Elsevier

NeuroImage

Volume 179, 1 October 2018, Pages 102-116
NeuroImage

Spatial frequency supports the emergence of categorical representations in visual cortex during natural scene perception

https://doi.org/10.1016/j.neuroimage.2018.06.033Get rights and content
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Highlights

  • Low spatial frequencies support generalizable neural responses to natural scenes.

  • Categorical representations of scenes arise in visual cortex within 200 ms.

  • We find simultaneous processing of low- and high-level visual features.

  • Low- and high-level neural network layers have overlapping representations.

Abstract

In navigating our environment, we rapidly process and extract meaning from visual cues. However, the relationship between visual features and categorical representations in natural scene perception is still not well understood. Here, we used natural scene stimuli from different categories and filtered at different spatial frequencies to address this question in a passive viewing paradigm. Using representational similarity analysis (RSA) and cross-decoding of magnetoencephalography (MEG) data, we show that categorical representations emerge in human visual cortex at ∼180 ms and are linked to spatial frequency processing. Furthermore, dorsal and ventral stream areas reveal temporally and spatially overlapping representations of low and high-level layer activations extracted from a feedforward neural network. Our results suggest that neural patterns from extrastriate visual cortex switch from low-level to categorical representations within 200 ms, highlighting the rapid cascade of processing stages essential in human visual perception.

Keywords

Multivariate pattern analysis (MVPA)
Representational similarity analysis (RSA)
Magnetoencephalography (MEG)
Convolutional neural network (CNN)
Scene categorization

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