imagery in research papers

Tools and Resources
Customer Services
Affective Science
Biological Foundations of Psychology
Clinical Psychology: Disorders and Therapies
Cognitive Psychology/Neuroscience
Developmental Psychology
Educational/School Psychology
Forensic Psychology
Health Psychology
History and Systems of Psychology
Individual Differences
Methods and Approaches in Psychology
Neuropsychology
Organizational and Institutional Psychology
Personality
Psychology and Other Disciplines
Social Psychology
Sports Psychology
Share This Facebook LinkedIn Twitter

Article contents

Psychological imagery in sport and performance.

Krista J. Munroe-Chandler Krista J. Munroe-Chandler The University of Windsor
and Michelle D. Guerrero Michelle D. Guerrero The University of Windsor
https://doi.org/10.1093/acrefore/9780190236557.013.228
Published online: 26 April 2017

Imagery, which can be used by anyone, is appealing to performers because it is executed individually and can be performed at anytime and anywhere. The breadth of the application of imagery is far reaching. Briefly, imagery is creating or recreating experiences in one’s mind. From the early theories of imagery (e.g., psychoneuromuscular) to the more recent imagery models (e.g., PETTLEP), understanding the way in which imagery works is essential to furthering our knowledge and developing strong research and intervention programs aimed at enhanced performance. The measurement of imagery ability and frequency provides a way of monitoring the progression of imagery use and imagery ability. Despite the individual differences known to impact imagery use (e.g., type of task, imagery perspective, imagery speed), imagery remains a key psychological skill integral to a performer’s success.

mental imagery
imagery ability
imagery theories
imagery models
imagery perspective
law enforcement

Introduction

All individuals, regardless of age, gender, or skill level, are capable of using imagery as a means to enhance cognitive, behavioral, and affective outcomes. In the sport domain, athletes use imagery in training, competition, and rehabilitation. Elsewhere, imagery has been widely utilized by other performers including military personnel, surgeons, and musicians.

Everything I make as a producer, I visualize it as a DJ first. And all those beats, I test them as a DJ. (David Guetta) I have a system of ridding my mind of negative thoughts. I visualize myself writing them down on a piece of paper. Then I imagine myself crumpling up the paper, lighting it on fire, and burning it to a crisp. (Bruce Lee)

The breadth of the application of imagery is far reaching, as demonstrated by these quotations from famous musician David Guetta and legendary martial artist Bruce Lee, illustrating that imagery can be used in different disciplines and for different functions. An often cited definition of imagery is:

an experience that mimics real experience. We can be aware of “seeing” an image, feeling movements as an image, or experiencing an image of smell, tastes, or sounds without actually experiencing the real thing. Sometimes people find that it helps to close their eyes. It differs from dreams in that we are awake and conscious when we form an image. (White & Hardy, 1998 , p. 389)

As just described, imagery is multisensory such that it can include the sense of sight, taste, sound, smell, and touch. This description provides insight into why the term imagery is used instead of “visualization,” which denotes only the sense of sight. In addition, the individual is awake and consciously aware when imaging and as such not dreaming. In essence, imagery is creating, or recreating, the entirety of an experience in one’s mind.

From early theories of imagery to more recent imagery models, the ways in which imagery is used to enhance performance will be explored. Measurement of imagery ability and frequency, which has been assessed primarily through the use of self-report, will be discussed, along with various factors influencing imagery use, including ability, speed, age, skill level and perspective. The uses of imagery in sport, exercise, and performance domains will be examined and avenues for future research suggested.

Theories and Models

For many years, researchers have been interested in the way in which imagery is used and applied by individuals. When individuals image they first retrieve information from memory to create or recreate an experience in their mind (Morris, Spittle, & Watt, 2005 ). Through a combination of imagery sub-processes, such as image transformation (e.g., rotation of an image), scanning (e.g., detecting details of an image), and maintenance (e.g., sustaining an image for some time), vivid and controllable images are generated. Despite the appeal of the simplistic explanation, a deeper understanding of how imagery works is necessary. As such, several theories have been proposed (psychoneuromuscular, bioinformational, triple code). Notwithstanding support and criticism of each of these theories, together they provide a foundation that continues to guide the development and refinement of imagery research and therefore warrant exploration and explanation. The most commonly discussed theories in sport, exercise, and performance psychology are presented along with an overview on the conceptual models of imagery.

The psychoneuromuscular theory (Jacobson, 1930 ) notes that when an individual mentally imagines a skill, the activated neural pathways are identical to those activated when physically performing the skill. The feedback one receives from the muscle innervation of the imagined skill enables the individual to make adjustments in motor behavior. Through measurement of electromyographical (EMG) activity, wherein the innervations when imaging are much smaller in magnitude than when physically performing, empirical support for the psychoneuromuscular theory has been found. Despite this, Hall ( 2001 ) has noted the failure of the psychoneuromuscular theory to examine the various types of imagery and Feltz and Landers ( 1983 ) have criticized the validity of this theory because of methodological concerns.

In bioinformation theory, Lang ( 1979 ) suggests that mental images comprise both stimulus proposition and stimulus response. Stimulus proposition refers to the content or characteristics of the image, such as a competitive swimmer imagining her surroundings and her opponents. Stimulus response, on the other hand, refers to the physiological and affective reaction experienced by the individual imaging. For example, that same swimmer may feel tightness in her shoulders due to the anxiety experienced when imagining the swim meet or she may neglect external stimuli such as the crowd cheering after imagining a personal best time. Images that contain both stimulus proposition and response are most effective in enhancing performance. Although not often acknowledged, Lang introduced the concept of meaning to the image, enhancing the relevance of the theory. Research supporting the bioinformational theory has found that imagery scripts containing more frequent use of response propositions, compared to stimulus propositions, elicit greater physiological reactions (Bakker, Boschker, & Chung, 1996 ). Although an improvement over earlier theories, the bioinformational theory lacks explanation regarding the motivational types of imagery (Hall, 2001 ).

Elaborating upon the bioinformational theory’s stimulus proposition and response characteristics, Ahsen’s ( 1984 ) tripe code theory added a third characteristic—the meaning of the image. Ahsen argued that no two people would have the same imagery experience even if provided with the same imagery instructions. Individuals bring their own unique set of experiences with them and view these experience through their individual lenses, thereby allowing for a different meaning of the image to emerge. As such, the most effective images are those that are realistic and vivid, evoke psychophysiological responses, and impart significance to the individual. However, as noted in the literature (Morris et al., 2005 ), this model neglects the cognitive effects of imagery, which is an important consideration for skill acquisition and learning.

The aforementioned concepts provide theoretical underpinning for imagery use; however, exploration of this topic also requires an examination of the different models of imagery, which are also essential for furthering our understanding of imagery use. Indeed, most of the recent performance imagery research (e.g., sport, exercise) has developed as a result of Paivio’s ( 1985 ) analytic model. It is well established that imagery has cognitive and motivational functions that operate at a general or specific level. The cognitive general (CG) function entails imaging strategies, game plans, or routines (e.g., a fast break in basketball), whereas the cognitive specific (CS) function involves imaging specific skills (e.g., follow through on a free throw). The motivational general (MG) function of imagery involves imaging physiological arousal levels and emotions (e.g., staying calm when taking a penalty shot), and the motivational specific (MS) function of imagery includes imaging individual goals (e.g., winning the championship). In an extension of Paivio’s work, Hall, Mack, Paivio, and Hausenblas ( 1998 ) further divided the motivational general function into a motivational general–arousal (MG-A) function, encompassing imagery associated with arousal and stress, and a motivational general–mastery (MG-M) function, representing imagery associated with being mentally tough, in control, and self-confident.

Guided by Paivio’s ( 1985 ) model, Martin, Moritz, and Hall ( 1999 ) developed the Applied Model of Imagery Use in Sport (AMIUS) to explain the way in which athletes use imagery to improve athletic performance. According to AMIUS, the sport situation influences the types of imagery used, which are then associated with various cognitive, affective, and behavioral outcomes. Further, the relationship between the imagery type (five functions of imagery as noted: CS, CG, MS, MG-A, MG-M) and the outcome is moderated by various individual differences, such as imagery ability.

As a model of imagery use, the AMIUS offers several benefits. From a research perspective, the AMIUS provides simple, practical, and testable relationships. From an applied perspective, the model offers guidance for imagery interventions. There is ample support for the AMIUS such that the type of imagery should match the desired outcome, or as summarized by Short, Monsma, and Short ( 2004 ), “what you see, is what you get” (p. 342). That is, if a performer wishes to improve his confidence, he should engage in MG-M imagery. However, some researchers (e.g., Bernier & Fournier, 2010 ; Nordin & Cumming, 2008 ) have found that images can serve multiple functions for an athlete and have argued that function (why athletes image) and content (what athletes image) are not identical and therefore should be separated. Indeed, the original belief that the type of imagery should match its intended outcome is not as clear as was once thought.

Drawing on the AMIUS, Munroe-Chandler and Gammage ( 2005 ) developed an applied model for exercise settings. The exercise model differs from the AMIUS in that the antecedents include factors beyond the physical setting (e.g., exerciser’s goals and experiences), efficacy beliefs mediate the function-outcome relationship, and the individual differences that moderate the relationship extend beyond imagery ability (e.g., frequency of exercise, age). This model has allowed for the refinement and development of exercise imagery research (e.g., Andersson & Moss, 2011 ; Najafabadi, Memari, Kordi, Shayestehfar, & Eshghi, 2015 ).

With over a decade of research guided by the AMIUS, Cumming and Williams ( 2013 ) proposed a revised model of deliberate imagery use applicable for many performers (e.g., athletes, dancers, musicians). The revised model considers “who” is imaging (age, gender, competitive level), “what” is being imaged (the type), and “why” performers use imagery (the function). Most important, however, the revised model recognizes the personal meaning as the link between the imagery type and function. Cumming and Williams note that the types of imagery are often combined to achieve a specific outcome (e.g., cognitive and motivational types of images are important sources of confidence; Levy, Perry, Nicholls, Larkin, & Davies, 2014 ), and therefore offers a more flexible framework than the original AMIUS.

Apart from the previously mentioned models, some sport psychology researchers have called for models of imagery to be grounded in neuroscience; the PETTLEP is one such model (Holmes & Collins, 2001 ). The PETTLEP model was developed to guide imagery interventions and is based on functional equivalence, which suggests that processes that occur in the brain during imagery mimic the processes that occur during actual movement. Seven key factors are identified to help guide imagery interventions; physical, environment, task, timing, learning, emotion, and perspective. Although there have been some studies examining the model’s components in isolation (e.g., O & Munroe-Chandler, 2008 ), more research is needed testing multiple elements of the model (cf., Smith, Wright, Allsopp, & Westhead, 2007 ) and in different contexts. Sophisticated neuroimaging techniques such as functional magnetic resonance imagery (fMRI) and positron emission tomography (PET), as well as mental chronometry (informs about the temporal coupling between real and simulated movements), have allowed researchers to test functional equivalence and to gain a greater understanding between imagery and movement.

Measurement

The measurement of imagery ability and imagery frequency have often been assessed in the sport, exercise, and performance imagery research. Given that imagery is an internal mental skill, its assessment has typically relied on the self-report questionnaires allowing individuals to subjectively report their imagery use and ability. More recent research, however, has combined self-report with other indices of imagery experiences such as chronometry or functional magnetic resonance imagery (fMRI) (Guillot & Collet, 2005 ).

As noted in the Applied Model of Imagery Use in Sport (AMIUS), imagery ability is one of the most important factors impacting imagery effectiveness. One’s ability to image includes various dimensions such as vividness, controllability, and maintenance (Morris, Spittle, & Watt, 2005 ). Although some performers may initially be better imagers than others, imagery is a skill that can be improved with practice (Rodgers, Hall, & Buckolz, 1991 ). From an applied perspective, the measurement of imagery ability is important as it leads to more individualized, and therefore effective, imagery interventions. Further, the measurement of imagery ability can be used as an imagery intervention screening procedure, thereby ensuring adequate imagery ability prior to the commencement of the intervention. Although there are numerous imagery ability questionnaires, the focus will be on the two most commonly used in the performance (sport) domain due to their inclusion of both movement and visual imagery.

The Movement Imagery Questionnaire (MIQ; Hall & Pongrac, 1983 ) assesses both visual and kinesthetic imagery. Although it was readily used for some time as a measure of imagery ability, Hall and Martin ( 1997 ) revised the MIQ (Movement Imagery Questionnaire–Revised; MIQ-R), reducing the number of items and thus minimizing the amount of time needed to complete the questionnaire. Those completing the MIQ-R are instructed to physically complete the movement sequence (i.e., knee raise, arm movement, waist bend, and jump) and then resume the starting position and recreate the experience using visual imagery, and finally using kinesthetic imagery. Participants are then asked to rate the quality of imagery on a 7-point Likert scale from 1 ( very easy to picture/feel) to 7 ( very difficult to picture/feel) . Given that the MIQ and MIQ-R did not distinguish between internal and external visual imagery perspective, Williams et al. ( 2012 ) developed the MIQ-3 to more fully capture an individual’s imagery ability. The MIQ-3 assesses external visual imagery (e.g., looking through your own eyes while performing the movement), internal visual imagery (e.g., watching yourself performing the movement), and kinesthetic imagery (e.g., feeling yourself do the movement). Although the MIQ-3 has shown to be a reliable and valid measure (Williams et al., 2012 ), because of the recentness of its development, more research is warranted using this measure.

The Vividness of Movement Imagery Questionnaire (Isaac, Marks, & Russell, 1986 ) assesses one’s ability to use visual imagery. It requires the participant to rate the 24 items on the vividness of imagery from 1 ( perfectly clear and as vivid as normal vision ) to 5 ( no image at all; you only know that you are thinking of the skill ). The revised VMIQ-2 (Roberts, Callow, Hardy, Markland, & Bringer, 2008 ) assesses the vividness of both visual and kinesthetic imagery. The 12-item VMIQ-2 scale asks respondents to imagine a variety of motor tasks (e.g., running, kicking a stone) and then rate the image on two perspectives of visual imagery (external and internal), as well as kinesthetically. All items are measured on a 5-point Likert scale ranging from 1 ( perfectly clear and as vivid as normal vision ) to 5 ( no image at all; you only know that you are thinking of the skill ). The VMIQ-2 has shown adequate reliability as well as adequate factorial, concurrent, and construct validity (Roberts et al., 2008 ).

All measurement tools are subject to criticism, and the imagery ability measures are not exempt. The instructions from the VMIQ-2 ask participants to draw on their memory of common movements, whereas the MIQ-3 requires participants to execute a movement first prior to imagining it, thereby relying on short-term memory. It may be argued that imaging a common movement (kicking a ball; VMIQ-2) may be easier for the participant than imaging a less common movement (raising your knee as high as possible so that you are standing on your left leg with your right leg flexed [bent] at the knee; MIQ-3). Conversely, a more common movement such as running up the stairs may elicit varying interpretations from the participant, thus leading to discrepancies in imagery content.

Gregg and Hall ( 2006 ) developed the Motivational Imagery Ability Measure for Sport (MIAMS) to assess motivational imagery abilities, which had yet to be included in any previous imagery ability measure. The MIAMS assesses the ability of an athlete to use MG-A and MG-M imagery, wherein the participant images the scene and then rates the image on an ease subscale 1 ( not at all easy to form ) to 7 ( very easy to form ) and an emotion subscale 1( no emotion ) to 7 ( very strong emotion ). Psychometric properties of the questionnaire have proved favorable, with acceptable model fit and adequate internal consistencies for the subscales (Gregg & Hall, 2006 ).

Of course, the various measures of imagery ability can be employed together to provide a more comprehensive assessment of an athlete’s overall imagery ability. Individuals who are more adept at imagery are more likely to engage these practices, and greater imagery use will likely result in enhanced imagery ability (Gregg, Hall, McGowan, & Hall, 2011 ). This is significant because research conclusively demonstrates that individual differences in imagery ability will have an impact on the effectiveness of imagery, and that high imagery ability leads to the ultimate goal: improved performance on a variety of motor tasks (Hall, 2001 ).

In addition to imagery ability, measuring a performer’s use of imagery allows researchers, and practitioners, to determine one’s frequency of a specific type of imagery and also enables them to see changes from pre- to post-intervention. The various questionnaires assessing the frequency of imagery use in sport, exercise, and active play will be addressed.

The Sport Imagery Questionnaire (SIQ; Hall, Mack, Paivio, & Hausenblas, 1998 ; Hall, Stevens, & Paivio, 2005 ) is the most widely used measure of imagery frequency in the sport domain (Morris et al., 2005 ). It is a general measure of imagery used for athletes of any sport at any competitive level. The self-report questionnaire comprises 30 items assessing the five functions of imagery (CS, CG, MS, MG-A, MG-M). All items are scored on a 7-point Likert scale anchored by 1 ( rarely ) and 7 ( often ). The SIQ has shown strong psychometric properties (i.e., reliability, validity) for athletes 14 years and older (Hall et al., 2005 ).

Given the research evidence supporting young athletes’ use of imagery (e.g., Munroe-Chandler, Hall, Fishburne, & Shannon, 2005 ), the Sport Imagery Questionnaire for Children (SIQ-C; Hall, Munroe-Chandler, Fishburne, O, & Hall, 2009 ) was developed for those young athletes aged 7–14 years. The SIQ-C includes 21 items, which assesses the same five functions as those identified in the adult version (CS, CG, MS, MG-A, MG-M). The items are rated on a 5-point Likert scale anchored at 1 ( not at all ) and 5 ( very often ), making it more appropriate for young children. Since its development, the SIQ-C has reported adequate internal consistencies for all subscales (Hall et al., 2009 ).

For researchers in the field of exercise imagery, two questionnaires have dominated: the Exercise Imagery Questionnaire (EIQ; Hausenblas, Hall, Rodgers, & Munroe, 1999 ) and the Exercise Imagery Inventory (EII; Giacobbi, Hausenblas, & Penfield, 2005 ). The nine-item EIQ was developed from qualitative responses from exercisers reporting their use imagery for three main purposes: appearance, energy, and technique. Exercisers are asked to rate their imagery use on the three aforementioned subscales using a 9-point scale, anchored by 1 ( never ) and 9 ( always ). Strong reliabilities are reported for all three subscales (Hausenblas et al., 1999 ; Rodgers, Munroe, & Hall, 2001 ).

The EII was developed as a result of qualitative evidence indicating exercisers’ use of imagery for purposes beyond those of appearance, energy, and technique. In fact, exercisers were found to use imagery for the following purposes: appearance or health, exercise technique, exercise self-efficacy, and exercise feelings. As a result of these findings, the EII includes questions that assess appearance, energy and technique imagery as well as exercise self-efficacy and exercise feeling imagery. The EII is a 19-item self-report measure of exercise frequency rated on a 7-point Likert scale (1 = rarely and 7 = often ). Support for the four-factor model across a variety of samples has been reported (Giacobbi et al., 2005 ).

The revised version of the EII (EII-R; Giacobbi, Tuccitto, Buman, & Munroe-Chandler, 2010 ) measures the same four subscales of the original version, in addition to exercise routines. This modification allowed for the measurement of the five functions of imagery, which were suggested in the applied model of exercise imagery use (Munroe-Chandler & Gammage, 2005 ). Results from a confirmatory factor analysis for the EII-R has demonstrated good fit indices (Giacobbi et al., 2010 ).

The Children’s Active Play Imagery Questionnaire (CAPIQ; Cooke, Munroe-Chandler, Hall, Tobin, & Guerrero, 2014 ) assesses the frequency of imagery use in children during their active play. The measure consists of 11 items, each rated on a 5-point Likert scale from 1 ( not at all ) to 5 ( very often ), assessing one of the three subscales (capability, fun, and social). Capability imagery refers to the practice of movements, social imagery refers to the engagement of active play activities either by oneself or with others, and fun imagery refers to feelings of satisfaction. The items were developed from active play research as well as qualitative focus groups with children examining their use of imagery during their leisure time physical activity (Tobin, Nadalin, Munroe-Chandler, & Hall, 2013 ). The CAPIQ has demonstrated adequate internal consistencies for all three subscales (Cooke et al., 2014 ) and contributes to the measurement of imagery use in a physical activity setting other than organized sport.

Factors Affecting Imagery

Researchers have identified a wide range of factors that have been found to influence imagery effectiveness, including imagery ability, image speed, age, skill level, and perspective.

Both Martin, Moritz, and Hall ( 1999 ) and Munroe-Chandler and Gammage ( 2005 ) have proposed that the relationship between imagery use and desired outcome is moderated by various individual differences, especially the ability to image. That is, better imagery ability leads to better performance on a variety of motor tasks (Hall, 2001 ). This was supported in an applied study wherein tennis players with better imagery ability showed greater improvements in tennis serve return accuracy than those athletes with lower imagery ability (Robin et al., 2007 ). Individual differences in imagery ability has been noted in early imagery research (cf., MacIntyre, Moran, Collet, & Guillot, 2013 ). Some have noted that novice performers may not be as skilled at imagining given their lack of ability to develop knowledge of the spatial and kinesthetic requirements of the task (Driskell, Copper, & Moran, 1994 ). Regardless of individual differences in imagery ability, there is sufficient evidence to show that imagery ability can improve with practice (Cooley, Williams, Burns, & Cumming, 2013 ).

Cumming et al. ( 2016 ) developed a structured, imagery exercise known as layered stimulus and response training (LSRT) designed to improve imagery ability. By generating images in a layered fashion, starting with a simple image and gradually incorporating additional information in subsequent layers, imagery ability improves. After each layer, the individual evaluates the image by reflecting on various aspects of the image. For example, what aspects were strong, easy, vague, or difficult to image? Earlier studies have implemented LSRT in a single imagery session, with the intent of enhancing individuals’ imagery ability prior to receiving an imagery intervention (e.g., Cumming, Olphin, & Law, 2007 ), and more recently for improving actual motor skill performance (Williams, Cooley, & Cumming, 2013 ).

Image Speed

Regarding the Timing element of the PETTLEP model, Holmes and Collins ( 2001 ) have recommended that athletes image primarily in real-time speed, due to the accurate representation of movement tempo and relative timing duration in one’s images. In a large-scale study examining athletes’ voluntary use of image speed (O & Hall, 2009 ), both recreational and competitive athletes reported using three image speeds depending on the function of imagery being employed and the stage of learning of the athlete. Real-time images were used most often by athletes regardless of imagery function or stage of learning. However, when learning or developing a skill or strategy, slow-motion images were used most often (which supports recent findings with novice golfers; Shirazipour, Munroe-Chandler, Loughead, & Vander Laan, 2016 ), and when imaging skills or strategies that had been mastered fast-motion images were used most often. Subsequent qualitative research by O and Hall ( 2013 ) substantiated those findings and defined voluntary image speed manipulation as that which “occurs when an athlete consciously and purposefully selects a speed at which to image” (p. 11).

The cognitive development of the individual, most often distinguished by age, is another factor influencing imagery use. Much of the research conducted by Kosslyn and colleagues (e.g., Kosslyn, Margolis, Barrett, Goldknopf, & Daly, 1990 ) in the general psychology domain notes differences in imagery use between children and adults. More specifically, it is not until age 14 that children are able to image similarly to their adult counterparts. Age differences also holds true in the sport, exercise, and active play domain. For example, child-specific imagery measures have been developed to adequately assess their use of imagery in various domains (i.e., SIQ-C, CAPIQ). Findings from an imagery intervention study (Munroe-Chandler, Hall, Fishburne, Murphy, & Hall, 2012 ) did identify age-related results, such that only the younger athletes (7–10 years) performed faster on a soccer task, when compared to the older athletes (10–14 years). Noted age differences are also evident in the active play setting such that only the older age cohorts (11–14 years) reported picturing themselves playing alone rather than with others (Tobin, Nadalin, Munroe-Chandler, & Hall, 2013 ). In the exercise domain, Milne, Burke, Hall, Nederhof, and Gammage ( 2006 ) found that younger exercisers ( M age = 22 years) reported using more appearance imagery than the older exercisers ( M age = 71 years). Although these findings offer some preliminary evidence for age differences, further research is needed in order to truly understand the effects of age on performers’ use of imagery.

Skill Level

One of the most consistent findings from the performance imagery literature is that higher skilled performers report using imagery more often than lower skilled performers (Cumming & Hall, 2002 ; Hall, Mack, Paivio, & Hausenblaus, 1998 ; Hausenblas, Hall, Rodgers, & Munroe, 1999 ). In the sport domain, although it had been suggested that novice athletes should use imagery more frequently than elite athletes, simply for the purposes of the learning, and development, of new strategies and skills (Hall, 2001 ), research supports benefits for highly skilled athletes (e.g., Arvinen-Barrow, Weigand, Thomas, Hemmings, & Walley, 2007 ). This finding is consistent in the exercise imagery field, wherein experienced exercisers use imagery more often than less experienced exercisers (Gammage, Hall, & Rodgers, 2000 ), and in the performing arts field wherein higher level ballet dancers report using more imagery than their lower level counterparts (Nordin & Cumming, 2008 ). Moving forward, researchers should consider other ways to assess skill level. Currently, skill level has been dichotomized as novice vs. elite or experienced vs non-experienced. This is problematic given the self-report nature of this dichotomy and the possibility that minimal differences in skill may exist between those two groups (Arvinen-Barrow et al., 2007 ). In the revised model of deliberate imagery use, Cumming and Williams ( 2013 ) suggest that in addition to the skill level of the athlete, other relevant individual characteristics to consider are experience with and confidence using imagery.

Imagery Perspective

Morris and Spittle ( 2012 ) noted that imagery perspective is a key factor impacting an athlete’s use of imagery. Indeed, a special issue of the Journal of Mental Imagery ( 2012 ) was dedicated solely to imagery perspective. Performers can image the execution of a skill from their own vantage point (internal imagery) or they can view themselves from the perspective of an external observer, as if they were a spectator in the stands watching a performance (external imagery). Early sport imagery researchers advocated the use of an internal perspective (Vealey, 1986 ), while others have found the perspective to be dependent upon the task. That is, tasks relying heavily on the use of form (e.g., gymnastics) are most effective when imaged from an external perspective (White & Hardy, 1995 ). Some researchers (Munroe, Giacobbi, Hall, & Weinberg, 2000 ; Smith, Wright, Allsopp, & Westhead, 2007 ) support athletes using a combination of internal and external perspectives. In the academic domain, Vasquez and Buehler ( 2007 ) found that students demonstrate increased motivation when they imagine the task from a third-person perspective. In a study examining imagery in five different disciplines (i.e., education, medicine, music, psychology, and sport), imagery was most often performed from an internal perspective (Schuster et al., 2011 ).

Other Factors

Scholars have recently acknowledged the scant research assessing the influence of personality characteristics on imagery use and its effectiveness (Roberts, Callow, Hardy, Woodman, & Thomas, 2010 ). In an effort to fill this gap, Roberts et al. ( 2010 ) examined the interactive effects of imagery perspective and narcissism on motor performance. Given that narcissists enjoy looking at themselves from the point of others, it was hypothesized that those high in narcissism would score higher on external visual imagery and better on their motor performance when compared to those low in narcissism. This hypothesis was supported using two independent samples. As such, it seems as though personality characteristics (i.e., narcissism) may influence the effectiveness of psychological skills and thereby require additional investigation.

Another factor that has recently been examined within the imagery domain is emotion regulation. Anuar, Cumming, and Williams ( 2016 ) believed that athletes’ emotion regulation may be associated with their imagery ability given that both imagery and emotion regulation are linked with emotions and memory. Indeed, their results indicated that athletes who change how they think about a particular situation scored higher on imagery ability. This study is the first of its kind, and future research examining individual characteristics and imagery is warranted.

Imagery as a Means to Improving Performance

Drawing on the various imagery models and empirical support, athletes use imagery for various motivational purposes (i.e., motivational general–mastery [MG-M], motivational general–arousal [MG-A], motivational specific [MS]). Most of the motivational imagery interventions have targeted the MG-M imagery function, and results from these studies are promising. In one study, a MG-M imagery intervention was implemented with four elite junior badminton players (Callow, Hardy, & Hall, 2001 ). The imagery scripts were designed to elicit images of being focused and confident, and included both response and stimulus propositions. Following the completion of the intervention, all but one badminton player showed significant improvements in their sport confidence. Other researchers employing single-subject multiple-baseline designs have found that MG-M imagery improved young squash players’ self-efficacy (O, Munroe-Chandler, Hall, & Hall, 2014 ) and high-performance golfers’ flow states (Nicholls, Polman, & Holt, 2005 ). Recently, MG-M imagery sessions were delivered to young athletes with an intellectual disability in an attempt to increase their perceptions of their sport competence (Catenacci, Harris, Langdon, Scott, & Czech, 2016 ). Results indicated that perceptions of sport competence improved from baseline to post-intervention for three of the five athletes, with two of the three athletes maintaining these changes upon commencement of the intervention. The benefits of MG-M imagery have also been underscored in several cross-sectional studies, providing evidence for a positive link between MG-M imagery and performance, state and trait sport confidence, self-efficacy, collective efficacy (see Cumming & Ramsey, 2009 , for review), and mental toughness (Mattie & Munroe-Chandler, 2012 ).

Imagery has also been used as a means to achieve desirable somatic and emotional experiences associated with sport-related stress, arousal, and anxiety (MG-A imagery). It is generally argued that MG-A imagery may be more beneficial for athletes who experience debilitative interpretations of pre-competitive anxiety as opposed to those who experience facilitative interpretations (Martin, Moritz, & Hall, 1999 ). For example, a female fencer who is feeling unusually sluggish prior to competition might use MG-A imagery to psych herself up, while a male mixed martial arts fighter who is abnormally restless before the start of a competition might use MG-A imagery to reduce his anxiety. Though MG-A images have been negatively associated with athletes’ self-reported cognitive and somatic anxiety (Monsma & Overby, 2004 ), few studies have examined the direct effects of MG-A imagery on competitive anxiety. Investigators of past studies have typically delivered multicomponent interventions, which have included MG-A imagery along with other psychological skills (e.g., relaxation, breathing; Thomas, Maynard, & Hanton, 2007 ). Adopting a multicomponent psychological skills package makes it virtually impossible to determine precisely how much MG-A imagery contributed to any observed changes. Nevertheless, findings from other studies have contributed to researchers’ existing understanding of the MG-A imagery–competitive anxiety relationship (Cumming, Olphin, & Law, 2007 ; Mellalieu, Hanton, & Thomas, 2009 ). Specifically, imagery scripts that contained MG-A images (psyching up imagery, anxiety imagery, and coping imagery) led to greater increases in athletes’ heart rate and anxiety intensity (Cumming et al., 2007 ), while individualized MG-A imagery scripts led to more facilitative interpretations of symptoms related to competitive anxiety (Mellalieu et al., 2009 ).

Within the sport psychology literature, few interventions have focused exclusively on goal-based images (MS imagery). This is likely because goal- or outcome-based images (e.g., qualifying for a competition, winning a medal) are least often used by athletes. Rather, coaches and sport practitioners often encourage their athletes to focus on process goals (e.g., completing stretching exercises prior to competition) rather than outcome goals. In a sample with beginner golfers, participants who imaged executing the perfect stroke as well as sinking the golf ball (performance and outcome imagery group) had better performance and set higher goals for themselves compared to participants who imaged executing the perfect stroke only (performance group) and the participants who received no intervention (control group; Martin & Hall, 1995 ). Additionally, athletes who used MS imagery more frequently also reported greater goal achievement, state and trait sport confidence, and self-efficacy (Cumming & Ramsey, 2009 ).

In addition to motivational purposes, athletes have reported using imagery for cognitive purposes (i.e., cognitive specific [CS] and cognitive general [CG]). Using cognitive imagery to enhance skill acquisition and performance (CS imagery) has received the most attention among researchers (Morris, Spittle, & Watt, 2005 ). Investigators examining the positive effects of CS imagery have found significant improvements in young soccer players’ time to complete a soccer task (Munroe-Chandler, Hall, Fishburne, Murphy, & Hall, 2012 ) as well as adult equestrian riders’ performance and self-efficacy for a specific skill (Davies, Boxall, Szekeres, & Greenlees, 2014 ). In another study, 7- to 10-year-old athletes who imaged the proper execution of a table tennis serve significantly improved their serve accuracy and quality (Li-Wei, Qi-Wei, Orlick, & Zitzelsberger, 1992 ). Furthermore, CS imagery has been positively associated with gymnasts’ performance at competition (Simonsmeier & Buecker, 2017 ) and trait confidence (Abma, Fry, Li, & Relyea, 2002 ).

