divergent thinking hypothesis

ORIGINAL RESEARCH article

A new perspective on the multidimensionality of divergent thinking tasks.

$\r\nBoris Forthmann*$

1 Institut für Psychologie, University of Münster, Münster, Germany
2 Fakultät Statistik, TU Dortmund University, Dortmund, Germany
3 Institut für Psychologie, University of Graz, Graz, Austria

In the presented work, a shift of perspective with respect to the dimensionality of divergent thinking (DT) tasks is introduced moving from the question of multidimensionality across DT scores (i.e., fluency, flexibility, or originality) to the question of multidimensionality within one holistic score of DT performance (i.e., snapshot ratings of creative quality). We apply IRTree models to test whether unidimensionality assumptions hold in different task instructions for snapshot scoring of DT tests across Likert-scale points and varying levels of fluency. It was found that evidence for unidimensionality across scale points was stronger with be-creative instructions as compared to be-fluent instructions which suggests better psychometric quality of ratings when be-creative instructions are used. In addition, creative quality latent variables pertaining to low-fluency and high-fluency ideational pools shared around 50% of variance which suggests both strong overlap, and evidence for differentiation. The presented approach allows to further examine the psychometric quality of subjective ratings and to examine new questions with respect to within-item multidimensionality in DT.

Introduction

Divergent thinking (DT) tasks are one of the most important proxies of creative thinking ( Runco and Acar, 2012 ). For example, they are frequently used in research on the link of intelligence and creativity (e.g., Karwowski et al., 2016 ) and have been shown to predict creative achievement above intelligence ( Kim, 2008 ). These tasks typically ask participants to come up with either many or creative ideas in order to solve a given problem. A classic research issue relating to such tasks is their underlying dimensionality; this has been discussed and researched since the 1950’s. Most of the studies in this vein addressed the factorial validity of Guilford’s classic four divergent-thinking abilities (i.e., fluency, flexibility, elaboration, and originality) in several common tests and batteries (e.g., Hargreaves and Bolton, 1972 ; Follman et al., 1973 ; Plass et al., 1974 ; Aliotti et al., 1975 ; Heausler and Thompson, 1988 ).

In the current work, however, we focus on the dimensionality of creative quality. In line with Forthmann et al. (2017a) , we refer to creative quality scores as all scorings that have a clear definitional link to the concept of creativity. Prominently, originality scores fit this notion of creative quality well, because originality is one of the defining characteristics of creativity (e.g., Runco and Jaeger, 2012 ). Thus, all commonly used indicators of originality, such as uncommonness, remoteness, and cleverness (see Wilson et al., 1953 ), are examples of creative quality scores (see Forthmann et al., 2017a ). Moreover, this definition of creative quality allows including usefulness-based scores as it is the second defining characteristic of creativity (e.g., Runco and Jaeger, 2012 ). Beyond the two-componential standard definition of creativity, the definition of creative quality is open to other conceptualizations of creativity such as Simonton’s three-componential definition ( Simonton, 2012 , 2018 ) or Kharkhurin’s four-criterion definition ( Kharkhurin, 2014 ), for example.

Various examples of quality scores can be found in the literature on DT. First, originality has been scored with respect to uncommonness (e.g., Runco and Charles, 1993 ; Mouchiroud and Lubart, 2001 ), remoteness (e.g., Christensen et al., 1957 ; Piers et al., 1960 ; Parnes, 1961 ), and cleverness (e.g., French et al., 1963 ; Mullins, 1963 ; Forthmann et al., 2017a ). Second, usefulness has been scored independent of originality (e.g., Runco and Charles, 1993 ; Diedrich et al., 2015 ; Olteteanu and Falomir, 2016 ). Third, both components of the standard definition of creativity – originality and usefulness – were combined in an all-in-all creativity score (e.g., Runco and Mraz, 1992 ; Runco and Charles, 1993 ; Mouchiroud and Lubart, 2001 ; Storm and Patel, 2014 ; Diedrich et al., 2015 ). Clearly, there is no unanimous way to score DT responses for creative quality (see also Reiter-Palmon et al., 2019 ). In the current work, a creative quality score that is based on all classic indicators of originality will be used because this approach has been used more extensively over recent years (e.g., Silvia et al., 2008 , 2009 ; Hass, 2015 , 2017 ; Hofelich Mohr et al., 2016 ).

Subjective ratings with Likert-scales are often used to score creative quality (e.g., Silvia et al., 2008 ). In particular, providing subjective ratings for a person’s ideational pool (all generated responses as a whole; see description below) will be in the focus of the current work ( Runco and Mraz, 1992 ; Silvia et al., 2009 ). In fact, these ratings raise new questions with respect to dimensionality issues of DT tests. By a combination of ratings and a particular item-response modeling technique, so called IRTrees ( De Boeck and Partchev, 2012 ), we show how to assess whether providing few or many ideas might induce multidimensionality in rated creative quality. Is there a relationship of quality in low-fluency sets and quality in high-fluency sets within the same person? Clearly, for the measurement of quality it is fundamental to know if the ability to be creative is being measured no matter how many ideas are generated. In fact, it is discussable if high-fluency and low-fluency ideational pools are unidimensional with respect to creative quality. Strategies that are efficient in terms of fluency (such as retrieval from memory) are most likely not conducive to idea quality which requires more demanding strategies ( Gilhooly et al., 2007 ). Thus, differences in cognitive processes when people are fluent and when they are not may affect the dimensionality of creative quality scores.

In a similar vein, studies on the “be-creative” effect in DT are informative ( Christensen et al., 1957 ; Harrington, 1975 ; Runco, 1986 ; Runco et al., 2005 ; Nusbaum et al., 2014 ; for be-creative effects in other creative performance tasks see, for example, Chen et al., 2005 ; Niu and Liu, 2009 ; Rosen et al., 2017 ). In this line of research, standard instructions (be-fluent instructions) with a focus on quantity of responses ( think of as many ideas as possible ) are compared with explicit instructions to be creative (be-creative instructions; think of ideas that are creative, original, uncommon, clever, and so forth ). Substantial variation in participants’ strategies is expected, when the task goal remains opaque as in be-fluent instructions ( Harrington, 1975 ; Nusbaum et al., 2014 ). On the contrary, “be-creative” instructions are assumed to homogenize participants’ mindset toward the task and more demanding strategies can be expected to be used from the beginning ( Nusbaum et al., 2014 ). Guilford (1968) argued also that explicit instructions to generate rather creative responses are likely to change the cognitive processes during idea generation. He expected a stronger involvement of evaluative processing with explicit instructions, which is in line with recent work by Nusbaum et al. (2014) . As a consequence, the involvement of evaluative processing should be more homogeneous across participants when receiving explicit instructions to be creative as compared to be-fluent instructions. Thus, multidimensionality of creative quality of low-fluency and high-fluency ideational pools seem to be more likely under be-fluent instructions.

Moreover, the introduced modeling technique allows addressing another issue regarding the dimensionality of DT quality. This issue is related to the subjective scoring of DT quality. Subjective scorings of DT have gained prominence over the last years, but they were used also in the early years of DT research. One example of subjective scoring is the so-called snapshot scoring or scoring of ideational pools ( Runco and Mraz, 1992 ; Silvia et al., 2009 ). Here raters see a full ideational pool of a participant and give a rating of the set’s overall creative quality. These ratings are typically given on a 5-point Likert scale and here the question can be asked: Do we measure the same quality at the lower end and the upper end of the scale? Or to put it differently: Is one latent trait underlying the full rating scale? This question has been coined ordinal hypothesis by De Boeck and Partchev (2012) .

An IRTree Modeling Approach for Divergent-Thinking Creative Quality Ratings

The model we use here is an IRT model for rating scales. It is inspired by the sequential model of Tutz (1990) . In order to understand the model, it is helpful to use a linear response tree structure of the Likert scale (see Figure 1 ). A linear response tree is comprised of end nodes Y (i.e., the concrete response categories of the Likert scale) and internal nodes Y ∗ ( De Boeck and Partchev, 2012 ). As shown in Figure 1 , internal nodes can be understood as binary-coded sub-items. For example, the sub-item for the first internal node ( Y 1 * in Figure 1 ) in the tree is coded zero when an ideational pool receives a rating of one, and the sub-item is coded one when the ideational pool received a rating larger than one. The sub-item for the second internal node ( Y 2 * in Figure 1 ) in the tree is coded zero when an ideational pool receives a rating of two, and the sub-item is coded one when the ideational pool received a rating larger than two, and so forth. Hence, the ratings on the Likert scale are divided into binary-coded sub-items Y p i r * for each of the r =1,…, R internal nodes. Please note that for simplicity we will use the term node from here onward to only refer to internal nodes of a response tree. Each node in the linear tree represents a branch with probability to remain at a given category m = 1,…, M - 1 in contrast to all possible higher categories ( De Boeck and Partchev, 2012 ). The model is based on the sub-items and can be formulated as:

with θ p = (θ p 1 ,…,θ pr ) being the node-specific latent variables (with person number p = 1,…, P ) and β ir (with item number i = 1,…, I ) denoting a node-specific item threshold. For unidimensional models, only one latent ability is assumed to underlie the linear response tree. We will also use the term node-unidimensionality to refer to such unidimensional models. Moreover, as outlined in De Boeck and Partchev (2012) , such a linear response tree can be combined with latent variable trees, which leads to other multidimensional modeling options. For example, a bi-factor structure can be proposed and tested as an underlying structure of a linear response tree.

Figure 1. Linear response tree for the creative quality ratings. The Y r * represent sub-items ( De Boeck and Partchev, 2012 ) and the Y the response categories for the ratings.

Aim of the Current Study

The aim of the current study is to provide new insights into the dimensionality of DT by means of a proof of concept study using IRTree modeling. The IRTree modeling framework allows assessing dimensionality issues that are at the heart of frequently used scoring procedures in divergent-thinking assessment such as snapshot scoring of ideational pools ( Runco and Mraz, 1992 ; Silvia et al., 2009 ). Related to this scoring procedure, we show how to test a unidimensional model underlying the rating scale against a multidimensional alternative that includes a different latent variable at each of the respective nodes. Unidimensionality of ratings in snapshot scoring with respect to the used scale is in fact fundamental to the method, but it has not yet been assessed in the literature. It is important to know if the ability to score better as the lowest scale point is the same ability that allows a person to move from the second highest to the highest scale point of the response tree.

Moreover, by means of IRTree modeling, it is possible to take a different view on the relationship between fluency and creative quality ratings. We can assess if the latent dimensions underlying low-fluent and high-fluent creative quality are related or even tap to the same latent variable. This question was tested for standard and be-creative instructions. The goal of this research is to illustrate a methodological approach to new dimensionality issues of DT which moves from the question of multidimensionality across DT scores to the question of multidimensionality across the scale of DT scores and across ideational pools of varying size.

Materials and Methods

Participants.

The data set to illustrate the above ideas was taken from Forthmann et al. (2016) . This dataset was gathered for a large project and used in previous publications on DT assessment issues ( Forthmann et al., 2016 , 2017a , b ) and the relationship between multicultural experiences and DT performance ( Forthmann et al., 2018 ). However, the analyses in this study are unique to this work and go beyond any of the issues tackled in the above-mentioned articles. In that study eight Alternate Uses Tasks (AUTs; Wallach and Kogan, 1965 ; Guilford, 1967 ) were administered online. Four of the tasks were administered with a be-fluent instruction and the other four with a be-creative instruction. The AUTs were scored for fluency and subjective ratings of creative quality of the full ideational pools were given by two raters. The raters were asked to give holistic ratings of creative quality reflecting the three classic originality indicators – uncommonness, remoteness, and cleverness – according to the rating scheme provided in Silvia et al. (2008) : Uncommonness: Any response that is given by a lot of people is common, by definition ; remoteness: creative uses for an object are far from everyday uses and obvious responses ; and cleverness: clever ideas strike people as insightful, ironic, humorous, fitting, or smart . In addition, the coding scheme refers also to task appropriateness: A random or inappropriate response would be uncommon but not creative . For more details with respect to these scoring dimensions see Silvia et al. (2008 ; p. 85). The ratings were provided on a 5-point Likert-scale as depicted in Figure 1 . Absolute agreement intra-class correlations for the average scores indicated good inter-rater reliability according to Cicchetti’s criteria ( Cicchetti, 2001 ), ICC(2,2) = 0.701, 95%-CI: [0.669, 0.743]. The final sample for data analysis was N = 249 (age: M = 23.48, SD = 6.44; gender: 79.12% female and 20.88% male). For more details please consult Forthmann et al. (2016) .

This study was carried out in accordance with the recommendations for online studies provided by the ethics committee of the department of psychology of the University in Münster. These guidelines are in accordance with the guidelines provided by the German foundation for online research (Deutsche Gesellschaft für Online Forschung). All subjects gave written informed consent prior to participation. An ethics approval was not required as per institutional and national guidelines.

Data Preparation and Statistical Analysis

The data were prepared with R functions provided by the IRTrees package ( De Boeck and Partchev, 2012 ). In order to fit the proposed models, the function dendrify() was used to expand and reshape the data from a wide-format matrix into a long-format matrix of sub-item responses (see De Boeck and Partchev, 2012 ). The data frame that is built by this function includes an indicator variable for the nodes, so that unidimensionality vs. multidimensionality for the nodes can directly be tested.

Analogous to Partchev and De Boeck (2012) median splits were used in order to separate low-fluency ideational pools from high-fluency ideational pools. Moreover, a methodological sensitivity analysis was performed to assess the differences between median-splits (with respect to fluency) within items and within persons. For the within-item median split all medians for every AUT task were calculated separately for both instructions. An indicator variable for low-fluency vs. high-fluency ideational pools was then formed by a binary coding. The indicator was coded one if fluency for a given set was strictly below (instead of below or equals) the respective median since this led to a more balanced distribution of the indicator variable. The within-person split was analogous, but here the median values were calculated for each person and both instruction types separately. Based on these medians the same indicator variables were built. Moreover, possible differences between raters’ central tendency of ratings were statistically controlled by including a corresponding fixed effect in all estimated models.

De Boeck and Partchev (2012) used maximum-likelihood methods to fit their models. For the present paper, however, we prefer a Bayesian approach for two reasons: First, random effects in the models refer to latent traits and latent trait correlations are of particular interest for our research question. Typical fitting procedures only return a point estimate for these correlations. Bayesian methods return the full posterior distribution of the correlations including standard errors and credible intervals (i.e., Bayesian confidence intervals). These provide a better understanding of the correlation structure and facilitate its interpretation. This is particularly relevant, if the posterior distribution of correlations is skewed, in which case the maximum-likelihood estimate is a poor measure of central tendency. Second, Bayesian methods have some advantages when it comes to model comparison. When using maximum-likelihood procedures, model comparisons are usually performed either by means of significance tests or by comparing information criteria. The former often lack statistical power when the number of observations is small, but for large data sets, even tiny and practically irrelevant differences in model fit will often get significant. These drawbacks are partially addressed by information criteria such as the AIC, but it remains unclear how much the criteria of two models should differ to indicate substantial differences in model fit. For recently developed Bayesian specific information criteria, namely an approximation of the leave-one-out cross-validation (LOO), standard errors can be computed giving a much better sense of uncertainty in those criteria ( Vehtari et al., 2017 ).

All statistical analyses were performed in R ( R Core Team, 2013 ) using the brms package ( Bürkner, 2016 ), which is based upon the probabilistic programming language Stan ( Carpenter et al., 2017 ). It is further noteworthy, that refitting the models by means of the package lme4 ( Bates et al., 2015 ) which uses restricted maximum likelihood estimation did not lead to relevant differences of the presented findings. The R code and data file are available in Open Science Framework 1 .

Dimensionality Underlying the Nodes

A unidimensional linear-response tree model was compared with a model that assumed different latent variables for each of the corresponding nodes. This comparison was initially conducted for the full data set (thus, instruction as an influencing variable was ignored). This comparison was in favor of the multidimensional model as indicated by the difference in LOO criteria (see Table 1 ). When inspecting the correlation matrix of this multidimensional model it was apparent that latent variables referring to nodes of close proximity were more strongly correlated as compared to distant nodes. The three highest nodes correlated fairly strong with each other, whereas the lowest node correlated moderately ( r = 0.39) with its next node and appeared to be unrelated to the other nodes. Thus, the ability to depart from the most common ideas (the ability to generate a response that receives a rating >1) appeared to be loosely related to the other abilities underlying the response tree. However, this pattern of results might be a reflection of different underlying strategies that either opt for quantity with the be-fluent instruction – participants are likely to accept low-quality ideas to increase fluency more readily here – or for quality with the be-creative instruction. Consequently, the dimensionality underlying the nodes was tested again for both instruction types separately.

Table 1. Comparison of unidimensional models vs. multidimensional models with respect to the nodes for the full model and for the be-creative instruction and be-fluent instruction.

For the instruction-specific tests of node-specific multidimensionality the item-parameters for the fourth node in the be-fluent instruction and the first node in the be-creative instruction were not identified due to lack of idea sets that received such ratings. According to the LOO information criterion, the multidimensional node model fit better in the be-fluent instruction, whereas for the be-creative instruction the unidimensional model was better (see Table 1 ). From the correlation estimates for latent variables of these models it is evident that support for unidimensionality of the nodes was stronger with a be-creative instruction as compared to the be-fluent instruction. The inter-correlations of latent variables underlying node 1, node 2, and node 3 ranged from r = 0.37 to r = 0.76 in the be-fluent instruction, whereas for the be-creative instruction the range was r = 0.73 to r = 0.89.

Discriminating Creative Quality for Ideational Pools of High and Low Fluency

In order to account for insufficient information in the data at the first node in the be-creative instruction and the fourth node in the be-fluent instruction, those were again omitted. Then, separate analyses for both instructions were conducted (however, the interested reader will find results for the full dataset in the supplementary material or in the OSF see text footnote 1). Initially, the competing models were compared by means of the LOO criterion. With respect to the median splitting method, it was found that using within-person split indicators for high-fluency and low-fluency ideational pools performed better in terms of model fit as compared to the unidimensional model, and the within-item split indicators. Generally, modeling two latent variables for high vs. low-fluency sets increased model fit as compared to a unidimensional model (see Tables 2 , 3 ).

Table 2. Correlations of latent variables and model comparison results for a differentiation of low-fluency and high-fluency creative quality for be-fluent instructions only.

Table 3. Correlations of latent variables and model comparison results for a differentiation of low-fluency and high-fluency creative quality for be-creative instructions only.

We then inspected the pattern of correlations and found estimates for within-item split of r = 0.66 (be-fluent; see Table 2 ) and r = 0.73 (be-creative; see Table 3 ) which implies that at least 44% of variation can be considered as common. Similarly, for the within-person split correlations of r = 0.71 (be-fluent; see Table 2 ) and r = 0.70 (be-creative; see Table 3 ) were found which implies an amount of shared variation of 49%. Thus, besides a strong construct overlap of creative quality for low-fluency and high-fluency ideational pools, there is also evidence for differentiation at the latent level. Moreover, the strong overlap of credible intervals for each of the correlations should be noted which indicates that the found slight differences between correlation estimates are negligible. Consequently, construct differentiation of low-fluency and high-fluency ideational pools with respect to creative quality latent variables can be considered to be similar for both instructions and both splitting methods (within-person or within-item).

The dimensionality of divergent-thinking indices such as originality and fluency has been an intriguing endeavor since the early years of creativity research. In the current study, we introduced IRTrees as an interesting model alternative to study the underlying dimensionality of DT. For example, we assessed the dimensionality of creative quality ratings over the rating scale in order to test the ordinal hypothesis of ratings ( De Boeck and Partchev, 2012 ). In addition, within the IRTree approach, we shifted the focus from the relationship of quality and quantity and the corresponding debate on the lack of discriminant validity of fluency and originality toward a quality-quality relationship when creative quality is assessed for high-production and low-production ideational pools within two common important instructions. Altogether, this study represents a new perspective on the dimensionality of divergent-thinking scores away from classical factor analytic approaches.

