The mechanism of mental scanning in foveal vision View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

1978-09

AUTHORS

T. Inui, M. Kawato, R. Suzuki

ABSTRACT

In order to perceive a visual pattern which includes several elemental pictures, the perceiver must allot his cognitive resources to suitably selected parts of the pattern and scan them in sequence. Even when the visual field is small and eye-movement is not required, such scanning is found. We called it ‘mental scanning’ and performed psychological experiments to investigate the mechanism. The tasks were to discern whether the elemental pictures in a pattern are all the same (SP) or not (DP). The per cents correct of the task were measured for various exposure durations. We defined the threshold as the exposure duration at which 75% correct answers were obtained. Our main findings are as follows. The threshold for SP is proportional to the number of picture elements, while the threshold for DP is constant. It appears that two modes of mental scanning exist. One is serial processing for SP, and the other is parallel processing for DP. We proposed a two-layered neural network model having the following characteristics. 1) Information is transmitted as two types of signals through two separate channels; one is the transient signals to the Y layer and the other is the sustained signals slowly conducted to the X layer. 2) Interactions among neurons in the Y layer are lateral inhibitory, while those in the X layer are self-excitatory and lateralinhibitory. 3) Every neuron in the Y layer sends inhibitory signals to every neuron in the X layer except one with the same receptive field. Under these conditions, the dynamics of neurons in the X layer is represented by a set of certain equations. From phase plane analysis and numerical integration, the model appears to have an ability to account for various experimental results. More... »

PAGES

147-155

References to SciGraph publications

  • 1971-01. Dynamics of “neuron ring” in KYBERNETIK
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/bf00337143

    DOI

    http://dx.doi.org/10.1007/bf00337143

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1029473117

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/708797


    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/2008/1109", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Neurosciences", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/11", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Medical and Health Sciences", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Darkness", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Humans", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Light", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Mathematics", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Models, Biological", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Time Factors", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Vision, Ocular", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Visual Perception", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "Osaka University", 
              "id": "https://www.grid.ac/institutes/grid.136593.b", 
              "name": [
                "Faculty of Human Sciences, Osaka University, Japan"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Inui", 
            "givenName": "T.", 
            "id": "sg:person.016502475521.02", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016502475521.02"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Osaka University", 
              "id": "https://www.grid.ac/institutes/grid.136593.b", 
              "name": [
                "Faculty of Engineering Science, Osaka University, Japan"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Kawato", 
            "givenName": "M.", 
            "id": "sg:person.01230705277.42", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01230705277.42"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Osaka University", 
              "id": "https://www.grid.ac/institutes/grid.136593.b", 
              "name": [
                "Faculty of Engineering Science, Osaka University, Japan"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Suzuki", 
            "givenName": "R.", 
            "id": "sg:person.013656713222.33", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013656713222.33"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "https://doi.org/10.1016/s0006-3495(72)86068-5", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1008475623"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1037/0033-295x.83.1.1", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1014488660"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/0006-8993(73)90171-6", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1018278176"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/0006-8993(73)90171-6", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1018278176"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1113/jphysiol.1975.sp011028", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1039421650"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1037/h0093759", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1039505086"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1113/jphysiol.1973.sp010239", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1049722917"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/bf00270832", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1049987421", 
