Retrospective model-based inference guides model-free credit assignment View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2019-12

AUTHORS

Rani Moran, Mehdi Keramati, Peter Dayan, Raymond J. Dolan

ABSTRACT

An extensive reinforcement learning literature shows that organisms assign credit efficiently, even under conditions of state uncertainty. However, little is known about credit-assignment when state uncertainty is subsequently resolved. Here, we address this problem within the framework of an interaction between model-free (MF) and model-based (MB) control systems. We present and support experimentally a theory of MB retrospective-inference. Within this framework, a MB system resolves uncertainty that prevailed when actions were taken thus guiding an MF credit-assignment. Using a task in which there was initial uncertainty about the lotteries that were chosen, we found that when participants' momentary uncertainty about which lottery had generated an outcome was resolved by provision of subsequent information, participants preferentially assigned credit within a MF system to the lottery they retrospectively inferred was responsible for this outcome. These findings extend our knowledge about the range of MB functions and the scope of system interactions. More... »

PAGES

750

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/s41467-019-08662-8

DOI

http://dx.doi.org/10.1038/s41467-019-08662-8

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1112134541

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30765718


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/2008/1401", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Economic Theory", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/14", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Economics", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Wellcome Centre for Human Neuroimaging", 
          "id": "https://www.grid.ac/institutes/grid.450002.3", 
          "name": [
            "Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, 10-12 Russell Square, WC1B 5EH, London, UK", 
            "Wellcome Centre for Human Neuroimaging, University College London, WC1N 3BG, London, United Kingdom"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Moran", 
        "givenName": "Rani", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Wellcome Centre for Human Neuroimaging", 
          "id": "https://www.grid.ac/institutes/grid.450002.3", 
          "name": [
            "Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, 10-12 Russell Square, WC1B 5EH, London, UK", 
            "Wellcome Centre for Human Neuroimaging, University College London, WC1N 3BG, London, United Kingdom", 
            "Department of Psychology, City, University of London, EC1R 0JD, London, UK"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Keramati", 
        "givenName": "Mehdi", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Max Planck Institute for Biological Cybernetics", 
          "id": "https://www.grid.ac/institutes/grid.419501.8", 
          "name": [
            "Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, 10-12 Russell Square, WC1B 5EH, London, UK", 
            "Gatsby Computational Neuroscience Unit, University College London, W1T 4JG, London, UK", 
            "Max Planck Institute for Biological Cybernetics, Max Plank-Ring 8, 72076, Tuebingen, Germany"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Dayan", 
        "givenName": "Peter", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Wellcome Centre for Human Neuroimaging", 
          "id": "https://www.grid.ac/institutes/grid.450002.3", 
          "name": [
            "Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, 10-12 Russell Square, WC1B 5EH, London, UK", 
            "Wellcome Centre for Human Neuroimaging, University College London, WC1N 3BG, London, United Kingdom"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Dolan", 
        "givenName": "Raymond J.", 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1126/science.275.5306.1593", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1001523695"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.neuron.2013.08.009", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1001823734"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.neuron.2010.04.016", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004293988"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.7554/elife.13747", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1005111759"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1523/jneurosci.0564-07.2007", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009136726"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1073/pnas.1506367112", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010959713"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/srep13874", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1012056064", 
          "https://doi.org/10.1038/srep13874"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.cogpsych.2015.01.005", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1012135509"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3389/fncom.2010.00146", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1014541634"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0893-6080(99)00046-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1016131651"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1037/a0030844", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017887428"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.neuron.2011.02.027", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020297092"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1152/jn.00024.2007", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020471944"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1460-9568.2004.03095.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025200754"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.3758/lb.37.4.289", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025371165", 
          "https://doi.org/10.3758/lb.37.4.289"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf00114726", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025781975", 
