Proceedings of the Annual Meeting of the Cognitive Science Society

The mind adeptly registers statistical regularities inexperience, often incidentally. We use a visual statisticallearning paradigm to study incidental learning of predictiverelations among animated events. We ask what kinds ofstatistics participants automatically compute, even whentracking such statistics is task-irrelevant and largely implicit.We find that participants are sensitive to a quantity governingassociative learning, DP, independently of conditionalprobabilities and chunk frequencies, as previously considered.DP specifically reflects the uniqueness, as well as strength, ofconditional probabilities; we find that uniqueness is equallyaffected by a single strong alternative predictor as by severalweak predictors. Performance is well captured with anadapted version of the Rescorla-Wagner delta learning rule(Rescorla & Wagner, 1972). We conclude that incidentalpredictive learning is governed by considerations ofuniqueness, and that this is computed by normalizingconditional probabilities by events’ base-rates. This opens thepossibility of common mechanisms between statisticallearning, associative learning, and causal inference.