Individual differences during acquisition predict shifts in generalization

• Intra-dimensional discrimination learning only led to peak shifts in generalization gradients for some individuals.
• Both discrimination acquisition and decision bias in training related to the degree of shift.
• Individual differences were not easily accounted for by any one class of peak shift theory.

Learning to distinguish subtle differences in objects or events can impact how one generalizes. In some cases, training can cause novel events to appear more familiar or attractive than those actually experienced during training: the peak shift effect. This study examined whether individual differences in learning led to systematic patterns of generalization. Participants were trained to identify simulated birdsongs, and then tested on their ability to identify a target song presented among several similar songs that differed in pitch. Initial analysis showed that those attaining moderate proficiency at discriminating songs during training were more likely to shift than those performing poorly or proficiently. However, a neural network trained to output individuals’ gradient dynamics using only performance during training as input found an additional set of training variables that predicted shift. Specifically, one subset of shifters had highly conservative response biases accompanied by very little change to perceptual sensitivity in training. These findings suggest that discrimination learning may only lead to generalization shifts in some individuals, and that all individuals who shift may not do so for the same reason.