Perceptual learning of oriented gratings as revealed by classification images

Abstract

Classification image analysis is a psychophysical technique in which noise components of stimuli are analyzed to produce an image that reveals critical features of a task. Here we use classification images to gain greater understanding of perceptual learning. To achieve reasonable classification images within a single session, we developed an efficient classification image procedure that employed designer noise and a low-dimensional stimulus space. Subjects were trained across ten sessions to detect the orientation of a grating masked in noise, with an eleventh, test, session conducted using a stimulus orthogonal to the trained stimulus. As with standard perceptual learning studies, subjects showed improvements in performance metrics of accuracy, threshold, and reaction times. The clarity of the classification images and their correlation to an ideal target also improved across training sessions in an orientation-specific manner. Furthermore, image-based analyses revealed aspects of performance that could not be observed with standard performance metrics. Subjects with threshold improvements learned to use pixels across a wider area of the image, and, apposed to subjects without threshold improvements, showed improvements in both the bright and dark parts of the image. We conclude that classification image analysis is an important complement to traditional metrics of perceptual learning.