              "https://doi.org/10.1007/bf00270832"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/bf00270832", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1049987421", 
              "https://doi.org/10.1007/bf00270832"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/bf00270832", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1049987421", 
              "https://doi.org/10.1007/bf00270832"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1152/jn.1975.38.3.475", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1074791180"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1152/jn.1972.35.6.915", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1080489376"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1152/jn.1973.36.3.409", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1080535762"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1152/jn.1976.39.2.354", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1082677130"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1152/jn.1977.40.2.189", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1082814048"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "1978-09", 
        "datePublishedReg": "1978-09-01", 
        "description": "In order to perceive a visual pattern which includes several elemental pictures, the perceiver must allot his cognitive resources to suitably selected parts of the pattern and scan them in sequence. Even when the visual field is small and eye-movement is not required, such scanning is found. We called it \u2018mental scanning\u2019 and performed psychological experiments to investigate the mechanism. The tasks were to discern whether the elemental pictures in a pattern are all the same (SP) or not (DP). The per cents correct of the task were measured for various exposure durations. We defined the threshold as the exposure duration at which 75% correct answers were obtained. Our main findings are as follows. The threshold for SP is proportional to the number of picture elements, while the threshold for DP is constant. It appears that two modes of mental scanning exist. One is serial processing for SP, and the other is parallel processing for DP. We proposed a two-layered neural network model having the following characteristics. 1) Information is transmitted as two types of signals through two separate channels; one is the transient signals to the Y layer and the other is the sustained signals slowly conducted to the X layer. 2) Interactions among neurons in the Y layer are lateral inhibitory, while those in the X layer are self-excitatory and lateralinhibitory. 3) Every neuron in the Y layer sends inhibitory signals to every neuron in the X layer except one with the same receptive field. Under these conditions, the dynamics of neurons in the X layer is represented by a set of certain equations. From phase plane analysis and numerical integration, the model appears to have an ability to account for various experimental results.", 
        "genre": "research_article", 
        "id": "sg:pub.10.1007/bf00337143", 
        "inLanguage": [
          "en"
        ], 
        "isAccessibleForFree": false, 
        "isPartOf": [
          {
            "id": "sg:journal.1081741", 
            "issn": [
              "0340-1200", 
              "1432-0770"
            ], 
            "name": "Biological Cybernetics", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "3", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "30"
          }
        ], 
        "name": "The mechanism of mental scanning in foveal vision", 
        "pagination": "147-155", 
        "productId": [
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/bf00337143"
            ]
          }, 
          {
            "name": "readcube_id", 
            "type": "PropertyValue", 
            "value": [
              "8c67cc3f0d1425bd6bfebd481fe1a17a3b3f14afc3b0e4f548a8af9916e8f186"
            ]
          }, 
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1029473117"
            ]
          }, 
          {
            "name": "nlm_unique_id", 
            "type": "PropertyValue", 
            "value": [
              "7502533"
            ]
          }, 
          {
            "name": "pubmed_id", 
            "type": "PropertyValue", 
            "value": [
              "708797"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1007/bf00337143", 
          "https://app.dimensions.ai/details/publication/pub.1029473117"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2019-04-15T08:48", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000374_0000000374/records_119714_00000000.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "http://link.springer.com/10.1007/BF00337143"
      }
    ]
     

