Perceptual learning reflects external noise filtering and internal noise reduction through channel reweighting

Abstract

To investigate the nature of plasticity in the adult visual system, perceptual learning was measured in a peripheral orientation discrimination task with systematically varying amounts of external (environmental) noise. The signal contrasts required to achieve threshold were reduced by a factor or two or more after training at all levels of external noise. The strong quantitative regularities revealed by this novel paradigm ruled out changes in multiplicative internal noise, changes in transducer nonlinearites, and simple attentional tradeoffs. Instead, the regularities specify the mechanisms of perceptual learning at the behavioral level as a combination of external noise exclusion and stimulus enhancement via additive internal noise reduction. The findings also constrain the neural architecture of perceptual learning. Plasticity in the weights between basic visual channels and decision is sufficient to account for perceptual learning without requiring the retuning of visual mechanisms.

Figure 1

Procedure and results of the perceptual learning experiment. (a) Layout of the displays. Threshold estimation by a 3/1 (b) and 2/1 staircase (c) (19). (d) Contrast thresholds for orientation discrimination were estimated at each of eight levels of environmental (external) noise. Contrasts at threshold are shown averaged over observers and pairs of days. Contrast thresholds for the more stringent criterion (e) and the less stringent criterion (f). Smooth curves are fits of the Perceptual Template Model (PTM) (Fig. 2). Perceptual learning at the two criterion levels identifies the improvements as a hybrid of external noise exclusion and signal enhancement.

Figure 2

Signature patterns of change in performance during perceptual learning associated with signal enhancement, external noise exclusion, and internal multiplicative noise reduction. (a) The Perceptual Template Model (–18). (b) Practice enhances the stimulus as processed by the perceptual template, leading to improvement in performance only in the region where performance is limited by internal noise (A_a reduction). (c) Practice improves external noise exclusion by changing the perceptual template, resulting in improved thresholds at high levels of external noise (A_f reduction). (e) Practice reduces internal multiplicative noise, yielding improvements in performance over the range of external noise (A_m reduction).

Figure 3

Schematic of early visual system channels and the changes with perceptual learning. A Gabor patch in noise is processed by visual channels tuned to different spatial frequencies and orientations (14). Channels tuned to a range of spatial frequencies and their corresponding bandpass stimuli are illustrated. Each channel has additive and multiplicative noise as well as nonlinearities. Early in training, several channels are active and connected to a decision structure. After training, the most closely tuned channel has the most active inputs to a decision module, whereas that of other channels has been reduced.