A layered neural architecture for the consolidation, maintenance, and updating of representations in visual working memory

Neural interactions in working memory explain decreased recall precision and similarity-based feature repulsion

Over the last several years, the study of working memory (WM) for simple visual features (e.g., colors, orientations) has been dominated by perspectives that assume items in WM are stored independently of one another. Evidence has revealed, however, systematic biases in WM recall which suggest that items in WM interact during active maintenance. In the present study, we report two experiments that replicate a repulsion bias between metrically similar colors during active storage in WM. We also observed that metrically similar colors were stored with lower resolution than a unique color held actively in mind at the same time. To account for these effects, we report quantitative simulations of two novel neurodynamical models of WM. In both models, the unique behavioral signatures reported here emerge directly from laterally-inhibitory neural interactions that serve to maintain multiple, distinct neural representations throughout the WM delay period. Simulation results show that the full pattern of empirical findings was only obtained with a model that included an elaborated spatial pathway with sequential encoding of memory display items. We discuss implications of our findings for theories of visual working memory more generally.

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).

Learning words in space and time: Contrasting models of the suspicious coincidence effect

In their 2007b Psychological Review paper, Xu and Tenenbaum found that early word learning follows the classic logic of the "suspicious coincidence effect:" when presented with a novel name ('fep') and three identical exemplars (three Labradors), word learners generalized novel names more narrowly than when presented with a single exemplar (one Labrador). Xu and Tenenbaum predicted the suspicious coincidence effect based on a Bayesian model of word learning and demonstrated that no other theory captured this effect. Recent empirical studies have revealed, however, that the effect is influenced by factors seemingly outside the purview of the Bayesian account. A process-based perspective correctly predicted that when exemplars are shown sequentially, the effect is eliminated or reversed (Spencer, Perone, Smith, & Samuelson, 2011). Here, we present a new, formal account of the suspicious coincidence effect using a generalization of a Dynamic Neural Field (DNF) model of word learning. The DNF model captures both the original finding and its reversal with sequential presentation. We compare the DNF model's performance with that of a more flexible version of the Bayesian model that allows both strong and weak sampling assumptions. Model comparison results show that the dynamic field account provides a better fit to the empirical data. We discuss the implications of the DNF model with respect to broader contrasts between Bayesian and process-level models.

The development of working memory

Working memory is a central cognitive system that plays key role in development, with increases in working memory capacity and speed of processing as children move from infancy through adolescence. Here, I focus on two questions: what neural processes underlie working memory and how do these processes change over development? Answers to these questions lie in computer simulations of artificial neural network models that shed light on how development happens. These models open up new avenues for optimizing clinical interventions aimed at boosting the working memory abilities of at-risk infants.

Increased repulsion of working memory representations in schizophrenia

Computational neuroscience models propose that working memory (WM) involves recurrent excitatory feedback loops that maintain firing over time along with lateral inhibition that prevents the spreading of activity to other feature values. In behavioral paradigms, this lateral inhibition appears to cause a repulsion of WM representations away from each other and from other strong sources of input. Recent computational models of schizophrenia have proposed that reduction in the strength of inhibition relative to strength of excitation may underlie impaired cognition, and this leads to the prediction that repulsion effects should be reduced in people with schizophrenia spectrum disorders (PSZ) relative to healthy control subjects (HCS). We tested this hypothesis in 2 experiments measuring WM repulsion effects. In Experiment 1, 45 PSZ and 32 HCS remembered the location of a single object relative to a centrally presented visual landmark and reported this location after a short delay. The reported location was repelled away from the landmark in both groups, but this repulsion effect was increased rather than decreased in PSZ relative to HCS. In Experiment 2, 41 PSZ and 34 HCS remembered 2 sequentially presented orientations and reported each orientation after a short delay. The reported orientations were biased away from each other in both groups, and this repulsion effect was again more pronounced in PSZ than in HCS. Contrary to the widespread hypothesis of reduced inhibition in schizophrenia, we provide robust evidence from 2 experiments showing that the behavioral performance of PSZ exhibited an exaggeration rather than a reduction of competitive inhibition. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Test of a dynamic neural field model: spatial working memory is biased away from distractors

