Integrated deep visual and semantic attractor neural networks predict fMRI pattern-information along the ventral object processing pathway.

Scientific Reports
Barry J DevereuxL K Tyler

Abstract

Recognising an object involves rapid visual processing and activation of semantic knowledge about the object, but how visual processing activates and interacts with semantic representations remains unclear. Cognitive neuroscience research has shown that while visual processing involves posterior regions along the ventral stream, object meaning involves more anterior regions, especially perirhinal cortex. Here we investigate visuo-semantic processing by combining a deep neural network model of vision with an attractor network model of semantics, such that visual information maps onto object meanings represented as activation patterns across features. In the combined model, concept activation is driven by visual input and co-occurrence of semantic features, consistent with neurocognitive accounts. We tested the model's ability to explain fMRI data where participants named objects. Visual layers explained activation patterns in early visual cortex, whereas pattern-information in perirhinal cortex was best explained by later stages of the attractor network, when detailed semantic representations are activated. Posterior ventral temporal cortex was best explained by intermediate stages corresponding to initial semantic processing, w...Continue Reading

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Citations

Aug 21, 2018·Journal of Cognitive Neuroscience·Alex ClarkeLorraine K Tyler
Oct 18, 2019·Proceedings of the National Academy of Sciences of the United States of America·W James MurdochBin Yu
Feb 7, 2020·Journal of Cognitive Neuroscience·Grace W Lindsay
Jun 24, 2020·Journal of Cognitive Neuroscience·John M HendersonTaylor R Hayes
Sep 26, 2019·Scientific Reports·Rose BruffaertsAlex Clarke
Aug 9, 2019·Annual Review of Vision Science·Thomas Serre
Jan 20, 2021·Nature Human Behaviour·Rebecca L JacksonMatthew A Lambon Ralph
Jun 30, 2021·Nature Reviews. Neuroscience·Friedemann PulvermüllerThomas Wennekers

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Methods Mentioned

BETA
RSA

Software Mentioned

searchlight
R rms package
MATLAB
CARET
Caffe
RSA
lmerTest
Hemera
lmerTest R

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