Beyond pain: Modeling decision-making deficits in chronic pain

Autores
Hess, Leonardo Emanuel; Haimovici, Ariel; Muñoz, Miguel Angel; Montoya, Pedro
Año de publicación
2014
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Risky decision-making seems to be markedly disrupted in patients with chronic pain, probably due to the high cost that impose pain and negative mood on executive control functions. Patients' behavioral performance on decision-making tasks such as the Iowa Gambling Task (IGT) is characterized by selecting cards more frequently from disadvantageous than from advantageous decks, and by switching often between competing responses in comparison with healthy controls (HCs). In the present study, we developed a simple heuristic model to simulate individuals' choice behavior by varying the level of decision randomness and the importance given to gains and losses. The findings revealed that the model was able to differentiate the behavioral performance of patients with chronic pain and HCs at the group, as well as at the individual level. The best fit of the model in patients with chronic pain was yielded when decisions were not based on previous choices and when gains were considered more relevant than losses. By contrast, the best account of the available data in HCs was obtained when decisions were based on previous experiences and losses loomed larger than gains. In conclusion, our model seems to provide useful information to measure each individual participant extensively, and to deal with the data on a participant-by-participant basis.
Fil: Hess, Leonardo Emanuel. Universidad Nacional de Rosario. Facultad de Ciencias Médicas; Argentina
Fil: Haimovici, Ariel. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Muñoz, Miguel Angel. Universidad de las Islas Baleares; España. Universidad de Granada; España
Fil: Montoya, Pedro. Universidad de las Islas Baleares; España
Materia
CHRONIC PAIN
COGNITION
DECISION-MAKING
EMOTION
MODELING
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/84493
Acceder
id CONICETDig_287bc5c8e03de216eeb7aae381b5a842
oai_identifier_str oai:ri.conicet.gov.ar:11336/84493
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Beyond pain: Modeling decision-making deficits in chronic painHess, Leonardo EmanuelHaimovici, ArielMuñoz, Miguel AngelMontoya, PedroCHRONIC PAINCOGNITIONDECISION-MAKINGEMOTIONMODELINGhttps://purl.org/becyt/ford/5.1https://purl.org/becyt/ford/5Risky decision-making seems to be markedly disrupted in patients with chronic pain, probably due to the high cost that impose pain and negative mood on executive control functions. Patients' behavioral performance on decision-making tasks such as the Iowa Gambling Task (IGT) is characterized by selecting cards more frequently from disadvantageous than from advantageous decks, and by switching often between competing responses in comparison with healthy controls (HCs). In the present study, we developed a simple heuristic model to simulate individuals' choice behavior by varying the level of decision randomness and the importance given to gains and losses. The findings revealed that the model was able to differentiate the behavioral performance of patients with chronic pain and HCs at the group, as well as at the individual level. The best fit of the model in patients with chronic pain was yielded when decisions were not based on previous choices and when gains were considered more relevant than losses. By contrast, the best account of the available data in HCs was obtained when decisions were based on previous experiences and losses loomed larger than gains. In conclusion, our model seems to provide useful information to measure each individual participant extensively, and to deal with the data on a participant-by-participant basis.Fil: Hess, Leonardo Emanuel. Universidad Nacional de Rosario. Facultad de Ciencias Médicas; ArgentinaFil: Haimovici, Ariel. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Muñoz, Miguel Angel. Universidad de las Islas Baleares; España. Universidad de Granada; EspañaFil: Montoya, Pedro. Universidad de las Islas Baleares; EspañaFrontiers Research Foundation2014-08info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/84493Hess, Leonardo Emanuel; Haimovici, Ariel; Muñoz, Miguel Angel ; Montoya, Pedro; Beyond pain: Modeling decision-making deficits in chronic pain; Frontiers Research Foundation; Frontiers in Behavioral Neuroscience; 8; AUG; 8-2014; 1-81662-5153CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.3389/fnbeh.2014.00263info:eu-repo/semantics/altIdentifier/url/https://www.frontiersin.org/articles/10.3389/fnbeh.2014.00263/fullinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T10:04:49Zoai:ri.conicet.gov.ar:11336/84493instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-03 10:04:50.08CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Beyond pain: Modeling decision-making deficits in chronic pain
title Beyond pain: Modeling decision-making deficits in chronic pain
spellingShingle Beyond pain: Modeling decision-making deficits in chronic pain
Hess, Leonardo Emanuel
CHRONIC PAIN
COGNITION
DECISION-MAKING