Attention facilitates the encoding (e.g., Awh, Anllo-Vento, & Hillyard, J Cognit Neurosci 12(5), 840-847, 2000) and maintenance of locations in spatial working memory (Awh, Vogel, & Oh, Atten, Percept Psychophys 78(4), 1043-1063, 2006). When individuals shift their attention during the maintenance period of a spatial working memory task, their memory of a target location tends to be biased in the direction of the attentional shift (Johnson & Spencer, 2016). Dynamic field theory predicts that in certain conditions, inhibitory mechanisms will result in biases away from distractors presented during the maintenance period of the task. Specifically, dynamic field theory predicts that memory responses will be biased toward distractors that are near the target location and biased away from distractors that are farther from the target location. In two experiments, the current study tested adults in a spatial memory task that required memorization of a single target location. On a subset of trials, a distractor appeared during the memory delay at different distances and directions from the target location. In contrast to the prediction, memory responses were biased away from distractors that were near the target location and not biased by distractors that were far from the target location, providing challenges for, dynamic field theory and other theories of spatial working memory.

Retroactive Interference in Visual Short-Term Memory

Abstract. Retroactive interference occurs when new information disrupts the retention of an existing representation, but its effects on visual short-term memory remain poorly understood. The present study examined three factors predicted to influence domain-specific retroactive interference, including the type of distractor, its temporal position, and the length of the retention interval. Participants compared target and test objects over a brief interval that either was unfilled or contained a similar or dissimilar distractor occurring 200 ms or 1.5 s after the target offset. Retention was influenced by the temporal position of the distractor and its relationship with the to-be-remembered target. Specifically, retroactive interference was only observed following the presentation of a dissimilar distractor that occurred 1.5 s after the target. These results suggest that novel distractors may be particularly interfering.

Drift in Neural Population Activity Causes Working Memory to Deteriorate Over Time

Short-term memories are thought to be maintained in the form of sustained spiking activity in neural populations. Decreases in recall precision observed with increasing number of memorized items can be accounted for by a limit on total spiking activity, resulting in fewer spikes contributing to the representation of each individual item. Longer retention intervals likewise reduce recall precision, but it is unknown what changes in population activity produce this effect. One possibility is that spiking activity becomes attenuated over time, such that the same mechanism accounts for both effects of set size and retention duration. Alternatively, reduced performance may be caused by drift in the encoded value over time, without a decrease in overall spiking activity. Human participants of either sex performed a variable-delay cued recall task with a saccadic response, providing a precise measure of recall latency. Based on a spike integration model of decision making, if the effects of set size and retention duration are both caused by decreased spiking activity, we would predict a fixed relationship between recall precision and response latency across conditions. In contrast, the drift hypothesis predicts no systematic changes in latency with increasing delays. Our results show both an increase in latency with set size, and a decrease in response precision with longer delays within each set size, but no systematic increase in latency for increasing delay durations. These results were quantitatively reproduced by a model based on a limited neural resource in which working memories drift rather than decay with time.

SIGNIFICANCE STATEMENT Rapid deterioration over seconds is a defining feature of short-term memory, but what mechanism drives this degradation of internal representations? Here, we extend a successful population coding model of working memory by introducing possible mechanisms of delay effects. We show that a decay in neural signal over time predicts that the time required for memory retrieval will increase with delay, whereas a random drift in the stored value predicts no effect of delay on retrieval time. Testing these predictions in a multi-item memory task with an eye movement response, we identified drift as a key mechanism of memory decline. These results provide evidence for a dynamic spiking basis for working memory, in contrast to recent proposals of activity-silent storage.

Restoration of fMRI Decodability Does Not Imply Latent Working Memory States

Recent imaging studies have challenged the prevailing view that working memory is mediated by sustained neural activity. Using machine learning methods to reconstruct memory content, these studies found that previously diminished representations can be restored by retrospective cueing or other forms of stimulation. These findings have been interpreted as evidence for an activity-silent working memory state that can be reactivated dependent on task demands. Here, we test the validity of this conclusion by formulating a neural process model of working memory based on sustained activity and using this model to emulate a spatial recall task with retro-cueing. The simulation reproduces both behavioral and fMRI results previously taken as evidence for latent states, in particular the restoration of spatial reconstruction quality following an informative cue. Our results demonstrate that recovery of the decodability of an imaging signal does not provide compelling evidence for an activity-silent working memory state.