          "https://doi.org/10.1007/bf00114726"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1371/journal.pcbi.1002055", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028648676"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.neuron.2013.09.007", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032043566"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/cercor/13.4.400", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1033197334"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1098/rspb.2010.1607", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034425657"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nn1560", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035060211", 
          "https://doi.org/10.1038/nn1560"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nn1560", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035060211", 
          "https://doi.org/10.1038/nn1560"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1460-9568.2005.04218.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035725450"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1460-9568.2005.04218.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035725450"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0893-6080(02)00048-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040074190"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/npp.2009.131", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041054380", 
          "https://doi.org/10.1038/npp.2009.131"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/npp.2009.131", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041054380", 
          "https://doi.org/10.1038/npp.2009.131"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.neuron.2013.11.028", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041731794"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nn.2902", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044175804", 
          "https://doi.org/10.1038/nn.2902"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nn.3981", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047010599", 
          "https://doi.org/10.1038/nn.3981"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/14640748108400816", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047483454"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/14640748108400816", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047483454"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1162/neco.2006.18.7.1637", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049023728"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1073/pnas.1609094113", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049316642"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jmp.2003.11.004", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050436877"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.neuron.2008.10.043", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050504002"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/b978-1-55860-141-3.50030-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051599773"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1371/journal.pcbi.1005020", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052095887"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tnn.1998.712192", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061716400"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.cub.2017.02.026", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1084069013"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nn.4520", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1084129393", 
          "https://doi.org/10.1038/nn.4520"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1073/pnas.1712479114", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1092669609"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/s41467-018-04397-0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1103889264", 
          "https://doi.org/10.1038/s41467-018-04397-0"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.2517-6161.1995.tb02031.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1110458929"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.2517-6161.1995.tb02031.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1110458929"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2019-12", 
    "datePublishedReg": "2019-12-01", 
    "description": "An extensive reinforcement learning literature shows that organisms assign credit efficiently, even under conditions of state uncertainty. However, little is known about credit-assignment when state uncertainty is subsequently resolved. Here, we address this problem within the framework of an interaction between model-free (MF) and model-based (MB) control systems. We present and support experimentally a theory of MB retrospective-inference. Within this framework, a MB system resolves uncertainty that prevailed when actions were taken thus guiding an MF credit-assignment. Using a task in which there was initial uncertainty about the lotteries that were chosen, we found that when participants' momentary uncertainty about which lottery had generated an outcome was resolved by provision of subsequent information, participants preferentially assigned credit within a MF system to the lottery they retrospectively inferred was responsible for this outcome. These findings extend our knowledge about the range of MB functions and the scope of system interactions.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1038/s41467-019-08662-8", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": true, 
    "isFundedItemOf": [
      {
        "id": "sg:grant.3639086", 
        "type": "MonetaryGrant"
      }
    ], 
    "isPartOf": [
      {
        "id": "sg:journal.1043282", 
        "issn": [
          "2041-1723"
        ], 
        "name": "Nature Communications", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "10"
      }
    ], 
    "name": "Retrospective model-based inference guides model-free credit assignment", 
    "pagination": "750", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "4ef128a3c6dc3d058103fbf27edbf1543e436e7c1716aaf94fa209fd08302dcb"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "30765718"
        ]
      }, 
      {
        "name": "nlm_unique_id", 
        "type": "PropertyValue", 
        "value": [
          "101528555"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1038/s41467-019-08662-8"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1112134541"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1038/s41467-019-08662-8", 
      "https://app.dimensions.ai/details/publication/pub.1112134541"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T12:53", 
    "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/v90/0000000364_0000000364/records_72853_00000001.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://www.nature.com/articles/s41467-019-08662-8"
  }
]
 

