Artículo

Wang, L.; Amalric, M.; Fang, W.; Jiang, X.; Pallier, C.; Figueira, S.; Sigman, M.; Dehaene, S. "Representation of spatial sequences using nested rules in human prefrontal cortex" (2019) NeuroImage. 186:245-255
Estamos trabajando para incorporar este artículo al repositorio
Consulte el artículo en la página del editor
Consulte la política de Acceso Abierto del editor

Abstract:

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. © 2018

Registro:

Documento: Artículo
Título:Representation of spatial sequences using nested rules in human prefrontal cortex
Autor:Wang, L.; Amalric, M.; Fang, W.; Jiang, X.; Pallier, C.; Figueira, S.; Sigman, M.; Dehaene, S.
Filiación:Institute of Neuroscience, Key Laboratory of Primate Neurobiology, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China
Collège de France, Paris, France
Cognitive Neuroimaging Unit, CEA DSV/I2BM, INSERM, Université Paris-Sud, Université Paris-Saclay, NeuroSpin Center, Gif/Yvette, 91191, France
Sorbonne Universités, UPMC Univ Paris 06, IFD, 4 place Jussieu, Paris, France
Key Laboratory of Brain Functional Genomics, Institute of Cognitive Neuroscience, School of Psychology and Cognitive Science, East China Normal University, Shanghai, 200062, China
Department of Computer Science, FCEN, University of Buenos Aires and ICC-CONICET, Argentina
Laboratorio de Neurociencia, Universidad Torcuato Di Tella, Buenos Aires, Argentina
CONICET (Consejo Nacional de Investigaciones Científicas y Técnicas), Argentina
Facultad de Lenguas y Educación, Universidad Nebrija, Madrid, Spain
Palabras clave:adult; Article; controlled study; dorsal inferior prefrontal cortex; dorsolateral prefrontal cortex; female; gaze; human; human experiment; language processing; learning; male; memory consolidation; nerve cell network; neuroanatomy; neurophysiology; normal human; prediction; prefrontal cortex; priority journal; working memory
Año:2019
Volumen:186
Página de inicio:245
Página de fin:255
DOI: http://dx.doi.org/10.1016/j.neuroimage.2018.10.061
Título revista:NeuroImage
Título revista abreviado:NeuroImage
ISSN:10538119
CODEN:NEIME
Registro:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_10538119_v186_n_p245_Wang

Referencias:

