An MEG signature corresponding to an axiomatic model of reward prediction error

Optimal decision-making is guided by evaluating the outcomes of previous decisions. Prediction errors are theoretical teaching signals which integrate two features of an outcome: its inherent value and prior expectation of its occurrence. To uncover the magnetic signature of prediction errors in the...

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Autores: Talmi, Deborah, Fuentemilla Garriga, Lluís, Litvak, Vladimir, Düzel, Emrah, Dolan, Raymond J.
Formato: artículo
Estado:Versión publicada
Fecha de publicación:2011
País:España
Recursos:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:2445/117585
Acesso em linha:https://hdl.handle.net/2445/117585
Access Level:acceso abierto
Palavra-chave:Diagnòstic per la imatge
Presa de decisions
Encèfal
Codis de correcció d'errors (Teoria de la informació)
Diagnostic imaging
Decision making
Encephalon
Error-correcting codes (Information theory)
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spelling An MEG signature corresponding to an axiomatic model of reward prediction errorTalmi, DeborahFuentemilla Garriga, LluísLitvak, VladimirDüzel, EmrahDolan, Raymond J.Diagnòstic per la imatgePresa de decisionsEncèfalCodis de correcció d'errors (Teoria de la informació)Diagnostic imagingDecision makingEncephalonError-correcting codes (Information theory)Optimal decision-making is guided by evaluating the outcomes of previous decisions. Prediction errors are theoretical teaching signals which integrate two features of an outcome: its inherent value and prior expectation of its occurrence. To uncover the magnetic signature of prediction errors in the human brain we acquired magnetoencephalographic (MEG) data while participants performed a gambling task. Our primary objective was to use formal criteria, based upon an axiomatic model (Caplin and Dean, 2008a), to determine the presence and timing profile of MEG signals that express prediction errors. We report analyses at the sensor level, implemented in SPM8, time locked to outcome onset. We identified, for the first time, a MEG signature of prediction error, which emerged approximately 320 ms after an outcome and expressed as an interaction between outcome valence and probability. This signal followed earlier, separate signals for outcome valence and probability, which emerged approximately 200 ms after an outcome. Strikingly, the time course of the prediction error signal, as well as the early valence signal, resembled the Feedback-Related Negativity (FRN). In simultaneously acquired EEG data we obtained a robust FRN, but the win and loss signals that comprised this difference wave did not comply with the axiomatic model. Our findings motivate an explicit examination of the critical issue of timing embodied in computational models of prediction errors as seen in human electrophysiological data.Elsevier B.V.2017201720112017info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersion11 p.application/pdfhttps://hdl.handle.net/2445/117585Articles publicats en revistes (Cognició, Desenvolupament i Psicologia de l'Educació)reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésReproducció del document publicat a: https://doi.org/10.1016/j.neuroimage.2011.06.051Neuroimage, 2011, vol. 59, num. 1, p. 635-645https://doi.org/10.1016/j.neuroimage.2011.06.051cc-by (c) Talmi, Deborah et al., 2011http://creativecommons.org/licenses/by/3.0/esinfo:eu-repo/semantics/openAccessoai:recercat.cat:2445/1175852026-05-29T05:05:01Z
dc.title.none.fl_str_mv An MEG signature corresponding to an axiomatic model of reward prediction error
title An MEG signature corresponding to an axiomatic model of reward prediction error
spellingShingle An MEG signature corresponding to an axiomatic model of reward prediction error
Talmi, Deborah
Diagnòstic per la imatge
Presa de decisions
Encèfal
Codis de correcció d'errors (Teoria de la informació)
Diagnostic imaging
Decision making
Encephalon
Error-correcting codes (Information theory)
title_short An MEG signature corresponding to an axiomatic model of reward prediction error
title_full An MEG signature corresponding to an axiomatic model of reward prediction error
title_fullStr An MEG signature corresponding to an axiomatic model of reward prediction error
title_full_unstemmed An MEG signature corresponding to an axiomatic model of reward prediction error
title_sort An MEG signature corresponding to an axiomatic model of reward prediction error
dc.creator.none.fl_str_mv Talmi, Deborah
Fuentemilla Garriga, Lluís
Litvak, Vladimir
Düzel, Emrah
Dolan, Raymond J.
author Talmi, Deborah
author_facet Talmi, Deborah
Fuentemilla Garriga, Lluís
Litvak, Vladimir
Düzel, Emrah
Dolan, Raymond J.
author_role author
author2 Fuentemilla Garriga, Lluís
Litvak, Vladimir
Düzel, Emrah
Dolan, Raymond J.
author2_role author
author
author
author
dc.subject.none.fl_str_mv Diagnòstic per la imatge
Presa de decisions
Encèfal
Codis de correcció d'errors (Teoria de la informació)
Diagnostic imaging
Decision making
Encephalon
Error-correcting codes (Information theory)
topic Diagnòstic per la imatge
Presa de decisions
Encèfal
Codis de correcció d'errors (Teoria de la informació)
Diagnostic imaging
Decision making
Encephalon
Error-correcting codes (Information theory)
description Optimal decision-making is guided by evaluating the outcomes of previous decisions. Prediction errors are theoretical teaching signals which integrate two features of an outcome: its inherent value and prior expectation of its occurrence. To uncover the magnetic signature of prediction errors in the human brain we acquired magnetoencephalographic (MEG) data while participants performed a gambling task. Our primary objective was to use formal criteria, based upon an axiomatic model (Caplin and Dean, 2008a), to determine the presence and timing profile of MEG signals that express prediction errors. We report analyses at the sensor level, implemented in SPM8, time locked to outcome onset. We identified, for the first time, a MEG signature of prediction error, which emerged approximately 320 ms after an outcome and expressed as an interaction between outcome valence and probability. This signal followed earlier, separate signals for outcome valence and probability, which emerged approximately 200 ms after an outcome. Strikingly, the time course of the prediction error signal, as well as the early valence signal, resembled the Feedback-Related Negativity (FRN). In simultaneously acquired EEG data we obtained a robust FRN, but the win and loss signals that comprised this difference wave did not comply with the axiomatic model. Our findings motivate an explicit examination of the critical issue of timing embodied in computational models of prediction errors as seen in human electrophysiological data.
publishDate 2011
dc.date.none.fl_str_mv 2011
2017
2017
2017
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/2445/117585
url https://hdl.handle.net/2445/117585
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Reproducció del document publicat a: https://doi.org/10.1016/j.neuroimage.2011.06.051
Neuroimage, 2011, vol. 59, num. 1, p. 635-645
https://doi.org/10.1016/j.neuroimage.2011.06.051
dc.rights.none.fl_str_mv cc-by (c) Talmi, Deborah et al., 2011
http://creativecommons.org/licenses/by/3.0/es
info:eu-repo/semantics/openAccess
rights_invalid_str_mv cc-by (c) Talmi, Deborah et al., 2011
http://creativecommons.org/licenses/by/3.0/es
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 11 p.
application/pdf
dc.publisher.none.fl_str_mv Elsevier B.V.
publisher.none.fl_str_mv Elsevier B.V.
dc.source.none.fl_str_mv Articles publicats en revistes (Cognició, Desenvolupament i Psicologia de l'Educació)
reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869420111059746816
score 15,812429

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