Evidence for imagery as a means to learn and improve execution of strategies, game plans, and routines (CG imagery) has been equivocal (see Westlund, Pope, & Tobin, 2012 , for review). For instance, while improvements in basketball athletes’ strategy execution were observed following a CG imagery intervention (Guillot, Nadrowska, & Collet, 2009 ), soccer athletes who participated in a seven-week CG imagery intervention showed no improvements in strategy execution from baseline to post-intervention (Munroe-Chandler, Hall, Fishburne, & Shannon, 2005 ). However, researchers adopting correlational-based studies have shown that athletes who used CG imagery reported higher levels of confidence, self-efficacy, imagery ability, and cohesion in team sports (Westlund et al., 2012 ).

Imagery has long been recognized as a viable psychological technique that can directly modify exercise-related cognitions. Self-efficacy is a particularly good example of one cognition that continues to receive attention in literature. Weibull, Cumming, Cooley, Williams, and Burns ( 2015 ) examined whether a brief (one week) imagery intervention could increase barrier self-efficacy among a group of women who were interested in becoming more active. Findings indicated that participants who performed daily imagery for one week (experimental group) reported greater increases in barrier self-efficacy compared to those who did not perform imagery (control group). Note, however, that when preexisting exercise levels were controlled, there were no significant differences in barrier efficacy between groups. Nevertheless, findings from this study support the notion that imagery can have an influential effect on barrier self-efficacy in a short time frame. Evidence for the effectiveness of using imagery to increase exercise self-efficacy has also been found in other intervention studies, including Duncan, Rodgers, Hall, and Wilson ( 2011 ).

Imagery has also been used to modify individuals’ motivation toward exercise. Duncan, Hall, Wilson, and Rodgers ( 2012 ) implemented an eight-week imagery intervention and found that participants who listened to guided imagery scripts showed significantly greater increases in self-determined motivation than those who listened to health information sessions. In another study, imagery scripts combined with peer-mentoring led to significantly greater increases in self-determined motivation to exercise at the end of the intervention compared to those whose participation was limited to peer-mentoring only (Giacobbi, Dreisbach, Thurlow, Anand, & Garcia, 2014 ). Additional benefits of employing imagery in an exercise domain include increased revitalization and post-exercise valence (Stanley & Cumming, 2010 ) and implicit attitudes toward exercise (Markland, Hall, Duncan, & Simatovic, 2015 ).

Beyond changing individuals’ attitudes toward exercise, imagery can also significantly impact exercise behavior. For example, audio-administered imagery scripts led to significantly greater increases in self-reported exercise behavior in both adult (Andersson & Moss, 2011 ) and older adult (Kim, Newton, Sachs, Giacobbi, & Glutting, 2011 ) samples. Chan and Cameron ( 2012 ) also tested the effects of different imagery content on physical activity participation by looking at imagery’s impact on a group of inactive adults. Their findings indicated that imagery scripts linking images of participation in physical activity with achievement of goals were most effective in increasing self-reported physical activity as well as greater increases in goal orientation, intentions, and action planning.

Although few imagery interventions have utilized objective measures of physical activity, the research that has been conducted in this area illustrates positive impact of imagery. In a sample of adolescent girls, Najafabadi et al. ( 2015 ) developed imagery scripts that focused on benefits obtained from exercise (e.g., improved appearance, enhanced energy). Following the intervention, significantly greater levels of physical activity (as measured by accelerometers) and physical self-concept were found among females in the imagery group compared to those in the control group. In a separate study, school-aged children who were assigned to an imagery group showed greater levels of active play and self-determined motivation following a four-week intervention compared to children assigned to a control group (Guerrero, Tobin, Munroe-Chandler, & Hall, 2015 ).

The effects of mental imagery with video-modeling on front squat strength and self-efficacy was recently examined in a sample of adults (Buck, Hutchinson, Winter, & Thompson, 2016 ). From pre-test to post-test, participants who received the imagery script and video-modeling showed significant increases in their self-efficacy and front squat performance compared to those who received no intervention. In a recent systematic review examining the effects of various cognitive strategies (e.g., imagery) on strength performance, imagery was found to positively influence maximal strength (Tod, Edwards, McGuigan, & Lovell, 2015 ).

Performance

Along with sporting arenas and fitness facilities, researchers have explored the effects and application of imagery in other performance domains. For example, in musical settings, imagery use coupled with physical practice increased pianists’ and trombonists’ movement timing, music memorization, and self-efficacy (see Wright, Wakefield, & Smith, 2014 , for review). Imagery use, in the absence of physical practice, has also shown to have promising effects on performance. In this respect, auditory practice (listening to an audio recording and imagining finger movements) led to significantly fewer errors in pianists’ performance than with those who did not engage in auditory practice (Highben & Palmer, 2004 ). More recently, Braden, Osborne, and Wilson ( 2015 ) tested the effectiveness of a multi-component, preventative skills-based program in reducing musical performance anxiety. The intervention program in this study comprised various components, including psychoeducation, cognitive restructuring, relaxation techniques, identification of strengths, goal-setting, positive self-talk, and imagery. Students who received the eight-week program reported significantly less musical performance anxiety than participants who did not receive the program.

In medical settings, researchers have employed imagery interventions to improve two primary outcomes: skill acquisition and levels of stress. With respect to skill acquisition, researchers found that medical students who received two imagery sessions demonstrated greater skill in performing surgery on live rabbits than students who had studied a textbook (Sanders et al., 2008 ). Similar findings were established in a study with gynecology residents, with those in the imagery group showing significantly better performance of cystoscopies as well as higher self-perceived level of preparedness compared to those in the control group (Komesu et al., 2009 ). In another study, student nurses who received PETTLEP training performed significantly better on a psychomotor skill (i.e., blood pressure measurement) than those who did not (Wright, Hogard, Ellis, Smith, & Kelly, 2008 ).

Given its successful use in the medical context, it is perhaps unsurprising that imagery has also been shown to be an effective stress management technique for other healthcare professionals (Arora et al., 2011 ) who also experience high levels of performance stress (Prabhu, Smith, Yurko, Acker, & Stefanidis, 2010 ). Compared to their control counterparts, novice surgeons who received imagery training demonstrated reduced self-reported stress as well as decreased objective stress (heart rate and salivary cortisol; Arora et al., 2011 ). In a very recent intervention study, Ignacio et al. ( 2016 ) developed, implemented, and evaluated an imagery intervention designed to improve nursing students’ clinical performance and reduce stress. Although no changes in subjective or objective stress were found, participants did significantly improve their performance from pre- to post-test.

Similar to healthcare professionals, police officers are often faced with a variety of stressors and potentially traumatic events, making imagery an appropriate psychological technique for members of law enforcement. Arnetz, Arble, Backman, Lynch, and Lubin ( 2013 ) implemented a 10-week imagery and relaxation intervention designed to help police officers develop effective coping skills. Compared to those in the control group, participants who received imagery training reported better general health and problem-based coping as well as reduced stomach problems, sleep difficulties, and exhaustion. Similarly, an imagery training program with rookie police officers led to significantly less negative mood and stress compared to standard police training (Arnetz, Nevedal, Lumley, Backman, & Lubin, 2009 ). Additionally, participants who received imagery training also demonstrated better performance during a live critical incident simulation (Arnetz et al., 2009 ).

Future Directions

That imagery is a powerful psychological technique is undeniable. Imagery allows individuals to search through, skip over, and select images from their memories in order to re-experience past events. Imagery also allows individuals to travel through time to create and manipulate never-experienced events. As illustrated, there is ample evidence documenting the effectiveness of imagery in sport, exercise, and performance settings. However, less is known about the potential negative consequences of imagery. For instance, engaging in self-generated imagery of a task requiring physical self-control (i.e., handgrip squeeze) led to performance decreases in a subsequent handgrip task for those who performed imagery compared to those who rested quietly (Graham, Sonne, & Bray, 2014 ). Furthermore, under certain conditions, imagery has been shown to have a negative effect on golf putting performance (Beilock, Afremow, Rabe, & Carr, 2001 ) and levels of aspirations and academic performance (Pham & Taylor, 1999 ). Together these findings indicate that there may be a dark side to imagery that should be explored to ensure that potential deleterious practices do not counteract the positive benefits associated with imagery use. Thus, future research should specifically explore possible negative effects of imagery on behavior and cognitions, including whether specific types of imagery should be avoided in certain environments and, if so, whether this caveat would hold true for all performers (e.g., professional dancer vs. surgeon)? While some researchers have begun to answer these questions (e.g., Nordin & Cumming, 2005 ), a more thorough examination of when and what imagery types facilitate or hinder performance would certainly contribute to the existing imagery research.

Along similar lines, there is a considerable gap in the imagery research investigating the impact involuntary, intrusive images have on performance. Imagery is considered to be intrusive as it can capture attention, cause distractions, and provoke unpleasant physiological and emotional reactions (Brewin, Gregory, Lipton, & Burgess, 2010 ). Indeed, there is some evidence indicating that performers do experience intrusive images (e.g., Nordin & Cumming, 2005 ; Parker, Jones, & Lovell, 2015 ). For instance, professional dancers reported experiencing irrelevant images, which may be intrusive, spontaneous, and debilitative (Nordin & Cumming, 2005 ). More recently, a small percentage of university students who participated on either recreational, university, county, or national competition levels reported experiencing intrusive visual imagery (Parker et al., 2015 ). Clearly, more research is needed in order to develop a greater understanding of the existence and effect of intrusive imagery within performance settings.

While the body of literature on imagery in performance settings continues to grow, more research exploring the usefulness and applicability of imagery among diverse performers is needed. Virtually all individuals, regardless of their occupations, are required to perform at some point or another. Successful lawyers need to deliver persuading and emotionally moving closing statements to the members of the jury; stand-up comedians are required to provide entertainment by mastering the pace and timing of every joke. Individuals of such occupations could undoubtedly benefit from imagery. Furthermore, as eSports (online competitive gaming) and competitive eating continue to gain popularity, exploring the potential for imagery as a performance enhancement technique for competitive gamers and eaters appears timely. Competitive gamers could use imagery to learn or improve their ability to make crucial decisions and to effectively cope with pressure, whereas competitive eaters could use imagery to improve execution of new strategies and maintain motivation during a contest.

Mental Imagery in the Science and Practice of Cognitive Behaviour Therapy: Past, Present, and Future Perspectives

Open access
Published: 01 February 2021
Volume 14 , pages 160–181, ( 2021 )

Cite this article

You have full access to this open access article

Simon E. Blackwell ORCID: orcid.org/0000-0002-3313-7084 1

13k Accesses

18 Citations

29 Altmetric

Explore all metrics

Mental imagery has a long history in the science and practice of cognitive behaviour therapy (CBT), stemming from both behavioural and cognitive traditions. The past decade or so has seen a marked increase in both scientific and clinical interest in mental imagery, from basic questions about the processes underpinning mental imagery and its roles in everyday healthy functioning, to clinical questions about how dysfunctions in mental imagery can cause distress and impairment, and how mental imagery can be used within CBT to effect therapeutic change. This article reflects on the current state of mental imagery in the science and practice of CBT, in the context of past developments and with a view to future challenges and opportunities. An ongoing interplay between the various strands of imagery research and the many clinical innovations in this area is recommended in order to realise the full therapeutic potential of mental imagery in CBT.

Cognitive Behavior Therapy at the Crossroads

Chairwork in cognitive behavioural therapy: a narrative review.

Changing Biased Interpretations in CBT: A Brief History and Overview

Avoid common mistakes on your manuscript.

Mental imagery has played a role in cognitive behaviour therapy (CBT) throughout its history, but the past decade or so has seen an acceleration of interest in the use of mental imagery across an ever-growing range of disorders and treatment applications (Saulsman et al. 2019 ). Several converging factors have likely contributed to this rising interest in and use of imagery. First, there has been a broader scientific resurgence of interest in mental imagery, facilitated both by the advent of better neuroimaging techniques, allowing insights into the neural underpinnings of imagery (Kosslyn et al. 2001 ; Pearson et al. 2015 ), and by experimental work supporting the perceptual nature of much emotional memory (e.g. Andrade et al. 1997 ; Arntz et al. 2005 ; Brewin and Saunders 2001 ), and testing the assumption of a special relationship between imagery and emotion (e.g. Holmes and Mathews 2005 ). Second, there has been growing recognition and documentation of the presence of mental imagery dysfunctions across the whole range of psychopathology (e.g. Holmes and Hackmann 2004 ). Third, there has been increasing clinical interest in the potential applicability of methods developed within the context of trauma memories, such as imagery rescripting, to other kinds of distressing memories and problematic imagery more broadly (e.g. Arntz and Weertman 1999 ; Holmes et al. 2007a ). This convergence of scientific and clinical interest has led to a particularly fertile interplay between basic research and applied clinical work.

This article reflects on the current status of mental imagery in the science and clinical practice of CBT, in the context of past developments, and with a view to future challenges and opportunities. After a brief introduction to mental imagery, this article will consider past and present developments of relevance to CBT, organised into the following four broad categories of work spanning science and practice: the use of mental imagery as an experimental tool to test and develop underlying theories; understanding the role of mental imagery in healthy everyday functioning; understanding of the role of mental imagery dysfunctions in psychopathology; and the use of mental imagery as a therapeutic tool within CBT itself. Finally, building on this overview of the present state of the field, the article will consider future perspectives for imagery in CBT research and treatment development. The article is not intended as a comprehensive review (and does not consider broader applications of imagery within the CBT context, for example to therapist self-care, training, or supervision e.g. Bennett-Levy et al. 2009 ; Prasko et al. 2020 ), but rather highlights selected examples of past and current work to illustrate broader patterns and trends in the field. The ‘present status’ of CBT-relevant mental imagery research is very much informed by the programme of the 2019 World Congress of Behavioural and Cognitive Therapies (WCBCT; Heidenreich et al. 2019 ; Heidenreich and Tata 2019 ), at which the numerous presentations on the topic of mental imagery spanned disorder areas including depression, anxiety disorders, addictions, and psychosis, illustrating the breadth of clinical interest at the present time. By providing an overview of where mental imagery stands at the current crossroads of science and clinical practice, the article aims to stimulate reflection and promote continued interchange across the many strands of work relevant to understanding how we can make best use of mental imagery in CBT.

Why Focus on Mental Imagery?

Mental imagery can be defined as “representations and the accompanying experience of sensory information without a direct external stimulus” (Pearson et al. 2015 , p. 590), or in more colloquial terms as “‘seeing with the mind’s eye,’ ‘hearing with the mind’s ear,’ and so on” (Kosslyn et al. 2001 , p. 635). Most of us will be familiar with the experience of mental imagery in our daily lives, for example when we recall an event from memory and replay the scene in our mind, or when we think forward to an upcoming event and ‘pre-experience’ some anticipatory emotion, such as excitement or dread, as we play out in our mind’s eye the different ways in which the event could potentially unfold. Many of us will also be familiar with the problematic imagery reported by patients or clients in clinical practice, for example distressing memories that return uncontrollably again and again, or upsetting scenes of anticipated catastrophes or disappointments. However, despite the ubiquity of mental imagery, clinicians, researchers, and patients may not always be aware of the scientific basis for considering it as a form of thought deserving particular attention in CBT (e.g. Bell et al. 2015 ; Blackwell 2019 ).

From a basic research perspective, experimental investigations have shown that imagery-based thought can have particularly strong effects on emotion, cognition, and behaviour compared to non-imagery (e.g. verbal) thought (see e.g. Blackwell 2019 ; Holmes and Mathews 2010 , for overviews). This is most likely due to the nature of the representation of imagery at a neural level, which is very similar to that for actual perception of sensory input (Pearson et al. 2015 ), and leads to imagery having an ‘as-if reality’ quality (Ji et al. 2016 ). From a more clinical perspective, the presence of dysfunctional imagery has been documented across an increasing range of disorders (Ji et al. 2019 ), and thus neglecting imagery in theoretical conceptualisations or clinical assessments risks excluding a potential key contributor to distress, functional impairment, and symptom maintenance. This point is particularly important given that patients may not report imagery unless it is explicitly asked about (e.g. Beck et al. 1979 ; Hales et al. 2014 ). Finally, an awareness of mental imagery, its functions, and properties, open up opportunities for a range of powerful therapeutic applications, from incorporation of imagery into ‘standard’ therapy tools such as thought records (Josefowitz 2017 ), to use of imagery-based techniques such as imagery rescripting (e.g. Arntz 2012 ; Strachan et al. 2020 ), and even complete CBT treatment programmes that have a specific focus on imagery (e.g. Holmes et al. 2019 ; Jung and Steil 2013 ; McEvoy and Saulsman 2014 ; Taylor et al. 2019 ). Arguably, discussion of almost any psychological process, psychopathological phenomenon, or treatment technique is incomplete unless the potential role of mental imagery has been explicitly considered (Blackwell 2020 ).

Mental Imagery Across the Science and Practice of CBT

The next sections will consider four ways in which mental imagery is of relevance for the science and practice of CBT: use of mental imagery as an experimental tool; understanding the role of mental imagery in healthy everyday functioning; mental imagery dysfunctions in psychopathology; and the clinical application of mental imagery-based techniques within the practice of CBT.

Mental Imagery as an Experimental Tool

The practice and continued development of CBT is rooted in theories and models of the role of cognition, behaviour, emotion, and physiology in the maintenance of psychopathology, and these theories and models can be tested and informed by scientific investigation. From a basic science perspective, the properties of mental imagery, such as its effect on emotion, cognition, and behaviour (Blackwell 2019 ), and more broadly, ability to serve as a simulation of reality (Ji et al. 2016 ), mean that imagery can provide a valuable experimental tool to probe numerous aspects of the theories underpinning CBT approaches.

One example of where the ability of mental imagery to evoke emotional and physiological responses has long been capitalised upon is in the context of fear research, for example using imagery scripts as a lab-based analogue of actual exposure to a feared situation (e.g. Lang et al. 1980 ). In fact, many of these lab-based applications of imagery followed on from clinical observations of the surprising effectiveness of imaginal exposure in treating anxiety disorders, which led to the development of Peter Lang’s bio-informational theory of imagery (Ji et al. 2016 ; Lang 1977 , 1979 ). Thus, imagery scripts could be used to explore differential physiological responses to different kinds of situations, such as phobia-related, neutral, or positive scenarios, amongst people with different kinds of anxiety disorders in a way that might not be possible ‘in vivo’. Coupled with interest at the time in physiological responses to imagery in the context of treatment for anxiety disorders, and potential differences between imagery-based versus in vivo exposure (e.g. Marks et al. 1971 ; Mathews 1971 ), the controlled use of imagery scripts in laboratory situations represented a close connection between science and clinical practice. Interestingly, such research reflects an experimental use of imagery to understand aspects of disorders that are not imagery-related in themselves (e.g. fear responses). Imagery can of course also be used as an experimental tool to test theoretical statements about the role of imagery itself in disorders, for example the effects of negative observer-perspective imagery in the context of social phobia (e.g. Hirsch et al. 2005 ; Spurr and Stopa 2003 ), the potential role of imagery in driving craving in the context of substance use disorders (e.g. Harvey et al. 2005 ; May et al. 2004 ), or as an analogue of trauma memories or other distressing imagery to test the effects or mechanisms of potential therapeutic techniques (e.g. Engelhard et al. 2011 ).

While there is a multitude of ongoing work using imagery to test and build theory in experimental contexts, one area with particular relevance for CBT that has seen a recent re-ignition of interest is the use of imagery in fear conditioning paradigms (see Mertens et al. 2020 ). Fear conditioning, extinction, and related paradigms have been widely used to test and refine theories of anxiety disorders, including their development, remission, and treatment. Careful use of mental imagery within these paradigms offers a number of opportunities to increase their ecological validity and relevance to psychopathology and its treatment (Mertens et al. 2020 ). For example, research has shown that fear responses to stimuli, including avoidance (Krypotos et al. 2020 ), can be conditioned when aversive contingencies are only ever imagined and not experienced in reality (Mueller et al. 2019 ; Soeter and Kindt 2012 ). Mental images themselves can act as a conditioned stimulus, with conditioned responses generalising to actual stimulus presentation (Lewis et al. 2013 ). Further, imagery-based recall can provide a means to reactivate fear memories, potentially rendering them vulnerable to disruption (Grégoire and Greening 2019 ). An interesting parallel comes from work investigating mechanisms underlying the development of intrusive memories of trauma. This research has long used film stimuli as an analogue of a traumatic event in order to induce involuntary memories and study the variables modulating their characteristics and occurrence (James et al. 2016 ), but in fact, intrusive memories can also be induced when the experimental ‘trauma’ is never witnessed (via film) but only imagined (e.g. Krans et al. 2009 ; Mooren et al. 2019 ). The relevance to CBT of using mental imagery in conditioning and related paradigms becomes clear if we consider how much of our emotional world is essentially internal and rich in mental imagery, how many disorders are characterised by fears of events that did not or have not yet happened, but have only been ‘experienced’ via imagery, and how much imaginal rehearsal of negative contingencies occurs in the context of processes such as rumination and worry. Thus, these lines of research hold exciting potential for future developments in our theoretical understanding of anxiety disorders, as well as having clear links to potential clinical applications.

Mental Imagery in Healthy Everyday Functioning

A second way in which the science of mental imagery can inform CBT is via research uncovering the role of mental imagery in everyday healthy functioning. That is, the more we know about when we experience imagery in daily life, and the functions such imagery serves, the better able we may be to identify dysfunctions in such imagery or deficits that can be addressed within therapy. The study of ‘normal’ mental imagery in fact goes back to the beginnings of experimental psychology itself (e.g. Fechner 1860 ; see Roeckelein 2004 for an overview), and although investigations of mental imagery have gone hand in hand with discussions of its functions, for example in memory, problem-solving, or broader aspects of conscious experience (e.g. Betts 1909 ; Galton 1883 ; Marks 1999 ), this has not necessarily fed into CBT theory or practice (apart from, perhaps, where it has informed deliberate use of imagery within some therapeutic techniques e.g. Meichenbaum 1978 ). One exception might be the role of specific memory retrieval in planning and problem-solving (Williams 2006 ), whereby deficits in this are exhibited in overgeneral memory in depression (e.g. Sumner et al. 2010 ; Williams and Broadbent 1986 ) and are linked to various dysfunctions, such as in interpersonal problem-solving (e.g. Raes et al. 2005 ). This has informed approaches such as memory specificity training (Raes et al. 2009 ), and overgeneral memory has also been a target for mindfulness-based cognitive therapy (Williams et al. 2000 ). Although not conceived as a mental imagery dysfunction per se, there is an intrinsic link between overgeneral memory and mental imagery due to the role of imagery in retrieval processes and the experience of episodic memory recall (see e.g. Holmes et al. 2016a , for a discussion).

However, the past decade or so has seen an ever-increasing amount of research shedding light on the role of mental imagery in everyday functioning, in particular in relation to memory and future-oriented thinking, in a way that has clear implications for CBT. Contributions have come both from a neural mechanisms perspective (e.g. Addis and Schacter 2008 ; Hassabis and Maguire 2009 ; Miloyan et al. 2019 ; Schacter et al. 2012 ), and from studies inducing or recording the experience of voluntary and involuntary memories and future-oriented thoughts in controlled laboratory settings or daily life (e.g. Barzykowski and Niedźwieńska 2018 ; Berntsen 1996 ; Berntsen and Jacobsen 2008 ; Cole and Kvavilashvili 2019 ; D’Argembeau et al. 2011 ; Mace 2005 ). Although this research is often not framed from a mental imagery perspective, the memories, future projections, and other spontaneous thoughts recorded in such studies often have a rich imagery component. Further, these imagery-rich thoughts, whether deliberately generated or spontaneously occurring, appear to be common in daily life and are implicated in a number of important roles, for example in planning, decision making, motivating or prompting behaviour, maintaining one’s self-image, and emotion regulation. To illustrate this more concretely, if someone receives a message from a friend inviting them to a party the upcoming weekend, they might find themselves automatically imagining being there and chatting or dancing with their friends; how enjoyable this ‘feels’ in their imagination may influence whether they accept the invitation or find an excuse not to make it (imagery in decision making). If this image of being at the party generates particularly strong positive emotions, the individual may deliberately bring it to mind throughout the week to improve their mood when they are struggling at work (imagery in emotion regulation). Walking past a clothes shop the image may pop spontaneously to mind and prompt the individual to enter the shop and buy a new outfit for the party (imagery prompting behaviour). When the evening of the party comes, perhaps it is freezing cold and pouring with rain, such that the individual feels like cancelling and staying at home; however, imagining how much fun the party will be and ‘pre-experiencing’ this enjoyment (or perhaps the friend’s disappointment and subsequent complaining if they do not come) might shift the balance in favour of going out (imagery motivating behaviour). Before leaving the house, the individual may mentally go through their usual route to the party location in their mind, but ‘seeing’ the normally pleasant path through the park in the dark makes them consider taking a different route (imagery in planning). Finally, arriving at the party and seeing an old friend they have not met in years may trigger re-experiencing of memories of past shared experiences, activating a sense of being a loyal and valued friend (imagery in self-image). Of course, all of this may occur without experiencing much or any imagery at all. However, emotional thoughts often have some imagery-based component (Moritz et al. 2014 ), and engaging in imagery-rich thinking, such as ‘mental time travel’ into the past and future, appears to be almost a ‘default’ activity that our minds engage in when we are not occupied in task-directed thought (e.g. Schacter et al. 2007 ). Hence, biases, deficits, or disruptions in the frequency, quality, or content of such imagery could have far-reaching consequences and readily contribute to psychopathology, and provide suitable targets for treatments (Blackwell 2019 , 2020 ).

The research cited above generally investigates the potential roles of imagery in healthy functioning via examining the circumstances and effects of imagery’s presence. An interesting complement to this comes from research examining the effect of imagery’s absence . The effect of not thinking in images, but predominantly in verbal-linguistic forms, has long been studied in relation to worry and generalised anxiety disorder (e.g. Borkovec et al. 1993 ). However, recently there has been increased research interest in the phenomenon of aphantasia, in which individuals have little or no conscious experience of mental imagery (e.g. Dawes et al. 2020 ; Zeman et al. 2015 ). Research shows, for example, that individuals with aphantasia have different patterns of emotional response to written text (Wicken et al. 2019 ), highlighting a crucial role for mental imagery in generating emotional responses to emotional (text-based) information. Developing our understanding of how mental imagery occurs and its functions in everyday healthy functioning—whether by studying imagery’s presence or its absence—can help not only inform theories of dysfunction, but also development of intervention strategies to help compensate for or rebuild potential deficits in such functions where they appear to contribute to clinical impairment or distress.

Dysfunctional Mental Imagery

A third way in which the science of mental imagery can inform CBT is via the discovery, documentation, and exploration of dysfunctional experiences of mental imagery in psychopathology. This strand has perhaps the longest history, in that descriptions of people experiencing intrusive memories or imagined catastrophes are found throughout both historical clinical reports and literature more broadly. In fact, early cognitive therapy manuals explicitly noted the importance of enquiring about the occurrence of images (e.g. Beck 1976 ), and dysfunctional images have long been included in cognitive models not only of disorders in whose phenomenology they obviously play a key role, such as PTSD and obsessive-compulsive disorder, but also others where their role is more subtle, such as social phobia (e.g. Clark and Wells 1995 ; Rapee and Heimberg 1997 ). However, it is only relatively recently that detailed and systematic documentation and investigation of dysfunctional mental imagery across a wider range of disorders has started to occur (Holmes and Hackmann 2004 ).

Subsequently, over the past decade or so, it has become apparent that dysfunctions in mental imagery are widespread across psychopathology, from the experience of intrusive memories (e.g. Reynolds and Brewin 1999 ; Williams and Moulds 2008 ) or suicidal ‘flashforwards’ in the context of depression (e.g. Holmes et al. 2007b ), to a broad spectrum of imagery dysfunctions across a wide range of disorders, including psychosis (Malcolm et al. 2015 ), bipolar disorder (Di Simplicio et al. 2016 ; Hales et al. 2011 ), worry and generalised anxiety disorder (Hirsch et al. 2006 ; Tallon et al. 2020 ), eating disorders (Dugué et al. 2016 ; Kadriu et al. 2019 ), and incontinence phobia (Pajak et al. 2013 )—essentially, in the context of any disorder or clinical manifestation of distress in which this has been investigated. Importantly, although the presence of distressing images may be the most obvious example of mental imagery dysfunction, problems can also occur in the form of the absence or reduced quality of beneficial or adaptive imagery, for example difficulties in generating positive future imagery as observed in depression (Holmes et al. 2016a ), which have been linked to core features of the disorder such as reduced anticipatory pleasure (Hallford et al. 2020a ).

Of course, it is important not only to document the presence (or absence) of imagery in the context of psychopathology, but also to fully characterise such imagery and its consequences. This has been an increasing focus of recent research, for example via obtaining finer-grained descriptions of different facets of dysfunctional imagery (e.g. non-visual, such as olfactory or somatosensory, sensory modalities; Dobinson et al. 2020 ; Weßlau et al. 2016 ) and delineating its roles more precisely, for example linking the occurrence of intrusive imagery more closely to core aspects of functioning such as the self (Çili and Stopa 2015 ). Recent developments in this area have also included increased interest in the potential role of imagery dysfunctions amongst children and adolescents (e.g. Chapman et al. 2020 ; Pile and Lau 2020 ), other adult populations such as those with brain injury (Murphy et al. 2019 ), and explicit incorporation of imagery into a wider range of psychological models of disorders and disordered behaviour, such as suicide (O’Connor and Kirtley 2018 ). Alongside research into the role and functions of mental imagery in everyday healthy functioning, as mentioned in the previous section, this growing awareness of imagery dysfunctions across psychopathology can contribute to a comprehensive picture of imagery from health to disorder. Further, once mental imagery dysfunctions have been identified in a particular disorder or clinical presentation, these can then be targeted in therapy, as will be discussed in the next section.

Mental Imagery as a Therapeutic Tool within CBT

Mental imagery can be used in a multitude of ways within CBT, including as an assessment tool (e.g. imaginal reliving of a situation to identify automatic thoughts), within imagery-focussed techniques such as imaginal exposure or imagery rescripting, or as an additional component within techniques for which imagery is not itself the main focus, such as problem-solving or challenging negative automatic thoughts. There are several historical overviews of the development of imagery techniques in CBT and psychological therapy more broadly (Edwards 2007 , 2011 ; Singer 2006 ). What is interesting to note from such histories is the ubiquity of imagery not only across psychotherapies in general, but also specifically in the early development of cognitive and behavioural therapies. These included central roles of imagery in systematic desensitisation (Wolpe 1958 ), forms of rehearsal such as ‘stress inoculation’ (Meichenbaum 1974 ), ‘covert modelling’ (Kazdin 1973 ), or ‘rational restructuring’ (Goldfried and Davison 1976 ), and various aspects of the treatment of anxiety disorders and depression within cognitive therapy (Beck et al. 1979 ; Beck and Emery 1985 ). The development of mental imagery within CBT to some extent mirrors the development of CBT more generally, in that one major influence comes from the behavioural tradition, for example via imaginal exposure (Wolpe 1958 ), and another out of a more cognitive tradition such as the work of Beck (e.g. Beck et al. 1979 ). These traditions come together in later work where the focus is explicitly on behavioural experiments—active behaviour-based testing out of cognitions or beliefs (Bennett-Levy et al. 2004 )—for example in using video-recording of a speech task to test negative beliefs about the accuracy of a self-image in social phobia (e.g. McManus et al. 2009 ; Warnock-Parkes et al. 2017 ).

