On some two-way barriers between models and mechanisms View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

1990-03

AUTHORS

William R. Uttal

ABSTRACT

A number of recent as well as classic ideas suggest that there are constraints and limits on the explanatory role that computational, mathematical, and neural net models of visual and other cognitive processes can play that have not been generally appreciated. These ideas come from mathematics, automata theory, chaos theory, thermodynamics, neurophysiology, and psychology. Collectively, these ideas suggest that the neural or cognitive mechanisms underlying many kinds of formal models are untestable and unverifiable. Models may be good descriptions of perceptual and other cognitive processes, but they cannot in principle be reductive explanations nor can we use them to predict behavior at the molar level from what we know of the neural primitives. This discussion is an effort to clarify the appropriate meanings of these models, not to dissuade workers from forging ahead in the modeling endeavor, which I acknowledge is progressing and is making possible our increasingly deep appreciation of plausible and interesting cognitive processes. More... »

PAGES

188-203

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.3758/bf03207086

DOI

http://dx.doi.org/10.3758/bf03207086

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1042973002

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/2201003


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/2008/1701", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Psychology", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/17", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Psychology and Cognitive Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Brain", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Brain Mapping", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Humans", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Models, Neurological", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Models, Psychological", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Models, Theoretical", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Visual Perception", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "name": [
            "Department of Psychology, Arizona State Umversity, 85287, Tempe, AZ"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Uttal", 
        "givenName": "William R.", 
        "id": "sg:person.01032051630.55", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01032051630.55"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1515/9781400874736", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000715897"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1073/pnas.59.2.368", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000883374"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0042-6989(84)90178-0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002580331"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0042-6989(84)90178-0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002580331"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf00288902", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1005506502", 
          "https://doi.org/10.1007/bf00288902"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1085/jgp.42.6.1241", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011662427"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.3758/bf03201006", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018253211", 
          "https://doi.org/10.3758/bf03201006"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0893-6080(88)90021-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1021531933"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0893-6080(88)90021-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1021531933"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1037/0096-1523.13.3.335", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024435130"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-1-4684-2484-3_7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030568170", 
          "https://doi.org/10.1007/978-1-4684-2484-3_7"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0025-5564(72)90075-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034024153"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0042-6989(78)90143-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1046307422"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0042-6989(78)90143-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1046307422"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.3758/bf03207868", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1048500452", 
          "https://doi.org/10.3758/bf03207868"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0749-596x(88)90074-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049965401"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/sapm1970492135", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050384338"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1037/0033-295x.91.3.375", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050999350"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/scientificamerican0579-140", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1056542088", 
          "https://doi.org/10.1038/scientificamerican0579-140"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1068/p010371", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1058161819"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1068/p010371", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1058161819"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tit.1954.1057468", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061645449"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tsmc.1983.6313075", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061793454"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1126/science.238.4827.632", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062534870"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1126/science.3045969", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062585342"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1515/9781400882618-006", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1086534355"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "1990-03", 
    "datePublishedReg": "1990-03-01", 
    "description": "A number of recent as well as classic ideas suggest that there are constraints and limits on the explanatory role that computational, mathematical, and neural net models of visual and other cognitive processes can play that have not been generally appreciated. These ideas come from mathematics, automata theory, chaos theory, thermodynamics, neurophysiology, and psychology. Collectively, these ideas suggest that the neural or cognitive mechanisms underlying many kinds of formal models are untestable and unverifiable. Models may be good descriptions of perceptual and other cognitive processes, but they cannot in principle be reductive explanations nor can we use them to predict behavior at the molar level from what we know of the neural primitives. This discussion is an effort to clarify the appropriate meanings of these models, not to dissuade workers from forging ahead in the modeling endeavor, which I acknowledge is progressing and is making possible our increasingly deep appreciation of plausible and interesting cognitive processes.", 
    "genre": "research_article", 
    "id": "sg:pub.10.3758/bf03207086", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": true, 
    "isPartOf": [
      {
        "id": "sg:journal.1041037", 
        "issn": [
          "1943-3921", 
          "1943-393X"
        ], 
        "name": "Attention, Perception, & Psychophysics", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "2", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "48"
      }
    ], 
    "name": "On some two-way barriers between models and mechanisms", 
    "pagination": "188-203", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "ccb1e8146ebdd5f93be83e6b172efc627d3a5af4122c253db2b3169b87741eff"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "2201003"
        ]
      }, 
      {
        "name": "nlm_unique_id", 
        "type": "PropertyValue", 
        "value": [
          "0200445"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.3758/bf03207086"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1042973002"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.3758/bf03207086", 
      "https://app.dimensions.ai/details/publication/pub.1042973002"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-10T13:14", 
    "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/v90/0000000001_0000000264/records_8659_00000507.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.3758/BF03207086"
  }
]
 

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.10.3758/bf03207086'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.3758/bf03207086'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.3758/bf03207086'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.3758/bf03207086'


 

