Evidence map›Paper›PMID 29513219›Full record

ArticleeLife2018

Distinct contributions of functional and deep neural network features to representational similarity of scenes in human brain and behavior.

Iris Ia Groen, Michelle R Greene, Christopher Baldassano, Li Fei-Fei, Diane M Beck, Chris I Baker

Abstract read
In one paragraph

Article in eLife, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 83 papers.

0numbers the graph read from it
0cells of the map it votes in
83citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

83 citing papers in PubMed.

  1. Making models disagree to learn how brains compute.Nature reviews. Neuroscience · 2026
    Review
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  8. Brain Representation of Numerosity across the Senses and Presentation Formats.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025
    Article
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  16. Representation of locomotive action affordances in human behavior, brains, and deep neural networks.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  17. Article
  18. Cortical Encoding of Spatial Structure and Semantic Content in 3D Natural Scenes.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025
    Article
  19. Article
  20. Article

23 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Iris Ia GroenLaboratory of Brain and Cognition, National Institutes of Health, Bethesda, United States.ORCID http://orcid.org/0000-0002-5536-6128
Michelle R GreeneNeuroscience Program, Bates College, Maine, United States.
Christopher BaldassanoPrinceton Neuroscience Institute, Princeton University, Princeton, United States.ORCID http://orcid.org/0000-0003-3540-5019
Li Fei-FeiStanford Vision Lab, Stanford University, Stanford, United States.
Diane M BeckDepartment of Psychology, University of Illinois, Urbana-Champaign, United States.ORCID http://orcid.org/0000-0001-9802-5828
Chris I BakerLaboratory of Brain and Cognition, National Institutes of Health, Bethesda, United States.ORCID http://orcid.org/0000-0001-6861-8964

Funding

Object, face, body and scene representations in the human brainZIAMH002909 · NIMH · NATIONAL INSTITUTE OF MENTAL HEALTH · PI BAKER, CHRIS · 2009 to 2025
$25.5M
Intramural NIH HHS ZIA MH002909NIH HHS ZIAMH002909
6 · The paper itself

Abstract

Inherent correlations between visual and semantic features in real-world scenes make it difficult to determine how different scene properties contribute to neural representations. Here, we assessed the contributions of multiple properties to scene representation by partitioning the variance explained in human behavioral and brain measurements by three feature models whose inter-correlations were minimized

Indexed as

AdultBrainBrain MappingFemaleHumansMagnetic Resonance ImagingMaleNerve NetPattern Recognition, VisualPhotic StimulationSemanticsVisual Perceptionbehavioral categorizationcomputational modeldeep neural networkfMRIhumanneurosciencescene perceptionvariance partitioning

Identifiers

PMID29513219
PMCPMC5860866

What OpenQuestion holds

Textmetadata
LicenceCC0
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.