Distinguishing fine structure and summary representation of sound textures from neural activity

Abstract

The auditory system relies on both local and summary representations; acoustic local features exceeding system constraints are compacted into a set of summary statistics. Such compression is pivotal for sound-object recognition. Here, we assessed whether computations subtending local and statistical representations of sounds could be distinguished at the neural level. A computational auditory model was employed to extract auditory statistics from natural sound textures (i.e., fire, rain) and to generate synthetic exemplars where local and statistical properties were controlled. Twenty-four human participants were passively exposed to auditory streams while the EEG was recorded. Each stream could consist of short, medium, or long sounds to vary the amount of acoustic information. Short and long sounds were expected to engage local or summary statistics representations, respectively. Data revealed a clear dissociation. Compared to summary-based ones, auditory-evoked responses based on local information were selectively greater in magnitude in short sounds. Opposite patterns emerged for longer sounds. Neural oscillations revealed that local features and summary statistics rely on neural activity occurring at different temporal scales, faster (beta) or slower (theta-alpha). These dissociations emerged automatically without explicit engagement in a discrimination task. Overall, this study demonstrates that the auditory system developed distinct coding mechanisms to discriminate changes in the acoustic environment based on fine structure and summary representations. SIGNIFICANCE STATEMENTPrior to this study, it was unknown whether we could measure auditory discrimination based on local temporal features or spectrotemporal statistics properties of sounds from brain responses. Results show that the two auditory modes of sound discrimination (local and summary statistics) are automatically attuned to the temporal resolution (high or low) at which a change has occurred. In line with the temporal resolutions of auditory statistics, faster or slower neural oscillations (temporal scales) code sound changes based on local or summary representations. These findings expand our knowledge of some fundamental mechanisms underlying the function of the auditory system.