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Capacity estimates increase from about 1. On no-change trials, the items in working memory suppress locations tuned to those items in the contrast layer. With development, stronger neural interactions enable the model to encode more items in the working memory layer and maintain the peaks more stably over the delay which, in turn, leads the model to respond more accurately at higher set sizes.

The model also accounts for the types of errors children and adults make. For instance, a false alarm occurs on no-change trials when the working memory layer loses a peak for one of the items in the sample array, disinhibiting sites tuned to the item in the contrast layer. This leads the contrast layer to detect the item as novel. These errors are more common in children than adults. Simulating the visual change detection task built upon the simulations of both spatial cognition and infant visual exploration.

Unlike position discrimination, however, the stimuli include multiple items at set sizes greater than one, similar to the infant change preference task.

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Johnson et al. These simulations showed that the recurrent excitatory and inhibitory interactions within the contrast and working memory layers constrains the number of items that can be maintained simultaneously: more peaks in the working memory layer generate more inhibition, which feeds back into both the contrast layer and working memory layer to prevent more peaks from forming.

With the demonstration by Johnson et al. Strengthening connectivity in the model over development had three consequences for change detection performance. First, it increased the stability of working memory peaks, which led them to be less susceptible to interference from noise and other items in working memory, meaning that once an item was encoded it was more likely to be maintained.

Second, reduced interference allowed the model to maintain more items simultaneously throughout the delay, effectively increasing its capacity. Third, the model more robustly detected change. The result was more correct identification of same on no-change trials correct rejections and fewer misses on change trials i. Together the simulations by Johnson et al.


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The implementation of both the infant change preference and visual change detection tasks within a single model architecture allowed Simmering to address an inconsistency in the literature: behavioral results with infants suggested that capacity reached adult-like levels by 10 months of age Ross-Sheehy et al. Simmering generated three empirical predictions and one simulation prediction. Second, she predicted when the same participants were tested in both tasks, the change preference task would yield higher estimates than change detection.

Third, despite these discrepant estimates, Simmering predicted performance would be correlated across tasks because both tasks depended on the same underlying neurocognitive processes of recognizing familiarity and detecting novelty. Lastly, the computational prediction Simmering tested was changes in connectivity derived from the Spatial Precision Hypothesis could account for developmental changes in performance across both tasks.

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Furthermore, simulating both tasks in the same age group showed how the structure of these tasks affected the functioning of the memory system. Similar to prior simulations Perone et al. However, Simmering also showed that the same memory system could indeed hold more items in the more supportive change preference task through the repeated presentations see Simmering, , Chapter 4. These empirical and computational results addressed gaps in the VWM literature regarding how capacity estimates were understood across tasks, the source of age-related increases in capacity, and the reasons capacity is limited.

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These insights were gained by specifying how cognition and behavior relate within each task. Emergence is a central concept for understanding how capacity limits arise and influence behavior. The number of items that can be held in memory is an emergent produce of the nature of the interactions that encode, maintain, and compare representations. These processes are all affected by changes in stability, with increasing stability allowing for more items to be encoded and maintained as well as more accurate comparison and decision processes, and smaller differences across task contexts Simmering, Simulating the performance in the change-preference task for infants and for young children and adults highlights the continuity of neurocognitive processes that support looking task behavior over development.

Showing how the same model could predict connections in performance from this looking task to the canonical change detection task with young children and adults provides strong support for continuity of processes across tasks. The simulations we presented here illustrate the key theoretical constructs of emergence, stability, and continuity in behavior, cognition, and development.

Table 1 summarizes the simulations we have described, focusing on the central changes in cognitive processes that arise through increased stability and the correspondence of these changes to behavioral phenomena. Increased stability over development has five important consequences illustrated in these simulations: 1 activation builds more quickly in the contrast layer for faster encoding; stronger activation produces peaks in the working memory layer that are 2 encoded more accurately and 3 maintained more accurately through delays i. Although, these changes all arise from implementing the Spatial Precision Hypothesis, we highlight only the most relevant changes in processes per task.

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Table 1. Cognitive and behavioral consequences of increased stability over development. Emergence is illustrated in Table 1 through the differences in which cognitive processes are most relevant to each task as well as the correspondence of the same cognitive process to different outcomes across tasks. For example, one consequence of increased stability is how quickly input is encoded; this change is most dramatic during infancy Perone and Spencer, b but continues to occur through early childhood into adulthood Simmering, However, this developmental change has little influence in the Piagetian A-not-B task or spatial recall because these tasks allow ample time for slower encoding.

Across tasks that use shorter stimulus presentations, the effect of faster encoding on behavior differs according to how representations are used in the task.

