Connectionist and dynamical systems approaches explain human thought, language and behavior in terms of the emergent consequences of a large number of simple noncognitive processes. We view the entities that serve as the basis for structured probabilistic approaches as abstractions that are occasionally useful but often misleading: they have no real basis in the actual processes that give rise to linguistic and cognitive abilities or to the development of these abilities. Although structured probabilistic approaches can be useful in determining what would be optimal under certain assumptions, we propose that connectionist, dynamical systems, and related approaches, which focus on explaining the mechanisms that give rise to cognition, will be essential in achieving a full understanding of cognition and development.

PDP++ is a freely available, open source software package designed to support the development, simulation, and analysis of research-grade connectionist models of cognitive processes. It supports most popular parallel distributed processing paradigms and artificial neural network architectures, and it also provides an implementation of the LEABRA computational cognitive neuroscience framework. Models are typically constructed and examined using the PDP++ graphical user interface, but the system may also be extended through the incorporation of user-written C++ code. This article briefly reviews the features of PDP++, focusing on its utility for teaching cognitive modeling concepts and skills to university undergraduate and graduate students. An informal evaluation of the software as a pedagogical tool is provided, based on the author’s classroom experiences at three research universities and several conference-hosted tutorials.

An explicit, rule-based, category-learning task with abstract visual stimuli was administered to 50 healthy older adults and 48 younger adults. Accuracy and reaction time (RT) were examined for the effects of age, perceptual abilities, rule memory, rule complexity, stimulus novelty, and response competition. Older adults performed at equivalent levels to younger adults when applying a simple rule, but showed performance decrements when applying a more complex rule. The age effect interacted with both stimulus novelty and response competition, and was not eliminated after controlling for basic perceptual abilities and rule memory. The authors suggest that older adults show category learning deficits in conditions that require enhanced cognitive control. These results are discussed in reference to the growing body of literature regarding age-related change in executive abilities and frontal lobe function.

Human cognitive control is uniquely flexible and has been shown to depend on prefrontal cortex (PFC). But exactly how the biological mechanisms of the PFC support flexible cognitive control remains a profound mystery. Existing theoretical models have posited powerful task-specific PFC representations, but not how these develop. We show how this can occur when a set of PFC-specific neural mechanisms interact with breadth of experience to self organize abstract rule-like PFC representations that support flexible generalization in novel tasks. The same model is shown to apply to benchmark PFC tasks (Stroop and Wisconsin card sorting), accurately simulating the behavior of neurologically intact and frontally damaged people.

Computational models of cognition often exhibit complex dynamics that are difficult to discern without the use of visualization tools. Current tools often provide insight only to the modeling expert, however, and they provide limited functionality for communicating model dynamics to the nonexpert, as is needed during scientific presentations and in educational settings. We present NAV, the Node Activity Visualizer, an easy-to-use and portable software tool that interactively transforms the output of cognitive modeling simulators into presentation quality animations of model performance.

Many purported demonstrations of irrational behavior rely on the assumption that participants believe key task parameters that are merely asserted by experimenters. For example, previous researchers have found that participants who first reported confidence in items presented in a yes–no format did not change confidence to the degree prescribed by the normative model when those same items were later presented in a forced-choice format. A crucial assumption, however, was that participants fully believed the assertion that the forced-choice items were mutually exclusive and exhaustive. In this article, the authors derive and test a new normative model in which it is not assumed that participants fully believe the assertion. Two visual identification experiments show that the new normative model provides a compelling account of participants’ confidence reports.

We present a computational model of the intradimensional/ extradimensional (ID/ED) task (a variant of the Wisconsin card sorting task) that simulates the performance of intact and frontally lesioned monkeys on three different kinds of rule changes (Dias et al., 1997, J Neurosci 17:9285–9297). Although Dias et al. interpret the lesion data as supporting a model in which prefrontal cortex is organized into different processing functions, our model suggests an alternative account based on representational content. A key aspect of the model is that prefrontal cortex representations are organized according to different levels of abstraction, with orbital areas encoding more specific featural information and dorsolateral areas encoding more abstract dimensional information. This representational scheme of the model is integrated with two additional key elements: (i) activation-based working memory representations controlled by a dynamic gating mechanism that simulates the hypothesized phasic actions of dopaminergic neuromodulation in prefrontal cortex, which acts to stabilize or destabilize frontal representations based on success in the task; and (ii) a weight- based associative learning system simulating posterior cortex and other subcortical areas, where the stimulus–response mappings are encoded. Frontal cortex contributes to the task via top-down activation-based biasing of task-appropriate features and dimensions in this posterior cortex system — this top-down biasing is specifically important for overcoming prepotent associations after a sorting rule reverses. The ability of the model to capture the double-dissociation observed by Dias et al. with orbital versus dorsolateral lesions supports the validity of these principles, many of which have also been useful in accounting for other frontal phenomena.

Yes-no and forced-choice tasks are common in psychology, but the empirical relation between reported confidence in the 2 tasks has been unclear. The authors examined this relation with 2 experiments. The general experimental method had participants first report confidence in the truth of each of many general knowledge statements (a yes-no task) then report confidence in them again when the statements were put into pairs where it was known that one statement was true and one was false (a forced-choice task). At issue was how confidence in the statements changed between the yes-no task and the forced-choice task. Two models, including the normative one, were ruled out as descriptive models. A linear model and a multiplicative model remain viable contenders.