When novel stimuli trigger a previously learned response, this can be due to failure to perceive the novel stimulus as different from the trained stimulus (perception), or active extrapolation of learned properties from the trained stimulus (induction). To date, there has been little investigation of how individual differences in perceptual ability relate to differences in induction. In this paper, we perform cluster analysis in six datasets (four published datasets and two unpublished datasets, N = 992 total) to examine the relationship between individual differences in perception and induction, as well as the utility of perception in predicting generalization gradients. The datasets were obtained from predictive learning tasks where participants learned associations between different colored cues and the presence or absence of a hypothetical outcome. In these datasets, stimulus perception and response generalization (expectancy ratings) were assessed in separate phases. Using cluster analyses, we identified similar subgroups of good and bad perceivers in all six datasets, with distinct patterns of response generalization between these subgroups. Based on the differences in stimulus perception, we could predict where across the stimulus range generalized responses would differ between subgroups as well as the direction of the difference. Furthermore, participants classified as good perceivers were more likely to report a similarity generalization rule than a relational or linear rule, providing evidence that individual differences in perception predict differences in induction. These findings suggest that greater consideration should be given to inter-individual variability in perception and induction and their relationship in explaining response generalization.