Visual learning with reliable and unreliable features.

Existing studies of sensory integration demonstrate how the reliabilities of perceptual cues or features influence perceptual decisions. However, these studies tell us little about the influence of feature reliability on visual learning. In this article, we study the implications of feature reliability for perceptual learning in the context of binary classification tasks. We find that finite sets of training data (i.e., the stimuli and corresponding class labels used on training trials) contain different information about a learner's parameters associated with reliable versus unreliable features. In particular, the statistical information provided by a finite number of training trials strongly constrains the set of possible parameter values associated with unreliable features, but only weakly constrains the parameter values associated with reliable features. Analyses of human subjects' performances reveal that subjects were sensitive to this statistical information. Additional analyses examine why subjects were sub-optimal visual learners.