Promises and challenges of human computational ethology
Abstract
The movements an organism makes provide insights into its internal states and motives. This principle is the foundation of the new field of computational ethology, which links rich automatic measurements of natural behaviors to motivational states and neural activity. Computational ethology has proven transformative for animal behavioral neuroscience. This success raises the question of whether rich automatic measurements of behavior can similarly drive progress in human neuroscience and psychology. New technologies for capturing and analyzing complex behaviors in real and virtual environments enable us to probe the human brain during naturalistic dynamic interactions with the environment that so far were beyond experimental investigation. Inspired by nonhuman computational ethology, we explore how these new tools can be used to test important questions in human neuroscience. We argue that application of this methodology will help human neuroscience and psychology extend limited behavioral measurements such as reaction time and accuracy, permit novel insights into how the human brain produces behavior, and ultimately reduce the growing measurement gap between human and animal neuroscience.
Additional Information
© 2021 Elsevier. Available online 17 June 2021. This work was supported by National Institute of Mental Health grant 2P50MH094258, a Chen Institute award (P2026052), and Templeton Foundation grant TWCF0366 (all to D.M.). T.W. is supported by a Wellcome Trust Sir Henry Wellcome Fellowship (206460/17/Z). This work is also supported by the National Institutes of Health (NIH) National Institute of Neurological Disorders and Stroke (NINDS; NS103802 and NS117838), the McKnight Foundation (Technological Innovations Award in Neuroscience to N.S.), and a Keck Junior Faculty Award (to N.S.). We thank Matthew Botvinick for feedback on an earlier version of this paper.Attached Files
Accepted Version - nihms-1714145.pdf
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Additional details
- PMCID
- PMC8769712
- Eprint ID
- 109597
- DOI
- 10.1016/j.neuron.2021.05.021
- Resolver ID
- CaltechAUTHORS:20210626-183437479
- 2P50MH094258
- NIH
- National Institute of Mental Health (NIMH)
- P2026052
- Tianqiao and Chrissy Chen Institute for Neuroscience
- TWCF0366
- John Templeton Foundation
- 206460/17/Z
- Wellcome Trust
- NS103802
- NIH
- NS117838
- NIH
- McKnight Foundation
- National Institute of Neurological Disorders and Stroke (NINDS)
- UCLA
- Created
-
2021-06-28Created from EPrint's datestamp field
- Updated
-
2022-07-27Created from EPrint's last_modified field
- Caltech groups
- Tianqiao and Chrissy Chen Institute for Neuroscience