Highlighting in Early Childhood: Learning Biases Through Attentional Shifting

The literature on human and animal learning suggests that individuals attend to and act on cues differently based on the order in which they were learned. Recent studies have proposed that one specific type of learning outcome, the highlighting effect, can serve as a framework for understanding a number of early cognitive milestones. However, little is known how this learning effect itself emerges among children, whose memory and attention are much more limited compared to adults. Two experiments were conducted using different versions of the general highlighting paradigm: Experiment 1 tested 3 to 6 year olds with a newly developed image-based version of the paradigm, which was designed specifically to test young children. Experiment 2 tested the validity of an image-based implementation of the highlighting paradigm with adult participants. The results from Experiment 1 provide evidence for the highlighting effect among children 3-6 years old, and they suggest age-related differences in dividing attention among multiple cues during learning. Experiment 2 replicated results from previous studies by showing robust biases for both image-based and text-based versions of the highlighting task. This study suggests that sensitivity to learning order emerges early through the process of cued attention, and the role of the highlighting effect in early language learning is discussed.

Developmental improvements in the resolution and capacity of visual working memory share a common source

The nature of visual working memory (VWM) representations is currently a source of debate between characterizations as slot-like versus a flexibly-divided pool of resources. Recently, a dynamic neural field model has been proposed as an alternative account that focuses more on the processes by which VWM representations are formed, maintained, and used in service of behavior. This dynamic model has explained developmental increases in VWM capacity and resolution through strengthening excitatory and inhibitory connections. Simulations of developmental improvements in VWM resolution suggest that one important change is the accuracy of comparisons between items held in memory and new inputs. Thus, the ability to detect changes is a critical component of developmental improvements in VWM performance across tasks, leading to the prediction that capacity and resolution should correlate during childhood. Comparing 5- to 8-year-old children's performance across color discrimination and change detection tasks revealed the predicted correlation between estimates of VWM capacity and resolution, supporting the hypothesis that increasing connectivity underlies improvements in VWM during childhood. These results demonstrate the importance of formalizing the processes that support the use of VWM, rather than focusing solely on the nature of representations. We conclude by considering our results in the broader context of VWM development.

Feature-Based Change Detection Reveals Inconsistent Individual Differences in Visual Working Memory Capacity

Visual working memory (VWM) is a key cognitive system that enables people to hold visual information in mind after a stimulus has been removed and compare past and present to detect changes that have occurred. VWM is severely capacity limited to around 3–4 items, although there are robust individual differences in this limit. Importantly, these individual differences are evident in neural measures of VWM capacity. Here, we capitalized on recent work showing that capacity is lower for more complex stimulus dimension. In particular, we asked whether individual differences in capacity remain consistent if capacity is shifted by a more demanding task, and, further, whether the correspondence between behavioral and neural measures holds across a shift in VWM capacity. Participants completed a change detection (CD) task with simple colors and complex shapes in an fMRI experiment. As expected, capacity was significantly lower for the shape dimension. Moreover, there were robust individual differences in behavioral estimates of VWM capacity across dimensions. Similarly, participants with a stronger BOLD response for color also showed a strong neural response for shape within the lateral occipital cortex, intraparietal sulcus (IPS), and superior IPS. Although there were robust individual differences in the behavioral and neural measures, we found little evidence of systematic brain-behavior correlations across feature dimensions. This suggests that behavioral and neural measures of capacity provide different views onto the processes that underlie VWM and CD. Recent theoretical approaches that attempt to bridge between behavioral and neural measures are well positioned to address these findings in future work.

Unraveling the sub-processes of selective attention: insights from dynamic modeling and continuous behavior

Selective attention biases information processing toward stimuli that are relevant for achieving our goals. However, the nature of this bias is under debate: Does it solely rely on the amplification of goal-relevant information or is there a need for additional inhibitory processes that selectively suppress currently distracting information? Here, we explored the processes underlying selective attention with a dynamic, modeling-based approach that focuses on the continuous evolution of behavior over time. We present two dynamic neural field models incorporating the diverging theoretical assumptions. Simulations with both models showed that they make similar predictions with regard to response times but differ markedly with regard to their continuous behavior. Human data observed via mouse tracking as a continuous measure of performance revealed evidence for the model solely based on amplification but no indication of persisting selective distracter inhibition.

