research study on memory

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Cognitive neuroscience perspective on memory: overview and summary

Sruthi sridhar.

1 Department of Psychology, Mount Allison University, Sackville, NB, Canada

Abdulrahman Khamaj

2 Department of Industrial Engineering, College of Engineering, Jazan University, Jazan, Saudi Arabia

Manish Kumar Asthana

3 Department of Humanities and Social Sciences, Indian Institute of Technology Roorkee, Roorkee, India

4 Department of Design, Indian Institute of Technology Roorkee, Roorkee, India

Associated Data

The original contributions presented in this study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

This paper explores memory from a cognitive neuroscience perspective and examines associated neural mechanisms. It examines the different types of memory: working, declarative, and non-declarative, and the brain regions involved in each type. The paper highlights the role of different brain regions, such as the prefrontal cortex in working memory and the hippocampus in declarative memory. The paper also examines the mechanisms that underlie the formation and consolidation of memory, including the importance of sleep in the consolidation of memory and the role of the hippocampus in linking new memories to existing cognitive schemata. The paper highlights two types of memory consolidation processes: cellular consolidation and system consolidation. Cellular consolidation is the process of stabilizing information by strengthening synaptic connections. System consolidation models suggest that memories are initially stored in the hippocampus and are gradually consolidated into the neocortex over time. The consolidation process involves a hippocampal-neocortical binding process incorporating newly acquired information into existing cognitive schemata. The paper highlights the role of the medial temporal lobe and its involvement in autobiographical memory. Further, the paper discusses the relationship between episodic and semantic memory and the role of the hippocampus. Finally, the paper underscores the need for further research into the neurobiological mechanisms underlying non-declarative memory, particularly conditioning. Overall, the paper provides a comprehensive overview from a cognitive neuroscience perspective of the different processes involved in memory consolidation of different types of memory.

Introduction

Memory is an essential cognitive function that permits individuals to acquire, retain, and recover data that defines a person’s identity ( Zlotnik and Vansintjan, 2019 ). Memory is a multifaceted cognitive process that involves different stages: encoding, consolidation, recovery, and reconsolidation. Encoding involves acquiring and processing information that is transformed into a neuronal representation suitable for storage ( Liu et al., 2021 ; Panzeri et al., 2023 ). The information can be acquired through various channels, such as visual, auditory, olfactory, or tactile inputs. The acquired sensory stimuli are converted into a format the brain can process and retain. Different factors such as attention, emotional significance, and repetition can influence the encoding process and determine the strength and durability of the resulting memory ( Squire et al., 2004 ; Lee et al., 2016 ; Serences, 2016 ).

Consolidation includes the stabilization and integration of memory into long-term storage to increase resistance to interference and decay ( Goedert and Willingham, 2002 ). This process creates enduring structural modification in the brain and thereby has consequential effects on the function by reorganizing and strengthening neural connections. Diverse sources like sleep and stress and the release of neurotransmitters can influence memory consolidation. Many researchers have noted the importance of sleep due to its critical role in enabling a smooth transition of information from transient repositories into more stable engrams (memory traces) ( McGaugh, 2000 ; Clawson et al., 2021 ; Rakowska et al., 2022 ).

Retrieval involves accessing, selecting, and reactivating or reconstructing the stored memory to allow conscious access to previously encoded information ( Dudai, 2002 ). Retrieving memories depends on activating relevant neural pathways while reconstructing encoded information. Factors like contextual or retrieval cues and familiarity with the material can affect this process. Forgetting becomes a possibility if there are inadequate triggers for associated memory traces to activate upon recall. Luckily, mnemonic strategies and retrieval practice offer effective tools to enhance recovery rates and benefit overall memory performance ( Roediger and Butler, 2011 ).

Previous research implied that once a memory has been consolidated, it becomes permanent ( McGaugh, 2000 ; Robins, 2020 ). However, recent studies have found an additional phase called “reconsolidation,” during which stored memories, when reactivated, enter a fragile or liable state and become susceptible to modification or update ( Schiller et al., 2009 ; Asthana et al., 2015 ). The process highlights the notion that memory is not static but a dynamic system influenced by subsequent encounters. The concept of reconsolidation has much significance in memory modification therapies and interventions, as it offers a promising opportunity to target maladaptive or traumatic memories for modification specifically. However, more thorough investigations are needed to gain insight into the mechanisms and concrete implications of employing memory reconsolidation within therapeutic settings ( Bellfy and Kwapis, 2020 ).

The concept of memory is not reducible to a single unitary phenomenon; instead, evidence suggests that it can be subdivided into several distinct but interrelated constituent processes and systems ( Richter-Levin and Akirav, 2003 ). There are three major types of human memory: working memory, declarative memory (explicit), and non-declarative memory (implicit). All these types of memories involve different neural systems in the brain. Working memory is a unique transient active store capable of manipulating information essential for many complex cognitive operations, including language processing, reasoning, and judgment ( Atkinson and Shiffrin, 1968 ; Baddeley and Logie, 1999 ; Funahashi, 2017 ; Quentin et al., 2019 ). Previous models suggest the existence of three components that make up the working memory ( Baddeley and Hitch, 1974 ; Baddeley, 1986 ). One master component, the central executive, controls the two dependent components, the phonological loop (speech perception and language comprehension) and the visuospatial sketchpad (visual images and spatial impressions processing). Some models mention a third component known as the episodic buffer. It is theorized that the episodic buffer serves as an intermediary between perception, long-term memory, and two components of working memory (the phonological loop and visuospatial sketchpad) by storing integrated episodes or chunks from both sources ( Baddeley, 2000 ). Declarative memory (explicit memory) can be recalled consciously, including facts and events that took place in one’s life or information learned from books. It encompasses memories of both autobiographical experiences and memories associated with general knowledge. It is usually associated with the hippocampus–medial temporal lobe system ( Thompson and Kim, 1996 ; Ober, 2014 ). Non-declarative memory (implicit memory) refers to unconscious forms of learning such as skills, habits, and priming effects; this type of implicit learning does not involve conscious recollection but can include motor skill tasks that often require no thought prior to execution nor later recall upon completion. This type of memory usually involves the amygdala and other systems ( Thompson and Kim, 1996 ; Ober, 2014 ).

Working memory

Working memory is primarily associated with the prefrontal and posterior parietal cortex ( Sarnthein et al., 1998 ; Todd and Marois, 2005 ). Working memory is not localized to a single brain region, and research suggests that it is an emergent property arising from functional interactions between the prefrontal cortex (PFC) and the rest of the brain ( D’Esposito, 2007 ). Neuroimaging studies have explored the neural basis for the three components proposed by Baddeley and Hitch (1974) , the Central executive, the phonological loop, and the visuospatial sketch pad; there is evidence for the existence of a fourth component called the episodic buffer ( Baddeley, 2000 ).

The central executive plays a significant role in working memory by acting as the control center ( Shallice, 2002 ). It facilitates critical functions like attention allocation and coordination between the phonological loop and the visuospatial sketchpad ( Yu et al., 2023 ). Recent findings have illuminated the dual-functional network regulation, the cingulo-opercular network (CON) and the frontoparietal network (FPN), that underpins the central executive system ( Yu et al., 2023 ). The CON comprises the dorsal anterior cingulate cortex (dACC) and anterior insula (AI). In contrast, the FPN encompasses various regions, such as the dorsolateral prefrontal cortex (DLPFC) and frontal eye field (FEF), along with the intraparietal sulcus (IPS) ( Yu et al., 2023 ). Neuroimaging research has found evidence that elucidates the neural underpinnings of the executive attention control system to the dorsolateral prefrontal cortex (DLPFC) and the anterior cingulate cortex (ACC) ( Jung et al., 2022 ). The activation patterns indicate that the CON may have a broader top-down control function across the working memory process. At the same time, the FPN could be more heavily implicated in momentary control or processing at the trial level ( Yu et al., 2023 ). Evidence suggests that the central executive interacts with the phonological loop and visuospatial sketchpad to support working memory processes ( Baddeley, 2003 ; Buchsbaum, 2010 ; Menon and D’Esposito, 2021 ). The function, localization, and neural basis of this interaction are thought to involve the activation of specific brain regions associated with each component of working memory, as discussed in detail below.

The phonological loop is divided into two components: a storage system that maintains information (a few seconds) and a component involving subvocal rehearsal—which maintains and refreshes information in the working memory. Neuroanatomically, the phonological loop is represented in the Brodmann area (BA) 40 in the parietal cortex and the rehearsal components in BA 44 and 6, both situated in the frontal cortex ( Osaka et al., 2007 ). The left inferior frontal gyrus (Broca’s area) and the left posterior superior temporal gyrus (Wernicke’s area) has been proposed to play a critical role in supporting phonological and verbal working memory tasks, specifically the subvocal rehearsal system of the articulatory loop ( Paulesu et al., 1993 ; Buchsbaum et al., 2001 ; Perrachione et al., 2017 ). The phonological store in verbal short-term memory has been localized at the left supramarginal gyrus ( Graves et al., 2008 ; Perrachione et al., 2017 ).

Studies utilizing neuroimaging techniques have consistently yielded results indicating notable activation in these brain regions during phonological activities like recalling non-words and maintaining verbal information in memory ( Awh et al., 1996 ; Graves et al., 2008 ). During tasks that require phonological rehearsal, there was an increase in activation in the left inferior frontal gyrus ( Paulesu et al., 1993 ). Researchers have noted an increase in activity within the superior temporal gyrus-which plays a significant role in auditory processing-in individuals performing tasks that necessitate verbal information maintenance and manipulation ( Smith et al., 1998 ; Chein et al., 2003 ).

Additionally, lesion studies have provided further confirmation regarding the importance of these regions. These investigations have revealed that impairment in performing phonological working memory tasks can transpire following damage inflicted upon the left hemisphere, particularly on perisylvian language areas ( Koenigs et al., 2011 ). It is common for individuals with lesions affecting regions associated with the phonological loop, such as the left inferior frontal gyrus and superior temporal gyrus, to have difficulty performing verbal working memory tasks. Clinical cases involving patients diagnosed with aphasia and specific language impairments have highlighted challenges related to retaining and manipulating auditory information. For example, those who sustain damage specifically within their left inferior frontal gyrus often struggle with tasks involving phonological rehearsal and verbal working memory activities, and therefore, they tend to perform poorly in tasks that require manipulation or repetition of verbal stimuli ( Saffran, 1997 ; Caplan and Waters, 2005 ).

The visuospatial sketchpad is engaged in the temporary retention and manipulation of visuospatial facts, including mental pictures, spatial associations, and object placements ( Miyake et al., 2001 ). The visuospatial sketchpad is localized to the right hemisphere, including the occipital lobe, parietal and frontal areas ( Osaka et al., 2007 ). Ren et al. (2019) identified the localization of the visuospatial sketchpad, and these areas were the right infero-lateral prefrontal cortex, lateral pre-motor cortices, right inferior parietal cortex, and the dorsolateral occipital cortices ( Burbaud et al., 1999 ; Salvato et al., 2021 ). Moreover, the posterior parietal cortex and the intraparietal sulcus have been implicated in spatial working memory ( Xu and Chun, 2006 ). Additionally, some evidence is available for an increase in brain regions associated with the visuospatial sketchpad during tasks involving mental imagery and spatial processing. Neuroimaging studies have revealed increased neural activation in some regions of the parietal cortex, mainly the superior and posterior parietal cortex, while performing mental rotation tasks ( Cohen et al., 1996 ; Kosslyn et al., 1997 ). However, further research is needed to better understand the visuospatial working memory and its integration with other cognitive processes ( Baddeley, 2003 ). Lesions to the regions involving the visuospatial sketchpad can have detrimental effects on visuospatial working memory tasks. Individuals with lesions to the posterior parietal cortex may exhibit deficits in mental rotation tasks and may be unable to mentally manipulate the visuospatial representation ( Buiatti et al., 2011 ). Moreover, studies concerning lesions have shown that damage to the parietal cortex can result in short-term deficits in visuospatial memory ( Shafritz et al., 2002 ). Damage to the occipital cortex can lead to performance impairments in tasks that require the generation and manipulation of mental visual images ( Moro et al., 2008 ).

The fourth component of the working memory, termed episodic buffer, was proposed by Baddeley (2000) . The episodic buffer is a multidimensional but essentially passive store that can hold a limited number of chunks, store bound features, and make them available to conscious awareness ( Baddeley et al., 2010 ; Hitch et al., 2019 ). Although research has suggested that episodic buffer is localized to the hippocampus ( Berlingeri et al., 2008 ) or the inferior lateral parietal cortex, it is thought to be not dependent on a single anatomical structure but instead can be influenced by the subsystems of working memory, long term memory, and even through perception ( Vilberg and Rugg, 2008 ; Baddeley et al., 2010 ). The episodic buffer provides a crucial link between the attentional central executive and the multidimensional information necessary for the operation of working memory ( Baddeley et al., 2011 ; Gelastopoulos et al., 2019 ).

The interdependence of the working memory modules, namely the phonological loop and visuospatial sketchpad, co-relates with other cognitive processes, for instance, spatial cognition and attention allocation ( Repovs and Baddeley, 2006 ). It has been found that the prefrontal cortex (PFC) and posterior parietal cortex (PPC) have a crucial role in several aspects of spatial cognition, such as the maintenance of spatially oriented attention and motor intentions ( Jerde and Curtis, 2013 ). The study by Sellers et al. (2016) and the review by Ikkai and Curtis (2011) posits that other brain areas could use the activity in PFC and PPC as a guide and manifest outputs to guide attention allocation, spatial memory, and motor planning. Moreover, research indicates that verbal information elicits an activation response in the left ventrolateral prefrontal cortex (VLPFC) when retained in the phonological loop, while visuospatial information is represented by a corresponding level of activity within the right homolog region ( Narayanan et al., 2005 ; Wolf et al., 2006 ; Emch et al., 2019 ). Specifically, the study by Yang et al. (2022) investigated the roles of two regions in the brain, the right inferior frontal gyrus (rIFG) and the right supra-marginal gyrus (rSMG), as they relate to spatial congruency in visual working memory tasks. A change detection task with online repetitive transcranial magnetic stimulation applied concurrently at both locations during high visual WM load conditions determined that rIFG is involved in actively repositioning the location of objects. At the same time, rSMG is engaged in passive perception of the stability of the location of objects.

Recent academic studies have found evidence to support the development of a new working memory model known as the state-based model ( D’Esposito and Postle, 2015 ). This theoretical model proposes that the allocation of attention toward internal representations permits short-term retention within working memory ( Ghaleh et al., 2019 ). The state-based model consists of two main categories: activated LTM models and sensorimotor recruitment models; the former largely focuses upon symbolic stimuli categorized under semantic aspects, while the latter has typically been applied to more perceptual tasks in experiments. This framework posits that prioritization through regulating cognitive processes provides insight into various characteristics across different activity types, including capacity limitations, proactive interference, etcetera ( D’Esposito and Postle, 2015 ). For example, the paper by Ghaleh et al. (2019) provides evidence for two separate mechanisms involved in maintenance of auditory information in verbal working memory: an articulatory rehearsal mechanism that relies more heavily on left sensorimotor areas and a non-articulatory maintenance mechanism that critically relies on left superior temporal gyrus (STG). These findings support the state-based model’s proposal that attentional allocation is necessary for short-term retention in working memory.

State-based models were found to be consistent with the suggested storage mechanism as they do not require representation transfer from one dedicated buffer type; research has demonstrated that any population of neurons and synapses may serve as such buffers ( Maass and Markram, 2002 ; Postle, 2006 ; Avraham et al., 2017 ). The review by D’Esposito and Postle (2015) examined the evidence to determine whether a persistent neural activity, synaptic mechanisms, or a combination thereof support representations maintained during working memory. Numerous neural mechanisms have been hypothesized to support the short-term retention of information in working memory and likely operate in parallel ( Sreenivasan et al., 2014 ; Kamiński and Rutishauser, 2019 ).

Persistent neural activity is the neural mechanism by which information is temporarily maintained ( Ikkai and Curtis, 2011 ; Panzeri et al., 2023 ). Recent review by Curtis and Sprague (2021) has focused on the notion that persistent neural activity is a fundamental mechanism for memory storage and have provided two main arcs of explanation. The first arc, mainly underpinned by empirical evidence from prefrontal cortex (PFC) neurophysiology experiments and computational models, posits that PFC neurons exhibit sustained firing during working memory tasks, enabling them to store representations in their active state ( Thuault et al., 2013 ). Intrinsic persistent firing in layer V neurons in the medial PFC has been shown to be regulated by HCN1 channels, which contribute to the executive function of the PFC during working memory episodes ( Thuault et al., 2013 ). Additionally, research has also found that persistent neural firing could possibly interact with theta periodic activity to sustain each other in the medial temporal, prefrontal, and parietal regions ( Düzel et al., 2010 ; Boran et al., 2019 ). The second arc involves advanced neuroimaging approaches which have, more recently, enabled researchers to decode content stored within working memories across distributed regions of the brain, including parts of the early visual cortex–thus extending this framework beyond just isolated cortical areas such as the PFC. There is evidence that suggests simple, stable, persistent activity among neurons in stimulus-selective populations may be a crucial mechanism for sustaining WM representations ( Mackey et al., 2016 ; Kamiński et al., 2017 ; Curtis and Sprague, 2021 ).

Badre (2008) discussed the functional organization of the PFC. The paper hypothesized that the rostro-caudal gradient of a function in PFC supported a control hierarchy, whereas posterior to anterior PFC mediated progressively abstract, higher-order controls ( Badre, 2008 ). However, this outlook proposed by Badre (2008) became outdated; the paper by Badre and Nee (2018) presented an updated look at the literature on hierarchical control. This paper supports neither a unitary model of lateral frontal function nor a unidimensional abstraction gradient. Instead, separate frontal networks interact via local and global hierarchical structures to support diverse task demands. This updated perspective is supported by recent studies on the hierarchical organization of representations within the lateral prefrontal cortex (LPFC) and the progressively rostral areas of the LPFC that process/represent increasingly abstract information, facilitating efficient and flexible cognition ( Thomas Yeo et al., 2011 ; Nee and D’Esposito, 2016 ). This structure allows the brain to access increasingly abstract action representations as required ( Nee and D’Esposito, 2016 ). It is supported by fMRI studies showing an anterior-to-posterior activation movement when tasks become more complex. Anatomical connectivity between areas also supports this theory, such as Area 10, which has projections back down to Area 6 but not vice versa.

Finally, studies confirm that different regions serve different roles along a hierarchy leading toward goal-directed behavior ( Badre and Nee, 2018 ). The paper by Postle (2015) exhibits evidence of activity in the prefrontal cortex that reflects the maintenance of high-level representations, which act as top-down signals, and steer the circulation of neural pathways across brain networks. The PFC is a source of top-down signals that influence processing in the posterior and subcortical regions ( Braver et al., 2008 ; Friedman and Robbins, 2022 ). These signals either enhance task-relevant information or suppress irrelevant stimuli, allowing for efficient yet effective search ( D’Esposito, 2007 ; D’Esposito and Postle, 2015 ; Kerzel and Burra, 2020 ). The study by Ratcliffe et al. (2022) provides evidence of the dynamic interplay between executive control mechanisms in the frontal cortex and stimulus representations held in posterior regions for working memory tasks. Moreover, the review by Herry and Johansen (2014) discusses the neural mechanisms behind actively maintaining task-relevant information in order for a person to carry out tasks and goals effectively. This review of data and research suggests that working memory is a multi-component system allowing for both the storage and processing of temporarily active representations. Neural activity throughout the brain can be differentially enhanced or suppressed based on context through top-down signals emanating from integrative areas such as PFC, parietal cortex, or hippocampus to actively maintain task-relevant information when it is not present in the environment ( Herry and Johansen, 2014 ; Kerzel and Burra, 2020 ).

