Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
1-2001
Abstract
To find out how the representations of structured visual objects depend on the co-occurrence statistics of their constituents, we exposed subjects to a set of composite images with tight control exerted over (1) the conditional probabilities of the constituent fragments, and (2) the value of Barlow's criterion of "suspicious coincidence" (the ratio of joint probability to the product of marginals). We then compared the part verification response times for various probe/target combinations before and after the exposure. For composite probes, the speedup was much larger for targets that contained pairs of fragments perfectly predictive of each other, compared to those that did not. This effect was modulated by the significance of their co-occurrence as estimated by Barlow's criterion. For lone-fragment probes, the speedup in all conditions was generally lower than for composites. These results shed light on the brain's strategies for unsupervised acquisition of structural information in vision.
Keywords
Learning, modeling, visual struction, structural information, brain
Discipline
Cognition and Perception
Research Areas
Psychology
Publication
Advances in Neural Information Processing Systems 14 (NIPS 2001): Proceedings of the 2001 Conference
Volume
14
First Page
19
Last Page
26
ISBN
9780262042062
Publisher
MIT Press
City or Country
Cambridge, MA
Citation
Edelman, Shimon, Benjamin P. Hiles, YANG Hwajin and Nathan Intrator. 2002. "Probabilistic Principles in Unsupervised Learning of Visual Structure: Human Data and a Model." In Advances in Neural Information Processing Systems, 14, 19-26. Cambridge, MA: MIT Press.
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://books.nips.cc/papers/files/nips14/CS03.pdf
Comments
Paper presented at Neural Information Processing Systems 2001