Current understanding of the development of quantity representations is based primarily on performance in the number-line task. We posit that the data from number-line tasks reflect the observer's underlying representation of quantity, together with the cognitive strategies and skills required to equate line length and quantity. Here, we specify a…

Algorithms for approximate Bayesian inference, such as those based on sampling (i.e., Monte Carlo methods), provide a natural source of models of how people may deal with uncertainty with limited cognitive resources. Here, we consider the idea that individual differences in working memory capacity (WMC) may be usefully modeled in terms of the…

Most accounts of the Stroop effect (Stroop, 1935) emphasize its negative aspect, namely, that in particular situations, processing of an irrelevant stimulus dimension interferes with participants' performance of the instructed task. In contrast, this paper emphasizes the fact that, even with that interference, participants actually can (and…

Emerging parallel processing and increased flexibility during the acquisition of cognitive skills form a combination that is hard to reconcile with rule-based models that often produce brittle behavior. Rule-based models can exhibit these properties by adhering to 2 principles: that the model gradually learns task-specific rules from instructions…

The notion of optimality is often invoked informally in the literature on metacognitive control. We provide a precise formulation of the optimization problem and show that optimal time allocation strategies depend critically on certain characteristics of the learning environment, such as the extent of time pressure, and the nature of the uptake…

Children improve at word learning during the 2nd year of life--sometimes dramatically. This fact has suggested a change in mechanism, from associative learning to a more referential form of learning. This article presents an associative exemplar-based model that accounts for the improvement without a change in mechanism. It provides a unified…

Reports results of studies with an unsupervised learning paradigm called competitive learning which is examined using computer simulation and formal analysis. When competitive learning is applied to parallel networks of neuron-like elements, many potentially useful learning tasks can be accomplished. (Author)