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Abstract
Recurrent neural networks can be trained to recognize strings generated by context-free grammars, but the ability of the networks to do so depends on their having an appropriate set of initial connection weights. Simulations of evolution were performed on populations of simple recurrent networks where the selection criterion was the ability of the networks to recognize strings generated by grammars. The networks evolved sets of initial weights from which they could reliably learn to recognize the strings. In order to recognize if a string was generated by a given context-free grammar, it is necessary to use a stack or counter to keep track of the depth of embedding in the string. The networks that evolved in our simulations are able to use the values passed along their recurrent connections for this purpose. Furthermore, populations of networks can evolve a bias towards learning the underlying regularities in a class of related languages. These results suggest a new explanation for the ``critical period'' effects observed in the acquisition of language and other cognitive faculties. Instead of being the result of an exogenous maturational process, the degraded acquisition ability may be the result of the values of innately specified initial weights diverging in response to training on spurious input.BibTex
@inproceedings{batali94innateBiases,
author={J. Batali},
title={Innate biases and critical periods: Combining evolution and learning in the acquisition of syntax},
year={1994},
pages={160-171},
address={Cambridge, MA},
editor={R. Brooks and P. Maes},
publisher={MIT Press},
booktitle={Artificial Life IV},
url={http://www.isrl.uiuc.edu/~amag/langev/paper/batali94innateBiases.html}
}
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