Augmented Hebbian reweighting: interactions between feedback and training accuracy in perceptual learning

Feedback plays an interesting role in perceptual learning. The complex pattern of empirical results concerning the role of feedback in perceptual learning rules out both a pure supervised mode and a pure unsupervised mode of learning and leads some researchers to the proposal that feedback may change the learning rate through top-down control but does not act as a teaching signal in perceptual learning (M. H. Herzog & M. Fahle, 1998). In this study, we tested the predictions of an augmented Hebbian reweighting model (AHRM) of perceptual learning (A. Petrov, B. A. Dosher, & Z.-L. Lu, 2005), in which feedback influences the effective rate of learning by serving as an additional input and not as a direct teaching signal. We investigated the interactions between feedback and training accuracy in a Gabor orientation identification task over six training days. The accelerated stochastic approximation method was used to track threshold contrasts at particular performance accuracy levels throughout training. Subjects were divided into 4 groups: high training accuracy (85% correct) with and without feedback, and low training accuracy (65%) with and without feedback. Contrast thresholds improved in the high training accuracy condition, independent of the feedback condition. However, thresholds improved in the low training accuracy condition only in the presence of feedback but not in the absence of feedback. The results are both qualitatively and quantitatively consistent with the predictions of the augmented Hebbian learning model and are not consistent with pure supervised error correction or pure Hebbian learning models.