Deep Gestalt Reasoning Model: Interpreting Electrophysiological Signals Related to Cognition

Andras Lorincz, Aron Fothi, Bryar O. Rahman, Viktor Varga; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017, pp. 2789-2797

Abstract

We are to join deep input-output processing and Gestalt Laws driven cognition under deterministic world assumption. We consider every feedforward input-output system as a sensor: including units performing holistic recognition. A mathematical theorem is also a sensor: it senses the consequences upon receiving its conditions. Systems seeking consistencies between the outputs of sensor are cognitive units. Such units are involved in cognition. Sensor and cognitive units complement each other. We argue that the goal of learning is to turn components of the cognitive system into feedforward holistic units for gaining speed in cognition. We put forth a model for self-training of the holistic units. We connect our concepts to certain electrophysiological signals and cognitive phenomena, including evoked response potentials, working memory, and consciousness. We demonstrate the working of the two complementary systems on low level situation analysis in videos.

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[bibtex]
@InProceedings{Lorincz_2017_ICCV,
author = {Lorincz, Andras and Fothi, Aron and Rahman, Bryar O. and Varga, Viktor},
title = {Deep Gestalt Reasoning Model: Interpreting Electrophysiological Signals Related to Cognition},
booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV) Workshops},
month = {Oct},
year = {2017}
}