  • Amalric, M., Dehaene, S., Origins of the brain networks for advanced mathematics in expert mathematicians (2016) Proc. Natl. Acad. Sci. U. S. A., 113, pp. 4909-4917
  • Amalric, M., Dehaene, S., Cortical circuits for mathematical knowledge: evidence for a major subdivision within the brain's semantic networks (2017) Philos. Trans. R. Soc. Lond. B Biol. Sci., 373
  • Amalric, M., Wang, L., Pica, P., Figueira, S., Sigman, M., Dehaene, S., The language of geometry: fast comprehension of geometrical primitives and rules in human adults and preschoolers (2017) PLoS Comput. Biol., 13
  • Badre, D., D'Esposito, M., Is the rostro-caudal axis of the frontal lobe hierarchical? (2009) Nat. Rev. Neurosci., 10, pp. 659-669
  • Badre, D., Kayser, A.S., D'Esposito, M., Frontal cortex and the discovery of abstract action rules (2010) Neuron, 66, pp. 315-326
  • Badre, D., Nee, D.E., Frontal cortex and the hierarchical control of behavior (2018) Trends Cognit. Sci., 22, pp. 170-188
  • Bahlmann, J., Blumenfeld, R.S., D'Esposito, M., The rostro-caudal Axis of frontal cortex is sensitive to the domain of stimulus information (2015) Cerebr. Cortex, 25, pp. 1815-1826
  • Balaguer, J., Spiers, H., Hassabis, D., Summerfield, C., Neural mechanisms of hierarchical planning in a virtual subway network (2016) Neuron, 90, pp. 893-903
  • Basirat, A., Dehaene, S., Dehaene-Lambertz, G., A hierarchy of cortical responses to sequence violations in three-month-old infants (2014) Cognition, 132, pp. 137-150
  • Bor, D., Duncan, J., Wiseman, R.J., Owen, A.M., Encoding strategies dissociate prefrontal activity from working memory demand (2003) Neuron, 37, pp. 361-367
  • Braver, T.S., Cohen, J.D., Nystrom, L.E., Jonides, J., Smith, E.E., Noll, D.C., A parametric study of prefrontal cortex involvement in human working memory (1997) Neuroimage, 5, pp. 49-62
  • Dehaene, S., The Number Sense (2011), Oxford Univ Press New York; Dehaene, S., Changeux, J.P., A hierarchical neuronal network for planning behavior (1997) Proc. Natl. Acad. Sci. U. S. A., 94, pp. 13293-13298
  • Dehaene, S., Meyniel, F., Wacongne, C., Wang, L., Pallier, C., The neural representation of sequences: from transition probabilities to algebraic patterns and linguistic trees (2015) Neuron, 88, pp. 2-19
  • Dehaene-Lambertz, G., Hertz-Pannier, L., Dubois, J., Meriaux, S., Roche, A., Sigman, M., Dehaene, S., Functional organization of perisylvian activation during presentation of sentences in preverbal infants (2006) Proc. Natl. Acad. Sci. U. S. A., 103, pp. 14240-14245
  • Desrochers, T.M., Chatham, C.H., Badre, D., The necessity of rostrolateral prefrontal cortex for higher-level sequential behavior (2015) Neuron, 87, pp. 1357-1368
  • Fitch, W.T., Toward a computational framework for cognitive biology: unifying approaches from cognitive neuroscience and comparative cognition (2014) Phys. Life Rev., 11, pp. 329-364
  • Fitch, W.T., Friederici, A.D., Artificial grammar learning meets formal language theory: an overview (2012) Philos. Trans. R. Soc. Lond. B Biol. Sci., 367, pp. 1933-1955
  • Fitch, W.T., Hauser, M.D., Computational constraints on syntactic processing in a nonhuman primate (2004) Science, 303, pp. 377-380
  • Greenfield, P.M., Language, tools, and brain - the ontogeny and phylogeny of hierarchically organized sequential behavior (1991) Behav. Brain Sci., 14, pp. 531-550
  • Hauser, M.D., Chomsky, N., Fitch, W.T., The faculty of language: what is it, who has it, and how did it evolve? (2002) Science, 298, pp. 1569-1579
  • Hunt, R.H., Aslin, R.N., Statistical learning in a serial reaction time task: access to separable statistical cues by individual learners (2001) J. Exp. Psychol. Gen., 130, pp. 658-680
  • Jansen, A.R., Marriott, K., Yelland, G.W., Comprehension of algebraic expressions by experienced users of mathematics (2003) Q. J. Exp. Psychol., 56, pp. 3-30
  • Jeon, H.A., Hierarchical processing in the prefrontal cortex in a variety of cognitive domains (2014) Front. Syst. Neurosci., 8, p. 223
  • Kabdebon, C., Pena, M., Buiatti, M., Dehaene-Lambertz, G., Electrophysiological evidence of statistical learning of long-distance dependencies in 8-month-old preterm and full-term infants (2015) Brain Lang., 148, pp. 25-36
  • Koechlin, E., Jubault, T., Broca's area and the hierarchical organization of human behavior (2006) Neuron, 50, pp. 963-974
  • Koechlin, E., Ody, C., Kouneiher, F., The architecture of cognitive control in the human prefrontal cortex (2003) Science, 302, pp. 1181-1185
  • Koechlin, E., Summerfield, C., An information theoretical approach to prefrontal executive function (2007) Trends Cognit. Sci., 11, pp. 229-235
  • Kriegeskorte, N., Mur, M., Bandettini, P., Representational similarity analysis - connecting the branches of systems neuroscience (2008) Front. Syst. Neurosci., 2, p. 4
  • Lancaster, J.L., Woldorff, M.G., Parsons, L.M., Liotti, M., Freitas, C.S., Rainey, L., Kochunov, P.V., Fox, P.T., Automated Talairach atlas labels for functional brain mapping (2000) Hum. Brain Mapp., 10, pp. 120-131
  • Lashley, K.S., The problem of serial order in behavior (1951) Cerebral Mechanisms in Behavior; the Hixon Symposium, pp. 112-146. , L.A. Jeffress Wiley Oxford, England
  • Makuuchi, M., Bahlmann, J., Anwander, A., Friederici, A.D., Segregating the core computational faculty of human language from working memory (2009) Proc. Natl. Acad. Sci. U. S. A., 106, pp. 8362-8367
  • Marcus, G.F., Vijayan, S., Bandi Rao, S., Vishton, P.M., Rule learning by seven-month-old infants (1999) Science, 283, pp. 77-80
  • Maruyama, M., Pallier, C., Jobert, A., Sigman, M., Dehaene, S., The cortical representation of simple mathematical expressions (2012) Neuroimage, 61, pp. 1444-1460
  • Ming, L., Vitányi, P., An Introduction to Kolmogorov Complexity and its Applications (1997), Springer Heidelberg; Nee, D.E., D'Esposito, M., The hierarchical organization of the lateral prefrontal cortex (2016) Elife, 5
  • Neubert, F.X., Mars, R.B., Thomas, A.G., Sallet, J., Rushworth, M.F., Comparison of human ventral frontal cortex areas for cognitive control and language with areas in monkey frontal cortex (2014) Neuron, 81, pp. 700-713
  • O'Reilly, R.C., The what and How of prefrontal cortical organization (2010) Trends Neurosci., 33, pp. 355-361
  • Pallier, C., Devauchelle, A.D., Dehaene, S., Cortical representation of the constituent structure of sentences (2011) Proc. Natl. Acad. Sci. U. S. A., 108, pp. 2522-2527
  • Patel, A.D., Language, music, syntax and the brain (2003) Nat. Neurosci., 6, pp. 674-681
  • Pinel, P., Thirion, B., Meriaux, S., Jobert, A., Serres, J., Le Bihan, D., Poline, J.B., Dehaene, S., Fast reproducible identification and large-scale databasing of individual functional cognitive networks (2007) BMC Neurosci., 8, p. 91
  • Rilling, J.K., Glasser, M.F., Preuss, T.M., Ma, X., Zhao, T., Hu, X., Behrens, T.E., The evolution of the arcuate fasciculus revealed with comparative DTI (2008) Nat. Neurosci., 11, pp. 426-428
  • Romano, S., Sigman, M., Figueira, S., LT2C2: a language of thought with Turing-computable Kolmogorov complexity (2013) Pap. Phys., p. 50001
  • Rosenbaum, D.A., Kenny, S.B., Derr, M.A., Hierarchical control of rapid movement sequences (1983) J. Exp. Psychol. Hum. Percept. Perform., 9, pp. 86-102
  • Saffran, J.R., Wilson, D.P., From syllables to syntax: multilevel statistical learning by 12-month-old infants (2003) Infancy, 4, pp. 273-284
  • Schenker, N.M., Hopkins, W.D., Spocter, M.A., Garrison, A.R., Stimpson, C.D., Erwin, J.M., Hof, P.R., Sherwood, C.C., Broca's area homologue in chimpanzees (Pan troglodytes): probabilistic mapping, asymmetry, and comparison to humans (2010) Cerebr. Cortex, 20, pp. 730-742
  • Schneider, D.W., Logan, G.D., Hierarchical control of cognitive processes: switching tasks in sequences (2006) J. Exp. Psychol. Gen., 135, pp. 623-640
  • Schneider, E., Maruyama, M., Dehaene, S., Sigman, M., Eye gaze reveals a fast, parallel extraction of the syntax of arithmetic formulas (2012) Cognition, 125, pp. 475-490
  • Smaers, J.B., Gomez-Robles, A., Parks, A.N., Sherwood, C.C., Exceptional evolutionary expansion of prefrontal cortex in great Apes and humans (2017) Curr. Biol., 27, p. 1549
  • Varley, R.A., Klessinger, N.J., Romanowski, C.A., Siegal, M., Agrammatic but numerate (2005) Proc. Natl. Acad. Sci. U. S. A., 102, pp. 3519-3524
  • Verwey, W.B., Abrahamse, E.L., de Kleine, E., Cognitive processing in new and practiced discrete keying sequences (2010) Front. Psychol., 1, p. 32
  • Wang, L., Uhrig, L., Jarraya, B., Dehaene, S., Representation of numerical and sequential patterns in macaque and human brains (2015) Curr. Biol., 25, pp. 1966-1974
  • Wendelken, C., Chung, D., Bunge, S.A., Rostrolateral prefrontal cortex: domain-general or domain-sensitive? (2012) Hum. Brain Mapp., 33, pp. 1952-1963
  • Werchan, D.M., Collins, A.G., Frank, M.J., Amso, D., Role of prefrontal cortex in learning and generalizing hierarchical rules in 8-month-old infants (2016) J. Neurosci., 36, pp. 10314-10322
  • Wilson, B., Kikuchi, Y., Sun, L., Hunter, D., Dick, F., Smith, K., Thiele, A., Petkov, C.I., Auditory sequence processing reveals evolutionarily conserved regions of frontal cortex in macaques and humans (2015) Nat. Commun., 6, p. 8901
  • Wilson, B., Marslen-Wilson, W.D., Petkov, C.I., Conserved sequence processing in primate frontal cortex (2017) Trends Neurosci., 40, pp. 72-82
  • Xu, F., Tenenbaum, J.B., Word learning as Bayesian inference (2007) Psychol. Rev., 114, pp. 245-272

