Elsevier

NeuroImage

Volume 153, June 2017, Pages 346-358
NeuroImage

Dynamics of scene representations in the human brain revealed by magnetoencephalography and deep neural networks

https://doi.org/10.1016/j.neuroimage.2016.03.063Get rights and content
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Abstract

Human scene recognition is a rapid multistep process evolving over time from single scene image to spatial layout processing. We used multivariate pattern analyses on magnetoencephalography (MEG) data to unravel the time course of this cortical process. Following an early signal for lower-level visual analysis of single scenes at ~100 ms, we found a marker of real-world scene size, i.e. spatial layout processing, at ~250 ms indexing neural representations robust to changes in unrelated scene properties and viewing conditions. For a quantitative model of how scene size representations may arise in the brain, we compared MEG data to a deep neural network model trained on scene classification. Representations of scene size emerged intrinsically in the model, and resolved emerging neural scene size representation. Together our data provide a first description of an electrophysiological signal for layout processing in humans, and suggest that deep neural networks are a promising framework to investigate how spatial layout representations emerge in the human brain.

Keywords

Scene perception
Spatial layout
Magnetoencephalography
Deep neural network
Representational similarity analysis

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