In this talk, I aim to integrate two approaches to understanding the computations that underlie the development of the human mind. One tradition holds that the mind is best characterized as a computer-like manipulator of symbols, the other that the mind is best characterized as a large network of neurons works in parallel. In the first part of my talk, I show how these apparently conflicting traditions can be reconciled, using some experimental data on "language learning" in human infants as a case study. In the second part of the talk, I will focus on what it might mean for a particular neural network to be "innate".
For the colloquium series schedule, see the UMD Computational Linguistics Colloquium Series web page at http://umiacs.umd.edu/~resnik/cl_colloquium/. If you are interested in meeting with the speaker, please contact Denise Best (denise@cfar.umd.edu).