In addition, Yu et al. (2022) examined how brain regions from the ventral stream pathway to the prefrontal cortex were activated during working memory (WM) gate opening and closing. They defined gate opening as the switch from maintenance to updating and gate closing as the switch from updating to maintenance. The data suggested that cognitive branching increases during the WM gating process, thus correlating the gating process and an information approach to the PFC function. The temporal cortices, lingual gyrus (BA19), superior frontal gyri including frontopolar cortices, and middle and inferior parietal regions are involved in processes of estimating whether a response option available will be helpful for each case. During gate closing, on the other hand, medial and superior frontal regions, which have been associated with conflict monitoring, come into play, as well as orbitofrontal and dorsolateral prefrontal processing at later times when decreasing activity resembling stopping or downregulating cognitive branching has occurred, confirming earlier theories about these areas being essential for estimation of usefulness already stored within long-term memories ( Yu et al., 2022 ).

Declarative and non-declarative memory

The distinctions between declarative and non-declarative memory are often based on the anatomical features of medial temporal lobe regions, specifically those involving the hippocampus ( Squire and Zola, 1996 ; Squire and Wixted, 2011 ). In the investigation of systems implicated in the process of learning and memory formation, it has been posited that the participation of the hippocampus is essential for the acquisition of declarative memories ( Eichenbaum and Cohen, 2014 ). In contrast, a comparatively reduced level of hippocampal involvement may suffice for non-declarative memories ( Squire and Zola, 1996 ; Williams, 2020 ).

Declarative memory (explicit) pertains to knowledge about facts and events. This type of information can be consciously retrieved with effort or spontaneously recollected without conscious intention ( Dew and Cabeza, 2011 ). There are two types of declarative memory: Episodic and Semantic. Episodic memory is associated with the recollection of personal experiences. It involves detailed information about events that happened in one’s life. Semantic memory refers to knowledge stored in the brain as facts, concepts, ideas, and objects; this includes language-related information like meanings of words and mathematical symbol values along with general world knowledge (e.g., capitals of countries) ( Binder and Desai, 2011 ). The difference between episodic and semantic memory is that when one retrieves episodic memory, the experience is known as “remembering”; when one retrieves information from semantic memory, the experience is known as “knowing” ( Tulving, 1985 ; Dew and Cabeza, 2011 ). The hippocampus, medial temporal lobe, and the areas in the diencephalon are implicated in declarative memory ( Richter-Levin and Akirav, 2003 ; Derner et al., 2020 ). The ventral parietal cortex (VPC) is involved in declarative memory processes, specifically episodic memory retrieval ( Henson et al., 1999 ; Davis et al., 2018 ). The evidence suggests that VPC and hippocampus is involved in the retrieval of contextual details, such as the location and timing of the event, and the information is critical for the formation of episodic memory ( Daselaar, 2009 ; Hutchinson et al., 2009 ; Wiltgen et al., 2010 ). The prefrontal cortex (PFC) is involved in the encoding (medial PFC) and retrieval (lateral PFC) of declarative memories, specifically in the integration of information across different sensory modalities ( Blumenfeld and Ranganath, 2007 ; Li et al., 2010 ). Research also suggests that the amygdala may modulate other brain regions involved with memory processing, thus, contributing to an enhanced recall of negative or positive experiences ( Hamann, 2001 ; Ritchey et al., 2008 ; Sendi et al., 2020 ). Maintenance of the integrity of hippocampal circuitry is essential for ensuring that episodic memory, along with spatial and temporal context information, can be retained in short-term or long-term working memory beyond 15 min ( Ito et al., 2003 ; Rasch and Born, 2013 ). Moreover, studies have suggested that the amygdala plays a vital role in encoding and retrieving explicit memories, particularly those related to emotionally charged stimuli which are supported by evidence of correlations between hippocampal activity and amygdala modulation during memory formation ( Richter-Levin and Akirav, 2003 ; Qasim et al., 2023 ).

Current findings in neuroimaging studies assert that a vast array of interconnected brain regions support semantic memory ( Binder and Desai, 2011 ). This network merges information sourced from multiple senses alongside different cognitive faculties necessary for generating abstract supramodal views on various topics stored within our consciousness. Modality-specific sensory, motor, and emotional system within these brain regions serve specialized tasks like language comprehension, while larger areas of the brain, such as the inferior parietal lobe and most of the temporal lobe, participate in more generalized interpretation tasks ( Binder and Desai, 2011 ; Kuhnke et al., 2020 ). These regions lie at convergences of multiple perceptual processing streams, enabling increasingly abstract, supramodal representations of perceptual experience that support a variety of conceptual functions, including object recognition, social cognition, language, and the remarkable human capacity to remember the past and imagine the future ( Binder and Desai, 2011 ; Binney et al., 2016 ). The following section will discuss the processes underlying memory consolidation and storage within declarative memory.

Non-declarative (implicit) memories refer to unconscious learning through experience, such as habits and skills formed from practice rather than memorizing facts; these are typically acquired slowly and automatically in response to sensory input associated with reward structures or prior exposure within our daily lives ( Kesner, 2017 ). Non-declarative memory is a collection of different phenomena with different neural substrates rather than a single coherent system ( Camina and Güell, 2017 ). It operates by similar principles, depending on local changes to a circumscribed brain region, and the representation of these changes is unavailable to awareness ( Reber, 2008 ). Non-declarative memory encompasses a heterogenous collection of abilities, such as associative learning, skills, and habits (procedural memory), priming, and non-associative learning ( Squire and Zola, 1996 ; Camina and Güell, 2017 ). Studies have concluded that procedural memory for motor skills depends upon activity in diverse set areas such as the motor cortex, striatum, limbic system, and cerebellum; similarly, perceptual skill learning is thought to be associated with sensory cortical activation ( Karni et al., 1998 ; Mayes, 2002 ). Research suggests that mutual connections between brain regions that are active together recruit special cells called associative memory cells ( Wang et al., 2016 ; Wang and Cui, 2018 ). These cells help integrate, store, and remember related information. When activated, these cells trigger the recall of memories, leading to behaviors and emotional responses. This suggests that co-activated brain regions with these mutual connections are where associative memories are formed ( Wang et al., 2016 ; Wang and Cui, 2018 ). Additionally, observational data reveals that priming mechanisms within distinct networks, such as the “repetition suppression” effect observed in visual cortical areas associated with sensory processing and in the prefrontal cortex for semantic priming, are believed to be responsible for certain forms of conditioning and implicit knowledge transfer experiences exhibited by individuals throughout their daily lives ( Reber, 2008 ; Wig et al., 2009 ; Camina and Güell, 2017 ). However, further research is needed to better understand the mechanisms of consolidation in non-declarative memory ( Camina and Güell, 2017 ).

The process of transforming memory into stable, long-lasting from a temporary, labile memory is known as memory consolidation ( McGaugh, 2000 ). Memory formation is based on the change in synaptic connections of neurons representing the memory. Encoding causes synaptic Long-Term potentiation (LTP) or Long-Term depression (LTD) and induces two consolidation processes. The first is synaptic or cellular consolidation which involves remodeling synapses to produce enduring changes. Cellular consolidation is a short-term process that involves stabilizing the neural trace shortly after learning via structural brain changes in the hippocampus ( Lynch, 2004 ). The second is system consolidation, which builds on synaptic consolidation where reverberating activity leads to redistribution for long-term storage ( Mednick et al., 2011 ; Squire et al., 2015 ). System consolidation is a long-term process during which memories are gradually transferred to and integrated with cortical neurons, thus promoting their stability over time. In this way, memories are rendered less susceptible to forgetting. Hebb postulated that when two neurons are repeatedly activated simultaneously, they become more likely to exhibit a coordinated firing pattern of activity in the future ( Langille, 2019 ). This proposed enduring change in synchronized neuronal activation was consequently termed cellular consolidation ( Bermudez-Rattoni, 2010 ).

The following sections of this paper incorporate a more comprehensive investigation into various essential procedures connected with memory consolidation- namely: long-term potentiation (LTP), long-term depression (LTD), system consolidation, and cellular consolidation. Although these mechanisms have been presented briefly before this paragraph, the paper aims to offer greater insight into each process’s function within the individual capacity and their collective contribution toward memory consolidation.

Synaptic plasticity mechanisms implicated in memory stabilization

Long-Term Potentiation (LTP) and Long-Term Depression (LTP) are mechanisms that have been implicated in memory stabilization. LTP is an increase in synaptic strength, whereas LTD is a decrease in synaptic strength ( Ivanco, 2015 ; Abraham et al., 2019 ).

Long-Term Potentiation (LTP) is a phenomenon wherein synaptic strength increases persistently due to brief exposures to high-frequency stimulation ( Lynch, 2004 ). Studies of Long-Term Potentiation (LTP) have led to an understanding of the mechanisms behind synaptic strengthening phenomena and have provided a basis for explaining how and why strong connections between neurons form over time in response to stimuli.

The NMDA receptor-dependent LTP is the most commonly described LTP ( Bliss and Collingridge, 1993 ; Luscher and Malenka, 2012 ). In this type of LTP, when there is high-frequency stimulation, the presynaptic neuron releases glutamate, an excitatory neurotransmitter. Glutamate binds to the AMPA receptor on the postsynaptic neuron, which causes the neuron to fire while opening the NMDA receptor channel. The opening of an NMDA channel elicits a calcium ion influx into the postsynaptic neuron, thus initiating a series of phosphorylation events as part of the ensuing molecular cascade. Autonomously phosphorylated CaMKII and PKC, both actively functional through such a process, have been demonstrated to increase the conductance of pre-existing AMPA receptors in synaptic networks. Additionally, this has been shown to stimulate the introduction of additional AMPA receptors into synapses ( Malenka and Nicoll, 1999 ; Lynch, 2004 ; Luscher and Malenka, 2012 ; Bailey et al., 2015 ).

There are two phases of LTP: the early phase and the late phase. It has been established that the early phase LTP (E-LTP) does not require RNA or protein synthesis; therefore, its synaptic strength will dissipate in minutes if late LTP does not stabilize it. On the contrary, late-phase LTP (L-LTP) can sustain itself over a more extended period, from several hours to multiple days, with gene transcription and protein synthesis in the postsynaptic cell ( Frey and Morris, 1998 ; Orsini and Maren, 2012 ). The strength of presynaptic tetanic stimulation has been demonstrated to be a necessary condition for the activation of processes leading to late LTP ( Luscher and Malenka, 2012 ; Bailey et al., 2015 ). This finding is supported by research examining synaptic plasticity, notably Eric Kandel’s discovery that CREB–a transcription factor–among other cytoplasmic and nuclear molecules, are vital components in mediating molecular changes culminating in protein synthesis during this process ( Kaleem et al., 2011 ; Kandel et al., 2014 ). Further studies have shown how these shifts ultimately lead to AMPA receptor stabilization at post-synapses facilitating long-term potentiation within neurons ( Luscher and Malenka, 2012 ; Bailey et al., 2015 ).

The “synaptic tagging and capture hypothesis” explains how a weak event of tetanization at synapse A can transform to late-LTP if followed shortly by the strong tetanization of a different, nearby synapse on the same neuron ( Frey and Morris, 1998 ; Redondo and Morris, 2011 ; Okuda et al., 2020 ; Park et al., 2021 ). During this process, critical plasticity-related proteins (PRPs) are synthesized, which stabilize their own “tag” and that from the weaker synaptic activity ( Moncada et al., 2015 ). Recent evidence suggests that calcium-permeable AMPA receptors (CP-AMPARs) are involved in this form of heterosynaptic metaplasticity ( Park et al., 2018 ). The authors propose that the synaptic activation of CP-AMPARs triggers the synthesis of PRPs, which are then engaged by the weak induction protocol to facilitate LTP on the independent input. The paper also suggests that CP-AMPARs are required during the induction of LTP by the weak input for the full heterosynaptic metaplastic effect to be observed ( Park et al., 2021 ). Additionally, it has been further established that catecholamines such as dopamine plays an integral part in memory persistence by inducing PRP synthesis ( Redondo and Morris, 2011 ; Vishnoi et al., 2018 ). Studies have found that dopamine release in the hippocampus can enhance LTP and improve memory consolidation ( Lisman and Grace, 2005 ; Speranza et al., 2021 ).

Investigations into neuronal plasticity have indicated that synaptic strength alterations associated with certain forms of learning and memory may be analogous to those underlying Long-Term Potentiation (LTP). Research has corroborated this notion, demonstrating a correlation between these two phenomena ( Lynch, 2004 ). The three essential properties of Long-Term Potentiation (LTP) that have been identified are associativity, synapse specificity, and cooperativity ( Kandel and Mack, 2013 ). These characteristics provide empirical evidence for the potential role of LTP in memory formation processes. Specifically, associativity denotes the amplification of connections when weak stimulus input is paired with a powerful one; synapse specificity posits that this potentiating effect only manifests on synaptic locations exhibiting coincidental activity within postsynaptic neurons, while cooperativity suggests stimulated neuron needs to attain an adequate threshold of depolarization before LTP can be induced again ( Orsini and Maren, 2012 ).

There is support for the idea that memories are encoded by modification of synaptic strengths through cellular mechanisms such as LTP and LTD ( Nabavi et al., 2014 ). The paper by Nabavi et al. (2014) shows that fear conditioning, a type of associative memory, can be inactivated and reactivated by LTD and LTP, respectively. The findings of the paper support a causal link between these synaptic processes and memory. Moreover, the paper suggests that LTP is used to form neuronal assemblies that represent a memory, and LTD could be used to disassemble them and thereby inactivate a memory ( Nabavi et al., 2014 ). Hippocampal LTD has been found to play an essential function in regulating synaptic strength and forming memories, such as long-term spatial memory ( Ge et al., 2010 ). However, it is vital to bear in mind that studies carried out on LTP exceed those done on LTD; hence the literature on it needs to be more extensive ( Malenka and Bear, 2004 ; Nabavi et al., 2014 ).

Cellular consolidation and memory

For an event to be remembered, it must form physical connections between neurons in the brain, which creates a “memory trace.” This memory trace can then be stored as long-term memory ( Langille and Brown, 2018 ). The formation of a memory engram is an intricate process requiring neuronal depolarization and the influx of intracellular calcium ( Mank and Griesbeck, 2008 ; Josselyn et al., 2015 ; Xu et al., 2017 ). This initiation leads to a cascade involving protein transcription, structural and functional changes in neural networks, and stabilization during the quiescence period, followed by complete consolidation for its success. Interference from new learning events or disruption caused due to inhibition can abort this cycle leading to incomplete consolidation ( Josselyn et al., 2015 ).

Cyclic-AMP response element binding protein (CREB) has been identified as an essential transcription factor for memory formation ( Orsini and Maren, 2012 ). It regulates the expression of PRPs and enhances neuronal excitability and plasticity, resulting in changes to the structure of cells, including the growth of dendritic spines and new synaptic connections. Blockage or enhancement of CREB in certain areas can affect subsequent consolidation at a systems level–decreasing it prevents this from occurring, while aiding its presence allows even weak learning conditions to produce successful memory formation ( Orsini and Maren, 2012 ; Kandel et al., 2014 ).

Strengthening weakly encoded memories through the synaptic tagging and capture hypothesis may play an essential role in cellular consolidation. Retroactive memory enhancement has also been demonstrated in human studies, mainly when items are initially encoded with low strength but later paired with shock after consolidation ( Dunsmoor et al., 2015 ). The synaptic tagging and capture theory (STC) and its extension, the behavioral tagging hypothesis (BT), have both been used to explain synaptic specificity and the persistence of plasticity ( Moncada et al., 2015 ). STC proposed that electrophysiological activity can induce long-term changes in synapses, while BT postulates similar effects of behaviorally relevant neuronal events on learning and memory models. This hypothesis proposes that memory consolidation relies on combining two distinct processes: setting a “learning tag” and synthesizing plasticity-related proteins ( De novo protein synthesis, increased CREB levels, and substantial inputs to nearby synapses) at those tagged sites. BT explains how it is possible for event episodes with low-strength inputs or engagements can be converted into lasting memories ( Lynch, 2004 ; Moncada et al., 2015 ). Similarly, the emotional tagging hypothesis posits that the activation of the amygdala in emotionally arousing events helps to mark experiences as necessary, thus enhancing synaptic plasticity and facilitating transformation from transient into more permanent forms for encoding long-term memories ( Richter-Levin and Akirav, 2003 ; Zhu et al., 2022 ).

Cellular consolidation, the protein synthesis-dependent processes observed in rodents that may underlie memory formation and stabilization, has been challenging to characterize in humans due to the limited ability to study it directly ( Bermudez-Rattoni, 2010 ). Additionally, multi-trial learning protocols commonly used within human tests as opposed to single-trial experiments conducted with non-human subjects suggest there could be interference from subsequent information that impedes individual memories from being consolidated reliably. This raises important questions regarding how individuals can still form strong and long-lasting memories when exposed to frequent stimuli outside controlled laboratory conditions. Although this phenomenon remains undiscovered by science, it is of utmost significance for gaining a deeper understanding of our neural capacities ( Genzel and Wixted, 2017 ).

The establishment of distributed memory traces requires a narrow temporal window following the initial encoding process, during which cellular consolidation occurs ( Nader and Hardt, 2009 ). Once this period ends and consolidation has been completed, further protein synthesis inhibition or pharmacological disruption will be less effective at altering pre-existing memories and interfering with new learning due to the stabilization of the trace in its new neuronal network connections ( Nader and Hardt, 2009 ). Thus, systems consolidation appears critical for the long-term maintenance of memory within broader brain networks over extended periods after their formation ( Bermudez-Rattoni, 2010 ).

System consolidation and memory

Information is initially stored in both the hippocampus and neocortex ( Dudai et al., 2015 ). The hippocampus subsequently guides a gradual process of reorganization and stabilization whereby information present within the neocortex becomes autonomous from that in the hippocampal store. Scholars have termed this phenomenon “standard memory consolidation model” or “system consolidation” ( Squire et al., 2015 ).

The Standard Model suggests that information acquired during learning is simultaneously stored in both the hippocampus and multiple cortical modules. Subsequently, it posits that over a period of time which may range from weeks to months or longer, the hippocampal formation directs an integration process by which these various elements become enclosed into single unified structures within the cortex ( Gilboa and Moscovitch, 2021 ; Howard et al., 2022 ). These newly learned memories are then assimilated into existing networks without interference or compression when necessary ( Frankland and Bontempi, 2005 ). It is important to note that memory engrams already exist within cortical networks during encoding. They only need strengthening through links enabled by hippocampal assistance-overtime allowing remote memory storage without reliance on the latter structure. Data appears consistent across studies indicating that both AMPA-and NMDA receptor-dependent “tagging” processes occurring within the cortex are essential components of progressive rewiring, thus enabling longer-term retention ( Takeuchi et al., 2014 ; Takehara-Nishiuchi, 2020 ).