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.10.1038/s41467-019-08662-8'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1038/s41467-019-08662-8'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1038/s41467-019-08662-8'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1038/s41467-019-08662-8'


 

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

223 TRIPLES      21 PREDICATES      69 URIs      21 LITERALS      9 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1038/s41467-019-08662-8 schema:about anzsrc-for:14
2 anzsrc-for:1401
3 schema:author N75f6017a74044a4db000c788ac461d1f
4 schema:citation sg:pub.10.1007/bf00114726
5 sg:pub.10.1038/nn.2902
6 sg:pub.10.1038/nn.3981
7 sg:pub.10.1038/nn.4520
8 sg:pub.10.1038/nn1560
9 sg:pub.10.1038/npp.2009.131
10 sg:pub.10.1038/s41467-018-04397-0
11 sg:pub.10.1038/srep13874
12 sg:pub.10.3758/lb.37.4.289
13 https://doi.org/10.1016/b978-1-55860-141-3.50030-4
14 https://doi.org/10.1016/j.cogpsych.2015.01.005
15 https://doi.org/10.1016/j.cub.2017.02.026
16 https://doi.org/10.1016/j.jmp.2003.11.004
17 https://doi.org/10.1016/j.neuron.2008.10.043
18 https://doi.org/10.1016/j.neuron.2010.04.016
19 https://doi.org/10.1016/j.neuron.2011.02.027
20 https://doi.org/10.1016/j.neuron.2013.08.009
21 https://doi.org/10.1016/j.neuron.2013.09.007
22 https://doi.org/10.1016/j.neuron.2013.11.028
23 https://doi.org/10.1016/s0893-6080(02)00048-5
24 https://doi.org/10.1016/s0893-6080(99)00046-5
25 https://doi.org/10.1037/a0030844
26 https://doi.org/10.1073/pnas.1506367112
27 https://doi.org/10.1073/pnas.1609094113
28 https://doi.org/10.1073/pnas.1712479114
29 https://doi.org/10.1080/14640748108400816
30 https://doi.org/10.1093/cercor/13.4.400
31 https://doi.org/10.1098/rspb.2010.1607
32 https://doi.org/10.1109/tnn.1998.712192
33 https://doi.org/10.1111/j.1460-9568.2004.03095.x
34 https://doi.org/10.1111/j.1460-9568.2005.04218.x
35 https://doi.org/10.1111/j.2517-6161.1995.tb02031.x
36 https://doi.org/10.1126/science.275.5306.1593
37 https://doi.org/10.1152/jn.00024.2007
38 https://doi.org/10.1162/neco.2006.18.7.1637
39 https://doi.org/10.1371/journal.pcbi.1002055
40 https://doi.org/10.1371/journal.pcbi.1005020
41 https://doi.org/10.1523/jneurosci.0564-07.2007
42 https://doi.org/10.3389/fncom.2010.00146
43 https://doi.org/10.7554/elife.13747
44 schema:datePublished 2019-12
45 schema:datePublishedReg 2019-12-01
46 schema:description An extensive reinforcement learning literature shows that organisms assign credit efficiently, even under conditions of state uncertainty. However, little is known about credit-assignment when state uncertainty is subsequently resolved. Here, we address this problem within the framework of an interaction between model-free (MF) and model-based (MB) control systems. We present and support experimentally a theory of MB retrospective-inference. Within this framework, a MB system resolves uncertainty that prevailed when actions were taken thus guiding an MF credit-assignment. Using a task in which there was initial uncertainty about the lotteries that were chosen, we found that when participants' momentary uncertainty about which lottery had generated an outcome was resolved by provision of subsequent information, participants preferentially assigned credit within a MF system to the lottery they retrospectively inferred was responsible for this outcome. These findings extend our knowledge about the range of MB functions and the scope of system interactions.
47 schema:genre research_article
48 schema:inLanguage en
49 schema:isAccessibleForFree true
50 schema:isPartOf N4b9d2556840546489b5904865e978be1
51 N4d3a01d3fb5147449b8b7f59c9dc45b8
52 sg:journal.1043282
53 schema:name Retrospective model-based inference guides model-free credit assignment
54 schema:pagination 750
55 schema:productId N2e810a73997442c8a19906cff44595ac
56 N389986bf1c274444a7197dda277191b1
57 N72c5c8b195d149bab04b67caaf21378d
58 N845060256bf44fed973e91c1c97004a4
59 Nf8cf0baea8624c7f8305a04dbcbb3823
60 schema:sameAs https://app.dimensions.ai/details/publication/pub.1112134541
61 https://doi.org/10.1038/s41467-019-08662-8
62 schema:sdDatePublished 2019-04-11T12:53
63 schema:sdLicense https://scigraph.springernature.com/explorer/license/
64 schema:sdPublisher N4363672c7c38498f83d6308141e95efc
65 schema:url https://www.nature.com/articles/s41467-019-08662-8
66 sgo:license sg:explorer/license/
67 sgo:sdDataset articles
68 rdf:type schema:ScholarlyArticle
69 N2e810a73997442c8a19906cff44595ac schema:name pubmed_id
70 schema:value 30765718
71 rdf:type schema:PropertyValue
72 N389986bf1c274444a7197dda277191b1 schema:name readcube_id
73 schema:value 4ef128a3c6dc3d058103fbf27edbf1543e436e7c1716aaf94fa209fd08302dcb