A probabilistic palimpsest model of visual short-term memory

Working memory plays a key role in cognition, and yet its mechanisms remain much debated. Human performance on memory tasks is severely limited; however, the two major classes of theory explaining the limits leave open questions about key issues such as how multiple simultaneously-represented items can be distinguished. We propose a palimpsest model, with the occurrent activity of a single population of neurons coding for several multi-featured items. Using a probabilistic approach to storage and recall, we show how this model can account for many qualitative aspects of existing experimental data. In our account, the underlying nature of a memory item depends entirely on the characteristics of the population representation, and we provide analytical and numerical insights into critical issues such as multiplicity and binding. We consider representations in which information about individual feature values is partially separate from the information about binding that creates single items out of multiple features. An appropriate balance between these two types of information is required to capture fully the different types of error seen in human experimental data. Our model provides the first principled account of misbinding errors. We also suggest a specific set of stimuli designed to elucidate the representations that subjects actually employ.

Humans can remember several visual items for a few seconds and recall them; however, performance deteriorates surprisingly quickly with the number of items that must be stored. Along with increasingly inaccurate recollection, subjects make association errors, sometimes apparently recalling the wrong item altogether. No current model accounts for these data fully. We discuss a simple model that focuses attention on the population representations that are putatively involved, and thereby on limits to the amount of information that can be stored and recalled. We use theoretical and numerical methods to examine the characteristics and performance of our model.

Beyond slots and resources: grounding cognitive concepts in neural dynamics

Research over the past decade has suggested that the ability to hold information in visual working memory (VWM) may be limited to as few as three to four items. However, the precise nature and source of these capacity limits remains hotly debated. Most commonly, capacity limits have been inferred from studies of visual change detection, in which performance declines systematically as a function of the number of items that participants must remember. According to one view, such declines indicate that a limited number of fixed-resolution representations are held in independent memory "slots." Another view suggests that such capacity limits are more apparent than real, but emerge as limited memory resources are distributed across more to-be-remembered items. Here we argue that, although both perspectives have merit and have generated and explained impressive amounts of empirical data, their central focus on the representations--rather than processes--underlying VWM may ultimately limit continuing progress in this area. As an alternative, we describe a neurally grounded, process-based approach to VWM: the dynamic field theory. Simulations demonstrate that this model can account for key aspects of behavioral performance in change detection, in addition to generating novel behavioral predictions that have been confirmed experimentally. Furthermore, we describe extensions of the model to recall tasks, the integration of visual features, cognitive development, individual differences, and functional imaging studies of VWM. We conclude by discussing the importance of grounding psychological concepts in neural dynamics, as a first step toward understanding the link between brain and behavior.

The co-development of looking dynamics and discrimination performance

The study of looking dynamics and discrimination form the backbone of developmental science and are central processes in theories of infant cognition. Looking dynamics and discrimination change dramatically across the 1st year of life. Surprisingly, developmental changes in looking and discrimination have not been studied together. Recent simulations of a dynamic neural field (DNF) model of infant looking and memory suggest that looking and discrimination do change together over development and arise from a single neurodevelopmental mechanism. We probed this claim by measuring looking dynamics and discrimination along continuous, metrically organized dimensions in 5-, 7-, and 10-month-old infants (N = 119). The results showed that looking dynamics and discrimination changed together over development and are linked within individuals. Quantitative simulations of a DNF model provide insights into the processes that underlie developmental change in looking dynamics and discrimination. Simulation results support the view that these changes might arise from a single neurodevelopmental mechanism.

