Expectations boost the reconstruction of auditory features from electrophysiological responses to noisy speech

Abstract

Online speech processing imposes significant computational demands on the listening brain. Predictive coding provides an elegant account of the way this challenge is met through the exploitation of prior knowledge. While such accounts have accrued considerable evidence at the sublexical- and word-levels, relatively little is known about the predictive mechanisms that support sentence-level processing. Here, we exploit the 'pop-out' phenomenon (i.e. dramatic improvement in the intelligibility of degraded speech following prior information) to investigate the psychophysiological correlates of sentence comprehension. We recorded electroencephalography and pupillometry from 21 humans (10 females) while they rated the clarity of full sentences that had been degraded via noise-vocoding or sine-wave synthesis. Sentence pop-out was reliably elicited following visual presentation of the corresponding written sentence, despite never hearing the undistorted speech. No such effect was observed following incongruent or no written information. Pop-out was associated with improved reconstruction of the acoustic stimulus envelope from low-frequency EEG activity, implying that pop-out is mediated via top-down signals that enhance the precision of cortical speech representations. Spectral analysis revealed that pop-out was accompanied by a reduction in theta-band power, consistent with predictive coding accounts of acoustic filling-in and incremental sentence processing. Moreover, delta- and alpha-band power, as well as pupil diameter, were increased following the provision of any written information. We interpret these findings as evidence of a transition to a state of active listening, whereby participants selectively engage attentional and working memory processes to evaluate the congruence between expected and actual sensory input.

Expectations boost the reconstruction of auditory features from electrophysiological responses to noisy speech

“…De-identified raw and preprocessed data are openly available on the Open Science Framework platform: https://osf.io/5qxds ( Corcoran et al. 2021 ).…”

Oscillatory and aperiodic neural activity jointly predict language learning

“…Indeed, a considerable proportion of work examining the oscillatory correlates of higher-order language processing have not explicitly accounted for modulations in broadband aperiodic activity (e.g., Bonhage et al, 2017; Corcoran et al, 2022; Kepinska et al, 2017; Lewis et al, 2016; Mai, Minett, & Wang, 2016; Prat et al, 2016; Rossi & Prystauka, 2020; c.f., Cao et al, 2022), making it difficult to determine whether oscillatory activity parsimoniously explains behavioural outcomes. By separating oscillatory and aperiodic components, we have demonstrated that the aperiodic exponent flattens across time, while, for example, theta and alpha power increase across time throughout the language learning phase.…”