Covariation, Structure and Generalization: Building Blocks of Causal Cognition

Abstract

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