Covariation, Structure and Generalization: Building Blocks of Causal Cognition

摘要: Theories of causal cognition describe how animals code cognitive primitives such as strength, directionality relations, and other variables that allow inferences on the effect interventions links. We argue these importantly generalization can be studied within an animal learning framework. Causal maps Bayesian approaches provide a normative framework for studying cognition, associative theory provides algorithms computing acquisition data-driven knowledge.