Bayesian adaptive model estimation to solve the speed accuracy tradeoff problem in psychophysical experiments

Abstract Most psychological experiments measure human cognitive function through the response time and accuracy of the response to a set of stimuli. Since response time and accuracy complement each other, it is often difficult to interpret cognitive performance based on only one dependent measuremen...

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Autores principales:	Jongsoo Baek, Hae-Jeong Park
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/749ac5cd5e314f87b7a25dfc677657e6
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spelling	oai:doaj.org-article:749ac5cd5e314f87b7a25dfc677657e62021-12-02T17:24:11ZBayesian adaptive model estimation to solve the speed accuracy tradeoff problem in psychophysical experiments10.1038/s41598-021-97772-92045-2322https://doaj.org/article/749ac5cd5e314f87b7a25dfc677657e62021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-97772-9https://doaj.org/toc/2045-2322Abstract Most psychological experiments measure human cognitive function through the response time and accuracy of the response to a set of stimuli. Since response time and accuracy complement each other, it is often difficult to interpret cognitive performance based on only one dependent measurement and raises a speed-accuracy tradeoff (SAT) problem. In overcoming this problem, SAT experimental paradigms and models that integrate response time and accuracy have been proposed to understand information processing in human cognitive function. However, due to a lengthy SAT experiment for reliable model estimation, SAT experiments' practical limitations have been pointed out. Thus, these limitations call for an efficient technique to shorten the number of trials required to estimate the SAT function reliably. Instead of using a block's stimulus-onset asynchrony (SOA) accuracy with long block-based task trials, we introduced a Bayesian SAT function estimation using trial-by-trial response time and correctness, which makes SAT tasks flexible and easily extendable to multiple trials. We then proposed a Bayesian adaptive method to select optimal SOA by maximizing information gain to estimate model parameters. Simulation results showed that the proposed Bayesian adaptive estimation was highly efficient and robust for accuracy and precision of estimating SAT function by enabling "multiple-step ahead search."Jongsoo BaekHae-Jeong ParkNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jongsoo Baek Hae-Jeong Park Bayesian adaptive model estimation to solve the speed accuracy tradeoff problem in psychophysical experiments
description	Abstract Most psychological experiments measure human cognitive function through the response time and accuracy of the response to a set of stimuli. Since response time and accuracy complement each other, it is often difficult to interpret cognitive performance based on only one dependent measurement and raises a speed-accuracy tradeoff (SAT) problem. In overcoming this problem, SAT experimental paradigms and models that integrate response time and accuracy have been proposed to understand information processing in human cognitive function. However, due to a lengthy SAT experiment for reliable model estimation, SAT experiments' practical limitations have been pointed out. Thus, these limitations call for an efficient technique to shorten the number of trials required to estimate the SAT function reliably. Instead of using a block's stimulus-onset asynchrony (SOA) accuracy with long block-based task trials, we introduced a Bayesian SAT function estimation using trial-by-trial response time and correctness, which makes SAT tasks flexible and easily extendable to multiple trials. We then proposed a Bayesian adaptive method to select optimal SOA by maximizing information gain to estimate model parameters. Simulation results showed that the proposed Bayesian adaptive estimation was highly efficient and robust for accuracy and precision of estimating SAT function by enabling "multiple-step ahead search."
format	article
author	Jongsoo Baek Hae-Jeong Park
author_facet	Jongsoo Baek Hae-Jeong Park
author_sort	Jongsoo Baek
title	Bayesian adaptive model estimation to solve the speed accuracy tradeoff problem in psychophysical experiments
title_short	Bayesian adaptive model estimation to solve the speed accuracy tradeoff problem in psychophysical experiments
title_full	Bayesian adaptive model estimation to solve the speed accuracy tradeoff problem in psychophysical experiments
title_fullStr	Bayesian adaptive model estimation to solve the speed accuracy tradeoff problem in psychophysical experiments
title_full_unstemmed	Bayesian adaptive model estimation to solve the speed accuracy tradeoff problem in psychophysical experiments
title_sort	bayesian adaptive model estimation to solve the speed accuracy tradeoff problem in psychophysical experiments
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/749ac5cd5e314f87b7a25dfc677657e6
work_keys_str_mv	AT jongsoobaek bayesianadaptivemodelestimationtosolvethespeedaccuracytradeoffprobleminpsychophysicalexperiments AT haejeongpark bayesianadaptivemodelestimationtosolvethespeedaccuracytradeoffprobleminpsychophysicalexperiments
_version_	1718380916988968960

Bayesian adaptive model estimation to solve the speed accuracy tradeoff problem in psychophysical experiments

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