However, the claim of a resurgence in interest in imagery within CBT in recent years implies that there was a dip in such interest at some point; certainly, reading through CBT manuals from the past 20–30 years, one would often get the impression that mental imagery was not figuring largely in clinicians’ or researchers’ thinking. It may be that there was a relative focus on other areas or that imagery simply became ‘crowded out’ via development of and preference for other techniques. Whatever the reason, it is definitely the case that imagery-based techniques can now be found across an increasingly broad spectrum of CBT applications. One example comes through the use of imagery rescripting across an ever-growing range of disorders and problem areas (Arntz 2012 ). Imagery rescripting most commonly involves reliving a distressing memory but imagining events turning out differently—for example, fighting back or being rescued from an attacker in case of an assault, or viewing the event through the eyes of an adult and intervening in the case of childhood abuse (e.g. Holmes et al. 2007a ; Raabe et al. 2015 ). Within the CBT tradition, imagery rescripting was developed in the context of memories of childhood trauma, and schema therapy for people with diagnoses of personality disorders; the rationale, or at least one hypothesised mechanism of action, was that via incorporation of corrective information via imagery, the meanings, appraisals, and emotions associated with the distressing memory would be modified. Although originally applied to distressing or core dysfunctional memories, in fact, the technique can be applied to any kind of imagery, and the past decade has seen an ever-increasing range of its applications as clinicians and researchers have realised the therapeutic possibilities. This has included using imagery rescripting of distressing memories as a stand-alone treatment for depression (Brewin et al. 2009 ), including within the context of a self-help approach (Moritz et al. 2018 ), to voice-hearing (Paulik et al. 2019 ) and nightmares (Sheaves et al. 2019 ) in the context of psychosis, to distressing memories in OCD (Basile et al. 2018 ; Veale et al. 2015 ), test anxiety (Maier et al. 2020 ), binge eating disorder (Dugué et al. 2019 ), social anxiety (Norton and Abbott 2016 ; Wild et al. 2007 ), and much more (see also Morina et al. 2017 for a meta-analysis). This increase in clinical applications of imagery rescripting has been accompanied by interest in the underlying mechanisms, investigated in experimental studies (e.g. Kunze et al. 2019 ; Siegesleitner et al. 2020 ).

Alongside the increased application and development of specific techniques, such as imagery rescripting, recent years have also seen greater attention paid to imagery within CBT approaches more generally (e.g. increased consideration of imagery within the second edition of Mind over Mood; Greenberger and Padesky 2015 ). Further, a range of CBT therapies have been developed where imagery is the main or even sole focus, for example in imagery-focused CBT approaches for social anxiety (McEvoy and Saulsman 2014 ), bipolar disorder (Holmes et al. 2016b ), psychosis (Taylor et al. 2019 ), or self-harm (Di Simplicio et al. 2020 ). These imagery-focussed treatments conceptualise specific experiences of imagery as being central to a core symptom or component of a disorder; broadly speaking, it then follows that a targeted focus on this imagery-based mechanism holds potential to bring about substantial improvements.

In addition to the expansion and refinement of imagery-focussed CBT approaches that target maladaptive imagery, another recent trend has been in the increased development of various techniques designed to foster positive or adaptive imagery. Some examples come from the context of depression, in which a difficulty imagining positive future events has been linked to symptoms such as anhedonia and lack of motivation. Approaches have included practice in generating vivid and specific imagery of future events (Hallford et al. 2020b ; Hallford et al. 2020c ) or rewarding experiences (Linke and Wessa 2017 ), or computerised cognitive training approaches involving repeated generation of positive imagery (e.g. Blackwell et al. 2015 ; Dainer-Best et al. 2018 ). Other examples not restricted to depression build on research indicating a role for imagery in goal-directed behaviour. These aim to increase engagement in healthy desired behaviour, for example via imagery of the steps required to carry out the behaviour and of the rewarding outcome (e.g. Renner et al. 2019 ; Solbrig et al. 2018 ). Further, there is continued interest in examining the effects and potential benefits of compassion-focussed imagery across a range of applications (e.g. Campbell et al. 2019 ; McEwan and Gilbert 2016 ; Naismith et al. 2019 ). In fact, there are now a huge variety of approaches involving repeated rehearsal of positive imagery or memories that are in various stages of development and testing (e.g. see Hitchcock et al. 2017 , for a review and meta-analysis including a range of these approaches).

The sheer amount of work exploring different ways of using imagery in therapy is testament to the promise seen in imagery-focussed techniques and approaches. However, apart from a small number of cases in which imagery-based work is part of an evidence-based treatment (e.g. prolonged exposure or trauma-focussed CBT for PTSD; Ehlers et al. 2005 ; Foa et al. 2007 ), most of these imagery-based treatments or techniques are still under development and evaluation; which of these should ultimately be integrated into routine clinical practice, and how best this can be achieved, remain open questions for future research.

Future Perspectives

As outlined in this paper, the past decade or so has seen an explosion of interest in mental imagery across the whole CBT spectrum, from basic research to treatment innovation; this is manifested in an ever-increasing proliferation of new research papers and opens up many exciting future possibilities. Of course, within each specific line of research—whether using imagery to probe theory, investigating functions of healthy mental imagery, uncovering imagery dysfunctions, or developing new treatment techniques—there will be many areas of particular promise and future directions that could be highlighted. Further, across all of these lines of research and clinical innovation, there is a great need for much more work focussing on developmental, life-span, and cross-cultural aspects of imagery. However, in line with the aim of this paper to provide an overview of the field, this final section will take a step back from the details of the work discussed in the previous sections and instead consider a broader perspective on the future of imagery in the science and practice of CBT. This will start by addressing research perspectives and then move on to clinical innovation and implementation, before considering what this means for the individual researcher or clinician.

Research Perspectives

There is now a vast breadth of research examining mental imagery from neural to behavioural levels, and ideally the development and optimisation of imagery-based treatment techniques in CBT would draw on this rich source of information to inspire new, and hone existing, approaches. However, the sheer quantity and variety of the research can be overwhelming, and within the literature, there is often little true integration between different levels of understanding or perspectives on imagery (e.g. through psychopathology, healthy functioning, and neural levels of explanation) beyond passing references in the introduction and discussion sections of papers. Further, because mental imagery itself comes into play across so many psychological processes, much research of relevance for understanding imagery and its functions is spread across work in memory, planning, goals, decision making, and other aspects of cognition in papers that may often make no explicit reference to the imagery-related components of cognition involved in these functions. There are also many overlaps between imagery-focussed research and other concepts such as embodied cognition (e.g. Palmiero et al. 2019 ), and with other non-clinical fields of psychology such as sports psychology (e.g. Cumming and Williams 2012 ); these fields may use different or only partially overlapping terminologies to describe similar phenomenon, and may sometimes even represent repetitions of the same research in parallel under different names. Such diversity in concepts and terminology is not itself necessarily a problem; in fact, it can provide additional perspectives leading to new insights and ideas, as well as conceptual replications of important phenomena. However, it provides a challenge to researchers or clinicians trying to gain a comprehensive understanding of the science underpinning mental imagery and how this might inform clinical practice.

Not only is the mental imagery literature characterised by this vast breadth of research, but there has also been an ever-increasing focus on finer and finer details within each specific field of investigation. This attention to detail is necessary and highly important. For example when enquiring about the presence of imagery or asking someone to imagine a particular scene, the precise nature of the imagery being experienced or to be generated needs to be closely specified; research needs to avoid using imagery as a blunt tool (simply asking participants to imagine something without specific instructions as to how) but rather as a scalpel or probe aiming for pinpoint-precision to answer the more specific questions that are increasingly of interest. From a basic research perspective, a finer-grained understanding of imagery and its impact can benefit from methodological advances, such as the increasing accessibility and convenience of ecological momentary assessment (EMA) approaches to capture spontaneous thoughts and images in (close to) real-time (e.g. Beaty et al. 2019 ; Slofstra et al. 2017 ), or use of virtual reality to create more ecologically valid but highly controlled environments for the encoding (e.g. Schweizer et al. 2018 ) and retrieval (e.g. Zlomuzica et al. 2018 ) of image-based memories. However, given the essentially internal and subjective nature of the experience of mental imagery, precision and attention to detail in constructing task instructions, or in eliciting descriptions of images from participants or patients, will always be a pre-requisite for informative research in mental imagery, and can in itself go a long way to achieving precisely-tailored imagery experiences in experimental settings and clinical settings. Similarly, within a treatment context, there undoubtedly remain many important insights that can be gained through careful clinical observation and questioning, which may then feed into new research questions or treatment techniques.

How can the needs for both inter-disciplinary integration and an ever-increasing focus on fine detail, as discussed above, be reconciled? A usable mental imagery-focussed ‘grand theory of everything’ aiming to integrate all our research knowledge is probably not only unfeasible but also not necessarily useful for many practical purposes. One more manageable alternative is to focus on specific, restricted, phenomena thought to have key clinical relevance, and within this specific focus try to incorporate and synthesise the relevant research findings and insights from other fields, disciplines, and perspectives, thus drawing on the richest possible information to understand and treat the specific phenomenon under investigation. The experience of distressing intrusive imagery is one example where this kind of approach has been explicitly discussed (e.g. Singh et al. 2020 ; Visser et al. 2018 ). If an ultimate aim of the research is to improve treatment outcomes, then applying a comprehensive inter-disciplinary perspective within a narrow focus on a specific clinically relevant imagery target may provide a feasible way to synthesise many sources of information and perspectives without losing sight of important details.

Clinical Innovation and Implementation Perspectives

Clinical innovation in the use of imagery in CBT may encounter a similar challenge to that facing research in this area: There is now simply so much published that it can be difficult to get a good grasp of what exactly has been done before and why it might have been effective or otherwise. Given the long-standing use of imagery in CBT and psychological therapy more broadly, ‘new’ techniques or approaches may sometimes be presented that appear to be very similar to existing but perhaps neglected ideas from the past, leading to a sense of re-invention of the wheel. This is not a phenomenon that is limited to imagery, but is well-illustrated in this area given the use of imagery across so many different formats of psychotherapy and, superficially at least, the limited scope of things one might ask someone to imagine. Thus, someone might read about a new technique and think: isn’t this just imaginal exposure under another name, or with some extra ‘features’ that may or may not add anything? Although wheels are probably re-invented at regular intervals within psychological therapy research and practice, particularly given that certain techniques or ideas tend to come in and out of fashion over time, it is also important to consider that a certain amount of re-appraisal of ‘old’ techniques is inevitable as a field develops, and is a necessary first step before improving such techniques: theories and models develop over time (hopefully), and before a technique can be applied and improved it needs to be explainable within the framework being used by the researcher or clinician. As an extreme example, a clinician or researcher may discover a technique used by Freud that, when they try it, is amazingly effective with their patients. However, they are unlikely to take Freud’s rationale for the treatment’s use but rather will need to re-explain it within their own working therapy model (perhaps adjusting the model if required). This in turn will likely suggest different ways to improve its application than those that would have been suggested by Freud’s initial conceptualisation of the treatment technique’s mechanism. As a perhaps more subtle example from within imagery work, Wolpe conceptualised the role of relaxation in his systematic desensitisation procedure as providing ‘reciprocal inhibition’ (Wolpe 1958 ). However, a later suggestion was that if relaxation had a beneficial effect in imaginal exposure, this may have been via allowing patients to generate more vivid imagery (Mathews 1971 ); this alternative conceptualisation would lead to quite different approaches to improving the treatment’s effectiveness. To take a more recent example unrelated to imagery, viewing exposure through the lens of inhibitory learning rather than previous ideas around habituation (Craske et al. 2014 ) also leads to divergent routes for improving this technique, albeit from the same procedural starting point. Thus some degree of re-invention of older techniques, or pulling in of techniques from other schools of psychotherapy and re-evaluating these in terms of CBT models, is a necessary part of continued treatment development and provides one potentially very valuable way of increasing our treatments’ effectiveness. However, this should be carried out with an awareness of the past work in order to avoid unnecessary repetition and waste, and re-branding or overcomplicating for its own sake should of course be avoided. Clinical innovation, whether from development of completely novel approaches or via re-evaluation of existing techniques, therefore represents an area where the interface of science and clinical practice is particularly important. New imagery techniques may be derived via a number of routes, for example via translation from basic research or via intuitive experimentation by clinicians in practice. However, in order to take such techniques forward, they should ideally be linked to mechanisms that can be articulated and tested in order to avoid a treatment being developed in an unhelpful direction simply because the initial putative mechanism was in fact not the one responsible for the beneficial effects (e.g. initial theories underlying the development of EMDR were almost certainly incorrect; Van den Hout and Engelhard 2012 ).

Given the ever-increasing number of apparently successful imagery-based techniques and CBT therapy approaches, one further clinical challenge for the future is the dissemination of these new approaches and their integration into routine clinical practice. If an imagery-focused therapy is developed for application in the context of a disorder for which there are already established CBT variants, and appears efficacious, what happens next? Should this new therapy be viewed as a replacement, an alternative option to choose from, or as providing a catalogue of ideas and techniques to be drawn on and integrated into the ‘standard’ approach? Perhaps we would wish to conduct a trial to compare this new treatment to existing ones, or to identify which treatment may be more indicated for which patients? However, such trials (particularly ones aiming to identify patient subgroups) require huge participant numbers, time, and therapist resources. Of course, this problem is not specific to new imagery-based approaches, but rather across the whole of CBT as researchers and clinicians continue to try to develop more effective therapies and more effective ways of tailoring therapy choices to individuals (see e.g. Dunn et al. 2019 , for a broader perspective). One way forwards gaining increasing traction is to move away from ‘brand-name’ therapies and towards a focus on mechanisms and methods to target them (e.g. Hofmann and Hayes 2019 ; Holmes et al. 2018 ). The efficacy of an imagery-based approach, or incorporation of imagery into a technique, in targeting a specific mechanism could potentially be tested via experimental studies or ‘micro-trials’ amongst treatment-seeking individuals before incorporation into broader treatment frameworks, for example flexible ‘modularised’ therapy approaches (e.g. Black et al. 2018 ; Evans et al. 2020 ).

Implications for the Individual Researcher or Clinician

What does the current state of imagery in the science and practice of CBT imply for the ongoing work of individual researchers and clinicians? One clear implication is that clinicians and researchers need to have an awareness of mental imagery, its properties, and its potential dysfunctional manifestations in psychopathology, keeping it in mind when developing research concepts or conducting therapy. From a research perspective, given that imagery appears to be interwoven into so many cognitive and emotional processes, not considering whether or how imagery may play a role in any particular mechanism under investigation may result in an important facet of this mechanism being missed. From a clinical perspective, not asking about imagery risks missing out on important aspects of a patient’s experience, and potentially key drivers of distress, impairment, or even risk (e.g. imagery of suicide or self-injury; Hales et al. 2011 ; Weßlau et al. 2015 ), and rules out the possibility of making use of simple and efficacious imagery-based treatment techniques. Or to frame this more positively, considering and asking about imagery opens up many opportunities for valuable clinical and research insights and a vast array of treatment possibilities. Further, understanding the basic science underlying imagery can lead to potential methods for augmenting ‘standard’ therapy techniques in a fairly uncomplicated manner (e.g. as illustrated in relation to thought records by Josefowitz 2017 ).

As a note of caution, there will of course be many individuals (both patients and therapists) who experience little imagery and do not find it a useful working tool in therapy. Further, given limited time and resources, principles of parsimony need to be applied—there are likely many situations in which using imagery-based techniques will not be necessary and may even distract from other, in specific cases more efficacious, aspects of therapies (especially if these are less attractive to therapists e.g. Waller 2009 ). Hence, delineating the boundaries of imagery’s utility in therapy will also remain an important consideration. However, simply remembering to ask oneself “could there be a role for imagery here?” has great potential for opening up many avenues in both the science and practice of CBT.

Conclusions

Mental imagery has a long and varied history in the science and practice of CBT, and the recognition of its powerful effects has led to ever-increasing inclusion of imagery into theories informing treatment and into treatment protocols themselves. This leads to a current situation that is both full of exciting potential but also potentially overwhelming, given the now quite impressive breadth of the field. Challenges for future research and clinical practice include navigating the need to both go further and further into specific details, but at the same time to make trans-disciplinary connections and maintain an overview, retain the principles of parsimony in theory and clinical practice, and be on the lookout for limitations, counter-indications, and boundaries to the utility of imagery in treatment. Returning to basic principles of what exactly imagery is and how it occurs (to the best of our current understanding), and keeping a close eye on hypothesised mechanisms—and testing these wherever possible—can help to navigate this complexity. Both research and clinical outcomes indicate that imagery can have profound effects on emotions, cognitions, beliefs, and behaviour, and there is plenty of scope to improve our understanding of how best to harness or leverage this potential to effect therapeutic change in CBT.

Addis, D. R., & Schacter, D. L. (2008). Constructive episodic simulation: temporal distance and detail of past and future events modulate hippocampal engagement. Hippocampus, 18 (2), 227–237. https://doi.org/10.1002/hipo.20405 .

Article PubMed Google Scholar

Andrade, J., Kavanagh, D. J., & Baddeley, A. D. (1997). Eye-movements and visual imagery: a working memory approach to the treatment of post-traumatic stress disorder. British Journal of Clinical Psychology, 36 (2), 209–223.

Arntz, A. (2012). Imagery rescripting as a therapeutic technique: review of clinical trials, basic studies, and research agenda. Journal of Experimental Psychopathology, 3 , 121–126. https://doi.org/10.5127/jep.024211 .

Article Google Scholar

Arntz, A., & Weertman, A. (1999). Treatment of childhood memories: theory and practice. Behaviour Research and Therapy, 37 (8), 715–740.

Arntz, A., de Groot, C., & Kindt, M. (2005). Emotional memory is perceptual. Journal of Behavior Therapy and Experimental Psychiatry, 36 (1), 19–34.

Barzykowski, K., & Niedźwieńska, A. (2018). Priming involuntary autobiographical memories in the lab. Memory, 26 (2), 277–289. https://doi.org/10.1080/09658211.2017.1353102 .

Basile, B., De Sanctis, B., Fadda, S., Luppino, O. I., Perdighe, C., Saliani, A. M., Tenore, K., & Mancini, F. (2018). Early life experiences in OCD and other disorders: a retrospective observational study using Imagery with Re-scripting. Clinical Neuropsychiatry: Journal of Treatment Evaluation, 15 (5), 299–305.

Google Scholar

Beaty, R. E., Seli, P., & Schacter, D. L. (2019). Thinking about the past and future in daily life: an experience sampling study of individual differences in mental time travel. Psychological Research, 83 (4), 805–816. https://doi.org/10.1007/s00426-018-1075-7 .

Beck, A. T. (1976). Cognitive Therapy and the emotional disorders . New York: International Universities Press.

Beck, A. T., & Emery, G. (1985). Anxiety disorders and phobias: a cognitive perspective . New York: Basic Books.

Beck, A. T., Rush, J. A., Shaw, F. B., & Emery, G. (1979). Cognitive therapy of depression . New York: Guilford Press.

Bell, T., Mackie, L., & Bennett-Levy, J. (2015). ‘Venturing towards the dark side’: the use of imagery interventions by recently qualified cognitive-behavioural therapists. Clinical Psychology & Psychotherapy, 22 (6), 591–603. https://doi.org/10.1002/cpp.1920 .

Bennett-Levy, J., Butler, G., Fennell, M., Hackman, A., Mueller, M., & Westbrook, D. (2004). Oxford Guide to Behavioural Experiments in Cognitive Therapy . Oxford: OUP Oxford.

Book Google Scholar

Bennett-Levy, J., Thwaites, R., Chaddock, A., & Davis, M. (2009). Reflective practice in cognitive behavioural therapy: the engine of lifelong learning. In J. Stedmon & R. Dallos (Eds.), Reflective Practice In Psychotherapy And Counselling . Maidenhead: Open University Press.

Berntsen, D. (1996). Involuntary autobiographical memories. Applied Cognitive Psychology, 10 (5), 435–454. https://doi.org/10.1002/(sici)1099-0720(199610)10:5<435::aid-acp408>3.0.co;2-l .

Berntsen, D., & Jacobsen, A. S. (2008). Involuntary (spontaneous) mental time travel into the past and future. Consciousness and Cognition, 17 , 1093–1104.

Betts, G. H. (1909). The distribution and functions of mental imagery . New York: Columbia University.

Black, M., Hitchcock, C., Bevan, A., Leary, C. O., Clarke, J., Elliott, R., Watson, P., LaFortune, L., Rae, S., Gilbody, S., Kuyken, W., Johnston, D., Newby, J. M., & Dalgleish, T. (2018). The HARMONIC trial: study protocol for a randomised controlled feasibility trial of Shaping Healthy Minds—a modular transdiagnostic intervention for mood, stressor-related and anxiety disorders in adults. BMJ Open, 8 (8), e024546. https://doi.org/10.1136/bmjopen-2018-024546 .

Article PubMed PubMed Central Google Scholar

Blackwell, S. E. (2019). Mental imagery: From basic research to clinical practice. Journal of Psychotherapy Integration, 29 , 235–247. https://doi.org/10.1037/int0000108 .

Blackwell, S. E. (2020). Emotional mental imagery. In A. Abraham (Ed.), The Cambridge Handbook of the Imagination . Cambridge: Cambridge University Press.

Blackwell, S. E., Browning, M., Mathews, A., Pictet, A., Welch, J., Davies, J., Watson, P., Geddes, J. R., & Holmes, E. A. (2015). Positive imagery-based cognitive bias modification as a web-based treatment tool for depressed adults: a randomized controlled trial. Clinical Psychological Science, 3 (1), 91–111. https://doi.org/10.1177/2167702614560746 .

Borkovec, T. D., Lyonfields, J. D., Wiser, S. L., & Deihl, L. (1993). The role of worrisome thinking in the suppression of cardiovascular-response to phobic imagery. Behaviour Research and Therapy, 31 (3), 321–324.

Brewin, C. R., & Saunders, J. (2001). The effect of dissociation at encoding on intrusive memories for a stressful film. British Journal of Medical Psychology, 74 (4), 467–472.

Brewin, C. R., Wheatley, J., Patel, T., Fearon, P., Hackmann, A., Wells, A., Fisher, P., & Myers, S. (2009). Imagery rescripting as a brief stand-alone treatment for depressed patients with intrusive memories. Behaviour Research and Therapy, 47 (7), 569–576.

Campbell, I. N., Gallagher, M., McLeod, H. J., O’Neill, B., & McMillan, T. M. (2019). Brief compassion focused imagery for treatment of severe head injury. Neuropsychological Rehabilitation, 29 (6), 917–927. https://doi.org/10.1080/09602011.2017.1342663 .

Chapman, J., Halldorsson, B., & Creswell, C. (2020). Mental imagery in social anxiety in children and young people: a systematic review. Clinical Child and Family Psychology Review, 23 (3), 379–392. https://doi.org/10.1007/s10567-020-00316-2 .

Çili, S., & Stopa, L. (2015). Intrusive mental imagery in psychological disorders: is the self the key to understanding Maintenance? Frontiers in Psychiatry, 6 , 103. https://doi.org/10.3389/fpsyt.2015.00103 .

Clark, D. M., & Wells, A. (1995). A cognitive model of social phobia. In R. G. Heimberg, M. Liebowitz, D. Hope, & F. Schneier (Eds.), Social Phobia: Diagnosis, Assement and Treatment (pp. 69–93). New York: Guilford Press.

Cole, S., & Kvavilashvili, L. (2019). Spontaneous future cognition: the past, present and future of an emerging topic. Psychological Research, 83 (4), 631–650. https://doi.org/10.1007/s00426-019-01193-3 .

Craske, M. G., Treanor, M., Conway, C. C., Zbozinek, T., & Vervliet, B. (2014). Maximizing exposure therapy: an inhibitory learning approach. Behaviour Research and Therapy, 58 , 10–23. https://doi.org/10.1016/j.brat.2014.04.006 .

Cumming, J., & Williams, S. E. (2012). The role of imagery in performance. In S. M. Murphy (Ed.), Handbook of sport and performance psychology (pp. 213–232). Oxford: Oxford University Press.

Chapter Google Scholar

D’Argembeau, A., Renaud, O., & Van der Linden, M. (2011). Frequency, characteristics and functions of future-oriented thoughts in daily life. Applied Cognitive Psychology, 25 (1), 96–103. https://doi.org/10.1002/acp.1647 .

Dainer-Best, J., Shumake, J. D., & Beevers, C. G. (2018). Positive imagery training increases positive self-referent cognition in depression. Behaviour Research and Therapy, 111 , 72–83. https://doi.org/10.1016/j.brat.2018.09.010 .

Dawes, A. J., Keogh, R., Andrillon, T., & Pearson, J. (2020). A cognitive profile of multi-sensory imagery, memory and dreaming in aphantasia. Scientific Reports, 10 (1), 10022. https://doi.org/10.1038/s41598-020-65705-7 .

Di Simplicio, M., Renner, F., Blackwell, S. E., Mitchell, H., Stratford, H. J., Watson, P., Myers, N., Nobre, A. C., Lau-Zhu, A., & Holmes, E. A. (2016). An investigation of mental imagery in bipolar disorder: exploring “the mind’s eye.”. Bipolar Disorders, 18 , 669–683. https://doi.org/10.1111/bdi.12453 .

Di Simplicio, M., Appiah-Kusi, E., Wilkinson, P., Watson, P., Meiser-Stedman, C., Kavanagh, D. J., & Holmes, E. A. (2020). Imaginator: A Proof-of-concept feasibility trial of a brief imagery-based psychological intervention for young people who self-harm. Suicide and Life-threatening Behavior, 50 (3), 724–740. https://doi.org/10.1111/sltb.12620 .

Dobinson, K. A., Norton, A. R., & Abbott, M. J. (2020). The relationship between negative self-imagery and social anxiety in a clinically diagnosed sample. Cognitive Therapy and Research, 44 (1), 156–170. https://doi.org/10.1007/s10608-019-10051-w .

Dugué, R., Keller, S., Tuschen-Caffier, B., & Jacob, G. A. (2016). Exploring the mind’s eye: contents and characteristics of mental images in overweight individuals with binge eating behaviour. Psychiatry Research, 246 , 554–560. https://doi.org/10.1016/j.psychres.2016.10.028 .

Dugué, R., Renner, F., Austermann, M., Tuschen-Caffier, B., & Jacob, G. A. (2019). Imagery rescripting in individuals with binge-eating behavior: an experimental proof-of-concept study. International Journal of Eating Disorders, 52 (2), 183–188. https://doi.org/10.1002/eat.22995 .

Dunn, B. D., O’Mahen, H., Wright, K., & Brown, G. (2019). A commentary on research rigour in clinical psychological science: how to avoid throwing out the innovation baby with the research credibility bath water in the depression field. Behaviour Research and Therapy, 120 , 103417. https://doi.org/10.1016/j.brat.2019.103417 .

Edwards, D. (2007). Restructuring implicational meaning through memory-based imagery: some historical notes. Journal of Behavior Therapy and Experimental Psychiatry, 38 (4), 306–316.

Edwards, D. (2011). Invited essay: From ancient shamanic healing to twenty-first century psychotherapy: the central role of imagery methods in effecting psychological change. In E. A. Holmes & A. Hackmann, Oxford Guide to Imagery in Cognitive Therapy (pp. xxxiii–xlii). Oxford: Oxford University Press.

Ehlers, A., Clark, D. M., Hackmann, A., McManus, F., & Fennell, M. (2005). Cognitive therapy for post-traumatic stress disorder: development and evaluation. Behaviour Research and Therapy, 43 (4), 413–431. https://doi.org/10.1016/j.brat.2004.03.006 .

Engelhard, I. M., van den Hout, M. A., & Smeets, M. A. M. (2011). Taxing working memory reduces vividness and emotional intensity of images about the Queen’s Day tragedy. Journal of Behavior Therapy and Experimental Psychiatry, 42 (1), 32–37. https://doi.org/10.1016/j.jbtep.2010.09.004 .

Evans, S. C., Weisz, J. R., Carvalho, A. C., Garibaldi, P. M., Bearman, S. K., Chorpita, B. F., & Research Network on Youth Mental Health. (2020). Effects of standard and modular psychotherapies in the treatment of youth with severe irritability. Journal of Consulting and Clinical Psychology, 88 (3), 255–268. https://doi.org/10.1037/ccp0000456 .

Fechner, G. T. (1860). Elemente der Psychophysik . Leipzig: Breitkopf und Härtel.

Foa, E. B., Hembree, E. A., & Rothbaum, B. O. (2007). Prolonged exposure therapy for PTSD: emotional processing of traumatic experiences . Oxford: Oxford University Press.

Galton, F. (1883). Inquiries into human faculty and its development . London: Dent.

Goldfried, M. R., & Davison, G. C. (1976). Clinical behavior therapy . New York: Holt, Rinehart, and Winston.

Greenberger, D., & Padesky, C. A. (2015). Mind over mood, Second Edition: Change How You Feel by Changing the Way You Think . New York: Guilford Publications.

Grégoire, L., & Greening, S. G. (2019). Opening the reconsolidation window using the mind’s eye: extinction training during reconsolidation disrupts fear memory expression following mental imagery reactivation. Cognition, 183 , 277–281. https://doi.org/10.1016/j.cognition.2018.12.001 .

Hales, S. A., Deeprose, C., Goodwin, G. M., & Holmes, E. A. (2011). Cognitions in bipolar disorder versus unipolar depression: Imagining suicide. Bipolar Disorders, 13 (7–8), 651–661. https://doi.org/10.1111/j.1399-5618.2011.00954.x .

Hales, S. A., Blackwell, S. E., Di Simplicio, M., Iyadurai, L., Young, K., & Holmes, E. A. (2014). Imagery-based cognitive-behavioral assessment. In G. P. Brown & D. A. Clark (Eds.), Assessment in Cognitive Therapy . New York: Guilford Press.

Hallford, D. J., Barry, T. J., Austin, D. W., Raes, F., Takano, K., & Klein, B. (2020a). Impairments in episodic future thinking for positive events and anticipatory pleasure in major depression. Journal of Affective Disorders, 260 , 536–543. https://doi.org/10.1016/j.jad.2019.09.039 .

Hallford, D. J., Sharma, M. K., & Austin, D. W. (2020b). Increasing anticipatory pleasure in major depression through enhancing episodic future thinking: a randomized single-case series trial. Journal of Psychopathology and Behavioral Assessment, 42 , 751–764. https://doi.org/10.1007/s10862-020-09820-9 .

Hallford, D. J., Yeow, J. J. E., Fountas, G., Herrick, C. A., Raes, F., & D’Argembeau, A. (2020c). Changing the future: an initial test of future specificity training (FeST). Behaviour Research and Therapy, 131 , 103638. https://doi.org/10.1016/j.brat.2020.103638 .

Harvey, K., Kemps, E., & Tiggemann, M. (2005). The nature of imagery processes underlying food cravings. British Journal of Health Psychology, 10 (Pt 1), 49–56.

Hassabis, D., & Maguire, E. A. (2009). The construction system of the brain. Philosophical Transactions of the Royal Society B, 364 (1521), 1263–1271. https://doi.org/10.1098/rstb.2008.0296 .

Heidenreich, T., & Tata, P. (2019). Proceedings of the 9th World Congress of Behavioural & Cognitive Therapies, Berlin, July 17th–20th 2019: Vol. I: Research, Applied Issues . Tübingen: dgvt-Verlag.

Heidenreich, T., Tata, P., & Blackwell, S. (2019). Proceedings of the 9th World Congress of Behavioural & Cognitive Therapies, Berlin, July 17th–20th 2019: Vol. II: Posters . Tübingen: dgvt-Verlag.

Hirsch, C. R., Mathews, A., Clark, D. M., Williams, R., & Morrison, J. (2005). The causal role of negative imagery in social anxiety: a test in confident public speakers. Journal of Behavior Therapy and Experimental Psychiatry, 37 (2), 159–170.

Hirsch, C. R., Clark, D. M., & Mathews, A. (2006). Imagery and interpretations in social phobia: support for the combined cognitive biases hypothesis. Behavior Therapy, 37 (3), 223–236.

Hitchcock, C., Werner-Seidler, A., Blackwell, S. E., & Dalgleish, T. (2017). Autobiographical episodic memory-based training for the treatment of mood, anxiety and stress-related disorders: a systematic review and meta-analysis. Clinical Psychology Review, 52 , 92–107. https://doi.org/10.1016/j.cpr.2016.12.003 .

Hofmann, S. G., & Hayes, S. C. (2019). The Future of intervention science: process-based therapy. Clinical Psychological Science: A Journal of the Association for Psychological Science, 7 (1), 37–50. https://doi.org/10.1177/2167702618772296 .

Holmes, E. A., & Hackmann, A. (2004). A healthy imagination? Editorial for the special issue of memory: mental imagery and memory in psychopathology. Memory, 12 (4), 387–388. https://doi.org/10.1080/09658210444000124 .

Holmes, E. A., & Mathews, A. (2005). Mental imagery and emotion: A special relationship? Emotion, 5 (4), 489–497. https://doi.org/10.1037/1528-3542.5.4.489 .

