Working Memory Model Research Articles

Validation of Oxford Cognitive Screen: Executive Function (OCS-EF), a tablet-based executive function assessment tool amongst adolescent females in rural South Africa.

Short, reliable, easily administered executive function (EF) assessment tools are needed to measure EF in low- and middle-income countries, particularly in sub-Saharan Africa given the prevalence of human immunodeficiency virus (HIV)-associated neurocognitive disorder. We administered Oxford Cognitive Screen-Executive Function (OCS-EF) to 932 rural South African females (mean age 19.7 years). OCS-EF includes seven tasks: two hot inhibition tasks (a modified Iowa Gambling Task, emotional go/no-go) and five cool EF tasks, two switching tasks (visuospatial rule-finding, geometric trails) and three working memory tasks (digit recall, selection and figure drawing). We performed confirmatory factor analysis testing whether a three-factor, two-factor hot-cool, two-factor working memory and inhibition/switching, or one-factor EF model fitted the data better. The three-factor (switching, inhibition and working memory) model had the best local and global fit (χ2 (11) 24.21, p=0.012; RMSEA 0.036; CFI 0.920; CD 0.617). We demonstrated the feasibility of OCS-EF administration by trained laypeople, the tripartite structure of EF amongst adolescent females and the factorial validity of OCS-EF in this population and context. OCS-EF tablet-based cognitive assessment tool can be administered by trained laypeople and is a valid tool for assessing cognition at scale amongst adolescents in rural South Africa and similar environments.

A Computational Model of Working Memory Based on Spike-Timing-Dependent Plasticity.

Working memory is closely involved in various cognitive activities, but its neural mechanism is still under exploration. The mainstream view has long been that persistent activity is the neural basis of working memory, but recent experiments have observed that activity-silent memory can also be correctly recalled. The underlying mechanism of activity-silent memory is considered to be an alternative scheme that rejects the theory of persistent activity. We propose a working memory model based on spike-timing-dependent plasticity (STDP). Different from models based on spike-rate coding, our model adopts temporal patterns of action potentials to represent information, so it can flexibly encode new memory representation. The model can work in both persistent and silent states, i.e., it is compatible with both of these seemingly conflicting neural mechanisms. We conducted a simulation experiment, and the results are similar to the real experimental results, which suggests that our model is plausible in biology.

Working Memory in Pediatric Epilepsy: A Systematic Review and Meta-Analysis.

Working memory is a multicomponent system that is supported by overlapping specialized networks in the brain. Baddeley's working memory model includes four components: the phonological loop, visuo-spatial sketchpad, the central executive, and episodic buffer. The aim of this review was to establish the gravity and pattern of working memory deficits in pediatric epilepsy. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement guided electronic searches. Sixty-five studies were included in the review. Meta-analyses revealed significant impairments across each working memory component: phonological loop (g = 0.739), visuo-spatial sketchpad (g = 0.521), and central executive (g = 0.560) in children with epilepsy compared to controls. The episodic buffer was not examined. The pattern of impairments, however, differed according to the site and side of seizure focus. This suggests that working memory components are differentially vulnerable to the location of seizure focus in the developing brain.

Modeling Working Memory in a Spiking Neuron Network Accompanied by Astrocytes.

We propose a novel biologically plausible computational model of working memory (WM) implemented by a spiking neuron network (SNN) interacting with a network of astrocytes. The SNN is modeled by synaptically coupled Izhikevich neurons with a non-specific architecture connection topology. Astrocytes generating calcium signals are connected by local gap junction diffusive couplings and interact with neurons via chemicals diffused in the extracellular space. Calcium elevations occur in response to the increased concentration of the neurotransmitter released by spiking neurons when a group of them fire coherently. In turn, gliotransmitters are released by activated astrocytes modulating the strength of the synaptic connections in the corresponding neuronal group. Input information is encoded as two-dimensional patterns of short applied current pulses stimulating neurons. The output is taken from frequencies of transient discharges of corresponding neurons. We show how a set of information patterns with quite significant overlapping areas can be uploaded into the neuron-astrocyte network and stored for several seconds. Information retrieval is organized by the application of a cue pattern representing one from the memory set distorted by noise. We found that successful retrieval with the level of the correlation between the recalled pattern and ideal pattern exceeding 90% is possible for the multi-item WM task. Having analyzed the dynamical mechanism of WM formation, we discovered that astrocytes operating at a time scale of a dozen of seconds can successfully store traces of neuronal activations corresponding to information patterns. In the retrieval stage, the astrocytic network selectively modulates synaptic connections in the SNN leading to successful recall. Information and dynamical characteristics of the proposed WM model agrees with classical concepts and other WM models.

