Previous research suggests that children can infer causal relations from patterns of events. However, what appear to be cases of causal inference may simply reduce to children recognizing relevant associations among events, and responding based on those associations. To examine this claim, in Experiments 1 and 2, children were introduced to a “blicket detector”, a machine that lit up and played music when certain objects were placed upon it. Children observed patterns of contingency between objects and the machine’s activation that (...) required them to use indirect evidence to make causal inferences. Critically, associative models either made no predictions, or made incorrect predictions about these inferences. In general, children were able to make these inferences, but some developmental differences between 3- and 4- year-olds were found. We suggest that children’s causal inferences are not based on recognizing associations, but rather that children develop a mechanism for Bayesian structure learning. Experiment 3 explicitly tests a prediction of this account. Children were asked to make an inference about ambiguous data based on the base-rate of certain events occurring. Fouryear-olds, but not 3-year-olds were able to make this inference. (shrink)

Although the developmental and adult literatures on causal understanding appear at first glance to have little in common, in fact this appearance is illusory, and the idea of two theories helps to bring the two literatures in contact with each other.

Book description: The capacity to represent and think about time is one of the most fundamental and least understood aspects of human cognition and consciousness. This book throws new light on central issues in the study of the mind by uniting, for the first time, psychological and philosophical approaches dealing with the connection between temporal representation and memory. Fifteen specially written essays by leading psychologists and philosophers investigate the way in which time is represented in memory, and the role memory (...) plays in our ability to reason about time. They offer insights into current theories of memory processes and of the mechanisms and cognitive abilities underlying temporal judgements, and draw out fundamental issues concerning the phenomenology and epistemology of memory and our understanding of time. The chapters are arranged into four sections, each focused on one area of current research: Keeping Track of Time, and Temporal Representation; Memory, Awareness and the Past; Memory and Experience; Knowledge and the Past: The Epistemology and Metaphysics of Time. A general introduction gives an overview of the topics discussed and makes explicit central themes which unify the different philosophical and psychological approaches. (shrink)

In this book John Campbell shows that the general structural features of human thought can be seen as having their source in the distinctive ways in which we...

We propose that children employ specialized cognitive systems that allow them to recover an accurate “causal map” of the world: an abstract, coherent, learned representation of the causal relations among events. This kind of knowledge can be perspicuously understood in terms of the formalism of directed graphical causal models, or “Bayes nets”. Children’s causal learning and inference may involve computations similar to those for learning causal Bayes nets and for predicting with them. Experimental results suggest that 2- to 4-year-old children (...) construct new causal maps and that their learning is consistent with the Bayes net formalism. (shrink)

Previous research suggests that children can infer causal relations from patterns of events. However, what appear to be cases of causal inference may simply reduce to children recognizing relevant associations among events, and responding based on those associations. To examine this claim, in Experiments 1 and 2, children were introduced to a “blicket detector,” a machine that lit up and played music when certain objects were placed upon it. Children observed patterns of contingency between objects and the machine's activation that (...) required them to use indirect evidence to make causal inferences. Critically, associative models either made no predictions, or made incorrect predictions about these inferences. In general, children were able to make these inferences, but some developmental differences between 3- and 4-year-olds were found. We suggest that children's causal inferences are not based on recognizing associations, but rather that children develop a mechanism for Bayesian structure learning. Experiment 3 explicitly tests a prediction of this account. Children were asked to make an inference about ambiguous data based on the base rate of certain events occurring. Four-year-olds, but not 3-year-olds were able to make this inference. (shrink)

We outline a cognitive and computational account of causal learning in children. We propose that children employ specialized cognitive systems that allow them to recover an accurate “causal map” of the world: an abstract, coherent representation of the causal relations among events. This kind of knowledge can be perspicuously represented by the formalism of directed graphical causal models, or “Bayes nets”. Human causal learning and inference may involve computations similar to those for learnig causal Bayes nets and for predicting with (...) them. Preliminary experimental results suggest that 2- to 4-year-old children spontaneously construct new causal maps and that their learning is consistent with the Bayes-Net formalism. (shrink)