The variability of natural scenes places perception in the realm of statistical inference. In this context, perceptual tasks may be optimized if the invariant statistical structure of sensory cues is built into the neural processing. We investigated this question in human sound localization, where interaural time difference (ITD) is a key sensory cue for inferring the direction of acoustic signals in the horizontal plane. ITD statistics were estimated from human head-related transfer functions (HRTFs) and properties of cochlear filters. As previously shown, ITD changed with azimuth following a sigmoid relationship, whose slope was steepest at the center in most frequencies. However, ITD was more variable over time for sounds located in the periphery compared to the center, in a frequency-dependent manner. We tested the hypothesis that these statistics, ITD rate of change with azimuth (ITDrc) and ITD variability (ITDv), are anticipated by the human brain, influencing spatial discriminability and novelty detection. Our results showed that thresholds for discriminating ITD changes could be better predicted by a model relying on both ITDrc and ITDv than on ITDrc alone. In addition, we tested how novelty detection in a spatial oddball paradigm was weighted by anticipated ITD statistics, as indexed by the amplitude of the mismatch negativity (MMN) brain signal, a pre-attentive indicator of novelty detection. ITDrc and ITDv predicted MMN amplitude, showing a significantly stronger effect of ITD statistics corresponding to repeated (standard) than sporadic (deviants) stimuli. We further show that these statistics could be represented in parameters underlying ITD discriminability postulated by classic neural models. These results show that spatial discriminability thresholds and novelty detection are consistent with a representation of anticipated ITD statistics in the brain, supporting the hypothesis that high-order statistics are built into human perceptual processes.
- Natural statistics
- Sound localization
ASJC Scopus subject areas
- Biochemistry, Genetics and Molecular Biology(all)
- Agricultural and Biological Sciences(all)
- Immunology and Microbiology(all)
- Pharmacology, Toxicology and Pharmaceutics(all)