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Seeing the Error in My “Bayes”: A Quantified Degree of Belief Change Correlates with Children’s Pupillary Surprise Responses Following Explicit Predictions
Bayesian models allow us to investigate children’s belief revision alongside physiological states, such as “surprise”. Recent work finds that pupil dilation (or the “pupillary surprise response”) following expectancy violations is predictive of belief revision. How can probabilistic models inform th...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9955423/ https://www.ncbi.nlm.nih.gov/pubmed/36832578 http://dx.doi.org/10.3390/e25020211 |
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author | Colantonio, Joseph Bascandziev, Igor Theobald, Maria Brod, Garvin Bonawitz, Elizabeth |
author_facet | Colantonio, Joseph Bascandziev, Igor Theobald, Maria Brod, Garvin Bonawitz, Elizabeth |
author_sort | Colantonio, Joseph |
collection | PubMed |
description | Bayesian models allow us to investigate children’s belief revision alongside physiological states, such as “surprise”. Recent work finds that pupil dilation (or the “pupillary surprise response”) following expectancy violations is predictive of belief revision. How can probabilistic models inform the interpretations of “surprise”? Shannon Information considers the likelihood of an observed event, given prior beliefs, and suggests stronger surprise occurs following unlikely events. In contrast, Kullback–Leibler divergence considers the dissimilarity between prior beliefs and updated beliefs following observations—with greater surprise indicating more change between belief states to accommodate information. To assess these accounts under different learning contexts, we use Bayesian models that compare these computational measures of “surprise” to contexts where children are asked to either predict or evaluate the same evidence during a water displacement task. We find correlations between the computed Kullback–Leibler divergence and the children’s pupillometric responses only when the children actively make predictions, and no correlation between Shannon Information and pupillometry. This suggests that when children attend to their beliefs and make predictions, pupillary responses may signal the degree of divergence between a child’s current beliefs and the updated, more accommodating beliefs. |
format | Online Article Text |
id | pubmed-9955423 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-99554232023-02-25 Seeing the Error in My “Bayes”: A Quantified Degree of Belief Change Correlates with Children’s Pupillary Surprise Responses Following Explicit Predictions Colantonio, Joseph Bascandziev, Igor Theobald, Maria Brod, Garvin Bonawitz, Elizabeth Entropy (Basel) Article Bayesian models allow us to investigate children’s belief revision alongside physiological states, such as “surprise”. Recent work finds that pupil dilation (or the “pupillary surprise response”) following expectancy violations is predictive of belief revision. How can probabilistic models inform the interpretations of “surprise”? Shannon Information considers the likelihood of an observed event, given prior beliefs, and suggests stronger surprise occurs following unlikely events. In contrast, Kullback–Leibler divergence considers the dissimilarity between prior beliefs and updated beliefs following observations—with greater surprise indicating more change between belief states to accommodate information. To assess these accounts under different learning contexts, we use Bayesian models that compare these computational measures of “surprise” to contexts where children are asked to either predict or evaluate the same evidence during a water displacement task. We find correlations between the computed Kullback–Leibler divergence and the children’s pupillometric responses only when the children actively make predictions, and no correlation between Shannon Information and pupillometry. This suggests that when children attend to their beliefs and make predictions, pupillary responses may signal the degree of divergence between a child’s current beliefs and the updated, more accommodating beliefs. MDPI 2023-01-21 /pmc/articles/PMC9955423/ /pubmed/36832578 http://dx.doi.org/10.3390/e25020211 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Colantonio, Joseph Bascandziev, Igor Theobald, Maria Brod, Garvin Bonawitz, Elizabeth Seeing the Error in My “Bayes”: A Quantified Degree of Belief Change Correlates with Children’s Pupillary Surprise Responses Following Explicit Predictions |
title | Seeing the Error in My “Bayes”: A Quantified Degree of Belief Change Correlates with Children’s Pupillary Surprise Responses Following Explicit Predictions |
title_full | Seeing the Error in My “Bayes”: A Quantified Degree of Belief Change Correlates with Children’s Pupillary Surprise Responses Following Explicit Predictions |
title_fullStr | Seeing the Error in My “Bayes”: A Quantified Degree of Belief Change Correlates with Children’s Pupillary Surprise Responses Following Explicit Predictions |
title_full_unstemmed | Seeing the Error in My “Bayes”: A Quantified Degree of Belief Change Correlates with Children’s Pupillary Surprise Responses Following Explicit Predictions |
title_short | Seeing the Error in My “Bayes”: A Quantified Degree of Belief Change Correlates with Children’s Pupillary Surprise Responses Following Explicit Predictions |
title_sort | seeing the error in my “bayes”: a quantified degree of belief change correlates with children’s pupillary surprise responses following explicit predictions |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9955423/ https://www.ncbi.nlm.nih.gov/pubmed/36832578 http://dx.doi.org/10.3390/e25020211 |
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