Trade-off between capacity and precision in visuospatial working memory

Limitations in the performance of working memory (WM) tasks have been characterized in terms of the number of items retained (capacity) and in terms of the precision with which the information is retained. The neural mechanisms behind these limitations are still unclear. Here we used a biological constrained computational model to study the capacity and precision of visuospatial WM. The model consists of two connected networks of spiking neurons. One network is responsible for storage of information. The other provides a nonselective excitatory input to the storage network. Simulations showed that this excitation boost could temporarily increase storage capacity but also predicted that this would be associated with a decrease in precision of the memory. This prediction was subsequently tested in a behavioral (38 participants) and fMRI (22 participants) experiment. The behavioral results confirmed the trade-off effect, and the fMRI results suggest that a frontal region might be engaged in the trial-by-trial control of WM performance. The average effects were small, but individuals differed in the amount of trade-off, and these differences correlated with the frontal activation. These results support a two-module model of WM where performance is determined both by storage capacity and by top-down influence, which can vary on a trial-by-trial basis, affecting both the capacity and precision of WM.

Autonomy in action: linking the act of looking to memory formation in infancy via dynamic neural fields

Looking is a fundamental exploratory behavior by which infants acquire knowledge about the world. In theories of infant habituation, however, looking as an exploratory behavior has been deemphasized relative to the reliable nature with which looking indexes active cognitive processing. We present a new theory that connects looking to the dynamics of memory formation and formally implement this theory in a Dynamic Neural Field model that learns autonomously as it actively looks and looks away from a stimulus. We situate this model in a habituation task and illustrate the mechanisms by which looking, encoding, working memory formation, and long-term memory formation give rise to habituation across multiple stimulus and task contexts. We also illustrate how the act of looking and the temporal dynamics of learning affect each other. Finally, we test a new hypothesis about the sources of developmental differences in looking.

Models provide specificity: Testing a proposed mechanism of visual working memory capacity development

Numerous studies have established that visual working memory has a limited capacity, and that capacity increases during childhood. However, debate continues over the source of capacity limits and its developmental increase. Simmering (2008) adapted a computational model of spatial cognitive development, the Dynamic Field Theory, to explain not only the source of capacity limitations but also the developmental mechanism. According to the model, capacity is limited by the balance between excitation and inhibition that maintains multiple neural representations simultaneously. Moreover, development is implemented according to the Spatial Precision Hypothesis, which proposes that excitatory and inhibitory connections strengthen throughout early childhood. Critically, these changes in connectivity result in increasing precision and stability of neural representations over development. Here we test this developmental mechanism by probing children's memory in a single-item change detection task. Results confirmed the model's predictions, providing further support for this account of visual working memory capacity development.

Word learning emerges from the interaction of online referent selection and slow associative learning

Classic approaches to word learning emphasize referential ambiguity: In naming situations, a novel word could refer to many possible objects, properties, actions, and so forth. To solve this, researchers have posited constraints, and inference strategies, but assume that determining the referent of a novel word is isomorphic to learning. We present an alternative in which referent selection is an online process and independent of long-term learning. We illustrate this theoretical approach with a dynamic associative model in which referent selection emerges from real-time competition between referents and learning is associative (Hebbian). This model accounts for a range of findings including the differences in expressive and receptive vocabulary, cross-situational learning under high degrees of ambiguity, accelerating (vocabulary explosion) and decelerating (power law) learning, fast mapping by mutual exclusivity (and differences in bilinguals), improvements in familiar word recognition with development, and correlations between speed of processing and learning. Together it suggests that (a) association learning buttressed by dynamic competition can account for much of the literature; (b) familiar word recognition is subserved by the same processes that identify the referents of novel words (fast mapping); (c) online competition may allow the children to leverage information available in the task to augment performance despite slow learning; (d) in complex systems, associative learning is highly multifaceted; and (e) learning and referent selection, though logically distinct, can be subtly related. It suggests more sophisticated ways of describing the interaction between situation- and developmental-time processes and points to the need for considering such interactions as a primary determinant of development.

Contributions of Dynamic Systems Theory to Cognitive Development

This paper examines the contributions of dynamic systems theory to the field of cognitive development, focusing on modeling using dynamic neural fields. A brief overview highlights the contributions of dynamic systems theory and the central concepts of dynamic field theory (DFT). We then probe empirical predictions and findings generated by DFT around two examples-the DFT of infant perseverative reaching that explains the Piagetian A-not-B error, and the DFT of spatial memory that explain changes in spatial cognition in early development. A systematic review of the literature around these examples reveals that computational modeling is having an impact on empirical research in cognitive development; however, this impact does not extend to neural and clinical research. Moreover, there is a tendency for researchers to interpret models narrowly, anchoring them to specific tasks. We conclude on an optimistic note, encouraging both theoreticians and experimentalists to work toward a more theory-driven future.

