Plinko
Eliciting beliefs to build better models of statistical learning and mental model updating
Bibliographic Data
| ID | 9619499 |
|---|---|
| Authors | Peter A V DiBerardino (0000-0001-7607-9278, Department of Psychology University of Waterloo Waterloo Ontario Canada), Alexandre L S Filipowicz (0000-0002-1311-386X, Toyota Research Institute Los Altos California USA), James Danckert (0000-0001-8093-066X, Department of Psychology University of Waterloo Waterloo Ontario Canada), Britt Anderson (0000-0001-5310-1495, Department of Psychology University of Waterloo Waterloo Ontario Canada, corresponding author) |
| Year | 2024 |
| Volume | 115 |
| Issue | 4 |
| Pages | 759-786 |
| Publication date | 2024-11-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | British Journal of Psychology (JOURNAL) |
| Journal identifiers | ISSN: 0007-1269 • E-ISSN: 2044-8295 |
| Publisher | Wiley (PUBLISHER • GB) |
| DOI | 10.1111/bjop.12724 |
| PMID | 39096484 |
| OpenAlex | W4401284752 |
| Language | EN |
| References cited | 81 |
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 to 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 various priors that cluster around prototypical probability distributions that in turn influence learning. In follow‐up studies, 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). These data reveal 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
Bayesian inference · Bayesian probability · Cognition · Cognitive psychology · Data mining · Machine learning · Measure (data warehouse) · Prior probability · Artificial Intelligence · Bayesian Modeling and Causal Inference · Child and Animal Learning Development · Computer Science · Psychology · Statistical Mechanics and Entropy
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| Highly cited | No |
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