Deep gestalt reasoning model: Interpreting electrophysiological signals related to cognition

A. Lőrincz, Áron Fóthi, Bryar O. Rahman, Viktor Varga

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publicationProceedings - 2017 IEEE International Conference on Computer Vision Workshops, ICCVW 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2789-2797
Number of pages9
Volume2018-January
ISBN (Electronic)9781538610343
DOIs
Publication statusPublished - Jan 19 2018
Event16th IEEE International Conference on Computer Vision Workshops, ICCVW 2017 - Venice, Italy
Duration: Oct 22 2017Oct 29 2017

Other

Other16th IEEE International Conference on Computer Vision Workshops, ICCVW 2017
CountryItaly
CityVenice
Period10/22/1710/29/17

Fingerprint

Sensors
Cognitive systems
Data storage equipment
Processing

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Vision and Pattern Recognition

Cite this

Lőrincz, A., Fóthi, Á., Rahman, B. O., & Varga, V. (2018). Deep gestalt reasoning model: Interpreting electrophysiological signals related to cognition. In Proceedings - 2017 IEEE International Conference on Computer Vision Workshops, ICCVW 2017 (Vol. 2018-January, pp. 2789-2797). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCVW.2017.328

Deep gestalt reasoning model : Interpreting electrophysiological signals related to cognition. / Lőrincz, A.; Fóthi, Áron; Rahman, Bryar O.; Varga, Viktor.

Proceedings - 2017 IEEE International Conference on Computer Vision Workshops, ICCVW 2017. Vol. 2018-January Institute of Electrical and Electronics Engineers Inc., 2018. p. 2789-2797.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Lőrincz, A, Fóthi, Á, Rahman, BO & Varga, V 2018, Deep gestalt reasoning model: Interpreting electrophysiological signals related to cognition. in Proceedings - 2017 IEEE International Conference on Computer Vision Workshops, ICCVW 2017. vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 2789-2797, 16th IEEE International Conference on Computer Vision Workshops, ICCVW 2017, Venice, Italy, 10/22/17. https://doi.org/10.1109/ICCVW.2017.328
Lőrincz A, Fóthi Á, Rahman BO, Varga V. Deep gestalt reasoning model: Interpreting electrophysiological signals related to cognition. In Proceedings - 2017 IEEE International Conference on Computer Vision Workshops, ICCVW 2017. Vol. 2018-January. Institute of Electrical and Electronics Engineers Inc. 2018. p. 2789-2797 https://doi.org/10.1109/ICCVW.2017.328
Lőrincz, A. ; Fóthi, Áron ; Rahman, Bryar O. ; Varga, Viktor. / Deep gestalt reasoning model : Interpreting electrophysiological signals related to cognition. Proceedings - 2017 IEEE International Conference on Computer Vision Workshops, ICCVW 2017. Vol. 2018-January Institute of Electrical and Electronics Engineers Inc., 2018. pp. 2789-2797
@inproceedings{8d6b10187eb247c48f3b139aeeca0324,
title = "Deep gestalt reasoning model: Interpreting electrophysiological signals related to cognition",
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.",
author = "A. Lőrincz and {\'A}ron F{\'o}thi and Rahman, {Bryar O.} and Viktor Varga",
year = "2018",
month = "1",
day = "19",
doi = "10.1109/ICCVW.2017.328",
language = "English",
volume = "2018-January",
pages = "2789--2797",
booktitle = "Proceedings - 2017 IEEE International Conference on Computer Vision Workshops, ICCVW 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

TY - GEN

T1 - Deep gestalt reasoning model

T2 - Interpreting electrophysiological signals related to cognition

AU - Lőrincz, A.

AU - Fóthi, Áron

AU - Rahman, Bryar O.

AU - Varga, Viktor

PY - 2018/1/19

Y1 - 2018/1/19

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85046259559&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85046259559&partnerID=8YFLogxK

U2 - 10.1109/ICCVW.2017.328

DO - 10.1109/ICCVW.2017.328

M3 - Conference contribution

AN - SCOPUS:85046259559

VL - 2018-January

SP - 2789

EP - 2797

BT - Proceedings - 2017 IEEE International Conference on Computer Vision Workshops, ICCVW 2017

PB - Institute of Electrical and Electronics Engineers Inc.

ER -