Characterizing perceptual learning using regression statistics: Development of a perceptual calibration index

Abstract

Abstract Perceptual learning, improvement in perceptual skill with practice, can improve both accuracy and consistency of perceptual reports. Regression statistics can quantify ongoing calibration of perceptible scalar properties (i.e., improvements in accuracy and consistency) because, ideally, actual and perceived values are linearly related. Changes in variance accounted for (r2) track changes in consistency, and changes in both slope and intercept track changes in accuracy. Conjoint changes in all three regression statistics, obscured in separate plots, can be seen simultaneously in a perceptual calibration state space diagram, with the regression statistics as axes, in which an attractor (r2 = 1.00, slope = 1.00, intercept = 0.00) represents optimal performance. Decreases in the distance between the attractor and successive points in the state space, each representing perceptual performance, quantify perceptual learning; that distance is a perceptual calibration index. To show the utility of the perceptual calibration index, we illustrate its use in an experiment on wielding hand-held objects.