Behavioral dynamics and neural grounding of a dynamic field theory of multi-object tracking

The ability to dynamically track moving objects in the environment is crucial for efficient interaction with the local surrounds. Here, we examined this ability in the context of the multi-object tracking (MOT) task. Several theories have been proposed to explain how people track moving objects; however, only one of these previous theories is implemented in a real-time process model, and there has been no direct contact between theories of object tracking and the growing neural literature using ERPs and fMRI. Here, we present a neural process model of object tracking that builds from a Dynamic Field Theory of spatial cognition. Simulations reveal that our dynamic field model captures recent behavioral data examining the impact of speed and tracking duration on MOT performance. Moreover, we show that the same model with the same trajectories and parameters can shed light on recent ERP results probing how people distribute attentional resources to targets vs. distractors. We conclude by comparing this new theory of object tracking to other recent accounts, and discuss how the neural grounding of the theory might be effectively explored in future work.

Grounding word learning in space

Humans and objects, and thus social interactions about objects, exist within space. Words direct listeners' attention to specific regions of space. Thus, a strong correspondence exists between where one looks, one's bodily orientation, and what one sees. This leads to further correspondence with what one remembers. Here, we present data suggesting that children use associations between space and objects and space and words to link words and objects—space binds labels to their referents. We tested this claim in four experiments, showing that the spatial consistency of where objects are presented affects children's word learning. Next, we demonstrate that a process model that grounds word learning in the known neural dynamics of spatial attention, spatial memory, and associative learning can capture the suite of results reported here. This model also predicts that space is special, a prediction supported in a fifth experiment that shows children do not use color as a cue to bind words and objects. In a final experiment, we ask whether spatial consistency affects word learning in naturalistic word learning contexts. Children of parents who spontaneously keep objects in a consistent spatial location during naming interactions learn words more effectively. Together, the model and data show that space is a powerful tool that can effectively ground word learning in social contexts.

Object features fail independently in visual working memory: evidence for a probabilistic feature-store model

The world is composed of features and objects and this structure may influence what is stored in working memory. It is widely believed that the content of memory is object-based: Memory stores integrated objects, not independent features. We asked participants to report the color and orientation of an object and found that memory errors were largely independent: Even when one of the object's features was entirely forgotten, the other feature was often reported. This finding contradicts object-based models and challenges fundamental assumptions about the organization of information in working memory. We propose an alternative framework involving independent self-sustaining representations that may fail probabilistically and independently for each feature. This account predicts that the degree of independence in feature storage is determined by the degree of overlap in neural coding during perception. Consistent with this prediction, we found that errors for jointly encoded dimensions were less independent than errors for independently encoded dimensions.

Stronger neural dynamics capture changes in infants' visual working memory capacity over development

Visual working memory (VWM) capacity has been studied extensively in adults, and methodological advances have enabled researchers to probe capacity limits in infancy using a preferential looking paradigm. Evidence suggests that capacity increases rapidly between 6 and 10 months of age. To understand how the VWM system develops, we must understand the relationship between the looking behavior used to study VWM and underlying cognitive processes. We present a dynamic neural field model that captures both real-time and developmental processes underlying performance. Three simulation experiments show how looking is linked to VWM processes during infancy and how developmental changes in performance could arise through increasing neural connectivity. These results provide insight into the sources of capacity limits and VWM development more generally.

A New Clustering Approach on the Basis of Dynamical Neural Field

In this letter, we present a new hierarchical clustering approach based on the evolutionary process of Amari's dynamical neural field model. Dynamical neural field theory provides a theoretical framework macroscopically describing the activity of neuron ensemble. Based on it, our clustering approach is essentially close to the neurophysiological nature of perception. It is also computationally stable, insensitive to noise, flexible, and tractable for data with complex structure. Some examples are given to show the feasibility.