Integrated deep visual and semantic attractor neural networks predict fMRI pattern-information along the ventral object processing pathway.
Nature Publishing Group
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Devereux, B. J., Clarke, A., & Tyler, L. (2018). Integrated deep visual and semantic attractor neural networks predict fMRI pattern-information along the ventral object processing pathway.. Scientific reports, 8 (1), 10636. https://doi.org/10.1038/s41598-018-28865-1
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, when visual information has the greatest influence on the emerging semantic representation. These results provide proof of principle of how a mechanistic model of combined visuo-semantic processing can account for pattern-information in the ventral stream.
Visual Cortex, Visual Pathways, Humans, Magnetic Resonance Imaging, Brain Mapping, Pattern Recognition, Visual, Models, Neurological, Semantics, Image Processing, Computer-Assisted, Female, Male, Perirhinal Cortex
This research was funded by an Advanced Investigator grant to LKT from the European Research Council under the European Community's Seventh Framework Programme (FP7/2007-2013)/ ERC Grant agreement n° 249640, and an ERC Advanced Investigator grant to LKT under the Horizon 2020 Research and Innovation Programme (2014-2020 ERC Grant agreement no 669820).
European Research Council (249640)
ECH2020 EUROPEAN RESEARCH COUNCIL (ERC) (669820)
External DOI: https://doi.org/10.1038/s41598-018-28865-1
This record's URL: https://www.repository.cam.ac.uk/handle/1810/283566
Attribution 4.0 International
Licence URL: https://creativecommons.org/licenses/by/4.0/