The curse of dimensionality, which has been widely studied in statistics and machine learning, occurs when additional features cause the size of the feature space to grow so quickly that learning classification rules becomes increasingly difficult. How do people overcome the curse of dimensionality when acquiring real-world categories that have…

This article assesses how people reason with categories whose features are related in causal cycles. Whereas models based on causal graphical models (CGMs) have enjoyed success modeling category-based judgments as well as a number of other cognitive phenomena, CGMs are only able to represent causal structures that are acyclic. A number of new…

Six experiments investigated variables predicted to influence subjects' tendency to classify items by a single property ("rule-based" responding) instead of overall similarity, following the paradigm of Norenzayan et al. (2002, "Cognitive Science"), who found that European Americans tended to give more "logical"…

Linguistic and non-linguistic pattern learning have been studied separately, but we argue for a comparative approach. Analogous inductive problems arise in phonological and visual pattern learning. Evidence from three experiments shows that human learners can solve them in analogous ways, and that human performance in both cases can be captured by…

Understanding the role of "representations" in cognitive science is a fundamental problem facing the emerging framework of embodied, situated, dynamical cognition. To make progress, I follow the approach proposed by an influential representational skeptic, Randall Beer: building artificial agents capable of minimally cognitive behaviors and…

Over the history of the study of visual perception there has been great success at discovering countless visual illusions. There has been less success in organizing the overwhelming variety of illusions into empirical generalizations (much less explaining them all via a unifying theory). Here, this article shows that it is possible to…

Many of the problems studied in cognitive science are inductive problems, requiring people to evaluate hypotheses in the light of data. The key to solving these problems successfully is having the right inductive biases--assumptions about the world that make it possible to choose between hypotheses that are equally consistent with the observed…

It has been suggested that iconic graphical signs evolve into symbolic graphical signs through repeated usage. This article reports a series of interactive graphical communication experiments using a "pictionary" task to establish the conditions under which the evolution might occur. Experiment 1 rules out a simple repetition based account in…