Learning optimal integration of arbitrary features in a perceptual discrimination task

• Published in: Journal of vision (Charlottesville, Va.) Va.), 2008-02, Vol.8 (2), p.3.1-316
• Main Authors: Michel, Melchi M, Jacobs, Robert A
• Format: Article
• Language: English
• Subjects: Discrimination (Psychology) - physiology; Humans; Learning - physiology; Pattern Recognition, Visual - physiology; Photic Stimulation
• ISSN: 1534-7362; 1534-7362
• DOI: 10.1167/8.2.3

A number of studies have demonstrated that people often integrate information from multiple perceptual cues in a statistically optimal manner when judging properties of surfaces in a scene. For example, subjects typically weight the information based on each cue to a degree that is inversely proportional to the variance of the distribution of a scene property given a cue's value. We wanted to determine whether subjects similarly use information about the reliabilities of arbitrary low-level visual features when making image-based discriminations, as in visual texture discrimination. To investigate this question, we developed a modification of the classification image technique and conducted two experiments that explored subjects' discrimination strategies using this improved technique. We created a basis set consisting of 20 low-level features and created stimuli by linearly combining the basis vectors. Subjects were trained to discriminate between two prototype signals corrupted with Gaussian feature noise. When we analyzed subjects' classification images over time, we found that they modified their decision strategies in a manner consistent with optimal feature integration, giving greater weight to reliable features and less weight to unreliable features. We conclude that optimal integration is not a characteristic specific to conventional visual cues or to judgments involving three-dimensional scene properties. Rather, just as researchers have previously demonstrated that people are sensitive to the reliabilities of conventionally defined cues when judging the depth or slant of a surface, we demonstrate that they are likewise sensitive to the reliabilities of arbitrary low-level features when making image-based discriminations.