Using subjective ratings is not free of criticism (see Runco, 2008 ) and testing the null hypothesis of node unidimensionality offers a way to empirically test how well they work and under which circumstances. For example, these findings highlight recent suggestions made by Reiter-Palmon et al. (2019) to carefully take instruction-scoring fit into account. Instruction-scoring fit refers to the congruence of instructions and scoring, for example, a be-creative instruction resonates well with a scoring of creative quality. While instruction-scoring fit is already preferable from a conceptual perspective, the findings of the current study highlight the importance of instruction-scoring fit because node-unidimensionality as a desirable psychometric property of subjective ratings was better supported when instructions and scoring were congruent. Thus, a Likert-type scale seems to work only in the intended way here if raters see responses of participants who were instructed to focus on quality rather than quantity. Most likely, this finding can be attributed to more consistent behavior as a consequence of a clear goal that is set by means of the instructions (e.g., Harrington, 1975 ; Nusbaum et al., 2014 ). Thus, this work presents a starting point to further investigate if unidimensionality underlying the rating scale can be maintained only for be-creative instructions, which should be tested in other samples (perhaps relying on a larger rater sample) in order to corroborate the observations here.

Tentatively, we conclude that subjective ratings of creative quality require instruction-scoring fit to be psychometrically sound. However, instruction-scoring fit should not be viewed as a dichotomy. For example, the original test material of the AUT from the Guilford group ( Wilson et al., 1960 ) instructs participants to think of uses that are different from an object’s most common use (which is also provided for each object during the test procedure). Thus, instruction-scoring fit for creative quality and the original Guilford instruction would be slightly higher as compared to the fully unrestricted be-fluent instruction used in the current study. Instruction-scoring fit could even be further enhanced when instructions to avoid the most common use ( Wilson et al., 1960 ) are further supplemented by commonly generated example ideas which are also required to be avoided ( Shin et al., 2018 ; George and Wiley, 2019 ). Hence, it is yet unclear which degree of instruction-scoring fit (with explicit instructions to be creative representing maximum instruction-scoring fit when creative quality is of interest) is required for subjective ratings to be node-unidimensional.

In addition, with be-fluent instructions it is efficient to quickly retrieve as many ideas from memory as possible (similar to verbal fluency tasks; for a related discussion see Nusbaum et al., 2014 ). Idea sets resulting from such a strategy reflect production ability and are likely to receive rather low ratings on a 5-point scale. Otherwise some participants may still receive higher-ratings even when be-fluent instructions are given due to their need to be original, need for cognition, or other motivational state and in order to reach such higher ratings more demanding strategies are necessary. Consequently, such differences in underlying cognitive processes and strategies to generate idea sets of varying quality may have caused the observed lack of node-unidimensionality. In fact, from experimental psychology it is known that differences in cognitive processes may influence the dimensionality of latent variable models ( Oberauer et al., 2005 ).

For measurement of creative quality, a unidimensional model that outperforms multidimensional alternatives is desirable. This seems to be particularly the case when participants receive a be-creative instruction. If participants look for imaginative ideas und do not focus on quantity, fluency can be conceived as a byproduct and differentiating for fluency should not affect unidimensionality of creative quality. However, it was illustrated that high-fluency and low-fluency ideational pools differ in terms of creative quality at the latent level (around 50% of the variance is shared). But how did this heterogeneity of latent traits emerge?

One potential explanation here is to assume that differential effects of practice, exhaustion, and current motivation affected the dimensionality here. First, it has been demonstrated that at late time points in DT different and more elaborate strategies are more likely to be used ( Gilhooly et al., 2007 ; see also below). Thus, if eight tasks are administered such strategies can directly be used in later tasks if they are encountered/used while working on one of the previous tasks. Second, if participants get more and more exhausted over the course of testing, it is likely that cognitive resources are used in different ways. Similarly, Haager et al. (2016) showed that production tasks are perceived to be less and less interesting over the course of time, while at the same time performance drops. As a consequence, motivational states toward the end of a test session with multiple DT tasks are likely to be less beneficial for performance. Most likely, all of the above factors have affected dimensionality in the current sample. Furthermore, early positions of the objects during test administration in the current sample were more likely to result in fluency scores above the median and, consequently, indicators for high-fluency and low-fluency latent variables were unevenly coded across their position during administration.

Another explanation for the differentiation of creative quality between low-fluency and high-fluency ideational pools could be that raters judged the quality of pools with varying numbers of responses in a slightly different way. In fact, it has been demonstrated that the amount of information that needs to be judged has a detrimental effect on rater agreement when DT responses are rated for creative quality ( Forthmann et al., 2017b ). Thus, it should not be overlooked that processing on the side of the raters might potentially influence the results here beyond the statistical control of rater severity effects as it was applied in the current study.

The differentiation between low-fluency and high-fluency ideational pools (reflecting differences in speed of the response generation) bears further interesting opportunities for the study of within-item multidimensionality. Ratings can be obtained for individual responses generated for the same item, and responses could also be grouped according to certain characteristics that can be assumed to influence within-item dimensionality. For example, a well-known phenomenon is the serial order effect (for example, Beaty and Silvia, 2012 ) in DT. It describes the tendency to generate ideas of better quality (for example, more original and remote ideas) toward the end of the allotted time on task. This effect points to differences in the underlying cognitive processes at the beginning and at the end of a test session. Consequently, it might be suggested that different abilities are involved over the time course and this hypothesis can be tested by the method outlined here.

Finally, it needs to be acknowledged that the current study is indeed limited to the creative quality scoring that was used, namely a holistic scoring of uncommonness, remoteness, and cleverness. Forthmann et al. (2017a) found strong correlations between such holistic ratings and ratings of the cleverness dimension only ( r = 0.82 for latent variables), it is likely that subjective ratings for the cleverness dimension yield similar results as compared to the current work. It is, however, unclear if results generalize to other originality indicators, indicators of the usefulness component of the standard definition of creativity, or other indicators relating to components specific to alternative conceptions of creativity (e.g., Simonton, 2012 , 2018 ; Kharkhurin, 2014 ). In relation to this, it should further be mentioned that a test of node-unidimensionality is a specific issue for subjective ratings relying on Likert-type scales, whereas the question of multidimensionality of creative quality as a function of the ideational pool size (low-fluency vs. high-fluency) might also be addressable for other ways to score creative quality (e.g., statistical frequency as an indicator of originality). In this regards, future studies are indeed needed to further expand our knowledge.

How to Detect and Deal With Technical Problems

In order to apply IRTrees in future studies, our work provides guidance on how to deal with possible technical problems such as not enough information at some of the nodes. Here it was demonstrated that the available information strongly depended on the instruction. That is, not enough information was available for the fourth node with a be-fluent instruction and for the first node with a be-creative instruction. As a consequence, all available data for those nodes in instruction-specific models were excluded. In future attempts, the available information at all nodes should be initially checked. For example, it normally occurs that standard errors for fixed effects of non-informative nodes are unrealistically high. Furthermore, in Bayesian applications non-informative nodes can be detected by convergence problems and high correspondence of prior and posterior if proper priors are assigned to the respective parameters.

Overall Conclusion

In the current work, IRTrees were introduced as a method that allows a change of perspective on the multidimensionality of DT tasks. The methodology looks promising in order to further explore the psychometric quality of subjective ratings of DT ideational pools (which has been traditionally a hot topic in DT research). Moreover, new studies on within-item multidimensionality of DT and also multidimensionality due to practice, exhaustion, and motivation are promising future applications of the presented method.

Ethics Statement

This study was carried out in accordance with the recommendations for online studies provided by the institutional ethics committee. These guidelines are in accordance with the guidelines provided by the German foundation for online research (Deutsche Gesellschaft für Online Forschung). All subjects gave informed consent prior to participation. An ethics approval was not required as per institutional and national guidelines.

Author Contributions

BF designed the study, wrote the initial draft, analyzed the data, and revised the manuscript. P-CB analyzed the data and revised the manuscript. CS wrote the initial draft and revised the manuscript. MB revised the manuscript. HH designed the study and revised the manuscript.

This research was supported by grant HO 1286/11-1 of the German Research Foundation (DFG) to HH.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

^ https://osf.io/3rf24/

Aliotti, N. C., Britt, M. F., and Haskins, G. P. (1975). Relationships among creativity, intelligence, and achievement measures in upward bound students. Psychol. Sch. 12, 423–427. doi: 10.1002/1520-6807(197510)12:4<423::AID-PITS2310120409>3.0.CO;2-Y

CrossRef Full Text | Google Scholar

Bates, D., Maechler, M., Bolker, B., and Walker, S. (2015). lme4: Linear Mixed-Effects Models Using Eigen and S4 . R Package Version 1.1-8. Available at: http://CRAN.R-project.org/package=lme4

Google Scholar

Beaty, R. E., and Silvia, P. J. (2012). Why do ideas get more creative across time? An executive interpretation of the serial order effect in divergent thinking tasks. Psychol. Aesthet. Creat. Arts 6, 309–319. doi: 10.1037/a0029171

Bürkner, P. C. (2016). brms: an R package for Bayesian multilevel models using Stan. J. Stat. Softw. 80, 1–28. doi: 10.18637/jss.v080.i01

Carpenter, B., Gelman, A., Hoffman, M., Lee, D., Goodrich, B., Betancourt, M., et al. (2017). Stan: a probabilistic programming language. J. Stat. Softw. 20, 1–37. doi: 10.18637/jss.v076.i01

PubMed Abstract | CrossRef Full Text | Google Scholar

Chen, C., Kasof, J., Himsel, A., Dmitrieva, J., Dong, Q., and Xue, G. (2005). Effects of explicit instruction to “be creative” across domains and cultures. J. Creat. Behav. 39, 89–110. doi: 10.1002/j.2162-6057.2005.tb01252.x

Christensen, P. R., Guilford, J. P., and Wilson, R. C. (1957). Relations of creative responses to working time and instructions. J. Exp. Psychol. 53, 82–88. doi: 10.1037/h0045461

Cicchetti, D. V. (2001). The precision of reliability and validity estimates re-visited: distinguishing between clinical and statistical significance of sample size requirements. J. Clin. Exp. Neuropsychol. 23, 695–700. doi: 10.1076/jcen.23.5.695.1249

De Boeck, P., and Partchev, I. (2012). IRTrees: tree-based item response models of the GLMM family. J. Stat. Softw. 48, 1–28. doi: 10.18637/jss.v048.c01

Diedrich, J., Benedek, M., Jauk, E., and Neubauer, A. C. (2015). Are creative ideas novel and useful? Psychol. Aesthet. Creat. Arts 9, 35–40. doi: 10.1037/a0038688

Follman, J., Uprichard, E., Malone, W., and Coop, R. (1973). Multivariate analyses of the relationship between creativity and intelligence. Paper presented at the annual meeting of the American Educational Research Association , New Orleans.

Forthmann, B., Gerwig, A., Holling, H., C_elik, P., Storme, M., and Lubart, T. (2016). The be-creative effect in divergent thinking: the interplay of instruction and object frequency. Intelligence 57, 25–32. doi: 10.1016/j.intell.2016.03.005

Forthmann, B., Holling, H., Çelik, P., Storme, M., and Lubart, T. (2017a). Typing speed as a confounding variable and the measurement of quality in divergent thinking. Creat. Res. J. 29, 257–269. doi: 10.1080/10400419.2017.1360059

Forthmann, B., Holling, H., Zandi, N., Gerwig, A., Çelik, P., Storme, M., et al. (2017b). Missing creativity: the effect of cognitive workload on rater (dis-)agreement in subjective divergent-thinking scores. Think. Skills Creat. 23, 129–139. doi: 10.1016/j.tsc.2016.12.005

Forthmann, B., Regehr, S., Seidel, J., Holling, H., Çelik, P., Storme, M., et al. (2018). Revisiting the interactive effect of multicultural experiences and openness to experience on divergent thinking. Int. J. Intercult. Relat. 63, 135–143. doi: 10.1016/j.ijintrel.2017.10.002

French, J. W., Ekstrom, R. B., and Price, L. A. (1963). Manual for Kit of Reference Tests for Cognitive Factors. Princeton, NJ: Educational Testing Service.

George, T., and Wiley, J. (2019). Need something different? Here’s what’s been done: effects of examples and task instructions on creative originality. Manuscript submitted for publication.

Gilhooly, K. J., Fioratou, E., Anthony, S. H., and Wynn, V. (2007). Divergent thinking: strategies and executive involvement in generating novel uses for familiar objects. Br. J. Psychol. 98, 611–625. doi: 10.1111/j.2044-8295.2007.tb00467.x

Guilford, J. P. (1967). The Nature of Human Intelligence. New York, NY: McGraw-Hill.

Guilford, J. P. (1968). Intelligence, creativity, and their educational implications. San Diego, CA: Robert R. Knapp

Haager, J. S., Kuhbandner, C., and Pekrun, R. (2016). To be bored or not to be bored-how task-related boredom influences creative performance. J. Creat. Behav. 52, 297–304. doi: 10.1002/jocb.154

Hargreaves, D. J., and Bolton, N. (1972). Selecting creativity tests for use in research. Br. J. Psychol. 63, 451–462. doi: 10.1111/j.2044-8295.1972.tb01295.x

Harrington, D. M. (1975). Effects of explicit instructions to “be creative” on the psychological meaning of divergent thinking test scores. J. Personal. 43, 434–454. doi: 10.1111/j.1467-6494.1975.tb00715.x

Hass, R. W. (2015). Feasibility of online divergent thinking assessment. Comput. Hum. Behav. 46, 85–93. doi: 10.1016/j.chb.2014.12.056

Hass, R. W. (2017). Semantic search during divergent thinking. Cognition 166, 344–357. doi: 10.1016/j.cognition.2017.05.039

Heausler, N. L., and Thompson, B. (1988). Structure of the torrance tests of creative thinking. Educ. Psychol. Measure. 48, 463–468. doi: 10.1177/0013164488482021

Hofelich Mohr, A., Sell, A., and Lindsay, T. (2016). Thinking inside the box: visual design of the response box affects creative divergent thinking in an online survey. Soc. Sci. Comput. Rev. 34, 347–359. doi: 10.1177/0894439315588736

Karwowski, M., Dul, J., Gralewski, J., Jauk, E., Jankowska, D. M., Gajda, A., et al. (2016). Is creativity without intelligence possible? A necessary condition analysis. Intelligence 57, 105–117. doi: 10.1016/j.intell.2016.04.006

Kharkhurin, A. V. (2014). Creativity. 4in1: four-criterion construct of creativity. Creat. Res. J. 26, 338–352. doi: 10.1080/10400419.2014.929424

Kim, K. H. (2008). Meta-analyses of the relationship of creative achievement to both IQ and divergent thinking test scores. J. Creat. Behav. 42, 106–130. doi: 10.1002/j.2162-6057.2008.tb01290.x

Mouchiroud, C., and Lubart, T. (2001). Children’s original thinking: an empirical examination of alternative measures derived from divergent thinking tasks. J. Genet. Psychol. 162, 382–401. doi: 10.1080/00221320109597491

Mullins, C. J. (1963). Prediction of creativity in a sample of research scientists. IEEE Trans. Eng. Manage. 10, 52–57. doi: 10.1109/TEM.1963.6446513

Niu, W., and Liu, D. (2009). Enhancing creativity: a comparison between effects of an indicative instruction “to be creative” and a more elaborate heuristic instruction on Chinese student creativity. Psychol. Aesthet. Creat. Arts 3, 93–98. doi: 10.1037/a0013660

Nusbaum, E. C., Silvia, P. J., and Beaty, R. E. (2014). Ready, set, create: what instructing people to “be creative” reveals about the meaning and mechanisms of divergent thinking. Psychol. Aesthet. Creat. Arts 8, 423–432. doi: 10.1037/a0036549

Oberauer, K., Wilhelm, O., and Schmiedek, F. (2005). “Experimental strategies in multivariate research,” in Multivariate Research Strategies: Festschrift in Honor of Werner W. Wittmann , eds A. Beauducel, B. Biehl, M. Bosnjak, W. Conrad, G. Schönberger, and D. Wagener (Aachen: Shaker Verlag), 119–149.

Olteteanu, A.-M., and Falomir, Z. (2016). Object replacement and object composition in a creative cognitive system. Towards a computational solver of the Alternative Uses Test. Cognit. Syst. Res. 39, 15–32. doi: 10.1016/j.cogsys.2015.12.011

Parnes, S. J. (1961). Effects of extended effort in creative problem solving. J. Educ. Psychol. 52, 117–122. doi: 10.1037/h0044650

Partchev, I., and De Boeck, P. (2012). Can fast and slow intelligence be differentiated? Intelligence 40, 23–32. doi: 10.1016/j.intell.2011.11.002

Piers, E. V., Daniels, J. M., and Quackenbush, J. F. (1960). The identification of creativity in adolescents. J. Educ. Psychol. 51, 346–351. doi: 10.1037/h0042669

Plass, H., Michael, J. J., and Michael, W. B. (1974). The factorial validity of the torrance tests of creative thinking for a sample of 111 sixth-grade children. Educ. Psychol. Measure. 34, 413–414. doi: 10.1177/001316447403400227

R Core Team (2013). R: A Language and Environment for Statistical Computing. Vienna: R Foundation for Statistical Computing.

Reiter-Palmon, R., Forthmann, B., and Barbot, B. (2019). Scoring divergent thinking tests: a review and systematic framework. Psychol. Aesthet. Creat. Arts. 13, 144–152. doi: 10.1037/aca0000227

Rosen, D. S., Kim, Y. E., Mirman, D., and Kounios, J. (2017). All you need to do is ask? The exhortation to be creative improves creative performance more for nonexpert than expert jazz musicians. Psychol. Aesthet. Creat. Arts 11, 420–427. doi: 10.1037/aca0000087

Runco, M. A. (1986). Maximal performance on divergent thinking tests by gifted, talented, and nongifted children. Psychol. Sch. 23, 308–315. doi: 10.1002/1520-6807(198607)23:3<308::AID-PITS2310230313>3.0.CO;2-V

Runco, M. A. (2008). Commentary: divergent thinking is not synonymous with creativity. Psychol. Aesthet. Creat. Arts 2, 93–96. doi: 10.1037/1931-3896.2.2.93

Runco, M. A., and Acar, S. (2012). Divergent thinking as an indicator of creative potential. Creat. Res. J. 24, 66–75. doi: 10.1080/10400419.2012.652929

Runco, M. A., and Charles, R. E. (1993). Judgments of originality and appropriateness as predictors of creativity. Personal. Ind. Diff. 15, 537–546. doi: 10.1016/0191-8869(93)90337-3

Runco, M. A., Illies, J. J., and Reiter-Palmon, R. (2005). Explicit instructions to be creative and original: a comparison of strategies and criteria as targets with three types of divergent thinking tests. Int. J. Creat. Prob. Solv. 15, 5–15.

