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Sloman, Steven A. – Cognitive Science, 2013
Judea Pearl won the 2010 Rumelhart Prize in computational cognitive science due to his seminal contributions to the development of Bayes nets and causal Bayes nets, frameworks that are central to multiple domains of the computational study of mind. At the heart of the causal Bayes nets formalism is the notion of a counterfactual, a representation…
Descriptors: Causal Models, Cognitive Psychology, Cognitive Science, Cognitive Processes
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Fernbach, Philip M.; Sloman, Steven A. – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2009
The authors proposed and tested a psychological theory of causal structure learning based on local computations. Local computations simplify complex learning problems via cues available on individual trials to update a single causal structure hypothesis. Structural inferences from local computations make minimal demands on memory, require…
Descriptors: Causal Models, Cues, Memory, Heuristics
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Hagmayer, York; Sloman, Steven A. – Journal of Experimental Psychology: General, 2009
Causal considerations must be relevant for those making decisions. Whether to bring an umbrella or leave it at home depends on the causal consequences of these options. However, most current decision theories do not address causal reasoning. Here, the authors propose a causal model theory of choice based on causal Bayes nets. The critical ideas…
Descriptors: Causal Models, Inferences, Decision Making, Intervention
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Chaigneau, Sergio E.; Barsalou, Lawrence W.; Sloman, Steven A. – Journal of Experimental Psychology: General, 2004
Theories typically emphasize affordances or intentions as the primary determinant of an object's perceived function. The HIPE theory assumes that people integrate both into causal models that produce functional attributions. In these models, an object's physical structure and an agent's action specify an affordance jointly, constituting the…
Descriptors: Inferences, Causal Models, Theories
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Lagnado, David A.; Sloman, Steven A. – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2006
How do people learn causal structure? In 2 studies, the authors investigated the interplay between temporal-order, intervention, and covariational cues. In Study 1, temporal order overrode covariation information, leading to spurious causal inferences when the temporal cues were misleading. In Study 2, both temporal order and intervention…
Descriptors: Time, Causal Models, Time Factors (Learning), Intervention