Binocular cues in the perception of distance of a point source of light

The ability to make egocentric distance estimates of a single point source of light, seen in darkness and without the cues of changing size and luminance, was investigated in sixteen observers. The attenuation required to maintain constant luminance, when the target was viewed from different distances, was shown to follow the inverse square law providing the angle subtended by the light was less than 20 s arc. Distance changes were also simulated by means of a split mirror which produced vergence cues, or by test lenses to provide accommodation cues. Over the range 0.5 to 9.2 m distance estimates were surprisingly accurate, although there was some overestimation of near and underestimation of far distances. Most observers made good judgements when only convergence cues were varied, whereas no observers made consistently good judgements when only accommodation cues were varied. The difficulties are discussed in terms of the accommodation-convergence link. When distance was simulated by changing convergence and accommodation cues, estimates were not as good as when real distance was changed. Since good estimates were made with brief target exposures, these judgements were not based on subsequent convergence or accommodation changes. It is suggested that the metric or reference against which the apparently absolute judgements were made was the efferent demand signal associated with a 'resting' position of convergence in darkness.     

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.