An integrated reweighting theory of perceptual learning

Abstract

Improvements in performance on visual tasks due to practice are often specific to a retinal position or stimulus feature. Many researchers suggest that specific perceptual learning alters selective retinotopic representations in early visual analysis. However, transfer is almost always practically advantageous, and it does occur. If perceptual learning alters location-specific representations, how does it transfer to new locations? An integrated reweighting theory explains transfer over retinal locations by incorporating higher level location-independent representations into a multilevel learning system. Location transfer is mediated through location-independent representations, whereas stimulus feature transfer is determined by stimulus similarity at both location-specific and location-independent levels. Transfer to new locations/positions differs fundamentally from transfer to new stimuli. After substantial initial training on an orientation discrimination task, switches to a new location or position are compared with switches to new orientations in the same position, or switches of both. Position switches led to the highest degree of transfer, whereas orientation switches led to the highest levels of specificity. A computational model of integrated reweighting is developed and tested that incorporates the details of the stimuli and the experiment. Transfer to an identical orientation task in a new position is mediated via more broadly tuned location-invariant representations, whereas changing orientation in the same position invokes interference or independent learning of the new orientations at both levels, reflecting stimulus dissimilarity. Consistent with single-cell recording studies, perceptual learning alters the weighting of both early and midlevel representations of the visual system.

Keywords: Hebbian models; reweighting models.

Fig. 1.

A schematic of the IRT includes location-independent representations (turquoise) and several location-specific representations (blue), each with internal additive noise (red). Learning reweights the connections from representation activations to a decision unit using Hebbian learning, with the benefit of bias control and feedback (SI Materials and Methods, Model Implementation of the Integrated Reweighting Theory). The location-independent weights to decision mediate location transfer, whereas the similarity of the optimized weight structure for different stimuli mediate feature/orientation transfer.

Fig. 2.

Contrast thresholds at 75% accuracy for the initial training phase (Left) and subsequent practice during the transfer phase (Right) after the task switch of the position (P), orientation (O), or both (OP), averaged over observers. Higher contrast threshold curves are for tests in high external noise, whereas lower contrast threshold curves are for tests without external noise. Error bars are estimated by Monte Carlo methods, and smooth curves represent best fitting power functions of practice.

Fig. 3.

Predictions of the best fitting IRT with the experimental data points. Model parameters of the best fitting model were estimated by grid search methods and are listed in Table 1.