An Empirical Evaluation of Models for How People Learn Cue Search Orders


We propose simple parameter-free models that predict how people learn environmental cue contingencies, use this information to measure the usefulness of cues, and in turn, use these measures 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 simple Bayesian and biased-Bayesian learning mechanisms for inferring these measures from experience. We evaluate the model predictions against people’s search behavior in an experiment in which people could freely search cues for information to decide between two stimuli. Our results show that people’s behavior is best predicted by models relying on cue measures maximizing short-term accuracy, rather than long-term exploration, and using the biased learning mechanism that increases the certainty of inferences about cue properties, but does not necessarily learn true environmental contingencies.

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