Recent studies have additionally demonstrated that the rate of system consolidation depends on an individual’s ability to relate new information to existing networks made up of connected neurons, popularly known as “schemas” ( Robin and Moscovitch, 2017 ). In situations where prior knowledge is present and cortical modules are already connected at the outset of learning, it has been observed that a hippocampal-neocortical binding process occurs similarly to when forming new memories ( Schlichting and Preston, 2015 ). The proposed framework involves the medial temporal lobe (MTL), which is involved in acquiring new information and binds different aspects of an experience into a single memory trace. In contrast, the medial prefrontal cortex (mPFC) integrates this information with the existing knowledge ( Zeithamova and Preston, 2010 ; van Kesteren et al., 2012 ). During consolidation and retrieval, MTL is involved in replaying memories to the neocortex, where they are gradually integrated with existing knowledge and schemas and help retrieve memory traces. During retrieval, the mPFC is thought to use existing knowledge and schemas to guide retrieval and interpretation of memory. This may involve the assimilation of newly acquired information into existing cognitive schemata as opposed to the comparatively slow progression of creating intercortical connections ( Zeithamova and Preston, 2010 ; van Kesteren et al., 2012 , 2016 ).

Medial temporal lobe structures are essential for acquiring new information and necessary for autobiographical (episodic) memory ( Brown et al., 2018 ). The consolidation of autobiographical memories depends on a distributed network of cortical regions. Brain areas such as entorhinal, perirhinal, and parahippocampal cortices are essential for learning new information; however, they have little impact on the recollection of the past ( Squire et al., 2015 ). The hippocampus is a region of the brain that forms episodic memories by linking multiple events to create meaningful experiences ( Cooper and Ritchey, 2019 ). It receives information from all areas of the association cortex and cingulate cortex, subcortical regions via the fornix, as well as signals originating within its entorhinal cortex (EC) and amygdala regarding emotionally laden or potentially hazardous stimuli ( Sorensen, 2009 ). Such widespread connectivity facilitates the construction of an accurate narrative underpinning each remembered episode, transforming short-term into long-term recollections ( Richter-Levin and Akirav, 2003 ).

Researchers have yet to establish a consensus regarding where semantic memory information is localized within the brain ( Roldan-Valadez et al., 2012 ). Some proponents contend that such knowledge is lodged within perceptual and motor systems, triggered when we initially associate with a given object. This point of view is supported by studies highlighting how neural activity occurs initially in the occipital cortex, followed by left temporal lobe involvement during processing and pertinent contributions to word selection/retrieval via activation of left inferior frontal cortices ( Patterson et al., 2007 ). Moreover, research indicates elevated levels of fusiform gyrus engagement (a ventral surface region encompassing both temporal lobes) occurring concomitantly with verbal comprehension initiatives, including reading and naming tasks ( Patterson et al., 2007 ).

Research suggests that the hippocampus is needed for a few years after learning to support semantic memory (factual information), yet, it is not needed for the long term ( Squire et al., 2015 ). However, some forms of memory remain dependent on the hippocampus, such as the retrieval of spatial memory ( Wiltgen et al., 2010 ). Similarly, the Multiple-trace theory ( Moscovitch et al., 2006 ), also known as the transformation hypothesis ( Winocur and Moscovitch, 2011 ), posits that hippocampal engagement is necessary for memories that retain contextual detail such as episodic memories. Consolidation of memories into the neocortex is theorized to involve a loss of specific finer details, such as temporal and spatial information, in addition to contextual elements. This transition ultimately results in an evolution from episodic memory toward semantic memory, which consists mainly of gist-based facts ( Moscovitch et al., 2006 ).

Sleep and memory consolidation

Sleep is an essential physiological process crucial to memory consolidation ( Siegel, 2001 ). Sleep is divided into two stages: Non-rapid Eye Movement (NREM) sleep and Rapid Eye Movement (REM) sleep. NREM sleep is divided into three stages: N1, N2, and N3 (AKA Slow Wave Sleep or SWS) ( Rasch and Born, 2013 ). Each stage displays unique oscillatory patterns and phenomena responsible for consolidating memories in distinct ways. The first stage, or N1 sleep, is when an individual transitions between wakefulness and sleep. This type of sleep is characterized by low-amplitude, mixed-frequency brain activity. N1 sleep is responsible for the initial encoding of memories ( Rasch and Born, 2013 ). The second stage, or N2 sleep, is characterized by the occurrence of distinct sleep spindles and K-complexes in EEG. N2 is responsible for the consolidation of declarative memories ( Marshall and Born, 2007 ). The third stage of sleep N3, also known as slow wave sleep (SWS), is characterized by low-frequency brain activity, slow oscillations, and high amplitude. The slow oscillations which define the deepest stage of sleep are trademark rhythms of NREM sleep. These slow oscillations are delta waves combined to indicate slow wave activity (SWA), which is implicated in memory consolidation ( Tononi and Cirelli, 2003 ; Stickgold, 2005 ; Kim et al., 2019 ). Sleep spindles are another trademark defining NREM sleep ( Stickgold, 2005 ). Ripples are high-frequency bursts, and when combined with irregularly occurring sharp waves (high amplitude), they form the sharp-wave ripple (SWR). These spindles and the SWRs coordinate the reactivation and redistribution of hippocampus-dependent memories to neocortical sites ( Ngo et al., 2020 ; Girardeau and Lopes-dos-Santos, 2021 ). The third stage is also responsible for the consolidation of procedural memories, such as habits and motor skills ( Diekelmann and Born, 2010 ). During SWS, there is minimal cholinergic activity and intermediate noradrenergic activity ( Datta and MacLean, 2007 ).

Finally, the fourth stage of sleep is REM sleep, characterized by phasic REMs and muscle atonia ( Reyes-Resina et al., 2021 ). During REM sleep, there is high cholinergic activity, serotonergic and noradrenergic activity are at a minimum, and high theta activity ( Datta and MacLean, 2007 ). REM sleep is also characterized by local increases in plasticity-related immediate-early gene activity, which might favor the subsequent synaptic consolidation of memories in the cortex ( Ribeiro, 2007 ; Diekelmann and Born, 2010 ; Reyes-Resina et al., 2021 ). The fourth stage of sleep is responsible for the consolidation of emotional memories and the integration of newly acquired memories into existing knowledge structures ( Rasch and Born, 2013 ). Studies indicate that the cholinergic system plays an imperative role in modifying these processes by toggling the entire thalamo-cortico-hippocampal network between distinct modes, namely high Ach encoding mode during active wakefulness and REM sleep and low Ach consolidation mode during quiet wakefulness and NREM sleep ( Bergmann and Staresina, 2017 ; Li et al., 2020 ). Consequently, improving neocortical hippocampal communication results in efficient memory encoding/synaptic plasticity, whereas hippocampo-neocortical interactions favor better systemic memory consolidation ( Diekelmann and Born, 2010 ).

The dual process hypothesis of memory consolidation posits that SWS facilitates declarative, hippocampus-dependent memory, whereas REM sleep facilitates non-declarative hippocampus-independent memory ( Maquet, 2001 ; Diekelmann and Born, 2010 ). On the other hand, the sequential hypothesis states that different sleep stages play a sequential role in memory consolidation. Memories are encoded during wakefulness, consolidated during NREM sleep, and further processed and integrated during REM sleep ( Rasch and Born, 2013 ). However, there is evidence present that contradicts the sequential hypothesis. A study by Goerke et al. (2013) found that declarative memories can be consolidated during REM sleep, suggesting that the relationship between sleep stages and memory consolidation is much more complex than a sequential model. Moreover, other studies indicate the importance of coordinating specific sleep phases with learning moments for optimal memory retention. This indicates that the timing of sleep has more influence than the specific sleep stages ( Gais et al., 2006 ). The active system consolidation theory suggests that an active consolidation process results from the selective reactivation of memories during sleep; the brain selectively reactivates newly encoded memories during sleep, which enhances and integrates them into the network of pre-existing long-term memories ( Born et al., 2006 ; Howard et al., 2022 ). Research has suggested that slow-wave sleep (SWS) and rapid eye movement (REM) sleep have complementary roles in memory consolidation. Declarative and non-declarative memories benefiting differently depending on which sleep stage they rely on ( Bergmann and Staresina, 2017 ). Specifically, during SWS, the brain actively reactivates and reorganizes hippocampo-neocortical memory traces as part of system consolidation. Following this, REM sleep is crucial for stabilizing these reactivated memory traces through synaptic consolidation. While SWS may initiate early plastic processes in hippocampo-neocortical memory traces by “tagging” relevant neocortico-neocortical synapses for later consolidation ( Frey and Morris, 1998 ), long-term plasticity requires subsequent REM sleep ( Rasch and Born, 2007 , 2013 ).

The active system consolidation hypothesis is not the only mechanism proposed for memory consolidation during sleep. The synaptic homeostasis hypothesis proposes that sleep is necessary for restoring synaptic homeostasis, which is challenged by synaptic strengthening triggered by learning during wake and synaptogenesis during development ( Tononi and Cirelli, 2014 ). The synaptic homeostasis hypothesis assumes consolidation is a by-product of the global synaptic downscaling during sleep ( Puentes-Mestril and Aton, 2017 ). The two models are not mutually exclusive, and the hypothesized processes probably act in concert to optimize the memory function of sleep ( Diekelmann and Born, 2010 ).

Non-rapid eye movement sleep plays an essential role in the systems consolidation of memories, with evidence showing that different oscillations are involved in this process ( Düzel et al., 2010 ). With an oscillatory sequence initiated by a slow frontal cortex oscillation (0.5–1 Hz) traveling to the medial temporal lobe and followed by a sharp-wave ripple (SWR) in the hippocampus (100–200 Hz). Replay activity of memories can be measured during this oscillatory sequence across various regions, including the motor cortex and visual cortex ( Ji and Wilson, 2006 ; Eichenlaub et al., 2020 ). Replay activity of memory refers to the phenomenon where the hippocampus replays previously experienced events during sharp wave ripples (SWRs) and theta oscillations ( Zielinski et al., 2018 ). During SWRs, short, transient bursts of high-frequency oscillations occur in the hippocampus. During theta oscillations, hippocampal spikes are ordered according to the locations of their place fields during behavior. These sequential activities are thought to play a role in memory consolidation and retrieval ( Zielinski et al., 2018 ). The paper by Zielinski et al. (2018) suggests that coordinated hippocampal-prefrontal representations during replay and theta sequences play complementary and overlapping roles at different stages in learning, supporting memory encoding and retrieval, deliberative decision-making, planning, and guiding future actions.

Additionally, the high-frequency oscillations of SWR reactivate groups of neurons attributed to spatial information encoding to align synchronized activity across an array of neural structures, which results in distributed memory creation ( Swanson et al., 2020 ; Girardeau and Lopes-dos-Santos, 2021 ). Parallel to this process is slow oscillation or slow-wave activity within cortical regions, which reflects synced neural firing and allows regulation of synaptic weights, which is in accordance with the synaptic homeostasis hypothesis (SHY). The SHY posits that downscaling synaptic strengths help incorporate new memories by avoiding saturation of resources during extended periods–features validated by discoveries where prolonged wakefulness boosts amplitude while it diminishes during stretches of enhanced sleep ( Girardeau and Lopes-dos-Santos, 2021 ).

During REM sleep, the brain experiences “paradoxical” sleep due to the similarity in activity to wakefulness. This stage plays a significant role in memory processing. Theta oscillations which are dominant during REM sleep, are primarily observed in the hippocampus, and these are involved in memory consolidation ( Landmann et al., 2014 ). There has been evidence of coherence between theta oscillations in the hippocampus, medial frontal cortex, and amygdala, which support their involvement in memory consolidation ( Popa et al., 2010 ). During REM sleep, phasic events such as ponto-geniculo-occipital waves originating from the brainstem coordinate activity across various brain structures and may contribute to memory consolidation processes ( Rasch and Born, 2013 ). Research has suggested that sleep-associated consolidation may be mediated by the degree of overlap between new and already known material whereby, if the acquired information is similar to the information one has learned, it is more easily consolidated during sleep ( Tamminen et al., 2010 ; Sobczak, 2017 ).

In conclusion, understanding more about how the brains cycle through different stages of sleep, including specific wave patterns, offers valuable insight into the ability to store memories effectively. While NREM sleep is associated with SWRs and slow oscillations, facilitating memory consolidation and synaptic downscaling, REM sleep, characterized by theta oscillations and phasic events, contributes to memory reconsolidation and the coordination of activity across brain regions. By exploring the interactions between sleep stages, oscillations, and memory processes, one may learn more about how sleep impacts brain function and cognition in greater detail.

Century has passed since we addressed memory, and several notable findings have moved from bench-to-bedside research. Several cross-talks between multidiscipline have been encouraged. Nevertheless, further research is needed into neurobiological mechanisms of non-declarative memory, such as conditioning ( Gallistel and Balsam, 2014 ). Modern research indicates that structural change that encodes information is likely at the level of the synapse, and the computational mechanisms are implemented at the level of neural circuitry. However, it also suggests that intracellular mechanisms realized at the molecular level, such as micro RNAs, should not be discounted as potential mechanisms. However, further research is needed to study the molecular and structural changes brought on by implicit memory ( Gallistel and Balsam, 2014 ).

The contribution of non-human animal studies toward our understanding of memory processes cannot be understated; hence recognizing their value is vital for moving forward. While this paper predominantly focused on cognitive neuroscience perspectives, some articles cited within this paper were sourced from non-human animal studies providing fundamental groundwork and identification of critical mechanisms relevant to human memories. A need persists for further investigation—primarily with humans—which can validate existing findings from non-human animals. Moving forward, it is prudent for researchers to bridge the gap between animal and human investigations done while exploring parallels and exploring unique aspects of human memory processes. By integrating findings from both domains, one can gain a more comprehensive understanding of the complexities of memory and its underlying neural mechanisms. Such investigations will broaden the horizon of our memory process and answer the complex nature of memory storage.

This paper attempted to provide an overview and summarize memory and its processes. The paper focused on bringing the cognitive neuroscience perspective on memory and its processes. This may provide the readers with the understanding, limitations, and research perspectives of memory mechanisms.

Data availability statement

Author contributions.

SS and MKA: conceptualization, framework, and manuscript writing. AK: review and editing of the manuscript. All authors contributed to the article and approved the submitted version.

Acknowledgments

We gratefully thank students and Indian Institute of Technology Roorkee (IITR) office staff for their conditional and unconditional support. We also thank the Memory and Anxiety Research Group (MARG), IIT Roorkee for its constant support.

Funding Statement

MKA was supported by the F.I.G. grant (IITR/SRIC/2741). The funding agency had no role in the preparation of the manuscript.

Conflict of interest

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.

Publisher’s note

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.

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Learn More Psychology

• Memory Psychology

10 Influential Memory Theories and Studies in Psychology

Discover the experiments and theories that shaped our understanding of how we develop and recall memories..

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How do our memories store information? Why is it that we can recall a memory at will from decades ago, and what purpose does forgetting information serve?

The human memory has been the subject of investigation among many 20th Century psychologists and remains an active area of study for today’s cognitive scientists. Below we take a look at some of the most influential studies, experiments and theories that continue to guide our understanding of the function of memory.

1 Multi-Store Model

(atkinson & shiffrin, 1968).

An influential theory of memory known as the multi-store model was proposed by Richard Atkinson and Richard Shiffrin in 1968. This model suggested that information exists in one of 3 states of memory: the sensory, short-term and long-term stores . Information passes from one stage to the next the more we rehearse it in our minds, but can fade away if we do not pay enough attention to it. Read More

Information enters the memory from the senses - for instance, the eyes observe a picture, olfactory receptors in the nose might smell coffee or we might hear a piece of music. This stream of information is held in the sensory memory store , and because it consists of a huge amount of data describing our surroundings, we only need to remember a small portion of it. As a result, most sensory information ‘ decays ’ and is forgotten after a short period of time. A sight or sound that we might find interesting captures our attention, and our contemplation of this information - known as rehearsal - leads to the data being promoted to the short-term memory store , where it will be held for a few hours or even days in case we need access to it.

The short-term memory gives us access to information that is salient to our current situation, but is limited in its capacity.

Therefore, we need to further rehearse information in the short-term memory to remember it for longer. This may involve merely recalling and thinking about a past event, or remembering a fact by rote - by thinking or writing about it repeatedly. Rehearsal then further promotes this significant information to the long-term memory store, where Atkinson and Shiffrin believed that it could survive for years, decades or even a lifetime.

Key information regarding people that we have met, important life events and other important facts makes it through the sensory and short-term memory stores to reach the long-term memory .

Learn more about Atkinson and Shiffrin’s Multi-Store Model

2 Levels of Processing

(craik & lockhart, 1972).

Fergus Craik and Robert Lockhart were critical of explanation for memory provided by the multi-store model, so in 1972 they proposed an alternative explanation known as the levels of processing effect . According to this model, memories do not reside in 3 stores; instead, the strength of a memory trace depends upon the quality of processing , or rehearsal , of a stimulus . In other words, the more we think about something, the more long-lasting the memory we have of it ( Craik & Lockhart , 1972). Read More

Craik and Lockhart distinguished between two types of processing that take place when we make an observation : shallow and deep processing. Shallow processing - considering the overall appearance or sound of something - generally leads to a stimuli being forgotten. This explains why we may walk past many people in the street on a morning commute, but not remember a single face by lunch time.

Deep (or semantic) processing , on the other hand, involves elaborative rehearsal - focusing on a stimulus in a more considered way, such as thinking about the meaning of a word or the consequences of an event. For example, merely reading a news story involves shallow processing, but thinking about the repercussions of the story - how it will affect people - requires deep processing, which increases the likelihood of details of the story being memorized.

In 1975, Craik and another psychologist, Endel Tulving , published the findings of an experiment which sought to test the levels of processing effect.

Participants were shown a list of 60 words, which they then answered a question about which required either shallow processing or more elaborative rehearsal. When the original words were placed amongst a longer list of words, participants who had conducted deeper processing of words and their meanings were able to pick them out more efficiently than those who had processed the mere appearance or sound of words ( Craik & Tulving , 1975).

Learn more about Levels of Processing here

3 Working Memory Model

(baddeley & hitch, 1974).

Whilst the Multi-Store Model (see above) provided a compelling insight into how sensory information is filtered and made available for recall according to its importance to us, Alan Baddeley and Graham Hitch viewed the short-term memory (STM) store as being over-simplistic and proposed a working memory model (Baddeley & Hitch, 1974), which replace the STM.

The working memory model proposed 2 components - a visuo-spatial sketchpad (the ‘inner eye’) and an articulatory-phonological loop (the ‘inner ear’), which focus on a different types of sensory information. Both work independently of one another, but are regulated by a central executive , which collects and processes information from the other components similarly to how a computer processor handles data held separately on a hard disk. Read More

According to Baddeley and Hitch, the visuo-spatial sketchpad handles visual data - our observations of our surroundings - and spatial information - our understanding of objects’ size and location in our environment and their position in relation to ourselves. This enables us to interact with objects: to pick up a drink or avoid walking into a door, for example.

The visuo-spatial sketchpad also enables a person to recall and consider visual information stored in the long-term memory. When you try to recall a friend’s face, your ability to visualize their appearance involves the visuo-spatial sketchpad.

