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

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Title: Plinko: Eliciting beliefs to build better models of statistical learning and mental model updating.
Authors: DiBerardino, Peter A. V., Filipowicz, Alexandre L. S., Danckert, James, Anderson, Britt
Source: British Journal of Psychology. Nov2024, Vol. 115 Issue 4, p759-786. 28p.
Subjects: Statistical models, Research funding, Probability theory, Descriptive statistics, Games, Learning strategies, Machine learning, Algorithms, Thought & thinking, Video games
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 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. [ABSTRACT FROM AUTHOR]
Copyright of British Journal of Psychology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: Plinko: Eliciting beliefs to build better models of statistical learning and mental model updating.
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  Data: <searchLink fieldCode="JN" term="%22British+Journal+of+Psychology%22">British Journal of Psychology</searchLink>. Nov2024, Vol. 115 Issue 4, p759-786. 28p.
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  Data: <searchLink fieldCode="DE" term="%22Statistical+models%22">Statistical models</searchLink><br /><searchLink fieldCode="DE" term="%22Research+funding%22">Research funding</searchLink><br /><searchLink fieldCode="DE" term="%22Probability+theory%22">Probability theory</searchLink><br /><searchLink fieldCode="DE" term="%22Descriptive+statistics%22">Descriptive statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Games%22">Games</searchLink><br /><searchLink fieldCode="DE" term="%22Learning+strategies%22">Learning strategies</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Thought+%26+thinking%22">Thought & thinking</searchLink><br /><searchLink fieldCode="DE" term="%22Video+games%22">Video games</searchLink>
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  Data: 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. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of British Journal of Psychology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1111/bjop.12724
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        Text: English
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      – SubjectFull: Research funding
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      – SubjectFull: Probability theory
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      – SubjectFull: Algorithms
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      – SubjectFull: Video games
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      – TitleFull: Plinko: Eliciting beliefs to build better models of statistical learning and mental model updating.
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              M: 11
              Text: Nov2024
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              Y: 2024
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