Performance-monitoring integrated reweighting model of perceptual learning

Abstract

Perceptual learning (PL) has been traditionally thought of as highly specific to stimulus properties, task and retinotopic position. This view is being progressively challenged, with accumulating evidence that learning can generalize (transfer) across various parameters under certain conditions. For example, retinotopic specificity can be diminished when the proportion of easy to hard trials is high, such as when multiple short staircases, instead of a single long one, are used during training. To date, there is a paucity of mechanistic explanations of what conditions affect transfer of learning. Here we present a model based on the popular Integrated Reweighting Theory model of PL but departing from its one-layer architecture by including a novel key feature: dynamic weighting of retinotopic-location-specific vs location-independent representations based on internal performance estimates of these representations. This dynamic weighting is closely related to gating in a mixture-of-experts architecture. Our dynamic performance-monitoring model (DPMM) unifies a variety of psychophysical data on transfer of PL, such as the short-vs-long staircase effect, as well as several findings from the double-training literature. Furthermore, the DPMM makes testable predictions and ultimately helps understand the mechanisms of generalization of PL, with potential applications to vision rehabilitation and enhancement.