    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/bf00337143'

    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/bf00337143'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/bf00337143'

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

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/bf00337143'


     

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

    153 TRIPLES      21 PREDICATES      49 URIs      29 LITERALS      17 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/bf00337143 schema:about N3f540525941d46dfbb7ca737d3f83a0a
    2 N494d3b6144d04d9499ebec91aa538477
    3 N56bfb500451b4e089da7e1cb583b08a2
    4 N697ff6bb9c0545209e863c6b3d7d0426
    5 N75ca7923caeb437f8918050e283f9ed2
    6 N932854c28e014ad5bd3a5f3d2330a7cc
    7 N9bcbbbd695ef47408a535c5011444812
    8 Nff683925080a43e18b54dc0d52ac14c7
    9 anzsrc-for:11
    10 anzsrc-for:1109
    11 schema:author Nc0f0962c6e4c4927b868aa1951ad197f
    12 schema:citation sg:pub.10.1007/bf00270832
    13 https://doi.org/10.1016/0006-8993(73)90171-6
    14 https://doi.org/10.1016/s0006-3495(72)86068-5
    15 https://doi.org/10.1037/0033-295x.83.1.1
    16 https://doi.org/10.1037/h0093759
    17 https://doi.org/10.1113/jphysiol.1973.sp010239
    18 https://doi.org/10.1113/jphysiol.1975.sp011028
    19 https://doi.org/10.1152/jn.1972.35.6.915
    20 https://doi.org/10.1152/jn.1973.36.3.409
    21 https://doi.org/10.1152/jn.1975.38.3.475
    22 https://doi.org/10.1152/jn.1976.39.2.354
    23 https://doi.org/10.1152/jn.1977.40.2.189
    24 schema:datePublished 1978-09
    25 schema:datePublishedReg 1978-09-01
    26 schema:description In order to perceive a visual pattern which includes several elemental pictures, the perceiver must allot his cognitive resources to suitably selected parts of the pattern and scan them in sequence. Even when the visual field is small and eye-movement is not required, such scanning is found. We called it ‘mental scanning’ and performed psychological experiments to investigate the mechanism. The tasks were to discern whether the elemental pictures in a pattern are all the same (SP) or not (DP). The per cents correct of the task were measured for various exposure durations. We defined the threshold as the exposure duration at which 75% correct answers were obtained. Our main findings are as follows. The threshold for SP is proportional to the number of picture elements, while the threshold for DP is constant. It appears that two modes of mental scanning exist. One is serial processing for SP, and the other is parallel processing for DP. We proposed a two-layered neural network model having the following characteristics. 1) Information is transmitted as two types of signals through two separate channels; one is the transient signals to the Y layer and the other is the sustained signals slowly conducted to the X layer. 2) Interactions among neurons in the Y layer are lateral inhibitory, while those in the X layer are self-excitatory and lateralinhibitory. 3) Every neuron in the Y layer sends inhibitory signals to every neuron in the X layer except one with the same receptive field. Under these conditions, the dynamics of neurons in the X layer is represented by a set of certain equations. From phase plane analysis and numerical integration, the model appears to have an ability to account for various experimental results.
    27 schema:genre research_article
    28 schema:inLanguage en
    29 schema:isAccessibleForFree false
    30 schema:isPartOf N41c9b35cd8bb4347982ede46f8d291c2
    31 Ne49ded85a9484681ab6fe36c01d2426a
    32 sg:journal.1081741
    33 schema:name The mechanism of mental scanning in foveal vision
    34 schema:pagination 147-155
    35 schema:productId N44086a62655546d6a2dfd849d14e27e0
    36 N6ab8b7659d3645898c4c6af6bdebb54c
    37 Nab797bb17508484799dae34895a94c7c
    38 Nc0da6c7b421a4f7aafee9053a8e6333b
    39 Nf6a75b1810684809a92e42577e8b37d9
    40 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029473117
    41 https://doi.org/10.1007/bf00337143
    42 schema:sdDatePublished 2019-04-15T08:48
    43 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    44 schema:sdPublisher N472d56c2de0b4e5f85901790f46a2454
    45 schema:url http://link.springer.com/10.1007/BF00337143
    46 sgo:license sg:explorer/license/
    47 sgo:sdDataset articles
    48 rdf:type schema:ScholarlyArticle
    49 N3559cfd808ce4e028436809502a92962 rdf:first sg:person.013656713222.33
    50 rdf:rest rdf:nil
    51 N3f540525941d46dfbb7ca737d3f83a0a schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    52 schema:name Light
    53 rdf:type schema:DefinedTerm
    54 N41c9b35cd8bb4347982ede46f8d291c2 schema:issueNumber 3
    55 rdf:type schema:PublicationIssue
    56 N44086a62655546d6a2dfd849d14e27e0 schema:name dimensions_id
    57 schema:value pub.1029473117
    58 rdf:type schema:PropertyValue
    59 N472d56c2de0b4e5f85901790f46a2454 schema:name Springer Nature - SN SciGraph project
    60 rdf:type schema:Organization
    61 N494d3b6144d04d9499ebec91aa538477 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    62 schema:name Models, Biological
    63 rdf:type schema:DefinedTerm
    64 N56bfb500451b4e089da7e1cb583b08a2 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    65 schema:name Time Factors