Runco, M. A., and Jaeger, G. J. (2012). The standard definition of creativity. Creat. Res. J. 24, 92–96. doi: 10.1080/10400419.2012.650092

Runco, M. A., and Mraz, W. (1992). Scoring divergent thinking tests using total ideational output and creativity index. Educ. Psychol. Measure. 52, 213–221. doi: 10.1177/001316449205200126

Shin, H., Cotter, K. N., Christensen, A. P., and Silvia, P. J. (2018). Creative fixation is no laughing matter: the effects of funny and unfunny examples on humor production. J. Creat. Behav. doi: 10.1002/jocb.383

Silvia, P. J., Martin, C., and Nusbaum, E. C. (2009). A snapshot of creativity: evaluating a quick and simple method for assessing divergent thinking. Think. Skills Creat. 4, 79–85. doi: 10.1016/j.tsc.2009.06.005

Silvia, P. J., Winterstein, B. P., Willse, J. T., Barona, C., Cram, J., Hess, K. I., et al. (2008). Assessing creativity with divergent thinking tasks: exploring the reliability and validity of new subjective scoring methods. Psychol. Aesthet. Creat. Arts 2, 68–85. doi: 10.1037/1931-3896.2.2.68

Simonton, D. K. (2012). Taking the US patent office criteria seriously: a quantitative three-criterion creativity definition and its implications. Creat. Res. J. 24, 97–106. doi: 10.1080/10400419.2012.676974

Simonton, D. K. (2018). Defining creativity: don’t we also need to define what is not creative? J. Creat. Behav. 52, 80–90. doi: 10.1002/jocb.137

Storm, B. C., and Patel, T. N. (2014). Forgetting as a consequence and enabler of creative thinking. J. Exp. Psychol. Learn. Mem. Cognit. 40, 1594–1609. doi: 10.1037/xlm0000006

Tutz, G. (1990). Sequential item response models with an ordered response. Br. J. Mathemat. Stat. Psychol. 43, 39–55. doi: 10.1111/j.2044-8317.1990.tb00925.x

Vehtari, A., Gelman, A., and Gabry, J. (2017). Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Stat. Comput. 27, 1413–1432. doi: 10.1007/s11222-016-9696-4

Wallach, M. A., and Kogan, N. (1965). Modes of Thinking in Young Children: A Study of the Creativity-Intelligence Distinction. New York, NY: Holt, Rinehart, & Winston.

Wilson, R. C., Christensen, P. R., Merrifield, P. R., and Guilford, J. P. (1960). Alternate Uses–Form A: Manual of Administration, Scoring, and Interpretation , 2nd Preliminary Edn. Beverly Hills, CA: Sheridan Supply Company.

Wilson, R. C., Guilford, J. P., and Christensen, P. R. (1953). The measurement of individual differences in originality. Psychol. Bull. 50, 362–370. doi: 10.1037/h0060857

Keywords : IRTrees, item-response theory, dimensionality, creativity, creative quality, fluency, divergent thinking

Citation: Forthmann B, Bürkner P-C, Szardenings C, Benedek M and Holling H (2019) A New Perspective on the Multidimensionality of Divergent Thinking Tasks. Front. Psychol. 10:985. doi: 10.3389/fpsyg.2019.00985

Received: 15 December 2018; Accepted: 15 April 2019; Published: 03 May 2019.

Reviewed by:

Copyright © 2019 Forthmann, Bürkner, Szardenings, Benedek and Holling. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Boris Forthmann, [email protected] ; [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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
My Bibliography
Collections
Citation manager

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

Relationship between Divergent Thinking and Intelligence: An Empirical Study of the Threshold Hypothesis with Chinese Children

Affiliations.

1 Beijing Key Laboratory of Learning and Cognition and Department of Psychology, Capital Normal University Beijing, China.
2 Beijing Key Laboratory of Learning and Cognition and Department of Psychology, Capital Normal UniversityBeijing, China; Beijing G&G Human Resource Development CenterBeijing, China.
PMID: 28275361
PMCID: PMC5319977
DOI: 10.3389/fpsyg.2017.00254

The threshold hypothesis is a classical and notable explanation for the relationship between creativity and intelligence. However, few empirical examinations of this theory exist, and the results are inconsistent. To test this hypothesis, this study investigated the relationship between divergent thinking (DT) and intelligence with a sample of 568 Chinese children aged between 11 and 13 years old using testing and questionnaire methods. The study focused on the breakpoint of intelligence and the moderation effect of openness on the relationship between intelligence and DT. The findings were as follows: (1) a breakpoint at the intelligence quotient (IQ) of 109.20 when investigating the relationship between either DT fluency or DT flexibility and intelligence. Another breakpoint was detected at the IQ of 116.80 concerning the correlation between originality and intelligence. The breakpoint of the relation between the composite score of creativity and intelligence occurred at the IQ of 110.10. (2) Openness to experience had a moderating effect on the correlation between the indicators of creativity and intelligence under the breakpoint. Above this point, however, the effect was not significant. The results suggested a relationship between DT and intelligence among Chinese children, which conforms to the threshold hypothesis. Besides, it remains necessary to explore the personality factors accounting for individual differences in the relationship between DT and intelligence.

Keywords: creativity; divergent thinking; intelligence; openness to experience; the threshold hypothesis.

PubMed Disclaimer

Related information

Linkout - more resources, full text sources.

Europe PubMed Central
Frontiers Media SA
PubMed Central

Other Literature Sources

scite Smart Citations

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.

Why You Need to Practice Divergent Thinking

Now is the time for divergent thinking..

Posted June 24, 2020 | Reviewed by Ekua Hagan

As I wrote in The Non-Obvious Guide to Being More Creative, No Matter Where You Work , “divergent thinking helps us generate many new ideas, imagine, be original, ask questions to spot problems, look for patterns, make unexpected connections, imagine, and see things from a variety of perspectives.”

Divergent thinking is ideational or visionary in nature. It involves rigorous gymnastics of the mind that lead to unexpected solutions. Divergent thinking spurs our thoughts beyond what exists, opening the door of thought to consider new possibilities.

Converge: Analyze Ideas and Form Solutions

Convergent thinking complements divergent thinking. We can take all the ideas and connections generated during divergence and probe them. Convergent thinking includes judgment. We weigh the options within a fixed set of information. We consider our particular situation, look at our goals and objectives, analyze the information before us, and decide what to do under the circumstances.

Most of us are well-practiced in convergent thinking. Much of school, especially with the growth of standardized tests, trains us almost exclusively in convergent thinking. As the use of these tests has increased and become more pressurized, teachers have adjusted their teaching styles to match them. Districts buy scripted curricula to align with test content, and many teachers are required to read straight from lesson plans without diverging.

Even more than before, convergent thinking dominates in our classrooms. Recent studies show that this is happening as early as preschool. Suffice it to say that we are steeped in convergent thinking from a very young age. Even so, we can still get better at using it as divergent thinking’s partner.

To have true creativity —the most robust form of thinking—we must have both divergent thinking and convergent thinking. Some people are more inclined toward divergent thinking, and others prefer convergent. This is a good thing, because, as I’ve said before, we need both. We need people who are passionate about pushing past the current reality to find new possibilities, and we also need people who are passionate about working through analytical details to craft solid solutions.

The problem is that convergence is an almost irresistible force that tends to dominate our lives and the world.

Don’t Let Convergence Take Over Your World

Convergent thinking in and of itself isn’t a bad thing, of course. As mentioned, it’s divergent thinking’s essential partner to achieve the truly robust cognition that is creativity. But when divergent thinking is compromised, and convergence takes over, the decline is imminent.

One definition of "converge" is to gradually change so as to become similar or develop something in common. As humans, our innate need to belong makes us more apt to take on a convergence mindset. This is true in all microcosms of society, from families to schools, to teams and businesses. The culture of the group puts pressure on the uniqueness of the individual.

Convergence is how traditions develop, how groups find cohesiveness, and how some people start to resemble their dogs. (There’s probably a much more scientific explanation for the last one!) But think about it. If we all continue to change toward one particular point of sameness, all of a sudden, we morph together in one giant blob of thought and action.

When we are no longer able to think for ourselves because we are so intertwined with the group, we lose the ability to find new solutions. The core of so many problems in the world is a lack of divergent thinking, a lack of considering the new possibilities that will get us out of ruts.

Balance Your Thinking to Be More Creative

When convergence takes over, it kills off divergence little by little until we find ourselves out of balance from a thinking standpoint. At work, this manifests as apathy and dissatisfaction. When we set out to improve our creativity, we realign our balance; using both divergent and convergent thinking is natural for human beings. Balanced thinking balances individuals, and they, in turn, nourish ecosystems away from decay and toward possibility.

3 Ways to Get Better at Divergent Thinking

We’re all pretty darn good at convergent thinking. But our divergent thinking tends to decrease over time. Here are three tips from The Non-Obvious Guide to Being More Creative, No Matter Where You Work to help you develop your divergent thinking.

1. Grow Your Thinking Flexibility: Minimize Trash. Lauren Singer has rethought her entire lifestyle so that her full year’s worth of trash can be contained in one small mason jar. And yes, ladies, that means she’s even found an alternative to those fun monthly feminine products.

What are five non-obvious things you can do today to reduce your own trash? Check out Singer’s Trash Is for Tossers website or her Instagram feed of the same name for inspiration. Even if she comes up with the ideas, it still takes flexibility for you to use them.

2. Free Your Thinking: Do Things Differently. Develop your freedom by doing things differently. What are three things that your organization does now because “we’ve always done it this way”? Choose one and think up and try an alternate approach.

3. Expand Your Thinking: Respond Like an Improv Actor. Imagine if, in the middle of an improv scene, one actor told the other, “You weren’t supposed to say that!” or “Don’t use that line!” The improv actor’s secret success tool is staying in a mentality of “Yes, and....” She has to say yes to the ridiculous and go with it to finish out the scene well. Today, practice responding to people’s ideas with, “Yes, and....” Repeat tomorrow.

www.washingtonpost.com/news/answer-sheet/wp/2016/01/12/what-to-do-when-…

Kathryn Haydon, MSc , is an innovation strategist, speaker, and author who helps teams and individuals activate and maximize their creative thinking and innovation potential.

Find a Therapist
Find a Treatment Center
Find a Psychiatrist
Find a Support Group
Find Online Therapy
United States
Brooklyn, NY
Chicago, IL
Houston, TX
Los Angeles, CA
New York, NY
Portland, OR
San Diego, CA
San Francisco, CA
Seattle, WA
Washington, DC
Asperger's
Bipolar Disorder
Chronic Pain
Eating Disorders
Passive Aggression
Personality
Goal Setting
Positive Psychology
Stopping Smoking
Low Sexual Desire
Relationships
Child Development
Self Tests NEW
Therapy Center
Diagnosis Dictionary
Types of Therapy

It’s increasingly common for someone to be diagnosed with a condition such as ADHD or autism as an adult. A diagnosis often brings relief, but it can also come with as many questions as answers.

Emotional Intelligence
Gaslighting
Affective Forecasting
Neuroscience

Divergent thinking modulates interactions between episodic memory and schema knowledge: Controlled and spontaneous episodic retrieval processes

Published: 16 January 2024
Volume 52 , pages 663–679, ( 2024 )

Cite this article

Michelle M. Ramey 1 &
Darya L. Zabelina 1

624 Accesses

3 Altmetric

Explore all metrics

The ability to generate novel ideas, known as divergent thinking, depends on both semantic knowledge and episodic memory. Semantic knowledge and episodic memory are known to interact to support memory decisions, but how they may interact to support divergent thinking is unknown. Moreover, it is debated whether divergent thinking relies on spontaneous or controlled retrieval processes. We addressed these questions by examining whether divergent thinking ability relates to interactions between semantic knowledge and different episodic memory processes. Participants completed the alternate uses task of divergent thinking, and completed a memory task in which they searched for target objects in schema-congruent or schema-incongruent locations within scenes. In a subsequent test, participants indicated where in each scene the target object had been located previously (i.e., spatial accuracy test), and provided confidence-based recognition memory judgments that indexed distinct episodic memory processes (i.e., recollection, familiarity, and unconscious memory) for the scenes. We found that higher divergent thinking ability—specifically in terms of the number of ideas generated—was related to (1) more of a benefit from recollection (a controlled process) and unconscious memory (a spontaneous process) on spatial accuracy and (2) beneficial differences in how semantic knowledge was combined with recollection and unconscious memory to influence spatial accuracy. In contrast, there were no effects with respect to familiarity (a spontaneous process). These findings indicate that divergent thinking is related to both controlled and spontaneous memory processes, and suggest that divergent thinking is related to the ability to flexibly combine semantic knowledge with episodic memory.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save.

Get 10 units per month
Download Article/Chapter or eBook
1 Unit = 1 Article or 1 Chapter
Cancel anytime

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Constructive episodic retrieval processes underlying memory distortion contribute to creative thinking and everyday problem solving

Divergent creative thinking in young and older adults: extending the effects of an episodic specificity induction.

Conceptual knowledge modulates memory recognition of common items: The selective role of item-typicality

Data availability.

The experiment was not preregistered. The data and code are available upon request.

The extent to which unconscious memory is its own process, or simply an expression of other types of memory (e.g., familiarity) below a threshold of subjective awareness is a subject of debate, and the present treatment is agnostic as to what type of representations or systems might underpin unconscious memory.

By convention, a BF 10 < 0.33 indicates substantial evidence for the null hypothesis, and a BF 10 < 0.01 indicates extreme evidence for the null (Jeffreys, 1961 ).

Addis, D. R., Pan, L., Musicaro, R., & Schacter, D. L. (2016). Divergent thinking and constructing episodic simulations. Memory, 24 (1), 89–97. https://doi.org/10.1080/09658211.2014.985591

Article PubMed Google Scholar

Andreasen, N. C. (2011). A journey into chaos: Creativity and the unconscious. Mens Sana Monographs, 9 (1), 42. https://doi.org/10.4103/0973-1229.77424

Article PubMed PubMed Central Google Scholar

Bastin, C., Besson, G., Simon, J., Delhaye, E., Geurten, M., Willems, S., & Salmon, E. (2019). An integrative memory model of recollection and familiarity to understand memory deficits. Behavioral and Brain Sciences , 42 . https://doi.org/10.1017/S0140525X19000621

Beaty, R. E., Silvia, P. J., Nusbaum, E. C., Jauk, E., & Benedek, M. (2014). The roles of associative and executive processes in creative cognition. Memory & Cognition, 42 (7), 1186–1197. https://doi.org/10.3758/S13421-014-0428-8

Article Google Scholar

Beaty, R. E., Thakral, P. P., Madore, K. P., Benedek, M., & Schacter, D. L. (2018). Core network contributions to remembering the past, imagining the future, and thinking creatively. Journal of Cognitive Neuroscience, 30 (12), 1–13. https://doi.org/10.1162/JOCN_A_01327

Beaty, R. E., Chen, Q., Christensen, A. P., Kenett, Y. N., Silvia, P. J., Benedek, M., & Schacter, D. L. (2020). Default network contributions to episodic and semantic processing during divergent creative thinking: A representational similarity analysis. NeuroImage, 209 , 116499. https://doi.org/10.1016/J.NEUROIMAGE.2019.116499

Benedek, M., & Jauk, E. (2018). Spontaneous and controlled processes. In K. C. R. Fox & K. Christoff (Eds.), The Oxford handbook of spontaneous thought: Mind-wandering, creativity, and dreaming (pp. 285–298). Oxford University Press.

Google Scholar

Benedek, M., Jauk, E., Fink, A., Koschutnig, K., Reishofer, G., Ebner, F., & Neubauer, A. C. (2014). To create or to recall? Neural mechanisms underlying the generation of creative new ideas. NeuroImage, 88 , 125–133. https://doi.org/10.1016/J.NEUROIMAGE.2013.11.021

Benedek, M., Jauk, E., Sommer, M., Arendasy, M., & Neubauer, A. C. (2014). Intelligence, creativity, and cognitive control: The common and differential involvement of executive functions in intelligence and creativity. Intelligence, 46 (1), 73–83. https://doi.org/10.1016/J.INTELL.2014.05.007

Benedek, M., Kenett, Y. N., Umdasch, K., Anaki, D., Faust, M., & Neubauer, A. C. (2017). How semantic memory structure and intelligence contribute to creative thought: A network science approach. Thinking and Reasoning, 23 (2), 158–183. https://doi.org/10.1080/13546783.2016.1278034

Benedek, M., Beaty, R. E., Schacter, D. L., & Kenett, Y. N. (2023). The role of memory in creative ideation. Nature Reviews Psychology, 2 (4), 246–257. https://doi.org/10.1038/s44159-023-00158-z

Boettcher, S. E. P., Draschkow, D., Dienhart, E., & Võ, M.L.-H. (2018). Anchoring visual search in scenes: Assessing the role of anchor objects on eye movements during visual search. Journal of Vision, 18 (13), 11. https://doi.org/10.1167/18.13.11

Carson, S. H., Peterson, J. B., & Higgins, D. M. (2005). Reliability, validity, and factor structure of the creative achievement questionnaire. Creativity Research Journal, 17 (1), 37–50. https://doi.org/10.1207/S15326934CRJ1701_4

Condon, D. M., & Revelle, W. (2014). The international cognitive ability resource: Development and initial validation of a public-domain measure. Intelligence, 43 (1), 52–64. https://doi.org/10.1016/J.INTELL.2014.01.004

de Leeuw, J. R. (2015). jsPsych: A JavaScript library for creating behavioral experiments in a web browser. Behavior Research Methods, 47 (1), 1–12. https://doi.org/10.3758/s13428-014-0458-y

Dewhurst, S. A., Thorley, C., Hammond, E. R., & Ormerod, T. C. (2011). Convergent, but not divergent, thinking predicts susceptibility to associative memory illusions. Personality and Individual Differences, 51 (1), 73–76. https://doi.org/10.1016/J.PAID.2011.03.018

Eichenbaum, H., Yonelinas, A. P., & Ranganath, C. (2007). The medial temporal lobe and recognition memory. Annual Review of Neuroscience, 30 , 123–152. https://doi.org/10.1146/annurev.neuro.30.051606.094328

Ellamil, M., Dobson, C., Beeman, M., & Christoff, K. (2012). Evaluative and generative modes of thought during the creative process. NeuroImage, 59 (2), 1783–1794. https://doi.org/10.1016/J.NEUROIMAGE.2011.08.008

Frith, E., Kane, M. J., Welhaf, M. S., Christensen, A. P., Silvia, P. J., & Beaty, R. E. (2021). Keeping creativity under control: Contributions of attention control and fluid intelligence to divergent thinking. Creativity Research Journal, 33 (2), 138–157. https://doi.org/10.1080/10400419.2020.1855906

George, T., & Wiley, J. (2019). Fixation, flexibility, and forgetting during alternate uses tasks. Psychology of Aesthetics, Creativity, and the Arts, 13 (3), 305–313. https://doi.org/10.1037/ACA0000173

Ghosh, V. E., & Gilboa, A. (2014). What is a memory schema? A historical perspective on current neuroscience literature. Neuropsychologia, 53 (1), 104–114. https://doi.org/10.1016/j.neuropsychologia.2013.11.010

Gilboa, A., & Marlatte, H. (2017). Neurobiology of schemas and schema-mediated memory. Trends in Cognitive Sciences, 21 (8), 618–631. https://doi.org/10.1016/j.tics.2017.04.013

Gilhooly, K., Fioratou, E., Anthony, S., & Wynn, V. (2007). Divergent thinking: Strategies and executive involvement in generating novel uses for familiar objects. British Journal of Psychology, 98 (4), 611–625. https://doi.org/10.1348/096317907X173421

Greve, A., Cooper, E., Tibon, R., & Henson, R. N. (2019). Knowledge is power: Prior knowledge aids memory for both congruent and incongruent events, but in different ways. Journal of Experimental Psychology: General, 148 (2), 325–341. https://doi.org/10.1037/xge0000498

Guilford, J. P. (1967). Creativity: Yesterday, today and tomorrow. The Journal of Creative Behavior, 1 (1), 3–14. https://doi.org/10.1002/J.2162-6057.1967.TB00002.X

Hannula, D. E., & Greene, A. J. (2012). The hippocampus reevaluated in unconscious learning and memory: At a tipping point? Frontiers in Human Neuroscience, 6 , 80. https://doi.org/10.3389/fnhum.2012.00080

Hannula, D. E., & Ranganath, C. (2009). The eyes have it: hippocampal activity predicts expression of memory in eye movements. Neuron, 63 (5), 592–599. https://doi.org/10.1016/j.neuron.2009.08.025

He, L., Kenett, Y. N., Zhuang, K., Liu, C., Zeng, R., Yan, T., Huo, T., & Qiu, J. (2020). The relation between semantic memory structure, associative abilities, and verbal and figural creativity. Thinking and Reasoning, 27 (2), 268–293. https://doi.org/10.1080/13546783.2020.1819415

Hemmer, P., & Steyvers, M. (2009). A Bayesian account of reconstructive memory. Topics in Cognitive Science, 1 (1), 189–202. https://doi.org/10.1111/j.1756-8765.2008.01010.x

Huba, G. J., Singer, J. L., Aneshensel, C. S., & Antrobus, J. S. (1982). The short imaginal processes inventory . Research Psychologist Press.

