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Title:Learning innate face preferences.
Authors: James Bednar ; Risto Miikkulainen
Date: 2003
Publication Title:Neural Computation
Publisher:MIT Press
Publication Type:Journal Article Publication Status:Published
Volume No:12(7) Page Nos:1721-1740
Newborn humans preferentially orient to face-like patterns at birth, but months of experience with faces is required for full face processing abilities to develop. Several models have been proposed for how the interaction of genetic and evironmental influences can explain this data. These models generally assume that the brain areas responsible for newborn orienting responses are not capable of learning and are physically separate from those that later learn from real faces. However, it has been difficult to reconcile these models with recent discoveries of face learning in newborns and young infants. We propose a general mechanism by which genetically specified and environment-driven preferences can coexist in the same visual areas. In particular, newborn face orienting may be the result of prenatal exposure of a learning system to internally generated input patterns, such as those found in PGO waves during REM sleep. Simulating this process with the HLISSOM biological model of the visual system, we demonstrate that the combination of learning and internal patterns is an efficient way to specify and develop circuitry for face perception. This prenatal learning can account for the newborn preferences for schematic and photographic images of faces, providing a computational explanation for how genetic influences interact with experience to construct a complex adaptive system. [This publication extends and supersedes results originally presented at the 2000 American Association for Artificial Intelligence conference.]
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Bibtex format
author = { James Bednar and Risto Miikkulainen },
title = {Learning innate face preferences.},
journal = {Neural Computation},
publisher = {MIT Press},
year = 2003,
volume = {12(7)},
pages = {1721-1740},
doi = {10.1162/089976603321891792},
url = {},

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