Determining mean and standard deviation of the strong gravity prior through simulations

Humans expect downwards moving objects to accelerate and upwards moving objects to decelerate. These results have been interpreted as humans maintaining an internal model of gravity. We have previously suggested an interpretation of these results within a Bayesian framework of perception: earth grav...

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Detalles Bibliográficos
Autores: Jörges, Björn, López-Moliner, Joan
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2020
País:España
Institución:Universidad de Barcelona
Repositorio:Dipòsit Digital de la UB
OAI Identifier:oai:diposit.ub.edu:2445/175621
Acceso en línea:https://hdl.handle.net/2445/175621
Access Level:acceso abierto
Palabra clave:Gravitació
Aprenentatge sensorial
Gravitation
Perceptual learning
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spelling Determining mean and standard deviation of the strong gravity prior through simulationsJörges, BjörnLópez-Moliner, JoanGravitacióAprenentatge sensorialGravitationPerceptual learningHumans expect downwards moving objects to accelerate and upwards moving objects to decelerate. These results have been interpreted as humans maintaining an internal model of gravity. We have previously suggested an interpretation of these results within a Bayesian framework of perception: earth gravity could be represented as a Strong Prior that overrules noisy sensory information (Likelihood) and therefore attracts the final percept (Posterior) very strongly. Based on this framework, we use published data from a timing task involving gravitational motion to determine the mean and the standard deviation of the Strong Earth Gravity Prior. To get its mean, we refine a model of mean timing errors we proposed in a previous paper (Jörges & López-Moliner, 2019), while expanding the range of conditions under which it yields adequate predictions of performance. This underscores our previous conclusion that the gravity prior is likely to be very close to 9.81 m/s2. To obtain the standard deviation, we identify different sources of sensory and motor variability reflected in timing errors. We then model timing responses based on quantitative assumptions about these sensory and motor errors for a range of standard deviations of the earth gravity prior, and find that a standard deviation of around 2 m/s2 makes for the best fit. This value is likely to represent an upper bound, as there are strong theoretical reasons along with supporting empirical evidence for the standard deviation of the earth gravity being lower than this value.Public Library of Science (PLoS)2020info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://hdl.handle.net/2445/175621Articles publicats en revistes (Cognició, Desenvolupament i Psicologia de l'Educació)reponame:Dipòsit Digital de la UBinstname:Universidad de BarcelonaInglésReproducció del document publicat a: https://doi.org/10.1371/journal.pone.0236732PLoS One, 2020, vol. 15, num. 8, p. e0236732https://doi.org/10.1371/journal.pone.0236732cc-by (c) Jörges, Björn et al., 2020http://creativecommons.org/licenses/by/3.0/esinfo:eu-repo/semantics/openAccessoai:diposit.ub.edu:2445/1756212026-05-27T06:46:51Z
dc.title.none.fl_str_mv Determining mean and standard deviation of the strong gravity prior through simulations
title Determining mean and standard deviation of the strong gravity prior through simulations
spellingShingle Determining mean and standard deviation of the strong gravity prior through simulations
Jörges, Björn
Gravitació
Aprenentatge sensorial
Gravitation
Perceptual learning
title_short Determining mean and standard deviation of the strong gravity prior through simulations
title_full Determining mean and standard deviation of the strong gravity prior through simulations
title_fullStr Determining mean and standard deviation of the strong gravity prior through simulations
title_full_unstemmed Determining mean and standard deviation of the strong gravity prior through simulations
title_sort Determining mean and standard deviation of the strong gravity prior through simulations
dc.creator.none.fl_str_mv Jörges, Björn
López-Moliner, Joan
author Jörges, Björn
author_facet Jörges, Björn
López-Moliner, Joan
author_role author
author2 López-Moliner, Joan
author2_role author
dc.subject.none.fl_str_mv Gravitació
Aprenentatge sensorial
Gravitation
Perceptual learning
topic Gravitació
Aprenentatge sensorial
Gravitation
Perceptual learning
description Humans expect downwards moving objects to accelerate and upwards moving objects to decelerate. These results have been interpreted as humans maintaining an internal model of gravity. We have previously suggested an interpretation of these results within a Bayesian framework of perception: earth gravity could be represented as a Strong Prior that overrules noisy sensory information (Likelihood) and therefore attracts the final percept (Posterior) very strongly. Based on this framework, we use published data from a timing task involving gravitational motion to determine the mean and the standard deviation of the Strong Earth Gravity Prior. To get its mean, we refine a model of mean timing errors we proposed in a previous paper (Jörges & López-Moliner, 2019), while expanding the range of conditions under which it yields adequate predictions of performance. This underscores our previous conclusion that the gravity prior is likely to be very close to 9.81 m/s2. To obtain the standard deviation, we identify different sources of sensory and motor variability reflected in timing errors. We then model timing responses based on quantitative assumptions about these sensory and motor errors for a range of standard deviations of the earth gravity prior, and find that a standard deviation of around 2 m/s2 makes for the best fit. This value is likely to represent an upper bound, as there are strong theoretical reasons along with supporting empirical evidence for the standard deviation of the earth gravity being lower than this value.
publishDate 2020
dc.date.none.fl_str_mv 2020
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/2445/175621
url https://hdl.handle.net/2445/175621
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Reproducció del document publicat a: https://doi.org/10.1371/journal.pone.0236732
PLoS One, 2020, vol. 15, num. 8, p. e0236732
https://doi.org/10.1371/journal.pone.0236732
dc.rights.none.fl_str_mv cc-by (c) Jörges, Björn et al., 2020
http://creativecommons.org/licenses/by/3.0/es
info:eu-repo/semantics/openAccess
rights_invalid_str_mv cc-by (c) Jörges, Björn et al., 2020
http://creativecommons.org/licenses/by/3.0/es
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Public Library of Science (PLoS)
publisher.none.fl_str_mv Public Library of Science (PLoS)
dc.source.none.fl_str_mv Articles publicats en revistes (Cognició, Desenvolupament i Psicologia de l'Educació)
reponame:Dipòsit Digital de la UB
instname:Universidad de Barcelona
instname_str Universidad de Barcelona
reponame_str Dipòsit Digital de la UB
collection Dipòsit Digital de la UB
repository.name.fl_str_mv
repository.mail.fl_str_mv
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