Motor memories of object dynamics are categorically organized
Abstract
The ability to predict the dynamics of objects, linking applied force to motion, underlies our capacity to perform many of the tasks we carry out on a daily basis. Thus, a fundamental question is how the dynamics of the myriad objects we interact with are organized in memory. Using a custom-built three-dimensional robotic interface that allowed us to simulate objects of varying appearance and weight, we examined how participants learned the weights of sets of objects that they repeatedly lifted. We find strong support for the novel hypothesis that motor memories of object dynamics are organized categorically, in terms of families, based on covariation in their visual and mechanical properties. A striking prediction of this hypothesis, supported by our findings and not predicted by standard associative map models, is that outlier objects with weights that deviate from the family-predicted weight will never be learned despite causing repeated lifting errors.
Data availability
All source data, analysis code, and figure generation code is available in the supplementary files.
Article and author information
Author details
Funding
No external funding was received for this work.
Reviewing Editor
- Matthew Crossley, Macquarie University, Australia
Ethics
Human subjects: All experiments were conducted in accordance with the 1964 Declaration of Helsinki, following protocol approved by the Columbia University Institutional Review Board (IRB-AAAR9148). Written informed consent was obtained from all participants prior to their participation.
Version history
- Received: June 24, 2021
- Preprint posted: July 14, 2021 (view preprint)
- Accepted: November 18, 2021
- Accepted Manuscript published: November 19, 2021 (version 1)
- Version of Record published: December 1, 2021 (version 2)
- Version of Record updated: July 26, 2022 (version 3)
Copyright
© 2021, Cesanek et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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