Holmes, E. A., & Mathews, A. (2010). Mental imagery in emotion and emotional disorders. Clinical Psychology Review, 30 (3), 349–362. https://doi.org/10.1016/j.cpr.2010.01.001 .

Holmes, E. A., Arntz, A., & Smucker, M. R. (2007a). Imagery rescripting in cognitive behaviour therapy: images, treatment techniques and outcomes. Journal of Behavior Therapy and Experimental Psychiatry, 38 (4), 297–305. https://doi.org/10.1016/j.jbtep.2007.10.007 .

Holmes, E. A., Crane, C., Fennell, M. J. V., & Williams, J. M. G. (2007b). Imagery about suicide in depression—“flash-forwards”? Journal of Behavior Therapy and Experimental Psychiatry, 38 (4), 423–434. https://doi.org/10.1016/j.jbtep.2007.10.004 .

Holmes, E. A., Blackwell, S. E., Burnett Heyes, S., Renner, F., & Raes, F. (2016a). Mental imagery in depression: phenomenology, potential mechanisms, and treatment implications. Annual Review of Clinical Psychology, 12 , 249–280. https://doi.org/10.1146/annurev-clinpsy-021815-092925 .

Holmes, E. A., Bonsall, M. B., Hales, S. A., Mitchell, H., Renner, F., Blackwell, S. E., Watson, P., Goodwin, G. M., & Di Simplicio, M. (2016b). Applications of time-series analysis to mood fluctuations in bipolar disorder to promote treatment innovation: a case series. Translational Psychiatry, 6 , e720. https://doi.org/10.1038/tp.2015.207 .

Holmes, E. A., Ghaderi, A., Harmer, C. J., Ramchandani, P. G., Cuijpers, P., Morrison, A. P., Roiser, J. P., Bockting, C. L. H., O’Connor, R. C., Shafran, R., Moulds, M. L., & Craske, M. G. (2018). The Lancet Psychiatry Commission on psychological treatments research in tomorrow’s science. The Lancet. Psychiatry, 5 (3), 237–286. https://doi.org/10.1016/S2215-0366(17)30513-8 .

Holmes, E. A., Hales, S. A., Young, K., & Simplicio, M. D. (2019). Imagery-based cognitive therapy for bipolar disorder and mood instability . New York: Guilford Publications.

James, E. L., Lau-Zhu, A., Clark, I. A., Visser, R. M., Hagenaars, M. A., & Holmes, E. A. (2016). The trauma film paradigm as an experimental psychopathology model of psychological trauma: Intrusive memories and beyond. Clinical Psychology Review, 47 , 106–142. https://doi.org/10.1016/j.cpr.2016.04.010 .

Ji, J. L., Burnett Heyes, S., MacLeod, C., & Holmes, E. A. (2016). Emotional mental imagery as simulation of reality: fear and beyond. A tribute to Peter Lang. Behavior Therapy, 47 (5), 702–719. https://doi.org/10.1016/j.beth.2015.11.004 .

Ji, J. L., Kavanagh, D. J., Holmes, E. A., MacLeod, C., & Simplicio, M. D. (2019). Mental imagery in psychiatry: conceptual & clinical implications. CNS Spectrums, 24 (1), 114–126. https://doi.org/10.1017/S1092852918001487 .

Josefowitz, N. (2017). Incorporating imagery into thought records: increasing engagement in balanced thoughts. Cognitive and Behavioral Practice, 24 (1), 90–100. https://doi.org/10.1016/j.cbpra.2016.03.005 .

Jung, K., & Steil, R. (2013). A randomized controlled trial on cognitive restructuring and imagery modification to reduce the feeling of being contaminated in adult survivors of childhood sexual abuse suffering from posttraumatic stress disorder. Psychotherapy and Psychosomatics, 82 (4), 213–220. https://doi.org/10.1159/000348450 .

Kadriu, F., Claes, L., Witteman, C., Norré, J., Vrieze, E., & Krans, J. (2019). Characteristics and content of intrusive images in patients with eating disorders. European Eating Disorders Review, 27 (5), 495–506. https://doi.org/10.1002/erv.2671 .

Kazdin, A. E. (1973). Covert modeling and the reduction of avoidance behavior. Journal of Abnormal Psychology, 81 , 87–95.

Kosslyn, S. M., Ganis, G., & Thompson, W. L. (2001). Neural foundations of imagery. Nature Reviews Neuroscience, 2 (9), 635–642. https://doi.org/10.1038/35090055 .

Krans, J., Näring, G., Holmes, E. A., & Becker, E. S. (2009). “I see what you are saying”: intrusive images from listening to a traumatic verbal report. Journal of Anxiety Disorders, 24 (1), 134–140. https://doi.org/10.1016/j.janxdis.2009.09.009 .

Krypotos, A.-M., Mertens, G., Leer, A., & Engelhard, I. M. (2020). Induction of conditioned avoidance via mental imagery. Behaviour Research and Therapy, 132 , 103652. https://doi.org/10.1016/j.brat.2020.103652 .

Kunze, A. E., Arntz, A., & Kindt, M. (2019). Investigating the effects of imagery rescripting on emotional memory: a series of analogue studies. Journal of Experimental Psychopathology, 10 (2), 2043808719850733. https://doi.org/10.1177/2043808719850733 .

Lang, P. J. (1977). Imagery in therapy: an information processing analysis of fear. Behavior Therapy, 8 (5), 862–886. https://doi.org/10.1016/S0005-7894(77)80157-3 .

Lang, P. J. (1979). A bio-informational theory of emotional imagery. Psychophysiology, 16 (6), 495–512. https://doi.org/10.1111/j.1469-8986.1979.tb01511.x .

Lang, P. J., Kozak, M. J., Miller, G. A., Levin, D. N., & McLean, A. (1980). Emotional imagery: conceptual structure and pattern of somato-visceral response. Psychophysiology, 17 (2), 179–192. https://doi.org/10.1111/j.1469-8986.1980.tb00133.x .

Lewis, D. E., O-Reilly, M. J., Khuu, S. K., & Pearson, J. (2013). Conditioning the mind’s eye: associative learning with voluntary mental imagery. Clinical Psychological Science, 1 (4), 390–400. https://doi.org/10.1177/2167702613484716 .

Linke, J., & Wessa, M. (2017). Mental imagery training increases wanting of rewards and reward sensitivity and reduces depressive symptoms. Behavior Therapy, 48 (5), 695–706. https://doi.org/10.1016/j.beth.2017.04.002 .

Mace, J. H. (2005). Priming involuntary autobiographical memories. Memory, 13 (8), 874–884.

Maier, A., Schaitz, C., Kröner, J., Connemann, B., & Sosic-Vasic, Z. (2020). Imagery rescripting: exploratory evaluation of a short intervention to reduce test anxiety in university students. Frontiers in Psychiatry, 11 , 84. https://doi.org/10.3389/fpsyt.2020.00084 .

Malcolm, C. P., Picchioni, M. M., & Ellett, L. (2015). Intrusive prospective imagery, posttraumatic intrusions and anxiety in schizophrenia. Psychiatry Research, 230 (3), 899–904. https://doi.org/10.1016/j.psychres.2015.11.029 .

Marks, D. F. (1999). Consciousness, mental imagery and action. British Journal of Psychology, 90 (4), 567–585. https://doi.org/10.1348/000712699161639 .

Marks, I., Marset, P., Boulougouris, J., & Huson, J. (1971). Physiological accompaniments of neutral and phobic imagery. Psychological Medicine, 1 , 299–307. https://doi.org/10.1017/S0033291700042264 .

Mathews, A. (1971). Psychophysiological approaches to the investigation of desensitisation and related processes. Psychological Bulletin, 76 , 73–91.

May, J., Andrade, J., Panabokke, N., & Kavanagh, D. J. (2004). Images of desire: cognitive models of craving. Memory, 12 (4), 447–461.

McEvoy, P. M., & Saulsman, L. M. (2014). Imagery-enhanced cognitive behavioural group therapy for social anxiety disorder: a pilot study. Behaviour Research and Therapy, 55 , 1–6. https://doi.org/10.1016/j.brat.2014.01.006 .

McEwan, K., & Gilbert, P. (2016). A pilot feasibility study exploring the practising of compassionate imagery exercises in a nonclinical population. Psychology and Psychotherapy: Theory, Research and Practice, 89 (2), 239–243. https://doi.org/10.1111/papt.12078 .

McManus, F., Clark, D. M., Grey, N., Wild, J., Hirsch, C., Fennell, M., Hackmann, A., Waddington, L., Liness, S., & Manley, J. (2009). A demonstration of the efficacy of two of the components of cognitive therapy for social phobia. Journal of Anxiety Disorders, 23 (4), 496–503. https://doi.org/10.1016/j.janxdis.2008.10.010 .

Meichenbaum, D. (1974). Cognitive behavior modification . New York: General Learning Press.

Meichenbaum, D. (1978). Why does using imagery in psychotherapy lead to change? In J. L. Singer & K. S. Pope (Eds.), The Power of Human Imagination: New Methods in Psychotherapy (pp. 381–394). Berlin: Springer US. https://doi.org/10.1007/978-1-4613-3941-0_13 .

Mertens, G., Krypotos, A.-M., & Engelhard, I. M. (2020). A review on mental imagery in fear conditioning research 100 years since the ‘Little Albert’ study. Behaviour Research and Therapy, 126 , 103556. https://doi.org/10.1016/j.brat.2020.103556 .

Miloyan, B., McFarlane, K. A., & Suddendorf, T. (2019). Measuring mental time travel: is the hippocampus really critical for episodic memory and episodic foresight? Cortex, 117 , 371–384. https://doi.org/10.1016/j.cortex.2019.01.020 .

Mooren, N., Krans, J., Näring, G., & van Minnen, A. (2019). Vantage perspective in analogue trauma memories: an experimental study. Cognition and Emotion, 33 (6), 1261–1270. https://doi.org/10.1080/02699931.2018.1538010 .

Morina, N., Lancee, J., & Arntz, A. (2017). Imagery rescripting as a clinical intervention for aversive memories: a meta-analysis. Journal of Behavior Therapy and Experimental Psychiatry, 55 , 6–15. https://doi.org/10.1016/j.jbtep.2016.11.003 .

Moritz, S., Hormann, C. C., Schroder, J., Berger, T., Jacob, G. A., Meyer, B., Holmes, E. A., Spath, C., Hautzinger, M., Lutz, W., & Klein, J. P. (2014). Beyond words: sensory properties of depressive thoughts. Cognition and Emotion, 28 (6), 1047–1056. https://doi.org/10.1080/02699931.2013.868342 .

Moritz, S., Ahlf-Schumacher, J., Hottenrott, B., Peter, U., Franck, S., Schnell, T., Peter, H., Schneider, B. C., & Jelinek, L. (2018). We cannot change the past, but we can change its meaning. a randomized controlled trial on the effects of self-help imagery rescripting on depression. Behaviour Research and Therapy, 104 , 74–83. https://doi.org/10.1016/J.BRAT.2018.02.007 .

Mueller, E. M., Sperl, M. F. J., & Panitz, C. (2019). Aversive imagery causes de novo fear conditioning. Psychological Science, 30 (7), 1001–1015. https://doi.org/10.1177/0956797619842261 .

Murphy, F. C., Peers, P. V., Blackwell, S. E., Holmes, E. A., & Manly, T. (2019). Anticipated and imagined futures: prospective cognition and depressed mood following brain injury. British Journal of Clinical Psychology, 58 (1), 91–109. https://doi.org/10.1111/bjc.12202 .

Naismith, I., Kerr, S., Mwale, A., & Feigenbaum, J. (2019). A thematic analysis of compassion-focused imagery for people with personality disorder: inhibitors, facilitators and clinical recommendations. Clinical Psychologist, 23 (3), 213–224. https://doi.org/10.1111/cp.12180 .

Norton, A. R., & Abbott, M. J. (2016). The efficacy of imagery rescripting compared to cognitive restructuring for social anxiety disorder. Journal of Anxiety Disorders, 40 , 18–28. https://doi.org/10.1016/j.janxdis.2016.03.009 .

O’Connor, R. C., & Kirtley, O. J. (2018). The integrated motivational–volitional model of suicidal behaviour. Philosophical Transactions of the Royal Society, B: Biological Sciences, 373 (1754), 20170268. https://doi.org/10.1098/rstb.2017.0268 .

Article PubMed Central Google Scholar

Pajak, R., Langhoff, C., Watson, S., & Kamboj, S. K. (2013). Phenomenology and thematic content of intrusive imagery in bowel and bladder obsession. Journal of Obsessive-Compulsive and Related Disorders, 2 (3), 233–240. https://doi.org/10.1016/j.jocrd.2013.04.005 .

Palmiero, M., Piccardi, L., Giancola, M., Nori, R., D’Amico, S., & Olivetti Belardinelli, M. (2019). The format of mental imagery: from a critical review to an integrated embodied representation approach. Cognitive Processing, 20 (3), 277–289. https://doi.org/10.1007/s10339-019-00908-z .

Paulik, G., Steel, C., & Arntz, A. (2019). Imagery rescripting for the treatment of trauma in voice hearers: a case series. Behavioural and Cognitive Psychotherapy, 47 (6), 709–725. https://doi.org/10.1017/S1352465819000237 .

Pearson, J., Naselaris, T., Holmes, E. A., & Kosslyn, S. M. (2015). Mental imagery: functional mechanisms and clinical applications. Trends in Cognitive Sciences, 19 (10), 590–602. https://doi.org/10.1016/j.tics.2015.08.003 .

Pile, V., & Lau, J. Y. F. (2020). Intrusive images of a distressing future: links between prospective mental imagery, generalized anxiety and a tendency to suppress emotional experience in youth. Behaviour Research and Therapy, 124 , 103508. https://doi.org/10.1016/j.brat.2019.103508 .

Prasko, J., Dicevicius, D., Ociskova, M., Krone, I., Slepecky, M., Albertina, M., Bagdonaviciene, L., & Juskiene, A. (2020). Imagery in cognitive behavioral supervision. Neuro Endocrinology Letters, 41 (1), 33–45.

PubMed Google Scholar

Raabe, S., Ehring, T., Marquenie, L., Olff, M., & Kindt, M. (2015). Imagery rescripting as stand-alone treatment for posttraumatic stress disorder related to childhood abuse. Journal of Behavior Therapy and Experimental Psychiatry, 48 , 170–176. https://doi.org/10.1016/j.jbtep.2015.03.013 .

Raes, F., Hermans, D., Williams, J. M. G., Demyttenaere, K., Sabbe, B., Pieters, G., & Eelen, P. (2005). Reduced specificity of autobiographical memories: a mediator between rumination and ineffective problem-solving in major depression? Journal of Affective Disorders, 87 , 331–335. https://doi.org/10.1016/j.jad.2005.05.004 .

Raes, F., Williams, J. G., & Hermans, D. (2009). Reducing cognitive vulnerability to depression: a preliminary investigation of memory specificity training (MEST) in inpatients with depressive symptomatology. Journal of Behavior Therapy and Experimental Psychiatry, 40 , 24–38.

Rapee, R. M., & Heimberg, R. G. (1997). A cognitive-behavioral model of anxiety in social phobia. Behaviour Research and Therapy, 35 , 741–756.

Renner, F., Murphy, F. C., Ji, J. L., Manly, T., & Holmes, E. A. (2019). Mental imagery as a “motivational amplifier” to promote activities. Behaviour Research and Therapy, 114 , 51–59. https://doi.org/10.1016/j.brat.2019.02.002 .

Reynolds, M., & Brewin, C. R. (1999). Intrusive memories in depression and posttraumatic stress disorder. Behaviour Research and Therapy, 37 (3), 201–215.

Roeckelein, J. E. (2004). Imagery in psychology: a reference guide . Westport: Greenwood Publishing Group.

Saulsman, L. M., Ji, J. L., & McEvoy, P. M. (2019). The essential role of mental imagery in cognitive behaviour therapy: what is old is new again. Australian Psychologist, 54 (4), 237–244. https://doi.org/10.1111/ap.12406 .

Schacter, D. L., Addis, D. R., & Buckner, R. L. (2007). Remembering the past to imagine the future: the prospective brain. Nature Reviews Neuroscience, 8 (Sept), 657–661.

Schacter, D. L., Addis, D. R., Hassabis, D., Martin, V. C., Spreng, R. N., & Szpunar, K. K. (2012). The future of memory: remembering, imagining, and the brain. Neuron, 76 (4), 677–694. https://doi.org/10.1016/j.neuron.2012.11.001 .

Schweizer, T., Renner, F., Sun, D., Kleim, B., Holmes, E. A., & Tuschen-Caffier, B. (2018). Psychophysiological reactivity, coping behaviour and intrusive memories upon multisensory virtual reality and script-driven imagery analogue trauma: a randomised controlled crossover study. Journal of Anxiety Disorders, 59 , 42–52. https://doi.org/10.1016/j.janxdis.2018.08.005 .

Sheaves, B., Holmes, E. A., Rek, S., Taylor, K. M., Nickless, A., Waite, F., Germain, A., Espie, C. A., Harrison, P. J., Foster, R., & Freeman, D. (2019). Cognitive behavioural therapy for nightmares for patients with persecutory delusions (Nites): an Assessor-Blind, Pilot Randomized Controlled Trial. Canadian Journal of Psychiatry. Revue Canadienne de Psychiatrie, 64 (10), 686–696. https://doi.org/10.1177/0706743719847422 .

Siegesleitner, M., Strohm, M., Wittekind, C. E., Ehring, T., & Kunze, A. E. (2020). Improving imagery rescripting treatments: comparing an active versus passive approach. Journal of Behavior Therapy and Experimental Psychiatry, 69 , 101578. https://doi.org/10.1016/j.jbtep.2020.101578 .

Singer, J. L. (2006). Imagery in psychotherapy . Washington, D.C.: American Psychological Association.

Singh, L., Espinosa, L., Ji, J. L., Moulds, M. L., & Holmes, E. A. (2020). Developing thinking around mental health science: the example of intrusive, emotional mental imagery after psychological trauma. Cognitive Neuropsychiatry, 25 (5), 348–363. https://doi.org/10.1080/13546805.2020.1804845 .

Slofstra, C., Klein, N. S., Nauta, M. H., Wichers, M., Batalas, N., & Bockting, C. L. H. (2017). Imagine your mood: study design and protocol of a randomized controlled micro-trial using app-based experience sampling methodology to explore processes of change during relapse prevention interventions for recurrent depression. Contemporary Clinical Trials Communications, 7 , 172–178. https://doi.org/10.1016/j.conctc.2017.07.003 .

Soeter, M., & Kindt, M. (2012). Erasing fear for an imagined threat event. Psychoneuroendocrinology, 37 (11), 1769–1779. https://doi.org/10.1016/j.psyneuen.2012.03.011 .

Solbrig, L., Whalley, B., Kavanagh, D. J., May, J., Parkin, T., Jones, R., & Andrade, J. (2018). Functional imagery training versus motivational interviewing for weight loss: a randomised controlled trial of brief individual interventions for overweight and obesity. International Journal of Obesity, 43 (4), 883–894. https://doi.org/10.1038/s41366-018-0122-1 .

Spurr, J. M., & Stopa, L. (2003). The observer perspective: effects on social anxiety and performance. Behaviour Research and Therapy, 41 (9), 1009–1028.

Strachan, L. P., Hyett, M. P., & McEvoy, P. M. (2020). Imagery rescripting for anxiety disorders and obsessive-compulsive disorder: recent advances and future directions. Current Psychiatry Reports, 22 (4), 17. https://doi.org/10.1007/s11920-020-1139-4 .

Sumner, J. A., Griffith, J. W., & Mineka, S. (2010). Overgeneral autobiographical memory as a predictor of the course of depression: a meta-analysis. Behaviour Research and Therapy, 48 (7), 614–625. https://doi.org/10.1016/j.brat.2010.03.013 .

Tallon, K., Ovanessian, M. M., Koerner, N., & Dugas, M. J. (2020). Mental imagery in generalized anxiety disorder: a comparison with healthy control participants. Behaviour Research and Therapy, 127 , 103571. https://doi.org/10.1016/j.brat.2020.103571 .

Taylor, C. D. J., Bee, P. E., Kelly, J., & Haddock, G. (2019). iMAgery-focused psychological therapy for persecutory delusions in psychosis (iMAPS): a novel treatment approach. Cognitive and Behavioral Practice, 26 (3), 575–588. https://doi.org/10.1016/j.cbpra.2018.10.002 .

Van den Hout, M. A., & Engelhard, I. M. (2012). How does EMDR work? Journal of Experimental Psychopathology, 3 (5), 724–738. https://doi.org/10.5127/jep.028212 .

Veale, D., Page, N., Woodward, E., & Salkovskis, P. (2015). Imagery Rescripting for obsessive compulsive disorder: a single case experimental design in 12 cases. Journal of Behavior Therapy and Experimental Psychiatry, 49 , 230–236. https://doi.org/10.1016/j.jbtep.2015.03.003 .

Visser, R. M., Lau-Zhu, A., Henson, R. N., & Holmes, E. A. (2018). Multiple memory systems, multiple time points: how science can inform treatment to control the expression of unwanted emotional memories. Philosophical Transactions of the Royal Society, B: Biological Sciences, 373 (1742), 20170209. https://doi.org/10.1098/rstb.2017.0209 .

Waller, G. (2009). Evidence-based treatment and therapist drift. Behaviour Research and Therapy, 47 (2), 119–127. https://doi.org/10.1016/j.brat.2008.10.018 .

Warnock-Parkes, E., Wild, J., Stott, R., Grey, N., Ehlers, A., & Clark, D. M. (2017). Seeing is believing: using video feedback in cognitive therapy for social anxiety disorder. Cognitive and Behavioral Practice, 24 (2), 245–255. https://doi.org/10.1016/j.cbpra.2016.03.007 .

Weßlau, C., Cloos, M., Höfling, V., & Steil, R. (2015). Visual mental imagery and symptoms of depression – results from a large-scale web-based study. BMC Psychiatry, 15 (1), 308. https://doi.org/10.1186/s12888-015-0689-1 .

Weßlau, C., Lieberz, K., Oertel-Knöchel, V., & Steil, R. (2016). Distressing visual mental images in depressed patients and healthy controls – are they one and the same? Psychiatry Research, 246 , 341–347. https://doi.org/10.1016/j.psychres.2016.09.034 .

Wicken, M., Keogh, R., & Pearson, J. (2019). The critical role of mental imagery in human emotion: insights from Aphantasia. BioRxiv , 726844. https://doi.org/10.1101/726844 .

Wild, J., Hackmann, A., & Clark, D. M. (2007). When the present visits the past: updating traumatic memories in social phobia. Journal of Behavior Therapy and Experimental Psychiatry, 38 (4), 386–401.

Williams, J. M. G. (2006). Capture rumination, functional avoidance, and executive control (CaRFAX): three processes that underie overgeneral memory. Cognition and Emotion, 20 (3/4), 548–568.

Williams, J. M. G., & Broadbent, K. (1986). Autobiographical memory in suicide attempters. Journal of Abnormal Psychology, 95 (2), 144–149. https://doi.org/10.1037/0021-843X.95.2.144 .

Williams, A. D., & Moulds, M. L. (2008). Negative appraisals and cognitive avoidance of intrusive memories in depression: a replication and extension. Depression and Anxiety, 25 , 26–33.

Williams, J. M. G., Teasdale, J. D., Segal, Z. V., & Soulsby, J. (2000). Mindfulness-based cognitive therapy reduces overgeneral autobiographical memory in formerly depressed patients. Journal of Abnormal Psychology, 109 (1), 150–155.

Wolpe, J. (1958). Psychotherapy by reciprocal inhibition . Palo Alto: Stanford University Press.

Zeman, A., Dewar, M., & Della Sala, S. (2015). Lives without imagery – congenital aphantasia. Cortex, 73 , 378–380. https://doi.org/10.1016/j.cortex.2015.05.019 .

Zlomuzica, A., Woud, M. L., Machulska, A., Kleimt, K., Dietrich, L., Wolf, O. T., Assion, H.-J., Huston, J. P., De Souza Silva, M. A., Dere, E., & Margraf, J. (2018). Deficits in episodic memory and mental time travel in patients with post-traumatic stress disorder. Progress in Neuro-Psychopharmacology and Biological Psychiatry, 83 , 42–54. https://doi.org/10.1016/j.pnpbp.2017.12.014 .

Download references

Open Access funding enabled and organized by Projekt DEAL.

Author information

Authors and affiliations.

Mental Health Research and Treatment Center, Faculty of Psychology, Ruhr-Universität Bochum, Massenbergstrasse 9-13, 44787, Bochum, Germany

Simon E. Blackwell

You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Simon E. Blackwell .

Ethics declarations

Conflict of interest.

The author conducts and publishes research on mental imagery, and applies for grant funding for such research, but otherwise declares that he has no known conflicts of interest.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Blackwell, S.E. Mental Imagery in the Science and Practice of Cognitive Behaviour Therapy: Past, Present, and Future Perspectives. J Cogn Ther 14 , 160–181 (2021). https://doi.org/10.1007/s41811-021-00102-0

Download citation

Accepted : 15 January 2021

Published : 01 February 2021

Issue Date : March 2021

DOI : https://doi.org/10.1007/s41811-021-00102-0

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Mental imagery
Cognitive behaviour therapy
Imagery rescripting
Imaginal exposure
Treatment mechanisms
Find a journal
Publish with us
Track your research

Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to upgrade your browser .

Enter the email address you signed up with and we'll email you a reset link.

We're Hiring!
Help Center

Imagery, affect, and the embodied mind: implications for reading and responding to literature

Related Papers

Anezka Kuzmicova

The objective of this article is twofold. In the first part, I discuss two issues central to any theoretical inquiry into mental imagery: embodiment and consciousness. I do so against the backdrop of second-generation cognitive science, more specifically the increasingly popular research framework of embodied cognition, and I consider two caveats attached to its current exploitation in narrative theory. In the second part, I attempt to cast new light on readerly mental imagery by offering a typology of what I propose to be its four basic varieties. The typology is grounded in the framework of embodied cognition and it is largely compatible with key neuroscientific and other experimental evidence produced within the framework.

Folia linguistica et litteraria

Mojtaba Pordel

In this article, I aim to theorize and formulate the understanding of literary text within an Embodied Cognitive Approach. After sketching out the analyses of literary text understanding conducted within the framework of the so-called Common Cognitive Approach, I will proceed to point out their shortcomings. I will then lay the scientific foundations of the Embodied Theory of Understanding Literary Text (ETULT) by referring to direct and indirect evidence from neurology, psychology and so on. I will introduce ETULT in detail, with the help of a fictional piece of evidence, Dante's Divine Comedy. I will also delineate the outlines of some field studies for the future, through developing questionnaires and brain scans (fMIR and EEG). In short, ETULT asserts that understanding literary texts is an embodied act, occurring processually on two levels of representation: Schematic and Embodied (The Two-Layered Representation Hypothesis or TLRH). Upon encountering a literary text, the reader forms a Blended Mediated World which is a fusion of the Text World and the Readerly World (The Blended Mediated World Hypothesis or BMWH). Within this mixed world, while those projected parts from the Text World which correspond with sensorimotor experiences of the reader are understood in an embodied way, the parts that lack embodied equivalence in the reader's sensorimotor experience function as Perceian Representamens, setting the reader in search of relevant Objects of Signs, which occur in the form of sensorimotor experiences (The Object-Search Hypothesis or OSH). The reader then becomes involved in a cycle of coming and going movements between the literary text and the socio-physical environment, demonstrating thus the processual nature of embodied understanding.

Theresa Schilhab

Karen Krasny

Frontiers in integrative neuroscience

grazia pulvirenti

According to ancient texts on poetics, the concept of representation is deeply bound to that of “mimesis;” this last was intended in two main ways: as “imitation” and as “world construction.” In Aristotle’s Poetics, mimesis is theorized as the main form of “world simulation,” giving rise to the complex universe of fiction. The concept of simulation plays a pivotal role in the neurocognitive theories on the embodied mind: within this frame, embodied simulation is intended as a functional prelinguistic activation of the human sensorimotor mechanism. This happens not only with regard to intercorporeality and intersubjectivity in the real world but also in relation to the process of imagination giving rise to literary imagery and to the reader’s reception of the fictional world, since human beings share a common sensorimotor apparatus. Imagination is a central concept in the recent neurocognitive studies since it plays a core role in human life and in artistic production and reception. Imagination has been considered as a complex emergent cognitive faculty deeply intertwined with perception, memory, and consciousness, shaping human life and transforming the limited horizon of our perceptual affective understanding, being, and acting. Although there is an immense bulk of literature on this topic, imagination is still an elusive concept: its definition and understanding change according to different heuristic frames—mainly the philosophical, aesthetic, poetic, and cognitive ones—giving rise to debates about its modalities and effects, particularly in relation to the construction of aesthetic and symbolic constraints. In this paper, we claim that scientific research may take advantage from the literary representation of the imaginative faculties, which occurs in specific tests characterized by dynamic images and motion. In such meta-representation of the imagination, we witness the phenomenological emergence of endogenous dynamic processes involving a cluster of cognitive faculties, activated by triggering the reader’s embodied simulation. One of the main German poets, Johann Wolfgang von Goethe, in the second part of hismasterwork Faust II, intuitively represents the very process of the imagination and its responding to embodied simulation with regard both to the author’s creative act and to its reception by the reader. At the crossway between literary and neurocognitive, this study aims to highlight the advantage offered to future transdisciplinary inquiries by the literaryrepresentation showing features and dynamics of the still mysterious phenomenon of the imagination.

Gestalt Theory

Pierre-Louis Patoine

This article is an attempt to link together the notions of representation and immersion within an interdisciplinary framework combining neuroscience, literary studies, and philosophy. What is representation? Can we define its mode of existence and describe its natural habitat? Does it live on the page of a novel, in the brain's circumvolutions, or in-between? Is it possible to intensify our experience of representation through immersive reading? How can we reach such immersive altered state of consciousness? What are its ethical and ecological implications? These are the questions we will be addressing in the following pages, in which we will first explore the neurophysiological conditions of immersive embodied reading, before considering its opposition to the productive, in-control cognitive styles promoted by our rationalist modernity.

Aline Moura

Taking into consideration an intellectual atmosphere attentive to the interchanges between embodied cognition and literature, the objective of this paper is to introduce definitions, mechanisms, and functionalities of bodily reactions to human existence and the production of knowledge, based mainly on the investigations of the Portuguese neuroscientist Antonio Damasio. He has published several books and is one of the most influential researchers on the reciprocity among body, brain, mind, and situated experiences. By analyzing his investigations, it becomes possible to develop theoretical and analytical repertoires capable of contemplating emotion resulting from contact with literary fiction as relevant to their understanding.

A long tradition of research including classical rhetoric, esthetics and poetics theory, formalism and structuralism, as well as current perspectives in (neuro)cognitive poetics has investigated structural and functional aspects of literature reception. Despite a wealth of literature published in specialized journals like Poetics, however, still little is known about how the brain processes and creates literary and poetic texts. Still, such stimulus material might be suited better than other genres for demonstrating the complexities with which our brain constructs the world in and around us, because it unifies thought and language, music and imagery in a clear, manageable way, most often with play, pleasure, and emotion (Schrott and Jacobs, 2011). In this paper, I discuss methods and models for investigating the neuronal and cognitive-affective bases of literary reading together with pertinent results from studies on poetics, text processing, emotion, or neuroaesthetics, and outline current challenges and future perspectives. Aesthetic value, then, is like the wind—we know of its existence only through its effects (Iser, 1976).

Ernest Goetz

Michael Kimmel

Literary works can be analyzed from the viewpoint of how frequently, how systematically, and at what level they engage the reader in somatic activation and imagery. Researchers need to (a) identify text cues that (co-)produce embodied effects and (b) evaluate their distinct somatization profiles. This paper surveys embodied simulation, a scene-bound effect lending itself to a textual approach most straightforwardly. After charting the wider terrain of literary somatization I shall single out two broad categories of cues for scene-bound effects (whereas global effects like suspense and “emergent” reader specific effects like dissatisfaction tend to elude linguistic analysis, so I won’t have much to say about them): My first focus addresses canonical imagery, which, by and large, subserves the functions of “being there” and character empathy. It spans descriptions of objects, persons, actions, and interactions in the storyworld, but also inner experience, i.e. pain, proprioception, and visceral affect. Under a second – in fact overlapping – heading, figurative language deserves attention. It comprises force-dynamic metaphors that cue our understanding of the causality of affect, psychodynamics, and protagonist interaction. Metaphors that augment already established simulative imagery by gestalt effects (double-projection, etc.) add to this. My overall aim is to pinpoint analytic hot spots by discussing the cue-effect relationship of some thirty linguistic devices with a view to case-studies and comparative analyses of “engagement profiles” of texts.