Citas:

---------- APA ----------
Wang, L., Amalric, M., Fang, W., Jiang, X., Pallier, C., Figueira, S., Sigman, M.,..., Dehaene, S. (2019) . Representation of spatial sequences using nested rules in human prefrontal cortex. NeuroImage, 186, 245-255.
http://dx.doi.org/10.1016/j.neuroimage.2018.10.061
---------- CHICAGO ----------
Wang, L., Amalric, M., Fang, W., Jiang, X., Pallier, C., Figueira, S., et al. "Representation of spatial sequences using nested rules in human prefrontal cortex" . NeuroImage 186 (2019) : 245-255.
http://dx.doi.org/10.1016/j.neuroimage.2018.10.061
---------- MLA ----------
Wang, L., Amalric, M., Fang, W., Jiang, X., Pallier, C., Figueira, S., et al. "Representation of spatial sequences using nested rules in human prefrontal cortex" . NeuroImage, vol. 186, 2019, pp. 245-255.
http://dx.doi.org/10.1016/j.neuroimage.2018.10.061
---------- VANCOUVER ----------
Wang, L., Amalric, M., Fang, W., Jiang, X., Pallier, C., Figueira, S., et al. Representation of spatial sequences using nested rules in human prefrontal cortex. NeuroImage. 2019;186:245-255.
http://dx.doi.org/10.1016/j.neuroimage.2018.10.061