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Psychophysics may be the game-changer for deep neural networks (DNNs) to imitate the human vision

Published online by Cambridge University Press:  06 December 2023

Keerthi S. Chandran Open the ORCID record for Keerthi S. Chandran [Opens in a new window] ,
Amrita Mukherjee Paul ,
Avijit Paul  and
Kuntal Ghosh Open the ORCID record for Kuntal Ghosh [Opens in a new window]
Keerthi S. Chandran
Affiliation:
Center for Soft Computing Research, Indian Statistical Institute, Kolkata, India keerthischandran@gmail.com Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India kuntal@isical.ac.in
Amrita Mukherjee Paul
Affiliation:
Center for Soft Computing Research, Indian Statistical Institute, Kolkata, India keerthischandran@gmail.com Applied Sciences, IIIT Allahabad, Prayagraj, UP, India rss2020501@iiita.ac.in
Avijit Paul
Affiliation:
Biomedical Engineering, Tufts University, Medford, MA, USA avijit.paul@tufts.edu
Kuntal Ghosh
Affiliation:
Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India kuntal@isical.ac.in
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Abstract

Psychologically faithful deep neural networks (DNNs) could be constructed by training with psychophysics data. Moreover, conventional DNNs are mostly monocular vision based, whereas the human brain relies mainly on binocular vision. DNNs developed as smaller vision agent networks associated with fundamental and less intelligent visual activities, can be combined to simulate more intelligent visual activities done by the biological brain.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 46 , 2023 , e388
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Copyright
Copyright © The Author(s), 2023. Published by Cambridge University Press

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