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Guy Wallis

Publications and source records attributed to Guy Wallis.

2 recordsLinked to original sources

An unexpected role for visual feedback in vehicle steering control.

Some motor tasks can be completed, quite literally, with our eyes shut. Most people can touch their nose without looking or reach for an object after only a brief glance at its location. This distinction leads to one of the defining questions of movement control: is information gleaned prior to starting the movement sufficient to complete the task (open loop), or is feedback about the progress of the movement required (closed loop)? One task that has commanded considerable interest in the literature over the years is that of steering a vehicle, in particular lane-correction and lane-changing tasks. Recent work has suggested that this type of task can proceed in a fundamentally open loop manner, with feedback mainly serving to correct minor, accumulating errors. This paper reevaluates the conclusions of these studies by conducting a new set of experiments in a driving simulator. We demonstrate that, in fact, drivers rely on regular visual feedback, even during the well-practiced steering task of lane changing. Without feedback, drivers fail to initiate the return phase of the maneuver, resulting in systematic errors in final heading. The results provide new insight into the control of vehicle heading, suggesting that drivers employ a simple policy of "turn and see," with only limited understanding of the relationship between steering angle and vehicle heading.

Automobile Driving↗

Using Spatio-temporal Correlations to Learn Invariant Object Recognition.

A competitive network is described which learns to classify objects on the basis of temporal as well as spatial correlations. This is achieved by using a Hebb-like learning rule which is dependent upon prior as well as current neural activity. The rule is shown to be capable of outperforming a supervised rule on the cross validation test of an invariant character recognition task, given a relatively small training set. It is also shown to outperform the supervised version of Fukushima's Neocognitron ([Fukushima, 1980]), on a larger training set. Copyright 1996 Elsevier Science Ltd.

Journal Article↗