Sound degradation type differentially affects neural indicators of cognitive workload and speech tracking

Abstract

Hearing-in-noise (HIN) is a challenging task that is essential to human functioning in social, vocational, and educational contexts. Successful speech perception in noisy settings is thought to rely in part on the brain's ability to enhance neural representations of attended speech. In everyday HIN situations, important features of speech (i.e., pitch, rhythm) may be degraded in addition to being embedded in noise. The impact of these differences in sound quality on experiences of workload and neural representations of speech will be important for informing our knowledge on the cognitive demands imposed by every-day difficult listening situations. We investigated HIN perception in 20 healthy adults using continuous speech that was either clean, spectrally degraded, or temporally degraded. Each sound condition was presented both with and without pink noise. Participants engaged in a selective listening task, in which a short-story was presented with varying sound quality, while EEG data were recorded. Neural correlates of cognitive workload were obtained using power levels of two frequency bands sensitive to task difficulty manipulations: alpha (8 – 12 Hz) and theta (4 – 8 Hz). Acoustic and linguistic features (speech envelope, word onsets, word surprisal) were decoded to reveal the degree to which speech was successfully encoded. Overall, alpha-theta power increased significantly when noise was added across sound conditions, while prediction accuracy of speech tracking decreased, suggesting that more effort was required to listen, and that the speech was not as successfully encoded. The temporal degradation also resulted in greater EEG power, possibly as a function of a compensatory mechanism to restore the important temporal information required for speech comprehension. Our findings suggest that measures related to cognitive workload and successful speech encoding are differentially affected by noise and sound degradations, which may help to inform future interventions that aim to mitigate these every-day challenges.