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

167 TRIPLES      21 PREDICATES      58 URIs      28 LITERALS      16 BLANK NODES

Subject Predicate Object
1 sg:pub.10.3758/bf03207086 schema:about N2cad37ada38c4a55987ee007910581f6
2 N3a900879c0cd47d19219d16b0bbb91f0
3 N3f3850aa33fd4f7694e09ac04beeb54b
4 N523a956aeade48458db73dde508ae7a5
5 N5ae46953f3e74bfeb0d4fd6063c7f6c0
6 Nf5f800615f12467da228910574292a57
7 Nfb260bdd9be64b02933a9c9dab98fa31
8 anzsrc-for:17
9 anzsrc-for:1701
10 schema:author N35092dd66c40409aa1d002af9706938b
11 schema:citation sg:pub.10.1007/978-1-4684-2484-3_7
12 sg:pub.10.1007/bf00288902
13 sg:pub.10.1038/scientificamerican0579-140
14 sg:pub.10.3758/bf03201006
15 sg:pub.10.3758/bf03207868
16 https://doi.org/10.1002/sapm1970492135
17 https://doi.org/10.1016/0025-5564(72)90075-2
18 https://doi.org/10.1016/0042-6989(78)90143-8
19 https://doi.org/10.1016/0042-6989(84)90178-0
20 https://doi.org/10.1016/0749-596x(88)90074-5
21 https://doi.org/10.1016/0893-6080(88)90021-4
22 https://doi.org/10.1037/0033-295x.91.3.375
23 https://doi.org/10.1037/0096-1523.13.3.335
24 https://doi.org/10.1068/p010371
25 https://doi.org/10.1073/pnas.59.2.368
26 https://doi.org/10.1085/jgp.42.6.1241
27 https://doi.org/10.1109/tit.1954.1057468
28 https://doi.org/10.1109/tsmc.1983.6313075
29 https://doi.org/10.1126/science.238.4827.632
30 https://doi.org/10.1126/science.3045969
31 https://doi.org/10.1515/9781400874736
32 https://doi.org/10.1515/9781400882618-006
33 schema:datePublished 1990-03
34 schema:datePublishedReg 1990-03-01
35 schema:description A number of recent as well as classic ideas suggest that there are constraints and limits on the explanatory role that computational, mathematical, and neural net models of visual and other cognitive processes can play that have not been generally appreciated. These ideas come from mathematics, automata theory, chaos theory, thermodynamics, neurophysiology, and psychology. Collectively, these ideas suggest that the neural or cognitive mechanisms underlying many kinds of formal models are untestable and unverifiable. Models may be good descriptions of perceptual and other cognitive processes, but they cannot in principle be reductive explanations nor can we use them to predict behavior at the molar level from what we know of the neural primitives. This discussion is an effort to clarify the appropriate meanings of these models, not to dissuade workers from forging ahead in the modeling endeavor, which I acknowledge is progressing and is making possible our increasingly deep appreciation of plausible and interesting cognitive processes.
36 schema:genre research_article
37 schema:inLanguage en
38 schema:isAccessibleForFree true
39 schema:isPartOf N1a41f250bf83462ebe98c615ec04aa13
40 N7bd248b56b7841188ea1eafd49e0e1a9
41 sg:journal.1041037
42 schema:name On some two-way barriers between models and mechanisms
43 schema:pagination 188-203
44 schema:productId N148bbd701c184038a9a760add65e6afc
45 N299d4b158a6c432b926143177ebc9c06
46 N48d019f60eb547f391e647032707d575
47 N79ad3ec82a8142c990117f3f57abb285
48 N9f4b7c099df74bc78d9da866d12ebfb6
49 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042973002
50 https://doi.org/10.3758/bf03207086
51 schema:sdDatePublished 2019-04-10T13:14
52 schema:sdLicense https://scigraph.springernature.com/explorer/license/
53 schema:sdPublisher N624b608295ab44d4af5b1587ff47c973
54 schema:url http://link.springer.com/10.3758/BF03207086
55 sgo:license sg:explorer/license/
56 sgo:sdDataset articles
57 rdf:type schema:ScholarlyArticle
58 N148bbd701c184038a9a760add65e6afc schema:name dimensions_id
59 schema:value pub.1042973002
60 rdf:type schema:PropertyValue
61 N1a41f250bf83462ebe98c615ec04aa13 schema:issueNumber 2
62 rdf:type schema:PublicationIssue
63 N299d4b158a6c432b926143177ebc9c06 schema:name pubmed_id
64 schema:value 2201003
65 rdf:type schema:PropertyValue
66 N2cad37ada38c4a55987ee007910581f6 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
67 schema:name Models, Theoretical
68 rdf:type schema:DefinedTerm
69 N35092dd66c40409aa1d002af9706938b rdf:first sg:person.01032051630.55
70 rdf:rest rdf:nil
71 N3a900879c0cd47d19219d16b0bbb91f0 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
72 schema:name Brain
73 rdf:type schema:DefinedTerm
74 N3f3850aa33fd4f7694e09ac04beeb54b schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
75 schema:name Models, Neurological
76 rdf:type schema:DefinedTerm
77 N48d019f60eb547f391e647032707d575 schema:name doi
78 schema:value 10.3758/bf03207086
79 rdf:type schema:PropertyValue
80 N523a956aeade48458db73dde508ae7a5 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
81 schema:name Brain Mapping
82 rdf:type schema:DefinedTerm
83 N5ae46953f3e74bfeb0d4fd6063c7f6c0 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
84 schema:name Humans
85 rdf:type schema:DefinedTerm
86 N624b608295ab44d4af5b1587ff47c973 schema:name Springer Nature - SN SciGraph project