Huttenlocher, J., Hedges, L. V., & Duncan, S. (1991). Categories and particulars: Prototype effects in estimating spatial location. Psychological Review, 98 (3), 352–376. https://doi.org/10.1037/0033-295X.98.3.352

Huttenlocher, J., Hedges, L. V., & Vevea, J. L. (2000). Why do categories affect stimulus judgment? Journal of Experimental Psychology: General, 129 (2), 220–241. https://doi.org/10.1037/0096-3445.129.2.220

Irish, M. (2016). Semantic memory as the essential scaffold for future-oriented mental time travel. In K. Michaelian, S. B. Klein, & K. K. Szpunar (Eds.), Seeing the future: Theoretical perspectives on future-oriented mental time travel (pp. 389–408). Oxford University Press. https://doi.org/10.1093/acprof:oso/9780190241537.003.0019

Chapter Google Scholar

Irish, M. (2020). On the interaction between episodic and semantic representations–constructing a unified account of imagination. In A. Abraham (Ed.), The Cambridge handbook of the imagination (pp. 447–465). Cambridge University Press. https://doi.org/10.1017/9781108580298.027

Jacoby, L. L., & Kelley, C. (1992). Unconscious influences of memory: Dissociations and automaticity. In A. D. Milner & M. D. Rugg (Eds.), The neuropsychology of consciousness (pp. 201–233). Academic Press. https://doi.org/10.1016/B978-0-12-498045-7.50015-3

Jeffreys, H. (1961). Theory of probability (3rd ed.). Clarendon.

Kenett, Y. N. (2018). Investigating creativity from a semantic network perspective. In Z. Kapoula, E. Volle, J. Renoult, & M. Andreatta (Eds.), Exploring transdisciplinarity in art and sciences (pp. 49–75). Springer. https://doi.org/10.1007/978-3-319-76054-4_3

Kenett, Y. N., Anaki, D., & Faust, M. (2014). Investigating the structure of semantic networks in low and high creative persons. Frontiers in Human Neuroscience, 8 , 407. https://doi.org/10.3389/fnhum.2014.00407

Kuznetsova, A., Brockhoff, P. B., & Christensen, R. H. B. (2017). lmerTest Package: Tests in Linear Mixed Effects Models. Journal of Statistical Software, 82 (13), 1–19. https://doi.org/10.18637/jss.v082.i13

Lampinen, J. M., Faries, J. M., Neuschatz, J. S., & Toglia, M. P. (2000). Recollections of Things Schematic: The Influence of Scripts on Recollective Experience. Applied Cognitive Psychology, 14 (6), 543–554. https://doi.org/10.1002/1099-0720(200011/12)14:6%3c543::AID-ACP674%3e3.0.CO;2-K

Lampinen, J. M., Copeland, S. M., & Neuschatz, J. S. (2001). Recollections of things schematic: Room schemas revisited. Journal of Experimental Psychology: Learning Memory and Cognition, 27 (5), 1211–1222. https://doi.org/10.1037/0278-7393.27.5.1211

Lee, C. S., & Therriault, D. J. (2013). The cognitive underpinnings of creative thought: A latent variable analysis exploring the roles of intelligence and working memory in three creative thinking processes. Intelligence, 41 (5), 306–320. https://doi.org/10.1016/J.INTELL.2013.04.008

Madore, K. P., Addis, D. R., & Schacter, D. L. (2015). Creativity and memory: Effects of an episodic-specificity induction on divergent thinking. Psychological Science, 26 (9), 1461–1468. https://doi.org/10.1177/0956797615591863

Madore, K. P., Jing, H. G., & Schacter, D. L. (2016). Divergent creative thinking in young and older adults: Extending the effects of an episodic specificity induction. Memory & Cognition, 44 (6), 974–988. https://doi.org/10.3758/S13421-016-0605-Z

Madore, K. P., Thakral, P. P., Beaty, R. E., Addis, D. R., & Schacter, D. L. (2019). Neural mechanisms of episodic retrieval support divergent creative thinking. Cerebral Cortex, 29 (1), 150–166. https://doi.org/10.1093/cercor/bhx312

Mednick, S. (1962). The associative basis of the creative process. Psychological Review, 69 (3), 220–232. https://doi.org/10.1037/H0048850

Miroshnik, K. G., Forthmann, B., Karwowski, M., & Benedek, M. (2023). The relationship of divergent thinking with broad retrieval ability and processing speed: A meta-analysis. Intelligence, 98 , 101739. https://doi.org/10.1016/j.intell.2023.101739

Nusbaum, E. C., & Silvia, P. J. (2011). Are intelligence and creativity really so different?. Fluid intelligence, executive processes, and strategy use in divergent thinking. Intelligence, 39 (1), 36–45. https://doi.org/10.1016/J.INTELL.2010.11.002

Palmiero, M., Fusi, G., Crepaldi, M., Borsa, V. M., & Rusconi, M. L. (2022). Divergent thinking and the core executive functions: a state-of-the-art review. Cognitive Processing, 23 (3), 341–366. https://doi.org/10.1007/S10339-022-01091-4

Persaud, K., Macias, C., Hemmer, P., & Bonawitz, E. (2021). Evaluating recall error in preschoolers: Category expectations influence episodic memory for color. Cognitive Psychology, 124 , 101357. https://doi.org/10.1016/j.cogpsych.2020.101357

Poincaré, H. (1914). Science and method . Dover.

Ramey, M. M., Yonelinas, A. P., & Henderson, J. M. (2019). Conscious and unconscious memory differentially impact attention: Eye movements, visual search, and recognition processes. Cognition, 185 , 71–82. https://doi.org/10.1016/J.COGNITION.2019.01.007

Ramey, M. M., Henderson, J. M., & Yonelinas, A. P. (2020). The spatial distribution of attention predicts familiarity strength during encoding and retrieval. Journal of Experimental Psychology: General, 149 (11), 2046–2062. https://doi.org/10.1037/xge0000758

Ramey, M. M., Yonelinas, A. P., & Henderson, J. M. (2020). Why do we retrace our visual steps? Semantic and episodic memory in gaze reinstatement. Learning and Memory, 27 (7), 275–283. https://doi.org/10.1101/lm.051227.119

Ramey, M. M., Henderson, J. M., & Yonelinas, A. P. (2022a). Eye movements dissociate between perceiving, sensing, and unconscious change detection in scenes. Psychonomic Bulletin & Review, 29 (6), 2122–2132. https://doi.org/10.3758/s13423-022-02122-z

Ramey, M. M., Henderson, J. M., & Yonelinas, A. P. (2022b). Episodic memory processes modulate how schema knowledge is used in spatial memory decisions. Cognition, 225 , 105111. https://doi.org/10.1016/J.COGNITION.2022.105111

Renoult, L., Irish, M., Moscovitch, M., & Rugg, M. D. (2019). From knowing to remembering: The semantic-episodic distinction. Trends in Cognitive Sciences, 23 (12), 1041–1057. https://doi.org/10.1016/J.TICS.2019.09.008

Ritter, S. M., & Dijksterhuis, A. (2014). Creativity-the unconscious foundations of the incubation period. Frontiers in Human Neuroscience, 8 , 215. https://doi.org/10.3389/FNHUM.2014.00215/BIBTEX

Ritter, S. M., van Baaren, R. B., & Dijksterhuis, A. (2012). Creativity: The role of unconscious processes in idea generation and idea selection. Thinking Skills and Creativity, 7 (1), 21–27. https://doi.org/10.1016/J.TSC.2011.12.002

Rosenthal, R., & Rosnow, R. L. (1991). Essentials of behavioral research: Methods and data analysis . McGraw-Hill.

Ryan, J. D., Althoff, R. R., Whitlow, S., & Cohen, N. J. (2000). Amnesia is a deficit in relational memory. Psychological Science, 11 (6), 454–461. https://doi.org/10.1111/1467-9280.00288

Seli, P., Risko, E. F., & Smilek, D. (2016). Assessing the associations among trait and state levels of deliberate and spontaneous mind wandering. Consciousness and Cognition, 41 , 50–56. https://doi.org/10.1016/J.CONCOG.2016.02.002

Silvia, P. J., Beaty, R. E., & Nusbaum, E. C. (2013). Verbal fluency and creativity: General and specific contributions of broad retrieval ability (Gr) factors to divergent thinking. Intelligence, 41 (5), 328–340. https://doi.org/10.1016/J.INTELL.2013.05.004

Smith, S. M., Ward, T. B., & Schumacher, J. S. (1993). Constraining effects of examples in a creative generation task. Memory & Cognition, 21 (6), 837–845. https://doi.org/10.3758/BF03202751/METRICS

Squire, L. R., Wixted, J. T., & Clark, R. E. (2007). Recognition memory and the medial temporal lobe: A new perspective. Nature Reviews. Neuroscience, 8 (11), 872. https://doi.org/10.1038/NRN2154

Sternberg, R. J., & Lubart, T. I. (1999). The concept of creativity: Prospects and parad. In R. J. Sternberg (Ed.), Handbook of creativity (pp. 3–15). Cambridge University Press https://psycnet.apa.org/record/1998-08125-001

Sweegers, C. C. G., Coleman, G. A., van Poppel, E. A. M., Cox, R., & Talamini, L. M. (2015). Mental schemas hamper memory storage of goal-irrelevant information. Frontiers in Human Neuroscience, 9 , 629. https://doi.org/10.3389/fnhum.2015.00629

Takeuchi, H., Taki, Y., Hashizume, H., Sassa, Y., Nagase, T., Nouchi, R., & Kawashima, R. (2011). Failing to deactivate: The association between brain activity during a working memory task and creativity. NeuroImage, 55 (2), 681–687. https://doi.org/10.1016/j.neuroimage.2010.11.052

Thakral, P. P., Madore, K. P., Kalinowski, S. E., & Schacter, D. L. (2020). Modulation of hippocampal brain networks produces changes in episodic simulation and divergent thinking. Proceedings of the National Academy of Sciences, 117 (23), 12729–12740. https://doi.org/10.1073/pnas.2003535117

Thakral, P. P., Devitt, A. L., Brashier, N. M., & Schacter, D. L. (2021). Linking creativity and false memory: Common consequences of a flexible memory system. Cognition, 217 , 104905. https://doi.org/10.1016/J.COGNITION.2021.104905

Thakral, P. P., Yang, A. C., Addis, D. R., & Schacter, D. L. (2021). Divergent thinking and constructing future events: dissociating old from new ideas. Memory, 29 (6), 729–743. https://doi.org/10.1080/09658211.2021.1940205

Thakral, P. P., Barberio, N. M., Devitt, A. L., & Schacter, D. L. (2023). Constructive episodic retrieval processes underlying memory distortion contribute to creative thinking and everyday problem solving. Memory & Cognition, 51 (5), 1125–1144. https://doi.org/10.3758/S13421-022-01377-0/FIGURES/7

van Kesteren, M. T. R., Ruiter, D. J., Fernández, G., & Henson, R. N. (2012). How schema and novelty augment memory formation. Trends in Neurosciences, 35 (4), 211–219. https://doi.org/10.1016/j.tins.2012.02.001

Võ, M.L.-H., Boettcher, S. E., & Draschkow, D. (2019). Reading scenes: How scene grammar guides attention and aids perception in real-world environments. Current Opinion in Psychology, 29 , 205–210. https://doi.org/10.1016/j.copsyc.2019.03.009

Weiss, S., Wilhelm, O., & Kyllonen, P. (2021). An improved taxonomy of creativity measures based on salient task attributes. Psychology of Aesthetics, Creativity, and the Arts. https://doi.org/10.1037/ACA0000434

Wixted, J. T. (2007). Dual-process theory and signal-detection theory of recognition memory. Psychological Review, 114 (1), 152–176. https://doi.org/10.1037/0033-295X.114.1.152

Wixted, J. T., Mickes, L., & Squire, L. R. (2010). Measuring recollection and familiarity in the medial temporal lobe. Hippocampus, 20 (11), 1195–1205. https://doi.org/10.1002/HIPO.20854

Yonelinas, A. P. (2002). The nature of recollection and familiarity: A review of 30 years of research. Journal of Memory and Language, 46 , 441–517. https://doi.org/10.1006/jmla.2002.2864

Yonelinas, A. P., Ramey, M. M., & Riddell, C. (2022). Recognition memory: The role of recollection and familiarity. In M. J. Kahana & A. D. Wagner (Eds.), The Oxford handbook of human memory. University Press.

Zabelina, D. L., & Condon, D. M. (2020). The Four-Factor Imagination Scale (FFIS): A measure for assessing frequency, complexity, emotional valence, and directedness of imagination. Psychological Research, 84 (8), 2287–2299. https://doi.org/10.1007/S00426-019-01227-W

Zabelina, D. L., & Ganis, G. (2018). Creativity and cognitive control: Behavioral and ERP evidence that divergent thinking, but not real-life creative achievement, relates to better cognitive control. Neuropsychologia, 118 , 20–28. https://doi.org/10.1016/J.NEUROPSYCHOLOGIA.2018.02.014

Zabelina, D. L., O’Leary, D., Pornpattananangkul, N., Nusslock, R., & Beeman, M. (2015). Creativity and sensory gating indexed by the P50: Selective versus leaky sensory gating in divergent thinkers and creative achievers. Neuropsychologia, 69 , 77–84. https://doi.org/10.1016/J.NEUROPSYCHOLOGIA.2015.01.034

Zabelina, D. L., Saporta, A., & Beeman, M. (2016). Flexible or leaky attention in creative people? Distinct patterns of attention for different types of creative thinking. Memory & Cognition, 44 (3), 488–498. https://doi.org/10.3758/s13421-015-0569-4

Download references

No funding was received for conducting this study.

Author information

Authors and affiliations.

Department of Psychological Science, University of Arkansas, 203A Memorial Hall, Fayetteville, AR, 72701, USA

Michelle M. Ramey & Darya L. Zabelina

You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Michelle M. Ramey .

Ethics declarations

Conflicts of interest.

The authors have no financial or proprietary interests in any material discussed in this article.

Ethics approval and consent

The methodology for this study was approved by the University institutional review board. Informed consent was obtained from all participants.

Additional information

Publisher's note.

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

Additional information about stimuli

The scene categories and targets consisted of kitchens (target: frying pan), dining rooms (target: wine glass), bedrooms (target: alarm clock), living rooms (target: coffee mug), and bathrooms (target: toothbrush cup). Eight different object exemplars were used per category, such that the visual features of the target object varied across different scenes within a category. In each scene, only one exemplar of the target object was present, and this was kept consistent across presentations. For example, in each living room scene, there was only one coffee mug present.

The congruent location for a target object was semantically consistent across all scenes in a category, such that targets were placed relative to larger objects with which the target objects co-occur with high probability in daily life (Boettcher et al., 2018 ; for review of scene grammar see Võ et al., 2019 ). Specifically, in bathroom scenes, the toothbrush cups were located next to sinks; in dining room scenes, the wine glasses were located on tables (within arm’s reach of a chair); in kitchen scenes, the pans were on stove burners; in bedroom scenes, the alarm clocks were on nightstands; and in living room scenes, the coffee mugs were on coffee tables. The spatial locations of the targets varied across scenes, as illustrated in Fig. 4 .

Distributions of target location midpoints within scenes. Note. A) Distribution of the target locations in congruent scenes. B) Distribution of target locations in incongruent scenes

Search time

The target was found on 98.9% of study phase trials. On trials in which the target was found, the average search time was (1) 2045 ms in congruent scenes and 2476 ms in incongruent trials ( p = .0001), and (2) 2,482 ms on first presentation and 2,040 ms on second presentation ( p < .0001). Thus, both semantic knowledge and episodic memory contributed to search speed in a similar fashion as to spatial accuracy.

Model equations

The equations for the models used for the primary (i.e., non-replication) analyses are specified below (Eqs 1 – 4 ). When these equations are discussed with respect to examining fluency and originality separately (in the Sensitivity Analyses section), the “AUT score” variable below was replaced with “fluency” or “originality,” depending on the analysis in question.

Recollection effects

For the difference between recollected and strength-matched familiar scenes, the analysis included old scenes that were given a response of 6 or 5, and the model was specified as:

For the congruency effects, the analysis included recollected scenes (old scenes that were given a response of 6) and was specified as:

Familiarity effects

For familiarity effects, the analyses included scenes across all levels of familiarity strength (old scenes that were given a response of 1-5). For the analysis that examined familiarity irrespective of congruency, the congruency parameter was removed:

Unconscious effects

For unconscious effects, analyses were conducted in old scenes given a response of “sure new,” and new scenes. For the analysis that examined unconscious memory irrespective of congruency, the congruency parameter was removed:

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Ramey, M.M., Zabelina, D.L. Divergent thinking modulates interactions between episodic memory and schema knowledge: Controlled and spontaneous episodic retrieval processes. Mem Cogn 52 , 663–679 (2024). https://doi.org/10.3758/s13421-023-01493-5

Download citation

Accepted : 07 November 2023

Published : 16 January 2024

Issue Date : April 2024

DOI : https://doi.org/10.3758/s13421-023-01493-5

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

Divergent thinking
Episodic memory
Schema knowledge
Recognition
Find a journal
Publish with us
Track your research

Divergent Thinking: What It Is, How It Works

Weird or out of the box thoughts can yield a cornucopia of good ideas.

“Bring Your Weird,” is one of the values at Panzura , a cloud-management software company based in San Jose, California. “We believe that different thinking is what makes us awesome, and we encourage everyone to be their authentic self at all times,” said Ed Peters, chief innovation officer.

What Is Divergent Thinking?

This “different thinking,” also known as divergent thinking, has resulted in many effective decisions for Panzura, including moving the company’s entire product-development and quality-assurance efforts to its Mexican nearshore unit, rather than nearshoring only parts of the process.

More on Leadership Skills 11 Essential Leadership Qualities for the Future of Work

In the 1950s, psychologist J.P. Guildford came up with the concept of convergent and divergent thinking . Convergent thinking is organized and linear, following certain steps to reach a single solution to a problem. Divergent thinking is more free-flowing and spontaneous, and it produces lots of ideas. Guilford considered divergent thinking more creative because of its ability to yield many solutions to problems.

“Divergent thinking is the ability to generate alternatives,” said Spencer Harrison, associate professor of organizational behavior at management school Insead. Divergent thinkers question the status quo. They reject “we’ve always done it this way” as a reason, he said.

Divergent thinking can and should involve convergent thinking, said Peters of Panzura. The two ways of thinking “are a yin and yang that can become a virtuous cycle and a source of great pride for the team members that create ideas, products and moments.”

Characteristics of Divergent Thinking

“All true thinking is divergent,” said Chris Nicholson, team lead at San Francisco-based Clipboard Health, which matches nurses with open shifts at healthcare facilities. “Everything else is imitation and doesn’t require thinking at all.”

Divergent thinking encompasses creativity, collaboration, open mindedness, attention to detail and other qualities.