Modeling Theories and Theorizing Models: an Attempted Replication of Miller-Cotto & Byrnes’ (2019) Comparison of Working Memory Models Using ECLS-K Data

Working memory is an essential mechanism in the cognitive learning process. However, its definitions and mechanisms remain a topic of debate. Miller-Cotto and Byrnes (Journal of Educational Psychology, 112(5), 1074–1084, 2020) reported a comparison of three models of working memory to determine which best accounted for data obtained from a national US dataset of young children over time (Early Childhood Longitudinal Study of Kindergarten). In comparing these models, which they derived from competing theories of working memory, they found that (1) one of the tested models best fit the available data and (2) the model parameters were similar across content domains, concluding that one specific theory of working memory was best supported. The study reported here is an attempted replication of this work. We implemented a multi-phase effort at replication, first undertaking a review of the literature surrounding competing theories of working memory to aid interpretation of results, then attempting a direct replication, and lastly applying an alternative modeling technique to appropriately differentiate between between- and within-person variance. Neither effort succeeded in fully replicating the original findings. Instead, we found that parsing between- and within-person variance is an essential strategy for appropriately interpreting the relationships between working memory and domain learning. Doing so led to support for different models across domains, and interpretation of aspects of these models through the lens of relevant theory suggested alternative strategies for examining these issues in future research.

How do neural processes give rise to cognition? Simultaneously predicting brain and behavior with a dynamic model of visual working memory.

There is consensus that activation within distributed functional brain networks underlies human thought. The impact of this consensus is limited, however, by a gap that exists between data-driven correlational analyses that specify where functional brain activity is localized using functional magnetic resonance imaging (fMRI), and neural process accounts that specify how neural activity unfolds through time to give rise to behavior. Here, we show how an integrative cognitive neuroscience approach may bridge this gap. In an exemplary study of visual working memory, we use multilevel Bayesian statistics to demonstrate that a neural dynamic model simultaneously explains behavioral data and predicts localized patterns of brain activity, outperforming standard analytic approaches to fMRI. The model explains performance on both correct trials and incorrect trials where errors in change detection emerge from neural fluctuations amplified by neural interaction. Critically, predictions of the model run counter to cognitive theories of the origin of errors in change detection. Results reveal neural patterns predicted by the model within regions of the dorsal attention network that have been the focus of much debate. The model-based analysis suggests that key areas in the dorsal attention network such as the intraparietal sulcus play a central role in change detection rather than working memory maintenance, counter to previous interpretations of fMRI studies. More generally, the integrative cognitive neuroscience approach used here establishes a framework for directly testing theories of cognitive and brain function using the combined power of behavioral and fMRI data. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Working Memory for Signs with Poor Visual Resolution: fMRI Evidence of Reorganization of Auditory Cortex in Deaf Signers.

Stimulus degradation adds to working memory load during speech processing. We investigated whether this applies to sign processing and, if so, whether the mechanism implicates secondary auditory cortex. We conducted an fMRI experiment where 16 deaf early signers (DES) and 22 hearing non-signers performed a sign-based n-back task with three load levels and stimuli presented at high and low resolution. We found decreased behavioral performance with increasing load and decreasing visual resolution, but the neurobiological mechanisms involved differed between the two manipulations and did so for both groups. Importantly, while the load manipulation was, as predicted, accompanied by activation in the frontoparietal working memory network, the resolution manipulation resulted in temporal and occipital activation. Furthermore, we found evidence of cross-modal reorganization in the secondary auditory cortex: DES had stronger activation and stronger connectivity between this and several other regions. We conclude that load and stimulus resolution have different neural underpinnings in the visual–verbal domain, which has consequences for current working memory models, and that for DES the secondary auditory cortex is involved in the binding of representations when task demands are low.

