Sequence detectors as a basis of grammar in the brain View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2003-05

AUTHORS

Friedemann Pulvermüller

ABSTRACT

Grammar processing may build upon serial-order mechanisms known from non-human species. A circuit similar to that underlying direction-sensitive movement detection in arthropods and vertebrates may become selective for sequences of words, thus yielding grammatical sequence detectors in the human brain. Sensitivity to the order of neuronal events arises from unequal connection strengths between two word specific neural units and a third element, the sequence detector. This mechanism, which critically depends on the dynamics of the neural units, can operate at the single neuron level and may be relevant at the level of neuronal ensembles as well. Due to the repeated occurrence of sequences, for example word strings, the sequence-sensitive elements become more firmly established and, by substitution of elements between strings, a process called auto-associative substitution learning (AASL) is triggered. AASL links the neuronal counterparts of the string elements involved in the substitution process to the sequence detector, thereby providing a brain basis of what can be described linguistically as the generalization of rules of grammar. A network of sequence detectors may constitute grammar circuits in the human cortex on which a separate set of mechanisms establishing temporary binding and recursion can operate. More... »

PAGES

87-103

References to SciGraph publications

  • 2001-01. Brain Size and Number of Neurons: An Exercise in Synthetic Neuroanatomy in JOURNAL OF COMPUTATIONAL NEUROSCIENCE
  • 1943-12. A logical calculus of the ideas immanent in nervous activity in BULLETIN OF MATHEMATICAL BIOLOGY
  • 1978. Cell Assemblies in the Cerebral Cortex in THEORETICAL APPROACHES TO COMPLEX SYSTEMS
  • 1969-06. Non-Holographic Associative Memory in NATURE
  • 1982. Neural Assemblies, An Alternative Approach to Artificial Intelligence in NONE
  • 1998. Cortex: Statistics and Geometry of Neuronal Connectivity in NONE
  • 1980-02. On associative memory in BIOLOGICAL CYBERNETICS
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s12064-003-0039-6

    DOI

    http://dx.doi.org/10.1007/s12064-003-0039-6

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1035650905


    Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
    Incoming Citations Browse incoming citations for this publication using opencitations.net

    JSON-LD is the canonical representation for SciGraph data.

    TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

    [
      {
        "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
        "about": [
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/06", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Biological Sciences", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "MRC Cognition and Brain Sciences Unit, 15 Chaucer Road, CB2 2EF, Cambridge, England", 
              "id": "http://www.grid.ac/institutes/grid.415036.5", 
              "name": [
                "MRC Cognition and Brain Sciences Unit, 15 Chaucer Road, CB2 2EF, Cambridge, England"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Pulverm\u00fcller", 
            "givenName": "Friedemann", 
            "id": "sg:person.01214737170.14", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01214737170.14"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "sg:pub.10.1038/222960a0", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1006268665", 
              "https://doi.org/10.1038/222960a0"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-642-93083-6_9", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1001516388", 
              "https://doi.org/10.1007/978-3-642-93083-6_9"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-662-03733-1", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1040027094", 
              "https://doi.org/10.1007/978-3-662-03733-1"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1023/a:1008920127052", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1017027965", 
              "https://doi.org/10.1023/a:1008920127052"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/bf02478259", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1028715170", 
              "https://doi.org/10.1007/bf02478259"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-642-81792-2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1052164481", 
              "https://doi.org/10.1007/978-3-642-81792-2"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/bf00337019", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1035422600", 
              "https://doi.org/10.1007/bf00337019"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2003-05", 
        "datePublishedReg": "2003-05-01", 
        "description": "Grammar processing may build upon serial-order mechanisms known from non-human species. A circuit similar to that underlying direction-sensitive movement detection in arthropods and vertebrates may become selective for sequences of words, thus yielding grammatical sequence detectors in the human brain. Sensitivity to the order of neuronal events arises from unequal connection strengths between two word specific neural units and a third element, the sequence detector. This mechanism, which critically depends on the dynamics of the neural units, can operate at the single neuron level and may be relevant at the level of neuronal ensembles as well. Due to the repeated occurrence of sequences, for example word strings, the sequence-sensitive elements become more firmly established and, by substitution of elements between strings, a process called auto-associative substitution learning (AASL) is triggered. AASL links the neuronal counterparts of the string elements involved in the substitution process to the sequence detector, thereby providing a brain basis of what can be described linguistically as the generalization of rules of grammar. A network of sequence detectors may constitute grammar circuits in the human cortex on which a separate set of mechanisms establishing temporary binding and recursion can operate.", 
        "genre": "article", 
        "id": "sg:pub.10.1007/s12064-003-0039-6", 
        "isAccessibleForFree": false, 
        "isPartOf": [
          {
            "id": "sg:journal.1019628", 
            "issn": [
              "1431-7613", 
              "1611-7530"
            ], 
            "name": "Theory in Biosciences", 
            "publisher": "Springer Nature", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "1", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "122"
          }
        ], 
        "keywords": [
          "serial-order mechanism", 
          "sequence of words", 
          "neural units", 
          "brain basis", 
          "non-human species", 
          "grammar processing", 
          "word strings", 
          "single neuron level", 
          "temporary binding", 
          "generalization of rules", 
          "human brain", 
          "human cortex", 
          "neuronal ensembles", 
          "neuron level", 
          "connection strength", 
          "neuronal events", 
          "grammar", 
          "words", 
          "occurrence of sequences", 
          "neuronal counterparts", 
          "brain", 
          "substitution of elements", 
          "learning", 
          "cortex", 
          "processing", 
          "movement detection", 
          "separate set", 
          "string", 
          "process", 
          "string elements", 
          "generalization", 
          "levels", 
          "third element", 
          "mechanism", 
          "basis", 
          "events", 
          "sequence detector", 
          "network", 
          "elements", 
          "counterparts", 
          "circuit", 
          "set", 
          "rules", 
          "sensitivity", 
          "units", 
          "sequence", 
          "strength", 
          "order", 
          "dynamics", 
          "substitution process", 
          "ensemble", 
          "detection", 
          "occurrence", 
          "recursion", 
          "detector", 
          "vertebrates", 
          "substitution", 
          "binding", 
          "species", 
          "arthropods"
        ], 
        "name": "Sequence detectors as a basis of grammar in the brain", 
        "pagination": "87-103", 
        "productId": [
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1035650905"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/s12064-003-0039-6"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1007/s12064-003-0039-6", 
          "https://app.dimensions.ai/details/publication/pub.1035650905"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2022-11-24T20:50", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-springernature-scigraph/baseset/20221124/entities/gbq_results/article/article_366.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://doi.org/10.1007/s12064-003-0039-6"
      }
    ]
     

