We study neural network models of discriminating between stimuli with
two similar angles, using the two-alternative forced choice (2AFC) par
adigm. Two network architectures are investigated: a two-layer percept
ron network and a gating network. In the two-layer network all hidden
units contribute to the decision at all angles, while in the other arc
hitecture the gating units select, for each stimulus, the appropriate
hidden units that will dominate the decision. We find that both archit
ectures can perform the task reasonably well for all angles. Perceptua
l learning has been modeled by training the networks to perform the ta
sk, using unsupervised Hebb learning algorithms with pairs of stimuli
at fixed angles theta and delta theta. Perceptual transfer is studied
by measuring the performance of the network on stimuli with theta' not
equal theta. The two-layer perceptron shows a partial transfer for an
gles that are within a distance a from theta, where a is the angular w
idth of the input tuning curves The change in performance due to learn
ing is positive for angles close to theta, but for \theta - theta'\ ap
proximate to a it is negative, i.e., its performance after training is
worse than before. In contrast, negative transfer can be avoided in t
he gating network by limiting the effects of learning to hidden units
that are optimized for angles that are close to the trained angle.