EEG decoding of semantic category reveals distributed representations for single concepts.
Achieving a clearer picture of categorial distinctions in the brain is essential for our understanding of the conceptual lexicon, but much more fine-grained investigations are required in order for this evidence to contribute to lexical research. Here we present a collection of advanced data-mining...
| Publicado en: | Brain & Language Vol. 117; no. 1; pp. 12 - 23 |
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| Autores principales: | , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Academic Press Inc.
Apr2011
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104871826&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104871826 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 0093934X 89A jtl: Brain & Language issn: 0093934X maglogo: N pubinfo: dt: Apr2011 vid: 117 iid: 1 pid: 735 pub: Academic Press Inc. place: Burlington, Massachusetts artinfo: ui: 104871826 2011012177 10.1016/j.bandl.2010.09.013 NLM21300399 104871826 ppf: 12 ppct: 11 formats: tig: atl: EEG decoding of semantic category reveals distributed representations for single concepts. aug: au: Murphy B Poesio M Bovolo F Bruzzone L Dalponte M Lakany H affil: Centre for Mind/Brain Sciences, University of Trento, Corso Bettini 31, 38068 Rovereto (TN), Italy. sug: subj: Algorithms Artificial Intelligence Brain Physiology Electroencephalography Semantics Signal Processing, Computer Assisted Adult Brain Mapping Methods Data Mining Methods Female Male Human Adult: 19-44 years Female Male ab: Achieving a clearer picture of categorial distinctions in the brain is essential for our understanding of the conceptual lexicon, but much more fine-grained investigations are required in order for this evidence to contribute to lexical research. Here we present a collection of advanced data-mining techniques that allows the category of individual concepts to be decoded from single trials of EEG data. Neural activity was recorded while participants silently named images of mammals and tools, and category could be detected in single trials with an accuracy well above chance, both when considering data from single participants, and when group-training across participants. By aggregating across all trials, single concepts could be correctly assigned to their category with an accuracy of 98%. The pattern of classifications made by the algorithm confirmed that the neural patterns identified are due to conceptual category, and not any of a series of processing-related confounds. The time intervals, frequency bands and scalp locations that proved most informative for prediction permit physiological interpretation: the widespread activation shortly after appearance of the stimulus (from 100ms) is consistent both with accounts of multi-pass processing, and distributed representations of categories. These methods provide an alternative to fMRI for fine-grained, large-scale investigations of the conceptual lexicon. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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