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Navigational Affordance Cortical Responses Explained by Scene-Parsing Model

  • Kshitij Dwivedi
  • Gemma RoigEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11131)

Abstract

Deep Neural Networks (DNNs) are the leading models for explaining the population responses of neurons in the visual cortex. Recent studies show that responses of some task-specific brain regions can also be explained by a DNN trained for classification. In this work, we propose that responses of task-specific brain regions are better explained by DNNs trained on a similar task. We first show that responses of scene selective visual areas like parahippocampal place area (PPA) and Occipital Place Area (OPA) are better explained by a DNN trained for scene classification than one trained for object classification. Next, we consider a particular case of OPA which has been shown to encode navigational affordances. We argue that a scene parsing task, which predicts the class of each pixel in the scene is more related to navigational affordances than scene classification. Our results show that the responses in OPA are better explained by the scene parsing model than the scene classification model.

Keywords

Deep Neural Networks Representational similarity analysis Occipital Place Area Neural Encoding 

Notes

Acknowledgement

This work was funded by the MOE SUTD SRG grant (SRG ISTD 2017 131). Kshitij Dwivedi was also funded by SUTD President’s Graduate Fellowship.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Singapore University of Technology and DesignSingaporeSingapore

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