Perceptual Categorization: Connectionist Modelling and Decision Rules

Although it is currently popular to model human associative learning using connectionist networks, the mechanism by which their output activations are converted to probabilities of response has received relatively little attention. Several possible models of this decision process are considered here, including a simple ratio rule, a simple difference rule, their exponential versions, and a winner-take-all network. Two categorization experiments that attempt to dissociate these models are reported. Analogues of the experiments were presented to a single-layer, feed-forward, delta-rule network. Only the exponential ratio rule and the winner-take-all architecture, acting on the networks' output activations that corresponded to responses available on test, were capable of fully predicting the mean response results. In addition, unlike the exponential ratio rule, the winner-take-all model has the potential to predict latencies. Further studies will be required to determine whether latencies produced under more stringent conditions conform to the model's predictions.     

Adaptation to displacing prisms in human infants.

This research reports a first attempt to study adaptation to laterally displacing prisms in human infants of ages six to ten months. Twenty infants were tested over four days under conditions similar to those used in adult adaptation studies. Evidence was found of vigorous adaptation when infants were exposed to base-right prisms under conditions of active exposure. Detailed consideration is given to the conditions and methods of studying adaptation in infants and implications for adult studies.