Neural correlates of causal power judgments

Abstract

Causal inference is a fundamental component of cognition and perception. Probabilistic theories of causal judgment (most notably causal Bayes networks) derive causal judgments using metrics that integrate contingency information. But human estimates typically diverge from these normative predictions. This is because human causal power judgments are typically strongly influenced by beliefs concerning underlying causal mechanisms, and because of the way knowledge is retrieved from human memory during the judgment process. Neuroimaging studies indicate that the brain distinguishes causal events from mere covariation, and also distinguishes between perceived and inferred causality. Areas involved in error prediction are also activated, implying automatic activation of possible exception cases during causal decision-making.

Keywords: causal judgment; causal power; causal reasoning; causality; neural correlates of causality.

Figure 1

A model of causal power values (W_c) as a function of belief that a causal mechanism underlies the contingency (B) and number of disablers for different values of α, a free parameter whose value is determined empirically. In the graph, B = 1, meaning that the decision-maker believes the contingency reflects a causal relationship. The function shows that the first few disablers retrieved have greater impact on causal power estimates than ones retrieved later.