This article reports results from two studies of how people answer counterfactual questions about simple machines. Participants learned about devices that have a specific configuration of components, and they answered questions of the form "If component X had not operated [failed], would component Y have operated?" The data from these…

Judea Pearl won the 2010 Rumelhart Prize in computational cognitive science due to his seminal contributions to the development of Bayes nets and causal Bayes nets, frameworks that are central to multiple domains of the computational study of mind. At the heart of the causal Bayes nets formalism is the notion of a counterfactual, a representation…

How do people attribute responsibility in situations where the contributions of multiple agents combine to produce a joint outcome? The prevalence of over-determination in such cases makes this a difficult problem for counterfactual theories of causal responsibility. In this article, we explore a general framework for assigning responsibility in…

How do human infants learn the causal dependencies between events? Evidence suggests that this remarkable feat can be achieved by observation of only a handful of examples. Many computational models have been produced to explain how infants perform causal inference without explicit teaching about statistics or the scientific method. Here, we…

Despite the fact that they play a prominent role in everyday speech, the representation and processing of fixed expressions during language production is poorly understood. Here, we report a study investigating the processes underlying fixed expression production. "Tip-of-the-tongue" (TOT) states were elicited for well-known idioms…

A key aim in biology and psychology is to identify fundamental principles underpinning the behavior of animals, including humans. Analyses of human language and the behavior of a range of non-human animal species have provided evidence for a common pattern underlying diverse behavioral phenomena: Words follow Zipf's law of brevity (the…

Young children spend a large portion of their time pretending about non-real situations. Why? We answer this question by using the framework of Bayesian causal models to argue that pretending and counterfactual reasoning engage the same component cognitive abilities: disengaging with current reality, making inferences about an alternative…

The ability to imagine objects undergoing rotation (mental rotation) improves markedly with practice, but an explanation of this plasticity remains controversial. Some researchers propose that practice speeds up the rate of a general-purpose rotation algorithm. Others maintain that performance improvements arise through the adoption of a new…

The ability of 3- and 4-year-old children to disregard advice from an overtly misleading informant was investigated across five studies (total "n" =212). Previous studies have documented limitations in young children's ability to reject misleading advice. This study was designed to test the hypothesis that these limitations are primarily due to an…

We respond to several important and valid concerns about our study ("The Prevalence of Folk Dualism in Early China," "Cognitive Science" 35: 997-1007) by Klein and Klein, defending our interpretation of our data. We also argue that, despite the undeniable challenges involved in qualitatively coding texts from ancient cultures, the standard tools…

This article proposes a new model of human concept learning that provides a rational analysis of learning feature-based concepts. This model is built upon Bayesian inference for a grammatically structured hypothesis space--a concept language of logical rules. This article compares the model predictions to human generalization judgments in several…

Computational models will play an important role in our understanding of human higher-order cognition. How can a model's contribution to this goal be evaluated? This article argues that three important aspects of a model of higher-order cognition to evaluate are (a) its ability to reason, solve problems, converse, and learn as well as people do;…

Model validation in computational cognitive psychology often relies on methods drawn from the testing of theories in experimental physics. However, applications of these methods to computational models in typical cognitive experiments can hide multiple, plausible sources of variation arising from human participants and from stochastic cognitive…

How children go about learning the general regularities that govern language, as well as keeping track of the exceptions to them, remains one of the challenging open questions in the cognitive science of language. Computational modeling is an important methodology in research aimed at addressing this issue. We must determine appropriate learning…

The recognition that human minds/brains are finite systems with limited resources for computation has led some researchers to advance the "Tractable Cognition thesis": Human cognitive capacities are constrained by computational tractability. This thesis, if true, serves cognitive psychology by constraining the space of computational-level theories…