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In looking tasks, faster encoding leads to faster recognition of familiarity and release of fixation, which corresponds to habituation rates or shift rates depending on whether the task includes one or two stimuli. In capacity-related tasks, faster encoding allows more items to be encoded, either across repetitions in the infant change preference task, or in a single presentation of the visual change detection task. By understanding how one cognitive system is coupled to behavioral systems, we gain a clearer picture of how a single developmental mechanism yields a vast array of performance differences across specific task contexts.

The focus on behavior as an emergent process has also provided a clearer picture of continuity of cognitive and developmental processes within and across tasks. As Table 1 shows, developmental change in stability corresponds to multiple cognitive changes that influence behavior across tasks and domains.

Despite the successful applications of the DNF model, there are some notable shortcomings.

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One shortcoming is DNF models focus primarily on how activation changes in real time to produce behavior, with less emphasis on the processes that support learning and development see Schlesinger and McMurray, , for discussion of timescales in models. Specifically, the only learning implemented in these model simulations was a simple Hebbian mechanism that accumulates a history from above-threshold activation in the excitatory layers.

Although this long-term memory trace was sufficient to capture key behavioral characteristics of the tasks we described here, it would likely not be capable of many forms of learning found in empirical studies. Another shortcoming is that the model has not, and likely cannot, simulate all the variants of the tasks presented here.

For example, in the visual change detection paradigm, objects were represented as a feature value along a single dimension i. Furthermore, the model simulations we presented only include one set of neurocognitive processes representing visuospatial information. Although these processes are involved in a broad range of behaviors, they are clearly not the only neurocognitive architectures needed to adapt across contexts.


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The DNF model and DFT principles it is grounded in are anchored to this historical vision and aspire to identify a set of principles to describe a wide array of behaviors Spencer et al. The study of cognition and its development has instead long been partitioned into sub-domains due to the complexity of the processes under investigation.

A by-product of this partitioning is minitheories that may or may not be able to be combined into a larger whole. The simulations we reviewed show how one neurocognitive system connect phenomena using different behavioral tasks over development. The models were initially developed to explain behavior in their own domain and developmental periods, but our synthesis shows that connecting them into a bigger whole exemplifies how general theoretical constructs can explain behavior, cognition, and development.

Our synthesis of DNF model simulations has implications for long-standing debates in psychology. One debate is centered on whether perception and cognition are separable processes and, in particular, the influence of cognition on perception for discussion, see Firestone and Scholl, In the DNF model, perceptual encoding in the contrast layer and cognitive memory formation in the working memory layer are interdependent. This interdependency is apparent in simulations of visual change detection.

When the working memory layer loses a peak associated with one of the items on the sample array, it perceives that item as new when the test array appears, leading to a false alarm. Another long-standing debate is whether cognition is domain-specific see Karmiloff-Smith, , , for review : the domain specific view posits that cognitive processes distinctly correspond to domains, whereas the domain general view is that a set of basic cognitive processes apply across domains.

Our synthesis of DNF model simulations showed how the same neurocognitive processes can support different behaviors across a wide array of tasks and developmental periods. The specification of the basic neurocognitive processes of encoding, maintenance, comparison, and long-term memory formation with different behavioral system in the DNF model allowed these connections to be made concretely. Our synthesis of DNF model simulations also has implications for our understanding of behavior.

The first implication is there is not a one-to-one mapping between behavior and cognition; rather, behavior emerges in context. For example, tasks designed to assess visual working memory capacity assumed that the target behavior i. The DNF model uses whatever it has available e. A classic example of emergence in a developing system is the stepping reflex Thelen et al.

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Newborn infants show step-like alternating leg movements when held upright over a flat surface; around 4 months of age, this behavior disappears, but then reappears as infants begin to walk. The pattern was originally attributed to a decrease in reflexive movement causing the disappearance of the behavior followed by an increase in voluntary control of movement causing the reappearance of the behavior. This example highlights the need to interpret behavior in context and not assign priority to internal components over external components for further discussion, see Fogel and Thelen, The second implication is cognition should be viewed as emerging in the context of the body perspective.

For example, the act of looking structures memory formation which, in turn, contributes to the maintenance or release of fixation. There is a continuous, mutually influential loop between cognition and behavior for similar discussion, see Pezzulo and Cisek, A striking empirical example of cognition being structured in relation to the body in early development is in the A-not-B task.

In particular, when young infants who normally make the A-not-B error are stood up in between the A trials and B trials, they no longer make the error Smith et al. The third and perhaps most crucial implication of our synthesis is stability is a critical component of behavior in the moment as well as a domain-general developmental mechanism. Stability refers to how reliably a system can exhibit a given state; Stability is a more general concept and can be seen in the motor domain.

These observations can be understood as an age-related increase in stability which improves suppression of competition e. Stability enabled the model to form a stable working memory peak on the B trial, suppressing competition from prior reaches to A, and guiding a correct reach to B.

In closing, simulations of the DNF model support the notion transitions in cognitive development are not qualitative in nature for discussion, see Kagan, but reflect the organization of a system in a task context with specific behavioral demands.