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Suboptimal Criterion Learning in Static and Dynamic Environments

Humans often make decisions based on uncertain sensory information. Signal detection theory (SDT) describes detection and discrimination decisions as a comparison of stimulus “strength” to a fixed decision criterion. However, recent research suggests that current responses depend on the recent histo...

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Autores principales: Norton, Elyse H., Fleming, Stephen M., Daw, Nathaniel D., Landy, Michael S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5242548/
https://www.ncbi.nlm.nih.gov/pubmed/28046006
http://dx.doi.org/10.1371/journal.pcbi.1005304
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author Norton, Elyse H.
Fleming, Stephen M.
Daw, Nathaniel D.
Landy, Michael S.
author_facet Norton, Elyse H.
Fleming, Stephen M.
Daw, Nathaniel D.
Landy, Michael S.
author_sort Norton, Elyse H.
collection PubMed
description Humans often make decisions based on uncertain sensory information. Signal detection theory (SDT) describes detection and discrimination decisions as a comparison of stimulus “strength” to a fixed decision criterion. However, recent research suggests that current responses depend on the recent history of stimuli and previous responses, suggesting that the decision criterion is updated trial-by-trial. The mechanisms underpinning criterion setting remain unknown. Here, we examine how observers learn to set a decision criterion in an orientation-discrimination task under both static and dynamic conditions. To investigate mechanisms underlying trial-by-trial criterion placement, we introduce a novel task in which participants explicitly set the criterion, and compare it to a more traditional discrimination task, allowing us to model this explicit indication of criterion dynamics. In each task, stimuli were ellipses with principal orientations drawn from two categories: Gaussian distributions with different means and equal variance. In the covert-criterion task, observers categorized a displayed ellipse. In the overt-criterion task, observers adjusted the orientation of a line that served as the discrimination criterion for a subsequently presented ellipse. We compared performance to the ideal Bayesian learner and several suboptimal models that varied in both computational and memory demands. Under static and dynamic conditions, we found that, in both tasks, observers used suboptimal learning rules. In most conditions, a model in which the recent history of past samples determines a belief about category means fit the data best for most observers and on average. Our results reveal dynamic adjustment of discrimination criterion, even after prolonged training, and indicate how decision criteria are updated over time.
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spelling pubmed-52425482017-02-28 Suboptimal Criterion Learning in Static and Dynamic Environments Norton, Elyse H. Fleming, Stephen M. Daw, Nathaniel D. Landy, Michael S. PLoS Comput Biol Research Article Humans often make decisions based on uncertain sensory information. Signal detection theory (SDT) describes detection and discrimination decisions as a comparison of stimulus “strength” to a fixed decision criterion. However, recent research suggests that current responses depend on the recent history of stimuli and previous responses, suggesting that the decision criterion is updated trial-by-trial. The mechanisms underpinning criterion setting remain unknown. Here, we examine how observers learn to set a decision criterion in an orientation-discrimination task under both static and dynamic conditions. To investigate mechanisms underlying trial-by-trial criterion placement, we introduce a novel task in which participants explicitly set the criterion, and compare it to a more traditional discrimination task, allowing us to model this explicit indication of criterion dynamics. In each task, stimuli were ellipses with principal orientations drawn from two categories: Gaussian distributions with different means and equal variance. In the covert-criterion task, observers categorized a displayed ellipse. In the overt-criterion task, observers adjusted the orientation of a line that served as the discrimination criterion for a subsequently presented ellipse. We compared performance to the ideal Bayesian learner and several suboptimal models that varied in both computational and memory demands. Under static and dynamic conditions, we found that, in both tasks, observers used suboptimal learning rules. In most conditions, a model in which the recent history of past samples determines a belief about category means fit the data best for most observers and on average. Our results reveal dynamic adjustment of discrimination criterion, even after prolonged training, and indicate how decision criteria are updated over time. Public Library of Science 2017-01-03 /pmc/articles/PMC5242548/ /pubmed/28046006 http://dx.doi.org/10.1371/journal.pcbi.1005304 Text en © 2017 Norton et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Norton, Elyse H.
Fleming, Stephen M.
Daw, Nathaniel D.
Landy, Michael S.
Suboptimal Criterion Learning in Static and Dynamic Environments
title Suboptimal Criterion Learning in Static and Dynamic Environments
title_full Suboptimal Criterion Learning in Static and Dynamic Environments
title_fullStr Suboptimal Criterion Learning in Static and Dynamic Environments
title_full_unstemmed Suboptimal Criterion Learning in Static and Dynamic Environments
title_short Suboptimal Criterion Learning in Static and Dynamic Environments
title_sort suboptimal criterion learning in static and dynamic environments
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5242548/
https://www.ncbi.nlm.nih.gov/pubmed/28046006
http://dx.doi.org/10.1371/journal.pcbi.1005304
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