Plinko: A Theory-Free Behavioral Measure of Priors for Statistical Learning and Mental Model Updating

• URL: http://arxiv.org/abs/2107.11477v1
• Date: Fri, 23 Jul 2021 22:27:30 GMT
• Title: Plinko: A Theory-Free Behavioral Measure of Priors for Statistical Learning and Mental Model Updating
• Authors: Peter A. V. DiBerardino, Alexandre L. S. Filipowicz, James Danckert, Britt Anderson
• Abstract summary: We present three experiments using "Plinko", a behavioral task in which participants estimate distributions of ball drops over all available outcomes. We show that participant priors cluster around prototypical probability distributions and that prior cluster membership may indicate learning ability. We verify that individual participant priors are reliable representations and that learning is not impeded when faced with a physically implausible ball drop distribution.
• Score: 62.997667081978825
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Probability distributions are central to Bayesian accounts of cognition, but behavioral assessments do not directly measure them. Posterior distributions are typically computed from collections of individual participant actions, yet are used to draw conclusions about the internal structure of participant beliefs. Also not explicitly measured are the prior distributions that distinguish Bayesian models from others by representing initial states of belief. Instead, priors are usually derived from experimenters' intuitions or model assumptions and applied equally to all participants. Here we present three experiments using "Plinko", a behavioral task in which participants estimate distributions of ball drops over all available outcomes and where distributions are explicitly measured before any observations. In Experiment 1, we show that participant priors cluster around prototypical probability distributions (Gaussian, bimodal, etc.), and that prior cluster membership may indicate learning ability. In Experiment 2, we highlight participants' ability to update to unannounced changes of presented distributions and how this ability is affected by environmental manipulation. Finally, in Experiment 3, we verify that individual participant priors are reliable representations and that learning is not impeded when faced with a physically implausible ball drop distribution that is dynamically defined according to individual participant input. This task will prove useful in more closely examining mechanisms of statistical learning and mental model updating without requiring many of the assumptions made by more traditional computational modeling methodologies.

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