Divergent thinking is creative , but it’s not creative thinking, which requires a complicated set of skills, Harrison said. Designers need to be empathetic to create suitable, organic solutions. That empathetic aspect of thinking is, in a way, divergent thinking because it leads to ideas, but it is not the sum and substance of divergent thinking, Harrison said.

“Engaging in divergent thinking while problem solving tends to result in more creative solutions.”

Divergent thinking and creativity are intertwined, said Taylor Sullivan, senior staff industrial-organizational psychologist at Codility , an HR tech company based in San Francisco. “Engaging in divergent thinking while problem solving tends to result in more creative solutions,” she said. “This is important because leader creativity has been shown to promote positive change and inspire followers,” she said. Creative problem-solving also enhances team performance, particularly when it involves brainstorming, Sullivan added.

Open Mind

“One of the key life lessons my father taught me was the importance of being willing to change your mind,” Sullivan said. Open-mindedness — the willingness to to consider new or different perspectives and ideas — is a hallmark of divergent thinking and is critical for effective leadership , she said.

Collaborative

Idea creation at Donut involves cross-department collaboration , said Arielle Shipper, vice president of operations at the New York-based company, which makes office communication tools. “We always pull in people from across the organization, even if the problem we’re working on doesn’t touch their direct role,” Shipper said. Representatives from product and engineering especially bring a perspective that helps tie products and the solutions, she said.

This collaboration involves getting input from everyone, even those who are reluctant to share thoughts, she said. “It’s important to me that everyone knows that their ideas are crucial for our work, even if they contradict what a more senior person is saying,” Shipper said. To spark conversation, she asks “is there anything you disagree with?” rather than “what do you think?” Asking the more tightly focused question, which Shipper calls a “simple but mindful shift in language” promotes a culture of acceptance and ideation.

Rethink Language

Along similar lines, Chris Nicholson and his team at Clipboard Health think divergently by escaping what he calls language traps, “when you realize that what’s happening is being obscured by the way people talk about it,” Nicholson said.

To illustrate: Clipboard Health believes that new hires should “raise the median” on the team they’re joining. That belief, though, led to rejecting people for the wrong reasons, for example not having a Ph.D on a team filled with Ph.Ds.

To get out of that language trap, the company settled on a multi-dimensional median for teams, meaning that candidates could excel in coding ability, humility or other skills .

Detail Oriented

“The devil is in the details,” said Leslie Ryan, managing director in cybersecurity and technology controls at JPMorgan Chase . “I have always thought outside the career and it has helped my career advance,” said Ryan, who has six direct reports and a team of 40.

Earlier in her career, Ryan’s employer wanted to outsource functions that many people thought couldn’t be outsourced. Trade support was one such function. “It typically required a person to be in proximity to the trader and details of the trade,” Ryan explained. By dissecting a trading assistant’s job, she was able to pinpoint certain functions, such as reconciliations and reporting, that could be outsourced.

Strategy

“I tend to see the bigger picture — strategically and long term,” said Chris Noble, CEO of New York-based cloud-tech company Cirrus Nexus, who considers himself a divergent thinker. “I look at things from a perspective of not what we can’t do, but imagining what can be and where we need to go,” he said. The quality, which Noble attributes in part to his dyslexia, helps him visualize unique and forward-thinking products for Cirrus Nexus.

More on Productivity Productive Downtime Is a Startup Leader’s Secret Weapon

Build Divergent Thinking Skills

Chris Nicholson of Clipboard Health honed his ability to think divergently when he was young; his family of six debated at the dinner table and his father enjoyed playing devil’s advocate. “That led us to see different perspectives,” he said. Nicholson thinks many people are able to think divergently, but perhaps are not in environments that foster it. Divergent thinking is “creative, reality focused, and persistent,” he said.

Ask Questions

When faced with a problem, Nicholson asks questions: “Why do we think this is a problem? What do we achieve if we solve it? What data, experience and customer interactions do we have that backs up our hypotheses?” This “discovery stage,” he said, helps management understand a problem before it builds solutions. “Explore the mystery first and relish the discomfort of not knowing, rather than building a plan based on misguided beliefs,” he said.

Let Thoughts Flow Freely

Free-flowing thought is a necessary step in divergent thinking, agreed Christine Andrukonis, founder and senior partner at leadership consultancy Notion Consulting, who considers divergent thinking a hallmark of leadership. “A great leader’s superpower is to be able to see into the future and anticipate what’s next, which requires divergent thinking,” she said.

“A great leader’s superpower is to be able to see into the future and anticipate what’s next, which requires divergent thinking.”

When presented with a problem, Andrukonis lets her thoughts flow freely and writes them down. Then she steps away to think about what she’s written down and perhaps identify patterns among the thoughts. She circles those patterns, steps away again, and then connects them to the bigger picture.

“My step-away moments are literally that — going for a walk, spending time with my family, or doing something creative like painting,” Andrukonis said. Stepping away does not involve a meeting or work-related task, she said.

Listen Actively

“When I face a problem, I innately begin thinking of different ways the problem can be solved,” said Daryl Hammett, general manager, global demand generation and operations at AWS , based in Seattle, Washington.

Soon after, though, Hammett starts tapping his team for feedback. “We always start with working back from the customers’ needs, so I actively seek the advice and viewpoints of a diverse range of people, listening to their thoughts about the problems, goals, and challenges they face,” he said.

By actively listening , he practices divergent thinking skills and builds solutions with his teams. “Problems are not linear,” he said. “They’re multi-dimensional and should be addressed from a variety of angles before the best solutions appear.”

To nurture divergent thinking, Hammett encourages his team to challenge him without fear of judgment. “I am always open to feedback and change,” he said. “Having two-way conversations helps me cut through the noise and put my people first.”

He also considers divergent thinking a mark of effective leadership — it helped him navigate the management challenges of the pandemic and helps lead his team with flexibility.

Both divergent and convergent thinking have their place in a leader’s skillset, said Spencer Harrison of Insead. Leaders who deal with stable and settled situations might benefit more from convergent thinking, while leaders with unstable, volatile environments might do well to think only divergently.

“What research suggests is that divergent thinking might help you see new possibilities, but you would still need convergent thinking to realize and execute on those possibilities,” he said. “That said, because education and organizations tend to over-reward conformity, divergent thinking is probably a bit more rare and therefore likely more valuable especially in the long run over the course of a career,” Harrison said.

Peters at Panzura has his own opinion. “Sometimes the divergent thinking path wins, much of the time it doesn’t,” he said. “We create more opportunities for divergence by repeating the saying: ‘You never lose. You win or you learn.’

Recent Career Development Articles

Is MasterClass right for me?

Take this quiz to find out.

Divergent Thinking: 5 Divergent Thinking Strategies

Written by MasterClass

Last updated: Feb 17, 2022 • 2 min read

Divergent thinking can be a valuable skill for problem-solving and creative ideation. Learn more about this type of thinking and how to use this method to find creative ideas.

Student Notebook

The Importance of Divergent Thinking for Research in Graduate School and Beyond

Educational
Graduate School
Higher Education
Interdisciplinary

As undergraduates, we are generally encouraged to practice concrete thought. Our goal is to find the “right” answers. In the context of undergraduate education, our success is often determined by our ability to spit back the information provided in textbooks and lectures; taking new divergent approaches to problems is rarely rewarded. This unidirectional flow of information from professors to students makes sense given that we all begin our studies as novice scientists. This arrangement promotes convergent thinking (Guilford, 1967), the concept that there is one correct answer to a problem.

However, when we make the transition to graduate school we find that the relationship between students and professors becomes bidirectional. In other words, both students and professors are expected to contribute original information to courses and research projects, promoting divergent thinking. A shift from convergent to divergent thinking — the generation of thoughts and perspectives from multiple viewpoints (Guilford, 1967) — accompanies this transition from unidirectional to bidirectional flows of information.

Convergent and divergent thinking are not mutually exclusive concepts; both play an important role in our research. Convergent thought provides a foundation for us to build upon as we gain more research experience. This foundation in the current best practices of our research areas provides common ground among colleagues from which we can advance future research. However, only by moving beyond commonly accepted practices and convergent thinking to develop divergent research ideas do we truly advance our science.

For example, over the last several decades there has been a major shift in the way that social psychologists think about social attitudes (Payne & Gawronski, 2010). Influences from cognitive psychology led to the development of assessment tools, such as the Implicit Association Test (Greenwald et al., 1998), designed to measure implicit attitudes. Overall, this resulted in a big change in the way psychological scientists think about and approach the topic of attitudes. Up until that point, explicit measures were the only tools psychologists had for assessing attitudes, and there was little empirical attention given to the possibility that attitudes could exist outside conscious awareness. We are now able to consider how both explicit and implicit attitudes relate to one another and predict various behaviors. The introduction of research on implicit attitudes added a new piece to the puzzle, giving us a more complete understanding of people’s attitudes.

In order to truly advance our science we need to push ourselves to engage in divergent thinking, starting in graduate school. As graduate students, we have a plethora of opportunities to improve our divergent thinking, developing the skills that will take our science to the next level.

Approaches to Improved Divergent Thinking

Diversify your course load. You can benefit from taking relevant courses outside of psychology, as they can offer new perspectives to your current approach to research. Psychology is an interdisciplinary science and has connections to biology, sociology, and psychiatry, to name just a few. I have taken classes outside psychology in Speech and Language Pathology, a department that certifies clinicians to treat patients with communication disorders resulting from stroke and traumatic brain injury. As a result, I have incorporated a treatment-focused approach which examines the long-term cognitive and neural changes associated with traumatic brain injury into my research. Viewing traumatic brain injury from this clinical perspective has inspired me to develop cognitive interventions for traumatic brain injury and prevention programs for at-risk populations. By taking courses outside of psychology, you too can expose yourself to alternative perspectives that may help shape the way you tackle research questions.

Collaboration. Collaboration with faculty and graduate students in other areas of psychology or even in other disciplines can also diversify your perspective. The unique benefit of collaboration is discourse that develops between you and your collaborator. Through this discourse, you both may gain new insights for present and future research. Take the example of the Implicit Association Test: The ways in which social psychologists studied attitudes changed as a result of influences from other scientists’ work — in this case, the work of cognitive psychologists. When we are exposed to alternative perspectives, we are more likely to engage in divergent thinking. Collaboration may provide opportunities to identify alternative approaches to our research questions and alternative explanations for our findings. Collaborative experiences may also propel you to bridge other schools of thought in your future research.

Grant Writing. Writing a grant can be an opportunity to explore research from multiple disciplinary viewpoints and ask original research questions. When reviewing literature prior to designing your research proposal, challenge yourself to read research from related disciplines. This will allow you to widen the scope of the research questions that you can ask. In my first semester of graduate school, I proposed an original research project in my application for a National Science Foundation Graduate Research Fellowship. To approach my research question of age-related cognitive decline from multiple perspectives, I incorporated literature from disciplines outside of psychology, such as gerontology and neuroscience. If you continue to read research from alternative disciplines, you will be able to use more flexible thinking to develop original research questions.

Present your research. By sharing your research with a greater network, you create more opportunities for feedback and collaboration from outside schools of thought. There are several opportunities in graduate school to exhibit your work. First, you can present your research at brown-bag style colloquia that are held within your department or graduate college. Second, to enlarge your research network, present a paper or poster at a national or regional conference. Some of my most developed research questions have come from interdisciplinary conversations with conference attendees. At a conference, you surround yourself with people of varying foci in your research area, which may even open the door to collaboration. Their feedback may stimulate you to see your research differently — leading to new research hypotheses and perspectives.

I encourage you to seek out the aforementioned experiences in your academic community to guide you towards alternative perspectives in your research area. Diversifying your course load, collaborating, writing grants, and presenting your research are just a few ways you can promote divergent thinking during your graduate education. An emphasis on divergent thinking will change the way you think about scientific literature and will help you ask original research questions. Moreover, divergent thinking will allow you to view complex problems from alternative angles, a skill which will facilitate better science and ultimately benefit you throughout your career.

Greenwald, A. G., McGhee, D. E., & Schwartz, J. L. K. (1998). Measuring individual differences in implicit cognition: The Implicit Association Test. Journal of Personality and Social Psychology, 74 (6), 1464–1480.

Guilford, J. P. (1967). The nature of human intelligence . New York: McGraw-Hill.

Payne, B. K., & Gawronski, B. (2010). A history of implicit social cognition: Where is it coming from? Where is it now? Where is it going? Handbook of implicit social cognition: Measurement, theory, and applications , 1–15.

I like some of the stuff you say but the way you define what “convergent” and “divergent” thinking are is puzzling.

I must admit I’ve never read Guilford and there aren’t any free samples on the internet so I can’t speak to details, but the way Peirce has defined convergence is simply an opinion that is fated to be agreed upon by all who investigate. So, the idea is simply application of an honest and curious attitude for truth seeking. That truth is not static and can change if we get data that doesn’t fit into the paradigm.

There is enough conflation by the reductionism/holism debate that has hindered progress. I should hope convergence/divergence doesn’t become its replacement.

One question that sprouted about from this reading was about the importance of learning convergent, one-sided thinking. As an undergraduate student, diagnosed as a divergent thinker at a very young age and living with it everyday (for good and bad), I felt compelled to say screw convergence. However I’d like to pose this as a question instead. A foundation is necessary for a tree to sprout branches, then leaves, no? Let’s talk about it. 🙂

APS regularly opens certain online articles for discussion on our website. Effective February 2021, you must be a logged-in APS member to post comments. By posting a comment, you agree to our Community Guidelines and the display of your profile information, including your name and affiliation. Any opinions, findings, conclusions, or recommendations present in article comments are those of the writers and do not necessarily reflect the views of APS or the article’s author. For more information, please see our Community Guidelines .

Please login with your APS account to comment.

About the Author

Patrick S. Ledwidge is a first-year doctoral student at the University of Nebraska–Lincoln, where he studies cognitive neuroscience. Broadly, his research interests include brain injury, atypical development, and aging. His current areas of research interest include traumatic brain injury, age-related cognitive decline, and the relationship between exercise and cognitive health. He can be contacted at [email protected] .

NSF Invites Grant Proposals for Studying Personalized Learning in the STEM Workforce

The National Science Foundation is interested in receiving new proposals and supplemental funding requests to support flexible personalized learning to prepare the science, technology, engineering, and mathematics workforce.

Professors’ Influence on Display in APS Membership Initiative

A new effort geared towards mentoring professors leads thousands of new students to join APS for 2017.

European Psychology Learning and Teaching Conference

The School of Education of the Paris Lodron University of Salzburg is hosting the next European Psychology Learning and Teaching (EUROPLAT) Conference on September 18–20, 2017 in Salzburg, Austria. The main theme of the conference

Privacy Overview

Cookie	Duration	Description
__cf_bm	30 minutes	This cookie, set by Cloudflare, is used to support Cloudflare Bot Management.

Cookie	Duration	Description
AWSELBCORS	5 minutes	This cookie is used by Elastic Load Balancing from Amazon Web Services to effectively balance load on the servers.

Cookie	Duration	Description
at-rand	never	AddThis sets this cookie to track page visits, sources of traffic and share counts.
CONSENT	2 years	YouTube sets this cookie via embedded youtube-videos and registers anonymous statistical data.
uvc	1 year 27 days	Set by addthis.com to determine the usage of addthis.com service.
_ga	2 years	The _ga cookie, installed by Google Analytics, calculates visitor, session and campaign data and also keeps track of site usage for the site's analytics report. The cookie stores information anonymously and assigns a randomly generated number to recognize unique visitors.
_gat_gtag_UA_3507334_1	1 minute	Set by Google to distinguish users.
_gid	1 day	Installed by Google Analytics, _gid cookie stores information on how visitors use a website, while also creating an analytics report of the website's performance. Some of the data that are collected include the number of visitors, their source, and the pages they visit anonymously.

Cookie	Duration	Description
loc	1 year 27 days	AddThis sets this geolocation cookie to help understand the location of users who share the information.
VISITOR_INFO1_LIVE	5 months 27 days	A cookie set by YouTube to measure bandwidth that determines whether the user gets the new or old player interface.
YSC	session	YSC cookie is set by Youtube and is used to track the views of embedded videos on Youtube pages.
yt-remote-connected-devices	never	YouTube sets this cookie to store the video preferences of the user using embedded YouTube video.
yt-remote-device-id	never	YouTube sets this cookie to store the video preferences of the user using embedded YouTube video.
yt.innertube::nextId	never	This cookie, set by YouTube, registers a unique ID to store data on what videos from YouTube the user has seen.
yt.innertube::requests	never	This cookie, set by YouTube, registers a unique ID to store data on what videos from YouTube the user has seen.

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

The PMC website is updating on October 15, 2024. Learn More or Try it out now .

Advanced Search
Journal List
HHS Author Manuscripts

Divergent Thinking and Constructing Episodic Simulations

Donna rose addis.

1 School of Psychology and the Centre for Brain Research, The University of Auckland, New Zealand

2 Department of Psychology, Harvard University, Cambridge, MA, USA

Regina Musicaro

Daniel l. schacter.

Divergent thinking likely plays an important role in simulating autobiographical events. We investigated whether divergent thinking is differentially associated with the ability to construct detailed imagined future and imagined past events as opposed to recalling past events. We also examined whether age differences in divergent thinking might underlie the reduced episodic detail generated by older adults. The richness of episodic detail comprising autobiographical events in young and older adults was assessed using the Autobiographical Interview. Divergent thinking abilities were measured using the Alternate Uses Task. Divergent thinking was significantly associated with the amount of episodic detail for imagined future events. Moreover, while age was significantly associated with imagined episodic detail, this effect was strongly related to age-related changes in episodic retrieval rather than divergent thinking.

Introduction

Divergent thinking – the ability to generate ideas by comparing and combining disparate forms of information in new ways – is closely linked to imagination ( Durndell & Wetherick, 1976 ; Mednick, 1962 ). Divergent thinking is related to the quality of imagination in children ( Russ, 2003 ), and mental imagery in adults ( Durndell & Wetherick, 1976 ; Forisha, 1978 ; Schmeidler, 1965 ). We suggest that divergent thinking is also associated with the ability to create detailed simulations of autobiographical events, such as possible future experiences.

Akin to divergent thinking, simulation is a form of “productive imagination” ( Burnham, 1892 ), involving the extraction of details from various episodic autobiographical memories which are recombined to create novel scenarios (for reviews of supporting evidence, see Addis & Schacter, 2012 ; Schacter et al., 2012 ; Szpunar, 2010 ). According to the constructive episodic simulation hypothesis, access to details in episodic memory is associated with the amount of episodic detail comprising simulations ( Schacter & Addis, 2007 ). Indeed, the level of detail comprising memories of past events is strongly correlated with that of simulations (see Schacter, Gaesser, & Addis, 2013 , for a review). However, retrieving details from memory is not sufficient; one has to organize disparate elements of information into a coherent form ( Addis & Schacter, 2012 ), which may require recruitment of creative thought processes ( Khatena, 1978 ), such as divergent thinking. Thus, the aim of this study is to investigate whether divergent thinking abilities are associated with the construction of detailed simulations over and above the ability to access detailed memories.

Little research has investigated the potential links between divergent thinking and autobiographical simulation. Ononye, Blinn-Pike and Smith (1993) found that performance on the Consequences Task ( Guilford, 1967 ), which requires the generation of possible consequences and responses to non-personal futuristic problems (e.g., everyone suddenly loses the ability to read and write), was significantly associated with that on the Future Problem-Solving Task, in which participants generated solutions for personal future problems (e.g., a dream job in a distant city). It is possible, however, that the similar structure of both tasks could explain, at least in part, the correlated performance. Moreover, while this study focused on the quantitative aspects of future thinking, divergent thinking may contribute to the quality of simulations – and may be particularly important for the richly detailed scenarios that typify episodic simulations ( Schacter & Addis, 2007 ). The present study attempts to address this issue.