The articulatory-phonological loop handles the sounds and voices that we hear. Auditory memory traces are normally forgotten but may be rehearsed using the ‘inner voice’; a process which can strengthen our memory of a particular sound.

Learn more about Baddeley and Hitch’s working memory model here

4 Miller’s Magic Number

(miller, 1956).

Prior to the working memory model, U.S. cognitive psychologist George A. Miller questioned the limits of the short-term memory’s capacity. In a renowned 1956 paper published in the journal Psychological Review , Miller cited the results of previous memory experiments, concluding that people tend only to be able to hold, on average, 7 chunks of information (plus or minus two) in the short-term memory before needing to further process them for longer storage. For instance, most people would be able to remember a 7-digit phone number but would struggle to remember a 10-digit number. This led to Miller describing the number 7 +/- 2 as a “magical” number in our understanding of memory. Read More

But why are we able to remember the whole sentence that a friend has just uttered, when it consists of dozens of individual chunks in the form of letters? With a background in linguistics, having studied speech at the University of Alabama, Miller understood that the brain was able to ‘chunk’ items of information together and that these chunks counted towards the 7-chunk limit of the STM. A long word, for example, consists of many letters, which in turn form numerous phonemes. Instead of only being able to remember a 7-letter word, the mind “recodes” it, chunking the individual items of data together. This process allows us to boost the limits of recollection to a list of 7 separate words.

Miller’s understanding of the limits of human memory applies to both the short-term store in the multi-store model and Baddeley and Hitch’s working memory. Only through sustained effort of rehearsing information are we able to memorize data for longer than a short period of time.

Read more about Miller’s Magic Number here

5 Memory Decay

(peterson and peterson, 1959).

Following Miller’s ‘magic number’ paper regarding the capacity of the short-term memory, Peterson and Peterson set out to measure memories’ longevity - how long will a memory last without being rehearsed before it is forgotten completely?

In an experiment employing a Brown-Peterson task, participants were given a list of trigrams - meaningless lists of 3 letters (e.g. GRT, PXM, RBZ) - to remember. After the trigrams had been shown, participants were asked to count down from a number, and to recall the trigrams at various periods after remembering them. Read More

The use of such trigrams makes it impracticable for participants to assign meaning to the data to help encode them more easily, while the interference task prevented rehearsal, enabling the researchers to measure the duration of short-term memories more accurately.

Whilst almost all participants were initially able to recall the trigrams, after 18 seconds recall accuracy fell to around just 10%. Peterson and Peterson’s study demonstrated the surprising brevity of memories in the short-term store, before decay affects our ability to recall them.

Learn more about memory decay here

6 Flashbulb Memories

(brown & kulik, 1977).

There are particular moments in living history that vast numbers of people seem to hold vivid recollections of. You will likely be able to recall such an event that you hold unusually detailed memories of yourself. When many people learned that JFK, Elvis Presley or Princess Diana died, or they heard of the terrorist attacks taking place in New York City in 2001, a detailed memory seems to have formed of what they were doing at the particular moment that they heard such news.

Psychologists Roger Brown and James Kulik recognized this memory phenomenon as early as 1977, when they published a paper describing flashbulb memories - vivid and highly detailed snapshots created often (but not necessarily) at times of shock or trauma. Read More

We are able to recall minute details of our personal circumstances whilst engaging in otherwise mundane activities when we learnt of such events. Moreover, we do not need to be personally connected to an event for it to affect us, and for it lead to the creation of a flashbulb memory.

Learn more about Flashbulb Memories here

7 Memory and Smell

The link between memory and sense of smell helps many species - not just humans - to survive. The ability to remember and later recognize smells enables animals to detect the nearby presence of members of the same group, potential prey and predators. But how has this evolutionary advantage survived in modern-day humans?

Researchers at the University of North Carolina tested the olfactory effects on memory encoding and retrieval in a 1989 experiment. Male college students were shown a series of slides of pictures of females, whose attractiveness they were asked to rate on a scale. Whilst viewing the slides, the participants were exposed to pleasant odor of aftershave or an unpleasant smell. Their recollection of the faces in the slides was later tested in an environment containing either the same or a different scent. Read More

The results showed that participants were better able to recall memories when the scent at the time of encoding matched that at the time of recall (Cann and Ross, 1989). These findings suggest that a link between our sense of smell and memories remains, even if it provides less of a survival advantage than it did for our more primitive ancestors.

8 Interference

Interference theory postulates that we forget memories due to other memories interfering with our recall. Interference can be either retroactive or proactive: new information can interfere with older memories (retroactive interference), whilst information we already know can affect our ability to memorize new information (proactive interference).

Both types of interference are more likely to occur when two memories are semantically related, as demonstrated in a 1960 experiment in which two groups of participants were given a list of word pairs to remember, so that they could recall the second ‘response’ word when given the first as a stimulus. A second group was also given a list to learn, but afterwards was asked to memorize a second list of word pairs. When both groups were asked to recall the words from the first list, those who had just learnt that list were able to recall more words than the group that had learnt a second list (Underwood & Postman, 1960). This supported the concept of retroactive interference: the second list impacted upon memories of words from the first list. Read More

Interference also works in the opposite direction: existing memories sometimes inhibit our ability to memorize new information. This might occur when you receive a work schedule, for instance. When you are given a new schedule a few months later, you may find yourself adhering to the original times. The schedule that you already knew interferes with your memory of the new schedule.

9 False Memories

Can false memories be implanted in our minds? The idea may sound like the basis of a dystopian science fiction story, but evidence suggests that memories that we already hold can be manipulated long after their encoding. Moreover, we can even be coerced into believing invented accounts of events to be true, creating false memories that we then accept as our own.

Cognitive psychologist Elizabeth Loftus has spent much of her life researching the reliability of our memories; particularly in circumstances when their accuracy has wider consequences, such as the testimonials of eyewitness in criminal trials. Loftus found that the phrasing of questions used to extract accounts of events can lead witnesses to attest to events inaccurately. Read More

In one experiment, Loftus showed a group of participants a video of a car collision, where the vehicle was travelling at a one of a variety of speeds. She then asked them the car’s speed using a sentence whose depiction of the crash was adjusted from mild to severe using different verbs. Loftus found when the question suggested that the crash had been severe, participants disregarded their video observation and vouched that the car had been travelling faster than if the crash had been more of a gentle bump (Loftus and Palmer, 1974). The use of framed questions, as demonstrated by Loftus, can retroactively interfere with existing memories of events.

James Coan (1997) demonstrated that false memories can even be produced of entire events. He produced booklets detailing various childhood events and gave them to family members to read. The booklet given to his brother contained a false account of him being lost in a shopping mall, being found by an older man and then finding his family. When asked to recall the events, Coan’s brother believed the lost in a mall story to have actually occurred, and even embellished the account with his own details (Coan, 1997).

Read more about false memories here

10 The Weapon Effect on Eyewitness Testimonies

(johnson & scott, 1976).

A person’s ability to memorize an event inevitably depends not just on rehearsal but also on the attention paid to it at the time it occurred. In a situation such as an bank robbery, you may have other things on your mind besides memorizing the appearance of the perpetrator. But witness’s ability to produce a testimony can sometimes be affected by whether or not a gun was involved in a crime. This phenomenon is known as the weapon effect - when a witness is involved in a situation in which a weapon is present, they have been found to remember details less accurately than a similar situation without a weapon. Read More

The weapon effect on eyewitness testimonies was the subject of a 1976 experiment in which participants situated in a waiting room watched as a man left a room carrying a pen in one hand. Another group of participants heard an aggressive argument, and then saw a man leave a room carrying a blood-stained knife.

Later, when asked to identify the man in a line-up, participants who saw the man carrying a weapon were less able to identify him than those who had seen the man carrying a pen (Johnson & Scott, 1976). Witnesses’ focus of attention had been distracted by a weapon, impeding their ability to remember other details of the event.

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Study sheds light on how neurons form long-term memories

On a late summer day in 1953, a young man who would soon be known as patient H.M. underwent experimental surgery. In an attempt to treat his debilitating seizures, a surgeon removed portions of his brain, including part of a structure called the hippocampus. The seizures stopped.

Unfortunately, for patient H.M., so too did time. When he woke up after surgery, he could no longer form new long-term memories, despite retaining normal cognitive abilities, language and short-term working memory. Patient H.M.’s condition ultimately revealed that the brain’s ability to create long-term memories is a distinct process that depends on the hippocampus.

Scientists had discovered where memories are made. But  how  they are made remained unknown.

Now, neuroscientists at Harvard Medical School (HMS) have taken a decisive step in the quest to understand the biology of long-term memory and find ways to intervene when memory deficits occur with age or disease.

Reporting in  Nature  on Dec. 9, they describe a newly identified mechanism that neurons in the adult mouse hippocampus use to regulate signals they receive from other neurons, in a process that appears critical for memory consolidation and recall.

The study was led by  Lynn Yap , HMS graduate student in neurobiology, and  Michael Greenberg , chair of neurobiology in the Blavatnik Institute at HMS.

“If we can better understand this process, we will have new handles on memory and how to intervene when things go wrong, …” Michael Greenberg, Blavatnik Institute at HMS

“Memory is essential to all aspects of human existence. The question of how we encode memories that last a lifetime is a fundamental one, and our study gets to the very heart of this phenomenon,” said Greenberg, the HMS Nathan Marsh Pusey Professor of Neurobiology and study corresponding author.

The researchers observed that new experiences activate sparse populations of neurons in the hippocampus that express two genes, Fos and Scg2. These genes allow neurons to fine-tune inputs from so-called inhibitory interneurons, cells that dampen neuronal excitation. In this way, small groups of disparate neurons may form persistent networks with coordinated activity in response to an experience.

“This mechanism likely allows neurons to better talk to each other so that the next time a memory needs to be recalled, the neurons fire more synchronously,” Yap said. “We think coincident activation of this Fos-mediated circuit is potentially a necessary feature for memory consolidation, for example, during sleep, and also memory recall in the brain.”

Circuit orchestration

In order to form memories, the brain must somehow wire an experience into neurons so that when these neurons are reactivated, the initial experience can be recalled. In their study, Greenberg, Yap and team set out to explore this process by looking at the gene Fos.

First  described  in neuronal cells by Greenberg and colleagues in 1986, Fos is expressed within minutes after a neuron is activated. Scientists have taken advantage of this property, using Fos as a marker of recent neuronal activity to identify brain cells that regulate thirst,  torpor , and many other behaviors.

Scientists hypothesized that Fos might play a critical role in learning and memory, but for decades, the precise function of the gene has remained a mystery.

To investigate, the researchers exposed mice to new environments and looked at pyramidal neurons, the principal cells of the hippocampus. They found that relatively sparse populations of neurons expressed Fos after exposure to a new experience. Next, they prevented these neurons from expressing Fos, using a virus-based tool delivered to a specific area of the hippocampus, which left other cells unaffected.

Mice that had Fos blocked in this manner showed significant memory deficits when assessed in a maze that required them to recall spatial details, indicating that the gene plays a critical role in memory formation.

The researchers studied the differences between neurons that expressed Fos and those that did not. Using  optogenetics  to turn inputs from different nearby neurons on or off, they discovered that the activity of Fos-expressing neurons was most strongly affected by two types of interneurons.

Neurons expressing Fos were found to receive increased activity-dampening, or inhibitory, signals from one distinct type of interneuron and decreased inhibitory signals from another type. These signaling patterns disappeared in neurons with blocked Fos expression.

“What’s critical about these interneurons is that they can regulate when and how much individual Fos-activated neurons fire, and also when they fire relative to other neurons in the circuit,” Yap said. “We think that at long last we have a handle on how Fos may in fact support memory processes, specifically by orchestrating this type of circuit plasticity in the hippocampus.”

Imagine the day

The researchers further probed the function of Fos, which codes for a transcription factor protein that regulates other genes. They used single-cell sequencing and additional genomic screens to identify genes activated by Fos and found that one gene in particular, Scg2, played a critical role in regulating inhibitory signals.

In mice with experimentally silenced Scg2, Fos-activated neurons in the hippocampus displayed a defect in signaling from both types of interneurons. These mice also had defects in theta and gamma rhythms, brain properties thought to be critical features of learning and memory.

Previous studies had shown that Scg2 codes for a neuropeptide protein that can be cleaved into four distinct forms, which are then secreted. In the current study, Yap and colleagues discovered that neurons appear to use these neuropeptides to fine-tune inputs they receive from interneurons.

Together, the team’s experiments suggest that after a new experience, a small group of neurons simultaneously express Fos, activating Scg2 and its derived neuropeptides, in order to establish a coordinated network with its activity regulated by interneurons.

“When neurons are activated in the hippocampus after a new experience, they aren’t necessarily linked together in any particular way in advance,” Greenberg said. “But interneurons have very broad axonal arbors, meaning they can connect with and signal to many cells at once. This may be how a sparse group of neurons can be linked together to ultimately encode a memory.”

The study findings represent a possible molecular- and circuit-level mechanism for long-term memory. They shed new light on the fundamental biology of memory formation and have broad implications for diseases of memory dysfunction.

The researchers note, however, that while the results are an important step in our understanding of the inner workings of memory, numerous unanswered questions about the newly identified mechanisms remain.

“We’re not quite at the answer yet, but we can now see many of the next steps that need to be taken,” Greenberg said. “If we can better understand this process, we will have new handles on memory and how to intervene when things go wrong, whether in age-related memory loss or neurodegenerative disorders such as Alzheimer’s disease.”

The findings also represent the culmination of decades of research, even as they open new avenues of study that will likely take decades more to explore, Greenberg added.

“I arrived at Harvard in 1986, just as my paper describing the discovery that neuronal activity can turn on genes was published,” he said. “Since that time, I’ve been imagining the day when we would figure out how genes like Fos might contribute to long-term memory.”

Additional authors include Noah Pettit, Christopher Davis, M. Aurel Nagy, David Harmin, Emily Golden, Onur Dagliyan, Cindy Lin, Stephanie Rudolph, Nikhil Sharma, Eric Griffith, and Christopher Harvey.

The study was supported by the National Institutes of Health (grants R01NS028829, R01NS115965, R01NS089521, T32NS007473 and F32NS112455), a Stuart H.Q. and Victoria Quan fellowship, a Harvard Department of Neurobiology graduate fellowship, an Aramont Fund.

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REVIEW article

Working memory from the psychological and neurosciences perspectives: a review.

• 1 Department of Neurosciences, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian, Malaysia
• 2 Center for Neuroscience Services and Research, Universiti Sains Malaysia, Kubang Kerian, Malaysia

Since the concept of working memory was introduced over 50 years ago, different schools of thought have offered different definitions for working memory based on the various cognitive domains that it encompasses. The general consensus regarding working memory supports the idea that working memory is extensively involved in goal-directed behaviors in which information must be retained and manipulated to ensure successful task execution. Before the emergence of other competing models, the concept of working memory was described by the multicomponent working memory model proposed by Baddeley and Hitch. In the present article, the authors provide an overview of several working memory-relevant studies in order to harmonize the findings of working memory from the neurosciences and psychological standpoints, especially after citing evidence from past studies of healthy, aging, diseased, and/or lesioned brains. In particular, the theoretical framework behind working memory, in which the related domains that are considered to play a part in different frameworks (such as memory’s capacity limit and temporary storage) are presented and discussed. From the neuroscience perspective, it has been established that working memory activates the fronto-parietal brain regions, including the prefrontal, cingulate, and parietal cortices. Recent studies have subsequently implicated the roles of subcortical regions (such as the midbrain and cerebellum) in working memory. Aging also appears to have modulatory effects on working memory; age interactions with emotion, caffeine and hormones appear to affect working memory performances at the neurobiological level. Moreover, working memory deficits are apparent in older individuals, who are susceptible to cognitive deterioration. Another younger population with working memory impairment consists of those with mental, developmental, and/or neurological disorders such as major depressive disorder and others. A less coherent and organized neural pattern has been consistently reported in these disadvantaged groups. Working memory of patients with traumatic brain injury was similarly affected and shown to have unusual neural activity (hyper- or hypoactivation) as a general observation. Decoding the underlying neural mechanisms of working memory helps support the current theoretical understandings concerning working memory, and at the same time provides insights into rehabilitation programs that target working memory impairments from neurophysiological or psychological aspects.

Introduction

Working memory has fascinated scholars since its inception in the 1960’s ( Baddeley, 2010 ; D’Esposito and Postle, 2015 ). Indeed, more than a century of scientific studies revolving around memory in the fields of psychology, biology, or neuroscience have not completely agreed upon a unified categorization of memory, especially in terms of its functions and mechanisms ( Cowan, 2005 , 2008 ; Baddeley, 2010 ). From the coining of the term “memory” in the 1880’s by Hermann Ebbinghaus, to the distinction made between primary and secondary memory by William James in 1890, and to the now widely accepted and used categorizations of memory that include: short-term, long-term, and working memories, studies that have tried to decode and understand this abstract concept called memory have been extensive ( Cowan, 2005 , 2008 ). Short and long-term memory suggest that the difference between the two lies in the period that the encoded information is retained. Other than that, long-term memory has been unanimously understood as a huge reserve of knowledge about past events, and its existence in a functioning human being is without dispute ( Cowan, 2008 ). Further categorizations of long-term memory include several categories: (1) episodic; (2) semantic; (3) Pavlovian; and (4) procedural memory ( Humphreys et al., 1989 ). For example, understanding and using language in reading and writing demonstrates long-term storage of semantics. Meanwhile, short-term memory was defined as temporarily accessible information that has a limited storage time ( Cowan, 2008 ). Holding a string of meaningless numbers in the mind for brief delays reflects this short-term component of memory. Thus, the concept of working memory that shares similarities with short-term memory but attempts to address the oversimplification of short-term memory by introducing the role of information manipulation has emerged ( Baddeley, 2012 ). This article seeks to present an up-to-date introductory overview of the realm of working memory by outlining several working memory studies from the psychological and neurosciences perspectives in an effort to refine and unite the scientific knowledge concerning working memory.

The Multicomponent Working Memory Model

When one describes working memory, the multicomponent working memory model is undeniably one of the most prominent working memory models that is widely cited in literatures ( Baars and Franklin, 2003 ; Cowan, 2005 ; Chein et al., 2011 ; Ashkenazi et al., 2013 ; D’Esposito and Postle, 2015 ; Kim et al., 2015 ). Baddeley and Hitch (1974) proposed a working memory model that revolutionized the rigid and dichotomous view of memory as being short or long-term, although the term “working memory” was first introduced by Miller et al. (1960) . The working memory model posited that as opposed to the simplistic functions of short-term memory in providing short-term storage of information, working memory is a multicomponent system that manipulates information storage for greater and more complex cognitive utility ( Baddeley and Hitch, 1974 ; Baddeley, 1996 , 2000b ). The three subcomponents involved are phonological loop (or the verbal working memory), visuospatial sketchpad (the visual-spatial working memory), and the central executive which involves the attentional control system ( Baddeley and Hitch, 1974 ; Baddeley, 2000b ). It was not until 2000 that another component termed “episodic buffer” was introduced into this working memory model ( Baddeley, 2000a ). Episodic buffer was regarded as a temporary storage system that modulates and integrates different sensory information ( Baddeley, 2000a ). In short, the central executive functions as the “control center” that oversees manipulation, recall, and processing of information (non-verbal or verbal) for meaningful functions such as decision-making, problem-solving or even manuscript writing. In Baddeley and Hitch (1974) ’s well-cited paper, information received during the engagement of working memory can also be transferred to long-term storage. Instead of seeing working memory as merely an extension and a useful version of short-term memory, it appears to be more closely related to activated long-term memory, as suggested by Cowan (2005 , 2008 ), who emphasized the role of attention in working memory; his conjectures were later supported by Baddeley (2010) . Following this, the current development of the multicomponent working memory model could be retrieved from Baddeley’s article titled “Working Memory” published in Current Biology , in Figure 2 ( Baddeley, 2010 ).