Loading Preview

Sorry, preview is currently unavailable. You can download the paper by clicking the button above.

RELATED PAPERS

David Miall

Willie van Peer

Cognitive Joyce

Marloes Mak

Redefining Literary Semiotics

Sirkka Knuuttila

Intervento al convegno Cognitive Futures in the Arts and Humanities. 10th Annual Conference: From 4E to 5E Cognition. About Emotions, Università degli Studi di Catania

Irene Orlandazzi

William McGinley, PhD

James R (Randy) Fromm

Anezka Kuzmicova , Anne Mangen , Research Centre KAIROS

Semiotica. Vol. 222. Pp. 101-112.

Frank Nuessel

Le Centre pour la Communication Scientifique Directe - HAL - Archive ouverte HAL

Martin Gliserman

Poetics Today

Naomi Rokotnitz

Scientific Study of Literature

Enthymema XVIII

grazia pulvirenti , Renata Gambino

Nayef Al-joulan

Rhiannon Corcoran

Mark Turner

European Journal of English Studies

Line Brandt

Frontiers in Communication

guillemette bolens

Craig Hamilton

Save citation to file

Email citation, add to collections.

Create a new collection
Add to an existing collection

Add to My Bibliography

Your saved search, create a file for external citation management software, your rss feed.

Search in PubMed
Search in NLM Catalog
Add to Search

Motor imagery: a window into the mechanisms and alterations of the motor system

Affiliation.

1 F.C. Donders Centre for Cognitive Neuroimaging, Radboud University Nijmegen, Nijmegen, Netherlands. [email protected]
PMID: 18387583
DOI: 10.1016/j.cortex.2007.09.002

Motor imagery is a widely used paradigm for the study of cognitive aspects of action control, both in the healthy and the pathological brain. In this paper we review how motor imagery research has advanced our knowledge of behavioral and neural aspects of action control, both in healthy subjects and clinical populations. Furthermore, we will illustrate how motor imagery can provide new insights in a poorly understood psychopathological condition: conversion paralysis (CP). We measured behavioral and cerebral responses with functional magnetic resonance imaging (fMRI) in seven CP patients with a lateralized paresis of the arm as they imagined moving the affected or the unaffected hand. Imagined actions were either implicitly induced by the task requirements, or explicitly instructed through verbal instructions. We previously showed that implicitly induced motor imagery of the affected limb leads to larger ventromedial prefrontal responses compared to motor imagery of the unaffected limb. We interpreted this effect in terms of greater self-monitoring of actions during motor imagery of the affected limb. Here, we report new data in support of this interpretation: inducing self-monitoring of actions of both the affected and the unaffected limb (by means of explicitly cued motor imagery) abolishes the activation difference between the affected and the unaffected hand in the ventromedial prefrontal cortex. Our results show that although implicit and explicit motor imagery both entail motor simulations, they differ in terms of the amount of action monitoring they induce. The increased self-monitoring evoked by explicit motor imagery can have profound cerebral consequences in a psychopathological condition.

PubMed Disclaimer

Publication types

Search in MeSH

LinkOut - more resources

Full text sources.

Elsevier Science
MedlinePlus Health Information

Miscellaneous

NCI CPTAC Assay Portal

Citation Manager

NCBI Literature Resources

MeSH PMC Bookshelf Disclaimer

The PubMed wordmark and PubMed logo are registered trademarks of the U.S. Department of Health and Human Services (HHS). Unauthorized use of these marks is strictly prohibited.

USC Libraries
Research Guides

Using Images and Non-Textual Materials in Presentations, Papers, Theses, and Dissertations

Documenting and Citing Images
Finding Images - Select Sources

Documenting and Citing Images/Photographs and Their Sources

Please note that this is advice on best practices and considerations in documenting and citing images and non-print materials. It does not represent legal advice on obtaining permissions.

Generally, images copied from other sources should not be used without permissions in publications or for commercial purposes. Many American academic institutions require graduate students to archive their finished and approved theses/dissertations in institutional electronic repositories and/or institutional libraries and repositories, and/or to post them on Proquest's theses database. Unpublished theses and dissertations are a form of scholarly dissemination. Someone else's images, like someone else's ideas, words or music, should be used with critical commentary, and need to be identified and cited. If a thesis/dissertation is revised for publication, waivers or permissions from the copyright holder(s) of the images and non-textual materials must be obtained. Best practices also apply to materials found on the internet and on social media, and, properly speaking, require identification, citation, and clearance of permissions, as relevant.

Use the following elements when identifying and citing an image, depending on the information you have available . It is your responsibility to do due diligence and document as much as possible about the image you are using:

Artist's/creator's name, if relevant;
Title of the work/image, if known, or description;
Ownership information (such as a person, estate, museum, library collection) and source of image;
Material, if known, particularly for art works;
Dimensions of the work, if known.

The Chicago Manual of Style online can be searched for norms on appropriate ways to caption illustrations, capitalize titles of visual works, or cite print materials that contain images.

Including images/photographs in a bibliography:

Best practice is to not include images within a bibliography of works cited. It is common, instead, to create a separate list of images (or figures) and their source, such as photographer (even if it's you) or collection. It may be useful to also include location, e.g., museum, geographic reference, address, etc.

Examples of Documenting Images

The image below is scanned from a published book. It can be used in a critical context within a presentation, classroom session, or paper/thesis, as follows:

[ Figure 1. This photograph from 1990 shows the Monument against Fascism designed by Jochen Gerz and Esther Shalev-Gerz, Hamburg, 1986-1993. Image from James Young, ed., Art of Memory: Holocaust Memorials in History (New York: Prestel, 1994), 70]

If you need to use this image in a published work, you will have to seek permission. For example, the book from which this image was scanned should have a section on photo credits which would help you identify the person/archive holding this image.

The image below was found through Google Images and downloaded from the internet. It can be used in a critical context within a presentation, classroom session, or paper/thesis, as follows:

[Figure 2. This image shows the interior of Bibliotheca Alexandrina designed by the Norwegian architecture firm Snøhetta in 2001. Image downloaded from https://mgkhs.com/gallery/alexandria in March 2016.]

If you want to use this image in a published work, you will have to do your best to track down its source to request permission to use. The web site or social media site where you found the image may not be an appropriate source, since it is common for people to repost images without attribution. Just because "everyone does it" does not mean that you should be using such materials without attribution or documentation. In this specific example, you may need to write to the photographer or to the architecture firm. If you have done due diligence and were unable to find the source, or have not received a response, you may be able to use an image found on the internet with appropriate documentation in a publication.

The image below was downloaded from a digitized historic collection of photographs held by an institutional archive. It can be used in a critical context within a presentation, classroom session, or paper/thesis, as follows:

[Figure 3. In the 1920s the urban landscape of Los Angeles started to change, as various developers began building multi-family apartment houses in sections previously zoned for single family dwellings. Seen in this photograph by Dick Whittington is the Warrington apartment building, which was completed in 1928, surrounded by older single family structures. Downloaded from the USC Digital Library in February 2016]

I f you plan to use this photograph in a publication, seek permission from the library/institution from whose digital archive you downloaded the image. Contact information is usually found in the record for the image.

The image below was taken by the author. It can be used in a critical context within a presentation, classroom session , paper/thesis, or a publication* as follows:

[Figure 4. Genex Tower, also known as West City Gate, is a residential tower located in New Belgrade. This example of late 20th century brutalist-style architecture was designed in 1977 by Mihajlo Mitrović. Photographed by the author in 2013.]

*Please note, if you re-photographed someone else's photograph or a work of art, or if you re-photographed a published image, you may not be able to publish your photograph without first seeking permission or credit for its content. If you have done due diligence and were unable to find the source or have not received a response, you may be able to use your image with appropriate documentation.

<< Previous: Fair Use
Next: Finding Images - Select Sources >>
Last Updated: Jan 19, 2023 3:12 PM
URL: https://libguides.usc.edu/fair_use

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

View all journals
Explore content
About the journal
Publish with us
Sign up for alerts
Open access
Published: 22 June 2020

A cognitive profile of multi-sensory imagery, memory and dreaming in aphantasia

Alexei J. Dawes 1 ,
Rebecca Keogh 1 ,
Thomas Andrillon 1 , 2 &
Joel Pearson 1

Scientific Reports volume 10 , Article number: 10022 ( 2020 ) Cite this article

72k Accesses

106 Citations

431 Altmetric

Metrics details

Neuroscience

For most people, visual imagery is an innate feature of many of our internal experiences, and appears to play a critical role in supporting core cognitive processes. Some individuals, however, lack the ability to voluntarily generate visual imagery altogether – a condition termed “aphantasia”. Recent research suggests that aphantasia is a condition defined by the absence of visual imagery, rather than a lack of metacognitive awareness of internal visual imagery. Here we further illustrate a cognitive “fingerprint” of aphantasia, demonstrating that compared to control participants with imagery ability, aphantasic individuals report decreased imagery in other sensory domains, although not all report a complete lack of multi-sensory imagery. They also report less vivid and phenomenologically rich autobiographical memories and imagined future scenarios, suggesting a constructive role for visual imagery in representing episodic events. Interestingly, aphantasic individuals report fewer and qualitatively impoverished dreams compared to controls. However, spatial abilities appear unaffected, and aphantasic individuals do not appear to be considerably protected against all forms of trauma symptomatology in response to stressful life events. Collectively, these data suggest that imagery may be a normative representational tool for wider cognitive processes, highlighting the large inter-individual variability that characterises our internal mental representations.

Insights into embodied cognition and mental imagery from aphantasia

Individuals who rarely adopt observer perspectives report richer scene-based autobiographical memories

Qualitative analysis of hotspots and intrusive memories after viewing an aversive film highlights their sensory and spatial features

Introduction.

Visual imagery, or seeing with the mind’s eye, contributes to essential cognitive processes such as episodic memory 1 , future event prospection 2 , visual working memory 3 , and dreaming 4 . By allowing us to re-live the past and simulate hypothetical futures, visual imagery enables us to flexibly and adaptively interpret the events we experience in the world 5 , and by extension appears to be an important precursor to our ability to plan effectively and engage in guided decision-making. Consequently, the frequency and content of maladaptive visual imagery are often defining features of mental illness 6 and mental imagery is often elevated in disorders characterised by hallucinations 7 , 8 .

One of the most significant findings to date is that despite the prevalence of visual imagery use in the wider population, and despite its functional utility in cognition, certain individuals lack the ability to visualise altogether – a condition recently termed “aphantasia” 9 . Beyond self-report measures, this condition is characterised by stark differences between individuals who can and cannot visualise on an objective measure of imagery’s sensory strength 10 . This suggests that rather than reflecting inaccurate phenomenological reports or poor population-specific metacognition, aphantasia appears to represent a veridical absence of voluntarily generated internal visual representations.

The potential impact of visual imagery absence on wider cognition remains unknown. No research to date has empirically verified whether this phenomenology extends to other internal experiences and mental processes. This presents us with a rare opportunity to extend a cognitive fingerprint of aphantasia, in order to better clarify the role of visual imagery in wider psychological functioning and explore the impact of its absence on the subjective lives of individuals with a “blind mind”. Here we investigated whether individuals with aphantasia report reduced imagery in other multi-sensory domains, and assessed self-reports of episodic memory ability and trauma symptomatology in response to stressful life events, in addition to reported mind-wandering frequency and dreaming phenomenology.

Participants

We compared a group of self-identified aphantasic individuals with two independent control groups of individuals with self-reported intact visual imagery on a range of questionnaires. The current study was approved by the UNSW Human Research Ethics Advisory Panel (HREAP-C) in line with National Health and Medical Research Council (NHRMC) guidelines on ethical human research. All participants gave informed consent before completing the study.

Given the need for more research in this area, we sought to collect data on as many aphantasic participants as possible. With the limited number of previous studies on aphantasia using small sample sizes of N = 10–20 9 , 10 , it was difficult to estimate required sample sizes for our study based on these results alone. We nevertheless used the limited data available to derive approximate effect sizes for group differences in these studies in the range of d = 1.0–3.0. Effect sizes in small sample studies are often inflated, however, and we expected weaker effects across multiple comparisons in our study, especially in non-imagery domain comparisons. Establishing a comparatively moderate expected effect size of d = 0.5, with 80% power and a highly conservative alpha of 0.0002 (see Statistical Analyses in Methods), we estimated that at least 170 participants would be required in each comparison group. Because our study was easily accessible online and received more participant responses than anticipated within our data collection window, we exceeded our sample size aim ( N = 170) and ceased data collection for our aphantasic participant group at the sample size reported below. We then collected an equivalent number of participants for our independent control groups. Sample sizes for the aphantasia group, control group 1 and control group 2 were approximately equal after data cleaning and exclusions ( n = 267, n = 203 and n = 197, respectively).

Aphantasia group

Aphantasic individuals in our study were recruited from online community research platforms ( https://www.facebook.com/sydneyaphantasiaresearch/ ) and participated in exchange for entry into a gift card prize draw. 317 aphantasic participants in total completed our study, of whom 33 participants were excluded from analysis due to missing data (not completing all questionnaires). An additional 17 participants were excluded from our aphantasic sample due to unclear reporting (e.g. scoring at ceiling on the Vividness of Visual Imagery Questionnaire (VVIQ; see Methods) in line with older versions of the scale that used reversed scoring compared to the current version of the scale). Our final sample of aphantasic individuals included for analysis contained 267 participants (48% females; mean age = 33.97 years, SD = 12.44, range = 17–75 years).

Control group 1 (MTurk)

Participants in our main control group were recruited using Amazon Mechanical Turk (MTurk) and were remunerated to complete the study. This main control group sample comprised of 205 participants, two of whom were excluded from final analysis due to study incompletion. Our final sample for our main control group thus consisted of 203 participants (35% females; mean age = 33.82 years, SD = 9.33, range = 20–70 years) who were matched on mean age with our aphantasic sample (mean age difference = 0.15 years, p = 0.89, BF 10 = 0.107).

Control group 2 (Undergraduates)

A second control group of 193 first-year undergraduate psychology students were tested using the same experimental design. Participants in our second control group (73% females; mean age = 19.33 years, SD = 3.69, range = 17–55 years) completed the study in exchange for course credit. All participants were included in final analysis (see section titled Control Group 2: Replication Analysis, in Results).

Aphantasia sample characteristics

Demographics.

A table of sample demographics for all groups can be found in the Supplementary Information (see Table S1 ). Our sample population of aphantasic participants were recruited from online community research platforms dedicated to the topic of visual imagery ability and aphantasia. Both participants who did and didn’t identify with a history of visual imagery absence were invited to participate in the study. Of the 267 participants in our sample who reported aphantasia, a majority reported English as their first language (83%, n = 220) and identified as White/Caucasian (88%, n = 235). 31 countries of residence were listed, with a majority of participants originating from the United States of America.

Clinical history

Of the aphantasic sample, 24% of participants reported a history of mental illness (compared to 18% in control group 1; χ 2 1,470 = 3.644, p = 0.06), 1% reported a history of epilepsy or seizures (compared to 8% in control group 1; χ 2 1,470 = 14.881, p < 0.001), 4% reported a neurological condition (compared to 7% in control group 1; χ 2 1,470 = 1.765, p = 0.184), 9% reported having suffered head injury or trauma at least once (compared to 9% in control group 1; χ 2 1,470 = 0.019, p = 0.890), and 0.7% reported having once suffered a stroke (compared to 6% in control group 1; χ 2 1,470 = 10.634, p < 0.01).

Imagery scores

Weak visual imagery ability is typically defined by a total score of 32 or less on the Vividness of Visual Imagery Questionnaire (VVIQ: see Imagery Questionnaires in Materials), a five-point Likert self-report scale which ranges from 16–80 9 , 11 . A total score of 32 is equivalent to rating one’s agreement on every questionnaire item at 2 (“Vague and dim”). On average, aphantasic participants in our sample scored 17.94 on the VVIQ (including 70% with total floor scores of 16), compared to 58.12 in control group 1 (see Imagery Results section) and 58.79 in control group 2 (see Table S2 in Supplementary Information).

Experimental procedure

Questionnaires were administered online using the Qualtrics research platform, and presented to each participant in random order. All participants completed a total of 206 questions in eight questionnaires. These questionnaires assessed self-reported multi-sensory imagery, episodic memory and future prospection, spatial abilities, mind-wandering and dreaming propensity, and response to stressful life events, as detailed below.

Imagery questionnaires

The Vividness of Visual Imagery Questionnaire (VVIQ 11 ; Marks, 1973) is a 16-item scale which asks participants to imagine a person as well as several scenes and rate the vividness of these mental images using a 5-point scale ranging from 1 (“No image at all, you only ‘know’ that you are thinking of the object”) to 5 (“Perfectly clear and <as> vivid as normal vision”). A single mean score on the VVIQ was computed for each participant. The Questionnaire upon Mental Imagery (QMI 12 ; Sheehan, 1967) asks participants to rate the clarity and vividness of a range of imagined stimuli in seven sensory domains (visual, auditory, tactile, kinesthetic, taste, olfactory, emotion) on a 7-point scale ranging from 1 (“I think of it, but do not have an image before me”) to 7 (“Very vivid and as clear as reality”). There are 35 items on the QMI in total, with five items corresponding to each of the seven sensory domains. The Object and Spatial Imagery Questionnaire (OSIQ 13 ; Blajenkova, Kozhevnikov, & Motes, 2006) is a 50-item scale which requires participants to indicate how well each of several statements on object imagery ability (e.g. “When I imagine the face of a friend, I have a perfectly clear and bright image”) and spatial imagery ability (e.g. “I am a good Tetris player”) applies to them on a 5-point scale ranging from 1 (“Totally disagree”) to 5 (“Totally agree”). There are 25 items each comprising the Object and Spatial imagery domains of the OSIQ, averaged to form a mean score on each domain.

Memory questionnaires

The Episodic Memory Imagery Questionnaire (EMIQ; on request) is a custom designed, 16-item self-report questionnaire which aims to assess the subjective vividness of episodic memory. Items on the EMIQ were partially derived from the VVIQ 11 scale (Marks, 1973) and modified for context. The EMIQ asks participants to remember several events or scenes from their life and rate the vividness of these scenes using a 5-point scale ranging from 1 (“No image at all, I only ‘know’ that I am recalling the memory”) to 5 (“Perfectly clear and as vivid as normal vision”). A single mean score on the EMIQ was computed for each participant. The Survey of Autobiographical Memory (SAM 14 ; Palombo, Williams, Abdi, & Levine, 2013) is a 26-item scale which measures participant agreement with a number of statements related to general episodic memory ability on a 5-point scale ranging from 1 (“Strongly disagree”) to 5 (“Strongly agree”). The scale is divided into 4 components: Event Memory (averaged across eight items, e.g. “When I remember events, in general I can recall people, what they looked like, or what they were wearing”), Future Events (averaged across six items; e.g. “When I imagine an event in the future, the event generates vivid mental images that are specific in time and place”), Factual Memory (averaged across six items; e.g. “I can learn and repeat facts easily, even if I don’t remember where I learned them”) and Spatial Memory (averaged across six items; e.g. “In general, my ability to navigate is better than most of my family/friends”).

Dreaming and daydreaming questionnaires

Part 1 of the Imaginal Process Inventory (IPI; 15 , 16 Giambra, 1980; Singer & Antrobus, 1963) consists of 24 items which assess the self-reported frequency of day dreams (or mind-wandering episodes) and night dreams on a 5-point agreement scale which differs on each question (e.g. “I recall my night dreams vividly”, ranging from a) “Rarely or never” through to e) “Once a night”). The Subjective Experiences Rating Scale (SERS 17 ; Kahan & Claudatos, 2016) comprises 39 questions which assess the qualitative content and subjective experience of participants’ night dreams generally (e.g. “During your dreams whilst asleep, <to what extent> do you experience colors”) on a 5-point rating scale ranging from 0 (“None”) to 4 (“A lot”). There are several sub-components of the scale which measure reported structural features of participants’ dreams (e.g. how bizarre one’s actions were, or how much perceived control participants experienced, during their dreams). The SERS is divided in our study into six dream components: Sensory, Affective, Cognitive, Spatial Complexity, Perspective and Lucidity. These components reflect typical SERS scale divisions, with the exception of Lucidity (in which we merge two existing components (Awareness and Control) of the previously published SERS scale 17 in order to improve the readability of Fig. 2 ).

Trauma response questionnaire

The Post-Traumatic Stress Disorder (PTSD) Checklist for DSM-5 (PCL-5 18 ; Weathers et al ., 2013) measures self-reported responses to stressful life events. It asks participants to indicate how much they have been bothered by a problem related to a stressful life event on a 5-point scale ranging from 1 (“Not at all”) to 5 (“Extremely”). The PCL-5 contains 20 questions which are broken into four clinically relevant symptom categories: Intrusions (e.g. “Repeated, disturbing, and unwanted memories of the stressful experience”), Avoidance (e.g. “Avoiding memories, thoughts, or feelings related to the stressful experience”), Negative Alterations in Cognitions and Mood (e.g. “Blaming yourself or someone else for the stressful experience and what happened after it”), and Arousal and Reactivity (e.g. “Feeling jumpy or easily startled”). PTSD diagnosis can only be established by a professional practitioner in a structured clinical interview, and although cut-off scores on the PCL-5 are often used as an adjunct screening tool, the scale is not used for diagnostic purposes here.

Statistical analyses

Non-parametric Mann-Whitney U hypothesis tests were conducted in SPSS 25.0 for Mac OS using Bonferroni adjusted alpha levels of α = 0.0002 (0.05/206 where 206 is the total number of question items across all questionnaires) to correct for multiple comparisons. Estimates of effect sizes r were computed using the following formula:

where Z is the Mann-Whitney standardized test statistic, N the total sample size of the combined groups, and r the output effect size estimate (comparable with Cohen’s d effect size interpretations 19 ). Because we adopted a highly conservative adjusted alpha, Mann-Whitney tests were supplemented by Bayesian analyses conducted in JASP. For all Bayesian analyses, a Cauchy prior of 0.707 was used. Bayes factors were used to help compare the weight of evidence for between-group differences across test comparisons, whilst Mann-Whitney tests were used to make overall inferences about test direction and significance. Bayes factors were interpreted according to common threshold guidelines 20 , where 1 = “No evidence”, 1–3 = “Anecdotal evidence”, 3–10 = “Moderate evidence”, 10–30 = “Strong evidence”, 30–100 = “Very strong evidence”, and >100 = “Extreme evidence”.

Data transformation

All analyses were conducted on raw data. Data visualisation for Fig. 1 only, however, was carried out on median-centered raw questionnaire data using the following transformation:

where y is the transformed score; x the raw individual item score for scale S , and S.min and S.max the lowest and highest possible scores on that scale, respectively. This transformation allows us to graphically compare results across scales, with a value of −0 .5 representing the lowest possible score, 0 the median score, and 0.5 the maximum possible score on each scale.

Summary of self-reported cognition questionnaires for individuals with aphantasia (red, n = 267) and control group 1 participants with visual imagery (blue, n = 203). Violin plots of median-centred scale scores with median (bold line), lower and upper quartiles (thin lines) and kernel density-smoothed frequency distribution (shaded area) coloured by group. Each pair of violin plots represents transformed raw data (see Data Transformation, Method). Stars to the right of group plot segments indicate Mann-Whitney test significance at threshold p < 0.0002.

We expected aphantasic individuals to report reduced visual imagery ability compared to controls, in line with previous findings 9 , 10 . There is some suggestion that auditory imagery may also be reduced in individuals who report visual imagery absence, however this evidence comes from case studies with limited sample sizes 1 . We therefore had no strong hypotheses regarding group differences in other multi-sensory imagery domains.

Given the proposed importance of mental imagery for the reliving of past life events 21 , we predicted that aphantasic individuals would report general alterations to episodic memory and future prospection processes, as well as reductions in episodic memory vividness.

Clinical research has traditionally placed heavy emphasis on the symptomatic role of visual imagery in mental health disorders including depression, social phobia, schizophrenia and post-traumatic stress disorder (PTSD), amongst others 6 . We therefore hypothesised that visual imagery absence might partially protect aphantasic individuals from experiencing some trauma symptomatology (such as vivid memory intrusions) in response to stressful past events.

Although neural measures suggest that dreaming is often characterised by vivid and objectively measurable internal visual experiences 4 , previous evidence on dreaming in aphantasia is somewhat inconclusive 22 . The overall impact of visual imagery absence on involuntary imagery processes (such as mind-wandering and dreaming whilst asleep) is therefore largely unclear, and we had no strong predictions regarding group differences in mind-wandering frequency, dream frequency, or dream phenomenology and content.

Lastly, we expected aphantasic self-reports of spatial imagery and spatial navigation abilities to align with data from previous studies suggesting that despite visual imagery absence, spatial abilities (as measured by questionnaires and performance on mental rotation and visuo-spatial tasks) appear to be largely preserved in aphantasia 10 , 22 .

The aim of the present study was to investigate the subjective impact of visual imagery absence on cognition. To achieve this, we compared self-reports of aphantasic individuals with those of general population individuals (with self-reported intact visual imagery) on several cognitive domains including multi-sensory imagery, episodic memory, trauma response, dreaming and daydreaming, and spatial abilities. The main results sections presented here all describe between-group tests comparing our aphantasic sample with our first control group of age-matched participants recruited from MTurk (see Tables S2 – 6 in Supplementary Information). For replication comparisons with our second control group sample of undergraduates, see section at end of Results titled “Control Group 2: Replication Analysis”.

Control Group 1: Main Comparisons

Imagery results.

We first examined group differences in visual imagery vividness. As expected based on previous findings 9 , 10 , aphantasic participants rated their visual imagery ability on the VVIQ as being significantly lower (17.94 ± 0.223, with many (70%) scoring at floor, i.e. 16) compared to control group 1 (58.12 ± 0.888; Mann-Whitney U = 427.5, p < 0.0002, r = 0.87, BF 10 = 1.41e 12 , 2-tailed; see Fig. 1 red section and Figure S1 in Supplementary Information; Fig. 1 depicts median-centered data with the aphantasia group denoted by red plots and control group 1 by blue plots throughout; Figures S1 – 5 show raw scale scores and distributions). This self-reported qualitative absence of visual imagery vividness was mirrored by significantly lower scores than controls on the object imagery component of the OSIQ (Mann-Whitney U = 372, p < 0.0002, r = 0.85, BF 10 = 446,931.23, 2-tailed; see Fig. 1 red section and Fig. S1), which measures the perceived ability to use imagery as a cognitive tool in task-relevant scenarios. Our data also showed that individuals with aphantasia not only report being unable to visualise, but also report comparatively reduced imagery, on average, in all other sensory modalities (measured using the QMI), including auditory ( U = 6,152, BF 10 = 5.01e 11 ), tactile ( U = 4,473, BF 10 = 4.90e 9 ), kinesthetic ( U = 5,151, BF 10 = 1.04e 11 ), taste ( U = 3,069.5, BF 10 = 4.82e 26 ), olfactory ( U = 3,439.5, BF 10 = 2.73e 9 ) and emotion ( U = 6,670.5, BF 10 = 4.81e 12 ) domains (all Mann-Whitney U-tests, p < 0.0002, r = 0.65–.78, 2-tailed; see Fig. 2a and Fig. S1). It is noteworthy, however, that despite reporting a near total absence of visual imagery on the QMI (Mann-Whitney U = 620.5, p < 0.0002, r = 0.87, BF 10 = 1.07e 9 , 2-tailed; see Fig. 2a ) and significantly lower total QMI scores overall compared to controls (Mann-Whitney U = 1,868.5, p < 0.0002, r = 0.79, BF 10 = 6.47e 12 , 2-tailed; see Fig. 1 red section, second panel from top), only 26.22% of aphantasic participants reported a complete lack of multi-sensory imagery altogether (rating each question in each QMI domain as “1: No sensory experience at all”). The remainder of our aphantasic sample (73.78%) reported some degree of imagery in non-visual sensory modalities (albeit significantly reduced compared to controls; see Fig. 1 red section, and Fig. 2a ), suggesting potential sub-categories of aphantasia.

Group differences in visual imagery ability on scale sub-components. Radar plots for ( a ) multi-sensory imagery; ( b ) trauma response; and ( c ) dreaming scales (SC. = Spatial Complexity; PSP. = Perspective; LUC. = Lucidity). Concentric dashed circles represent raw scale scores for each scale (e.g. a ; 1–7 Likert-type), with lowest possible item scores falling on innermost solid circle and highest possible item scores falling on outermost coloured circle; radial dashed lines denote item grouping for scale sub-components (e.g. c ; Intrusions, Avoidance, Negative Cognition and Mood, Arousal and Reactivity); central coloured lines (red = aphantasia group, blue = control group 1) represent raw total group scores on individual scale items, with translucent shading denoting standard-deviation.

Memory results

Aphantasic individuals described a significantly lower ability to remember specific life events in general (Event Memory component of the SAM; Mann-Whitney U = 8,865, p < 0.0002, r = 0.58, BF 10 = 4.68e 10 , 2-tailed; see Fig. 1 blue section) and reported almost no ability to generate visual sensory details when actively remembering past events (memory vividness on the EMIQ; Mann-Whitney U = 2,186.5, p < 0.0002, r = 0.81, BF 10 = 1.01e 15 , 2-tailed; see Fig. 1 blue section and Fig. S2 in Supplementary Information) compared to participants in control group 1. However, these self-reported reductions in reliving events were not confined to the past, with aphantasics as a group also reporting a near total inability to imagine future hypothetical events in any sensory detail (Future Events component of the SAM; Mann-Whitney U = 7,469.5, p < 0.0002, r = 0.63, BF 10 = 2.97e 10 , 2-tailed; see Fig. 1 blue section and Fig. S2). Self-reported factual (or semantic) memory, which is traditionally thought to provide a kind of ‘scaffold’ for event memories more widely 23 , also appeared to be lower in individuals unable to visualise compared to controls (Factual Memory component of the SAM; Mann-Whitney U = 18,601.5, p < 0.0002, r = 0.27, BF 10 = 156,732.50, 2-tailed; see Fig. 1 blue section and Fig. S2), although this effect was of a lower magnitude than the memory reductions reported above (see Fig. 1 blue section and Table S7 in Supplementary Information). The fourth scale component of the SAM (Spatial Memory) is grouped with the Spatial Imagery component of the OSIQ in results below (see Spatial Ability Results).

Trauma response results

Our data did not directly support the hypothesis that visual imagery absence might protect aphantasic individuals from trauma symptomology in response to stressful life events, with the aphantasia group scoring comparatively to control group 1 on the PCL-5 overall (total PCL-5 scores; Mann-Whitney U = 27,515, p = 0.776, r = 0.01, BF 10 = 0.12, 2-tailed; see Fig. 1 grey section and Figure S3 in Supplementary Information). An analysis of group differences on the four sub-components of this scale (Intrusions, Cognition and Mood, Avoidance, and Arousal) also revealed that there were no significant differences between the groups in reports of emotional arousal and reactivity associated with remembering stressful past events (Mann-Whitney U = 27,240, p = 0.924, r = 0.00, BF 10 = 0.11, 2-tailed; see Fig. 2b and Fig. S3). Compared to participants with visual imagery, individuals with aphantasia appeared to report fewer recurrent and involuntary memory intrusions (Mann-Whitney U = 22,739, p = 0.002, r = 0.14, BF 10 = 14.85, 2-tailed; see Fig. 2b and Fig. S3), lower engagement in avoidance behaviours (Mann-Whitney U = 23,164.5, p = 0.006, r = 0.13, BF 10 = 2.13, 2-tailed; see Fig. 2b and Fig. S3), and greater negative changes in cognition and mood (Mann-Whitney U = 30,960, p = 0.008, r = 0.12, BF 10 = 12.99, 2-tailed; see Fig. 2b and Fig. S3) in response to stressful life events, although none of these group differences survived Bonferroni correction for multiple comparisons, and effect size estimates were small ( r = 0.12–.14; see Table S7 in Supplementary Information). Interestingly, however, Bayesian analyses indicated strong evidence in favour of group differences on the Intrusions (BF 10 = 14.85) and Cognition and Mood (BF 10 = 12.99) sub-scales of the PCL-5 reported above.

