No Cover Image

Journal article 567 views 61 downloads

Breaking Deadlocks: Reward Probability and Spontaneous Preference Shape Voluntary Decisions and Electrophysiological Signals in Humans

Wojciech Zajkowski Orcid Logo, Dominik Krzemiński, Jacopo Barone, Lisa H. Evans, Jiaxiang Zhang Orcid Logo

Computational Brain and Behavior, Volume: 4, Issue: 2, Pages: 191 - 212

Swansea University Author: Jiaxiang Zhang Orcid Logo

  • 61251_VoR.pdf

    PDF | Version of Record

    © The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International License

    Download (1.49MB)

Check full text

DOI (Published version): 10.1007/s42113-020-00096-6

Abstract

Choosing between equally valued options is a common conundrum, for which classical decision theories predicted a prolonged response time (RT). This contrasts with the notion that an optimal decision maker in a stable environment should make fast and random choices, as the outcomes are indifferent. H...

Full description

Published in: Computational Brain and Behavior
ISSN: 2522-0861 2522-087X
Published: Springer Science and Business Media LLC 2021
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa61251
first_indexed 2022-09-26T13:48:42Z
last_indexed 2023-01-13T19:21:54Z
id cronfa61251
recordtype SURis
fullrecord <?xml version="1.0"?><rfc1807><datestamp>2022-09-26T14:51:06.2480656</datestamp><bib-version>v2</bib-version><id>61251</id><entry>2022-09-16</entry><title>Breaking Deadlocks: Reward Probability and Spontaneous Preference Shape Voluntary Decisions and Electrophysiological Signals in Humans</title><swanseaauthors><author><sid>555e06e0ed9a87608f2d035b3bde3a87</sid><ORCID>0000-0002-4758-0394</ORCID><firstname>Jiaxiang</firstname><surname>Zhang</surname><name>Jiaxiang Zhang</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2022-09-16</date><deptcode>MACS</deptcode><abstract>Choosing between equally valued options is a common conundrum, for which classical decision theories predicted a prolonged response time (RT). This contrasts with the notion that an optimal decision maker in a stable environment should make fast and random choices, as the outcomes are indifferent. Here, we characterize the neurocognitive processes underlying such voluntary decisions by integrating cognitive modelling of behavioral responses and EEG recordings in a probabilistic reward task. Human participants performed binary choices between pairs of unambiguous cues associated with identical reward probabilities at different levels. Higher reward probability accelerated RT, and participants chose one cue faster and more frequent over the other at each probability level. The behavioral effects on RT persisted in simple reactions to single cues. By using hierarchical Bayesian parameter estimation for an accumulator model, we showed that the probability and preference effects were independently associated with changes in the speed of evidence accumulation, but not with visual encoding or motor execution latencies. Time-resolved MVPA of EEG-evoked responses identified significant representations of reward certainty and preference as early as 120 ms after stimulus onset, with spatial relevance patterns maximal in middle central and parietal electrodes. Furthermore, EEG-informed computational modelling showed that the rate of change between N100 and P300 event-related potentials modulated accumulation rates on a trial-by-trial basis. Our findings suggest that reward probability and spontaneous preference collectively shape voluntary decisions between equal options, providing a mechanism to prevent indecision or random behavior.</abstract><type>Journal Article</type><journal>Computational Brain and Behavior</journal><volume>4</volume><journalNumber>2</journalNumber><paginationStart>191</paginationStart><paginationEnd>212</paginationEnd><publisher>Springer Science and Business Media LLC</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint>2522-0861</issnPrint><issnElectronic>2522-087X</issnElectronic><keywords>Decision making; Reward probability; Preference; EEG; Cognitive modelling</keywords><publishedDay>1</publishedDay><publishedMonth>6</publishedMonth><publishedYear>2021</publishedYear><publishedDate>2021-06-01</publishedDate><doi>10.1007/s42113-020-00096-6</doi><url/><notes/><college>COLLEGE NANME</college><department>Mathematics and Computer Science School</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>MACS</DepartmentCode><institution>Swansea University</institution><apcterm/><funders>This study was supported by a European Research Council starting grant (716321). WZ was supported by a PhD studentship from Cardiff University School of Psychology. DK was supported by a PhD studentship from the Engineering and Physical Sciences Research Council (1982622).