EMOTION
MODELING
title_short Beyond pain: Modeling decision-making deficits in chronic pain
title_full Beyond pain: Modeling decision-making deficits in chronic pain
title_fullStr Beyond pain: Modeling decision-making deficits in chronic pain
title_full_unstemmed Beyond pain: Modeling decision-making deficits in chronic pain
title_sort Beyond pain: Modeling decision-making deficits in chronic pain
dc.creator.none.fl_str_mv Hess, Leonardo Emanuel
Haimovici, Ariel
Muñoz, Miguel Angel
Montoya, Pedro
author Hess, Leonardo Emanuel
author_facet Hess, Leonardo Emanuel
Haimovici, Ariel
Muñoz, Miguel Angel
Montoya, Pedro
author_role author
author2 Haimovici, Ariel
Muñoz, Miguel Angel
Montoya, Pedro
author2_role author
author
author
dc.subject.none.fl_str_mv CHRONIC PAIN
COGNITION
DECISION-MAKING
EMOTION
MODELING
topic CHRONIC PAIN
COGNITION
DECISION-MAKING
EMOTION
MODELING
purl_subject.fl_str_mv https://purl.org/becyt/ford/5.1
https://purl.org/becyt/ford/5
dc.description.none.fl_txt_mv Risky decision-making seems to be markedly disrupted in patients with chronic pain, probably due to the high cost that impose pain and negative mood on executive control functions. Patients' behavioral performance on decision-making tasks such as the Iowa Gambling Task (IGT) is characterized by selecting cards more frequently from disadvantageous than from advantageous decks, and by switching often between competing responses in comparison with healthy controls (HCs). In the present study, we developed a simple heuristic model to simulate individuals' choice behavior by varying the level of decision randomness and the importance given to gains and losses. The findings revealed that the model was able to differentiate the behavioral performance of patients with chronic pain and HCs at the group, as well as at the individual level. The best fit of the model in patients with chronic pain was yielded when decisions were not based on previous choices and when gains were considered more relevant than losses. By contrast, the best account of the available data in HCs was obtained when decisions were based on previous experiences and losses loomed larger than gains. In conclusion, our model seems to provide useful information to measure each individual participant extensively, and to deal with the data on a participant-by-participant basis.
Fil: Hess, Leonardo Emanuel. Universidad Nacional de Rosario. Facultad de Ciencias Médicas; Argentina
Fil: Haimovici, Ariel. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Muñoz, Miguel Angel. Universidad de las Islas Baleares; España. Universidad de Granada; España
Fil: Montoya, Pedro. Universidad de las Islas Baleares; España
description Risky decision-making seems to be markedly disrupted in patients with chronic pain, probably due to the high cost that impose pain and negative mood on executive control functions. Patients' behavioral performance on decision-making tasks such as the Iowa Gambling Task (IGT) is characterized by selecting cards more frequently from disadvantageous than from advantageous decks, and by switching often between competing responses in comparison with healthy controls (HCs). In the present study, we developed a simple heuristic model to simulate individuals' choice behavior by varying the level of decision randomness and the importance given to gains and losses. The findings revealed that the model was able to differentiate the behavioral performance of patients with chronic pain and HCs at the group, as well as at the individual level. The best fit of the model in patients with chronic pain was yielded when decisions were not based on previous choices and when gains were considered more relevant than losses. By contrast, the best account of the available data in HCs was obtained when decisions were based on previous experiences and losses loomed larger than gains. In conclusion, our model seems to provide useful information to measure each individual participant extensively, and to deal with the data on a participant-by-participant basis.
publishDate 2014
dc.date.none.fl_str_mv 2014-08
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/84493
Hess, Leonardo Emanuel; Haimovici, Ariel; Muñoz, Miguel Angel ; Montoya, Pedro; Beyond pain: Modeling decision-making deficits in chronic pain; Frontiers Research Foundation; Frontiers in Behavioral Neuroscience; 8; AUG; 8-2014; 1-8
1662-5153
CONICET Digital
CONICET
url http://hdl.handle.net/11336/84493
identifier_str_mv Hess, Leonardo Emanuel; Haimovici, Ariel; Muñoz, Miguel Angel ; Montoya, Pedro; Beyond pain: Modeling decision-making deficits in chronic pain; Frontiers Research Foundation; Frontiers in Behavioral Neuroscience; 8; AUG; 8-2014; 1-8
1662-5153
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.3389/fnbeh.2014.00263
info:eu-repo/semantics/altIdentifier/url/https://www.frontiersin.org/articles/10.3389/fnbeh.2014.00263/full
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Frontiers Research Foundation
publisher.none.fl_str_mv Frontiers Research Foundation
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1842269878127427584
score 13.13397

Publicaciones similares