The Music-In-Noise Task (MINT): A Tool for Dissecting Complex Auditory Perception

The ability to segregate target sounds in noisy backgrounds is relevant both to neuroscience and to clinical applications. Recent research suggests that hearing-in-noise (HIN) problems are solved using combinations of sub-skills that are applied according to task demand and information availability. While evidence is accumulating for a musician advantage in HIN, the exact nature of the reported training effect is not fully understood. Existing HIN tests focus on tasks requiring understanding of speech in the presence of competing sound. Because visual, spatial and predictive cues are not systematically considered in these tasks, few tools exist to investigate the most relevant components of cognitive processes involved in stream segregation. We present the Music-In-Noise Task (MINT) as a flexible tool to expand HIN measures beyond speech perception, and for addressing research questions pertaining to the relative contributions of HIN sub-skills, inter-individual differences in their use, and their neural correlates. The MINT uses a match-mismatch trial design: in four conditions (Baseline, Rhythm, Spatial, and Visual) subjects first hear a short instrumental musical excerpt embedded in an informational masker of “multi-music” noise, followed by either a matching or scrambled repetition of the target musical excerpt presented in silence; the four conditions differ according to the presence or absence of additional cues. In a fifth condition (Prediction), subjects hear the excerpt in silence as a target first, which helps to anticipate incoming information when the target is embedded in masking sound. Data from samples of young adults show that the MINT has good reliability and internal consistency, and demonstrate selective benefits of musicianship in the Prediction, Rhythm, and Visual subtasks. We also report a performance benefit of multilingualism that is separable from that of musicianship. Average MINT scores were correlated with scores on a sentence-in-noise perception task, but only accounted for a relatively small percentage of the variance, indicating that the MINT is sensitive to additional factors and can provide a complement and extension of speech-based tests for studying stream segregation. A customizable version of the MINT is made available for use and extension by the scientific community.