Representation of spatial sequences using nested rules in human prefrontal cortex

Autores
Wang, Liping; Amalric, Marie; Fang, Wen; Jiang, Xinjian; Pallier, Christophe; Figueira, Santiago; Sigman, Mariano; Dehaene, Stanislas
Año de publicación
2019
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Memory for spatial sequences does not depend solely on the number of locations to be stored, but also on the presence of spatial regularities. Here, we show that the human brain quickly stores spatial sequences by detecting geometrical regularities at multiple time scales and encoding them in a format akin to a programming language. We measured gaze-anticipation behavior while spatial sequences of variable regularity were repeated. Participants’ behavior suggested that they quickly discovered the most compact description of each sequence in a language comprising nested rules, and used these rules to compress the sequence in memory and predict the next items. Activity in dorsal inferior prefrontal cortex correlated with the amount of compression, while right dorsolateral prefrontal cortex encoded the presence of embedded structures. Sequence learning was accompanied by a progressive differentiation of multi-voxel activity patterns in these regions. We propose that humans are endowed with a simple “language of geometry” which recruits a dorsal prefrontal circuit for geometrical rules, distinct from but close to areas involved in natural language processing
Fil: Wang, Liping. Chinese Academy of Sciences; República de China
Fil: Amalric, Marie. Collége de France; Francia. Université Paris Sud; Francia. Sorbonne University; Francia
Fil: Fang, Wen. East China Normal University.; China
Fil: Jiang, Xinjian. East China Normal University.; China
Fil: Pallier, Christophe. Université Paris Sud; Francia. Sorbonne University; Francia
Fil: Figueira, Santiago. Universidad de Buenos Aires. Facultad de Ingeniería. Instituto Tecnológico de Buenos Aires. Departamento de Informatica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Sigman, Mariano. Universidad Torcuato Di Tella; Argentina. Universidad Nebrija; España. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Dehaene, Stanislas. Collége de France; Francia. Université Paris Sud; Francia
Materia
spatial sequences
human prefrontal cortex
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/109134
Acceder
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repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Representation of spatial sequences using nested rules in human prefrontal cortexWang, LipingAmalric, MarieFang, WenJiang, XinjianPallier, ChristopheFigueira, SantiagoSigman, MarianoDehaene, Stanislasspatial sequenceshuman prefrontal cortexhttps://purl.org/becyt/ford/1.6https://purl.org/becyt/ford/1Memory for spatial sequences does not depend solely on the number of locations to be stored, but also on the presence of spatial regularities. Here, we show that the human brain quickly stores spatial sequences by detecting geometrical regularities at multiple time scales and encoding them in a format akin to a programming language. We measured gaze-anticipation behavior while spatial sequences of variable regularity were repeated. Participants’ behavior suggested that they quickly discovered the most compact description of each sequence in a language comprising nested rules, and used these rules to compress the sequence in memory and predict the next items. Activity in dorsal inferior prefrontal cortex correlated with the amount of compression, while right dorsolateral prefrontal cortex encoded the presence of embedded structures. Sequence learning was accompanied by a progressive differentiation of multi-voxel activity patterns in these regions. We propose that humans are endowed with a simple “language of geometry” which recruits a dorsal prefrontal circuit for geometrical rules, distinct from but close to areas involved in natural language processingFil: Wang, Liping. Chinese Academy of Sciences; República de ChinaFil: Amalric, Marie. Collége de France; Francia. Université Paris Sud; Francia. Sorbonne University; FranciaFil: Fang, Wen. East China Normal University.; ChinaFil: Jiang, Xinjian. East China Normal University.; ChinaFil: Pallier, Christophe. Université Paris Sud; Francia. Sorbonne University; FranciaFil: Figueira, Santiago. Universidad de Buenos Aires. Facultad de Ingeniería. Instituto Tecnológico de Buenos Aires. Departamento de Informatica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Sigman, Mariano. Universidad Torcuato Di Tella; Argentina. Universidad Nebrija; España. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Dehaene, Stanislas. Collége de France; Francia. Université Paris Sud; FranciaAcademic Press Inc Elsevier Science2019-02info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/109134Wang, Liping; Amalric, Marie; Fang, Wen; Jiang, Xinjian; Pallier, Christophe; et al.; Representation of spatial sequences using nested rules in human prefrontal cortex; Academic Press Inc Elsevier Science; Journal Neuroimag; 186; 2-2019; 245-2551053-8119CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://linkinghub.elsevier.com/retrieve/pii/S1053811918320330info:eu-repo/semantics/altIdentifier/doi/10.1016/j.neuroimage.2018.10.061info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-10T13:18:21Zoai:ri.conicet.gov.ar:11336/109134instacron: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:34982025-09-10 13:18:21.707CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Representation of spatial sequences using nested rules in human prefrontal cortex
title Representation of spatial sequences using nested rules in human prefrontal cortex
