title: A Bayesian Theory of Sequential Causal Learning and Abstract Transfer.
abstract: Two key research issues in the field of causal learning are how people acquire causal knowledge when observing data that are presented sequentially, and the level of abstraction at which learning takes place. Does sequential causal learning solely involve the acquisition of specific cause-effect links, or do learners also acquire knowledge about abstract causal constraints? Recent empirical studies have revealed that experience with one set of causal cues can dramatically alter subsequent learning and performance with entirely different cues, suggesting that learning involves abstract transfer, and such transfer effects involve sequential presentation of distinct sets of causal cues. It has been demonstrated that pre-training (or even post-training) can modulate classic causal learning phenomena such as forward and backward blocking. To account for these effects, we propose a Bayesian theory of sequential causal learning. The theory assumes that humans are able to consider and use several alternative causal generative models, each instantiating a different causal integration rule. Model selection is used to decide which integration rule to use in a given learning environment in order to infer causal knowledge from sequential data. Detailed computer simulations demonstrate that humans rely on the abstract characteristics of outcome variables (e.g., binary vs. continuous) to select a causal integration rule, which in turn alters causal learning in a variety of blocking and overshadowing paradigms. When the nature of the outcome variable is ambiguous, humans select the model that yields the best fit with the recent environment, and then apply it to subsequent learning tasks. Based on sequential patterns of cue-outcome co-occurrence, the theory can account for a range of phenomena in sequential causal learning, including various blocking effects, primacy effects in some experimental conditions, and apparently abstract transfer of causal knowledge.
authors.affiliation: Department of Psychology, University of California, Los Angeles.|Department of Statistics, University of California, Los Angeles.
authors.forename: Hongjing
authors.initials: H
authors.lastname: Lu
authors.affiliation: Department of Economics, University of California, Los Angeles.
authors.forename: Randall R
authors.initials: RR
authors.lastname: Rojas
authors.affiliation: Department of Psychology, KU Leuven.|Department of Clinical Psychology and Amsterdam Brain and Cognition, University of Amsterdam.
authors.forename: Tom
authors.initials: T
authors.lastname: Beckers
authors.affiliation: Department of Psychology, University of California, Los Angeles.|Department of Statistics, University of California, Los Angeles.
authors.forename: Alan L
authors.initials: AL
authors.lastname: Yuille
mesh_terms: D001245:Association Learning / Q000502:physiology*; D001499:Bayes Theorem; D003071:Cognition / Q000502:physiology; D003198:Computer Simulation; D003210:Concept Formation / Q000502:physiology*; D006801:Humans; D019359:Knowledge; D007858:Learning / Q000502:physiology*; D008960:Models, Psychological; D014163:Transfer, Psychology / Q000502:physiology*
keywords: Abstract transfer; Bayesian inference; Blocking; Causal learning; Model selection; Sequential causal inference