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Growing evidence suggests that selective reweighting of the read-out connections from the sensory representations plays a major role in perceptual learning. Here we instantiate this idea in a computational model that takes grayscale images as inputs and learns on a trial-by-trial basis. The model develops the multi-channel perceptual template model (PTM, Dosher & Lu, 1998, PNAS) and extends it with a biologically plausible learning rule. The stimuli are processed by standard orientation-and frequency-tuned representational units, divisively normalized. Learning occurs only in the read-out connections to a decision unit; the stimulus representations never change. An incremental Hebbian rule tracks the task-dependent predictive value of each unit, thereby improving the signal-to-noise ratio of their weighted combination. Each abrupt change in the environmental statistics induces a switch cost in the learning …