Plinko: Eliciting beliefs to build better models of statistical learning and mental model updating
This addresses the issue of assuming or inferring priors in cognitive science, providing a more direct measurement method for studying statistical learning and mental model updating.
The researchers tackled the problem of measuring prior beliefs in Bayesian cognition by having participants play a game called Plinko to directly elicit priors, showing that participants hold diverse priors that cluster around prototypical distributions and influence learning, with priors being stable over time and updating affected by environmental breaks.
Prior beliefs are central to Bayesian accounts of cognition, but many of these accounts do not directly measure priors. More specifically, initial states of belief heavily influence how new information is assumed to be utilized when updating a particular model. Despite this, prior and posterior beliefs are either inferred from sequential participant actions or elicited through impoverished means. We had participants play a version of the game "Plinko", to first elicit individual participant priors in a theoretically agnostic manner. Subsequent learning and updating of participant beliefs was then directly measured. We show that participants hold a variety of priors that cluster around prototypical probability distributions that in turn influence learning. In follow-up experiments we show that participant priors are stable over time and that the ability to update beliefs is influenced by a simple environmental manipulation (i.e. a short break). This data reveals the importance of directly measuring participant beliefs rather than assuming or inferring them as has been widely done in the literature to date. The Plinko game provides a flexible and fecund means for examining statistical learning and mental model updating.