title: How causal knowledge affects classification: A generative theory of categorization.
abstract: Several theories have been proposed regarding how causal relations among features of objects affect how those objects are classified. The assumptions of these theories were tested in 3 experiments that manipulated the causal knowledge associated with novel categories. There were 3 results. The 1st was a multiple cause effect in which a feature's importance increases with its number of causes. The 2nd was a coherence effect in which good category members are those whose features jointly corroborate the category's causal knowledge. These 2 effects can be accounted for by assuming that good category members are those likely to be generated by a category's causal laws. The 3rd result was a primary cause effect, in which primary causes are more important to category membership. This effect can also be explained by a generative account with an additional assumption: that categories often are perceived to have hidden generative causes.
authors.affiliation: Department of Psychology, New York University, New York, NY 10003, USA. bob.rehder@nyu.edu
authors.forename: Bob
authors.initials: B
authors.lastname: Rehder
authors.forename: ShinWoo
authors.initials: S
authors.lastname: Kim
mesh_terms: D001245:Association Learning; D015984:Causality; D003210:Concept Formation; D004193:Discrimination Learning; D006801:Humans; D016571:Neural Networks, Computer; D010364:Pattern Recognition, Visual; D011337:Probability Learning; D011340:Problem Solving; D011578:Psycholinguistics; D013223:Statistics as Topic; D013334:Students / Q000523:psychology