Understanding occipital and parietal contributions to visual working memory: Commentary on Xu (2020)

ABSTRACT In her commentary, Xu [2020. Revisit once more the sensory storage account of visual working memory. Visual Cognition, 28(5–8), 433–446] admonishes the reader that “To have a full understanding of the cognitive mechanisms underlying VWM [visual working memory], both behavioral and neural evidence needs to be taken into account. This is a must, and not a choice, for any study that attempts to capture the nature of VWM” (p. 11). Although we don’t disagree with this statement, our overall assessment of this commentary is that it, itself, fails to satisfy several “musts” and, consequently, does not pose a serious challenge for the sensory recruitment framework for understanding visual working memory. These “musts” include accurately characterizing the framework being critiqued, not favouring verbal models and intuition at the expense of formal quantitative models, and providing even-handed interpretation of the work of others. We’ll conclude with a summary of how the sensory recruitment framework can be incorporated into a broader working model of visual working memory.

How the Working Memory with Distributed Executive Control Model Accounts for Task Switching and Dual-Task Coordination Costs.

According to the working memory model with distributed executive control (WMDEC), working memory is not only used for temporary maintenance of information, but it also serves goal-directed action by maintaining task-related information. Such information may include the current action goal, the means selected to attain the goal, situational constraints, and interim processing results. A computational version of the WMDEC model was used to simulate human performance in a series of experiments that examined particular predictions regarding task switching costs, costs due to task and attention switching, to dual-task coordination in working memory tasks, and to experiments that required dual-task coordination of memorisation and task switching demands. The results of these simulations are reported and their implications for accounts of multi- and dual-tasking are discussed.

Dynamic functional connectome predicts individual working memory performance across diagnostic categories

Working memory impairment is a common feature of psychiatric disorders. Although its neural mechanisms have been extensively examined in healthy subjects or individuals with a certain clinical condition, studies investigating neural predictors of working memory in a transdiagnostic sample are scarce. The objective of this study was to create a transdiagnostic predictive working memory model from whole-brain functional connectivity using connectome-based predictive modeling (CPM), a recently developed machine learning approach. Resting-state functional MRI data from 242 subjects across 4 diagnostic categories (healthy controls and individuals with schizophrenia, bipolar disorder, and attention deficit/hyperactivity) were used to construct dynamic and static functional connectomes. Spatial working memory was assessed by the spatial capacity task. CPM was conducted to predict individual working memory from dynamic and static functional connectivity patterns. Results showed that dynamic connectivity-based CPM models successfully predicted overall working memory capacity and accuracy as well as mean reaction time, yet their static counterparts fell short in the prediction. At the neural level, we found that dynamic connectivity of the frontoparietal and somato-motor networks were negatively correlated with working memory capacity and accuracy, and those of the default mode and visual networks were positively associated with mean reaction time. Moreover, different feature selection thresholds, parcellation strategies and model validation methods as well as diagnostic categories did not significantly influence the prediction results. Our findings not only are coherent with prior reports that dynamic functional connectivity encodes more behavioral information than static connectivity, but also help advance the translation of cognitive “connectome fingerprinting” into real-world application.

Exact neural mass model for synaptic-based working memory.

