Understanding causal structure is a central task of human cognition. Causal learning underpins the development of our concepts and categories, our intuitive theories, and our capacities for planning, imagination and inference. During the last few years, there has been an interdisciplinary revolution in our understanding of learning and reasoning: Researchers in philosophy, psychology, and computation have discovered new mechanisms for learning the causal structure of the world. This new work provides a rigorous, formal basis for theory theories of concepts and cognitive development, and moreover, the causal learning mechanisms it has uncovered go dramatically beyond the traditional mechanisms of both nativist theories, such as modularity theories, and empiricist ones, such as association or connectionism.With this goal inmind, we focus on threeinterrelated questions(Table191). First ... What, if anything, domechanismsfor theoryguided learninghavein common with mechansismsfor learningatthismore abstract ... with the samelearning andreasoningchallenges (Anderson, 1990; Chater, 1999;Marr, 1982;Oaksford aamp;Shepard, 1987). Thisapproach proceedsintwostages. First, we identifyacore set of computational problems that intuitive theories helpto solve, focusingontherole oftheories inanbsp;...
|Title||:||Causal Learning: Psychology, Philosophy, and Computation|
|Author||:||Alison Gopnik, Laura Schulz|
|Publisher||:||Oxford University Press - 2007-03-12|