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Prior beliefs are central to Bayesian accounts of cognition, but many ofthese accounts do not directly measure priors. More specifically, initialstates of belief heavily influence how new information is assumed to beutilized when updating a particular model. Despite this, prior and posteriorbeliefs are either inferred from sequential participant actions or elicitedthrough impoverished means. We had participants play a version of the game"Plinko", to first elicit individual participant priors in a theoreticallyagnostic manner. Subsequent learning and updating of participant beliefs wasthen directly measured. We show that participants hold a variety of priors thatcluster around prototypical probability distributions that in turn influencelearning. In follow-up experiments we show that participant priors are stableover time and that the ability to update beliefs is influenced by a simpleenvironmental manipulation (i.e. a short break). This data reveals theimportance of directly measuring participant beliefs rather than assuming orinferring them as has been widely done in the literature to date. The Plinkogame provides a flexible and fecund means for examining statistical learningand mental model updating.

Plinko: Eliciting beliefs to build better models of statistical learning and mental model updating