74 rdf:type schema:PropertyValue
75 N4363672c7c38498f83d6308141e95efc schema:name Springer Nature - SN SciGraph project
76 rdf:type schema:Organization
77 N4b9d2556840546489b5904865e978be1 schema:volumeNumber 10
78 rdf:type schema:PublicationVolume
79 N4cc2457a070b4233a46332af042089f4 rdf:first Nb88a20b92151401dbfc51ded2b391ec3
80 rdf:rest rdf:nil
81 N4d3a01d3fb5147449b8b7f59c9dc45b8 schema:issueNumber 1
82 rdf:type schema:PublicationIssue
83 N5a05190d99c0460fae325c1b62389b14 rdf:first N9b1e226c7144435d969a13d91a0c47f5
84 rdf:rest Nef0d16c2a35a408c993358c3c7f5ea23
85 N72c5c8b195d149bab04b67caaf21378d schema:name doi
86 schema:value 10.1038/s41467-019-08662-8
87 rdf:type schema:PropertyValue
88 N75f6017a74044a4db000c788ac461d1f rdf:first N81354366482f4e09a0ab89104665d349
89 rdf:rest N5a05190d99c0460fae325c1b62389b14
90 N81354366482f4e09a0ab89104665d349 schema:affiliation https://www.grid.ac/institutes/grid.450002.3
91 schema:familyName Moran
92 schema:givenName Rani
93 rdf:type schema:Person
94 N845060256bf44fed973e91c1c97004a4 schema:name dimensions_id
95 schema:value pub.1112134541
96 rdf:type schema:PropertyValue
97 N9b1e226c7144435d969a13d91a0c47f5 schema:affiliation https://www.grid.ac/institutes/grid.450002.3
98 schema:familyName Keramati
99 schema:givenName Mehdi
100 rdf:type schema:Person
101 Nb88a20b92151401dbfc51ded2b391ec3 schema:affiliation https://www.grid.ac/institutes/grid.450002.3
102 schema:familyName Dolan
103 schema:givenName Raymond J.
104 rdf:type schema:Person
105 Nef0d16c2a35a408c993358c3c7f5ea23 rdf:first Nf5807f6346554562a4b57d0117f05989
106 rdf:rest N4cc2457a070b4233a46332af042089f4
107 Nf5807f6346554562a4b57d0117f05989 schema:affiliation https://www.grid.ac/institutes/grid.419501.8
108 schema:familyName Dayan
109 schema:givenName Peter
110 rdf:type schema:Person
111 Nf8cf0baea8624c7f8305a04dbcbb3823 schema:name nlm_unique_id
112 schema:value 101528555
113 rdf:type schema:PropertyValue
114 anzsrc-for:14 schema:inDefinedTermSet anzsrc-for:
115 schema:name Economics
116 rdf:type schema:DefinedTerm
117 anzsrc-for:1401 schema:inDefinedTermSet anzsrc-for:
118 schema:name Economic Theory
119 rdf:type schema:DefinedTerm
120 sg:grant.3639086 http://pending.schema.org/fundedItem sg:pub.10.1038/s41467-019-08662-8
121 rdf:type schema:MonetaryGrant
122 sg:journal.1043282 schema:issn 2041-1723
123 schema:name Nature Communications
124 rdf:type schema:Periodical
125 sg:pub.10.1007/bf00114726 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025781975
126 https://doi.org/10.1007/bf00114726
127 rdf:type schema:CreativeWork
128 sg:pub.10.1038/nn.2902 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044175804
129 https://doi.org/10.1038/nn.2902
130 rdf:type schema:CreativeWork
131 sg:pub.10.1038/nn.3981 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047010599
132 https://doi.org/10.1038/nn.3981
133 rdf:type schema:CreativeWork
134 sg:pub.10.1038/nn.4520 schema:sameAs https://app.dimensions.ai/details/publication/pub.1084129393
135 https://doi.org/10.1038/nn.4520
136 rdf:type schema:CreativeWork
137 sg:pub.10.1038/nn1560 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035060211
138 https://doi.org/10.1038/nn1560
139 rdf:type schema:CreativeWork
140 sg:pub.10.1038/npp.2009.131 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041054380
141 https://doi.org/10.1038/npp.2009.131
142 rdf:type schema:CreativeWork
143 sg:pub.10.1038/s41467-018-04397-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1103889264
144 https://doi.org/10.1038/s41467-018-04397-0
145 rdf:type schema:CreativeWork
146 sg:pub.10.1038/srep13874 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012056064
147 https://doi.org/10.1038/srep13874
148 rdf:type schema:CreativeWork
149 sg:pub.10.3758/lb.37.4.289 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025371165
150 https://doi.org/10.3758/lb.37.4.289
151 rdf:type schema:CreativeWork
152 https://doi.org/10.1016/b978-1-55860-141-3.50030-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051599773
153 rdf:type schema:CreativeWork
154 https://doi.org/10.1016/j.cogpsych.2015.01.005 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012135509
155 rdf:type schema:CreativeWork
156 https://doi.org/10.1016/j.cub.2017.02.026 schema:sameAs https://app.dimensions.ai/details/publication/pub.1084069013
157 rdf:type schema:CreativeWork
158 https://doi.org/10.1016/j.jmp.2003.11.004 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050436877
159 rdf:type schema:CreativeWork
160 https://doi.org/10.1016/j.neuron.2008.10.043 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050504002
161 rdf:type schema:CreativeWork