    66 rdf:type schema:DefinedTerm
    67 N697ff6bb9c0545209e863c6b3d7d0426 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    68 schema:name Darkness
    69 rdf:type schema:DefinedTerm
    70 N6ab8b7659d3645898c4c6af6bdebb54c schema:name nlm_unique_id
    71 schema:value 7502533
    72 rdf:type schema:PropertyValue
    73 N75ca7923caeb437f8918050e283f9ed2 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    74 schema:name Visual Perception
    75 rdf:type schema:DefinedTerm
    76 N932854c28e014ad5bd3a5f3d2330a7cc schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    77 schema:name Humans
    78 rdf:type schema:DefinedTerm
    79 N9bcbbbd695ef47408a535c5011444812 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    80 schema:name Mathematics
    81 rdf:type schema:DefinedTerm
    82 Nab797bb17508484799dae34895a94c7c schema:name doi
    83 schema:value 10.1007/bf00337143
    84 rdf:type schema:PropertyValue
    85 Nc0da6c7b421a4f7aafee9053a8e6333b schema:name readcube_id
    86 schema:value 8c67cc3f0d1425bd6bfebd481fe1a17a3b3f14afc3b0e4f548a8af9916e8f186
    87 rdf:type schema:PropertyValue
    88 Nc0f0962c6e4c4927b868aa1951ad197f rdf:first sg:person.016502475521.02
    89 rdf:rest Ncfd6b904c9dd4e76b7bf331fe6d4c778
    90 Ncfd6b904c9dd4e76b7bf331fe6d4c778 rdf:first sg:person.01230705277.42
    91 rdf:rest N3559cfd808ce4e028436809502a92962
    92 Ne49ded85a9484681ab6fe36c01d2426a schema:volumeNumber 30
    93 rdf:type schema:PublicationVolume
    94 Nf6a75b1810684809a92e42577e8b37d9 schema:name pubmed_id
    95 schema:value 708797
    96 rdf:type schema:PropertyValue
    97 Nff683925080a43e18b54dc0d52ac14c7 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    98 schema:name Vision, Ocular
    99 rdf:type schema:DefinedTerm
    100 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
    101 schema:name Medical and Health Sciences
    102 rdf:type schema:DefinedTerm
    103 anzsrc-for:1109 schema:inDefinedTermSet anzsrc-for:
    104 schema:name Neurosciences
    105 rdf:type schema:DefinedTerm
    106 sg:journal.1081741 schema:issn 0340-1200
    107 1432-0770
    108 schema:name Biological Cybernetics
    109 rdf:type schema:Periodical
    110 sg:person.01230705277.42 schema:affiliation https://www.grid.ac/institutes/grid.136593.b
    111 schema:familyName Kawato
    112 schema:givenName M.
    113 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01230705277.42
    114 rdf:type schema:Person
    115 sg:person.013656713222.33 schema:affiliation https://www.grid.ac/institutes/grid.136593.b
    116 schema:familyName Suzuki
    117 schema:givenName R.
    118 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013656713222.33
    119 rdf:type schema:Person
    120 sg:person.016502475521.02 schema:affiliation https://www.grid.ac/institutes/grid.136593.b
    121 schema:familyName Inui
    122 schema:givenName T.
    123 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016502475521.02
    124 rdf:type schema:Person
    125 sg:pub.10.1007/bf00270832 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049987421
    126 https://doi.org/10.1007/bf00270832
    127 rdf:type schema:CreativeWork
    128 https://doi.org/10.1016/0006-8993(73)90171-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018278176
    129 rdf:type schema:CreativeWork
    130 https://doi.org/10.1016/s0006-3495(72)86068-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008475623
    131 rdf:type schema:CreativeWork
    132 https://doi.org/10.1037/0033-295x.83.1.1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014488660
    133 rdf:type schema:CreativeWork
    134 https://doi.org/10.1037/h0093759 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039505086
    135 rdf:type schema:CreativeWork
    136 https://doi.org/10.1113/jphysiol.1973.sp010239 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049722917
    137 rdf:type schema:CreativeWork
    138 https://doi.org/10.1113/jphysiol.1975.sp011028 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039421650
    139 rdf:type schema:CreativeWork
    140 https://doi.org/10.1152/jn.1972.35.6.915 schema:sameAs https://app.dimensions.ai/details/publication/pub.1080489376
    141 rdf:type schema:CreativeWork
    142 https://doi.org/10.1152/jn.1973.36.3.409 schema:sameAs https://app.dimensions.ai/details/publication/pub.1080535762
    143 rdf:type schema:CreativeWork
    144 https://doi.org/10.1152/jn.1975.38.3.475 schema:sameAs https://app.dimensions.ai/details/publication/pub.1074791180
    145 rdf:type schema:CreativeWork
    146 https://doi.org/10.1152/jn.1976.39.2.354 schema:sameAs https://app.dimensions.ai/details/publication/pub.1082677130
    147 rdf:type schema:CreativeWork
    148 https://doi.org/10.1152/jn.1977.40.2.189 schema:sameAs https://app.dimensions.ai/details/publication/pub.1082814048
    149 rdf:type schema:CreativeWork
    150 https://www.grid.ac/institutes/grid.136593.b schema:alternateName Osaka University
    151 schema:name Faculty of Engineering Science, Osaka University, Japan
    152 Faculty of Human Sciences, Osaka University, Japan
    153 rdf:type schema:Organization
     




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


    ...