| Visual scientists have shown that people are capable of perceptual learning in a large variety of circumstances. Nonetheless, the mechanisms mediating such learning are poorly understood. How flexible are these mechanisms? How are they constrained? We investigated these questions in two studies of perceptual learning. In both studies, we modeled subjects as observers performing probabilistic perceptual inferences to determine how their use of the available sensory information changed as a result of training. The first study consisted of five experiments examining the mechanisms of perceptual cue acquisition. Subjects were placed in novel environments containing systematic statistical relationships among scene and perceptual variables. These relationships could be either consistent or inconsistent with the types of sensory relationships that occur in natural environments. We found that subjects' learning was biased to favor statistical relationships consistent with those found in natural environments and proposed a new constraint on early perceptual learning to account for these results, defined in terms of Bayesian networks. The second study examined the mechanisms of learning in image-based perceptual discrimination tasks. Previous studies have demonstrated that people can integrate information from multiple perceptual cues in a statistically optimal manner when judging properties of surfaces in a scene. We wanted to determine whether subjects can learn to integrate optimally across arbitrary low-level visual features when making image-based discriminations. To investigate this question, we developed a novel and efficient modification of the classification image technique and conducted two experiments that explored subjects' discrimination strategies using this improved technique. We found that, with practice, subjects modified their decision strategies in a manner consistent with optimal feature combination, giving greater weight to reliable features and less weight to unreliable features. Thus, 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 learning to make image-based discriminations. |
