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.
Highlights
Causal inference is a fundamental component of cognition and perception
In probabilistic theories of causal judgment, causal power is assessed through metrics that integrate contingency information
That is, the probability of the effect occurring in the presence of the cause minus the probability of the effect occurring in the absence of the cause. (This metric is referred to as ∆P by Cheng (1997) and as PNS by Pearl (2000))
Summary
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. For causes that prevent the effect, Cheng (1997) defined causal power as 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 memory during the judgment process.
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