1. Probabilistic Models of Cognition Conceptual Foundations Chater, Tenenbaum, & Yuille TICS, 10(7), 287-291 (2006)

2. Probabilistic Models Powerful recent approach to perception & cognition Probability theory and Bayesian inference Alternative to classical AI Focus on function over mechanism Suggest reasoning is generally optimal

3. Bayesian Inference Probabilities as beliefs about world –Not limiting frequencies Reasoning from effects to causes 200 “B”200 “R”100 “S” P(hypothesis | data)  P(data | hypothesis) * P(hypothesis) Prior beliefs + Data Bayes’ rule Posterior beliefs

4. Probabilistic Cognitive Models Hypotheses over structured representations –Grammars, casual networks, taxonomies, scenes Hierarchies of hypotheses Sophisticated learning and estimation techniques Application to language, vision, navigation, causal learning, categorization, memory, reasoning Mapping to heuristics and neural organization Round Red or green 4 legs, tail, ears Various colors “apple” “cat” Shape > color