Natural ITD statistics are built into human sound localization

Anticipating invariant statistical structure of sensory cues may optimize perception. We investigated this hypothesis in human sound localization, focusing on context-invariant interaural time difference (ITD) statistics. Natural ITD rate of change across azimuth (ITDrc) and a novel metric of ITD variability over time (ITDv) were estimated using head-related transfer functions (HRTFs) and modeled cochlear filters. Using protocols which disabled ITDrc estimation and delivered zero-ITDv stimuli, we found that spatial discriminability and novelty detection are consistent with the hypothesis that the brain represents and predicts natural ITD statistics, exploiting them for optimizing discrimination. We further show that natural ITD statistics could be represented under coding frameworks postulated by classic neural models, highlighting the validity of these models and providing potential explanation for the observed distribution of best ITDs across frequency found in mammalian brainstem. These findings support the hypothesis that natural ITD statistics are built into the neural substrate underlying perception.