Connectionist Models of Orientation Identification

Abstract

Abstract We have used connectionist simulations in an attempt to understand how orientation tuned units similar to those found in the visual cortex can be used to perform psychophysical tasks involving absolute identification of stimulus orientation. In one task, the observer (or the network) was trained to identify which of two possible orientations had been presented, whereas in a second task there were 10 possible orientations that had to be identified. By determining asymptotic performance levels with stimuli separated to different extents it is possible to generate a psychophysical function relating identification performance to stimulus separation. Comparisons between the performance functions of neural networks with those found for human subjects performing equivalent tasks led us to the following conclusions. Firstly, we found that the ‘psychometric functions’ generated for the networks could accurately mimic the performance of the human observers. Secondly, the most important orientation selectiv...