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For deep networks, the whole equals the sum of the parts

Published online by Cambridge University Press:  06 December 2023

Philip J. Kellman Open the ORCID record for Philip J. Kellman [Opens in a new window] ,
Nicholas Baker ,
Patrick Garrigan ,
Austin Phillips  and
Hongjing Lu
Philip J. Kellman
Affiliation:
Department of Psychology and David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA kellman@cognet.ucla.edu; https://kellmanlab.psych.ucla.edu/
Nicholas Baker
Affiliation:
Department of Psychology, Loyola University of Chicago, Chicago, IL, USA nbaker1@ucla.edu; https://www.luc.edu/psychology/people/staff/facultyandstaff/nicholasbaker/
Patrick Garrigan
Affiliation:
Department of Psychology, St. Joseph's University, Philadelphia, PA, USA patrick.garrigan@sju.edu; https://sjupsych.org/faculty_pg.php
Austin Phillips
Affiliation:
Department of Psychology, University of California, Los Angeles, Los Angeles, CA, USA asphillips@ucla.edu; https://kellmanlab.psych.ucla.edu/
Hongjing Lu
Affiliation:
Department of Psychology and Department of Statistics, University of California, Los Angeles, Los Angeles, CA, USA hongjing@ucla.edu; https://cvl.psych.ucla.edu/
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Abstract

Deep convolutional networks exceed humans in sensitivity to local image properties, but unlike biological vision systems, do not discover and encode abstract relations that capture important properties of objects and events in the world. Coupling network architectures with additional machinery for encoding abstract relations will make deep networks better models of human abilities and more versatile and capable artificial devices.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 46 , 2023 , e396
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Copyright
Copyright © The Author(s), 2023. Published by Cambridge University Press

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References

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