Day and night dream results

Here we found that although there was little evidence for or against (BF 10 = 1.93 and BF 01 = 0.518) a difference between groups in the reported frequency of day-dreaming (Mann-Whitney U = 23,001.5, p = 0.005, r = 0.13, 2-tailed, non-significant after Bonferroni correction; see Fig. 1 teal section and Figure S4 in Supplementary Information), aphantasic individuals did report experiencing significantly fewer night dreams than controls (Imaginal Process Inventory (IPI); Mann-Whitney U = 15,828.5, p < 0.0002, r = 0.37, BF 10 = 4.24e 6 , 2-tailed; see Fig. 1 teal section and Fig. S4). Interestingly, the reported qualitative content of these night dreams also differed between groups as measured by the SERS. Dream reports for aphantasic individuals reinforce a model of aphantasia as being primarily characterised by sensory deficits (Sensory; Mann-Whitney U = 15,087.5, p < 0.0002, 0.38, BF 10 = 5.46e 6 , 2-tailed) across all dream modalities (including olfactory, tactile, taste and auditory domains; see Fig. 2c and Fig. S4). Interestingly, aphantasic individuals also reported experiencing lower awareness and control during their dreams (Lucidity; Mann Whitney U = 19,473.0, p < 0.0002, r = 0.25, BF 10 = 1902.01, 2-tailed). We found some evidence that the dreams aphantasic participants report are characterised by less vivid emotions (Affective; Mann Whitney U = 23,463.0, p = 0.013, non-significant after Bonferroni correction, r = 0.11, BF 10 = 9.01, 2-tailed), and a less clear dreamer perspective (Perspective (PSP); Mann Whitney U = 22,070.5, p = 0.0004, r = 0.16, non-significant after Bonferroni correction, BF 10 = 127.28, 2-tailed) compared to participants in control group 1. However, there were no significant differences between the aphantasia group and control group 1 in the experience of within-dream cognition (e.g. planning or remembering (Cognitive); Mann Whitney U = 24,592.0, p = 0.085, r = 0.08, BF 10 = 1.05, 2-tailed) or the details of dreams’ spatial features (Spatial Complexity (SC); Mann Whitney U = 24,697.0, p = 0.092, r = 0.08, BF 10 = 0.31, 2-tailed). Interestingly, the only question on the SERS for which aphantasics scored significantly higher than control group 1 participants was an item in the Cognitive domain (see Fig. 2c ) which asks how much time participants spent thinking during their dreams (Mann-Whitney U = 34,401.5, p < 0.0002, BF 10 = 3.53e 3 ), which accords well with a reduction in the sensory qualities of dreams in aphantasia in favour of semanticised contents.

Spatial ability results

Aphantasic participants reported slightly lower spatial imagery ability on the spatial sub-component of the OSIQ when compared to control group 1 (Mann-Whitney U = 24,462, p = 0.001, r = 0.15, BF 10 = 14.65, 2-tailed; see Fig. 1 purple section and Figure S5 in Supplementary Information), although this effect was not significant after Bonferroni correction. Additionally, the scores of aphantasic individuals on the Spatial Memory component of the SAM (which includes items measuring reported spatial navigation and naturalistic spatial memory ability) were not significantly different from controls (SAM; Mann-Whitney U = 24,720, p = 0.1, r = 0.08, BF 10 = 0.23, 2-tailed; see Fig. 1 purple section and Fig. S5). These results demonstrate that overall there were no consistent differences in reported spatial abilities between aphantasic individuals and participants in control group 1.

Control Group 2: Replication Analysis

Although control group 1 was age-matched, it featured a higher ratio of males to females (see Table S1 ) in contrast to our aphantasic sample (which comprised of more females than males). Some of the variables included in this study (such as spatial ability and PTSD susceptibility) are known to be influenced by gender. To address this potential issue, we ran a replication analysis with a second control group of first-year undergraduate psychology students using the same experimental design (their raw data is depicted alongside our original control group and aphantasic sample in Figures S1 – 5 ).

Participants in our second control group ( n = 193) were recruited from a sample of undergraduate psychology students at the University of New South Wales, and completed the study in exchange for course credit. All participants in this second control group were included in final analysis (with no exclusions). These participants (mean age = 19.33 years, SD = 3.69, range = 17–55 years) were not matched on mean age with our aphantasic sample (mean age difference = 14.6 years, p < 0.01, BF 10 = 1.23e 10 ), but instead featured a higher proportion of females to males (73% females, compared to 48% females in our aphantasic sample and 35% females in control group 1 (our main control group of MTurk responders).

Comparison with this second control group revealed a similar overall pattern of group differences to those reported above, with few effect changes in imagery and memory related domains in particular (see Figures S1 – 5 and Tables S2 – 6 in Supplementary Information for a comparison of test results, as well as Table S7 for a comparison of effect sizes). Aphantasic participants scored significantly lower than control group 2 on all outcomes of the imagery and episodic memory questionnaires (all p < 0.0002, all r > 0.52, all BF 10 > 1.42e 8 ) with the exception of the factual memory component of the SAM (which was no longer significantly lower in aphantasics when compared to control group 2 after controlling for multiple comparisons; Mann-Whitney U = 21,496.0, p = 0.002, r = 0.14, BF 10 = 3.196, 2-tailed).

Although our Bayes analysis suggested strong evidence for higher total PCL-5 scores in control group 2 compared to the aphantasic group (Mann-Whitney U = 21,464.0, p = 0.002, r = 0.14, BF 10 = 12.76, 2-tailed), this effect was not significant after Bonferroni correction. However, the previously non-significant reduction in memory intrusions amongst aphantasic participants (compared to control group 1) was much stronger in this second group comparison (Mann-Whitney U = 15,134.5, p < 0.0002, r = 0.35, BF 10 = 2.20e 7 , 2-tailed), as were lower reports of avoidance behaviours by aphantasic individuals compared to control group 2 (Mann-Whitney U = 18,494.5, p < 0.0002, r = 0.24, BF 10 = 2494.67, 2-tailed). Compared to control group 2, however, aphantasic participants did not report significantly higher negative cognition and mood (Mann-Whitney U = 25,827.5, p = 0.97, r = 0.00, BF 10 = 0.12, 2-tailed) or arousal (Mann-Whitney U = 25,517.0, p = 0.12, r = 0.07, BF 10 = 0.34, 2-tailed) in response to stressful life events, in line with our main control group 1 comparisons.

Individuals with aphantasia reported significantly fewer night dreams than control group 2 (Mann-Whitney U = 17,156.0, p < 0.0002, r = 0.74, BF 10 = 21,124.12, 2-tailed). However, they also reported significantly less frequent mind-wandering compared to participants in control group 2 (Mann-Whitney U = 19,271.5, p < 0.0002, r = 0.29, BF 10 = 397.04, 2-tailed), in contrast to the results of our main analysis (which revealed no significant differences in mind-wandering reports between the aphantasic group and control group 1). Also in contrast to our initial dreaming results, aphantasic participants scored significantly lower than control group 2 on all components of the SERS (Sensory, Affective, Cognitive, Spatial Complexity, Perspective and Lucidity; all p < 0.0002, all r > 0.71, all BF 10 > 1.56e 7 ), including on some domains where there were no significant differences between aphantasic participants and age-matched participants in control group 1 (see Fig. S4 and Table S5 ). However, these findings may be partially explained by age-related decline in dream frequency and subjective recall 24 .

Lastly, there were no significant differences in reported spatial imagery ability on the OSIQ (Mann-Whitney U = 22,635.5, p = 0.03, r = 0.10, BF 10 = 0.88, 2-tailed) or spatial navigation ability on the SAM (Mann-Whitney U = 23,760.5, p = 0.15, r = 0.07, BF 10 = 0.23, 2-tailed) between the aphantasic group and control group 2, reinforcing our initial results as well as previous findings of preserved spatial (but not object) imagery in aphantasic participant samples 10 , 22 .

Here we found that individuals with aphantasia report significant reductions in sensory simulation across a range of volitional and non-volitional mental processes, and overall appear to demonstrate a markedly distinct pattern of cognition compared to individuals with visual imagery. Notably, aphantasic individuals reported significantly reduced imagery across all sensory modalities (and not just visual). However, only 26.22% of aphantasic participants reported a total absence of multi-sensory imagery altogether, raising important questions about the primary aetiology of aphantasia and suggesting possible sub-categories of aphantasia within a heterogeneous group. Aphantasic individuals’ episodic memory and ability to imagine future events were also reported to be significantly reduced compared to the two control populations. These findings attest to the recently established functional and anatomical overlap in brain networks supporting the flexible, constructive simulation of episodic events (whether they be real past events or hypothetical future events) 25 , and suggest that visual imagery may be an essential and unifying representational format potentiating these processes.

Interestingly, our data aligns with that of previous studies demonstrating unaffected spatial imagery abilities in aphantasia 10 , 22 , suggesting an important distinction between object imagery (low-level perceptual features of objects and scenes) and spatial imagery (spatial locations and relations in mental images) 26 . This distinction is indeed reflected at a neural level, with disparate brain pathways used for perceptual object processing and spatial locations, respectively 27 . Strikingly, cognitive differences in aphantasia were not limited to processes where visual imagery is typically deliberate and volitional, with aphantasic individuals in our study reporting significantly less frequent and less vivid instances of spontaneous imagery such as night dreams. These data suggest that any cognitive function (voluntary or involuntary 28 ) involving a sensory visual component is likely to be reduced in aphantasic individuals, and it is this generalised reduction in the sensory simulation of complex events and scenes that is most striking in aphantasia.

This work used a large-sample design to investigate reports of altered cognitive processes as a function of visual imagery absence. However, due to the self-described nature of the phenomenon in our online sample, it is prudent to rule out alternative explanations for the between-group differences seen here. Some authors have appropriately highlighted that visual imagery absence does not always present congenitally, but may be acquired as an associated symptom of neurological damage or psychopathology 29 . As a result, it is arguable that some aspects of our results may be more parsimoniously attributed to underlying psychogenic factors. Whilst plausible, we do not believe the reports of our sample here are best explained by this account. Only 9 out of 267 (3%) participants in our aphantasic sample reported acquired imagery loss, with the majority of participants reporting having lacked visual imagery capacity since birth. Additionally, there were no significant differences between our aphantasic sample and our main control group in the number of participants reporting a history of mental illness, neurological condition, or head injury/trauma – in fact, significantly fewer aphantasic participants reported a history of stroke or history of epilepsy/seizures compared to participants in control group 1 (see Sample Characteristics in Method, and Table S1 in Supplementary Information).

Importantly, a supplementary within-group analysis also showed that there were no significant differences between aphantasic participants with or without a reported history of mental illness/psychopathology on any of our primary imagery, memory, dreaming, or spatial ability outcome variables, after controlling for multiple comparisons (see Table S8 in Supplementary Information). Furthermore, the only significant within-group differences that were revealed by this supplementary analysis (such as significantly higher scores on some PCL-5 components in aphantasic individuals with a mental illness history compared to those without; see Table S8 ) are differences we might expect to find as a function of psychopathology status in any sample population, given the target variables of interest and clinical scope of the scale. Considering these factors together, it is unlikely that our main results are best explained by acquired or associated symptoms of psychogenic causes such as mental illness or psychopathology.

Aphantasic participants in our study were compared with two independent control groups of participants with visual imagery on a range of self-reported cognitive outcomes. It is important to note that that neither of these control groups were perfectly matched on demographic characteristics with our aphantasic sample. In our main group comparison, the ratio of females to males was significantly higher in the aphantasic group (48%) than in control group 1 (35%), despite these groups being matched on mean age. In order to account for the potential influence of sample characteristics (including gender) in our main control group of MTurk participants, we conducted a replication analysis with a second control group of undergraduate students featuring a higher ratio of females to males (73%). This second control group, however, was significantly younger in mean age (19 years) compared to the aphantasic sample (34 years; see Table S1 in Supplementary Information).

Despite these demographic discrepancies, the results of our replication analysis with control group 2 revealed a remarkably similar pattern of between-group effects to our main analysis (see Tables S2 – 6 in Supplementary Information). Additionally, a majority of the significant changes to our results that did occur are congruent with established effects of age and gender on cognitive outcomes. For example, our finding that undergraduate participants reported significantly more frequent memory intrusions and avoidance behaviours than aphantasic participants in response to stressful life events may be explained by the typically higher prevalence of PTSD diagnosis and symptomatology amongst females 30 (and younger females in particular 31 ). Similarly, our replication analysis results suggested that aphantasic participants reported significantly fewer mind-wandering episodes and qualitatively impoverished dream phenomenology in additional SERS domains, but only in comparison to the comparatively younger undergraduate control group 2 and not when compared to the age-matched control group 1 (see Fig. S4 and Table S5 in Supplementary Information). This is a pattern of results which accords well with findings of age-related decline in spontaneous mind-wandering 32 and subjective dream phenomenology 24 , respectively.

The few divergences in results between our main analysis (with control group 1) and replication analysis (with control group 2) are therefore largely consistent with previous research on the roles of age and gender in cognition. The overall equivalence of our results across these independent control group comparisons (despite demographic discrepancies between groups) suggests that our major findings are unlikely to be artifacts of sampling bias. Nevertheless, the interaction between demographic characteristics, imagery and cognition is potentially complex, and future research should overcome this limitation of our study design by implementing more precise selection criteria for matched control samples.

It is also important to highlight that our study assessed intergroup differences in cognition by using self-report outcomes which might be influenced by response biases. If aphantasic participants were motivated to respond in line with a self-identified lack of imagery (or even with perceived generalised cognitive deficits), for example, we would expect them to indiscriminately report at floor on all self-report measures of cognition, or at least on all scales measuring cognitive abilities typically thought to be reliant on visual imagery use. Their pattern of responses on some scales (particularly those measuring reported spatial abilities) suggests otherwise. On the SAM, aphantasic individuals reported no consistent reduction in spatial memory (or navigation) ability compared to controls, despite reporting memory deficits on all other components of this scale (see Fig. 1 blue and purple sections). More convincingly, aphantasic participants selectively reported deficits in object imagery but not spatial imagery on the OSIQ in our study, despite items corresponding to these two components being presented in randomised order within the same scale (see Fig. 1 blue and red sections). Lastly, previous research has shown that participants with self-described aphantasia do not just score at floor on self-report imagery questionnaires, but also exhibit lower scores than control group participants on a behavioural measure of sensory imagery strength which bypasses the need for self reports 10 , suggesting that response bias is not a most parsimonious explanation for presentations of self-described aphantasia. Demand characteristics cannot be unequivocally ruled out in the current study (as with any study of self-reports), and our findings should be validated with objective measures in future experiments. However, this study provides useful population-level data in order to highlight the veridical subjective differences that exist in a range of cognitive domains as a function of visual imagery absence.

There is strong theoretical impetus for future assessments of aphantasia, and our work highlights several areas of relevance that should be prioritised by future studies. For example, it is noteworthy that whilst the PCL-5 assesses one’s general response to stressful life events, it does not assess responses to recalling specific traumatic events 18 , nor does it have good measurement sensitivity for the imagery-based re-experiencing of such events. Whilst the overall pattern of our results suggests that aphantasic individuals do not appear to be markedly protected against all forms of trauma symptomatology, it may remain the case that they discernibly benefit from a reduced susceptibility to re-living these events in vivid sensory detail. Similarly, the self-report nature of our study does not allow for an objective, content-driven account of episodic memory function and phenomenology in aphantasia. Whilst some of the questions presented to participants on the EMIQ and on the SAM do ask them to report upon the visual experience of their memories, the distinction between remembering past life events and visually representing them is one which is not well delineated. There is therefore considerable scope for future experimental research to tease apart these separable component processes of episodic memory, and their relation to visual imagery absence in aphantasia.

Many other questions about aphantasia remain unanswered, including its longitudinal stability, the relative contribution of genetic and developmental factors to its aetiology, and its exact contribution to individual cognitive profiles. Our research presents an extended cognitive fingerprint of aphantasia and helps to clarify the role that visual imagery plays in wider consciousness and cognition. Visual imagery is a cognitive tool often taken for granted – an assumed precursor to our ability to think, learn, and simulate the world around us. This work demonstrates that such tools are not shared by everyone, and shines light on the rich but often invisible variations that exist in the internal world of the mind.

Greenberg, D. L. & Knowlton, B. J. The role of visual imagery in autobiographical memory. Mem. Cognit. 42 , 922–934 (2014).

Article Google Scholar

D’Argembeau, A. & Van der Linden, M. Individual differences in the phenomenology of mental time travel: The effect of vivid visual imagery and emotion regulation strategies. Conscious. Cogn. 15 , 342–50 (2006).

Pearson, J. & Keogh, R. Redefining visual working memory: A cognitive-strategy, brain-region approach. Curr. Dir. Psychol. Sci . 28, 266–273 (2019).

Horikawa, T. et al . Neural decoding of visual imagery during sleep. Science 340 , 639–42 (2013).

Article ADS CAS Google Scholar

Moulton, S. T. & Kosslyn, S. M. Imagining predictions: Mental imagery as mental emulation. Philos. Trans. R. Soc. B Biol. Sci. 364 , 1273–1280 (2009).

Pearson, J., Naselaris, T., Holmes, E. A. & Kosslyn, S. M. Mental imagery: Functional mechanisms and clinical applications. Trends Cogn. Sci. 19 , 590–602 (2015).

Shine, J. M. et al . Imagine that: Elevated sensory strength of mental imagery in individuals with Parkinson’s disease and visual hallucinations. Proc. R. Soc. B Biol. Sci. 282 , 20142047–20142047 (2014).

Benson, T. L. & Park, S. Exceptional visuospatial imagery in schizophrenia; implications for madness and creativity. Front. Hum. Neurosci. 7 , 756 (2013).

Zeman, A., Dewar, M. & Della Sala, S. Lives without imagery - Congenital aphantasia. Cortex. 73 , 378–80 (2015).

Keogh, R. & Pearson, J. The blind mind: No sensory visual imagery in aphantasia. Cortex 105 , 53–60 (2018).

Marks, D. F. Visual imagery differences in the recall of picture. Br. J. Psychol. 64 , 17–24 (1973).

Sheehan, P. W. A shortened form of Betts’ questionnaire upon mental imagery. J. Clin. Psychol. 23 , 386–389 (1967).

Article CAS Google Scholar

Blajenkova, O., Kozhevnikov, M. & Motes, M. A. Object-spatial imagery: A new self-report imagery questionnaire. Appl. Cogn. Psychol. 20 , 239–263 (2006).

Palombo, D. J., Williams, L. J., Abdi, H. & Levine, B. The survey of autobiographical memory (SAM): A novel measure of trait mnemonics in everyday life. Cortex 49 , 1526–1540 (2013).

Giambra, L. M. A factor analysis of the items of the imaginal processes inventory. J. Clin. Psychol. 36 , 383–409 (1980).

Singer, J. L. & Antrobus, J. S. A factor-analytic study of daydreaming and conceptually-related cognitive and personality variables. Percept. Mot. Skills 17 , 187–209 (1963).

Kahan, T. L. & Claudatos, S. Phenomenological features of dreams: Results from dream log studies using the Subjective Experiences Rating Scale (SERS). Conscious. Cogn. 41 , 159–176 (2016).

Weathers, F. W. et al . The PTSD Checklist for DSM-5 (PCL-5). Scale available from Natl. Cent. PTSD www.ptsd.va.gov (2013).

Rosenthal, R. Parametric measures of effect size. (1994).

Jeffreys, H. Theory of Probability. (Clarendon Press, 1961).

Schacter, D. L., Addis, D. R. & Buckner, R. L. Remembering the past to imagine the future: The prospective brain. Nat. Rev. Neurosci. 8 , 657–661 (2007).

Zeman, A. Z. J. et al . Loss of imagery phenomenology with intact visuo-spatial task performance: A case of ‘blind imagination’. Neuropsychologia 48 , 145–155 (2010).

Irish, M., Addis, D. R., Hodges, J. R. & Piguet, O. Considering the role of semantic memory in episodic future thinking: Evidence from semantic dementia. Brain 135 , 2178–2191 (2012).

Mangiaruga, A., Scarpelli, S., Bartolacci, C. & De Gennaro, L. Spotlight on dream recall: The ages of dreams. Nat. Sci. Sleep 10 , 1–12 (2018).

Schacter, D. L. et al . The future of memory: Remembering, imagining, and the brain. Neuron 76 , 677–694 (2012).

Sheldon, S., Amaral, R. & Levine, S. Individual differences in visual imagery determine how event information is remembered. Memory 25 , 360–369 (2017).

Goodale, M. A. & Milner, A. D. Separate visual pathways for perception and action. Trends Neurosci. 15 , 20–25 (1992).

Pearson, J. & Westbrook, F. Phantom perception: Voluntary and involuntary nonretinal vision. Trends in Cognitive Sciences 19 , 278–284 (2015).

de Vito, S. & Bartolomeo, P. Refusing to imagine? On the possibility of psychogenic aphantasia. A commentary on Zeman et al. (2015). Cortex 74 , 334–335 (2016).

Tolin, D. F. & Foa, E. B. Sex differences in trauma and posttraumatic stress disorder: A quantitative review of 25 years of research. Psychol. Bull. 132 , 959–992 (2006).

Ditlevsen, D. N. & Elklit, A. The combined effect of gender and age on post traumatic stress disorder: Do men and women show differences in the lifespan distribution of the disorder? Ann. Gen. Psychiatry 9 , 32 (2010).

Maillet, D. & Schacter, D. L. From mind wandering to involuntary retrieval: Age-related differences in spontaneous cognitive processes. Neuropsychologia 8 , 142–156 (2016).

Download references

Acknowledgements

We thank Marcus Wicken for his helpful insight on this project and ongoing collaboration. We also thank the aphantasic participants who gave their time to participate in this study and contribute feedback on our research. This work was supported by Australian NHMRC grants APP1046198 and APP1085404; J. Pearson’s Career Development Fellowship APP1049596; and an ARC discovery project DP140101560. T. Andrillon is supported by the International Brain Research Organization and the Human Frontiers Science Program (LT000362/2018-L). A. Dawes is supported by an Australian Government Research Training Program (RTP) Scholarship.

Author information

Authors and affiliations.

School of Psychology, The University of New South Wales, Sydney, New South Wales, Australia

Alexei J. Dawes, Rebecca Keogh, Thomas Andrillon & Joel Pearson

School of Psychological Sciences and Turner Institute for Brain and Mental Health, Monash University, Melbourne, Victoria, Australia

Thomas Andrillon

You can also search for this author in PubMed Google Scholar

Contributions

All authors developed the study concept. A. Dawes built the study design and collected the data. A. Dawes, R. Keogh and T. Andrillon performed data analysis. A. Dawes drafted the first version of the manuscript, and R. Keogh, T. Andrillon and J. Pearson provided critical revisions. All the authors approved the final manuscript for submission.

Corresponding author

Correspondence to Alexei J. Dawes .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary information., rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Dawes, A.J., Keogh, R., Andrillon, T. et al. A cognitive profile of multi-sensory imagery, memory and dreaming in aphantasia. Sci Rep 10 , 10022 (2020). https://doi.org/10.1038/s41598-020-65705-7

Download citation

Received : 28 December 2019

Accepted : 06 May 2020

Published : 22 June 2020

DOI : https://doi.org/10.1038/s41598-020-65705-7

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

This article is cited by

Predicting the subjective intensity of imagined experiences from electrophysiological measures of oscillatory brain activity.

Derek H. Arnold
Blake W. Saurels
Dietrich S. Schwarzkopf

Scientific Reports (2024)

Give your ideas a hand: the role of iconic hand gestures in enhancing divergent creative thinking

Gyulten Hyusein
Tilbe Göksun

Psychological Research (2024)

The Mind’s Eye is Not a Metaphor: Visuospatial Working Memory and Mental Imageries for Learning and Language Comprehension

Anisha Savarimuthu
R. Joseph Ponniah

Psychological Studies (2024)

Dreams share phenomenological similarities with task-unrelated thoughts and relate to variation in trait rumination and COVID-19 concern

Quentin Raffaelli
Eric S. Andrews
Jessica R. Andrews-Hanna

Scientific Reports (2023)

Emiko J. Muraki
Laura J. Speed
Penny M. Pexman

Nature Reviews Psychology (2023)

By submitting a comment you agree to abide by our Terms and Community Guidelines . If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Quick links

Explore articles by subject
Guide to authors
Editorial policies

Today's news
Reviews and deals
Climate change
2024 election
Newsletters
Fall allergies
Health news
Mental health
Sexual health
Family health
So mini ways
Unapologetically
Buying guides

Entertainment

How to Watch
My watchlist
Stock market
Biden economy
Personal finance
Stocks: most active
Stocks: gainers
Stocks: losers
Trending tickers
World indices
US Treasury bonds
Top mutual funds
Highest open interest
Highest implied volatility
Currency converter
Basic materials
Communication services
Consumer cyclical
Consumer defensive
Financial services
Industrials
Real estate
Mutual funds
Credit cards
Balance transfer cards
Cash back cards
Rewards cards
Travel cards
Online checking
High-yield savings
Money market
Home equity loan
Personal loans
Student loans
Options pit
Fantasy football
Pro Pick 'Em
College Pick 'Em
Fantasy baseball
Fantasy hockey
Fantasy basketball
Download the app
Daily fantasy
Scores and schedules
GameChannel
World Baseball Classic
Premier League
CONCACAF League
Champions League
Motorsports
Horse racing

New on Yahoo

Privacy Dashboard

What is mental imagery? Brain researchers explain the pictures in your mind and why they’re useful

Curious Kids is a series for children of all ages. If you have a question you’d like an expert to answer, send it to [email protected] .

Why are some people able to visualize scenarios in their minds, with colors and details, and some people are not? – Luiza, age 14, Goiânia, Brazil

Imagine you are in a soccer match, and it’s tied. Each team will begin taking penalty kicks. The crowd is roaring, and whether or not your team wins the game depends on your ability to hit the shot. As you imagine this scene, are you able to picture the scenario with colors and details?

Scientists are hard at work trying to understand why some people can visualize these kinds of scenarios more easily than others can. Even the same person can be better or worse at picturing things in their mind at different times.

As neuroscientists in the fields of physical therapy and psychology , we think about the ways people use mental imagery. Here is what researchers do know so far.

The brain and mental imagery

Mental imagery is the ability to visualize things and scenarios in your mind, without actual physical input.

For example, when you think about your best friends, you may automatically picture their faces in your head without actually seeing them in front of you. When you daydream about an upcoming vacation, you may see yourself on the sunny beach.

People who dream about taking a penalty kick could visualize themselves like they are watching a video of it in their mind. They may even experience the smell of the turf or hear the sounds that fans would make.

Scientists believe your primary visual cortex , located in the back of your brain, is involved in internal visualization . This is the same part of the brain that processes visual information from the eyes and that lets you see the world around you.

Another brain region, located in the very front of the brain, also contributes to mental imagery. This structure, called the prefrontal cortex, is in charge of executive functions – a group of high-level mental skills that allow you to concentrate, plan, organize and reason.

Scientists have found such skills to be, at least to some extent, related to one’s mental imagery ability. If someone is good at holding and manipulating large amounts of information in mind, this person can play with things like numbers or images in their mind on the go.

Experiencing and remembering

Most of the same brain areas are active both while you’re actually experiencing an event and also when you’re visualizing it from a memory in your head. For example, when you behold the beauty of the Grand Canyon, your brain creates a memory of the image. But that memory is not simply stored in a single place in the brain. It’s created when thousands of brain cells across different parts of the brain fire together. Later, when a sound, smell or image triggers the memory, this network of brain cells fires together again, and you may picture the Grand Canyon in your head as clearly as if it were in front of you.

Benefits of mental imagery

The ability to mentally visualize can be helpful .

Notice the look of concentration on a gymnast’s face before competition. The athlete is likely visualizing themselves executing a perfect rings routine in their mind. This visualization activates the same brain regions as when they physically perform on the rings, building their confidence and priming their brain for better success.

Athletes can use visualization to help them acquire skills more quickly and with less wear and tear on their bodies. Engineers and mechanics can use visualization to help them fix or design things.

Mental visualization can also help people relearn how to move their bodies after a brain injury . However, with additional practice, those who do not use visualization will eventually catch up .

Nature-nurture interactions

All is not lost if you have difficulty visualizing. It is possible that the ability to visualize in your mind is a combined effect of both how your individual brain works and your life experiences.

For example, taxi drivers in London need to navigate very complicated streets and, scientists found, experience changes to their brain structures over the course of their careers. In particular, they develop larger hippocampuses , a brain structure related to memory. Scientists believe that the training the taxi drivers went through – having to visualize a map of complex streets across London in daily driving – made them better at mental imagery via changes in their hippocampus.

And watching someone else do a physical action activates the same brain areas as creating your own internal mental imagery. If you want to be able to do something, watching a video of someone else doing it can be just as helpful as visualizing yourself doing it in your head . So even if you struggle with mental visualization, there are still ways to reap its benefits.

Hello, curious kids! Do you have a question you’d like an expert to answer? Ask an adult to send your question to [email protected] . Please tell us your name, age and the city where you live.

And since curiosity has no age limit – adults, let us know what you’re wondering, too. We won’t be able to answer every question, but we will do our best.

This article is republished from The Conversation , a nonprofit, independent news organization bringing you facts and trustworthy analysis to help you make sense of our complex world. It was written by: Lynne Gauthier , UMass Lowell and Jiabin Shen , UMass Lowell

How do our brains reconstruct the visual world?

Your brain thinks – but how?

How does your brain wake up from sleep?

Lynne Gauthier receives funding from US Army Soldier Center and has prior funding from PCORI, the American Heart Association, and the National MS Society.

Jiabin Shen has received funding from National Institutes of Health (NIH), Health Resources and Services Administration (HRSA), American Psychological Foundation (APF), and Society for Public Health Education (SOPHE).

Skip to main content
Keyboard shortcuts for audio player

Research News

The physics behind the most annoying thing that could ever happen to you: a paper cut, the physics behind a very annoying thing that could ever happen to you: a paper cut.

Scientists have figured out what type of paper is the most prone to cut skin. Kaare Jensen, associate professor of physics at the Technical University of Denmark, explains.

NPR transcripts are created on a rush deadline by an NPR contractor. This text may not be in its final form and may be updated or revised in the future. Accuracy and availability may vary. The authoritative record of NPR’s programming is the audio record.

Online Students

For All Online Programs

International Students

On Campus, need or have Visa

Campus Students

For All Campus Programs

Academic Referencing: How to Cite a Research Paper

A student holding a stack of books in a library working on academic referencing for their research paper.

Learning how to conduct accurate, discipline-specific academic research can feel daunting at first. But, with a solid understanding of the reasoning behind why we use academic citations coupled with knowledge of the basics, you’ll learn how to cite sources with accuracy and confidence.

Amanda Girard, a research support manager of Shapiro Library at SNHU.

When it comes to academic research, citing sources correctly is arguably as important as the research itself. "Your instructors are expecting your work to adhere to these professional standards," said Amanda Girard , research support manager of Shapiro Library at Southern New Hampshire University (SNHU).

With Shapiro Library for the past three years, Girard manages the library’s research support services, which includes SNHU’s 24/7 library chat and email support. She holds an undergraduate degree in professional writing and a graduate degree in library and information science. She said that accurate citations show that you have done your research on a topic and are knowledgeable about current ideas from those actively working in the field.

In other words, when you cite sources according to the academic style of your discipline, you’re giving credit where credit is due.

Why Cite Sources?

Citing sources properly ensures you’re following high academic and professional standards for integrity and ethics.

“When you cite a source, you can ethically use others’ research. If you are not adequately citing the information you claim in your work, it would be considered plagiarism ,” said Shannon Geary '16 , peer tutor at SNHU.

Geary has an undergraduate degree in communication from SNHU and has served on the academic support team for close to 2 years. Her job includes helping students learn how to conduct research and write academically.

“In academic writing, it is crucial to state where you are receiving your information from,” she said. “Citing your sources ensures that you are following academic integrity standards.”