A synaptic theory of Working Memory (WM) has been developed in the last decade as a possible alternative to the persistent spiking paradigm. In this context, we have developed a neural mass model able to reproduce exactly the dynamics of heterogeneous spiking neural networks encompassing realistic cellular mechanisms for short-term synaptic plasticity. This population model reproduces the macroscopic dynamics of the network in terms of the firing rate and the mean membrane potential. The latter quantity allows us to gain insight of the Local Field Potential and electroencephalographic signals measured during WM tasks to characterize the brain activity. More specifically synaptic facilitation and depression integrate each other to efficiently mimic WM operations via either synaptic reactivation or persistent activity. Memory access and loading are related to stimulus-locked transient oscillations followed by a steady-state activity in the β-γ band, thus resembling what is observed in the cortex during vibrotactile stimuli in humans and object recognition in monkeys. Memory juggling and competition emerge already by loading only two items. However more items can be stored in WM by considering neural architectures composed of multiple excitatory populations and a common inhibitory pool. Memory capacity depends strongly on the presentation rate of the items and it maximizes for an optimal frequency range. In particular we provide an analytic expression for the maximal memory capacity. Furthermore, the mean membrane potential turns out to be a suitable proxy to measure the memory load, analogously to event driven potentials in experiments on humans. Finally we show that the γ power increases with the number of loaded items, as reported in many experiments, while θ and β power reveal non monotonic behaviours. In particular, β and γ rhythms are crucially sustained by the inhibitory activity, while the θ rhythm is controlled by excitatory synapses.

Spatial Attention in Serial Order Working Memory: An EEG Study.

Theoretical models explaining serial order processing link order information to specified position markers. However, the precise characteristics of position marking have remained largely elusive. Recent studies have shown that space is involved in marking serial position of items in verbal working memory (WM). Furthermore, it has been suggested, but not proven, that accessing these items involves horizontal shifts of spatial attention. We used continuous electroencephalography recordings to show that memory search in serial order verbal WM involves spatial attention processes that share the same electrophysiological signatures as those operating on the visuospatial WM and external space. Accessing an item from a sequence in verbal WM induced posterior "early directing attention negativity" and "anterior directing attention negativity" contralateral to the position of the item in mental space (i.e., begin items on the left; end items on the right). In the frequency domain, we observed posterior alpha suppression contralateral to the position of the item. Our results provide clear evidence for the involvement of spatial attention in retrieving serial information from verbal WM. Implications for WM models are discussed.

Inhibiting glycolysis rescues memory impairment in an intellectual disability Gdi1-null mouse

ObjectivesGDI1 gene encodes for αGDI, a protein controlling the cycling of small GTPases, reputed to orchestrate vesicle trafficking. Mutations in human GDI1 are responsible for intellectual disability (ID). In mice with ablated Gdi1, a model of ID, impaired working and associative short-term memory was recorded. This cognitive phenotype worsens if the deletion of αGDI expression is restricted to neurons. However, whether astrocytes, key homeostasis providing neuroglial cells, supporting neurons via aerobic glycolysis, contribute to this cognitive impairment is unclear. MethodsWe carried out proteomic analysis and monitored [18F]-fluoro-2-deoxy-d-glucose uptake into brain slices of Gdi1 knockout and wild type control mice. d-Glucose utilization at single astrocyte level was measured by the Förster Resonance Energy Transfer (FRET)-based measurements of cytosolic cyclic AMP, d-glucose and L-lactate, evoked by agonists selective for noradrenaline and L-lactate receptors. To test the role of astrocyte-resident processes in disease phenotype, we generated an inducible Gdi1 knockout mouse carrying the Gdi1 deletion only in adult astrocytes and conducted behavioural tests. ResultsProteomic analysis revealed significant changes in astrocyte-resident glycolytic enzymes. Imaging [18F]-fluoro-2-deoxy-d-glucose revealed an increased d-glucose uptake in Gdi1 knockout tissue versus wild type control mice, consistent with the facilitated d-glucose uptake determined by FRET measurements. In mice with Gdi1 deletion restricted to astrocytes, a selective and significant impairment in working memory was recorded, which was rescued by inhibiting glycolysis by 2-deoxy-d-glucose injection. ConclusionsThese results reveal a new astrocyte-based mechanism in neurodevelopmental disorders and open a novel therapeutic opportunity of targeting aerobic glycolysis, advocating a change in clinical practice.

