Utilize este identificador para referenciar este registo: https://hdl.handle.net/1822/5780

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dc.contributor.authorErlhagen, Wolfram-
dc.contributor.authorMukovskiy, Albert-
dc.contributor.authorBicho, E.-
dc.date.accessioned2006-11-15T11:26:24Z-
dc.date.available2006-11-15T11:26:24Z-
dc.date.issued2006-
dc.identifier.citationErlhagen, W., Mukovskiy, A., & Bicho, E. (2006, April). A dynamic model for action understanding and goal-directed imitation. Brain Research. Elsevier BV. http://doi.org/10.1016/j.brainres.2006.01.114eng
dc.identifier.issn0006-8993eng
dc.identifier.urihttps://hdl.handle.net/1822/5780-
dc.description.abstractThe understanding of other individuals' actions is a fundamental cognitive skill for all species living in social groups. Recent neurophysiological evidence suggests that an observer may achieve the understanding by mapping visual information onto his own motor repertoire to reproduce the action effect. However, due to differences in embodiment, environmental constraints or motor skills, this mapping very often cannot be direct. In this paper, we present a dynamic network model which represents in its layers the functionality of neurons in different interconnected brain areas known to be involved in action observation/execution tasks. The model aims at substantiating the idea that action understanding is a continuous process which combines sensory evidence, prior task knowledge and a goal-directed matching of action observation and action execution. The model is tested in variations of an imitation task in which an observer with dissimilar embodiment tries to reproduce the perceived or inferred end-state of a grasping-placing sequence. We also propose and test a biologically plausible learning scheme which allows establishing during practice a goal-directed organization of the distributed network. The modeling results are discussed with respect to recent experimental findings in action observation/execution studies.eng
dc.description.sponsorshipThis work was supported by the European grants ArteSImit (IST-200-29689) and JAST (IST-2-003747-IP). We would like to thank Drs Harold Bekkering, Hein von Schie, Leonardo Fogassi and Giacomo Rizzolatti for numerous discussions about this work.por
dc.language.isoengeng
dc.publisherElseviereng
dc.rightsopenAccesseng
dc.subjectAction sequenceeng
dc.subjectMirror circuiteng
dc.subjectGraspingeng
dc.subjectHebbian learningeng
dc.subjectLearning by imitationeng
dc.subjectPlacingeng
dc.titleA dynamic model for action understanding and goal-directed imitationeng
dc.typearticlepor
dc.peerreviewedyeseng
sdum.pagination174-188eng
sdum.publicationstatuspublishedeng
sdum.volume1083eng
oaire.citationStartPage174por
oaire.citationEndPage188por
oaire.citationIssue1por
oaire.citationVolume1083por
dc.identifier.doi10.1016/j.brainres.2006.01.114por
dc.identifier.pmid16616516por
dc.subject.wosScience & Technologypor
sdum.journalBrain researchpor
Aparece nas coleções:CAlg - Artigos em revistas internacionais / Papers in international journals
DEI - Artigos em revistas internacionais
Offmath - Artigos (Papers)

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