87 rdf:type schema:Organization
88 N79ad3ec82a8142c990117f3f57abb285 schema:name nlm_unique_id
89 schema:value 0200445
90 rdf:type schema:PropertyValue
91 N7bd248b56b7841188ea1eafd49e0e1a9 schema:volumeNumber 48
92 rdf:type schema:PublicationVolume
93 N9f4b7c099df74bc78d9da866d12ebfb6 schema:name readcube_id
94 schema:value ccb1e8146ebdd5f93be83e6b172efc627d3a5af4122c253db2b3169b87741eff
95 rdf:type schema:PropertyValue
96 Ne051f00804ac414a80e5b0c2e8084372 schema:name Department of Psychology, Arizona State Umversity, 85287, Tempe, AZ
97 rdf:type schema:Organization
98 Nf5f800615f12467da228910574292a57 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
99 schema:name Models, Psychological
100 rdf:type schema:DefinedTerm
101 Nfb260bdd9be64b02933a9c9dab98fa31 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
102 schema:name Visual Perception
103 rdf:type schema:DefinedTerm
104 anzsrc-for:17 schema:inDefinedTermSet anzsrc-for:
105 schema:name Psychology and Cognitive Sciences
106 rdf:type schema:DefinedTerm
107 anzsrc-for:1701 schema:inDefinedTermSet anzsrc-for:
108 schema:name Psychology
109 rdf:type schema:DefinedTerm
110 sg:journal.1041037 schema:issn 1943-3921
111 1943-393X
112 schema:name Attention, Perception, & Psychophysics
113 rdf:type schema:Periodical
114 sg:person.01032051630.55 schema:affiliation Ne051f00804ac414a80e5b0c2e8084372
115 schema:familyName Uttal
116 schema:givenName William R.
117 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01032051630.55
118 rdf:type schema:Person
119 sg:pub.10.1007/978-1-4684-2484-3_7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030568170
120 https://doi.org/10.1007/978-1-4684-2484-3_7
121 rdf:type schema:CreativeWork
122 sg:pub.10.1007/bf00288902 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005506502
123 https://doi.org/10.1007/bf00288902
124 rdf:type schema:CreativeWork
125 sg:pub.10.1038/scientificamerican0579-140 schema:sameAs https://app.dimensions.ai/details/publication/pub.1056542088
126 https://doi.org/10.1038/scientificamerican0579-140
127 rdf:type schema:CreativeWork
128 sg:pub.10.3758/bf03201006 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018253211
129 https://doi.org/10.3758/bf03201006
130 rdf:type schema:CreativeWork
131 sg:pub.10.3758/bf03207868 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048500452
132 https://doi.org/10.3758/bf03207868
133 rdf:type schema:CreativeWork
134 https://doi.org/10.1002/sapm1970492135 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050384338
135 rdf:type schema:CreativeWork
136 https://doi.org/10.1016/0025-5564(72)90075-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034024153
137 rdf:type schema:CreativeWork
138 https://doi.org/10.1016/0042-6989(78)90143-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046307422
139 rdf:type schema:CreativeWork
140 https://doi.org/10.1016/0042-6989(84)90178-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002580331
141 rdf:type schema:CreativeWork
142 https://doi.org/10.1016/0749-596x(88)90074-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049965401
143 rdf:type schema:CreativeWork
144 https://doi.org/10.1016/0893-6080(88)90021-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021531933
145 rdf:type schema:CreativeWork
146 https://doi.org/10.1037/0033-295x.91.3.375 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050999350
147 rdf:type schema:CreativeWork
148 https://doi.org/10.1037/0096-1523.13.3.335 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024435130
149 rdf:type schema:CreativeWork
150 https://doi.org/10.1068/p010371 schema:sameAs https://app.dimensions.ai/details/publication/pub.1058161819
151 rdf:type schema:CreativeWork
152 https://doi.org/10.1073/pnas.59.2.368 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000883374
153 rdf:type schema:CreativeWork
154 https://doi.org/10.1085/jgp.42.6.1241 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011662427
155 rdf:type schema:CreativeWork
156 https://doi.org/10.1109/tit.1954.1057468 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061645449
157 rdf:type schema:CreativeWork
158 https://doi.org/10.1109/tsmc.1983.6313075 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061793454
159 rdf:type schema:CreativeWork
160 https://doi.org/10.1126/science.238.4827.632 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062534870
161 rdf:type schema:CreativeWork
162 https://doi.org/10.1126/science.3045969 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062585342
163 rdf:type schema:CreativeWork
164 https://doi.org/10.1515/9781400874736 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000715897
165 rdf:type schema:CreativeWork
166 https://doi.org/10.1515/9781400882618-006 schema:sameAs https://app.dimensions.ai/details/publication/pub.1086534355
167 rdf:type schema:CreativeWork
 




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


...