Low attention impairs optimal incorporation of prior knowledge in perceptual decisions

Autores
Morales, Jorge Luis; Solovey, Guillermo; Maniscalco, Brian; Rahnev, Drobomir; de Lange, Floris P.; Lau, Hakwan
Año de publicación
2015
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
When visual attention is directed away from a stimulus, neural processing is weak and strength and precision of sensory data decreases. From a computational perspective, in such situations observers should give more weight to prior expectations in order to behave optimally during a discrimination task. Here we test a signal detection theoretic model that counter-intuitively predicts subjects will do just the opposite in a discrimination task with two stimuli, one attended and one unattended: when subjects are probed to discriminate the unattended stimulus, they rely less on prior information about the probed stimulus’ identity. The model is in part inspired by recent findings that attention reduces trial-by-trial variability of the neuronal population response and that they use a common criterion for attended and unattended trials. In five different visual discrimination experiments, when attention was directed away from the target stimulus, subjects did not adjust their response bias in reaction to a change in stimulus presentation frequency despite being fully informed and despite the presence of performance feedback and monetary and social incentives. This indicates that subjects did not rely more on the priors under conditions of inattention as would be predicted by a Bayes-optimal observer model. These results inform and constrain future models of Bayesian inference in the human brain.
Fil: Morales, Jorge Luis. Columbia University; Estados Unidos
Fil: Solovey, Guillermo. Columbia University; Estados Unidos. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Cálculo; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física. Laboratorio de Neurociencia Integrativa; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Maniscalco, Brian. Columbia University; Estados Unidos. National Institutes of Health; Estados Unidos
Fil: Rahnev, Drobomir. Columbia University; Estados Unidos. University of California; Estados Unidos
Fil: de Lange, Floris P.. Radboud Universiteit Nijmegen. Donders Instituto Brain Cognition And Behavior. Snn Machine Learning Group; Países Bajos
Fil: Lau, Hakwan. Columbia University; Estados Unidos. Radboud Universiteit Nijmegen. Donders Instituto Brain Cognition And Behavior. Snn Machine Learning Group; Países Bajos. University of California; Estados Unidos
Materia
ATTENTION: DIVIDED ATTENTION AND INATTENTION
COGNITIVE AND ATTENTIONAL CONTROL
IDEAL OBSERVER BAYESIAN MODELS
SIGNAL DETECTION THEORY
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/44631

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spelling Low attention impairs optimal incorporation of prior knowledge in perceptual decisionsMorales, Jorge LuisSolovey, GuillermoManiscalco, BrianRahnev, Drobomirde Lange, Floris P.Lau, HakwanATTENTION: DIVIDED ATTENTION AND INATTENTIONCOGNITIVE AND ATTENTIONAL CONTROLIDEAL OBSERVER BAYESIAN MODELSSIGNAL DETECTION THEORYhttps://purl.org/becyt/ford/1.6https://purl.org/becyt/ford/1When visual attention is directed away from a stimulus, neural processing is weak and strength and precision of sensory data decreases. From a computational perspective, in such situations observers should give more weight to prior expectations in order to behave optimally during a discrimination task. Here we test a signal detection theoretic model that counter-intuitively predicts subjects will do just the opposite in a discrimination task with two stimuli, one attended and one unattended: when subjects are probed to discriminate the unattended stimulus, they rely less on prior information about the probed stimulus’ identity. The model is in part inspired by recent findings that attention reduces trial-by-trial variability of the neuronal population response and that they use a common criterion for attended and unattended trials. In five different visual discrimination experiments, when attention was directed away from the target stimulus, subjects did not adjust their response bias in reaction to a change in stimulus presentation frequency despite being fully informed and despite the presence of performance feedback and monetary and social incentives. This indicates that subjects did not rely more on the priors under conditions of inattention as would be predicted by a Bayes-optimal observer model. These results inform and constrain future models of Bayesian inference in the human brain.Fil: Morales, Jorge Luis. Columbia University; Estados UnidosFil: Solovey, Guillermo. Columbia University; Estados Unidos. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Cálculo; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física. Laboratorio de Neurociencia Integrativa; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Maniscalco, Brian. Columbia University; Estados Unidos. National Institutes of Health; Estados UnidosFil: Rahnev, Drobomir. Columbia University; Estados Unidos. University of California; Estados UnidosFil: de Lange, Floris P.. Radboud Universiteit Nijmegen. Donders Instituto Brain Cognition And Behavior. Snn Machine Learning Group; Países BajosFil: Lau, Hakwan. Columbia University; Estados Unidos. Radboud Universiteit Nijmegen. Donders Instituto Brain Cognition And Behavior. Snn Machine Learning Group; Países Bajos. University of California; Estados UnidosPsychonomic Society2015-08info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/44631Morales, Jorge Luis; Solovey, Guillermo; Maniscalco, Brian; Rahnev, Drobomir; de Lange, Floris P.; et al.; Low attention impairs optimal incorporation of prior knowledge in perceptual decisions; Psychonomic Society; Attention Perception & Psychophysics; 77; 6; 8-2015; 2021-20361943-39211943-393XCONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.3758/s13414-015-0897-2info:eu-repo/semantics/altIdentifier/url/https://link.springer.com/article/10.3758%2Fs13414-015-0897-2info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T14:38:21Zoai:ri.conicet.gov.ar:11336/44631instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-10-15 14:38:22.068CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Low attention impairs optimal incorporation of prior knowledge in perceptual decisions
title Low attention impairs optimal incorporation of prior knowledge in perceptual decisions
spellingShingle Low attention impairs optimal incorporation of prior knowledge in perceptual decisions
Morales, Jorge Luis
ATTENTION: DIVIDED ATTENTION AND INATTENTION
COGNITIVE AND ATTENTIONAL CONTROL
IDEAL OBSERVER BAYESIAN MODELS
SIGNAL DETECTION THEORY
title_short Low attention impairs optimal incorporation of prior knowledge in perceptual decisions
title_full Low attention impairs optimal incorporation of prior knowledge in perceptual decisions
title_fullStr Low attention impairs optimal incorporation of prior knowledge in perceptual decisions
title_full_unstemmed Low attention impairs optimal incorporation of prior knowledge in perceptual decisions
title_sort Low attention impairs optimal incorporation of prior knowledge in perceptual decisions
dc.creator.none.fl_str_mv Morales, Jorge Luis
Solovey, Guillermo
Maniscalco, Brian
Rahnev, Drobomir
de Lange, Floris P.
