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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/44631
Ver los metadatos del registro completo
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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 |
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CONICET Digital (CONICET) |
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CONICET Digital (CONICET) |
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Consejo Nacional de Investigaciones Científicas y Técnicas |
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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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13.22299 |