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

TL;DR

This study introduces the Plinko game as a novel method to directly elicit and measure individual prior beliefs, demonstrating their stability and influence on learning and updating processes in cognition.

Contribution

The paper presents a new experimental approach using Plinko to directly measure priors, revealing their clustering, stability, and impact on belief updating.

Findings

Participants hold diverse priors around prototypical distributions.

Priors are stable over time.

Belief updating can be influenced by environmental manipulations.

Abstract

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.