Incidental encoding of visual information in temporal reference frames in working memory

Visual events are structured in space and time, yet models of visual working memory (VWM) have largely relied on tasks emphasizing spatial aspects. Here, we show that temporal properties of visual events are incidentally encoded along with spatial properties. In five experiments, participants performed change-detection tasks, in which items had unique spatial and temporal coordinates at encoding. Crucially, neither space nor time was task-relevant. The key manipulation concerned the retrieval context: The test array was identical to the memory array either in its entire spatiotemporal structure, or only its spatial or temporal structure. Removing spatial or temporal information at retrieval resulted in costs, indicating that memory relied on both spatial and temporal context in which items were initially perceived. Encoding of spatiotemporal structure occurred incidentally, not strategically, as it was robust even when the retrieval context was perfectly predictable. However, spatial and temporal inter-item spacings influenced the weighting of spatial and temporal information: It favoured the domain in which items were more widely spaced, facilitating their individuation and, likely, access to representations. Across individuals, the weighting of spatial and temporal information varied substantially, but it remained consistent across sessions, suggesting stable preferences for coding in the spatial or temporal domain. No comparable incidental encoding occurred for other task-irrelevant feature dimensions (size or colour). We propose that temporal structure serves as fundamental a function in VWM as spatial structure, scaffolding events that unfold over time.

Interference control in working memory: Evidence for discriminant validity between removal and inhibition tasks.

Working memory (WM) is a system for maintenance of and access to a limited number of goal-relevant representations in the service of higher cognition. Because of its limited capacity, WM requires interference-control processes, allowing us to avoid being distracted by irrelevant information. Recent research has proposed two interference-control processes, which are conceptually similar: (1) an active, item-wise removal process assumed to remove no-longer relevant information from WM, and (2) an inhibitory process assumed to suppress the activation of distractors against competing, goal-relevant representations. The purpose of this study was to determine the extent to which the tasks used to assess removal and inhibition measure the same interference-control construct. Results showed acceptable to good reliabilities for nearly all measures. Similar to previous studies, a structural equation modeling approach identified a reliable latent variable of removal. However, also similar to some previous studies, no latent variable of inhibition could be established. This was the case even when the correlation matrix used to compute the latent variable of inhibition was disattenuated for imperfect reliability. Critically, the individual measures of inhibition were unrelated to the latent variable of removal. These results provide tentative support for the notion that removal is not related to the interference-control processes assessed in inhibition tasks. This suggests that the removal process should be conceptualized as a process independent of the concept of inhibition, as proposed in computational WM models that implement removal as the "unbinding" of a WM item from the context in which it occurred.

Declarative working memory: A bio-inspired cognitive architecture proposal

Memory is considered one of the most important functions since it allows us to code, store and retrieve knowledge. These qualities make it an indispensable function for a virtual creature. In general, memory can be classified based on the durability of the stored data in working memory and long-term memory. Working memory refers to the capacity to maintain temporarily a limited amount of information in mind, which can then be used to support various abilities, including learning, reasoning, planning and decision-making. Unlike short-term memory, working memory is not only a storage site, but it is also a framework of interacting processes that involve the temporary storage and manipulation of information in the service of performing complex cognitive activities. Declarative memory is a type of long-term memory related with the storage of facts and events. This research focuses on the development of a cognitive architecture for the type of working memory that maintains and manipulates declarative information. The construction of the model was grounded in theoretical evidence taken from cognitive sciences such as neuroscience and psychology, which gave us the components and their processes. The model was evaluated through a case study that covers the encoding, storing, and retrieval stages. Our hypothesis is that a virtual creature endowed with our working memory model will provide faster access to the information needed for the ongoing task. Therefore, it improves the planning and decision-making processes.

Working memory profiles of children with reading difficulties who are learning to read in Greek.

This study investigated working memory skills in a small group of 13 nine-year-old Greek children facing reading difficulties and a group of 14 age matched typical Greek readers. The children were assessed on working memory tasks measuring separately the four components of the working memory model of Baddeley and Hitch(1974) as revised by Baddeley(2000): the phonological loop, visuo-spatial sketchpad, episodic buffer and central executive. Both groups completed tests of accuracy of reading, speed of reading and text understanding. The children with reading difficulties performed significantly more poorly than typical readers on all aspects of working memory apart from visual-spatial short-term memory. These results indicate a similar verbal working memory impairment in Greek children with reading difficulties as in their English peers, despite the fact that they are learning to read a language with a transparent rather than an opaque orthography.