An Embedded-Processes Model of Working Memory

Notwithstanding the widespread use of the multicomponent working memory model, Cowan (1999 , 2005 ) proposed the embedded-processes model that highlights the roles of long-term memory and attention in facilitating working memory functioning. Arguing that the Baddeley and Hitch (1974) model simplified perceptual processing of information presentation to the working memory store without considering the focus of attention to the stimuli presented, Cowan (2005 , 2010 ) stressed the pivotal and central roles of working memory capacity for understanding the working memory concept. According to Cowan (2008) , working memory can be conceptualized as a short-term storage component with a capacity limit that is heavily dependent on attention and other central executive processes that make use of stored information or that interact with long-term memory. The relationships between short-term, long-term, and working memory could be presented in a hierarchical manner whereby in the domain of long-term memory, there exists an intermediate subset of activated long-term memory (also the short-term storage component) and working memory belongs to the subset of activated long-term memory that is being attended to ( Cowan, 1999 , 2008 ). An illustration of Cowan’s theoretical framework on working memory can be traced back to Figure 1 in his paper titled “What are the differences between long-term, short-term, and working memory?” published in Progress in Brain Research ( Cowan, 2008 ).

Alternative Models

Cowan’s theoretical framework toward working memory is consistent with Engle (2002) ’s view, in which it was posited that working memory capacity is comparable to directed or held attention information inhibition. Indeed, in their classic study on reading span and reading comprehension, Daneman and Carpenter (1980) demonstrated that working memory capacity, which was believed to be reflected by the reading span task, strongly correlated with various comprehension tests. Surely, recent and continual growth in the memory field has also demonstrated the development of other models such as the time-based resource-sharing model proposed by several researchers ( Barrouillet et al., 2004 , 2009 ; Barrouillet and Camos, 2007 ). This model similarly demonstrated that cognitive load and working memory capacity that were so often discussed by working memory researchers were mainly a product of attention that one receives to allocate to tasks at hand ( Barrouillet et al., 2004 , 2009 ; Barrouillet and Camos, 2007 ). In fact, the allocated cognitive resources for a task (such as provided attention) and the duration of such allocation dictated the likelihood of success in performing the tasks ( Barrouillet et al., 2004 , 2009 ; Barrouillet and Camos, 2007 ). This further highlighted the significance of working memory in comparison with short-term memory in that, although information retained during working memory is not as long-lasting as long-term memory, it is not the same and deviates from short-term memory for it involves higher-order processing and executive cognitive controls that are not observed in short-term memory. A more detailed presentation of other relevant working memory models that shared similar foundations with Cowan’s and emphasized the roles of long-term memory can be found in the review article by ( D’Esposito and Postle, 2015 ).

In addition, in order to understand and compare similarities and disparities in different proposed models, about 20 years ago, Miyake and Shah (1999) suggested theoretical questions to authors of different models in their book on working memory models. The answers to these questions and presentations of models by these authors gave rise to a comprehensive definition of working memory proposed by Miyake and Shah (1999 , p. 450), “working memory is those mechanisms or processes that are involved in the control, regulation, and active maintenance of task-relevant information in the service of complex cognition, including novel as well as familiar, skilled tasks. It consists of a set of processes and mechanisms and is not a fixed ‘place’ or ‘box’ in the cognitive architecture. It is not a completely unitary system in the sense that it involves multiple representational codes and/or different subsystems. Its capacity limits reflect multiple factors and may even be an emergent property of the multiple processes and mechanisms involved. Working memory is closely linked to LTM, and its contents consist primarily of currently activated LTM representations, but can also extend to LTM representations that are closely linked to activated retrieval cues and, hence, can be quickly activated.” That said, in spite of the variability and differences that have been observed following the rapid expansion of working memory understanding and its range of models since the inception of the multicomponent working memory model, it is worth highlighting that the roles of executive processes involved in working memory are indisputable, irrespective of whether different components exist. Such notion is well-supported as Miyake and Shah, at the time of documenting the volume back in the 1990’s, similarly noted that the mechanisms of executive control were being heavily investigated and emphasized ( Miyake and Shah, 1999 ). In particular, several domains of working memory such as the focus of attention ( Cowan, 1999 , 2008 ), inhibitory controls ( Engle and Kane, 2004 ), maintenance, manipulation, and updating of information ( Baddeley, 2000a , 2010 ), capacity limits ( Cowan, 2005 ), and episodic buffer ( Baddeley, 2000a ) were executive processes that relied on executive control efficacy (see also Miyake and Shah, 1999 ; Barrouillet et al., 2004 ; D’Esposito and Postle, 2015 ).

The Neuroscience Perspective

Following such cognitive conceptualization of working memory developed more than four decades ago, numerous studies have intended to tackle this fascinating working memory using various means such as decoding its existence at the neuronal level and/or proposing different theoretical models in terms of neuronal activity or brain activation patterns. Table 1 offers the summarized findings of these literatures. From the cognitive neuroscientific standpoint, for example, the verbal and visual-spatial working memories were examined separately, and the distinction between the two forms was documented through studies of patients with overt impairment in short-term storage for different verbal or visual tasks ( Baddeley, 2000b ). Based on these findings, associations or dissociations with the different systems of working memory (such as phonological loops and visuospatial sketchpad) were then made ( Baddeley, 2000b ). It has been established that verbal and acoustic information activates Broca’s and Wernicke’s areas while visuospatial information is represented in the right hemisphere ( Baddeley, 2000b ). Not surprisingly, many supporting research studies have pointed to the fronto-parietal network involving the dorsolateral prefrontal cortex (DLPFC), the anterior cingulate cortex (ACC), and the parietal cortex (PAR) as the working memory neural network ( Osaka et al., 2003 ; Owen et al., 2005 ; Chein et al., 2011 ; Kim et al., 2015 ). More precisely, the DLPFC has been largely implicated in tasks demanding executive control such as those requiring integration of information for decision-making ( Kim et al., 2015 ; Jimura et al., 2017 ), maintenance and manipulation/retrieval of stored information or relating to taxing loads (such as capacity limit) ( Osaka et al., 2003 ; Moore et al., 2013 ; Vartanian et al., 2013 ; Rodriguez Merzagora et al., 2014 ), and information updating ( Murty et al., 2011 ). Meanwhile, the ACC has been shown to act as an “attention controller” that evaluates the needs for adjustment and adaptation of received information based on task demands ( Osaka et al., 2003 ), and the PAR has been regarded as the “workspace” for sensory or perceptual processing ( Owen et al., 2005 ; Andersen and Cui, 2009 ). Figure 1 attempted to translate the theoretical formulation of the multicomponent working memory model ( Baddeley, 2010 ) to specific regions in the human brain. It is, however, to be acknowledged that the current neuroscientific understanding on working memory adopted that working memory, like other cognitive systems, involves the functional integration of the brain as a whole; and to clearly delineate its roles into multiple components with only a few regions serving as specific buffers was deemed impractical ( D’Esposito and Postle, 2015 ). Nonetheless, depicting the multicomponent working memory model in the brain offers a glimpse into the functional segregation of working memory.

TABLE 1. Working memory (WM) studies in the healthy brain.

FIGURE 1. A simplified depiction (adapted from the multicomponent working memory model by Baddeley, 2010 ) as implicated in the brain, in which the central executive assumes the role to exert control and oversee the manipulation of incoming information for intended execution. ACC, Anterior cingulate cortex.

Further investigation has recently revealed that other than the generally informed cortical structures involved in verbal working memory, basal ganglia, which lies in the subcortical layer, plays a role too ( Moore et al., 2013 ). Particularly, the caudate and thalamus were activated during task encoding, and the medial thalamus during the maintenance phase, while recorded activity in the fronto-parietal network, which includes the DLPFC and the parietal lobules, was observed only during retrieval ( Moore et al., 2013 ). These findings support the notion that the basal ganglia functions to enhance focusing on a target while at the same time suppressing irrelevant distractors during verbal working memory tasks, which is especially crucial at the encoding phase ( Moore et al., 2013 ). Besides, a study conducted on mice yielded a similar conclusion in which the mediodorsal thalamus aided the medial prefrontal cortex in the maintenance of working memory ( Bolkan et al., 2017 ). In another study by Murty et al. (2011) in which information updating, which is one of the important aspects of working memory, was investigated, the midbrain including the substantia nigra/ventral tegmental area and caudate was activated together with DLPFC and other parietal regions. Taken together, these studies indicated that brain activation of working memory are not only limited to the cortical layer ( Murty et al., 2011 ; Moore et al., 2013 ). In fact, studies on cerebellar lesions subsequently discovered that patients suffered from impairments in attention-related working memory or executive functions, suggesting that in spite of the motor functions widely attributed to the cerebellum, the cerebellum is also involved in higher-order cognitive functions including working memory ( Gottwald et al., 2004 ; Ziemus et al., 2007 ).

Shifting the attention to the neuronal network involved in working memory, effective connectivity analysis during engagement of a working memory task reinforced the idea that the DLPFC, PAR and ACC belong to the working memory circuitry, and bidirectional endogenous connections between all these regions were observed in which the left and right PAR were the modeled input regions ( Dima et al., 2014 ) (refer to Supplementary Figure 1 in Dima et al., 2014 ). Effective connectivity describes the attempt to model causal influence of neuronal connections in order to better understand the hidden neuronal states underlying detected neuronal responses ( Friston et al., 2013 ). Another similar study of working memory using an effective connectivity analysis that involved more brain regions, including the bilateral middle frontal gyrus (MFG), ACC, inferior frontal cortex (IFC), and posterior parietal cortex (PPC) established the modulatory effect of working memory load in this fronto-parietal network with memory delay as the driving input to the bilateral PPC ( Ma et al., 2012 ) (refer to Figure 1 in Ma et al., 2012 ).

Moving away from brain regions activated but toward the in-depth neurobiological side of working memory, it has long been understood that the limited capacity of working memory and its transient nature, which are considered two of the defining characteristics of working memory, indicate the role of persistent neuronal firing (see Review Article by D’Esposito and Postle, 2015 ; Zylberberg and Strowbridge, 2017 ; see also Silvanto, 2017 ), that is, continuous action potentials are generated in neurons along the neural network. However, this view was challenged when activity-silent synaptic mechanisms were found to also be involved ( Mongillo et al., 2008 ; Rose et al., 2016 ; see also Silvanto, 2017 ). Instead of holding relevant information through heightened and persistent neuronal firing, residual calcium at the presynaptic terminals was suggested to have mediated the working memory process ( Mongillo et al., 2008 ). This synaptic theory was further supported when TMS application produced a reactivation effect of past information that was not needed or attended at the conscious level, hence the TMS application facilitated working memory efficacy ( Rose et al., 2016 ). As it happens, this provided evidence from the neurobiological viewpoint to support Cowan’s theorized idea of “activated long-term memory” being a feature of working memory as non-cued past items in working memory that were assumed to be no longer accessible were actually stored in a latent state and could be brought back into consciousness. However, the researchers cautioned the use of the term “activated long-term memory” and opted for “prioritized long-term memory” because these unattended items maintained in working memory seemed to employ a different mechanism than items that were dropped from working memory ( Rose et al., 2016 ). Other than the synaptic theory, the spiking working memory model proposed by Fiebig and Lansner (2017) that borrowed the concept from fast Hebbian plasticity similarly disagreed with persistent neuronal activity and demonstrated that working memory processes were instead manifested in discrete oscillatory bursts.

Age and Working Memory

Nevertheless, having established a clear working memory circuitry in the brain, differences in brain activations, neural patterns or working memory performances are still apparent in different study groups, especially in those with diseased or aging brains. For a start, it is well understood that working memory declines with age ( Hedden and Gabrieli, 2004 ; Ziaei et al., 2017 ). Hence, older participants are expected to perform poorer on a working memory task when making comparison with relatively younger task takers. In fact, it was reported that decreases in cortical surface area in the frontal lobe of the right hemisphere was associated with poorer performers ( Nissim et al., 2017 ). In their study, healthy (those without mild cognitive impairments [MCI] or neurodegenerative diseases such as dementia or Alzheimer’s) elderly people with an average age of 70 took the n-back working memory task while magnetic resonance imaging (MRI) scans were obtained from them ( Nissim et al., 2017 ). The outcomes exhibited that a decrease in cortical surface areas in the superior frontal gyrus, pars opercularis of the inferior frontal gyrus, and medial orbital frontal gyrus that was lateralized to the right hemisphere, was significantly detected among low performers, implying an association between loss of brain structural integrity and working memory performance ( Nissim et al., 2017 ). There was no observed significant decline in cortical thickness of the studied brains, which is assumed to implicate neurodegenerative tissue loss ( Nissim et al., 2017 ).

Moreover, another extensive study that examined cognitive functions of participants across the lifespan using functional magnetic resonance imaging (fMRI) reported that the right lateralized fronto-parietal regions in addition to the ventromedial prefrontal cortex (VMPFC), posterior cingulate cortex, and left angular and middle frontal gyri (the default mode regions) in older adults showed reduced modulation of task difficulty, which was reflective of poorer task performance ( Rieck et al., 2017 ). In particular, older-age adults (55–69 years) exhibited diminished brain activations (positive modulation) as compared to middle-age adults (35–54 years) with increasing task difficulty, whereas lesser deactivation (negative modulation) was observed between the transition from younger adults (20–34 years) to middle-age adults ( Rieck et al., 2017 ). This provided insights on cognitive function differences during an individual’s lifespan at the neurobiological level, which hinted at the reduced ability or efficacy of the brain to modulate functional regions to increased difficulty as one grows old ( Rieck et al., 2017 ). As a matter of fact, such an opinion was in line with the Compensation-Related Utilization of Neural Circuits Hypothesis (CRUNCH) proposed by Reuter-Lorenz and Cappell (2008) . The CRUNCH likewise agreed upon reduced neural efficiency in older adults and contended that age-associated cognitive decline brought over-activation as a compensatory mechanism; yet, a shift would occur as task loads increase and under-activation would then be expected because older adults with relatively lesser cognitive resources would max out their ‘cognitive reserve’ sooner than younger adults ( Reuter-Lorenz and Park, 2010 ; Schneider-Garces et al., 2010 ).

In addition to those findings, emotional distractors presented during a working memory task were shown to alter or affect task performance in older adults ( Oren et al., 2017 ; Ziaei et al., 2017 ). Based on the study by Oren et al. (2017) who utilized the n-back task paired with emotional distractors with neutral or negative valence in the background, negative distractors with low load (such as 1-back) resulted in shorter response time (RT) in the older participants ( M age = 71.8), although their responses were not significantly more accurate when neutral distractors were shown. Also, lesser activations in the bilateral MFG, VMPFC, and left PAR were reported in the old-age group during negative low load condition. This finding subsequently demonstrated the results of emotional effects on working memory performance in older adults ( Oren et al., 2017 ). Further functional connectivity analyses revealed that the amygdala, the region well-known to be involved in emotional processing, was deactivated and displayed similar strength in functional connectivity regardless of emotional or load conditions in the old-age group ( Oren et al., 2017 ). This finding went in the opposite direction of that observed in the younger group in which the amygdala was strongly activated with less functional connections to the bilateral MFG and left PAR ( Oren et al., 2017 ). This might explain the shorter reported RT, which was an indication of improved working memory performance, during the emotional working memory task in the older adults as their amygdala activation was suppressed as compared to the younger adults ( Oren et al., 2017 ).

Interestingly, a contrasting neural connection outcome was reported in the study by Ziaei et al. (2017) in which differential functional networks relating to emotional working memory task were employed by the two studied groups: (1) younger ( M age = 22.6) and (2) older ( M age = 68.2) adults. In the study, emotional distractors with positive, neutral, and negative valence were presented during a visual working memory task and older adults were reported to adopt two distinct networks involving the VMPFC to encode and process positive and negative distractors while younger adults engaged only one neural pathway ( Ziaei et al., 2017 ). The role of amygdala engagement in processing only negative items in the younger adults, but both negative and positive distractors in the older adults, could be reflective of the older adults’ better ability at regulating negative emotions which might subsequently provide a better platform for monitoring working memory performance and efficacy as compared to their younger counterparts ( Ziaei et al., 2017 ). This study’s findings contradict those by Oren et al. (2017) in which the amygdala was found to play a bigger role in emotional working memory tasks among older participants as opposed to being suppressed as reported by Oren et al. (2017) . Nonetheless, after overlooking the underlying neural mechanism relating to emotional distractors, it was still agreed that effective emotional processing sustained working memory performance among older/elderly people ( Oren et al., 2017 ; Ziaei et al., 2017 ).

Aside from the interaction effect between emotion and aging on working memory, the impact of caffeine was also investigated among elders susceptible to age-related cognitive decline; and those reporting subtle cognitive deterioration 18-months after baseline measurement showed less marked effects of caffeine in the right hemisphere, unlike those with either intact cognitive ability or MCI ( Haller et al., 2017 ). It was concluded that while caffeine’s effects were more pronounced in MCI participants, elders in the early stages of cognitive decline displayed diminished sensitivity to caffeine after being tested with the n-back task during fMRI acquisition ( Haller et al., 2017 ). It is, however, to be noted that the working memory performance of those displaying minimal cognitive deterioration was maintained even though their brain imaging uncovered weaker brain activation in a more restricted area ( Haller et al., 2017 ). Of great interest, such results might present a useful brain-based marker that can be used to identify possible age-related cognitive decline.

Similar findings that demonstrated more pronounced effects of caffeine on elderly participants were reported in an older study, whereas older participants in the age range of 50–65 years old exhibited better working memory performance that offset the cognitive decline observed in those with no caffeine consumption, in addition to displaying shorter reaction times and better motor speeds than observed in those without caffeine ( Rees et al., 1999 ). Animal studies using mice showed replication of these results in mutated mice models of Alzheimer’s disease or older albino mice, both possibly due to the reported results of reduced amyloid production or brain-derived neurotrophic factor and tyrosine-kinase receptor. These mice performed significantly better after caffeine treatment in tasks that supposedly tapped into working memory or cognitive functions ( Arendash et al., 2006 ). Such direct effects of caffeine on working memory in relation to age was further supported by neuroimaging studies ( Haller et al., 2013 ; Klaassen et al., 2013 ). fMRI uncovered increased brain activation in regions or networks of working memory, including the fronto-parietal network or the prefrontal cortex in old-aged ( Haller et al., 2013 ) or middle-aged adults ( Klaassen et al., 2013 ), even though the behavioral measures of working memory did not differ. Taken together, these outcomes offered insight at the neurobiological level in which caffeine acts as a psychoactive agent that introduces changes and alters the aging brain’s biological environment that explicit behavioral testing might fail to capture due to performance maintenance ( Haller et al., 2013 , 2017 ; Klaassen et al., 2013 ).

