Supramodal representation of temporal priors calibrates interval timing

Visual timing and auditory timing influence each other when time intervals in the two modalities are drawn from two adjacent distributions and are randomly intermixed. A Bayesian model with a supramodal prior (distribution of intervals from both modalities) outperforms the model using sensory-specific priors in describing participants’ performance. A generalized model further reveals that the prior is represented as a weighted average of the distribution of time intervals from the two modalities, which differ individually.