Modeling Multiple Language Learning in a Developmental Cognitive Architecture

In this work, we model multiple natural language learning in a developmental neuroscience-inspired architecture. The artificial neural network with adaptive behavior exploited for language learning (ANNABELL) model, is a large-scale neural network, however, unlike most deep learning methods that solve natural language processing (NLP) tasks, it does not represent an empirical engineering solution for specific NLP problems; rather, its organization complies with findings from cognitive neuroscience, particularly the multicompartment working memory models. The system is appropriately trained to understand the level of cognitive development required for language acquisition and the robustness achieved in learning simultaneously four languages, using a corpus of text-based exchanges of developmental complexity. The selected languages, Greek, Italian and Albanian, besides English, differ significantly in structure and complexity. Initially, the system was validated in each language alone and was then compared with the open-ended cumulative training, in which languages are learned jointly, prior to querying with random language at random order. We aimed to assess if the model could learn the languages together to the same degree of skill as learning each apart. Moreover, we explored the generalization skill in multilingual context questions and the ability to elaborate a short text of preschool literature. We verified if the system could follow a dialogue coherently and cohesively, keeping track of its previous answers and recalling them in subsequent queries. The results show that the architecture developed broad language processing functionalities, with satisfactory performances in each language trained singularly, maintaining high accuracies when they are acquired cumulatively.

The role of the cerebellum in creativity and expert stone knapping

The purpose of this article is to review the evolution and function of the cerebellum, particularly in regard to its role in creativity and expert stone knapping. First, the article reviews the history of the cerebellum, its evolution and phylogenetics, and its concerted evolution with various brain regions. It also notes the critical role of the cerebellum and the cerebro-cerebellar network in its traditionally recognized control of gross and fine motor movements and in its exaptation for basic and higher level cognitive processes, particularly in regard to increasingly more sophisticated stone knapping. Also, reviewed are various theories, advanced over the past three decades, of how the cerebellum tweaks and refines thoughts, images, and ideas just as it refines and smooths motor movements. Baddeley’s working memory model is also prominently featured, as are the works of Ito, Vandervert, and others on the cerebellum’s role in novel problem-solving, insight, and creativity. Finally, this article reviews two “Cognitive Rubicons” in stone tool knapping, Theory of Mind and stone knapping, and Malafouris’ Material Engagement Theory. The article concludes with a novel hypothesis that the automaticity provided by the cerebellum derived from extensive practice in stone knapping may have freed Baddeley’s central executive from its attentional demands and allows the mind to “wander and wonder.”

Natural variation in the representational fidelity between multiple working memory items can explain which item guides attention

When holding a single item in working memory, visual attention is automatically guided towards objects that match its features, but results are mixed as to whether multiple concurrently stored items can also guide attention. These results are relevant to models of attention and working memory use the single-item guidance account as evidence for the structure of working memory comprising distinct states where one item is prioritized over all others (i.e., a special focus of attention). However, previous studies give little consideration to how memory strength for individual items affects guidance. We therefore test how multiple items interact with attention and how memory fidelity influences this relationship. Participants remembered one or two colors, then performed a visual search task, and finally reported one of the memory items using continuous report. We found reliable guidance when participants remembered one or two items, but the multiple-item effect could be explained by guidance from only one of the items on a proportion of trials. In Experiment 2, by precisely measuring memory for individual items, we show that items naturally vary in their representational fidelity, and that in general only items with the strongest representation guides attention. In Experiment 3, we demonstrate that no distinct states in working memory — i.e., no special focus of attention — is necessary to explain these single-item guidance effects. Instead, natural variation in representational fidelity between items can explain why some items have better memories than others and why those items guide attention. In particular, only very strong memories guide attention, and it is rare for more than one item to have such strong fidelity. These findings challenge current models of working memory and attention by suggesting that natural variation in representational fidelity, instead of distinct and prioritized memory states, determine which item guides attention on any given trial.