Coordinating Actions

The motor system composes complex actions by combining simpler submovements. This presumably involves sharing information about the progress of one submovement with the centres controlling another submovement, to ensure that the second happens in an appropriate relation to the first. This process is called coordination. In this paper Idiscuss evidence that coordinating actions indeed involves an active process of sharing information about the current state of movements. Coordination appears to be qualitatively different from the process of reacting to external stimuli. This may reflect the importance of predictive representations in coordination. Finally, the processes underlying coordination appear to be organized in a response-specific fashion, as a number of relatively independent circuits. The development and tuning of these circuits may, in part, be what makes an action "skilled".     

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.