    Download the RDF metadata as:  json-ld nt turtle xml License info

    HOW TO GET THIS DATA PROGRAMMATICALLY:

    JSON-LD is a popular format for linked data which is fully compatible with JSON.

    curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s12064-003-0039-6'

    N-Triples is a line-based linked data format ideal for batch operations.

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s12064-003-0039-6'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s12064-003-0039-6'

    RDF/XML is a standard XML format for linked data.

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s12064-003-0039-6'


     

    This table displays all metadata directly associated to this object as RDF triples.

    141 TRIPLES      21 PREDICATES      91 URIs      77 LITERALS      6 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/s12064-003-0039-6 schema:about anzsrc-for:06
    2 schema:author N8709eed00adc4cdf9d52cf13575c3f60
    3 schema:citation sg:pub.10.1007/978-3-642-81792-2
    4 sg:pub.10.1007/978-3-642-93083-6_9
    5 sg:pub.10.1007/978-3-662-03733-1
    6 sg:pub.10.1007/bf00337019
    7 sg:pub.10.1007/bf02478259
    8 sg:pub.10.1023/a:1008920127052
    9 sg:pub.10.1038/222960a0
    10 schema:datePublished 2003-05
    11 schema:datePublishedReg 2003-05-01
    12 schema:description Grammar processing may build upon serial-order mechanisms known from non-human species. A circuit similar to that underlying direction-sensitive movement detection in arthropods and vertebrates may become selective for sequences of words, thus yielding grammatical sequence detectors in the human brain. Sensitivity to the order of neuronal events arises from unequal connection strengths between two word specific neural units and a third element, the sequence detector. This mechanism, which critically depends on the dynamics of the neural units, can operate at the single neuron level and may be relevant at the level of neuronal ensembles as well. Due to the repeated occurrence of sequences, for example word strings, the sequence-sensitive elements become more firmly established and, by substitution of elements between strings, a process called auto-associative substitution learning (AASL) is triggered. AASL links the neuronal counterparts of the string elements involved in the substitution process to the sequence detector, thereby providing a brain basis of what can be described linguistically as the generalization of rules of grammar. A network of sequence detectors may constitute grammar circuits in the human cortex on which a separate set of mechanisms establishing temporary binding and recursion can operate.
    13 schema:genre article
    14 schema:isAccessibleForFree false
    15 schema:isPartOf N72107a40216940ffbc725d4f78f2aaeb
    16 N9f1350d79c784d9b963ad33bde2bbd33
    17 sg:journal.1019628
    18 schema:keywords arthropods
    19 basis
    20 binding
    21 brain
    22 brain basis
    23 circuit
    24 connection strength
    25 cortex
    26 counterparts
    27 detection
    28 detector
    29 dynamics
    30 elements
    31 ensemble
    32 events
    33 generalization
    34 generalization of rules
    35 grammar
    36 grammar processing
    37 human brain
    38 human cortex
    39 learning
    40 levels
    41 mechanism
    42 movement detection
    43 network
    44 neural units
    45 neuron level
    46 neuronal counterparts
    47 neuronal ensembles
    48 neuronal events
    49 non-human species
    50 occurrence
    51 occurrence of sequences
    52 order
    53 process
    54 processing
    55 recursion
    56 rules
    57 sensitivity
    58 separate set
    59 sequence
    60 sequence detector
    61 sequence of words
    62 serial-order mechanism
    63 set
    64 single neuron level
    65 species
    66 strength
    67 string
