Cognitive Approaches to Human Perception

immediately attend to the most discriminating region, the greater strength of negative connection between the input unit responsible for this region and upper layers of units would result in a rapid decrease of activation in the output unit. On the other hand, when a standard pattern is presented, the input unit in the most discriminating region remains off and does not propagate its high negative signal to the output unit. In consequence, the output unit is more likely to fire. In the case of untrained participants, who neither attend to nor weight heavily the most discriminating regions, the buildup and reduction of activity in the output unit is not so rapid.

One final connectionist approach to the explanation of the current results is a model of attentional learning containing a sequentially allocatable spotlight of attention mechanism ( Schreter & Latimer, 1992). This model, like the respondents in the present experiments, learns to focus its attentional spotlight on the most discriminating parts of the input field. After learning is finished the network starts scanning the input pattern at the most discriminative features and tends to limit scanning to those features that are necessary and sufficient to discriminate the patterns from each other. As such, the model displays many of the characteristics observed in the recognition of geometric forms by human participants.

This research was supported by University of Sydney Research Grants to the first author and an Australian Commonwealth Postgraduate Award to the second author. MacBrain is a trademark of Neurix, Boston, Massachusetts. MacLab is a trademark of ADInstruments, Dunedin, New Zealand.

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