title: Causal competition based on generic priors.
abstract: Although we live in a complex and multi-causal world, learners often lack sufficient data and/or cognitive resources to acquire a fully veridical causal model. The general goal of making precise predictions with energy-efficient representations suggests a generic prior favoring causal models that include a relatively small number of strong causes. Such "sparse and strong" priors make it possible to quickly identify the most potent individual causes, relegating weaker causes to secondary status or eliminating them from consideration altogether. Sparse-and-strong priors predict that competition will be observed between candidate causes of the same polarity (i.e., generative or else preventive) even if they occur independently. For instance, the strength of a moderately strong cause should be underestimated when an uncorrelated strong cause also occurs in the general learning environment, relative to when a weaker cause also occurs. We report three experiments investigating whether independently-occurring causes (either generative or preventive) compete when people make judgments of causal strength. Cue competition was indeed observed for both generative and preventive causes. The data were used to assess alternative computational models of human learning in complex multi-causal situations.
authors.affiliation: Department of Psychology, University of California, Los Angeles, United States. Electronic address: derekpowell@ucla.edu.
authors.forename: Derek
authors.initials: D
authors.lastname: Powell
authors.affiliation: Department of Psychology, University of California, Los Angeles, United States.
authors.forename: M Alice
authors.initials: MA
authors.lastname: Merrick
authors.affiliation: Department of Psychology, University of California, Los Angeles, United States; Department of Statistics, University of California, Los Angeles, United States.
authors.forename: Hongjing
authors.initials: H
authors.lastname: Lu
authors.affiliation: Department of Psychology, University of California, Los Angeles, United States.
authors.forename: Keith J
authors.initials: KJ
authors.lastname: Holyoak
mesh_terms: D001245:Association Learning; D001499:Bayes Theorem; D003163:Competitive Behavior; D003210:Concept Formation; D003463:Cues; D005260:Female; D006801:Humans; D007600:Judgment; D016014:Linear Models; D008297:Male; D008960:Models, Psychological; D055815:Young Adult
keywords: Bayesian models; Causal learning; Cue competition; Generic priors; Simplicity