Noise is intuitively thought to interfere with perceptual learning; However, human and machine learning studies suggest that, in certain contexts, variability may reduce overfitting and improve generalizability. Whereas previous studies have examined the effects of variability in learned stimuli or tasks, it is hitherto unknown what are the effects of variability in the temporal environment. Here, we examined this question in two groups of adult participants (N=40) presented with visual targets

more »

... at either random or fixed temporal routines and then tested on the same type of targets at a new fixed temporal routine. Findings reveal that participants of the random group performed better and adapted quicker following a change in the timing routine, relative to participants of the fixed group. Corroborated with eye-tracking and computational modeling, these findings suggest that prior exposure to temporal randomness improves the formation of new temporal expectations and enhances generalizability in a dynamic environment.