Object weight can be rapidly predicted, with low cognitive load, by exploiting learned associations between the weights and locations of objects
Abstract
We use a novel object support task using a three-dimensional robotic interface and virtual reality system to provide evidence that the locations of objects are used to predict their weights. Using location information, rather than the visual appearance of the objects, supports fast prediction, thereby avoiding processes that can be demanding on working memory.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Feb 01, 2023
- Source ID
- 10.1152/jn.00414.2022
Entities
People
- Daniel M. Wolpert
- Evan Cesanek
- James N. Ingram
- Randy Flanagan
- Zhaoran Zhang
Organizations
- Air Force Research Laboratory
- Columbia University
- National Institute of Neurological Disorders and Stroke
- Queen's University