With respect to physiological effects on cognitive functions (such as effects of caffeine on brain physiology), estradiol, the primary female sex hormone that regulates menstrual cycles, was found to also modulate working memory by engaging different brain activity patterns during different phases of the menstrual cycle ( Joseph et al., 2012 ). The late follicular (LF) phase of the menstrual cycle, characterized by high estradiol levels, was shown to recruit more of the right hemisphere that was associated with improved working memory performance than did the early follicular (EF) phase, which has lower estradiol levels although overall, the direct association between estradiol levels and working memory was inconclusive ( Joseph et al., 2012 ). The finding that estradiol levels modified brain recruitment patterns at the neurobiological level, which could indirectly affect working memory performance, presents implications that working memory impairment reported in post-menopausal women (older aged women) could indicate a link with estradiol loss ( Joseph et al., 2012 ). In 2000, post-menopausal women undergoing hormone replacement therapy, specifically estrogen, were found to have better working memory performance in comparison with women who took estrogen and progestin or women who did not receive the therapy ( Duff and Hampson, 2000 ). Yet, interestingly, a study by Janowsky et al. (2000) showed that testosterone supplementation counteracted age-related working memory decline in older males, but a similar effect was not detected in older females who were supplemented with estrogen. A relatively recent paper might have provided the explanation to such contradicting outcomes ( Schöning et al., 2007 ). As demonstrated in the study using fMRI, the nature of the task (such as verbal or visual-spatial) might have played a role as a higher level of testosterone (in males) correlated with activations of the left inferior parietal cortex, which was deemed a key region in spatial processing that subsequently brought on better performance in a mental-rotation task. In contrast, significant correlation between estradiol and other cortical activations in females in the midluteal phase, who had higher estradiol levels, did not result in better performance of the task compared to women in the EF phase or men ( Schöning et al., 2007 ). Nonetheless, it remains premature to conclude that age-related cognitive decline was a result of hormonal (estradiol or testosterone) fluctuations although hormones might have modulated the effect of aging on working memory.

Other than the presented interaction effects of age and emotions, caffeine, and hormones, other studies looked at working memory training in the older population in order to investigate working memory malleability in the aging brain. Findings of improved performance for the same working memory task after training were consistent across studies ( Dahlin et al., 2008 ; Borella et al., 2017 ; Guye and von Bastian, 2017 ; Heinzel et al., 2017 ). Such positive results demonstrated effective training gains regardless of age difference that could even be maintained until 18 months later ( Dahlin et al., 2008 ) even though the transfer effects of such training to other working memory tasks need to be further elucidated as strong evidence of transfer with medium to large effect size is lacking ( Dahlin et al., 2008 ; Guye and von Bastian, 2017 ; Heinzel et al., 2017 ; see also Karbach and Verhaeghen, 2014 ). The studies showcasing the effectiveness of working memory training presented a useful cognitive intervention that could partially stall or delay cognitive decline. Table 2 presents an overview of the age-related working memory studies.

TABLE 2. Working memory (WM) studies in relation to age.

The Diseased Brain and Working Memory

Age is not the only factor influencing working memory. In recent studies, working memory deficits in populations with mental or neurological disorders were also being investigated (see Table 3 ). Having identified that the working memory circuitry involves the fronto-parietal region, especially the prefrontal and parietal cortices, in a healthy functioning brain, targeting these areas in order to understand how working memory is affected in a diseased brain might provide an explanation for the underlying deficits observed at the behavioral level. For example, it was found that individuals with generalized or social anxiety disorder exhibited reduced DLPFC activation that translated to poorer n-back task performance in terms of accuracy and RT when compared with the controls ( Balderston et al., 2017 ). Also, VMPFC and ACC, representing the default mode network (DMN), were less inhibited in these individuals, indicating that cognitive resources might have been divided and resulted in working memory deficits due to the failure to disengage attention from persistent anxiety-related thoughts ( Balderston et al., 2017 ). Similar speculation can be made about individuals with schizophrenia. Observed working memory deficits might be traced back to impairments in the neural networks that govern attentional-control and information manipulation and maintenance ( Grot et al., 2017 ). The participants performed a working memory binding task, whereby they had to make sure that the word-ellipse pairs presented during the retrieval phase were identical to those in the encoding phase in terms of location and verbal information; results concluded that participants with schizophrenia had an overall poorer performance compared to healthy controls when they were asked to actively bind verbal and spatial information ( Grot et al., 2017 ). This was reflected in the diminished activation in the schizophrenia group’s ventrolateral prefrontal cortex and the PPC that were said to play a role in manipulation and reorganization of information during encoding and maintenance of information after encoding ( Grot et al., 2017 ).

TABLE 3. Working memory (WM) studies in the diseased brain.

In addition, patients with major depressive disorder (MDD) displayed weaker performance in the working memory updating domain in which information manipulation was needed when completing a visual working memory task ( Le et al., 2017 ). The working memory task employed in the study was a delayed recognition task that required participants to remember and recognize the faces or scenes as informed after stimuli presentation while undergoing fMRI scan ( Le et al., 2017 ). Subsequent functional connectivity analyses revealed that the fusiform face area (FFA), parahippocampal place area (PPA), and left MFG showed aberrant activity in the MDD group as compared to the control group ( Le et al., 2017 ). These brain regions are known to be the visual association area and the control center of working memory and have been implicated in visual working memory updating in healthy adults ( Le et al., 2017 ). Therefore, altered visual cortical functions and load-related activation in the prefrontal cortex in the MDD group implied that the cognitive control for visual information processing and updating might be impaired at the input or control level, which could have ultimately played a part in the depressive symptoms ( Le et al., 2017 ).

Similarly, during a verbal delayed match to sample task that asked participants to sub-articulatorly rehearse presented target letters for subsequent letter-matching, individuals with bipolar affective disorder displayed aberrant neural interactions between the right amygdala, which is part of the limbic system implicated in emotional processing as previously described, and ipsilateral cortical regions often concerned with verbal working memory, pointing out that the cortico-amygdalar connectivity was disrupted, which led to verbal working memory deficits ( Stegmayer et al., 2015 ). As an attempt to gather insights into previously reported hyperactivation in the amygdala in bipolar affective disorder during an articulatory working memory task, functional connectivity analyses revealed that negative functional interactions seen in healthy controls were not replicated in patients with bipolar affective disorder ( Stegmayer et al., 2015 ). Consistent with the previously described study about emotional processing effects on working memory in older adults, this reported outcome was suggestive of the brain’s failed attempts to suppress pathological amygdalar activation during a verbal working memory task ( Stegmayer et al., 2015 ).

Another affected group with working memory deficits that has been the subject of research interest was children with developmental disorders such as attention deficit/hyperactivity disorder (ADHD), developmental dyscalculia, and reading difficulties ( Rotzer et al., 2009 ; Ashkenazi et al., 2013 ; Wang and Gathercole, 2013 ; Maehler and Schuchardt, 2016 ). For instance, looking into the different working memory subsystems based on Baddeley’s multicomponent working memory model in children with dyslexia and/or ADHD and children with dyscalculia and/or ADHD through a series of tests, it was reported that distinctive working memory deficits by groups could be detected such that phonological loop (e.g., digit span) impairment was observed in the dyslexia group, visuospatial sketchpad (e.g., Corsi block tasks) deficits in the dyscalculia group, while central executive (e.g., complex counting span) deficits in children with ADHD ( Maehler and Schuchardt, 2016 ). Meanwhile, examination of working memory impairment in a delayed match-to-sample visual task that put emphasis on the maintenance phase of working memory by examining the brainwaves of adults with ADHD using electroencephalography (EEG) also revealed a marginally significantly lower alpha band power in the posterior regions as compared to healthy individuals, and such an observation was not significantly improved after working memory training (Cogmed working memory training, CWMT Program) ( Liu et al., 2016 ). The alpha power was considered important in the maintenance of working memory items; and lower working memory accuracy paired with lower alpha band power was indeed observed in the ADHD group ( Liu et al., 2016 ).

Not dismissing the above compiled results, children encountering disabilities in mathematical operations likewise indicated deficits in the working memory domain that were traceable to unusual brain activities at the neurobiological level ( Rotzer et al., 2009 ; Ashkenazi et al., 2013 ). It was speculated that visuospatial working memory plays a vital role when arithmetic problem-solving is involved in order to ensure intact mental representations of the numerical information ( Rotzer et al., 2009 ). Indeed, Ashkenazi et al. (2013) revealed that Block Recall, a variant of the Corsi Block Tapping test and a subtest of the Working Memory Test Battery for Children (WMTB-C) that explored visuospatial sketchpad ability, was significantly predictive of math abilities. In relation to this, studies investigating brain activation patterns and performance of visuospatial working memory task in children with mathematical disabilities identified the intraparietal sulcus (IPS), in conjunction with other regions in the prefrontal and parietal cortices, to have less activation when visuospatial working memory was deemed involved (during an adapted form of Corsi Block Tapping test made suitable for fMRI [ Rotzer et al., 2009 ]); in contrast the control group demonstrated correlations of the IPS in addition to the fronto-parietal cortical activation with the task ( Rotzer et al., 2009 ; Ashkenazi et al., 2013 ). These brain activity variations that translated to differences in overt performances between healthily developing individuals and those with atypical development highlighted the need for intervention and attention for the disadvantaged groups.

Traumatic Brain Injury and Working Memory

Physical injuries impacting the frontal or parietal lobes would reasonably be damaging to one’s working memory. This is supported in studies employing neuropsychological testing to assess cognitive impairments in patients with traumatic brain injury; and poorer cognitive performances especially involving the working memory domains were reported (see Review Articles by Dikmen et al., 2009 ; Dunning et al., 2016 ; Phillips et al., 2017 ). Research on cognitive deficits in traumatic brain injury has been extensive due to the debilitating conditions brought upon an individual daily life after the injury. Traumatic brain injuries (TBI) refer to accidental damage to the brain after being hit by an object or following rapid acceleration or deceleration ( Farrer, 2017 ). These accidents include falls, assaults, or automobile accidents and patients with TBI can be then categorized into three groups; (1) mild TBI with GCS – Glasgow Coma Scale – score of 13–15; (2) moderate TBI with GCS score of 9–12; and (3) severe TBI with GCS score of 3–8 ( Farrer, 2017 ). In a recently published meta-analysis that specifically looked at working memory impairments in patients with moderate to severe TBI, patients displayed reduced cognitive functions in verbal short-term memory in addition to verbal and visuospatial working memory in comparison to control groups ( Dunning et al., 2016 ). It was also understood from the analysis that the time lapse since injury and age of injury were deciding factors that influenced these cognitive deficits in which longer time post-injury or older age during injury were associated with greater cognitive decline ( Dunning et al., 2016 ).

Nonetheless, it is to be noted that such findings relating to age of injury could not be generalized to the child population since results from the pediatric TBI cases showed that damage could negatively impact developmental skills that could indicate a greater lag in cognitive competency as the child’s frontal lobe had yet to mature ( Anderson and Catroppa, 2007 ; Mandalis et al., 2007 ; Nadebaum et al., 2007 ; Gorman et al., 2012 ). These studies all reported working memory impairment of different domains such as attentional control, executive functions, or verbal and visuospatial working memory in the TBI group, especially for children with severe TBI ( Mandalis et al., 2007 ; Nadebaum et al., 2007 ; Gorman et al., 2012 ). Investigation of whether working memory deficits are domain-specific or -general or involve one or more mechanisms, has yielded inconsistent results. For example, Perlstein et al. (2004) found that working memory was impaired in the TBI group only when complex manipulation such as sequential coding of information is required and not accounted for by processing speed or maintenance of information, but two teams of researchers ( Perbal et al., 2003 ; Gorman et al., 2012 ) suggested otherwise. From their study on timing judgments, Perbal et al. (2003) concluded that deficits were not related to time estimation but more on generalized attentional control, working memory and processing speed problems; while Gorman et al. (2012) also attributed the lack of attentional focus to impairments observed during the working memory task. In fact, in a later study by Gorman et al. (2016) , it was shown that processing speed mediated TBI effects on working memory even though the mediation was partial. On the other hand, Vallat-Azouvi et al. (2007) reported impairments in the working memory updating domain that came with high executive demands for TBI patients. Also, Mandalis et al. (2007) similarly highlighted potential problems with attention and taxing cognitive demands in the TBI group.

From the neuroscientific perspective, hyper-activation or -connectivity in the working memory circuitry was reported in TBI patients in comparison with healthy controls when both groups engaged in working memory tasks, suggesting that the brain attempted to compensate for or re-establish lost connections upon the injury ( Dobryakova et al., 2015 ; Hsu et al., 2015 ; Wylie et al., 2015 ). For a start, it was observed that participants with mild TBI displayed increased activation in the right prefrontal cortex during a working memory task when comparing to controls ( Wylie et al., 2015 ). Interestingly, this activation pattern only occurred in patients who did not experience a complete recovery 1 week after the injury ( Wylie et al., 2015 ). Besides, low activation in the DMN was observed in mild TBI patients without cognitive recovery, and such results seemed to be useful in predicting recovery in patients in which the patients did not recover when hypoactivation (low activation) was reported, and vice versa ( Wylie et al., 2015 ). This might be suggestive of the potential of cognitive recovery simply by looking at the intensity of brain activation of the DMN, for an increase in activation of the DMN seemed to be superseded before cognitive recovery was present ( Wylie et al., 2015 ).

In fact, several studies lent support to the speculation mentioned above as hyperactivation or hypoactivation in comparison with healthy participants was similarly identified. When sex differences were being examined in working memory functional activity in mild TBI patients, hyperactivation was reported in male patients when comparing to the male control group, suggesting that the hyperactivation pattern might be the brain’s attempt at recovering impaired functions; even though hypoactivation was shown in female patients as compared to the female control group ( Hsu et al., 2015 ). The researchers from the study further explained that such hyperactivation after the trauma acted as a neural compensatory mechanism so that task performance could be maintained while hypoactivation with a poorer performance could have been the result of a more severe injury ( Hsu et al., 2015 ). Therefore, the decrease in activation in female patients, in addition to the observed worse performance, was speculated to be due to a more serious injury sustained by the female patients group ( Hsu et al., 2015 ).

In addition, investigation of the effective connectivity of moderate and severe TBI participants during a working memory task revealed that the VMPFC influenced the ACC in these TBI participants when the opposite was observed in healthy subjects ( Dobryakova et al., 2015 ). Moreover, increased inter-hemispheric transfer due to an increased number of connections between the left and right hemispheres (hyper-connectivity) without clear directionality of information flow (redundant connectivity) was also reported in the TBI participants ( Dobryakova et al., 2015 ). This study was suggestive of location-specific changes in the neural network connectivity following TBI depending on the cognitive functions at work, other than providing another support to the neural compensatory hypothesis due to the observed hyper-connectivity ( Dobryakova et al., 2015 ).

Nevertheless, inconsistent findings should not be neglected. In a study that also focused on brain connectivity analysis among patients with mild TBI by Hillary et al. (2011) , elevated task-related connectivity in the right hemisphere, in particular the prefrontal cortex, was consistently demonstrated during a working memory task while the control group showed greater left hemispheric activation. This further supported the right lateralization of the brain to reallocate cognitive resources of TBI patients post-injury. Meanwhile, the study did not manage to obtain the expected outcome in terms of greater clustering of whole-brain connections in TBI participants as hypothesized ( Hillary et al., 2011 ). That said, no significant loss or gain of connections due to the injury could be concluded from the study, as opposed to the hyper- or hypoactivation or hyper-connectivity frequently highlighted in other similar researches ( Hillary et al., 2011 ). Furthermore, a study by Chen et al. (2012) also failed to establish the same results of increased brain activation. Instead, with every increase of the working memory load, increase in brain activation, as expected to occur and as demonstrated in the control group, was unable to be detected in the TBI group ( Chen et al., 2012 ).

Taken all the insightful studies together, another aspect not to be neglected is the neuroimaging techniques employed in contributing to the literature on TBI. Modalities other than fMRI, which focuses on localization of brain activities, show other sides of the story of working memory impairments in TBI to offer a more holistic understanding. Studies adopting electroencephalography (EEG) or diffusor tensor imaging (DTI) reported atypical brainwaves coherence or white matter integrity in patients with TBI ( Treble et al., 2013 ; Ellis et al., 2016 ; Bailey et al., 2017 ; Owens et al., 2017 ). Investigating the supero-lateral medial forebrain bundle (MFB) that innervates and consequently terminates at the prefrontal cortex, microstructural white matter damage at the said area was indicated in participants with moderate to severe TBI by comparing its integrity with the control group ( Owens et al., 2017 ). Such observation was backed up by evidence showing that the patients performed more poorly on attention-loaded cognitive tasks of factors relating to slow processing speed than the healthy participants, although a direct association between MFB and impaired attentional system was not found ( Owens et al., 2017 ).

Correspondingly, DTI study of the corpus callosum (CC), which described to hold a vital role in connecting and coordinating both hemispheres to ensure competent cognitive functions, also found compromised microstructure of the CC with low fractional anisotropy and high mean diffusivity, both of which are indications of reduced white matter integrity ( Treble et al., 2013 ). This reported observation was also found to be predictive of poorer verbal or visuospatial working memory performance in callosal subregions connecting the parietal and temporal cortices ( Treble et al., 2013 ). Adding on to these results, using EEG to examine the functional consequences of CC damage revealed that interhemispheric transfer time (IHTT) of the CC was slower in the TBI group than the control group, suggesting an inefficient communication between the two hemispheres ( Ellis et al., 2016 ). In addition, the TBI group with slow IHTT as well exhibited poorer neurocognitive functioning including working memory than the healthy controls ( Ellis et al., 2016 ).

Furthermore, comparing the working memory between TBI, MDD, TBI-MDD, and healthy participants discovered that groups with MDD and TBI-MDD performed poorer on the Sternberg working memory task but functional connectivity on the other hand, showed that increased inter-hemispheric working memory gamma connectivity was observed in the TBI and TBI-MDD groups ( Bailey et al., 2017 ). Speculation provided for the findings of such neuronal state that was not reflected in the explicit working memory performance was that the deficits might not be detected or tested by the utilized Sternberg task ( Bailey et al., 2017 ). Another explanation attempting to answer the increase in gamma connectivity in these groups was the involvement of the neural compensatory mechanism after TBI to improve performance ( Bailey et al., 2017 ). Nevertheless, such outcome implies that behavioral performances or neuropsychological outcomes might not always be reflective of the functional changes happening in the brain.

Yet, bearing in mind that TBI consequences can be vast and crippling, cognitive improvement or recovery, though complicated due to the injury severity-dependent nature, is not impossible (see Review Article by Anderson and Catroppa, 2007 ; Nadebaum et al., 2007 ; Dikmen et al., 2009 ; Chen et al., 2012 ). As reported by Wylie et al. (2015) , cognitive improvement together with functional changes in the brain could be detected in individuals with mild TBI. Increased activation in the brain during 6-week follow-up was also observed in the mild TBI participants, implicating the regaining of connections in the brain ( Chen et al., 2012 ). Administration of certain cognitively enhancing drugs such as methylphenidate was reported to be helpful in improving working memory performance too ( Manktelow et al., 2017 ). Methylphenidate as a dopamine reuptake inhibitor was found to have modulated the neural activity in the left cerebellum which subsequently correlated with improved working memory performance ( Manktelow et al., 2017 ). A simplified summary of recent studies on working memory and TBI is tabulated in Table 4 .

