Confidence through consensus: A neural mechanism for uncertainty monitoring

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Models that integrate sensory evidence to a threshold can explain task accuracy, response times and confidence, yet it is still unclear how confidence is encoded in the brain. Classic models assume that confidence is encoded in some form of balance between the evidence integrated in favor and agains...

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Detalles Bibliográficos
Autor principal: Sigman, Mariano
Publicado: 2016
Materias:
consensus
nervous system
stimulus
stochastic model
uncertainty
algorithm
biological model
computer simulation
decision making
human
perception
reaction time
Algorithms
Computer Simulation
Consensus
Decision Making
Humans
Models, Neurological
Perception
Reaction Time
Uncertainty
Acceso en línea:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_20452322_v6_n_p_Paz
http://hdl.handle.net/20.500.12110/paper_20452322_v6_n_p_Paz
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Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
id paper:paper_20452322_v6_n_p_Paz
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spelling paper:paper_20452322_v6_n_p_Paz2023-06-08T16:33:26Z Confidence through consensus: A neural mechanism for uncertainty monitoring Sigman, Mariano consensus nervous system stimulus stochastic model uncertainty algorithm biological model computer simulation consensus decision making human perception reaction time Algorithms Computer Simulation Consensus Decision Making Humans Models, Neurological Perception Reaction Time Uncertainty Models that integrate sensory evidence to a threshold can explain task accuracy, response times and confidence, yet it is still unclear how confidence is encoded in the brain. Classic models assume that confidence is encoded in some form of balance between the evidence integrated in favor and against the selected option. However, recent experiments that measure the sensory evidence's influence on choice and confidence contradict these classic models. We propose that the decision is taken by many loosely coupled modules each of which represent a stochastic sample of the sensory evidence integral. Confidence is then encoded in the dispersion between modules. We show that our proposal can account for the well established relations between confidence, and stimuli discriminability and reaction times, as well as the fluctuations influence on choice and confidence. Fil:Sigman, M. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. 2016 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_20452322_v6_n_p_Paz http://hdl.handle.net/20.500.12110/paper_20452322_v6_n_p_Paz
institution Universidad de Buenos Aires
institution_str I-28
repository_str R-134
collection Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic consensus
nervous system
stimulus
stochastic model
uncertainty
algorithm
biological model
computer simulation
consensus
decision making
human
perception
reaction time
Algorithms
Computer Simulation
Consensus
Decision Making
Humans
Models, Neurological
Perception
Reaction Time
Uncertainty
spellingShingle consensus
nervous system
stimulus
stochastic model
uncertainty
algorithm
biological model
computer simulation
consensus
decision making
human
perception
reaction time
Algorithms
Computer Simulation
Consensus
Decision Making
Humans
Models, Neurological
Perception
Reaction Time
Uncertainty
Sigman, Mariano
Confidence through consensus: A neural mechanism for uncertainty monitoring
topic_facet consensus
nervous system
stimulus
stochastic model
uncertainty
algorithm
biological model
computer simulation
consensus
decision making
human
perception
reaction time
Algorithms
Computer Simulation
Consensus
Decision Making
Humans
Models, Neurological
Perception
Reaction Time
Uncertainty
description Models that integrate sensory evidence to a threshold can explain task accuracy, response times and confidence, yet it is still unclear how confidence is encoded in the brain. Classic models assume that confidence is encoded in some form of balance between the evidence integrated in favor and against the selected option. However, recent experiments that measure the sensory evidence's influence on choice and confidence contradict these classic models. We propose that the decision is taken by many loosely coupled modules each of which represent a stochastic sample of the sensory evidence integral. Confidence is then encoded in the dispersion between modules. We show that our proposal can account for the well established relations between confidence, and stimuli discriminability and reaction times, as well as the fluctuations influence on choice and confidence.
author Sigman, Mariano
author_facet Sigman, Mariano
author_sort Sigman, Mariano
title Confidence through consensus: A neural mechanism for uncertainty monitoring
title_short Confidence through consensus: A neural mechanism for uncertainty monitoring
title_full Confidence through consensus: A neural mechanism for uncertainty monitoring
title_fullStr Confidence through consensus: A neural mechanism for uncertainty monitoring
title_full_unstemmed Confidence through consensus: A neural mechanism for uncertainty monitoring
title_sort confidence through consensus: a neural mechanism for uncertainty monitoring
publishDate 2016
url https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_20452322_v6_n_p_Paz
http://hdl.handle.net/20.500.12110/paper_20452322_v6_n_p_Paz
work_keys_str_mv AT sigmanmariano confidencethroughconsensusaneuralmechanismforuncertaintymonitoring
_version_ 1768545393828691968

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