Perceptual Learning at Higher Trained Cutoff Spatial Frequencies Induces Larger Visual Improvements

Abstract

It is well known that extensive practice of a perceptual task can improve visual performance, termed perceptual learning. The goal of the present study was to evaluate the dependency of visual improvements on the features of training stimuli (i.e., spatial frequency). Twenty-eight observers were divided into training and control groups. Visual acuity (VA) and contrast sensitivity function (CSF) were measured and compared before and after training. All observers in the training group were trained in a monocular grating detection task near their individual cutoff spatial frequencies. The results showed that perceptual learning induced significant visual improvement, which was dependent on the cutoff spatial frequency, with a greater improvement magnitude and transfer of perceptual learning observed for those trained with higher spatial frequencies. However, VA significantly improved following training but was not related to the cutoff spatial frequency. The results may broaden the understanding of the nature of the learning rule and the neural plasticity of different cortical areas.

Keywords: contrast sensitivity function; cutoff spatial frequency; perceptual learning; visual acuity; visual improvement.

FIGURE 1

(A) Exponential function with the three parameters. (B) Time constant (γ) as a function of the cutoff spatial frequency. Each symbol represents an observer. The solid line is the best fitting regression line.

FIGURE 2

(A) CS improvement at the trained spatial frequency as a function of the cutoff spatial frequency. (B) AULCSF improvement as a function of the cutoff spatial frequency. The solid line is the best fitting regression line.

FIGURE 3

The bandwidth of perceptual learning as a function of the cutoff spatial frequency.

FIGURE 4

(A) The correlation of pre-training and post-training VA. The dashed line is the identity line with a slope of 1. The data points representing improved VA are located below this line. The solid line is the best-fitting linear model. (B) VA improvement as a function of the cutoff spatial frequency. The solid line is the best fitting regression line.