Vocal motor development in infancy provides a crucial foundation for language
development. Some significant early accomplishments include learning
to control the process of phonation (the production of sound at the larynx)
and learning to produce the sounds of one's language. Previous work has
shown that social reinforcement shapes the kinds of vocalizations infants
produce. We present a neural network model that provides an account of
how vocal learning may be guided by reinforcement. The model consists
of a self-organizing map that outputs to muscles of a realistic vocalization
synthesizer. Vocalizations are spontaneously produced by the network. If a
vocalization meets certain acoustic criteria, it is reinforced, and the weights
are updated to make similar muscle activations increasingly likely to recur.
We ran simulations of the model under various reinforcement criteria and
tested the types of vocalizations it produced after learning in the different
conditions. When reinforcement was contingent on the production of phonated (i.e. voiced) sounds, the network's post learning productions were almost always phonated, whereas when reinforcement was not contingent on phonation, the network's post-learning productions were almost always not phonated. When reinforcement was contingent on both phonation and proximity to English vowels as opposed to Korean vowels, the model's postlearning productions were more likely to resemble the English vowels and vice versa.