spellingShingle Representation of spatial sequences using nested rules in human prefrontal cortex
Wang, Liping
spatial sequences
human prefrontal cortex
title_short Representation of spatial sequences using nested rules in human prefrontal cortex
title_full Representation of spatial sequences using nested rules in human prefrontal cortex
title_fullStr Representation of spatial sequences using nested rules in human prefrontal cortex
title_full_unstemmed Representation of spatial sequences using nested rules in human prefrontal cortex
title_sort Representation of spatial sequences using nested rules in human prefrontal cortex
dc.creator.none.fl_str_mv Wang, Liping
Amalric, Marie
Fang, Wen
Jiang, Xinjian
Pallier, Christophe
Figueira, Santiago
Sigman, Mariano
Dehaene, Stanislas
author Wang, Liping
author_facet Wang, Liping
Amalric, Marie
Fang, Wen
Jiang, Xinjian
Pallier, Christophe
Figueira, Santiago
Sigman, Mariano
Dehaene, Stanislas
author_role author
author2 Amalric, Marie
Fang, Wen
Jiang, Xinjian
Pallier, Christophe
Figueira, Santiago
Sigman, Mariano
Dehaene, Stanislas
author2_role author
author
author
author
author
author
author
dc.subject.none.fl_str_mv spatial sequences
human prefrontal cortex
topic spatial sequences
human prefrontal cortex
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.6
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Memory for spatial sequences does not depend solely on the number of locations to be stored, but also on the presence of spatial regularities. Here, we show that the human brain quickly stores spatial sequences by detecting geometrical regularities at multiple time scales and encoding them in a format akin to a programming language. We measured gaze-anticipation behavior while spatial sequences of variable regularity were repeated. Participants’ behavior suggested that they quickly discovered the most compact description of each sequence in a language comprising nested rules, and used these rules to compress the sequence in memory and predict the next items. Activity in dorsal inferior prefrontal cortex correlated with the amount of compression, while right dorsolateral prefrontal cortex encoded the presence of embedded structures. Sequence learning was accompanied by a progressive differentiation of multi-voxel activity patterns in these regions. We propose that humans are endowed with a simple “language of geometry” which recruits a dorsal prefrontal circuit for geometrical rules, distinct from but close to areas involved in natural language processing
Fil: Wang, Liping. Chinese Academy of Sciences; República de China
Fil: Amalric, Marie. Collége de France; Francia. Université Paris Sud; Francia. Sorbonne University; Francia
Fil: Fang, Wen. East China Normal University.; China
Fil: Jiang, Xinjian. East China Normal University.; China
Fil: Pallier, Christophe. Université Paris Sud; Francia. Sorbonne University; Francia
Fil: Figueira, Santiago. Universidad de Buenos Aires. Facultad de Ingeniería. Instituto Tecnológico de Buenos Aires. Departamento de Informatica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Sigman, Mariano. Universidad Torcuato Di Tella; Argentina. Universidad Nebrija; España. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Dehaene, Stanislas. Collége de France; Francia. Université Paris Sud; Francia
description Memory for spatial sequences does not depend solely on the number of locations to be stored, but also on the presence of spatial regularities. Here, we show that the human brain quickly stores spatial sequences by detecting geometrical regularities at multiple time scales and encoding them in a format akin to a programming language. We measured gaze-anticipation behavior while spatial sequences of variable regularity were repeated. Participants’ behavior suggested that they quickly discovered the most compact description of each sequence in a language comprising nested rules, and used these rules to compress the sequence in memory and predict the next items. Activity in dorsal inferior prefrontal cortex correlated with the amount of compression, while right dorsolateral prefrontal cortex encoded the presence of embedded structures. Sequence learning was accompanied by a progressive differentiation of multi-voxel activity patterns in these regions. We propose that humans are endowed with a simple “language of geometry” which recruits a dorsal prefrontal circuit for geometrical rules, distinct from but close to areas involved in natural language processing
publishDate 2019
dc.date.none.fl_str_mv 2019-02
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/11336/109134
Wang, Liping; Amalric, Marie; Fang, Wen; Jiang, Xinjian; Pallier, Christophe; et al.; Representation of spatial sequences using nested rules in human prefrontal cortex; Academic Press Inc Elsevier Science; Journal Neuroimag; 186; 2-2019; 245-255
1053-8119
CONICET Digital
CONICET
url http://hdl.handle.net/11336/109134
identifier_str_mv Wang, Liping; Amalric, Marie; Fang, Wen; Jiang, Xinjian; Pallier, Christophe; et al.; Representation of spatial sequences using nested rules in human prefrontal cortex; Academic Press Inc Elsevier Science; Journal Neuroimag; 186; 2-2019; 245-255
1053-8119
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://linkinghub.elsevier.com/retrieve/pii/S1053811918320330
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.neuroimage.2018.10.061
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Academic Press Inc Elsevier Science
publisher.none.fl_str_mv Academic Press Inc Elsevier Science
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
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score 12.993085

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