Explicit Bayesian Reasoning with Frequencies, Probabilities, and Surprisals

Abstract

To explore human deviations from Bayes’ rule in numerically explicit problems, prior and likelihood probabilities or frequencies are manipulated and their effects on posterior probabilities or surprisals are measured. Results show that people use both priors and likelihoods in Bayesian directions, but the effect of likelihood information is stronger than that of prior information. Use of frequency information and surprisal measures increase deviations from Bayesian predictions. There is evidence that people do compute something like the standardizing marginal data term when asked for probability estimates, but not when asked for surprisal ratings.


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