According to Geary and Girard, several key reasons for citing sources are:

Access. Citing sources points readers to original sources. If anyone wants to read more on your topic, they can use your citations as a roadmap to access the original sources.
Attribution. Crediting the original authors, researchers and experts shows that you’re knowledgeable about current ideas from those actively working in the field and adhering to high ethical standards, said Girard.
Clarity. “By citing your sources correctly, your reader can follow along with your research,” Girard said.
Consistency. Adhering to a citation style provides a framework for presenting ideas within similar academic fields. “Consistent formatting makes accessing, understanding and evaluating an author's findings easier for others in related fields of study,” Geary said.
Credibility. Proper citation not only builds a writer's authority but also ensures the reliability of the work, according to Geary.

Ultimately, citing sources is a formalized way for you to share ideas as part of a bigger conversation among others in your field. It’s a way to build off of and reference one another’s ideas, Girard said.

How Do You Cite an Academic Research Paper?

Any time you use an original quote or paraphrase someone else’s ideas, you need to cite that material, according to Geary.

“The only time we do not need to cite is when presenting an original thought or general knowledge,” she said.

While the specific format for citing sources can vary based on the style used, several key elements are always included, according to Girard. Those are:

Title of source
Type of source, such as a journal, book, website or periodical

By giving credit to the authors, researchers and experts you cite, you’re building credibility. You’re showing that your argument is built on solid research.

“Proper citation not only builds a writer's authority but also ensures the reliability of the work,” Geary said. “Properly formatted citations are a roadmap for instructors and other readers to verify the information we present in our work.”

Common Citation Styles in Academic Research

Certain disciplines adhere to specific citation standards because different disciplines prioritize certain information and research styles . The most common citation styles used in academic research, according to Geary, are:

American Psychological Association, known as APA . This style is standard in the social sciences such as psychology, education and communication. “In these fields, research happens rapidly, which makes it exceptionally important to use current research,” Geary said.
Modern Language Association, known as MLA . This style is typically used in literature and humanities because of the emphasis on literature analysis. “When citing in MLA, there is an emphasis on the author and page number, allowing the audience to locate the original text that is being analyzed easily,” Geary said.
Chicago Manual of Style, known as Chicago . This style is typically used in history, business and sometimes humanities. “(Chicago) offers flexibility because of the use of footnotes, which can be seen as less distracting than an in-text citation,” Geary said.

The benefit of using the same format as other researchers within a discipline is that the framework of presenting ideas allows you to “speak the same language,” according to Girard.

APA Citation for College: A Brief Overview

Are you writing a paper that needs to use APA citation, but don’t know what that means? No worries. You’ve come to the right place.

How to Use MLA Formatting: A Brief Overview

Are you writing a paper for which you need to know how to use MLA formatting, but don’t know what that means? No worries. You’ve come to the right place.

How to Ensure Proper Citations

Keeping track of your research as you go is one of the best ways to ensure you’re citing appropriately and correctly based on the style that your academic discipline uses.

“Through careful citation, authors ensure their audience can distinguish between borrowed material and original thoughts, safeguarding their academic reputation and following academic honesty policies,” Geary said.

Some tips that she and Girard shared to ensure you’re citing sources correctly include:

Keep track of sources as you work. Writers should keep track of their sources every time an idea is not theirs, according to Geary. “You don’t want to find the perfect research study and misplace its source information, meaning you’d have to omit it from your paper,” she said.
Practice. Even experienced writers need to check their citations before submitting their work. “Citing requires us to pay close attention to detail, so always start your citation process early and go slow to ensure you don’t make mistakes,” said Geary. In time, citing sources properly becomes faster and easier.
Use an Online Tool . Geary recommends the Shapiro Library citation guide . You can find sample papers, examples of how to cite in the different academic styles and up-to-date citation requirements, along with information and examples for APA, MLA and Chicago style citations.
Work with a Tutor. A tutor can offer support along with tips to help you learn the process of academic research. Students at SNHU can connect with free peer tutoring through the Academic Support tab in their online courses, though many colleges and universities offer peer tutoring.

Find Your Program

How to cite a reference in academic writing.

A citation consists of two pieces: an in-text citation that is typically short and a longer list of references or works cited (depending on the style used) at the end of the paper.

“In-text citations immediately acknowledge the use of external source information and its exact location,” Geary said. While each style uses a slightly different format for in-text citations that reference the research, you may expect to need the page number, author’s name and possibly date of publication in parentheses at the end of a sentence or passage, according to Geary.

A longer entry listing the complete details of the resource you referenced should also be included on the references or works cited page at the end of the paper. The full citation is provided with complete details of the source, such as author, title, publication date and more, Geary said.

The two-part aspect of citations is because of readability. “You can imagine how putting the full citation would break up the flow of a paper,” Girard said. “So, a shortened version is used (in the text).”

“For example, if an in-text citation reads (Jones, 2024), the reader immediately knows that the ideas presented are coming from Jones’s work, and they can explore the comprehensive citation on the final page,” she said.

The in-text citation and full citation together provide a transparent trail of the author's process of engaging with research.

“Their combined use also facilitates further research by following a standardized style (APA, MLA, Chicago), guaranteeing that other scholars can easily connect and build upon their work in the future,” Geary said.

Developing and demonstrating your research skills, enhancing your work’s credibility and engaging ethically with the intellectual contributions of others are at the core of the citation process no matter which style you use.

A degree can change your life. Choose your program from 200+ SNHU degrees that can take you where you want to go.

A former higher education administrator, Dr. Marie Morganelli is a career educator and writer. She has taught and tutored composition, literature, and writing at all levels from middle school through graduate school. With two graduate degrees in English language and literature, her focus — whether teaching or writing — is in helping to raise the voices of others through the power of storytelling. Connect with her on LinkedIn .

Explore more content like this article

A student at a desk, typing on a computer

What is Considered Plagiarism And How to Avoid It

A person researching the difference between certificates and degrees on the laptop.

Degrees vs. Certificate Programs: What's the Difference?

An SNHU graduate at Commencement earning their degree

How Many Credits Do You Need to Graduate College?

About southern new hampshire university.

Two students walking in front of Monadnock Hall

SNHU is a nonprofit, accredited university with a mission to make high-quality education more accessible and affordable for everyone.

Founded in 1932, and online since 1995, we’ve helped countless students reach their goals with flexible, career-focused programs . Our 300-acre campus in Manchester, NH is home to over 3,000 students, and we serve over 135,000 students online. Visit our about SNHU page to learn more about our mission, accreditations, leadership team, national recognitions and awards.

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Publications
Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

Advanced Search
Journal List
v.37(5); 2017 Feb 1

Vividness of Visual Imagery Depends on the Neural Overlap with Perception in Visual Areas

Nadine dijkstra.

Radboud University, Donders Insitute for Brain, Cognition and Behaviour, 6525 EN, Nijmegen, The Netherlands

Sander E. Bosch

Marcel a.j. van gerven.

Author contributions: N.D., S.E.B., and M.A.J.v.G. designed research; N.D. and S.E.B. performed research; N.D. and S.E.B. analyzed data; N.D., S.E.B., and M.A.J.v.G. wrote the paper.

Research into the neural correlates of individual differences in imagery vividness point to an important role of the early visual cortex. However, there is also great fluctuation of vividness within individuals, such that only looking at differences between people necessarily obscures the picture. In this study, we show that variation in moment-to-moment experienced vividness of visual imagery, within human subjects, depends on the activity of a large network of brain areas, including frontal, parietal, and visual areas. Furthermore, using a novel multivariate analysis technique, we show that the neural overlap between imagery and perception in the entire visual system correlates with experienced imagery vividness. This shows that the neural basis of imagery vividness is much more complicated than studies of individual differences seemed to suggest.

SIGNIFICANCE STATEMENT Visual imagery is the ability to visualize objects that are not in our direct line of sight: something that is important for memory, spatial reasoning, and many other tasks. It is known that the better people are at visual imagery, the better they can perform these tasks. However, the neural correlates of moment-to-moment variation in visual imagery remain unclear. In this study, we show that the more the neural response during imagery is similar to the neural response during perception, the more vivid or perception-like the imagery experience is.

Introduction

Visual imagery allows us to think and reason about objects that are absent in the visual field by creating a mental image of them. This ability plays an important role in several cognitive processes, such as working memory, mental rotation, reasoning about future events, and many more ( Kosslyn et al., 2001 ). The vividness of visual imagery seems to be a key factor in these cognitive abilities, with more vivid imagery being linked to better performance on tasks requiring imagery ( Keogh and Pearson, 2011 , 2014 ; Albers et al. 2013 ).

There are great individual differences in how vividly people can generate a mental image ( Cui et al., 2007 ; Lee et al., 2012 ; Bergmann et al., 2016 ). However, within individuals there is also variation in imagery vividness: in some instances, imagery is much more vivid than in other instances ( Pearson et al., 2008 ). To date, the neural mechanisms underlying this moment-to-moment variation in experienced imagery vividness have remained unclear.

Previous work has shown that people who have more vivid visual imagery, as measured by the Vividness of Visual Imagery Questionnaire (VVIQ) ( Marks, 1973 ), show higher activity in early visual cortex during imagery ( Cui et al., 2007 ). Furthermore, individual differences in imagery precision and strength, as measured by the effect on subsequent binocular rivalry, are related to the size of V1, whereas individual differences in subjective imagery vividness correlate with prefrontal cortex volume but not with visual cortex anatomy ( Bergmann et al., 2016 ). Studies using multivariate analysis techniques have shown that there is overlap in stimulus representations between imagery and perception across the whole visual hierarchy, with more overlap in higher visual areas ( Reddy et al., 2010 ; Lee et al., 2012 ). However, only the overlap between perception and imagery in the primary visual cortex correlates with VVIQ scores and with imagery ability as measured by task performance ( Lee et al., 2012 ; Albers et al., 2013 ).

It remains unclear which of these neural correlates are important in determining moment-to-moment vividness of visual imagery and whether V1, especially the neural overlap with perception in V1, also relates to the variation of vividness within participants. In the current study, we investigated this question by having participants perform a retro-cue imagery task in the MRI scanner and rate their experienced vividness in every trial. First, we explored where in the brain activity correlates with vividness. Second, we investigated the overlap of category representations of perceived and imagined stimuli and in which areas this overlap is modulated by imagery vividness.

Materials and Methods

Participants..

Twenty-nine healthy adult volunteers with normal or corrected to normal vision gave written informed consent and participated in the experiment. Three participants were excluded: two because of insufficient data caused by scanner problems and one because of not finishing the task. Twenty-six participants (mean ± SD age = 24.31 ± 3.05 years; 18 female) were included in the reported analyses. The study was approved by the local ethics committee (CMO Arnhem-Nijmegen).

Experimental paradigm.

Before scanning we asked participants to fill in the VVIQ. This 16-item scale is summarized in a vividness score between 1 and 4 for each participant, where a score of 1 indicates high and 4 indicates low vividness.

The experimental paradigm is depicted in Figure 1 . We adapted a retro-cue working memory paradigm ( Harrison and Tong, 2009 ). In each trial, participants were shown two objects successively, followed by a cue indicating which of the two they subsequently should imagine. During imagery, a frame was presented within which subjects were asked to imagine the cued stimulus as vividly as possible. After this, they indicated their experienced vividness on a scale from 1 to 4, where 1 was low vividness and 4 was high vividness. Previous research has shown that such a subjective imagery rating shows high test-retest reliability and correlates with objective measures of imagery vividness ( Pearson et al., 2011 ; Bergmann et al., 2016 ).

An external file that holds a picture, illustration, etc.
Object name is zns9991794140001.jpg

Experimental paradigm. Participants were shown two objects for 2 s each with a random interstimulus interval (ISI) of between 1 and 3 s during which a fixation cross was shown. Next, another fixation cross was shown for 1–3 s after which a red cue was presented indicating which of the two objects the participant had to imagine. Subsequently, a frame was shown for 3.5 s on which the participant had to imagine the cued stimulus. After this, they had to rate their experienced imagery vividness on a scale from 1 (not vivid at all) to 4 (very vivid).

There were 20 of these trials per block. Each stimulus was perceived 60 times and imagined 30 times over the course of the whole experiment, resulting in nine blocks in total and a total scanner time of approximately 1.5 hours per participant.

Six images obtained from the World Wide Web were used as stimuli: 2 faces (Barack Obama and Emma Watson), 2 letters ('D' and 'I'), and 2 kinds of fruit (banana and apple). These three categories were chosen because they had, respectively, high, medium, and low Kolmogorov complexity, which is a measure that describes the complexity of an image in terms of its shortest possible description and can be approximated by its normalized compressed file size. It has been shown that the neural response in visual cortex is influenced by the Kolmogorov complexity of the stimulus ( Güclu and van Gerven, 2015 ). Furthermore, the within-category exemplars were chosen to be maximally different, such as to allow potential within-class differentiation. For the letters, this was quantified as the pair of images with the least pixel overlap.

fMRI acquisition.

Each block was recorded in a separate fMRI run, leading to 9 runs in total. In between runs, the participant had a break and indicated by means of a button press when they were ready for the experiment to continue. fMRI data were recorded on a Siemens 3T Prisma scanner with a Multiband 4 sequence (TR, 1.5 s; voxel size, 2 × 2 × 2 mm; TE, 39.6 ms) and a 32 channel head coil. For all participants, the field of view was tilted −25° from the transverse plane, using the Siemens AutoAlign Head software, resulting in the same tilt relative to the individual participant's head position. T1-weighted structural images (MPRAGE; voxel size, 1 × 1 × 1 mm; TR, 2.3 s) were also acquired for each participant.

fMRI data preprocessing.

Data were preprocessed using SPM8 (RRID: SCR_007037). Functional imaging data were motion corrected and coregistered to the T1 structural scan. No spatial or temporal smoothing was performed. A high-pass filter of 128 s was used to remove slow signal drift.

Univariate GLM analysis.

Before the multivariate analyses, we first ran a standard GLM in SPM8 in which we modeled the different regressors separately for each fMRI run. We modeled, per category, the perception events, imagery events, and the parametric modulation of the imagery response by vividness each in a separate regressor. The intertrial intervals were modeled as a baseline regressor during which there was no imagery. The visual cues, the presentation of the vividness instruction screen and the button presses, were included in separate nuisance regressors, along with subject movement in six additional regressors. This analysis gave us the β weight of each regressor for each voxel separately. Significance testing for univariate contrasts was done on the normalized smoothed t maps using FSL's cluster-based permutation technique (FSL, RRID: SCR_002823). To illustrate the parametric influence of vividness, a separate GLM was run in which the imagery response per vividness level was modeled in a separate regressor, collapsed over stimulus categories, and concatenated over runs.

Searchlight-based cross-validated MANOVA.

Numerous studies have shown that information about complex cognitive processes, such as visual imagery, is often more clearly present in patterns of neural responses than in the mean response amplitude pooled over voxels ( Kok et al., 2012 ; Tong et al., 2012 ; Albers et al., 2013 ; Bosch et al., 2014 ). Therefore, in this study, we focused on effects in the multivariate patterns of voxel responses.

We used the multivariate searchlight-based analysis technique developed by Allefeld and Haynes (2014 ). This analysis takes the parameter estimates of the GLM regressors per run as input and computes the multivariate “pattern distinctness” of any given contrast per searchlight. We chose a searchlight with a radius of 4 mm, leading to 33 voxels per sphere, in line with the findings of Kriegeskorte et al. (2006) , who showed that this size is optimal for most brain regions.

The pattern distinctness D of the two conditions in any contrast is defined as the magnitude of the between-condition covariance compared with the within-condition covariance ( Allefeld and Haynes, 2014 ). When there are only two conditions, which is the case in all our contrasts, D has a clear relationship to the Mahalanobis distance. Let

denote the Mahalanobis distance, where μ 1 and μ 2 are p × 1 vectors representing the means of the two conditions and Σ is a p × p matrix representing the data covariance, where p is the number of voxels per searchlight. The distinctness is related to the Mahalanobis distance as follows:

where n 1 and n 2 are the number of data points per condition.

As defined here, D is a squared distance measure and therefore cannot take on values smaller than zero. If D is close to zero or zero, estimation errors mostly increase the estimate. This problem is solved by implementing a leave-one-run-out cross-validation. This leads to the final, unbiased estimator of pattern distinctness as follows:

where m is the number of runs, p a correction for the searchlight size and f E the residual degrees of freedom per run.

Parametric modulation by vividness analysis.

We first wanted to investigate where in the brain the neural response was modulated by trial-by-trial differences in experienced imagery vividness. To this end, we used the above-mentioned technique with the contrast of the imagery × vividness parametric regressor per category versus the implicit baseline (i.e., the main effect of the parametric regressor). This analysis reveals for each category in which areas the pattern of voxel responses is modulated by the experienced vividness.

Representational overlap imagery and perception analysis.

Second, we were interested in revealing the similarity in neural category representations between perception and imagery and subsequently investigating where this was modulated by vividness. Previous studies used cross-decoding for this purpose ( Reddy et al., 2010 ; Lee et al., 2012 ; Albers et al., 2013 ). The rationale behind this technique is that, if stimulus representations are similar across two conditions (e.g., imagery and perception), you can use a classifier trained to decode the stimulus in one condition to decode the stimulus in the other condition. The accuracy of this cross-decoding is then interpreted as a measure of similarity or stability in representations over the two conditions. However, this is a rather indirect approach and depends highly on the exact classifier used.

Within the cross-validated MANOVA (cvMANOVA) framework (see above) representational overlap can be calculated much more directly. Overlap between two conditions can be seen as the complement of an interaction. An interaction tries to show that the representations or difference between conditions of one factor change under the conditions of another factor. When investigating overlap, we try to show that the representations remain stable under the levels of another factor. In the context of cvMANOVA, the pattern stability of one factor over the levels of another factor is defined as the main effect of that factor minus the interaction effect with the other factor. In the current analysis, the pattern stability of category over the levels of modality (perception vs imagery) is defined as follows:

where D ̂ ( C ) is the main effect of category over all levels of modality (perception and imagery trials together) and D ̂ ( M × C ) is the interaction between category and modality. D ̂ ( C \ M ) then reveals in which voxels the effect of category remained stable during imagery compared with perception.

Modulation of overlap by vividness analysis.

To investigate where the overlap between perception and imagery was influenced by the experienced imagery vividness, we had to identify those voxels that (1) represent the stimulus category during both perception and imagery and (2) are modulated by vividness. This effect is found in the stability in category effect between the perception response and the imagery × vividness response. This stability is calculated in a similar way as the stability of the category effect between the perception and imagery response as described above, but now instead of using the imagery regressor we used the imagery × vividness regressor.

Pooled permutation testing group statistics.

Stelzer et al. (2013 ) argued that the application of standard second-level statistics, including t tests, to MVPA measures is in many cases invalid due to violations of assumptions. Instead, they suggest permutation testing to generate the empirical null-distribution, thereby circumventing the need to rely on assumptions about this distribution. We followed their approach and performed permutation tests.

Single-subject permutations were generated by a sign-permutation procedure adapted for cross-validation as described by Allefeld and Haynes (2014 ). Because of computational limits, we generated 25 single-subject permutations per contrast. The permuted maps were subsequently normalized to MNI space. Second-level permutations were generated by randomly drawing (with replacement) 1 of the 25 permutation maps per subject and then averaging this selection to a group permutation ( Stelzer et al., 2013 ). For each voxel position, the empirical null-distribution was generated using 10,000 group permutation maps. p values were calculated per voxel as the right-tailed area of the histogram of permutated distinctness from the mean over subjects. Cluster correction was performed, ensuring that voxels were only identified as significant if they belonged to a cluster of at least 50 significant voxels. We corrected for multiple comparisons using FDR correction with a q value cutoff of 0.01.

Because the vividness regressor was not estimable in every run due to lack of variation in some runs, there were fewer runs available to estimate these contrasts. Therefore, these significance maps are based on 10 instead of 25 single subject permutations, but still 10,000 group-level permutations. Furthermore, two participants were removed from this analysis because they did not have enough variation in their responses to even produce 10 permutations. For this analysis, the q value cutoff was set to 0.05.

Behavioral results

Before the experiment, participants filled out the VVIQ, which is a self-report measure of people's ability to vividly imagine scenes and objects ( Marks, 1973 ; Cui et al., 2007 ). During the experiment, participants imagined previously seen, cued images and rated their vividness after each trial. First, we investigated whether the reported averaged vividness ratings and VVIQ scores were related. There was a significant negative correlation between the VVIQ and the averaged vividness ratings over trials ( r = −0.45, p = 0.02). Because the polarity of the two scales is reversed, this indicates that subjects with a higher imagery vividness as measured by the VVIQ also experienced on average more vivid imagery during the experiment.

Next, we explored whether experienced imagery vividness was influenced by stimulus category. We performed t tests between the vividness scores of the different stimulus categories. As shown in Figure 2 , there was a significant difference in vividness between letters (3.12 ± 0.59) and faces (2.80 ± 0.61; p = 0.006, t (25) = 3.01) and between faces and fruit (2.99 ± 0.53; p = 0.012, t (25) = 2.71). There was a nonsignificant difference between fruit and letters ( p = 0.076, t (25) = 1.85). Because the categories were of different complexity levels, this shows that vividness was modulated by stimulus complexity, such that imagery of simple stimuli was experienced as more vivid than imagery of more complex stimuli. This means that any effect of vividness on the neural responses aggregated over categories may be influenced by the effect of stimulus category. Therefore, we performed subsequent vividness analyses separately for each stimulus category.

An external file that holds a picture, illustration, etc.
Object name is zns9991794140002.jpg

Average trial-by-trial vividness ratings for the different stimulus categories. For each box: the central mark indicates the median, the edges of the box indicate the 25th and 75th percentiles, and the whisker indicates the minimum and maximum values. Each dot indicates the average for one participant. * p < 0.05. ** p < 0.01.

Univariate fMRI results

To investigate which brain areas were activated by the different phases in the imagery task, we contrasted activity during perception and imagery versus baseline. Both perception and imagery activated large parts of the visual cortex ( Fig. 3 ). Here activity is pooled over all imagery and perception trials so these results are not informative about overlap in stimulus representations.

An external file that holds a picture, illustration, etc.
Object name is zns9991794140003.jpg

Perception and imagery versus baseline. Blue-green represents t values for perception versus baseline. Red-yellow represents t values for imagery versus baseline. Shown t values were significant on the group level, FDR corrected for multiple comparisons.

To directly compare activity between perception and imagery, we contrasted the two conditions ( Fig. 4 ). Even though both conditions activated visual cortex with respect to baseline, we observed stronger activity during perception than imagery throughout the whole ventral visual stream. In contrast, imagery led to stronger activity in more anterior areas, including insula, left dorsal lateral prefrontal cortex, and medial frontal cortex.

An external file that holds a picture, illustration, etc.
Object name is zns9991794140004.jpg

Perception versus imagery. Blue-green represents t values for perception versus imagery. Red-yellow represents t values for imagery versus perception. Shown t values were significant on the group level, FDR corrected for multiple comparisons.

Parametric modulation by vividness

We first investigated where in the brain activity was modulated by experienced imagery vividness. To this end, we used the cvMANOVA analysis technique developed by Allefeld and Haynes (2014 ) (see Materials and Methods). This analysis investigates per searchlight whether the pattern of voxel responses is influenced by the experienced imagery vividness. In all three categories, there were significant clusters in early visual cortex, precuneus, medial frontal cortex, and right parietal cortex ( Fig. 5 ). This means that, in these regions, patterns of voxel responses were modulated by the experienced imagery vividness.

An external file that holds a picture, illustration, etc.
Object name is zns9991794140005.jpg

Parametric modulation by experienced imagery vividness per category. Shown distinctness values were significant at the group level.

Overlap between perception and imagery and modulation by vividness

Subsequently, we investigated the overlap in category representations between imagery and perception and where in the brain this overlap was influenced by experienced vividness. Within the cvMANOVA framework, overlap is defined as that part of the category effect that was similar for imagery and perception (see Materials and Methods). In all categories, there was large overlap between imagery and perception in the lateral occipital complex. For both the letter and face category, there was also high overlap in parietal and premotor areas ( Fig. 6 , red-yellow). Vividness modulated the overlap in all categories in the superior parietal cortex, in the fruit and face category in the entire visual cortex, and in the letter category in right inferior temporal cortex and left intraparietal sulcus ( Fig. 6 , blue-green).

An external file that holds a picture, illustration, etc.
Object name is zns9991794140006.jpg

Overlap in category representations between perception and imagery. Red-yellow represents that the overlap is shown that is not modulated by vividness. Blue-green represents the modulation by vividness. Shown distinctness values were significant on the group level.

To illustrate this finding more clearly, we ran a new GLM in which we modeled the imagery response for each vividness level separately. In Figure 7 , we plotted the difference between the main effect of perception and the main effect of imagery in early visual cortex, for each vividness level. More vivid imagery was associated with a smaller difference between perception and imagery.

An external file that holds a picture, illustration, etc.
Object name is zns9991794140007.jpg

Difference between main effect of perception and main effect of imagery, separately for the four vividness levels. The results are shown for a voxel in the early visual cortex that showed the highest overlap between the main effect of perception and the main effect of the most vivid imagery, as quantified by a conjunction analysis. MNI coordinates: 34, −96, 4.

In the present study, we investigated (1) in which brain areas activity was modulated by variation in experienced vividness and (2) where the overlap between perception and imagery was influenced by vividness. There was an effect of vividness on activation in the precuneus, right parietal cortex, medial frontal cortex, and parts of early visual cortex in all categories. We found overlap in category representations between imagery and perception in lateral occipital complex in all categories, and in inferior parietal and premotor cortex in the letter and face category. Furthermore, we found an effect of vividness on the overlap over the whole visual cortex for both the fruit and face categories, and in the superior parietal cortex for all categories. For letters, also the overlap in left intraparietal sulcus and right inferior temporal cortex was modulated by vividness.

Previous work has shown that individual differences in visual imagery vividness correlate with activation of early visual cortex during imagery ( Cui et al., 2007 ). Here, we show that this is also the case for trial-by-trial variation of imagery vividness. Furthermore, previous studies showed a correlation between imagery ability and overlap of neural representations with perception in early visual cortex ( Lee et al., 2012 ; Albers et al., 2013 ). In contrast, we found that within-participant fluctuations in vividness are related to the amount of overlap in the entire visual cortex as well as the parietal cortex. A possible explanation for this discrepancy is that previous studies defined the overlap across stimulus categories and looked at general imagery ability. In contrast, our current approach allowed us to define overlap within each stimulus category and relate it directly to the experienced vividness of those stimuli. This technique is much more sensitive and can reveal more fine-grained effects. The results indicate that the overlap of the neural representation in the entire visual system is important for participants' subjective experience.

We did not find a clear modulation of the overlap in the visual cortex in the letter category. In this category, the overlap in intraparietal sulcus and inferior temporal cortex was modulated instead. An explanation for this is that vividness of letters means something different from vividness of other stimuli. Letters were the least complex and so had the least visual details: a key factor in determining vividness ( Marks, 1973 ). This could therefore mean that other factors, such as semantic association or auditory imagery, determined vividness in the letter category.

In addition to effects in the visual cortex, we found that, in all categories the activity, but not the overlap, was modulated by vividness in precuneus, medial frontal, and right parietal cortex. Previous studies have also reported activation in these areas during visual imagery ( Ishai et al., 2000 ; Ganis et al., 2004 ; Mechelli et al., 2004 ; de Borst et al., 2012 ). It has been suggested that the precuneus is important for selecting relevant details during imagery ( Ganis et al., 2004 ). This is in line with our current findings because the amount of detail experienced during imagery plays an important role in judging experienced vividness. Furthermore, medial frontal activity has been associated with imagery performance ( de Borst et al., 2012 ), which in turn has been linked to experienced imagery vividness ( Keogh and Pearson, 2014 ). It has been suggested that the medial frontal cortex is important for the retrieval and integration of information during both working memory and imagery via connections to parietal and visual areas ( Onton et al., 2005 ; de Borst et al., 2012 ). Finally, right parietal cortex has been associated with attention, visual inspection, and percept stabilization, all factors that could influence the experienced vividness ( Trojano et al., 2000 ; de Borst et al., 2012 ; Zaretskaya et al., 2010 ).

Our findings crucially depend on the fluctuations in imagery vividness and associated overlap and activity over time. This begs the question what the origin of these fluctuations is. Fluctuations may be driven by variation in cortical excitability or large-scale reconfigurations of resting-state networks. For example, spontaneous changes within the default mode network and frontoparietal networks correlate with switches between an internal versus an external focus ( Smallwood et al., 2012 ; Van Calster et al., 2016 ). Furthermore, resting state oscillations within visual and motor cortices are related to changes in cortical excitability, which have an effect on behavior ( Fox et al., 2007 ; Romei et al., 2008 ). These spontaneous fluctuations could underlie the observed variability in experienced vividness within participants. More research is necessary to investigate this idea.

In addition to the neural correlates of imagery vividness, this study also provides novel insights with regard to the overlap in neural representations of imagined and perceived stimuli. We reveal a large overlap between perception and imagery in visual cortex. This is consistent with previous work showing that working memory, perception, and visual imagery have common representations in visual areas ( Reddy et al., 2010 ; Lee et al., 2012 ; Albers et al., 2013 ; Bosch et al., 2014 ). However, our study is the first to look at overlap between neural representations of imagery and perception beyond the visual cortex. Unexpectedly, we also found strong overlap in category representations for the letter and face category in inferior parietal and premotor cortex. Previous studies have already reported representations in parietal cortex of stimuli held in working memory ( Christophel et al., 2015 ; Lee and Kuhl, 2016 ). It may be the case that different cognitive functions rely on the same representations in both visual and parietal cortex.

Furthermore, premotor cortex activity during visual imagery has been associated with the spatial transformation of a mental object ( Sack et al., 2008 ; Oshio et al., 2010 ). However, we now show that stimulus representations in premotor cortex are shared between perception and imagery during a task that does not involve spatial transformations. This overlap also cannot be explained by motor preparation of the vividness response because during perception participants did not yet know which stimulus they had to imagine. One possible explanation for the overlap in the letter category is the fact that letters have a sensorimotor representation, such that the perception of letters activates areas in premotor cortex involved in writing ( Longcamp et al., 2003 ). Our results would imply that imagery of letters also activates premotor areas. However, this explanation is less likely to hold for faces. Because the overlap is more anterior for faces, this could also indicate the involvement of inferior frontal gyrus, a region that is known to be involved in the imagery of faces ( Ishai et al., 2002 ).

The fact that we did not find overlap in more anterior areas for the fruit category can be explained by the fact that the neural representation of the fruit category was less distinctive than that of the other categories. We calculated overlap as that part of the main category effect that was not different between perception and imagery. The main effect of the fruit category (how distinctive it was from the other categories) was much smaller than the main effect of the other categories, especially in more anterior brain areas. Therefore, the overlap in these areas was necessarily also smaller. This suggests that the fact that we did not find overlap in these areas is more likely due to low sensitivity than to true absence of overlap in these areas.

Because of the nature of our experimental task, it could be the case that our overlap findings are mainly driven by the imagery trials in which the second stimulus was cued. This could point to spillover of the BOLD response from the perception part of the trial, which would pose a problem for the general overlap results. To investigate this, we performed the overlap analysis separately for first and second cue trials ( Harrison and Tong, 2009 ). The peak activations for the first cue dataset were centered around lateral occipital complex, parietal, and premotor regions, which matches the main results. Furthermore, no salient differences were observed when comparing the results for the first and second cue. This shows that the effects cannot be explained by spillover effects of bottom-up perceptual processing. The modulation of overlap by vividness cannot be caused by spillover effects because these two are completely unrelated in our setup.

In conclusion, we showed that a network of areas, including both early and late visual areas, precuneus, right parietal cortex, and medial frontal cortex, is associated with the experienced vividness of visual imagery. The more anterior areas seem to be important for imagery-specific processes, whereas visual areas represent the visual features of the experience. This is apparent from the relation between experienced vividness and overlap with perception in these areas. Furthermore, our results show that the overlap in neural representations between imagery and perception, regardless of vividness, extends beyond the visual cortex to include also parietal and premotor/frontal areas.

This work was supported by The Netherlands Organization for Scientific Research VIDI Grant 639.072.513.

The authors declare no competing financial interests.