    68 string elements
    69 substitution
    70 substitution of elements
    71 substitution process
    72 temporary binding
    73 third element
    74 units
    75 vertebrates
    76 word strings
    77 words
    78 schema:name Sequence detectors as a basis of grammar in the brain
    79 schema:pagination 87-103
    80 schema:productId N132c1e9559f1438e82586eaa022ae68a
    81 Nf8d169c509264e409ef1fd8b079f7818
    82 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035650905
    83 https://doi.org/10.1007/s12064-003-0039-6
    84 schema:sdDatePublished 2022-11-24T20:50
    85 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    86 schema:sdPublisher N32d6177bd8c54e29902e703ee6ef9036
    87 schema:url https://doi.org/10.1007/s12064-003-0039-6
    88 sgo:license sg:explorer/license/
    89 sgo:sdDataset articles
    90 rdf:type schema:ScholarlyArticle
    91 N132c1e9559f1438e82586eaa022ae68a schema:name doi
    92 schema:value 10.1007/s12064-003-0039-6
    93 rdf:type schema:PropertyValue
    94 N32d6177bd8c54e29902e703ee6ef9036 schema:name Springer Nature - SN SciGraph project
    95 rdf:type schema:Organization
    96 N72107a40216940ffbc725d4f78f2aaeb schema:issueNumber 1
    97 rdf:type schema:PublicationIssue
    98 N8709eed00adc4cdf9d52cf13575c3f60 rdf:first sg:person.01214737170.14
    99 rdf:rest rdf:nil
    100 N9f1350d79c784d9b963ad33bde2bbd33 schema:volumeNumber 122
    101 rdf:type schema:PublicationVolume
    102 Nf8d169c509264e409ef1fd8b079f7818 schema:name dimensions_id
    103 schema:value pub.1035650905
    104 rdf:type schema:PropertyValue
    105 anzsrc-for:06 schema:inDefinedTermSet anzsrc-for:
    106 schema:name Biological Sciences
    107 rdf:type schema:DefinedTerm
    108 sg:journal.1019628 schema:issn 1431-7613
    109 1611-7530
    110 schema:name Theory in Biosciences
    111 schema:publisher Springer Nature
    112 rdf:type schema:Periodical
    113 sg:person.01214737170.14 schema:affiliation grid-institutes:grid.415036.5
    114 schema:familyName Pulvermüller
    115 schema:givenName Friedemann
    116 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01214737170.14
    117 rdf:type schema:Person
    118 sg:pub.10.1007/978-3-642-81792-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052164481
    119 https://doi.org/10.1007/978-3-642-81792-2
    120 rdf:type schema:CreativeWork
    121 sg:pub.10.1007/978-3-642-93083-6_9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001516388
    122 https://doi.org/10.1007/978-3-642-93083-6_9
    123 rdf:type schema:CreativeWork
    124 sg:pub.10.1007/978-3-662-03733-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040027094
    125 https://doi.org/10.1007/978-3-662-03733-1
    126 rdf:type schema:CreativeWork
    127 sg:pub.10.1007/bf00337019 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035422600
    128 https://doi.org/10.1007/bf00337019
    129 rdf:type schema:CreativeWork
    130 sg:pub.10.1007/bf02478259 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028715170
    131 https://doi.org/10.1007/bf02478259
    132 rdf:type schema:CreativeWork
    133 sg:pub.10.1023/a:1008920127052 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017027965
    134 https://doi.org/10.1023/a:1008920127052
    135 rdf:type schema:CreativeWork
    136 sg:pub.10.1038/222960a0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006268665
    137 https://doi.org/10.1038/222960a0
    138 rdf:type schema:CreativeWork
    139 grid-institutes:grid.415036.5 schema:alternateName MRC Cognition and Brain Sciences Unit, 15 Chaucer Road, CB2 2EF, Cambridge, England
    140 schema:name MRC Cognition and Brain Sciences Unit, 15 Chaucer Road, CB2 2EF, Cambridge, England
    141 rdf:type schema:Organization
     




    Preview window. Press ESC to close (or click here)


    ...