Lau, Hakwan
author Morales, Jorge Luis
author_facet Morales, Jorge Luis
Solovey, Guillermo
Maniscalco, Brian
Rahnev, Drobomir
de Lange, Floris P.
Lau, Hakwan
author_role author
author2 Solovey, Guillermo
Maniscalco, Brian
Rahnev, Drobomir
de Lange, Floris P.
Lau, Hakwan
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv ATTENTION: DIVIDED ATTENTION AND INATTENTION
COGNITIVE AND ATTENTIONAL CONTROL
IDEAL OBSERVER BAYESIAN MODELS
SIGNAL DETECTION THEORY
topic ATTENTION: DIVIDED ATTENTION AND INATTENTION
COGNITIVE AND ATTENTIONAL CONTROL
IDEAL OBSERVER BAYESIAN MODELS
SIGNAL DETECTION THEORY
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.6
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv When visual attention is directed away from a stimulus, neural processing is weak and strength and precision of sensory data decreases. From a computational perspective, in such situations observers should give more weight to prior expectations in order to behave optimally during a discrimination task. Here we test a signal detection theoretic model that counter-intuitively predicts subjects will do just the opposite in a discrimination task with two stimuli, one attended and one unattended: when subjects are probed to discriminate the unattended stimulus, they rely less on prior information about the probed stimulus’ identity. The model is in part inspired by recent findings that attention reduces trial-by-trial variability of the neuronal population response and that they use a common criterion for attended and unattended trials. In five different visual discrimination experiments, when attention was directed away from the target stimulus, subjects did not adjust their response bias in reaction to a change in stimulus presentation frequency despite being fully informed and despite the presence of performance feedback and monetary and social incentives. This indicates that subjects did not rely more on the priors under conditions of inattention as would be predicted by a Bayes-optimal observer model. These results inform and constrain future models of Bayesian inference in the human brain.
Fil: Morales, Jorge Luis. Columbia University; Estados Unidos
Fil: Solovey, Guillermo. Columbia University; Estados Unidos. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Cálculo; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física. Laboratorio de Neurociencia Integrativa; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Maniscalco, Brian. Columbia University; Estados Unidos. National Institutes of Health; Estados Unidos
Fil: Rahnev, Drobomir. Columbia University; Estados Unidos. University of California; Estados Unidos
Fil: de Lange, Floris P.. Radboud Universiteit Nijmegen. Donders Instituto Brain Cognition And Behavior. Snn Machine Learning Group; Países Bajos
Fil: Lau, Hakwan. Columbia University; Estados Unidos. Radboud Universiteit Nijmegen. Donders Instituto Brain Cognition And Behavior. Snn Machine Learning Group; Países Bajos. University of California; Estados Unidos
description When visual attention is directed away from a stimulus, neural processing is weak and strength and precision of sensory data decreases. From a computational perspective, in such situations observers should give more weight to prior expectations in order to behave optimally during a discrimination task. Here we test a signal detection theoretic model that counter-intuitively predicts subjects will do just the opposite in a discrimination task with two stimuli, one attended and one unattended: when subjects are probed to discriminate the unattended stimulus, they rely less on prior information about the probed stimulus’ identity. The model is in part inspired by recent findings that attention reduces trial-by-trial variability of the neuronal population response and that they use a common criterion for attended and unattended trials. In five different visual discrimination experiments, when attention was directed away from the target stimulus, subjects did not adjust their response bias in reaction to a change in stimulus presentation frequency despite being fully informed and despite the presence of performance feedback and monetary and social incentives. This indicates that subjects did not rely more on the priors under conditions of inattention as would be predicted by a Bayes-optimal observer model. These results inform and constrain future models of Bayesian inference in the human brain.
publishDate 2015
dc.date.none.fl_str_mv 2015-08
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/44631
Morales, Jorge Luis; Solovey, Guillermo; Maniscalco, Brian; Rahnev, Drobomir; de Lange, Floris P.; et al.; Low attention impairs optimal incorporation of prior knowledge in perceptual decisions; Psychonomic Society; Attention Perception & Psychophysics; 77; 6; 8-2015; 2021-2036
1943-3921
1943-393X
CONICET Digital
CONICET
url http://hdl.handle.net/11336/44631
identifier_str_mv Morales, Jorge Luis; Solovey, Guillermo; Maniscalco, Brian; Rahnev, Drobomir; de Lange, Floris P.; et al.; Low attention impairs optimal incorporation of prior knowledge in perceptual decisions; Psychonomic Society; Attention Perception & Psychophysics; 77; 6; 8-2015; 2021-2036
1943-3921
1943-393X
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.3758/s13414-015-0897-2
info:eu-repo/semantics/altIdentifier/url/https://link.springer.com/article/10.3758%2Fs13414-015-0897-2
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Psychonomic Society
publisher.none.fl_str_mv Psychonomic Society
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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