Proceedings of the Annual Meeting of the Cognitive Science Society

We propose simple parameter-free models that predict howpeople learn environmental cue contingencies, use this infor-mation to measure the usefulness of cues, and in turn, use thesemeasures to construct search orders. To develop the models,we consider a total of 8 previously proposed cue measures,based on cue validity and discriminability, and develop simpleBayesian and biased-Bayesian learning mechanisms for infer-ring these measures from experience. We evaluate the modelpredictions against people’s search behavior in an experimentin which people could freely search cues for information todecide between two stimuli. Our results show that people’sbehavior is best predicted by models relying on cue measuresmaximizing short-term accuracy, rather than long-term explo-ration, and using the biased learning mechanism that increasesthe certainty of inferences about cue properties, but does notnecessarily learn true environmental contingencies.