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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 decisionmaking. [ABSTRACT FROM AUTHOR]

People consider alternative causes when deciding whether a cause is responsible for an effect (diagnostic inference) but appear to neglect them when deciding whether an effect will occur (predictive inference). Five experiments were conducted to test a 2-part explanation of this phenomenon: namely, (a) that people interpret standard predictive queries as requests to estimate the probability of the effect in the presence of the cause alone, which renders alternative causes irrelevant, and (b) that the impact of disablers (inhibitory causes) on predictive judgments is underestimated, and this underestimation is wrongly interpreted as cause neglect. Experiment 1 showed that standard predictive queries are frequently interpreted as requests to estimate the likelihood of E given C alone. In Experiment 2, a causal Bayes network overestimated predictive inference when it was queried in the standard way, but this overestimation diminished when predictive inference was queried using an alternative wording. In Experiment 3, participants judged alternative causes to be relevant to diagnostic inference and both disablers and alternative causes to be relevant to predictive inference. In Experiment 4, disablers greatly overshadowed alternative causes in predictive judgments, but their impact on diagnostic judgments was negligible. In Experiment 5, the order of disabler retrieval influenced causal judgments. Taken together, these results indicate that human causal inference cannot be adequately modeled unless the manner in which knowledge is retrieved and applied is taken into consideration. [ABSTRACT FROM AUTHOR]

Abstract: Dack and Astington (Journal of Experimental Child Psychology 110 2011 94–114) attempted to replicate the deontic reasoning advantage among preschoolers reported by Cummins (Memory & Cognition 24 1996 823–829) and by Harris and Nuñez (Child Development. 67 1996 572–1591). Dack and Astington argued that the apparent deontic advantage reported by these studies was in fact an artifact due to a methodological confound, namely, inclusion of an authority in the deontic condition only. Removing this confound attenuated the effect in young children but had no effect on the reasoning of 7-year-olds and adults. Thus, removing reference to authority “explains away” young children’s apparent precocity at this type of reasoning. But this explanation rests on (a) a misunderstanding of norms as targets of deontic reasoning and (b) conclusions based on a sample size that was too small to detect the effect in young children. [Copyright &y& Elsevier]

Observes the factors of casual deduction of human reasoning. Conduction of experiments; More information; Discussion of the results.