An additional question is whether divergent thinking is similarly related to the simulation of any autobiographical episode, whether the imagined event is located in the future or the past. We have previously found imagined past events to be similar to future simulations, both in terms of the amount of episodic detail ( Addis, Musicaro, Pan, & Schacter, 2010 ) and neural correlates ( Addis, Pan, Vu, Laiser, & Schacter, 2009 ). However, these events may differ in terms of opportunity for flexible, divergent thought. Although the imagined future is somewhat constrained by past experiences and plans, it can still be conceived as many branching possibilities ( Goldie, 2009 ), while imagined past events are likely more constrained by what has actually occurred.

We also assessed whether previously-documented differences between young and older adults in the amount of episodic detail comprising imagined events (cf., Addis, Wong, & Schacter, 2008 ; Addis et al., 2010 ; Cole, Morrison, & Conway, 2013 ; Rendell, Bailey, Henry, Phillips, Gaskin, & Kliegel, 2012 ; for review, see Schacter et al., 2013 ) is associated with the ability to retrieve episodic details from memory, or whether divergent thinking might also be relevant. Age-related declines on the Alternate Uses Test have been reported (e.g., Alpaugh et al., 1982 ) and age is a significant predictor of divergent thinking ( Hendricks, 1999 ), but it remains unexplored whether declines or cohort differences in divergent thinking are important non-mnemonic factors in understanding age-related changes in the simulation of episodic events.

Materials and Methods

Thirty-six participants (18 young: 9 males; M age =21.89 years, SD age =3.61; 18 older: 7 males; M age =74.89, SD age =5.56) who had participated in another study examining imagined future, imagined past, and remembered past events ( Addis et al., 2010 ) came back into the laboratory for additional testing of divergent thinking. Participants gave informed written consent for all testing sessions in a manner approved by the Harvard Institutional Review Board. Participants were fluent in English, had no history of neurological or psychiatric impairment; all older adults had a mini-mental state examination score of 27 or higher, excluding dementia. Older adults had completed more years of education than younger adults (Older: M=16.39 years, SD=2.62; Younger: M=14.56, SD=2.09; p =.026).

For a detailed description of the episodic simulation task used during Phase 1 of this experiment, see Addis et al. (2010) . Briefly, during session 1, participants retrieved 35 memories from the past 5 years, and specified 3 details for each: a person, object, and location. In session 2, participants completed an adapted version of the Autobiographical Interview (AI, Levine, Svoboda, Hay, Winocur, & Moscovitch, 2002 ) with three conditions: imagine-future, imagine-past, recall-past. On each trial, they were shown sets of details from their own memories recalled in session 1. For “past-recall” trials (4 trials), the detail set comprised a person, location, and object from one memory and participants recalled the specified event. For past-imagine (4 trials) and future-imagine trial (4 trials), a set of person, location and object details drawn from different memories were shown 1 , and participants generated a plausible personal experience involving the specified details. For all conditions, events were required to be temporally and contextually specific (i.e., episodic) and located within five years from the present. General probes were given when needed to clarify instructions and encourage as much description of details as possible within the three minutes allocated for each trial. Trials were blocked according to condition; order was counterbalanced across participants. Transcribed interviews were scored using the standardized AI scoring procedure ( Levine et al., 2002 ; for more information, see Addis et al., 2010 ): each distinct detail was classified as internal (episodic information relating to the central event being described) or external (non-episodic information including semantic details, extended events and repetitions). The average number of internal and external details for each participant in each condition was used in these analyses.

Approximately one month (M=27.42 days, SD=36.79) after session 2, participants were invited to the laboratory to complete session 3; this delay did not differ between groups ( p =.265). Divergent thinking was assessed using the Alternative Uses Task ( Guilford, 1967 ). Participants were instructed to generate as many uses as possible for a given item within a minute. Six items were used: eyeglasses, shoes, keys, button, wooden pencil and automobile tire. Participant responses were recorded and scored for standard measures of divergent thinking: Fluency (total number of possible uses generated); flexibility (the number of distinct categories or groupings the responses could be divided into); appropriateness (appropriate uses received a score of 1 and inappropriate responses a score of 0); elaboration (0 points were given for brief descriptions of the use, e.g., “a doorstop”; 1 if more detail was given, e.g., “a doorstop to prevent a door slamming”; and 2 points if even further detail was given, e.g., “a doorstop to prevent a door slamming in a strong wind”); originality (calculated by comparing each response generated by a participant to the responses of all other participants; a score of 3 was assigned if less than 5% of other participants generated that response, 2 if 5–10% of other participants had the response, 1 if 10–15% of other participants had that response, and 0 if more than 15% of other participants gave that response). Flexibility, appropriateness and elaboration were scored by three independent raters blind to group membership (fluency and originality scores were not subjected to an inter-rater reliability analysis as these scores were based on counts of responses or the distribution of responses across participants). Inter-rater reliability of these three divergent thinking measures was high; using a two-way mixed model, the standardised Cronbach’s α was greater than .86 for each measure. As performance across the five measures was significantly inter-correlated ( r values, .604 to .998), scores were mean-centred and collapsed into a mean divergent thinking score for use in the regression analyses.

Episodic Simulation

We conducted a 3 (Condition: Past-Imagine, Future-Imagine, Past-Recall) × 2 (Detail: Internal, External) × 2 (Group: Young, Older) mixed factorial ANOVA with repeated factors of Condition and Detail and between factor of Group. Although this analysis was reported in Addis et al. (2010) , we repeated the analysis here with the subset of imagined events used in the current study. The same effects of interest as those previously reported were also evident here, and the average internal and external detail scores according to condition and age-group are provided in Table 1A . A main effect of Detail, F 1,34 =82.39, p <.001, reflected more internal than external details generated when describing events. There was also a crossover interaction of Detail and Group, F 1,34 =25.79, p <.001, where young adults generated more internal details than older adults ( p =.005) and older adults generated more external details than young adults ( p =.005). The main effect of Condition, F 2,68 = 39.11, p <.001, was driven by more detail generated for recalled events than imagined past and future events ( p values <.001). Internal details were strongly correlated across the three conditions ( r values >.68; p values <.001), as were external details ( r values >.59; p values <.001).

Autobiographical Interview and Alternate Uses Test: Group Performance and Correlations



	Mean (SD)

	Future-Imagine		Past-Imagine		Past-Recall

AI Score	Younger	Older	Younger	Older	Younger	Older
Internal Detail	51.75 (16.50)	32.15 (15.59)	52.58 (18.47)	39.67 (18.99)	60.56 (15.00)	45.17 (15.05)
External Detail	16.97 (11.66)	32.15 (15.59)	15.42 (8.77)	24.69 (9.46)	25.10 (15.47)	34.20 (15.78)



AUT Measure	Mean (SD)		Correlation with Internal Detail Scores

	Younger	Older	Future-Imagine	Past-Imagine	Past-Recall
Fluency	6.45 (2.55)	5.52 (2.39)	.402	.233	.097
Flexibility	4.55 (1.52)	3.833 (1.33)	.388	.222	.110
Originality	8.37 (4.97)	7.96 (5.34)	.292	.207	.022
Appropriateness	6.35 (2.47)	5.47 (2.40)	.413	.247	.111
Elaboration	4.25 (2.37)	4.20 (1.81)	.374	.304	.258

Divergent Thinking Measures

We conducted a series of independent sample t -tests to determine whether there were age differences in the divergent thinking measures (see Table 1B ). These analyses revealed that age did not affect performance on any of the five measures: Fluency ( t 34 =1.14, p =.26), flexibility ( t 34 =1.51, p =.14), appropriateness ( t 34 =1.10, p =.28), originality ( t 34 =.24, p =.81), elaboration ( t 34 =.07, p =.95), or the mean of these divergent thinking measures ( t 34 =.71, p =.49).

Relation between Divergent Thinking and Episodic Simulation

We were interested in whether divergent thinking abilities would be associated with the ability to generate detailed episodic simulations. We computed Pearson’s product-moment correlations between the five divergent thinking measures and imagine-future, imagine-past and recall-past internal detail scores. As shown in Table 1B , all five measures were significantly correlated with the amount of internal episodic details comprising imagined future events. The elaboration score was also significantly correlated with the internal detail score for imagined past events, and exhibited a trend for recalled past events ( p =.065). None of the divergent thinking measures significantly correlated with the external detail score (all p values >.084).

We conducted hierarchical linear regression analyses to determine whether the mean divergent thinking score could predict the amount of episodic detail comprising imagined future, imagined past and recalled events, even when age differences were controlled for (see Table 2A ). First, age (in years) was entered into the models and was a significant predictor of the internal detail score for all event conditions, explaining approximately 14–26% of the variance in internal detail scores across the conditions. This finding is consistent with the age-related decreases in internal detail scores evident in the ANOVA analyses. In contrast, mean divergent thinking was only a significant predictor of the number of internal details generated for imagined future events, explaining an additional 11% of variance over and above age. It is possible that divergent thinking may be a significant predictor of internal details for the past-imagine and past-recall conditions if age is not already entered into the model. However, another set of regression analyses (Model 1 in Table 2B ) with mean divergent thinking as the only predictor in the model indicated that this was not the case; once again, divergent thinking was a significant predictor of future-imagine internal detail only. We also ran this second set of regression analyses ( Table 2B ) to determine whether age could account for significant variance in the internal detail scores over and above divergent thinking. Indeed, this was the case for all conditions; even in the future-imagine condition, where mean divergent thinking was a significant predictor, age still accounted for a further 21% of variance.

Linear Regression Analyses of Age and Divergent Thinking on Internal Detail Scores



Model		R	R Change	Standardized Beta Coefficients

				Age	DT

	1. Age	.243	.243	−.493
	2. Age, DT	.357	.114	−.461	.339

	1. Age	.138	.138	−.371
	2. Age, DT	.185	.048	−.351	.219

	1. Age	.260	.260	−.509
	2. Age, DT	.263	.003	−.504	.057



Model		R	R Change	Standardized Beta Coefficients

				DT	Age

	1. DT	.146	.146	.382
	2. DT, Age	.357	.211	.339	−.461

	1. DT	.063	.063	.252
	2. DT, Age	.185	.122	.219	−.351

	1. DT	.011	.011	.104
	2. DT, Age	.263	.252	.057	−.504

DT= Divergent Thinking; DV=dependent variable.

Given the hypothesized link between retrieval of episodic detail and the amount of detail comprising imagined events, we also repeated the above hierarchical linear regressions for the imagined event conditions but included past-recall internal detail score as an additional variable in the model (see Table 3A ). Again, age emerged as a significant predictor for internal details, but only when the sole predictor in the model. Once the past-recall internal detail score was entered, age was no longer a significant predictor, suggesting that the effect of age may be strongly related to reduced access to episodic detail. The past-recall internal detail score, however, was a highly significant predictor of the amount of imagined internal detail, explaining 27–34% of variance over and above age. Importantly, even with this highly significant variable in the model, mean divergent thinking still emerged as a significant predictor of imagined internal detail, but again only for future events. We also re-ran these regression analyses ( Table 3B ) adding age as the last predictor, to determine whether age could significant account for variance in imagined internal detail over and above recalled internal detail and mean divergent thinking. Age was not a significant predictor for either of the imagine conditions. Interestingly, in the future condition, where divergent thinking was already entered as a significant predictor, the past-recall internal detail score still explained an additional 44% of the variance in internal detail ( Table 3B , Model 2). In fact, for both imagined conditions, the model with divergent thinking and past-recall internal detail as predictors ( Table 3B , Model 2) explained 51–59% of the variance in imagined internal detail, much higher than the model with divergent thinking and age as predictors (19–36% variance explained; Table 2 , Model 2).

Linear Regression Analyses of Age, Past-Recall Internal Detail Score and Divergent Thinking on Imagined Internal Detail Scores



Model	R	R Change	Standardized Beta Coefficients

			Age	Past-Recall	DT

1. Age	.243	.243	−.493
2. Age, Past-Recall	.515	.272	−.184	.606
3. Age, Past-Recall, DT	.607	.092	−.168	.582	.305

1. Age	.138	.138	−.371
2. Age, Past-Recall	.482	.344	−.024	.681
3. Age, Past-Recall, DT	.514	.032	−.014	.667	.181



Model	R	R Change	Standardized Beta Coefficients

			DT	Past-Recall	Age

1. DT	.146	.146	.382
2. DT, Past-Recall	.586	.440	.312	.667
3. DT, Past-Recall, Age	.607	.021	.305	.582	−.168

1. DT	.063	.063	.252
2. DT, Past-Recall	.514	.450	.181	.675
3. Dt, Past-Recall, Age	.514	.000	.181	.667	−.014

DT=Divergent Thinking; DV=dependent variable.

The pattern of results suggests that while divergent thinking is related to the amount of internal detail comprising imagined future events, this relationship is not evident for imagined past events. In order to explore the significance of this apparent interaction, we ran a 2 (Condition: Past-Imagine, Future-Imagine) ANCOVA with three covariates (Age Group, Past-Recall, Divergent Thinking) on the number of internal details. The key finding was that the Condition × Divergent Thinking interaction was not significant ( F 1,32 =0.42, p =.52), indicating that the relationship between divergent thinking was not significantly stronger for imagined future events relative to imagined past events.

The primary aim of this study was to determine whether divergent thinking abilities are associated with the construction of detailed future simulations over and above the ability to access episodic details, and if so, whether similar associations would be evident for imagined past events. We also explored whether previously documented differences between young and older adults in the amount of episodic detail comprising simulations are associated with age, memory ability, and/or divergent thinking. While we found support for the hypothesis that divergent thinking is a significant predictor of the imagined internal detail score over and above memory for episodic details, this finding applied most strongly to imagined future events. Although it was not clearly evident for imagined past events, the fact that we failed to observe a significant interaction between imagination condition and divergent thinking in the model that included future and past imagination as dependent variables and divergent thinking, past recall, and age as covariates indicates that we cannot draw strong conclusions concerning differences between future and past imagination. Moreover, while age was associated with the amount of detail comprising simulations, it appears that this effect may be strongly related to age-related changes in retrieving episodic detail.

However, our results do show clearly that divergent thinking abilities are strongly associated with future episodic detail. Importantly, the regression analyses showed that the predictive value of divergent thinking for future episodic detail was still evident even when variance due to age and retrieval of episodic detail (as indexed by the past-recall internal detail score) were accounted for. This observation is consistent with previous work linking divergent thinking with many of the cognitive processes required for future simulation, including mental imagery (e.g., Durndell & Wetherick, 1976 ), narrative abilities (e.g., Albert & Kormos, 2004 ) and associative processes (e.g., Mednick, 1962 ), as well as recent fMRI evidence ( Benedek et al., 2014 ) that divergent thinking recruits some of the same default network regions typically linked with future simulation (e.g., Schacter, Addis, & Buckner, 2007 ; Schacter et al., 2012 ). It is notable, however, that many of these processes are thought to be required when imagining past events, and to some extent when recalling past events – and divergent thinking was not clearly associated with these conditions. Other investigators have also reported differences in imagining future scenarios compared with imagining atemporal scenarios, with age-related deficits exaggerated during the former compared with the latter ( Rendell et al., 2012 ; for more general discussion, see Schacter et al., 2012 ). Although beyond the scope of this study, further work is needed to tease apart what aspects of the episodic content of future events (e.g., visuospatial content, narrative complexity) are most strongly related to particular forms of divergent thinking. Moreover, future work could address whether this relationship between divergent thinking and future simulation is still evident once individual differences in other related processes (e.g., general knowledge and vocabulary) are controlled for.

Interestingly, however, we did find one important similarity between imagined past and future events: that access to episodic detail was a strong predictor for both forms of imagination. This finding is consistent with reports that a number of amnesic patients who cannot access episodic details show some impairment in imagining events ( Hassabis, Kumaran, Vann, & Maguire, 2007 ; Klein, Loftus, & Kihlstrom, 2002 ; Kwan, Carson, Addis, & Rosenbaum, 2010 ; Tulving, 1985 ; for review, see Addis & Schacter, 2012 ). Moreover, recent evidence (Duff, Kurczek, Rubin, Cohen, & Tranel, 2014) indicates that a group of five amnesic patients were impaired in performance of the Torrance Tests of Creative Thinking, which tap divergent thinking processes. It is possible, however, that the degree of detail generated on these tasks reflects a general narrative style that is common to remembering and imagining any autobiographical episode. Speaking against this interpretation, we have previously found that episodic detail is associated with imagined detail even after controlling for narrative ability ( Gaesser, Sacchetti, Addis, & Schacter, 2011 ), and recent data indicate that access to episodic details on tasks that tap remembering and imagining can be dissociated from performance on a narrative description task ( Madore, Gaesser, & Schacter, 2014 ).

Once the past-recall internal score was entered into the model, age was no longer a significant predictor of imagined episodic detail, suggesting that the reduced ability of older adults to retrieve episodic details may underlie the age-related deficits in the episodic content of imagined events ( Schacter et al., 2013 ). However, we did recently report in another aging study examining future simulation that even after recalled episodic detail was entered into the regression model, age still predicted a small but significant portion of variance in the amount of future detail generated ( Gaesser et al., 2011 ). A key difference between the two studies is that Gaesser et al. provided participants with detailed visual stimuli in the recall and imagination conditions, and it is possible that this externally-provided detail may have slightly decreased the reliance on details retrieved from memory, allowing other age-related factors to emerge.

Although our findings demonstrate that divergent thinking abilities are associated with the generation of detailed future events, our results also suggest that age-related differences in future simulation are not simply due to cohort differences or age-related decline in divergent thinking abilities. We did not find any age-differences in divergent thinking despite previous findings to the contrary (e.g., Alpaugh, Parham, Cole, & Birren, 1982 ; McCrae, Arenberg, & Costa, 1987 ). Moreover, even when divergent thinking was entered into the model first, a significant age effect was still evident and the R 2 change was similar in magnitude to when age was entered into the model first. This pattern suggests that other group differences are likely more important in explaining age-related reductions in episodic content – such as retrieval of episodic detail. This interpretation, however, does not negate the finding that divergent thinking is an important individual difference to consider when assessing future simulation abilities, irrespective of age.

Finally, another important question for future work concerns whether future simulation is also related to convergent thinking – the ability to generate the best single solution to a particular problem – or whether the link is selective to divergent thinking. Recent evidence indicates that associative false memory effects in the Deese-Roediger-McDermott (DRM) paradigm, where presentation of multiple associated words that converge on a non-presented lure word results in a high false alarm rate to the lure word on a subsequent recognition test (for review, see Gallo, 2010 ), are linked with convergent but not divergent thinking ( Dewhurst, Thorley, Hammond, & Ormerod, 2011 ). Based on the present results and our characterization of future simulation as involving the generation of multiple alternative scenarios, we expect that future simulation will exhibit the opposite pattern, i.e., though related to divergent thinking, it will not be significantly related to convergent thinking abilities.

In summary, the current study confirms that individual differences in divergent thinking are associated with the capacity for imagining future episodes. Although imagining a detailed scenario strongly relies on mnemonic factors, such as the retrieval of episodic details, divergent thinking is an important ingredient for future episodic thought across the lifespan.

Acknowledgements

We thank Ryan Hunt, Melissa Inger and Jessica Mitchell for scoring of the Alternate Uses Test data, and Lucia Lee and Lara Markstein for transcribing and scoring AI data. This research was supported by National Institute on Aging grant AG08441, awarded to DLS. DRA was supported by a Rutherford Discovery Fellowship (RDF-10-UOA-024).

1 Note that in the original study, there were additional imagine trials in which details came from one or two memories (i.e., a recombination load manipulation). However, in order to match trial numbers across memory and imagine trials for this analysis, we only included data from the imagine trials in which recombination of details was maximal.