</funders><projectreference/><lastEdited>2022-09-26T14:51:06.2480656</lastEdited><Created>2022-09-16T09:39:45.9539880</Created><path><level id="1">Faculty of Science and Engineering</level><level id="2">School of Mathematics and Computer Science - Computer Science</level></path><authors><author><firstname>Wojciech</firstname><surname>Zajkowski</surname><orcid>0000-0001-6078-1724</orcid><order>1</order></author><author><firstname>Dominik</firstname><surname>Krzemi&#x144;ski</surname><order>2</order></author><author><firstname>Jacopo</firstname><surname>Barone</surname><order>3</order></author><author><firstname>Lisa H.</firstname><surname>Evans</surname><order>4</order></author><author><firstname>Jiaxiang</firstname><surname>Zhang</surname><orcid>0000-0002-4758-0394</orcid><order>5</order></author></authors><documents><document><filename>61251__25229__da50d0e5ca2e420cb0305f9d0eb1d87e.pdf</filename><originalFilename>61251_VoR.pdf</originalFilename><uploaded>2022-09-26T14:49:06.7825861</uploaded><type>Output</type><contentLength>1560585</contentLength><contentType>application/pdf</contentType><version>Version of Record</version><cronfaStatus>true</cronfaStatus><documentNotes>&#xA9; The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International License</documentNotes><copyrightCorrect>true</copyrightCorrect><language>eng</language><licence>http://creativecommonshorg/licenses/by/4.0/</licence></document></documents><OutputDurs/></rfc1807>
spelling 2022-09-26T14:51:06.2480656 v2 61251 2022-09-16 Breaking Deadlocks: Reward Probability and Spontaneous Preference Shape Voluntary Decisions and Electrophysiological Signals in Humans 555e06e0ed9a87608f2d035b3bde3a87 0000-0002-4758-0394 Jiaxiang Zhang Jiaxiang Zhang true false 2022-09-16 MACS Choosing between equally valued options is a common conundrum, for which classical decision theories predicted a prolonged response time (RT). This contrasts with the notion that an optimal decision maker in a stable environment should make fast and random choices, as the outcomes are indifferent. Here, we characterize the neurocognitive processes underlying such voluntary decisions by integrating cognitive modelling of behavioral responses and EEG recordings in a probabilistic reward task. Human participants performed binary choices between pairs of unambiguous cues associated with identical reward probabilities at different levels. Higher reward probability accelerated RT, and participants chose one cue faster and more frequent over the other at each probability level. The behavioral effects on RT persisted in simple reactions to single cues. By using hierarchical Bayesian parameter estimation for an accumulator model, we showed that the probability and preference effects were independently associated with changes in the speed of evidence accumulation, but not with visual encoding or motor execution latencies. Time-resolved MVPA of EEG-evoked responses identified significant representations of reward certainty and preference as early as 120 ms after stimulus onset, with spatial relevance patterns maximal in middle central and parietal electrodes. Furthermore, EEG-informed computational modelling showed that the rate of change between N100 and P300 event-related potentials modulated accumulation rates on a trial-by-trial basis. Our findings suggest that reward probability and spontaneous preference collectively shape voluntary decisions between equal options, providing a mechanism to prevent indecision or random behavior. Journal Article Computational Brain and Behavior 4 2 191 212 Springer Science and Business Media LLC 2522-0861 2522-087X Decision making; Reward probability; Preference; EEG; Cognitive modelling 1 6 2021 2021-06-01 10.1007/s42113-020-00096-6 COLLEGE NANME Mathematics and Computer Science School COLLEGE CODE MACS Swansea University This study was supported by a European Research Council starting grant (716321). WZ was supported by a PhD studentship from Cardiff University School of Psychology. DK was supported by a PhD studentship from the Engineering and Physical Sciences Research Council (1982622). 