Albers AM, Kok P, Toni I, Dijkerman HC, de Lange FP (2013) Shared representations for working memory and mental imagery in early visual cortex . Curr Biol 23 :1427–1431. 10.1016/j.cub.2013.05.065 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Allefeld C, Haynes JD (2014) Searchlight-based multi-voxel pattern analysis of fMRI by cross-validated MANOVA . Neuroimage 89 :345–357. 10.1016/j.neuroimage.2013.11.043 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Bergmann J, Genç E, Kohler A, Singer W, Pearson J (2016) Smaller primary visual cortex is associated with stronger, but less precise mental imagery . Cereb Cortex 26 :3838–3850. 10.1093/cercor/bhv186 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Bosch SE, Jehee JF, Fernández G, Doeller CF (2014) Reinstatement of associative memories in early visual cortex is signalled by the hippocampus . J Neurosci 35 :7493–7500. 10.1523/JNEUROSCI.0805-14.2014 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Christophel TB, Cichy RM, Hebart MN, Haynes JD (2015) Parietal and early visual cortices encode working memory content across mental transformations . Neuroimage 106 :198–206. 10.1016/j.neuroimage.2014.11.018 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Cui X, Jeter CB, Yang D, Montague PR, Eagleman DM (2007) Vividness of mental imagery: individual variability can be measured objectively . Vision Res 47 :474–478. 10.1016/j.visres.2006.11.013 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
de Borst AW, Sack AT, Jansma BM, Esposito F, de Martino F, Valente G, Roebroeck A, di Salle F, Goebel R, Formisano E (2012) Integration of “what” and “where” in frontal cortex during visual imagery of scenes . Neuroimage 60 :47–58. 10.1016/j.neuroimage.2011.12.005 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Fox MD, Raiche ME (2007) Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging . Nat Rev Neurosci 8 :700–711. 10.1038/nrn2201 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Ganis G, Thompson WL, Kosslyn SM (2004) Brain areas underlying visual mental imagery and visual perception: an fMRI study . Brain Res Cogn Brain Res 20 :226–241. 10.1016/j.cogbrainres.2004.02.012 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Güçlü U, van Gerven MA (2015) Deep neural networks reveal a gradient in the complexity of neural representations across the ventral stream . J Neurosci 35 :10005–10014. 10.1523/JNEUROSCI.5023-14.2015 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Harrison SA, Tong F (2009) Decoding reveals the contents of visual working memory in early visual areas . Nature 458 :632–635. 10.1038/nature07832 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Ishai A, Ungerleider LG, Haxby JV (2000) Distributed neural systems for the generation of visual images parts of the visual system mediate imagery for different types of visual information . Neuron 28 :979–990. 10.1016/S0896-6273(00)00168-9 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Ishai A, Haxby JV, Ungerleider LG (2002) Visual imagery of famous faces: effects of memory and attention revealed by fMRI . Neuroimage 17 :1729–1741. 10.1006/nimg.2002.1330 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Keogh R, Pearson J (2011) Mental imagery and visual working memory . PLoS One 6 :e29221. 10.1371/journal.pone.0029221 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Keogh R, Pearson J (2014) The sensory strength of voluntary visual imagery predicts visual working memory capacity . J Vis 14 :pii7. 10.1167/14.12.7 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Kok P, Jehee JF, de Lange FP (2012) Less is more: expectation sharpens representations in the primary visual cortex . Neuron 75 :265–270. 10.1016/j.neuron.2012.04.034 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Kosslyn SM, Ganis G, Thompson WL (2001) Neural foundations of imagery . Nat Rev Neurosci 2 :635–642. 10.1038/35090055 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Kriegeskorte N, Goebel R, Bandetteini P (2006) Information-based functional brain mapping . Proc Natl Acad Sci U S A 103 :3863–3868. 10.1073/pnas.0600244103 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Lee H, Kuhl BA (2016) Reconstructing perceived and retrieved faces from activity patterns in lateral parietal cortex . J Neurosci 36 :6069–6082. 10.1523/JNEUROSCI.4286-15.2016 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Lee SH, Kravitz DJ, Baker CI (2012) Disentangling visual imagery and perception of real-world objects . Neuroimage 59 :4064–4073. 10.1016/j.neuroimage.2011.10.055 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Longcamp M, Anton JL, Roth M, Velay JL (2003) Visual presentation of single letters activates a premotor area involved in writing . Neuroimage 19 :1492–1500. 10.1016/S1053-8119(03)00088-0 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Marks DF. (1973) Visual imagery differences in the recall of pictures . Br J Psychol 64 :17–24. 10.1111/j.2044-8295.1973.tb01322.x [ PubMed ] [ CrossRef ] [ Google Scholar ]
Mechelli A, Price CJ, Friston KJ, Ishai A (2004) Where bottom-up meets top-down: neuronal interactions during perception and imagery . Cereb Cortex 14 :1256–1265. 10.1093/cercor/bhh087 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Onton J, Delorme A, Makeig S (2005) Frontal midline EEG dynamics during working memory . Neuroimage 27 :341–356. 10.1016/j.neuroimage.2005.04.014 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Oshio R, Tanaka S, Sadato N, Sokabe M, Hanakawa T, Honda M (2010) Differential effect of double-pulse TMS applied to dorsal premotor cortex and precuneus during internal operation of visuospatial information . Neuroimage 49 :1108–1115. 10.1016/j.neuroimage.2009.07.034 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Pearson J, Clifford CW, Tong F (2008) The functional impact of mental imagery on conscious perception . Curr Biol 18 :982–986. 10.1016/j.cub.2008.05.048 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Pearson J, Rademaker RL, Tong F (2011) Evaluating the mind's eye: the metacognition of visual imagery . Psychol Sci 22 :1535–1542. 10.1177/0956797611417134 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Reddy L, Tsuchiya N, Serre T (2010) Reading the mind's eye: decoding category information during mental imagery . Neuroimage 50 :818–825. 10.1016/j.neuroimage.2009.11.084 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Romei V, Brodbeck V, Michel C, Amedi A, Pascual-Leone A, Thut G (2008) Spontaneous fluctuations in posterior a-band EEG activity reflect variability in excitability of human visual areas . Cereb Cortex 18 :2010–2018. 10.1093/cercor/bhm229 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Sack AT, Jacobs C, De Martino F, Staeren N, Goebel R, Formisano E (2008) Dynamic premotor-to-parietal interactions during spatial imagery . J Neurosci 28 :8417–8429. 10.1523/JNEUROSCI.2656-08.2008 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Smallwood J, Brown K, Baird B, Schooler JW (2012) Cooperation between the default mode network and the frontal-parietal network in the production of an internal train of thought . J Brain Res 1428 :60–70. 10.1016/j.brainres.2011.03.072 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Stelzer J, Chen Y, Turner R (2013) Statistical inference and multiple testing correction in classification-based multi-voxel pattern analysis (MVPA): random permutations and cluster size control . Neuroimage 65 :69–82. 10.1016/j.neuroimage.2012.09.063 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Tong F, Harrison SA, Dewey JA, Kamitani Y (2012) Relationship between BOLD amplitude and pattern classification of orientation-selective activity in the human visual cortex . Neuroimage 63 :1212–1222. 10.1016/j.neuroimage.2012.08.005 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Trojano L, Grossi D, Linden DE, Formisano E, Hacker H, Zanella FE, Goebel R, Di Salle F (2000) Matching two imagined clocks: the functional anatomy of spatial analysis in the absence of visual stimulation . Cereb Cortex 10 :473–481. 10.1093/cercor/10.5.473 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Van Calster L, D'Argembeau A, Salmon E, Peters F, Majerus S (2016) Fluctuations of attentional networks and default mode network during the resting state reflect variations in cognitive states: evidence from a novel resting-state experience sampling method . J Cogn Neurosci 29 :85–113. 10.1162/jocn_a_01025 [ PubMed ] [ CrossRef ] [ Google Scholar ]
Zaretskaya N, Thielscher A, Logothetis NK, Bartels A (2010) Disrupting parietal function prolongs dominance durations in binocular rivalry . Curr Biol 20 :2106–2111. 10.1016/j.cub.2010.10.046 [ PubMed ] [ CrossRef ] [ Google Scholar ]

Apply to UW
Programs & Majors
Cost & Financial Aid
Current Students
UW Libraries
Online Degrees & Programs
Degree Plans & Courses
Advising & Career Services
UW College of Law
Honors College
Academic Affairs
Geological Museum
All Colleges
Campus Recreation
Campus Maps
Housing & Dining
Transit & Parking
University Store
Student Organizations
Campus Activities
Campus Safety
Research & Economic Dev.
Wyoming INBRE
Neuroscience Center
Research at AMK Ranch
Technology Transfer Office
Supercomputing
Water Research
WY EPSCoR/IDeA
American Heritage Center
Where We Shine
About Laramie
Student Stories
Campus Fact Book
UWYO Magazine
Marketing & Brand Center
Administrative Resources
Strategic Plan
+Application Login
UW Homepage

UW Faculty Members Encouraged to Submit Papers for Release in Advance

News Releases
Back to 2023 Archives
Experts List
Subscribe to UW News
Submit News

Institutional Communications Bureau of Mines Building, Room 137 Laramie, WY 82071 Phone: (307) 766-2929 Email: [email protected]

Published August 26, 2024

University of Wyoming faculty members who have upcoming research papers that will be published in the following journals -- Nature, Science or the Proceedings of the National Academy of Sciences -- are encouraged to let UW Institutional Communications know well in advance.

UW Institutional Communications is primarily interested in writing media releases on research published in these journals but will publicize papers appearing in other peer-reviewed scientific journals as well.

For scheduling purposes, UW faculty members are encouraged to submit their papers as soon as they know they will be published. This is typically three to four weeks in advance. It is easier to work on a news release and obtain photos weeks in advance than learning only a few days before the paper is to be published. And UW Institutional Communications honors all publishing embargoes, so there is no need to worry about sending papers well in advance of the publication dates, says Ron Podell, a communications specialist in UW Institutional Communications.

To submit a paper, call Podell at (307) 460-0247 or email [email protected] .

SUGGESTED TOPICS
The Magazine
Newsletters
Managing Yourself
Managing Teams
Work-life Balance
The Big Idea
Data & Visuals
Reading Lists
Case Selections
HBR Learning
Topic Feeds
Account Settings
Email Preferences

Research: How to Build Consensus Around a New Idea

Devon Proudfoot
Wayne Johnson

Strategies for overcoming the disagreements that can stymie innovation.

Previous research has found that new ideas are seen as risky and are often rejected. New research suggests that this rejection can be due to people’s lack of shared criteria or reference points when evaluating a potential innovation’s value. In a new paper, the authors find that the more novel the idea, the more people differ on their perception of its value. They also found that disagreement itself can make people view ideas as risky and make them less likely to support them, regardless of how novel the idea is. To help teams get on the same page when it comes to new ideas, they suggest gathering information about evaluator’s reference points and developing criteria that can lead to more focused discussions.

Picture yourself in a meeting where a new idea has just been pitched, representing a major departure from your company’s standard practices. The presenter is confident about moving forward, but their voice is quickly overtaken by a cacophony of opinions from firm opposition to enthusiastic support. How can you make sense of the noise? What weight do you give each of these opinions? And what does this disagreement say about the idea?

DP Devon Proudfoot is an Associate Professor of Human Resource Studies at Cornell’s ILR School. She studies topics related to diversity and creativity at work.
Wayne Johnson is a researcher at the Utah Eccles School of Business. He focuses on evaluations and decisions about new information, including persuasion regarding creative ideas and belief change.

Partner Center

Suggestions or feedback?

MIT News | Massachusetts Institute of Technology

Machine learning
Sustainability
Black holes
Classes and programs

Departments

Aeronautics and Astronautics
Brain and Cognitive Sciences
Architecture
Political Science
Mechanical Engineering

Centers, Labs, & Programs

Abdul Latif Jameel Poverty Action Lab (J-PAL)
Picower Institute for Learning and Memory
Lincoln Laboratory
School of Architecture + Planning
School of Engineering
School of Humanities, Arts, and Social Sciences
Sloan School of Management
School of Science
MIT Schwarzman College of Computing

Study reveals the benefits and downside of fasting

Press contact :, media download.

A large glowing stem cell, with clocks and empty plates in background.

*Terms of Use:

Images for download on the MIT News office website are made available to non-commercial entities, press and the general public under a Creative Commons Attribution Non-Commercial No Derivatives license . You may not alter the images provided, other than to crop them to size. A credit line must be used when reproducing images; if one is not provided below, credit the images to "MIT."

Previous image Next image

Low-calorie diets and intermittent fasting have been shown to have numerous health benefits: They can delay the onset of some age-related diseases and lengthen lifespan, not only in humans but many other organisms.

Many complex mechanisms underlie this phenomenon. Previous work from MIT has shown that one way fasting exerts its beneficial effects is by boosting the regenerative abilities of intestinal stem cells, which helps the intestine recover from injuries or inflammation.

In a study of mice, MIT researchers have now identified the pathway that enables this enhanced regeneration, which is activated once the mice begin “refeeding” after the fast. They also found a downside to this regeneration: When cancerous mutations occurred during the regenerative period, the mice were more likely to develop early-stage intestinal tumors.

“Having more stem cell activity is good for regeneration, but too much of a good thing over time can have less favorable consequences,” says Omer Yilmaz, an MIT associate professor of biology, a member of MIT’s Koch Institute for Integrative Cancer Research, and the senior author of the new study.

Yilmaz adds that further studies are needed before forming any conclusion as to whether fasting has a similar effect in humans.

“We still have a lot to learn, but it is interesting that being in either the state of fasting or refeeding when exposure to mutagen occurs can have a profound impact on the likelihood of developing a cancer in these well-defined mouse models,” he says.

MIT postdocs Shinya Imada and Saleh Khawaled are the lead authors of the paper, which appears today in Nature .

Driving regeneration

For several years, Yilmaz’s lab has been investigating how fasting and low-calorie diets affect intestinal health. In a 2018 study , his team reported that during a fast, intestinal stem cells begin to use lipids as an energy source, instead of carbohydrates. They also showed that fasting led to a significant boost in stem cells’ regenerative ability.

However, unanswered questions remained: How does fasting trigger this boost in regenerative ability, and when does the regeneration begin?

“Since that paper, we’ve really been focused on understanding what is it about fasting that drives regeneration,” Yilmaz says. “Is it fasting itself that’s driving regeneration, or eating after the fast?”

In their new study, the researchers found that stem cell regeneration is suppressed during fasting but then surges during the refeeding period. The researchers followed three groups of mice — one that fasted for 24 hours, another one that fasted for 24 hours and then was allowed to eat whatever they wanted during a 24-hour refeeding period, and a control group that ate whatever they wanted throughout the experiment.

The researchers analyzed intestinal stem cells’ ability to proliferate at different time points and found that the stem cells showed the highest levels of proliferation at the end of the 24-hour refeeding period. These cells were also more proliferative than intestinal stem cells from mice that had not fasted at all.

“We think that fasting and refeeding represent two distinct states,” Imada says. “In the fasted state, the ability of cells to use lipids and fatty acids as an energy source enables them to survive when nutrients are low. And then it’s the postfast refeeding state that really drives the regeneration. When nutrients become available, these stem cells and progenitor cells activate programs that enable them to build cellular mass and repopulate the intestinal lining.”

Further studies revealed that these cells activate a cellular signaling pathway known as mTOR, which is involved in cell growth and metabolism. One of mTOR’s roles is to regulate the translation of messenger RNA into protein, so when it’s activated, cells produce more protein. This protein synthesis is essential for stem cells to proliferate.

The researchers showed that mTOR activation in these stem cells also led to production of large quantities of polyamines — small molecules that help cells to grow and divide.

“In the refed state, you’ve got more proliferation, and you need to build cellular mass. That requires more protein, to build new cells, and those stem cells go on to build more differentiated cells or specialized intestinal cell types that line the intestine,” Khawaled says.

Too much of a good thing

The researchers also found that when stem cells are in this highly regenerative state, they are more prone to become cancerous. Intestinal stem cells are among the most actively dividing cells in the body, as they help the lining of the intestine completely turn over every five to 10 days. Because they divide so frequently, these stem cells are the most common source of precancerous cells in the intestine.

In this study, the researchers discovered that if they turned on a cancer-causing gene in the mice during the refeeding stage, they were much more likely to develop precancerous polyps than if the gene was turned on during the fasting state. Cancer-linked mutations that occurred during the refeeding state were also much more likely to produce polyps than mutations that occurred in mice that did not undergo the cycle of fasting and refeeding.

“I want to emphasize that this was all done in mice, using very well-defined cancer mutations. In humans it’s going to be a much more complex state,” Yilmaz says. “But it does lead us to the following notion: Fasting is very healthy, but if you’re unlucky and you’re refeeding after a fasting, and you get exposed to a mutagen, like a charred steak or something, you might actually be increasing your chances of developing a lesion that can go on to give rise to cancer.”

Yilmaz also noted that the regenerative benefits of fasting could be significant for people who undergo radiation treatment, which can damage the intestinal lining, or other types of intestinal injury. His lab is now studying whether polyamine supplements could help to stimulate this kind of regeneration, without the need to fast.

“This fascinating study provides insights into the complex interplay between food consumption, stem cell biology, and cancer risk,” says Ophir Klein, a professor of medicine at the University of California at San Francisco and Cedars-Sinai Medical Center, who was not involved in the study. “Their work lays a foundation for testing polyamines as compounds that may augment intestinal repair after injuries, and it suggests that careful consideration is needed when planning diet-based strategies for regeneration to avoid increasing cancer risk.”

The research was funded, in part, by a Pew-Stewart Trust Scholar award, the Marble Center for Cancer Nanomedicine, the Koch Institute-Dana Farber/Harvard Cancer Center Bridge Project, and the MIT Stem Cell Initiative.

Share this news article on:

Press mentions, medical news today.

A new study led by researchers at MIT suggests that fasting and then refeeding stimulates cell regeneration in the intestines, reports Katharine Lang for Medical News Today . However, notes Lang, researchers also found that fasting “carries the risk of stimulating the formation of intestinal tumors.”

Prof. Ömer Yilmaz and his colleagues have discovered the potential health benefits and consequences of fasting, reports Max Kozlov for Nature . “There is so much emphasis on fasting and how long to be fasting that we’ve kind of overlooked this whole other side of the equation: what is going on in the refed state,” says Yilmaz.

MIT researchers have discovered how fasting impacts the regenerative abilities of intestinal stem cells, reports Ed Cara for Gizmodo . “The major finding of our current study is that refeeding after fasting is a distinct state from fasting itself,” explain Prof. Ömer Yilmaz and postdocs Shinya Imada and Saleh Khawaled. “Post-fasting refeeding augments the ability of intestinal stem cells to, for example, repair the intestine after injury.”

Previous item Next item

How early-stage cancer cells hide from the immune system

MIT biologists found that intestinal stem cells express high levels of a ketogenic enzyme called HMGCS2, shown in brown.

Study links certain metabolites to stem cell function in the intestine

Intestinal stem cells from mice that fasted for 24 hours, at right, produced much more substantial intestinal organoids than stem cells from mice that did not fast, at left.

Fasting boosts stem cells’ regenerative capacity

“Not only does the high-fat diet change the biology of stem cells, it also changes the biology of non-stem-cell populations, which collectively leads to an increase in tumor formation,” Omer Yilmaz says.

How diet influences colon cancer

More mit news.

Illustration of 5 spheres with purple and brown swirls. Below that, a white koala with insets showing just its head. Each koala has one purple point on either the forehead, ears, and nose.

A framework for solving parabolic partial differential equations

Read full story →

Feyisayo Eweje wears lab coat and gloves while sitting in a lab.

Designing better delivery for medical therapies

Saeed Miganeh poses standing in a hallway. A street scene is visible through windows in the background

Making a measurable economic impact

Faces of MIT: Jessica Tam

More than a dozen people sit around shared tables with laptops running App Inventor

First AI + Education Summit is an international push for “AI fluency”

President Kornbluth welcomes the Class of 2028

More news on MIT News homepage →

Massachusetts Institute of Technology 77 Massachusetts Avenue, Cambridge, MA, USA

Map (opens in new window)
Events (opens in new window)
People (opens in new window)
Careers (opens in new window)
Accessibility
Social Media Hub
MIT on Facebook
MIT on YouTube
MIT on Instagram

The Potential Long-Run Implications of a Permanently-Expanded Child Tax Credit

For many of those who worked to include an expanded Child Tax Credit in the 2021 American Rescue Plan, an important motivation was to test the feasibility and effectiveness of a permanent U.S. child allowance similar to those provided in other rich countries. Because this expansion was short-lived, however, evaluations of its effects cannot provide complete evidence on the long-run effects of a permanently expanded CTC. We leverage theoretical predictions from standard economic models, behavioral science, and child development frameworks, along with empirical evidence from literature evaluating previous long-term cash and quasi-cash transfers to families with children, to predict the likely long-run impacts of a permanent child allowance. We find that it would lead to increased future earnings and tax payments, improved health and longevity, and reduced health care, crime, and child protection costs; using conventional valuations, benefits to society outweigh costs nearly 10 to 1, with most benefits due to credit refundability.

There are no funding sources or material or relevant financial relationships to disclose. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.

MARC RIS BibTeΧ

Download Citation Data

More from NBER

In addition to working papers , the NBER disseminates affiliates’ latest findings through a range of free periodicals — the NBER Reporter , the NBER Digest , the Bulletin on Retirement and Disability , the Bulletin on Health , and the Bulletin on Entrepreneurship — as well as online conference reports , video lectures , and interviews .

2024, 16th Annual Feldstein Lecture, Cecilia E. Rouse," Lessons for Economists from the Pandemic" cover slide

IMAGES

Research Paper
(PDF) Best Research Papers in the Field of Sensors, Signals, and
Different Types of Research Papers Guide with Examples
Tips For How To Write A Scientific Research Paper
43+ Research Paper Examples
How to Write and Publish a Research Paper.pdf

COMMENTS

Psychological Imagery in Sport and Performance
The measurement of imagery ability and imagery frequency have often been assessed in the sport, exercise, and performance imagery research. Given that imagery is an internal mental skill, its assessment has typically relied on the self-report questionnaires allowing individuals to subjectively report their imagery use and ability.
Mental Imagery: Functional Mechanisms and Clinical Applications
Mental imagery research has weathered both disbelief of the phenomenon and inherent methodological limitations. Here we review recent behavioral, brain imaging, and clinical research that has reshaped our understanding of mental imagery. Research supports the claim that visual mental imagery is a depictive internal representation that functions ...
The human imagination: the cognitive neuroscience of visual mental imagery
Mental imagery can be advantageous, unnecessary and even clinically disruptive. With methodological constraints now overcome, research has shown that visual imagery involves a network of brain ...
The critical role of mental imagery in human emotion: insights from
(a) Participants. Sample size estimation was based on the theoretical centrality of mental imagery in amplifying emotional responses to thought and the appearance of large effect sizes in the limited existing research comparing aphantasics and the general population on imagery measures [10,14].For a two-independent-group t-test with a large effect size (Cohen's d = 1) and with α = 0.05 and ...
Different Mechanisms for Supporting Mental Imagery and Perceptual
Recent research suggests imagery is functionally equivalent to a weak form of visual perception. Here we report evidence across five independent experiments on adults that perception and imagery are supported by fundamentally different mechanisms: Whereas perceptual representations are largely formed via increases in excitatory activity, imagery representations are largely supported by ...
Mental Imagery in the Science and Practice of Cognitive Behaviour
The 'present status' of CBT-relevant mental imagery research is very much informed by the programme of the 2019 World Congress of Behavioural and Cognitive Therapies ... this is manifested in an ever-increasing proliferation of new research papers and opens up many exciting future possibilities. Of course, within each specific line of ...
Nature-Based Guided Imagery as an Intervention for State Anxiety
Guided imagery (GI) has been used as an effective intervention for anxiety by generating relaxing states through mental processes ( Martin et al., 1999; Holmes and Mathews, 2005; Apóstolo and Kolcaba, 2009 ). An explicit addition of the natural environment to a GI process might serve to overcome the issue of physical access to nature and ...
Mental imagery: Functional mechanisms and clinical applications
Mental imagery research has weathered both disbelief of the phenomenon and inherent methodological limitations. Here we review recent behavioral, brain imaging, and clinical research that has reshaped our understanding of mental imagery. Research supports the claim that visual mental imagery is a depictive internal representation that functions like a weak form of perception.
PDF The human imagination: the cognitive neuroscience of visual mental imagery
The use of imagery as a tool has been linked to many compound cognitive processes and imagery plays both symptomatic and mechanistic roles in neurological and mental disorders and treatments ...
Neural foundations of imagery
Remarkably little research has addressed auditory imagery per se. ... This paper is a landmark in the development of techniques to delineate topographically organized areas in the human brain ...
(PDF) Study of Visual Mental Imagery on the Aspects of Background
Mental imagery is an under-explored field in clinical psychology research but presents a topic of potential interest and relevance across many clinical disorders, including social phobia ...
Imagery Ability and Imagery Perspective Preference: A Study of Their
1. Introduction. In the past five decades, mental imagery has gathered a large body of research supporting its use in facilitating performance of motor and cognitive tasks (for meta-analytical reviews, see [1, 2]).Motor imagery, which involves simulating an action without its physical execution [], has been of particular interest to researchers in fields of sport and exercise and clinical ...
Visual Methodologies in Qualitative Research:
Visual methodologies are used to understand and interpret images (Barbour, 2014) and include photography, film, video, painting, drawing, collage, sculpture, artwork, graffiti, advertising, and cartoons.Visual methodologies are a new and novel approach to qualitative research derived from traditional ethnography methods used in anthropology and sociology.
The Multiple Uses of Guided Imagery
Guided imagery is a therapeutic approach that has been used for centuries. Through the use of mental imagery, the mind-body connection is activated to enhance an individual's sense of well-being, reduced stress, and reduced anxiety, and it has the ability to enhance the individual's immune system. There are research and data to support the use ...
Applied imagery for motivation: a person-centred model
In this paper, we will review current approaches to the use of imagery in sport, and propose a person-centred model called applied imagery for motivation (AIM) for practitioners to follow, and athletes to experience. We consider the amalgamation of cognitive, motivational and person-centred imagery from theory to practice.
The effect of mental imagery on skill performance in sport: A
Most research on imagery training is on closed self-paced motor skills, with significantly more studies of self-paced, closed motor skills than open motor skills included in imagery reviews ...
Imagery, affect, and the embodied mind: implications for reading and
In this paper, we claim that scientific research may take advantage from the literary representation of the imaginative faculties, which occurs in specific tests characterized by dynamic images and motion. ... Metaphors that augment already established simulative imagery by gestalt effects (double-projection, etc.) add to this. My overall aim ...
Motor imagery: a window into the mechanisms and alterations of the
Motor imagery is a widely used paradigm for the study of cognitive aspects of action control, both in the healthy and the pathological brain. In this paper we review how motor imagery research has advanced our knowledge of behavioral and neural aspects of action control, both in healthy subjects and clinical populations.
Guided imagery: Harnessing the power of imagination to ...
Guided Imagery is a powerful and well-researched self-care tool that can combat the stress response with even a brief practice. • Guided Imagery is a low cost, low effort, accessible practice that can elicit positive results in just a few minutes. • Guided Imagery engages all of the senses for a rich, desirable experience. •
Research Guides: Using Images and Non-Textual Materials in
This guide offers basic information on using images and media in research. Reasonable use of images and media in teaching, course papers, and graduate theses/dissertations is generally covered by fair use. ... It can be used in a critical context within a presentation, classroom session, or paper/thesis, as follows: [Figure 1. This photograph ...
A cognitive profile of multi-sensory imagery, memory and ...
Our research presents an extended cognitive fingerprint of aphantasia and helps to clarify the role that visual imagery plays in wider consciousness and cognition. Visual imagery is a cognitive ...
What is mental imagery? Brain researchers explain the pictures ...
As neuroscientists in the fields of physical therapy and psychology, we think about the ways people use mental imagery.Here is what researchers do know so far. The brain and mental imagery. Mental ...
The physics behind the most annoying thing that could ever happen to
MARTIN: So he and his colleagues got different kinds of paper - book paper, photo paper, Post-it notes - and they found a substitute for skin. JENSEN: We built a little robot, a little ninja ...
How to Cite a Research Paper
The most common citation styles used in academic research, according to Geary, are: American Psychological Association, known as APA. This style is standard in the social sciences such as psychology, education and communication. "In these fields, research happens rapidly, which makes it exceptionally important to use current research ...
Vividness of Visual Imagery Depends on the Neural Overlap with
After this, they indicated their experienced vividness on a scale from 1 to 4, where 1 was low vividness and 4 was high vividness. Previous research has shown that such a subjective imagery rating shows high test-retest reliability and correlates with objective measures of imagery vividness (Pearson et al., 2011; Bergmann et al., 2016).
UW Faculty Members Encouraged to Submit Papers for Release in Advance
University of Wyoming faculty members who have upcoming research papers that will be published in the following journals -- Nature, Science or the Proceedings of the National Academy of Sciences -- are encouraged to let UW Institutional Communications know well in advance. ... To submit a paper, call Podell at (307) 460-0247 or email rpodell ...
Research: How to Build Consensus Around a New Idea
New research suggests that this rejection can be due to people's lack of shared criteria or reference points when evaluating a potential innovation's value. In a new paper, the authors find ...
Study reveals the benefits and downside of fasting
MIT postdocs Shinya Imada and Saleh Khawaled are the lead authors of the paper, which appears today in Nature. Driving regeneration. For several years, Yilmaz's lab has been investigating how fasting and low-calorie diets affect intestinal health. ... The research was funded, in part, by a Pew-Stewart Trust Scholar award, the Marble Center ...
Early science and colossal stone engineering in Menga, a Neolithic
The research presented here proposes a completely innovative interpretation of how this colossal monument was built. ... R. Bradley, Access, style and imagery: The audience for prehistoric rock art in Atlantic Spain and Portugal, 4000-2000 BC. ... All data needed to evaluate the conclusions in the paper are present in the paper and/or the ...
The Potential Long-Run Implications of a Permanently-Expanded Child Tax
Founded in 1920, the NBER is a private, non-profit, non-partisan organization dedicated to conducting economic research and to disseminating research findings among academics, public policy makers, and business professionals.

Article contents

Introduction

Theories and Models

Measurement

Factors Affecting Imagery

Image Speed

Skill Level

Imagery Perspective

Other Factors

Imagery as a Means to Improving Performance

Performance

Future Directions

Further Reading

Related Articles

Mental Imagery in the Science and Practice of Cognitive Behaviour Therapy: Past, Present, and Future Perspectives

Cite this article

Similar content being viewed by others

Cognitive Behavior Therapy at the Crossroads

Changing Biased Interpretations in CBT: A Brief History and Overview

Why Focus on Mental Imagery?

Mental Imagery Across the Science and Practice of CBT

Mental Imagery as an Experimental Tool

Mental Imagery in Healthy Everyday Functioning

Dysfunctional Mental Imagery

Mental Imagery as a Therapeutic Tool within CBT

Future Perspectives

Research Perspectives

Clinical Innovation and Implementation Perspectives

Implications for the Individual Researcher or Clinician

Conclusions

Author information

Corresponding author

Ethics declarations

Additional information

Rights and permissions

About this article

Share this article

Imagery, affect, and the embodied mind: implications for reading and responding to literature

Related Papers

RELATED PAPERS

RELATED TOPICS

Save citation to file

Add to My Bibliography

Motor imagery: a window into the mechanisms and alterations of the motor system

Similar articles

Publication types

LinkOut - more resources

Miscellaneous

Using Images and Non-Textual Materials in Presentations, Papers, Theses, and Dissertations

Documenting and Citing Images/Photographs and Their Sources

Examples of Documenting Images

A cognitive profile of multi-sensory imagery, memory and dreaming in aphantasia

Similar content being viewed by others

Insights into embodied cognition and mental imagery from aphantasia

Individuals who rarely adopt observer perspectives report richer scene-based autobiographical memories

Qualitative analysis of hotspots and intrusive memories after viewing an aversive film highlights their sensory and spatial features

Participants

Aphantasia group

Control group 1 (MTurk)

Control group 2 (Undergraduates)

Aphantasia sample characteristics

Clinical history

Imagery scores

Experimental procedure

Imagery questionnaires

Memory questionnaires

Dreaming and daydreaming questionnaires

Trauma response questionnaire

Statistical analyses

Data transformation

Control Group 1: Main Comparisons

Memory results

Trauma response results

Day and night dream results

Spatial ability results

Control Group 2: Replication Analysis

Acknowledgements

Author information

Contributions

Corresponding author