Combining causal and similarity-based reasoning

Everyday inductive reasoning draws on many kinds of knowledge, including knowledge about relationships between properties and know ledge about relation- ships between objects. Previous accounts of inductive reasoning generally focus on just one kind of knowledge: models of causal reasoning oft en focus on relationships between properties, and models of similarity- based reasoning often focus on similarity relationships between objects. We present a Bayesian model of inductive reasoning that incorporates both kinds of know ledge, and show that it accounts well for human inferences about the properties o f biological species.</p

Combining causal and similarity-based reasoning. NIPS

Everyday inductive reasoning draws on many kinds of knowledge, including knowledge about causal relationships between properties and knowledge about similarity relationships between objects. Previous accounts of inductive reasoning generally focus on just one kind of knowledge: models of causal reasoning often focus on relationships between properties, and models of similarity-based reasoning often focus on similarity relationships between objects. We present a model of inductive reasoning that incorporates both kinds of knowledge. Two experiments show that our model makes accurate quantitative predictions in a task where subjects combine knowledge about causal relationships between properties with knowledge about similarity between biological species.