Addis DR, Musicaro R, Pan L, Schacter DL. Episodic simulation of past and future events in older adults: Evidence from an experimental recombination task. Psychology & Aging. 2010; 25 :369–376. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Addis DR, Pan L, Vu M-A, Laiser N, Schacter DL. Constructive episodic simulation of the future and the past: Distinct subsystems of a core brain network mediate imagining and remembering. Neuropsychologia. 2009; 47 :2222–2238. [ PubMed ] [ Google Scholar ]
Addis DR, Schacter DL. The hippocampus and imagining the future: Where do we stand? Frontiers in Human Neuroscience. 2012; 5 :173. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Addis DR, Wong AT, Schacter DL. Age-related changes in the episodic simulation of future events. Psychological Science. 2008; 19 :33–41. [ PubMed ] [ Google Scholar ]
Albert A, Kormos J. Creativity and narrative task performance: An exploratory study. Language Learning. 2004; 54 :277–310. [ Google Scholar ]
Alpaugh PK, Parham IA, Cole KD, Birren JE. Creativity in adulthood and old age: an exploratory study. Educational Gerontology. 1982; 8 :101–116. [ Google Scholar ]
Benedek M, Jauk E, Fink A, Koschutnig K, Reishofer G, Ebner F, Neubauer AC. To create or to recall? Neural mechanisms underlying the generation of new creative ideas. NeuroImage. 2014; 88 :125–133. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Burnham WH. Individual differences in the imagination of children. Pedagogical Seminary. 1892; 2 :204–225. [ Google Scholar ]
Cole SN, Morrison CM, Conway MA. Episodic future thinking: Linking neuropsychological performance with episodic detail in young and old adults. Quarterly Journal of Experimental Psychology. 2013; 66 :1687–1706. [ PubMed ] [ Google Scholar ]
Dewhurst SA, Thorley C, Hammond ER, Ormerod TC. Convergent, but not divergent, thinking predicts susceptibility to associative memory illusions. Personality and Individual Differences. 2011; 51 :73–76. [ Google Scholar ]
Duff MC, Kurczek J, Rubin R, Cohen NJ, Tranel D. Hippocampal amnesia disrupts creative thinking. Hippocampus. 2013; 23 :1143–1149. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Durndell AJ, Wetherick NE. The relation of reported imagery to cognitive performance. British Journal of Psychology. 1976; 67 :501–506. [ Google Scholar ]
Forisha B. Creativity and imagery in men and women. Perceptual and Motor Skills. 1978; 47 :1255–1264. [ PubMed ] [ Google Scholar ]
Gaesser B, Sacchetti DC, Addis DR, Schacter DL. Characterizing age-related changes in remembering the past and imagining the future. Psychology and Aging. 2011; 26 :80–84. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Gallo DA. False memories and fantastic beliefs: 15 years of the DRM illusion. Memory and Cognition. 2010; 38 :833–848. [ PubMed ] [ Google Scholar ]
Goldie P. Narrative thinking, emotion, and planning. The Journal of Aesthetics and Art Criticism. 2009; 67 :97–106. [ Google Scholar ]
Guilford JP. The Nature of Human Intelligence. New York: McGraw Hill; 1967. [ Google Scholar ]
Hassabis D, Kumaran D, Vann SD, Maguire EA. Patients with hippocampal amnesia cannot imagine new experiences. Proceedings of the National Academy of Sciences of the United States of America. 2007; 104 :1726–1731. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Hendricks J. Creativity over the life course - a call for a relational perspective. International Journal of Aging and Human Development. 1999; 48 :85–111. [ PubMed ] [ Google Scholar ]
Khatena J. Identification and stimulation of creative imagination imagery. The Journal of Creative Behaviour. 1978; 12 :30–38. [ Google Scholar ]
Klein SB, Loftus J, Kihlstrom JF. Memory and temporal experience: The effects of episodic memory loss on an amnesic patient's ability to remember the past and imagine the future. Social Cognition. 2002; 20 :353–379. [ Google Scholar ]
Kwan D, Carson N, Addis DR, Rosenbaum RS. Deficits in past remembering extend to future imagining in a case of developmental amnesia. Neuropsychologia. 2010; 48 :3179–3186. [ PubMed ] [ Google Scholar ]
Levine B, Svoboda E, Hay JF, Winocur G, Moscovitch M. Aging and autobiographical memory: dissociating episodic from semantic retrieval. Psychology & Aging. 2002; 17 :677–689. [ PubMed ] [ Google Scholar ]
Madore KP, Gaesser B, Schacter DL. Constructive episodic simulation: Dissociable effects of a specificity induction on remembering, imagining, and describing in young and older adults. Journal of Experimental Psychology: Learning, Memory, & Cognition. 2014; 40 :609–622. [ PMC free article ] [ PubMed ] [ Google Scholar ]
McCrae RR, Arenberg D, Costa PT. Declines in divergent thinking with age: cross-sectional, longitudinal, and cross-sequential analyses. Psychology and Aging. 1987; 2 :130–137. [ PubMed ] [ Google Scholar ]
Mednick SA. The associative basis of the creative process. Psychological Review. 1962; 69 :220–232. [ PubMed ] [ Google Scholar ]
Ononye GC, Blinn-Pike LM, Smith DE. Creativity and future time perspective: Exploring fantasy and realistic measures. Creativity Research Journal. 1993; 6 :449–456. [ Google Scholar ]
Rendell PG, Bailey PE, Henry JD, Phillips LH, Gaskin S, Kliegel M. Older adults have greater difficulty imagining future rather than atemporal experiments. Psychology and Aging. 2012; 27 :1089–1098. [ PubMed ] [ Google Scholar ]
Russ SW. Play and creativity: Developmental issues. Scandinavian Journal of Educational Research. 2003; 47 :291–303. [ Google Scholar ]
Schacter DL, Addis DR. The cognitive neuroscience of constructive memory: Remembering the past and imagining the future. Philosophical Transactions of the Royal Society of London B. 2007; 362 :773–786. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Schacter DL, Addis DR, Buckner RL. Remembering the past to imagine the future: The prospective brain. Nature Reviews Neuroscience. 2007; 8 :657–661. [ PubMed ] [ Google Scholar ]
Schacter DL, Addis DR, Hassabis D, Martin VC, Spreng RN, Szpunar KK. The future of memory: Remembering, imagining, and the brain. Neuron. 2012; 76 :677–694. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Schacter DL, Gaesser B, Addis DR. Remembering the past and imagining the future in the elderly. Gerontology. 2013; 59 :143–151. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Schmeidler GR. Visual imagery correlated to a measure of creativity. Journal of Consulting Psychology. 1965; 29 :78–80. [ PubMed ] [ Google Scholar ]
Szpunar KK. Episodic future thought: An emerging concept. Perspectives on Psychological Science. 2010; 5 :142–162. [ PubMed ] [ Google Scholar ]
Tulving E. Memory and consciousness. Canadian Psychologist. 1985; 25 :1–12. [ Google Scholar ]

Information

Author Services

Initiatives

You are accessing a machine-readable page. In order to be human-readable, please install an RSS reader.

All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess .

Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications.

Feature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive positive feedback from the reviewers.

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

Original Submission Date Received: .

Active Journals
Find a Journal
Journal Proposal
Proceedings Series
For Authors
For Reviewers
For Editors
For Librarians
For Publishers
For Societies
For Conference Organizers
Open Access Policy
Institutional Open Access Program
Special Issues Guidelines
Editorial Process
Research and Publication Ethics
Article Processing Charges
Testimonials
Preprints.org
SciProfiles
Encyclopedia

Article Menu

Subscribe SciFeed
Recommended Articles
Google Scholar
on Google Scholar
Table of Contents

Find support for a specific problem in the support section of our website.

Please let us know what you think of our products and services.

Visit our dedicated information section to learn more about MDPI.

JSmol Viewer

Decomposing the true score variance in rated responses to divergent thinking-tasks for assessing creativity: a multitrait–multimethod analysis.

1. Introduction

Model-implied ICCs can be computed for the DT-scores within a specific rating procedure (construct) that only consider variability of true scores and separate measurement error;
DT-object-specific variability can be separated from measurement error;
The model allows for the computation of additional informative relative true-score variance components such as various forms of consistency and method specificity;
Using Bayesian methods, credibility intervals (CRIs) for all relative variances (mentioned in 1. and 3.) can be computed;
Rater-effects (variability across raters) can be separated from interaction-effects (variability across rater–target interactions) which allows one to investigate whether raters consistently maintain their standards across targets;
Due to the flexibility of SEM, the model can be extended to include attributes of raters in order to predict differences in raters, for example (the same is true for rater–target interactions).

2. Defining an Appropriate Cross-Classified CTC(M − 1) Model for DT-Ratings

3. variance decomposition, 4. empirical application, 4.1. the data, 4.2. analytic strategy, 4.3. results and discussion.

For example, there could be two participants who have the same number of good quality ideas, but one of the two has several more low-quality ideas. On average, these two performances may differ a great deal, but if the upper tails of their distributions are considered, the performances of both persons are much more alike. (p. 261)

5. General Discussion

5.1. substantive deliberations, 5.2. modifications, extensions, useful applications, and limitations of the model, 6. conclusions, supplementary materials, institutional review board statement, informed consent statement, data availability statement, acknowledgments, conflicts of interest, appendix a. variance decomposition in the divergent thinking two-level model (dttl), appendix b. prior-specifications within the dtcc and the dttl-b of the presented application.

Asparouhov, Tihomir, and Bengt Muthén. 2021. Bayesian Analysis of Latent Variable Models Using Mplus . Los Angeles: Muthén & Muthén. Available online: http://www.statmodel.com/download/BayesAdvantages18.pdf (accessed on 20 July 2024).
Bishop, Jacob, Christian Geiser, and David A. Cole. 2015. Modeling latent growth with multiple indicators: A comparison of three approaches. Psychological Methods 20: 43–62. [ Google Scholar ] [ CrossRef ] [ PubMed ]
Bollen, Kenneth A. 1989. Structural Equations with Latent Variables . New York: Wiley. [ Google Scholar ]
Browne, Michael W., and Robert Cudeck. 1992. Alternative ways of assessing model fit. Sociological Methods & Research 21: 230–58. [ Google Scholar ] [ CrossRef ]
Campbell, Donald T., and Donald W. Fiske. 1959. Convergent and discriminant validation by the multitrait-multimethod matrix. Psychological Bulletin 56: 81–105. [ Google Scholar ] [ CrossRef ]
Chen, Feinian, Patrick J. Curran, Kenneth A. Bollen, James Kirby, and Pamela Paxton. 2008. An empirical evaluation of the use of fixed cut-off points in RMSEA test statistic in structural equation models. Sociological Methods & Research 36: 462–94. [ Google Scholar ] [ CrossRef ]
Costa, Paul T., and Robert R. McCrae. 1989. The NEO PI/FFI Manual Supplement . Odessa: Psychological Assessment Resources. [ Google Scholar ]
Curran, Patrick J. 2003. Have multilevel models been structural equation models all along? Multivariate Behavioral Research 38: 529–69. [ Google Scholar ] [ CrossRef ] [ PubMed ]
DeYoung, Colin G., Lena C. Quilty, and Jordan B. Peterson. 2007. Between facets and domains: 10 aspects of the Big Five. Journal of Personality and Social Psychology 93: 880–96. [ Google Scholar ] [ CrossRef ]
Eid, Michael. 2000. A multitrait-multimethod model with minimal assumptions. Psychometrika 65: 241–61. [ Google Scholar ] [ CrossRef ]
Eid, Michael, and Ed Diener. 2004. Global Judgments of Subjective Well-Being: Situational Variability and Long-Term Stability. Social Indicators Research 65: 245–77. [ Google Scholar ] [ CrossRef ]
Eid, Michael, Fridtjof W. Nussbeck, Christian Geiser, David A. Cole, Mario Gollwitzer, and Tanja Lischetzke. 2008. Structural equation modeling of multitrait-multimethod data: Different models for different types of methods. Psychological Methods 13: 230–53. [ Google Scholar ] [ CrossRef ]
Eid, Michael, Tanja Lischetzke, Fridtjof W. Nussbeck, and Lisa I. Trierweiler. 2003. Separating trait effects from trait-specific method effects in multitrait-multimethod models: A multiple indicator CT-C( M -1) model. Psychological Methods 8: 38–60. [ Google Scholar ] [ CrossRef ]
Forthmann, Boris, and Philipp Doebler. 2022. Fifty years later and still working: Rediscovering Paulus et al.’s (1970) automated ‘scoring of divergent thinking tests. Psychology of Aesthetics, Creativity, and the Arts . Advance online publication . [ Google Scholar ] [ CrossRef ]
Forthmann, Boris, David Jendryczko, Jana Scharfen, Ruben Kleinkorres, Mathias Benedek, and Heinz Holling. 2019. Creative ideation, broad retrieval ability, and processing speed: A confirmatory study of nested cognitive abilities. Intelligence 75: 59–72. [ Google Scholar ] [ CrossRef ]
Forthmann, Boris, Heinz Holling, Pınar Çelik, Martin Storme, and Todd Lubart. 2017. Typing Speed as a Confounding Variable and the Measurement of Quality in Divergent Thinking. Creativity Research Journal 29: 257–69. [ Google Scholar ] [ CrossRef ]
Forthmann, Boris, Sue H. Paek, Denis Dumas, Baptiste Barbot, and Heinz Holling. 2020. Scrutinizing the basis of originality in divergent thinking tests: On the measurement precision of response propensity estimates. British Journal of Educational Psychology 90: 683–99. [ Google Scholar ] [ CrossRef ] [ PubMed ]
French, John W., Ruth B. Ekstrom, and Leighton A. Price. 1963. Manual for Kit of Reference Tests for Cognitive Factors . Princeton: Educational Testing Service. [ Google Scholar ]
Geiser, Christian, and Ginger Lockhart. 2012. A comparison of four approaches to account for method effects in latent state–trait analyses. Psychological Methods 17: 255–83. [ Google Scholar ] [ CrossRef ] [ PubMed ]
Geiser, Christian, and Trenton G. Simmons. 2021. Do method effects generalize across traits (and what if they don’t)? Journal of Personality 89: 382–401. [ Google Scholar ] [ CrossRef ]
Geiser, Christian, Michael Eid, and Fridtjof W. Nussbeck. 2008. On the Meaning of the Variance and Covariance Components in Multitrait-Multimethod Models: What Happens if Items Do Not Measure the Intended Constructs? Psychological Methods 13: 49–67. [ Google Scholar ] [ CrossRef ]
Gelman, Andrew, and Donald B. Rubin. 1992. Inference from iterative simulation using multiple sequences. Statistical Science 7: 457–72. [ Google Scholar ] [ CrossRef ]
Gelman, Andrew, Xiao-Li Meng, and Hal Stern. 1996. Posterior predictive assessment of model fitness via realized discrepancies. Statistica Sinica 6: 733–60. [ Google Scholar ]
Guilford, Joy Paul. 1966. Measurement and creativity. Theory into Practice 5: 185–89. [ Google Scholar ] [ CrossRef ]
Guilford, Joy Paul. 1967. The Nature of Human Intelligence . New York: McGraw-Hill. [ Google Scholar ]
Hu, Li-tze, and Peter M. Bentler. 1999. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal 6: 1–55. [ Google Scholar ] [ CrossRef ]
Jäger, Adolf O., Heinz Holling, Franzis Preckel, Ralf Schulze, Miriam Vock, Heinz-Martin Süß, and André Beauducel. 2006. BIS-HB: Berliner Intelligenzstrukturtest für Jugendliche: Begabungs- und Hochbegabungsdiagnostik—Manual . Göttingen: Hogrefe. [ Google Scholar ]
Jendryczko, David, and Fridtjof W. Nussbeck. 2022. Estimating and investigating multiple constructs multiple indicators social relations models with and without roles within the traditional structural equation modeling framework: A tutorial. Psychological Methods . Advance online publication . [ Google Scholar ] [ CrossRef ] [ PubMed ]
Jendryczko, David, and Fridtjof W. Nussbeck. 2024a. A correlated traits correlated (methods–1) multitrait-multimethod model for augmented round-robin data. British Journal of Mathematical and Statistical Psychology 77: 1–30. [ Google Scholar ] [ CrossRef ] [ PubMed ]
Jendryczko, David, and Fridtjof W. Nussbeck. 2024b. Latent Reciprocal Engagement and Accuracy Variables in Social Relations Structural Equation Modeling. Multivariate Behavioral Research . Advance online publication . [ Google Scholar ] [ CrossRef ]
Jeon, Minjeong, and Frank Rijmen. 2014. Recent developments in maximum likelihood estimation of MTMM models for categorical data. Frontiers in Psychology 5: 269. [ Google Scholar ] [ CrossRef ]
Johnson, Donald M., George L. Parrott, and R. Paul Stratton. 1968. Production and judgment of solutions to five problems. Journal of Educational Psychology 59: 1–21. [ Google Scholar ] [ CrossRef ]
Kim, Kyung Hee. 2008. Meta-analyses of the relationship of creative achievement to both IQ and divergent thinking test scores. Journal of Creative Behavior 42: 106–30. [ Google Scholar ] [ CrossRef ]
Koch, Tobias, Jana Holtmann, Johannes Bohn, and Michael Eid. 2018. Explaining general and specific factors in longitudinal, multi-method, and bifactor models: Some caveats and recommendations. Psychological Methods 23: 505–23. [ Google Scholar ] [ CrossRef ]
Koch, Tobias, Martin Schultze, Minjeong Jeon, Fridtjof W. Nussbeck, Anna-Katharina Praetorius, and Michael Eid. 2016. A Cross-Classified CFA-MTMM Model for Structurally Different and Nonindependent Interchangeable Methods. Multivariate Behavioral Research 51: 67–85. [ Google Scholar ] [ CrossRef ]
Long, J. Scott, and Laurie H. Ervin. 2000. Using heteroscedasticity consistent standard errors in the linear regression model. The American Statistician 54: 217–24. [ Google Scholar ] [ CrossRef ]
Lubart, Todd, Chantal Pacteau, Anne-Yvonne Jacquet, and Xavier Caroff. 2010. Children’s creative potential: An empirical study of measurement issues. Learning and Individual Differences 20: 388–92. [ Google Scholar ] [ CrossRef ]
Mehta, Paras D., and Michael C. Neale. 2005. People are variables too: Multilevel structural equations modeling. Psychological Methods 10: 259–84. [ Google Scholar ] [ CrossRef ] [ PubMed ]
Mullins, Cecil J. 1963. Prediction of creativity in a sample of research scientists. IEEE Transactions on Engineering Management 10: 52–57. [ Google Scholar ] [ CrossRef ]
Muthén, Linda K., and Bengt O. Muthén. 1998–2017. Mplus User’s Guide , 8th ed. Los Angeles: Muthén & Muthén. Available online: https://www.statmodel.com/download/usersguide/MplusUserGuideVer_8.pdf (accessed on 20 July 2024).
Nusbaum, Emily C., Paul J. Silvia, and Roger E. Beaty. 2014. Ready, set, create: What instructing people to “be creative” reveals about the meaning and mechanisms of divergent thinking. Psychology of Aesthetics, Creativity, and the Arts 8: 423–32. [ Google Scholar ] [ CrossRef ]
Nussbeck, Fridtjof W., Michael Eid, and Tanja Lischetzke. 2006. Analysing multitrait–multimethod data with structural equation models for ordinal variables applying the WLSMV estimator: What sample size is needed for valid results? British Journal of Mathematical and Statistical Psychology 59: 195–213. [ Google Scholar ] [ CrossRef ]
Nussbeck, Fridtjof W., Michael Eid, Christian Geiser, Delphine S. Courvoisier, and Tanja Lischetzke. 2009. A CTC( M −1) model for different types of raters. Methodology 5: 88–98. [ Google Scholar ] [ CrossRef ]
Pihko, Elina, Anne Virtanen, Veli-Matti Saarinen, Sebastian Pannasch, Lotta Hirvenkari, Timo Tossavainen, Arto Haapala, and Riitta Hari. 2011. Experiencing art: The influence of expertise and painting abstraction level. Frontiers in Human Neuroscience 5: 94. [ Google Scholar ] [ CrossRef ]
Preckel, Franzis, Christina Wermer, and Frank M. Spinath. 2011. The interrelationship between speeded and unspeeded divergent thinking and reasoning, and the role of mental speed. Intelligence 39: 378–88. [ Google Scholar ] [ CrossRef ]
R Core Team. 2013. R: A Language and Environment for Statistical Computing . Vienna: R Foundation for Statistical Computing. Available online: http://www.R-project.org/ (accessed on 20 July 2024).
Runco, Mark A., and Garrett J. Jaeger. 2012. The standard definition of creativity. Creativity Research Journal 24: 92–96. [ Google Scholar ] [ CrossRef ]
Runco, Mark A., and Selcuk Acar. 2010. Do tests of divergent thinking have an experiential bias? Psychology of Aesthetics, Creativity, and the Arts 4: 144–48. [ Google Scholar ] [ CrossRef ]
Runco, Mark A., and Selcuk Acar. 2012. Divergent thinking as an indicator of creative potential. Creativity Research Journal 24: 66–75. [ Google Scholar ] [ CrossRef ]
Schmitt, Manfred J., and Rolf Steyer. 1993. A latent state-trait model (not only) for social desirability. Personality and Individual Differences 14: 519–29. [ Google Scholar ] [ CrossRef ]
Silvia, Paul J., Christopher Martin, and Emily C. Nusbaum. 2009. A snapshot of creativity: Evaluating a quick and simple method for assessing divergent thinking. Thinking Skills and Creativity 4: 79–85. [ Google Scholar ] [ CrossRef ]
Steyer, Rolf. 1989. Models of classical psychometric test theory as stochastic measurement models: Representation, uniqueness, meaningfulness, identifiability, and testability. Methodika 3: 25–60. [ Google Scholar ]
Stratton, R. Paul, and Robert Brown. 1972. Improving creative thinking by training in the production and/or judgment of solutions. Journal of Educational Psychology 63: 390–97. [ Google Scholar ] [ CrossRef ] [ PubMed ]
Vogt, Stine, and Svein Magnussen. 2007. Expertise in pictorial perception: Eye-movement patterns and visual memory in artists and laymen. Perception 36: 91–100. [ Google Scholar ] [ CrossRef ]
Wallach, Michael A., and Cliff W. Wing, Jr. 1969. The Talented Student: A Validation of the Creativity-Intelligence Distinction . New York: Holt, Rinehart & Winston. [ Google Scholar ]
Wallach, Michael A., and Nathan Kogan. 1965. Modes of Thinking in Young Children: A Study of the Creativity-Intelligence Distinction . New York: Holt, Rinehart & Winston. [ Google Scholar ]
West, Stephen G., Aaron B. Taylor, and Wei Wu. 2012. Model fit and model selection in structural equation modeling. In Handbook of Structural Equation Modeling . Edited by Rick H. Hoyle. New York: Guilford Press, pp. 209–31. [ Google Scholar ]
Wilson, Robert C., Joy P. Guilford, and Paul R. Christensen. 1953. The measurement of individual differences in originality. Psychological Bulletin 50: 362–70. [ Google Scholar ] [ CrossRef ]
Zimmerman, Donald W. 1975. Probability spaces, Hilbert spaces, and the axioms of test theory. Psychometrika 40: 395–412. [ Google Scholar ] [ CrossRef ]