2022-09-26T14:51:06.2480656 2022-09-16T09:39:45.9539880 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Wojciech Zajkowski 0000-0001-6078-1724 1 Dominik Krzemiński 2 Jacopo Barone 3 Lisa H. Evans 4 Jiaxiang Zhang 0000-0002-4758-0394 5 61251__25229__da50d0e5ca2e420cb0305f9d0eb1d87e.pdf 61251_VoR.pdf 2022-09-26T14:49:06.7825861 Output 1560585 application/pdf Version of Record true © The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International License true eng http://creativecommonshorg/licenses/by/4.0/
title Breaking Deadlocks: Reward Probability and Spontaneous Preference Shape Voluntary Decisions and Electrophysiological Signals in Humans
spellingShingle Breaking Deadlocks: Reward Probability and Spontaneous Preference Shape Voluntary Decisions and Electrophysiological Signals in Humans
Jiaxiang Zhang
title_short Breaking Deadlocks: Reward Probability and Spontaneous Preference Shape Voluntary Decisions and Electrophysiological Signals in Humans
title_full Breaking Deadlocks: Reward Probability and Spontaneous Preference Shape Voluntary Decisions and Electrophysiological Signals in Humans
title_fullStr Breaking Deadlocks: Reward Probability and Spontaneous Preference Shape Voluntary Decisions and Electrophysiological Signals in Humans
title_full_unstemmed Breaking Deadlocks: Reward Probability and Spontaneous Preference Shape Voluntary Decisions and Electrophysiological Signals in Humans
title_sort Breaking Deadlocks: Reward Probability and Spontaneous Preference Shape Voluntary Decisions and Electrophysiological Signals in Humans
author_id_str_mv 555e06e0ed9a87608f2d035b3bde3a87
author_id_fullname_str_mv 555e06e0ed9a87608f2d035b3bde3a87_***_Jiaxiang Zhang
author Jiaxiang Zhang
author2 Wojciech Zajkowski
Dominik Krzemiński
Jacopo Barone
Lisa H. Evans
Jiaxiang Zhang
format Journal article
container_title Computational Brain and Behavior
container_volume 4
container_issue 2
container_start_page 191
publishDate 2021
institution Swansea University
issn 2522-0861
2522-087X
doi_str_mv 10.1007/s42113-020-00096-6
publisher Springer Science and Business Media LLC
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
document_store_str 1
active_str 0
description Choosing between equally valued options is a common conundrum, for which classical decision theories predicted a prolonged response time (RT). This contrasts with the notion that an optimal decision maker in a stable environment should make fast and random choices, as the outcomes are indifferent. Here, we characterize the neurocognitive processes underlying such voluntary decisions by integrating cognitive modelling of behavioral responses and EEG recordings in a probabilistic reward task. Human participants performed binary choices between pairs of unambiguous cues associated with identical reward probabilities at different levels. Higher reward probability accelerated RT, and participants chose one cue faster and more frequent over the other at each probability level. The behavioral effects on RT persisted in simple reactions to single cues. By using hierarchical Bayesian parameter estimation for an accumulator model, we showed that the probability and preference effects were independently associated with changes in the speed of evidence accumulation, but not with visual encoding or motor execution latencies. Time-resolved MVPA of EEG-evoked responses identified significant representations of reward certainty and preference as early as 120 ms after stimulus onset, with spatial relevance patterns maximal in middle central and parietal electrodes. Furthermore, EEG-informed computational modelling showed that the rate of change between N100 and P300 event-related potentials modulated accumulation rates on a trial-by-trial basis. Our findings suggest that reward probability and spontaneous preference collectively shape voluntary decisions between equal options, providing a mechanism to prevent indecision or random behavior.
published_date 2021-06-01T08:14:59Z
_version_ 1821392554808049664
score 11.047804

Similar Items