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Accession Number ADA578263
Title Reasoning, Learning, and Classifying with Uncertain Causal Models.
Publication Date Nov 2012
Media Count 15p
Personal Author B. Rehder
Abstract The last 20 years have seen a growing interest in the role of causal knowledge in numerous areas of cognition. Many studies have investigated how causal relations are learned from observed correlations. Others have tested their impact on various forms of reasoning, including inference, interventions, decision making, analogy, and classification. The original goal of this proposal (intended to cover 3 years of research but funded for 18 months) was to study three aspects of human causal reasoning. The first aspect is how people reason causally under uncertainty, that is, when beliefs are held with less than complete confidence. We address a particular application in which a representation of uncertainty resolves how inconsistencies among beliefs are resolved. The second aspect is how individuals reason with 'conjunctive causes, ' that is, when a cause only operates when it is accompanied by one or more other causes (e.g., a spark only yields fire when there is also fuel and oxygen). The third aspect concerns how people reason with inhibitory causes, that is, factors that disable or deactivate a causal mechanism. Research on the first two of these topics was conducted and is near completion, and is described in this paper.
Keywords Bayes theorem
Bayesian networks
Belief integration
Belief revision
Causal graphical models
Causal learning
Causal reasoning
Causal uncertainty
Cognitive science
Conjunctive causes
Experimental psychology
Independent causes
Interactive causes

Source Agency Non Paid ADAS
NTIS Subject Category 92B - Psychology
57T - Psychiatry
72F - Statistical Analysis
Corporate Author New York Univ., NY.
Document Type Technical report
Title Note Final rept. Sep 2010-Mar 2012.
NTIS Issue Number 1321
Contract Number FA9550-10-1-0466

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