TABLE 4. Working memory (WM) studies in the TBI group.

General Discussion and Future Direction

In practice, all of the aforementioned studies contribute to the working memory puzzle by addressing the topic from different perspectives and employing various methodologies to study it. Several theoretical models of working memory that conceptualized different working memory mechanisms or domains (such as focus of attention, inhibitory controls, maintenance and manipulation of information, updating and integration of information, capacity limits, evaluative and executive controls, and episodic buffer) have been proposed. Coupled with the working memory tasks of various means that cover a broad range (such as Sternberg task, n-back task, Corsi block-tapping test, Wechsler’s Memory Scale [WMS], and working memory subtests in the Wechsler Adult Intelligence Scale [WAIS] – Digit Span, Letter Number Sequencing), it has been difficult, if not highly improbable, for working memory studies to reach an agreement upon a consistent study protocol that is acceptable for generalization of results due to the constraints bound by the nature of the study. Various data acquisition and neuroimaging techniques that come with inconsistent validity such as paper-and-pen neuropsychological measures, fMRI, EEG, DTI, and functional near-infrared spectroscopy (fNIRS), or even animal studies can also be added to the list. This poses further challenges to quantitatively measure working memory as only a single entity. For example, when studying the neural patterns of working memory based on Cowan’s processes-embedded model using fMRI, one has to ensure that the working memory task selected is fMRI-compatible, and demands executive control of attention directed at activated long-term memory (domain-specific). That said, on the one hand, there are tasks that rely heavily on the information maintenance such as the Sternberg task; on the other hand, there are also tasks that look into the information manipulation updating such as the n-back or arithmetic task. Meanwhile, the digit span task in WAIS investigates working memory capacity, although it can be argued that it also encompasses the domain on information maintenance and updating-. Another consideration involves the different natures (verbal/phonological and visuospatial) of the working memory tasks as verbal or visuospatial information is believed to engage differing sensory mechanisms that might influence comparison of working memory performance between tasks of different nature ( Baddeley and Hitch, 1974 ; Cowan, 1999 ). For instance, though both are n-back tasks that includes the same working memory domains, the auditory n-back differs than the visual n-back as the information is presented in different forms. This feature is especially crucial with regards to the study populations as it differentiates between verbal and visuospatial working memory competence within individuals, which are assumed to be domain-specific as demonstrated by vast studies (such as Nadler and Archibald, 2014 ; Pham and Hasson, 2014 ; Nakagawa et al., 2016 ). These test variations undeniably present further difficulties in selecting an appropriate task. Nevertheless, the adoption of different modalities yielded diverging outcomes and knowledge such as behavioral performances, functional segregation and integration in the brain, white matter integrity, brainwave coherence, and oxy- and deoxyhaemoglobin concentrations that are undeniably useful in application to different fields of study.

In theory, the neural efficiency hypothesis explains that increased efficiency of the neural processes recruit fewer cerebral resources in addition to displaying lower activation in the involved neural network ( Vartanian et al., 2013 ; Rodriguez Merzagora et al., 2014 ). This is in contrast with the neural compensatory hypothesis in which it attempted to understand diminished activation that is generally reported in participants with TBI ( Hillary et al., 2011 ; Dobryakova et al., 2015 ; Hsu et al., 2015 ; Wylie et al., 2015 ; Bailey et al., 2017 ). In the diseased brain, low activation has often been associated with impaired cognitive function ( Chen et al., 2012 ; Dobryakova et al., 2015 ; Wylie et al., 2015 ). Opportunely, the CRUNCH model proposed within the field of aging might be translated and integrated the two hypotheses here as it suitably resolved the disparity of cerebral hypo- and hyper-activation observed in weaker, less efficient brains as compared to healthy, adept brains ( Reuter-Lorenz and Park, 2010 ; Schneider-Garces et al., 2010 ). Moreover, other factors such as the relationship between fluid intelligence and working memory might complicate the current understanding of working memory as a single, isolated construct since working memory is often implied in measurements of the intelligence quotient ( Cowan, 2008 ; Vartanian et al., 2013 ). Indeed, the process overlap theory of intelligence proposed by Kovacs and Conway (2016) in which the constructs of intelligence were heavily scrutinized (such as general intelligence factors, g and its smaller counterparts, fluid intelligence or reasoning, crystallized intelligence, perceptual speed, and visual-spatial ability), and fittingly connected working memory capacity with fluid reasoning. Cognitive tests such as Raven’s Progressive Matrices or other similar intelligence tests that demand complex cognition and were reported in the paper had been found to correlate strongly with tests of working memory ( Kovacs and Conway, 2016 ). Furthermore, in accordance with such views, in the same paper, neuroimaging studies found intelligence tests also activated the same fronto-parietal network observed in working memory ( Kovacs and Conway, 2016 ).

On the other hand, even though the roles of the prefrontal cortex in working memory have been widely established, region specificity and localization in the prefrontal cortex in relation to the different working memory domains such as manipulation or delayed retention of information remain at the premature stage (see Review Article by D’Esposito and Postle, 2015 ). It has been postulated that the neural mechanisms involved in working memory are of high-dimensionality and could not always be directly captured and investigated using neurophysiological techniques such as fMRI, EEG, or patch clamp recordings even when comparing with lesion data ( D’Esposito and Postle, 2015 ). According to D’Esposito and Postle (2015) , human fMRI studies have demonstrated that a rostral-caudal functional gradient related to level of abstraction required of working memory along the frontal cortex (in which different regions in the prefrontal cortex [from rostral to caudal] might be associated with different abstraction levels) might exist. Other functional gradients relating to different aspects of working memory were similarly unraveled ( D’Esposito and Postle, 2015 ). These proposed mechanisms with different empirical evidence point to the fact that conclusive understanding regarding working memory could not yet be achieved before the inconsistent views are reconciled.

Not surprisingly, with so many aspects of working memory yet to be understood and its growing complexity, the cognitive neuroscience basis of working memory requires constant research before an exhaustive account can be gathered. From the psychological conceptualization of working memory as attempted in the multicomponent working memory model ( Baddeley and Hitch, 1974 ), to the neural representations of working memory in the brain, especially in the frontal regions ( D’Esposito and Postle, 2015 ), one important implication derives from the present review of the literatures is that working memory as a psychological construct or a neuroscientific mechanism cannot be investigated as an isolated event. The need for psychology and neuroscience to interact with each other in an active feedback cycle exists in which this cognitive system called working memory can be dissected at the biological level and refined both empirically, and theoretically.

In summary, the present article offers an account of working memory from the psychological and neuroscientific perspectives, in which theoretical models of working memory are presented, and neural patterns and brain regions engaging in working memory are discussed among healthy and diseased brains. It is believed that working memory lays the foundation for many other cognitive controls in humans, and decoding the working memory mechanisms would be the first step in facilitating understanding toward other aspects of human cognition such as perceptual or emotional processing. Subsequently, the interactions between working memory and other cognitive systems could reasonably be examined.

Author Contributions

WC wrote the manuscript with critical feedback and consultation from AAH. WC and AAH contributed to the final version of the manuscript. JA supervised the process and proofread the manuscript.

This work was supported by the Transdisciplinary Research Grant Scheme (TRGS) 203/CNEURO/6768003 and the USAINS Research Grant 2016.

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.

The reviewer EB and handling Editor declared their shared affiliation.

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Keywords : working memory, neuroscience, psychology, cognition, brain, central executive, prefrontal cortex, review

Citation: Chai WJ, Abd Hamid AI and Abdullah JM (2018) Working Memory From the Psychological and Neurosciences Perspectives: A Review. Front. Psychol. 9:401. doi: 10.3389/fpsyg.2018.00401

Received: 24 November 2017; Accepted: 09 March 2018; Published: 27 March 2018.

Reviewed by:

Copyright © 2018 Chai, Abd Hamid and Abdullah. 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 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: Aini Ismafairus Abd Hamid, [email protected]

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Stanford researchers observe memory formation in real time

By Alan Toth

Why is it that someone who hasn’t ridden a bicycle in decades can likely jump on and ride away without a wobble, but could probably not recall more than a name or two from their 3rd grade class?

This may be because physical skills — dubbed motor memories by neuroscientists — are encoded differently in our brains than our memories for names or facts.

Now, a new study by scientists with the Wu Tsai Neurosciences Institute is revealing exactly how motor memories are formed and why they are so persistent. It may even help illuminate the root causes of movement disorders like Parkinson’s disease.

“We think motor memory is unique,” said Jun Ding , an associate professor of neurosurgery and of neurology. “Some studies on Alzheimer’s disease included participants who were previously musicians and couldn’t remember their own families, but they could still play beautiful music. Clearly, there’s a huge difference in the way that motor memories are formed.”

Memories are thought to be encoded in the brain in the pattern of activity in networks of hundreds or thousands of neurons, sometimes distributed across distant brain regions. The concept of such a memory trace — sometimes called a memory engram — has been around for more than a century, but identifying exactly what an engram is and how it is encoded has proven extremely challenging. Previous studies have shown that some forms of learning activate specific neurons, which reactivate when the learned memory is recalled. However, whether memory engram neurons exist for motor skill learning remains unknown.

Ding and postdoctoral scholars Richard Roth and Fuu-Jiun Hwang wanted to know how these engram-like groups of cells get involved in learning and remembering a new motor skill.

“When you’re first learning to shoot a basketball, you use a very diverse set of neurons each time you throw, but as you get better, you use a more refined set that’s the same every time,” said Roth. “These refined neuron pathways were thought to be the basis of a memory engram, but we wanted to know exactly how these pathways emerge.”

In their new study, published July 8, 2022 in Neuron , the researchers trained mice to use their paws to reach food pellets through a small slot. Using genetic wizardry developed by the lab of Liqun Luo , a Wu Tsai Neurosciences Institute colleague in the Department of Biology, the researchers were able to identify specific neurons in the brain’s motor cortex — an area responsible for controlling movements — that were activated during the learning process. The researchers tagged these potential engram cells with a fluorescent marker so they could see if they also played a role in recalling the memory later on.

When the researchers tested the animals’ memory of this new skill weeks later, they found that those mice that still remembered the skill showed increased activity in the same neurons that were first identified during the learning period, showing that these neurons were responsible for encoding the skill: the researchers had observed the formation of memory engrams.

But how do these particular groups of neurons take on responsibility for learning a new task in the first place? And how do they actually improve the animal’s performance?

To answer these questions, the researchers zoomed in closer. Using two-photon microscopy to observe these living circuits in action, they observed the so-called “engram neurons” reprogram themselves as the mice learned. Motor cortex engram cells took on new synaptic inputs — potentially reflecting information about the reaching movement — and themselves formed powerful new output connections in a distant brain region called the dorsolateral striatum — a key waystation through which the engram neurons can exert refined control over the animal’s movements. It was the first time anyone had observed the creation of new synaptic pathways on the same neuron population — both at the input and the output levels — in these two brain regions.

The ability to trace new memories forming in the mouse brain allowed the research team to weigh in on a long-standing debate about how skills are stored in the brain: are they controlled from one central memory trace, or engram, or is the memory redundantly stored across many different brain areas? Though this study cannot discount the idea of centralized memory, it does lend credibility to the opposing theory. Another fascinating question is whether the activation of these engram neurons is required for the performance of already learned motor tasks. The researchers speculated that by suppressing the activity of neurons that had been identified as part of the motor cortex memory engram, the mice probably still would be able to perform the task.

“Think of memory like a highway. If 101 and 280 are both closed, you could still get to Stanford from San Francisco, it would just take a lot longer,” said Ding.   

These findings suggest that, in addition to being dispersed, motor memories are highly redundant. The researchers say that as we repeat learned skills, we are continually reinforcing the motor engrams by building new connections — refining the skill. It’s what is meant by the term muscle memory — a refined, highly redundant network of motor engrams used so frequently that the associated skill seems automatic.

Ding believes that this constant repetition is one reason for the persistence of motor memory, but it’s not the only reason. Memory persistence may also be affected by a skill being associated with a reward, perhaps through the neurotransmitter dopamine. Though the research team did not directly address it in this study, Ding’s previous work in Parkinson’s disease suggests the connection.

“Current thinking is that Parkinson’s disease is the result of these motor engrams being blocked, but what if they’re actually being lost and people are forgetting these skills?” said Ding. “Remember that even walking is a motor skill that we all learned once, and it can potentially be forgotten.”

It’s a question that the researchers hope to answer in a follow-up study, because it may be the key to developing effective treatments for motor disorders. If Parkinson’s disease is the result of blocked motor memories, then patients should be able to improve their movement abilities by practicing and reinforcing these motor skills. On the other hand, if Parkinson’s destroys motor engrams and inhibits the creation of new ones — by targeting motor engram neurons and their synaptic connection observed in the team’s new study — then a completely different approach must be taken to deliver effective treatments.

“Our next goal is to understand what’s happening in movement disorders like Parkinson’s,” Ding said. “Obviously, we’re still a long way from a cure, but understanding how motor skills form is critical if we want to understand why they’re disrupted by disease.”

The research was published July 8 in Neuron: https://doi.org/10.1016/j.neuron.2022.06.006

Study authors were Fuu-Jiun Hwang, Richard H. Roth, Yu-Wei Wu, Yue Sun, Destany K. Kwon, Yu Liu, and Jun B. Ding.

The research was supported by the National Institutes of Health (NIH) and National Institute for Neurological Disease and Stroke (NINDS); the Klingenstein Foundation's Aligning Science Across Parkinson’s initiative; and GG gift fund, the Stanford School of Medicine Dean’s Postdoctoral Fellowship; and Parkinson’s Foundation Postdoctoral Fellowship.

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Study finds that memory complaints can predict biological changes in the brain

by Brigham and Women’s Hospital

A new study adds further evidence that when a patient or family member notices signs of persistent memory loss, it's important to speak with a doctor. While there are many reasons why someone's memory may change, researchers from Mass General Brigham who are studying patients prior to diagnosis with Alzheimer's disease found changes in the brain when patients and their study partners—those who could answer questions about their daily cognitive function—reported a decline in cognition.

Using imaging, the researchers found reports of cognitive decline were associated with accumulation of tau tangles—a hallmark of Alzheimer's disease. Results are published in Neurology .

"Something as simple as asking about memory complaints can track with disease severity at the preclinical stage of Alzheimer's disease," said senior author Rebecca E. Amariglio, Ph.D., of the Department of Neurology at Brigham and Women's Hospital. Amariglio is a clinical neuropsychologist at both Brigham and Women's Hospital and Massachusetts General Hospital, the founding members of Mass General Brigham.

"We now understand that changes in the brain due to Alzheimer's disease start well before patients show clinical symptoms detected by a doctor. There is increasing evidence that individuals themselves or a close family member may notice changes in memory, even before a clinical measure picks up evidence of cognitive impairment."

The new study, led by first author Michalina F. Jadick, included researchers from across the Brigham and Mass General. The research team designed their study to include participants from the "Anti-Amyloid Treatment in Asymptomatic AD/Longitudinal Evaluation of Amyloid Risk (A4/LEARN) and Neurodegeneration" studies and the "Harvard Aging Brain Study" and affiliated studies.

Participants were cognitively unimpaired individuals at risk but not yet diagnosed with Alzheimer's disease. Each participant and respective study partner completed evaluations of cognitive function for the participant. Each participant also underwent PET imaging to detect levels of tau and amyloid beta .

Across 675 participants, the team found that both amyloid and tau were associated with greater self-reported decline in cognitive function. The team also found that subjective reports from patients and their partners complemented objective tests of cognitive performance.

The authors note that the study was limited by the fact that most participants were white and highly educated. Future studies that include more diverse participants and follow participants in the longer term are needed.

Amariglio cautions that noticing a change in cognition does not mean that one should leap to the conclusion that a person has Alzheimer's disease. However, a patient's or family member's concerns should not be dismissed if they are worried about cognition.

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First hints of memory problems associated with changes in the brain

Memory problems reported by study participants and their partners linked to higher tau.

People who report early memory problems and whose partners also suspect they have memory problems have higher levels of tau tangles in the brain, a biomarker associated with Alzheimer's disease, according to a study published Neurology ® , the medical journal of the American Academy of Neurology.

Subjective cognitive decline is when a person reports memory and thinking problems before any decline is large enough to show up on standard tests.

"Understanding the earliest signs of Alzheimer's disease is even more important now that new disease-modifying drugs are becoming available," said study author Rebecca E. Amariglio, PhD, of Harvard Medical School in Boston. "Our study found early suspicions of memory problems by both participants and the people who knew them well were linked to higher levels of tau tangles in the brain."

The study involved 675 adults with an average age of 72 who did not have cognitive impairment on formal testing. All had brain scans for amyloid plaques. Of this group, 60% had elevated levels of amyloid, meaning they were at risk for developing cognitive impairment due to Alzheimer's disease even though, at the time of the scan, they were cognitively normal. Participants did not know if they had elevated levels of amyloid.

Each participant had a study partner -- a spouse, child or friend -- who could answer questions about the participant's thinking and memory skills and ability to perform daily tasks. In 65% of cases, partners lived with participants.

Each participant and their partner completed a questionnaire to assess the participant's subjective cognitive decline. Questions included, "Compared to one year ago, do you feel that your memory has declined substantially?" and "Compared to one year ago, do you have more difficulty managing money?" Participants' and partners' scores were recorded with higher scores indicating greater complaints about memory.

Researchers also reviewed brain scans for levels of tau tangles. Greater tau is also a risk factor for Alzheimer's disease and is at higher levels in people with elevated amyloid.

Researchers found participants with higher levels of tau tangles in the brain had higher scores of complaints on the memory questionnaire. Their partners also scored them higher. This association was stronger in participants who had elevated levels of amyloid plaques.

"Our study included a high percentage of people with elevated amyloid, and for this reason we were able to also see that memory complaints were associated with higher tau tangles," said Amariglio. "Our findings suggest that asking older people who have elevated Alzheimer's disease biomarkers about subjective cognitive decline may be valuable for early detection. This is particularly important since it is predicted that treatments given at the earliest diagnosable form of the disease will be the most effective in slowing the disease."

Limitations of the study include that most participants were white and highly educated. Amariglio noted future studies should follow people for longer periods of time and include more participants from other racial and ethnic groups, as well as people with different levels of education.

• Alzheimer's
• Intelligence
• Neuroscience
• Disorders and Syndromes
• Alzheimer's disease
• Memory bias
• Limbic system
• Memory-prediction framework
• Cognitive psychology
• Lead poisoning

Story Source:

Materials provided by American Academy of Neurology . Note: Content may be edited for style and length.

Journal Reference :

• Michalina F. Jadick, Talia Robinson, Michelle E. Farrell, Hannah Klinger, Rachel F. Buckley, Gad A. Marshall, Patrizia Vannini, Dorene M. Rentz, Keith A. Johnson, Reisa A. Sperling, Rebecca E. Amariglio. Associations Between Self and Study Partner Report of Cognitive Decline With Regional Tau in a Multicohort Study . Neurology , 2024; 102 (12) DOI: 10.1212/WNL.0000000000209447

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These 5 foods can slow aging in your brain, new study finds

A diet filled with brain foods is one of the best ways to prevent dementia and cognitive decline as you age, by frances largeman-roth, rdn | today • published may 28, 2024 • updated on may 28, 2024 at 5:50 pm.

