Confidence through consensus: A neural mechanism for uncertainty monitoring

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
Autores principales: Paz, L., Insabato, A., Zylberberg, A., Deco, G., Sigman, M.
Formato: JOUR
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:http://hdl.handle.net/20.500.12110/paper_20452322_v6_n_p_Paz
Aporte de:
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
id todo:paper_20452322_v6_n_p_Paz
record_format dspace
spelling todo:paper_20452322_v6_n_p_Paz2023-10-03T16:38:15Z Confidence through consensus: A neural mechanism for uncertainty monitoring Paz, L. Insabato, A. Zylberberg, A. Deco, G. Sigman, M. 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. JOUR info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar 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
Paz, L.
Insabato, A.
Zylberberg, A.
Deco, G.
Sigman, M.
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.
format JOUR
author Paz, L.
Insabato, A.
Zylberberg, A.
Deco, G.
Sigman, M.
author_facet Paz, L.
Insabato, A.
Zylberberg, A.
Deco, G.
Sigman, M.
author_sort Paz, L.
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
url http://hdl.handle.net/20.500.12110/paper_20452322_v6_n_p_Paz
work_keys_str_mv AT pazl confidencethroughconsensusaneuralmechanismforuncertaintymonitoring
AT insabatoa confidencethroughconsensusaneuralmechanismforuncertaintymonitoring
AT zylberberga confidencethroughconsensusaneuralmechanismforuncertaintymonitoring
AT decog confidencethroughconsensusaneuralmechanismforuncertaintymonitoring
AT sigmanm confidencethroughconsensusaneuralmechanismforuncertaintymonitoring
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