Cited by 168Visual perception and processing mechanismsFace Recognition and PerceptionVisual Attention and Saliency Detection

Interaction between Scene and Object Processing Revealed by Human fMRI and MEG Decoding

Open access
Brandman, Talia, Peelen, Marius V.·2017·The Journal of Neuroscience

Abstract

Scenes strongly facilitate object recognition, such as when we make out the shape of a distant boat on the water. Yet, although known to interact in perception, neuroimaging research has primarily provided evidence for separate scene- and object-selective cortical pathways. This raises the question of how these pathways interact to support context-based perception. Here we used a novel approach in human fMRI and MEG studies to reveal supra-additive scene-object interactions. Participants (men and women) viewed degraded objects that were hard to recognize when presented in isolation but easy to recognize within their original scene context, in which no other associated objects were present. fMRI decoding showed that the multivariate representation of the objects9 category (animate/inanimate) in object-selective cortex was strongly enhanced by the presence of scene context, even though the scenes alone did not evoke category-selective response patterns. This effect in object-selective cortex was correlated with concurrent activity in scene-selective regions. MEG decoding results revealed that scene-based facilitation of object processing peaked at 320 ms after stimulus onset, 100 ms later than peak decoding of intact objects. Together, results suggest that expectations derived from scene information, processed in scene-selective cortex, feed back to shape object representations in visual cortex. These findings characterize, in space and time, functional interactions between scene- and object-processing pathways. SIGNIFICANCE STATEMENT Although scenes and objects are known to contextually interact in visual perception, the study of high-level vision has mostly focused on the dissociation between their selective neural pathways. The current findings are the first to reveal direct facilitation of object recognition and neural representation by scene background, even in the absence of contextually associated objects. Using a multivariate approach to both fMRI and MEG, we characterize the functional neuroanatomy and neural dynamics of such scene-based object facilitation. Finally, the correlation of this effect with scene-selective activity suggests that, although functionally distinct, scene and object processing pathways do interact at a perceptual level to fill in for insufficient visual detail.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1523/jneurosci.0582-17.2017

Cites

50 of 50 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Object representations are modulated by scene context.

    “Indeed, there is evidence that the neural representation of simple stimulus features is influenced by higher‐level object representations 115 , 129 and object representations are modulated by scene context.”
  • Neural representations of objects are enhanced by the presence of scene context, and this enhancement is correlated with the engagement of scene-selective cortical regions.

    “For example, neural representations of objects are enhanced by the presence of scene context, and this enhancement is correlated with the engagement of sceneselective cortical regions [32]”

    From How Do Expectations Shape Perception? (2018) · cites this paper as [32]

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Authors
Brandman, T., & Peelen, M. V.
Affiliations
University of Trento (IT)
Venue
The Journal of Neuroscience
Published
2017-07-07
Volume
37
Issue
32
Pages
7700-7710
ISSN
  • 0270-6474 (ISSN-L)
  • 1529-2401
OpenAlex
W2732001149
Cited by
168
References
50
Citations by year
2026: 18 · 2025: 31 · 2024: 16 · 2023: 21 · 2022: 15 · 2021: 18 · 2020: 22 · 2019: 16 · 2018: 10 · 2017: 1
FWCI
7.69
Type
journal article
Language
en
Open access
Locations
  • Journal of Neuroscience (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
  • Radboud Repository (Radboud University) (submittedVersion, other-oa)
  • Radboud Repository (Radboud University) (submittedVersion)
Funders
  • European Commission (HORIZON2020, 659778)
  • Provincia Autonoma di Trento
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Face Recognition and Perception
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
Keywords
scene-selective cortex, object-selective cortex, object recognition, scene context, neural dynamics, multivariate decoding, visual cortex
MeSH terms
Adult, Female, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Pattern Recognition, Visual, Photic Stimulation, Visual Cortex, Visual Perception, Magnetoencephalography, Young Adult

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review