One of the best ways to keep your mind working well and prevent dementia and cognitive decline is to eat a diet full of brain foods.

The most common type of dementia, Alzheimer’s disease, affects nearly 6 million Americans and is expected to rise to 14 million by 2060 due to our aging population. Cognitive decline — an impairment of memory, decision-making and ability to learn — develops due to aging neurons and the slowing down of the speed at which the brain functions. It’s directly linked to the aging process and leads to worsening memory, attention and brain processing. 

Beyond the calories that are burned by running all the many functions of the brain, there are specific foods that help support our brain’s activity. Here's what to know about so-called brain foods.

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New research on foods and brain aging

A  new study published in the journal Nature Aging  points to specific nutrients that can contribute to slower aging in the brain. The 100 participants between 65 and 75 years old completed questionnaires, underwent various physical and cognitive tests, MRI scans and had their blood plasma drawn after fasting.

Researchers found that one group had signs of slower aging and also ate a specific nutrient profile. The nutrients in the blood that were prevalent in participants with slower aging were:

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• Fatty acids, found in seafood and some  healthy cooking oils
• Antioxidants, found in berries, garlic, tomatoes, nuts and plenty of other fruits and vegetables
• Carotenoids, found in spinach, kale, carrots, broccoli and some fruits
• Vitamin E, found in fruits, vegetables, seafood, seeds and nuts and more
• Choline, found in egg yolks, beef, dairy and some veggies

Many foods that make up the Mediterranean diet are high in these nutrients,  the researchers noted . While most previous research on foods and brain health have relied on food questionnaires, this research is one of the first to use blood biomarkers, brain scans and cognitive testing.

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What is the no. 1 best food for brain health.

As a registered dietitian, I would say this is the best food to boost your brain health:

Studies have shown that eating  just one seafood meal per week  has been linked with a lowered risk of both Alzheimer's and dementia. Our brains are mostly made up of omega-3 fatty acids called EPA and DHA, so it makes sense that foods that contain these fats would help support brain health.

Omega-3  has been shown to help protect the brain with its anti-inflammatory effects, ability to help create new neurons, and power to help  clear the brain of plaques , one of the signs of Alzheimer’s. The best-known sources of EPA and DHA on the planet are high-quality seafood, like wild Alaskan salmon, sablefish and halibut. Sardines are another source of omega-3s. Wild-caught seafood is sustainably caught and also has lower contaminants than farm-raised seafood.

What foods help with brain health?

The micronutrient  choline  is finally getting the attention it deserves for  its role in brain health , including memory, thinking, mood and more. Higher levels of choline intake are thought to support brain function, which may decrease the risk of some types of dementia, including Alzheimer’s. One of the best dietary sources of choline is the egg. One large egg provides 150 milligrams, about 25% of the daily requirement for men and 35% for women.

You’ll find choline (plus nearly half of an egg’s protein and many other vitamins and minerals) in the yolk, so be sure to eat the whole egg.  According to the American Heart Association , eggs can be included as part of a heart-smart diet for healthy adults. 

Research has found that  eating walnuts  may be linked with improved cognitive function and memory in groups at high risk for age-related cognitive impairment, and reduced risk of Alzheimer's disease. The nut is also linked with a reduced risk of other diseases, such as cardiovascular disease or Type 2 diabetes, which are both risk factors for developing dementia. Whether you’re munching on walnuts for heart or brain health, you can feel good knowing that you’re covering both bases. 

Known for being rich in antioxidants and polyphenols, berries contain several disease-fighting compounds. Research has found that  eating berries  has a protective effect against  cardiovascular disease , cancer and Alzheimer’s. A major contributor to Alzheimer’s and other chronic diseases is inflammation. Both strawberries and  blueberries  have anti-inflammatory benefits.

A  study on strawberries  found that when older adults, ages 60 to 75, were given the equivalent of 2 cups of strawberries daily for 90 days, they showed improvement in memory and learning tests. In a similar  study,  participants who ate the equivalent of 1 cup of blueberries daily were tested on verbal learning and task switching and had significantly fewer errors on both tests at 45 and 90 days. 

Known for their gut health and bone benefits, prunes are also great for your brain. Prunes are high in potassium and a source of vitamin B6 and copper, all micronutrients that contribute to normal functioning of the nervous system. What’s more,  studies  on prunes show that the dried fruit has anti-inflammatory, antioxidant and memory-improving characteristics. The benefits are likely due to the high content of anthocyanin, a blue plant pigment. 

Citrus fruits

One of the markers of Alzheimer’sdDisease is neurodegeneration. The peel of a small citrus fruit from Okinawa, Japan called shikuwasa lime (also called citrus depressa) is rich in a plant compound called  nobiletin . Nobiletin has been found to protect nerve cells and provide anti-inflammatory benefits and is looking promising as a potential treatment for Alzheimer’s. The good news is that  this important compound  can also be found in mandarins, oranges, tangerines and grapefruits. 

Cocoa powder and dark chocolate

Cocoa beans are rich in flavanols, which help fight inflammation in our body and can  increase blood flow to the brain . Choosing  dark chocolate  over milk chocolate helps you get more of the protective polyphenols.

Extra virgin olive oil

As the staple of the  Mediterranean diet , extra virgin olive oil is rich in polyphenols and vitamin E. A  2023 study  done at the Harvard T.H. Chan School of Public Health found that daily consumption of more than half a tablespoon of EVOO had a 28% lower risk of dying from dementia compared to never or rarely consuming olive oil. The study also found that replacing just one teaspoon of margarine or mayo with the same amount of EVOO daily was associated with an 8 to 14% lower risk of dying from dementia. 

Tips to sharpen your memory  

In addition to what we eat, there are other habits to work on to support brain health, Dr. Andrew Budson, author of "Why We Forget and How to Remember Better," tells TODAY.com.

Here are some strategies to remember things better:

• Focus your attention on whatever it is that you want to remember.
• Organize whatever it is that you want to remember, whether it is by reviewing the sights, sounds, smells, thoughts, and feelings of an experience or the material you need to memorize for a presentation or exam.
• Understand what you want to remember, such as the deeper meaning or implications of an episode of your life or the individual elements of your presentation or exam
• Relate what you are learning to things you already know or have experienced

In addition to the tips above, you may want to ditch some habits that can hinder memory over time, Budson says. These include:

• Not correcting bad habits immediately. Break bad habits right away or they will become part of your routine. For example, don’t leave your keys, wallet, cell phone where they are difficult to find— even once —or you may find yourself frequently hunting around the house looking for them.
• Not paying attention to where you are or what you are doing. This is the No. 1 reason people have trouble finding their car, keys, phone, etc. Stop and pay attention to where you parked and where you put down your keys, for example.
• Not engaging in  aerobic exercise  regularly. Aerobic exercise releases growth factors from the brain that help to grow new brain cells in the hippocampus, the part of the brain that forms new memories.
• Being  sedentary  and watching too much television. There are new studies that suggest that even when controlling for vigorous exercise, it is still important to not be sedentary and not to watch more than one hour of television per day.
• Eating too much unhealthy food. Everyone can get away with eating dessert, red meat, butter, soda, refined sugar and flour once in a while, but it is important that the majority of one’s diet be from the  Mediterranean  menu, including fish, olive oil, fruits, and vegetables, nuts and beans, and whole grains.
• While Budson doesn’t recommend any particular supplements for brain health, he does encourage people to have their  vitamin D  and B12 levels checked by your physician at least once every decade after age 40. Both vitamin D and  B-12  are necessary for proper  memory  function.

This story first appeared on TODAY.com . More from TODAY :

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Study reveals brain mechanisms behind speech impairment in Parkinson’s

Most Parkinson’s disease patients struggle with speech problems. New research by Stanford Medicine scientists uncovers the brain connections that could be essential to preserving speech.

May 28, 2024 - By Nina Bai

Research by Stanford Medicine scientists may explain why some treatments for Parkinson’s — developed mainly to target motor symptoms — can improve speech impairments while other treatments make them worse. Lightspring /Shutterstock.com

Parkinson’s disease is most well-known and well-studied for its motor impairments — tremors, stiffness and slowness of movement. But less visible symptoms such as trouble with memory, attention and language, which also can profoundly impact a person’s quality of life, are less understood. A new study by Stanford Medicine researchers reveals the brain mechanisms behind one of the most prevalent, yet often overlooked, symptoms of the disease — speech impairment.

Based on brain imaging from Parkinson’s patients, the researchers identified specific connections in the brain that may determine the extent of speech difficulties.

The findings , reported May 20 in the Proceedings of the National Academy of Sciences , could help explain why some treatments for Parkinson’s — developed mainly to target motor symptoms — can improve speech impairments while other treatments make them worse.

More than a motor disorder

“Parkinson’s disease is a very common neurological disorder, but it’s mostly considered a motor disorder,” said Weidong Cai , PhD, clinical associate professor of psychiatry and behavioral sciences and the lead author of the new study. “There’s been lots of research on how treatments such as medications and deep brain stimulation can help improve motor function in patients, but there was limited understanding about how these treatments affect cognitive function and speech.”

Over 90% of people with Parkinson’s experience difficulties with speech, an intricate neurological process that requires motor and cognitive control. Patients may struggle with a weak voice, slurring, mumbling and stuttering.

“Speech is a complex process that involves multiple cognitive functions, such as receiving auditory feedback, organizing thoughts and producing the final vocal output,” Cai said.

The senior author of the study is Vinod Menon , PhD, professor of psychiatry and behavioral sciences and director of the Stanford Cognitive and Systems Neuroscience Laboratory .

The researchers set out to study how levodopa, a common Parkinson’s drug that replaces the dopamine lost from the disease, affects overall cognitive function. They focused on the subthalamic nucleus, a small, pumpkin-seed-shaped region deep within the brain.

Weidong Cai

The subthalamic nucleus is known for its role in inhibiting motor activity, but there are clues to its involvement in other functions. For example, deep brain stimulation, which uses implanted electrodes to stimulate the subthalamic nucleus, has proven to be a powerful way to relieve motor symptoms for Parkinson’s patients — but a common side effect is worsened speech impairment.

Same test, different scores

In the new study, 27 participants with Parkinson’s disease and 43 healthy controls, all older than 60, took standard tests of motor and cognitive functioning. The participants with Parkinson’s took the tests while on and off their medication.

As expected, the medication improved motor functioning in the patients, with those having the most severe symptoms improving the most.

The test for cognitive functioning offered a surprise. The test, known as the Symbol Digit Modalities Test, is given in two forms — oral and written. Patients are provided with nine symbols, each matched with a number — a plus sign for the number 7, for example. They are then asked to translate a string of symbols into numbers, either speaking or writing down their answers, depending on the version of the test.

As a group, the patients’ performance on both versions of the cognitive test was little affected by medication. But taking a closer look, the researchers noticed that the subset of patients who performed particularly poorly on the spoken version of the test without medication improved their spoken performance on the medication. Their written test scores did not change significantly.

“It was quite interesting to find this dissociation between the written and oral version of the same test,” Cai said.

The dissociation suggested that the medication was not enhancing general cognitive functions such as attention and working memory, but it was selectively improving speech.

“Our research unveiled a previously unrecognized impact of dopaminergic drugs on the speech function of Parkinson’s patients,” Menon said.

Uncovering connections

Next, the researchers analyzed fMRI brain scans of the participants, looking at how the subthalamic nucleus interacted with brain networks dedicated to various functions, including hearing, vision, language and executive control.

Vinod Menon

They found that different parts of the subthalamic nucleus interacted with different networks.

In particular, they discovered that improvements on the oral version of the test correlated with better functional connectivity between the right side of the subthalamic nucleus and the brain’s language network.

Using a statistical model, they could even predict a patient’s improvement on the oral test based on changes in their brain’s functional connectivity.

“Here we’re not talking about an anatomical connection,” Cai explained. Rather, functional connectivity between brain regions means the activity in these regions is closely coordinated, as if they are talking to each other.

“We discovered that these medications influence speech by altering the functional connectivity between the subthalamic nucleus and crucial language networks,” Menon said. “This insight opens new avenues for therapeutic interventions tailored specifically to improve speech without deteriorating other cognitive abilities.”

This newly identified interaction between the subthalamic nucleus and the language network could serve as a biological indicator of speech behavior — in Parkinson’s as well as other speech disorders like stuttering.

Such a biomarker could be used to monitor treatment outcomes and inspire new therapies. “Of course, you can directly observe the outcome of a medication by observing behavior, but I think to have a biomarker in the brain will provide more useful information for the future development of drugs,” Cai said. 

The findings also provide a detailed map of the subthalamic nucleus, which could guide neurosurgeons performing deep brain stimulation in avoiding damage to an area critical to speech function. “By identifying key neural maps and connections that predict speech improvement, we can craft more effective treatment plans that are both precise and personalized for Parkinson’s disease patients,” Menon said.

The study received funding from the National Institutes of Health (grants P50 AG047366, P30 AG066515, RF1 NS086085, R21 DC017950-S1, R01 NS115114, R01 MH121069 and K99 AG071837) and the Alzheimer’s Association.

About Stanford Medicine

Stanford Medicine is an integrated academic health system comprising the Stanford School of Medicine and adult and pediatric health care delivery systems. Together, they harness the full potential of biomedicine through collaborative research, education and clinical care for patients. For more information, please visit med.stanford.edu .

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11 Transforming Student Learning with Effective Study Techniques

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Effective study techniques can significantly enhance student learning and academic performance. In today’s fast-paced educational environment, students face numerous challenges, from managing multiple assignments and homework to balancing extracurricular activities. Developing strong study habits is essential for success in both school and college.

Understanding how to study efficiently can make a significant difference in a student’s academic journey. By implementing the right techniques, students can improve their comprehension, retention, and overall performance. This article explores various study methods and provides valuable tips for students looking to transform their learning experiences with paperwriter .

The Importance of Effective Study Techniques

Effective study techniques are crucial for students aiming to achieve their academic goals. These techniques help in better time management, reducing stress, and improving understanding of complex subjects. With the right approach, students can make their study sessions more productive and less overwhelming.

One of the biggest challenges students face is the sheer volume of information they need to learn and retain. Effective study techniques can help break down this information into manageable chunks, making it easier to digest and remember. Moreover, students who develop good study habits early on are more likely to succeed in their future academic and professional endeavors.

Creating a Conducive Study Environment

A conducive study environment is essential for effective learning. Students should choose a quiet, comfortable place with minimal distractions to focus on their studies. A well-organized study space can significantly enhance concentration and productivity.

Tips for Creating a Conducive Study Environment:

• Choose a quiet location: Find a place free from noise and interruptions.
• Ensure good lighting: Proper lighting reduces eye strain and improves focus.
• Organize your materials: Keep all necessary supplies within reach to avoid unnecessary distractions.
• Comfortable seating: Choose a chair and desk that provide good support to maintain good posture.

Time Management and Scheduling

Time management is a critical skill for students. Balancing school, college, assignments, and homework can be challenging. Effective scheduling ensures that students allocate sufficient time for each subject and activity.

Strategies for Better Time Management:

• Create a study schedule: Plan your study sessions and stick to the schedule.
• Set priorities: Focus on the most important tasks first.
• Break tasks into smaller steps: Divide large assignments into manageable parts.
• Use a planner: Keep track of deadlines, assignments, and exams.

Active Learning Techniques

Active learning involves engaging with the material actively rather than passively reading or listening. This approach enhances understanding and retention.

Effective Active Learning Techniques:

• Summarization: Summarize key points in your own words.
• Questioning: Ask questions about the material and seek answers.
• Discussion: Discuss topics with classmates to gain different perspectives.
• Application: Apply what you have learned to real-world scenarios.

The Role of Technology in Studying

Technology can be a powerful tool for enhancing learning. Various apps and online resources can help students manage their study time, organize notes, and access educational materials.

Useful Technological Tools for Students:

• Note-taking apps: Apps like Evernote and OneNote help organize and store notes efficiently.
• Study apps: Apps like Quizlet and Anki offer flashcards and quizzes for effective revision.
• Time management apps: Tools like Trello and Todoist help students plan and track their tasks.( or visit https://do-my-math.com/ )
• Online resources: Websites like Khan Academy and Coursera provide additional learning materials.

Enhancing Memory and Retention

Improving memory and retention is vital for academic success. Students can employ various techniques to boost their ability to remember and recall information.

Techniques to Enhance Memory and Retention:

• Mnemonics: Use mnemonic devices to remember complex information.
• Visualization: Create mental images to associate with the material.
• Repetition: Review material regularly to reinforce learning.
• Teaching others: Explaining concepts to others helps solidify understanding.

Staying Motivated and Managing Stress

Maintaining motivation and managing stress are essential components of effective studying. Students need to find ways to stay motivated and cope with academic pressures.

Tips for Staying Motivated and Managing Stress:

• Set realistic goals: Set achievable short-term and long-term goals.
• Take breaks: Regular breaks prevent burnout and improve focus.
• Stay positive: Maintain a positive attitude towards learning.
• Seek support: Reach out to teachers, peers, or counselors for help when needed.

Incorporating effective study techniques can transform the learning experience for students. By creating a conducive study environment, managing time efficiently, engaging in active learning, utilizing technology, enhancing memory, and staying motivated, students can achieve academic success. Remember, the key to effective studying lies in consistency and dedication. With the right approach, every student can improve their learning outcomes and reach their full potential.

Effective study habits not only help students excel in their current studies but also prepare them for future challenges. By implementing these strategies, students can turn studying into a more enjoyable and rewarding experience, ultimately leading to better academic performance and personal growth. Investing time in developing good study habits today will pay off in the long run, making the journey through school and college a successful one.

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POSTPONED: Stories Half Shared

28 May 2024, 11:00 am–12:00 pm

Memory, Postmemory, and Narrative Biography Construction in the Postwar Ukrainian-British Diaspora, an Overview. A SSEES Research Student seminar with Laura Osadciw

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Violent, and often distressing, the experiences of the two million or so Ukrainians who were displaced as a result of the Second World War did not always lend themselves to fireside stories. In many cases they became half-shared anecdotes, only hinting at the experiences that had gone before, fragments of larger pictures which removed more difficult, troubling, or traumatic elements. For some, these barriers were more easily broken down, with stories shared at social gatherings, around the dinner table, and with pride and expression in the past, in Ukraine, and in the ties to one's homeland that continued to tug. For others these experiences and identities were entirely invisible, stories of silence which left in their wake a new set of questions, interpretations, and uncomfortable reserve.

In this presentation I will put forward the broad overview of my research into this postwar group, exploring the ways in which this first generation of Ukrainians chose to share their stories, and the impact of these initial narrative decisions on the later generations of both their families, and the Ukrainian diaspora as a whole.

Considering memory, postmemory, and narrative co-creation, I will question the role of storytelling in history, and how this generation of Ukrainians who settled in Britain after the Second World War offers valuable insight into the role of narration in identity construction and reconstruction, and the experiences of later generations as they grapple with their own history.

Laura is an MPhil Candidate at The School of Slavonic and East European Studies, UCL. Her research explores the Postwar Ukrainian British Diaspora and the intergenerational impact of their storytelling, silence, and cultural engagement. More broadly, she is interested in the role of memory and storytelling in history, and the place of postmemory here. Before this, she completed an internship with the State Hermitage in St. Petersburg, and has worked with several museums in curation and display creation. She received both her Bachelor’s and Master’s degrees from King’s College London.

Image credit: Private collection