Task and Spatial Frequency Effects on Face Specialization

Abstract

There is strong evidence that face processing is localized in the brain. The double dissociation between prosopagnosia, a face recognition deficit occurring after brain damage, and visual object agnosia, difficulty recognizing otber kinds of complex objects, indicates tbat face and non(cid:173) face object recognition may be served by partially independent mecha(cid:173) nisms in the brain. Is neural specialization innate or learned? We sug(cid:173) gest that this specialization could be tbe result of a competitive learn(cid:173) ing mechanism that, during development, devotes neural resources to the tasks they are best at performing. Furtber, we suggest that the specializa(cid:173) tion arises as an interaction between task requirements and developmen(cid:173) tal constraints. In this paper, we present a feed-forward computational model of visual processing, in which two modules compete to classify input stimuli. When one module receives low spatial frequency infor(cid:173) mation and the other receives high spatial frequency information, and the task is to identify the faces while simply classifying the objects, the low frequency network shows a strong specialization for faces. No otber combination of tasks and inputs shows this strong specialization. We take these results as support for the idea that an innately-specified face processing module is unnecessary.

Cite

Text

Dailey and Cottrell. "Task and Spatial Frequency Effects on Face Specialization." Neural Information Processing Systems, 1997.

Markdown

[Dailey and Cottrell. "Task and Spatial Frequency Effects on Face Specialization." Neural Information Processing Systems, 1997.](https://mlanthology.org/neurips/1997/dailey1997neurips-task/)

BibTeX

@inproceedings{dailey1997neurips-task,
  title     = {{Task and Spatial Frequency Effects on Face Specialization}},
  author    = {Dailey, Matthew N. and Cottrell, Garrison W.},
  booktitle = {Neural Information Processing Systems},
  year      = {1997},
  pages     = {17-23},
  url       = {https://mlanthology.org/neurips/1997/dailey1997neurips-task/}
}