Click here to enlarge figure

Target	Rater	Y	Y	Y	Y	Y	Y
1	1	3	3	2	NA	NA	NA
1	2	NA	NA	NA	3.00	2.25	3.00
1	3	3	2	2	NA	NA	NA
1	4	4	3	3	NA	NA	NA
1	5	4	3	3	NA	NA	NA
1	6	NA	NA	NA	4.00	5.00	3.00
1	7	NA	NA	NA	2.00	3.00	3.00
2	1	2	4	3	NA	NA	NA
2	2	NA	NA	NA	3.00	3.50	1.75
2	3	2	4	2	NA	NA	NA
2	4	3	4	2	NA	NA	NA
2	5	3	4	2	NA	NA	NA
2	6	NA	NA	NA	4.00	4.50	2.75
2	7	NA	NA	NA	2.00	3.50	2.00

			DTCC						DTTL-B						DTTL-ML
Parameter	Y	Y	Y	Y	Y	Y	Y	Y	Y	Y	Y	Y	Y	Y	Y	Y	Y	Y
	2.993	3.066	3.026	2.907	3.132	2.927	2.996	3.055	3.024	2.908	3.124	2.922	2.993	3.069	3.030	2.904	3.129	2.926
	1	0.489	0.498	1	0.540	0.459	1	0.510	0.514	1	0.531	0.461	1	0.521	0.508	1	0.598	0.497
	1	0.715	0.502	1	1.108	0.583	-	-	-	-	-	-	-	-	-	-	-	-
	1	0.997	0.908	1	0.932	1.004	1	0.917	0.762	1	1.114	0.596	1	0.775	0.638	1	1.163	0.613
	0.324	0.261	0.273	0.158	0.209	0.188	0.346	0.257	0.274	0.183	0.207	0.208	0.321	0.262	0.275	0.208	0.195	0.209
	0.588			0.223			0.564			0.195			0.579			0.156
		0.468	0.429		0.210	0.182		0.469	0.424		0.207	0.180		0.456	0.426		0.170	0.137
	0.055			0.328			-			-			-			-
	0.047			0.004			0.084			0.253			0.109			0.240
			0.316 (.876)						0.310 (.941)						0.298 (.994)
			0.187 (.420)						0.182 (.411)						0.181 (.410)
			0.285 (.912)						0.282 (.908)						0.162 (.942)
			0.075 (.260)						0.079 (.274)						0.058 (.232)
			0.073 (.245)						0.066 (.224)						0.054 (.200) *
			0.251 (.901)						0.251 (.913)						0.231 (.958)
			0.051 (.263)						0.049 (.259)						0.007 (.045) *
L2Con		.231	.254		.237	.205		.238	.259		.210	.188		.256	.259		.247	.219
L2OMS		.769	.746		.763	.795		.762	.741		.790	.812		.744	.741		.753	.781
L1Con		.201	.228		.091	.126		.213	.238		.095	.133		.232	.241		.101	.145
L1OMS		.672	.674		.306	.520		.681	.680		.357	.575		.672	.688		.310	.515
MIICC	.846	.885	.911	.401	.405	.663	.870	.897	.921	.435	.457	.714	.842	.904	.928	.394	.411	.660
RMS	.080	.041	.022	.590	.589	.321	-	-	-	-	-	-	-	-	-	-	-	-
IMS	.065	.065	.060	.006	.004	.009	-	-	-	-	-	-	-	-	-	-	-	-
UMS	.154	.115	.089	.599	.595	.337	.130	.103	.079	.565	.543	.286	.158	.096	.072	.606	.589	.340
REL	.688	.730	.701	.780	.767	.655	.654	.730	.697	.710	.738	.602	.680	.721	.693	.655	.738	.559

The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

Jendryczko, D. Decomposing the True Score Variance in Rated Responses to Divergent Thinking-Tasks for Assessing Creativity: A Multitrait–Multimethod Analysis. J. Intell. 2024 , 12 , 95. https://doi.org/10.3390/jintelligence12100095

Jendryczko D. Decomposing the True Score Variance in Rated Responses to Divergent Thinking-Tasks for Assessing Creativity: A Multitrait–Multimethod Analysis. Journal of Intelligence . 2024; 12(10):95. https://doi.org/10.3390/jintelligence12100095

Jendryczko, David. 2024. "Decomposing the True Score Variance in Rated Responses to Divergent Thinking-Tasks for Assessing Creativity: A Multitrait–Multimethod Analysis" Journal of Intelligence 12, no. 10: 95. https://doi.org/10.3390/jintelligence12100095

Article Metrics

Article access statistics, supplementary material.

ZIP-Document (ZIP, 6605 KiB)

Further Information

Mdpi initiatives, follow mdpi.

Subscribe to receive issue release notifications and newsletters from MDPI journals

IMAGES

What Is Divergent Thinking? [Definition and Examples]
What Is Divergent Thinking? [Definition and Examples]
What is divergent thinking innovation, and how does it work?
Use divergent thinking to generate fresh ideas in your next brainstorm
02_four-pillars-of-divergent-thinking
Divergent Thinking

VIDEO

Types of Thinking // Convergent , Divergent , Critical , Reflective & Lateral Thinking // T & L 💯
Divergent thinking: Exploring the world of neurodiversity (part 1)
Divergent Thinking: Unleashing Your Creative Marketing Potential
Divergent Thinking (Explained for Beginners in 2 Minutes)
The Cursed Castle: Find Your Way Out!
Are We Living in a Simulation? The Mind-Bending Hypothesis

COMMENTS

The Relationship between Intelligence and Divergent Thinking—A Meta-Analytic Update
This paper provides a meta-analytic update on the relationship between intelligence and divergent thinking (DT), as research on this topic has increased, and methods have diversified since Kim's meta-analysis in 2005. A three-level meta-analysis was used to analyze 849 correlation coefficients from 112 studies with an overall N = 34,610.
Divergent thinking
Divergent thinking is a thought process used to generate creative ideas by exploring many possible solutions. It typically occurs in a spontaneous, free-flowing, "non-linear" manner, such that many ideas are generated in an emergent cognitive fashion. ... [10] this hypothesis was examined more closely and "found positive mood participants were ...
Relationship between Divergent Thinking and Intelligence: An Empirical
However, few empirical examinations of this theory exist, and the results are inconsistent. To test this hypothesis, this study investigated the relationship between divergent thinking (DT) and intelligence with a sample of 568 Chinese children aged between 11 and 13 years old using testing and questionnaire methods.
The Relationship between Intelligence and Divergent Thinking—A ...
This paper provides a meta-analytic update on the relationship between intelligence and divergent thinking (DT), as research on this topic has increased, and methods have diversified since Kim's meta-analysis in 2005. A three-level meta-analysis was used to analyze 849 correlation coefficients from 112 studies with an overall N = 34,610. The overall effect showed a significant positive ...
A New Perspective on the Multidimensionality of Divergent Thinking Tasks
Introduction. Divergent thinking (DT) tasks are one of the most important proxies of creative thinking (Runco and Acar, 2012).For example, they are frequently used in research on the link of intelligence and creativity (e.g., Karwowski et al., 2016) and have been shown to predict creative achievement above intelligence ().These tasks typically ask participants to come up with either many or ...
Divergent Thinking
Two things should be emphasized: one is that divergence indicates that thinking is moving in different directions. It may also benefit from leaps, where the gap between ideas is large. Barron (1995) referred to this kind of thing as the interstices of thought, and for some people, those gaps might be sizeable.
Divergent Thinking
Divergent thinking refers to the cognitive process of generating a variety of creative possibilities and novel associations, often assessed by tasks like thinking of multiple unusual uses for a common object. It is strongly associated with Openness/Intellect in personality traits and is a key factor in creative idea generation. AI generated ...
Divergent Thinking
Divergent thinking is the ability to take different directions from the prevailing modes of thought or expression. As the name implies, this type of thinking breaks away from established concepts and produces novel ideas, which can serve as the basis for the development of a creative product. Divergent thinking often produces multiple new ideas.
Divergent thinking and constructing future events: Dissociating old
Divergent thinking refers to the ability to generate creative ideas by combining diverse kinds of information in novel ways (Guilford, 1967).In the laboratory, the Alternate Uses Task (AUT) has been frequently used to measure divergent thinking ability (Guilford, 1967).In this task, participants are presented an object cue word, such as 'newspaper', and asked to generate unusual and ...
Reasoning outside the box: Divergent thinking is related to logical
This study explored the hypothesis that divergent thinking, a key component of creativity, is a unique predictive factor of logical reasoning. A total of 96 adults completed a divergent thinking task and logical reasoning problems with varying forms and contents. Cognitive capacity was measured as a confounding factor.
Relationship between Divergent Thinking and Intelligence: An Empirical
The threshold hypothesis is a classical and notable explanation for the relationship between creativity and intelligence. However, few empirical examinations of this theory exist, and the results are inconsistent. ... To test this hypothesis, this study investigated the relationship between divergent thinking (DT) and intelligence with a sample ...
Linguistic Relativity in Creative Thought: How Divergent Thinking in
Divergent thinking - the ability to generate many varied, original, and elaborate responses - increases the chance that an original yet appropriate idea can be developed. ... In a weaker sense, the hypothesis suggests that our conceptualizations and perception of reality may be systematically influenced by the language we speak (for an ...
The relationship between intelligence and divergent thinking—A meta
This paper provides a meta-analytic update on the relationship between intelligence and divergent thinking (DT), as research on this topic has increased, and methods have diversified since Kim's meta-analysis in 2005. A three-level meta-analysis was used to analyze 849 correlation coefficients from 112 studies with an overall N = 34,610. The overall effect showed a significant positive ...
Why You Need to Practice Divergent Thinking
Now is the time for divergent thinking. As I wrote in The Non-Obvious Guide to Being More Creative, No Matter Where You Work, "divergent thinking helps us generate many new ideas, imagine, be ...
Divergent thinking modulates interactions between episodic memory and
The ability to generate novel ideas, known as divergent thinking, depends on both semantic knowledge and episodic memory. Semantic knowledge and episodic memory are known to interact to support memory decisions, but how they may interact to support divergent thinking is unknown. Moreover, it is debated whether divergent thinking relies on spontaneous or controlled retrieval processes. We ...
Divergent Thinking: What It Is, How It Works
Convergent thinking is organized and linear, following certain steps to reach a single solution to a problem. Divergent thinking is more free-flowing and spontaneous, and it produces lots of ideas. Guilford considered divergent thinking more creative because of its ability to yield many solutions to problems. "Divergent thinking is the ...
Divergent Thinking: 5 Divergent Thinking Strategies
1. Tapping creative potential: Applying divergent thinking to business problems can create valuable and lasting insight. 2. Encourages flexibility: Creative thinkers tend to be more flexible. This can in turn make them better at adapting to change, collaborating, and taking on new risks and increased responsibilities. 3.
Relational reasoning and divergent thinking: An examination of the
One critical caveat to note here about the existing literature on the threshold hypothesis, is that nearly all extant studies of the TH explicitly examine the relation between creative abilities or achievement (e.g., divergent thinking) and a generalized cognitive attribute termed intelligence (e.g., Preckel et al., 2006).
The Importance of Divergent Thinking for Research in Graduate School
A shift from convergent to divergent thinking — the generation of thoughts and perspectives from multiple viewpoints (Guilford, 1967) — accompanies this transition from unidirectional to bidirectional flows of information. Convergent and divergent thinking are not mutually exclusive concepts; both play an important role in our research.
The Controversial Effect of Age on Divergent Thinking Abilities: A
Divergent thinking (DT) is considered as an indicator of creative potential and a predictor of creative achievement. Furthermore, it is also conceptualized as an indicator of cognitive reserve (CR) in healthy elderly. CR refers to a functional benefit that can potentially offer protection against brain pathologies and is thereby considered a ...
Is the More the Better? The Role of Divergent Thinking in Creative
It is often thought that divergent thinking is the base for solving problem creatively, for the more ideas an individual generates, the more likely he will hit the answer. This idea however has never been tested empirically, partly due to a lack of measurable index regarding the creativity of hypothesis generation. The current research therefore aimed at 1) defining an index featuring ...
Divergent Thinking and Constructing Episodic Simulations
Divergent thinking - the ability to generate ideas by comparing and combining disparate forms of information in new ways ... While we found support for the hypothesis that divergent thinking is a significant predictor of the imagined internal detail score over and above memory for episodic details, this finding applied most strongly to ...
Relationship between divergent thinking and intelligence: An empirical
The threshold hypothesis is a classical and notable explanation for the relationship between creativity and intelligence. However, few empirical examinations of this theory exist, and the results are inconsistent. To test this hypothesis, this study investigated the relationship between divergent thinking (DT) and intelligence with a sample of 568 Chinese children aged between 11 and 13 years ...
Decomposing the True Score Variance in Rated Responses to Divergent
It is shown how the Correlated Traits Correlated Methods Minus One (CTC(M − 1)) Multitrait-Multimethod model for cross-classified data can be modified and applied to divergent thinking (DT)-task responses scored for miscellaneous aspects of creative quality by several raters. In contrast to previous Confirmatory Factor Analysis approaches to analyzing DT-tasks, this model explicitly takes ...
Using design thinking hands-on learning to improve artificial
Another study suggested that thinking efficiency can be enhanced by teaching reasoning and content knowledge (Ellerton, 2022), although this is different for divergent thinking. Still, our findings generally align with these previous studies and explain why the most significant positive effects of DT on the creative process were observed in the ...

ORIGINAL RESEARCH article

Introduction

An IRTree Modeling Approach for Divergent-Thinking Creative Quality Ratings

Aim of the Current Study

Materials and Methods

Data Preparation and Statistical Analysis

Dimensionality Underlying the Nodes

Discriminating Creative Quality for Ideational Pools of High and Low Fluency

How to Detect and Deal With Technical Problems

Overall Conclusion

Ethics Statement

Author Contributions

Conflict of Interest Statement

Save citation to file

Add to My Bibliography

Relationship between Divergent Thinking and Intelligence: An Empirical Study of the Threshold Hypothesis with Chinese Children

Similar articles

Related information

Other Literature Sources

Why You Need to Practice Divergent Thinking

Converge: Analyze Ideas and Form Solutions

Don’t Let Convergence Take Over Your World

Balance Your Thinking to Be More Creative

3 Ways to Get Better at Divergent Thinking

Divergent thinking modulates interactions between episodic memory and schema knowledge: Controlled and spontaneous episodic retrieval processes

Cite this article

Access this article

Similar content being viewed by others

Constructive episodic retrieval processes underlying memory distortion contribute to creative thinking and everyday problem solving

Conceptual knowledge modulates memory recognition of common items: The selective role of item-typicality

Author information

Corresponding author

Ethics declarations

Ethics approval and consent

Additional information

Additional information about stimuli

Search time

Model equations

Recollection effects

Familiarity effects

Unconscious effects

Rights and permissions

About this article

Share this article

Divergent Thinking: What It Is, How It Works

What Is Divergent Thinking?

Characteristics of Divergent Thinking

Open Mind

Collaborative

Rethink Language

Detail Oriented

Strategy

Build Divergent Thinking Skills

Ask Questions

Let Thoughts Flow Freely

Listen Actively

Recent Career Development Articles

Divergent Thinking: 5 Divergent Thinking Strategies

The Importance of Divergent Thinking for Research in Graduate School and Beyond

Approaches to Improved Divergent Thinking

About the Author

NSF Invites Grant Proposals for Studying Personalized Learning in the STEM Workforce

Professors’ Influence on Display in APS Membership Initiative

European Psychology Learning and Teaching Conference

Privacy Overview

Divergent Thinking and Constructing Episodic Simulations

Regina Musicaro

Introduction

Materials and Methods

Episodic Simulation

Divergent Thinking Measures

Relation between Divergent Thinking and Episodic Simulation

Acknowledgements

Information

Initiatives

Article Menu

JSmol Viewer

1. Introduction

2. Defining an Appropriate Cross-Classified CTC(M − 1) Model for DT-Ratings

5. General Discussion