162 https://doi.org/10.1016/j.neuron.2010.04.016 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004293988
163 rdf:type schema:CreativeWork
164 https://doi.org/10.1016/j.neuron.2011.02.027 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020297092
165 rdf:type schema:CreativeWork
166 https://doi.org/10.1016/j.neuron.2013.08.009 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001823734
167 rdf:type schema:CreativeWork
168 https://doi.org/10.1016/j.neuron.2013.09.007 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032043566
169 rdf:type schema:CreativeWork
170 https://doi.org/10.1016/j.neuron.2013.11.028 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041731794
171 rdf:type schema:CreativeWork
172 https://doi.org/10.1016/s0893-6080(02)00048-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040074190
173 rdf:type schema:CreativeWork
174 https://doi.org/10.1016/s0893-6080(99)00046-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016131651
175 rdf:type schema:CreativeWork
176 https://doi.org/10.1037/a0030844 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017887428
177 rdf:type schema:CreativeWork
178 https://doi.org/10.1073/pnas.1506367112 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010959713
179 rdf:type schema:CreativeWork
180 https://doi.org/10.1073/pnas.1609094113 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049316642
181 rdf:type schema:CreativeWork
182 https://doi.org/10.1073/pnas.1712479114 schema:sameAs https://app.dimensions.ai/details/publication/pub.1092669609
183 rdf:type schema:CreativeWork
184 https://doi.org/10.1080/14640748108400816 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047483454
185 rdf:type schema:CreativeWork
186 https://doi.org/10.1093/cercor/13.4.400 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033197334
187 rdf:type schema:CreativeWork
188 https://doi.org/10.1098/rspb.2010.1607 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034425657
189 rdf:type schema:CreativeWork
190 https://doi.org/10.1109/tnn.1998.712192 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061716400
191 rdf:type schema:CreativeWork
192 https://doi.org/10.1111/j.1460-9568.2004.03095.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1025200754
193 rdf:type schema:CreativeWork
194 https://doi.org/10.1111/j.1460-9568.2005.04218.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1035725450
195 rdf:type schema:CreativeWork
196 https://doi.org/10.1111/j.2517-6161.1995.tb02031.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1110458929
197 rdf:type schema:CreativeWork
198 https://doi.org/10.1126/science.275.5306.1593 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001523695
199 rdf:type schema:CreativeWork
200 https://doi.org/10.1152/jn.00024.2007 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020471944
201 rdf:type schema:CreativeWork
202 https://doi.org/10.1162/neco.2006.18.7.1637 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049023728
203 rdf:type schema:CreativeWork
204 https://doi.org/10.1371/journal.pcbi.1002055 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028648676
205 rdf:type schema:CreativeWork
206 https://doi.org/10.1371/journal.pcbi.1005020 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052095887
207 rdf:type schema:CreativeWork
208 https://doi.org/10.1523/jneurosci.0564-07.2007 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009136726
209 rdf:type schema:CreativeWork
210 https://doi.org/10.3389/fncom.2010.00146 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014541634
211 rdf:type schema:CreativeWork
212 https://doi.org/10.7554/elife.13747 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005111759
213 rdf:type schema:CreativeWork
214 https://www.grid.ac/institutes/grid.419501.8 schema:alternateName Max Planck Institute for Biological Cybernetics
215 schema:name Gatsby Computational Neuroscience Unit, University College London, W1T 4JG, London, UK
216 Max Planck Institute for Biological Cybernetics, Max Plank-Ring 8, 72076, Tuebingen, Germany
217 Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, 10-12 Russell Square, WC1B 5EH, London, UK
218 rdf:type schema:Organization
219 https://www.grid.ac/institutes/grid.450002.3 schema:alternateName Wellcome Centre for Human Neuroimaging
220 schema:name Department of Psychology, City, University of London, EC1R 0JD, London, UK
221 Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, 10-12 Russell Square, WC1B 5EH, London, UK
222 Wellcome Centre for Human Neuroimaging, University College London, WC1N 3BG, London, United Kingdom
223 rdf:type schema:Organization
 




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


...