Effects of Age on Cortical Tracking of Word-Level Features of Continuous Competing Speech.
TL;DR: The authors investigated effects of age on cortical tracking of these word-level features within a two-talker speech mixture, and their relationship with self-reported difficulties with speech-in-noise understanding.
Abstract: Speech-in-noise comprehension difficulties are common among the elderly population, yet traditional objective measures of speech perception are largely insensitive to this deficit, particularly in the absence of clinical hearing loss. In recent years, a growing body of research in young normal-hearing adults has demonstrated that high-level features related to speech semantics and lexical predictability elicit strong centro-parietal negativity in the EEG signal around 400 ms following the word onset. Here we investigate effects of age on cortical tracking of these word-level features within a two-talker speech mixture, and their relationship with self-reported difficulties with speech-in-noise understanding. While undergoing EEG recordings, younger and older adult participants listened to a continuous narrative story in the presence of a distractor story. We then utilized forward encoding models to estimate cortical tracking of four speech features: (1) word onsets, (2) "semantic" dissimilarity of each word relative to the preceding context, (3) lexical surprisal for each word, and (4) overall word audibility. Our results revealed robust tracking of all features for attended speech, with surprisal and word audibility showing significantly stronger contributions to neural activity than dissimilarity. Additionally, older adults exhibited significantly stronger tracking of word-level features than younger adults, especially over frontal electrode sites, potentially reflecting increased listening effort. Finally, neuro-behavioral analyses revealed trends of a negative relationship between subjective speech-in-noise perception difficulties and the model goodness-of-fit for attended speech, as well as a positive relationship between task performance and the goodness-of-fit, indicating behavioral relevance of these measures. Together, our results demonstrate the utility of modeling cortical responses to multi-talker speech using complex, word-level features and the potential for their use to study changes in speech processing due to aging and hearing loss.
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TL;DR: The neural tracking framework enables the analysis of neural responses (EEG) to continuous natural speech, e.g., a story or a podcast as mentioned in this paper, which allows for objective investigation of a range of auditory and linguistic processes in the brain during natural speech perception.
Abstract: The neural tracking framework enables the analysis of neural responses (EEG) to continuous natural speech, e.g., a story or a podcast. This allows for objective investigation of a range of auditory and linguistic processes in the brain during natural speech perception. This approach is more ecologically valid than traditional auditory evoked responses and has great potential for both research and clinical applications. In this article, we review the neural tracking framework and highlight three prominent examples of neural tracking analyses. This includes the neural tracking of the fundamental frequency of the voice (f0), the speech envelope and linguistic features. Each of these analyses provides a unique point of view into the hierarchical stages of speech processing in the human brain. f0-tracking assesses the encoding of fine temporal information in the early stages of the auditory pathway, i.e. from the auditory periphery up to early processing in the primary auditory cortex. This fundamental processing in (mostly) subcortical stages forms the foundation of speech perception in the cortex. Envelope tracking reflects bottom-up and top-down speech-related processes in the auditory cortex, and is likely necessary but not sufficient for speech intelligibility. To study neural processes more directly related to speech intelligibility, neural tracking of linguistic features can be used. This analysis focuses on the encoding of linguistic features (e.g. word or phoneme surprisal) in the brain. Together these analyses form a multi-faceted and time-effective objective assessment of the auditory and linguistic processing of an individual.
12 citations
TL;DR: In this paper , neural tracking of the fundamental frequency of the voice (f0), the speech envelope and linguistic features is discussed. But the authors focus on the early stages of the auditory pathway, i.e., from the auditory periphery up to early processing in the primary auditory cortex.
9 citations
TL;DR: In this article , the authors investigated the effect of age on acoustic and linguistic processing of speech and found that older subjects showed shorter latencies for early acoustic responses to speech, while younger subjects showed slower responses to early utterances.
8 citations
TL;DR: This work uses a dual-talker continuous speech paradigm to demonstrate how a key parameter of experimental design, the quantity of acquired data, influences TRF analyses fit to either individual data (subject-specific analyses), or group data (generic analyses).
Abstract: In recent years, temporal response function (TRF) analyses of non-invasive recordings of neural activity evoked by continuous naturalistic stimuli have become increasingly popular for characterizing response properties within the auditory hierarchy. However, despite this rise in TRF usage, relatively few educational resources for these tools exist. Here we use a dual-talker continuous speech paradigm to demonstrate how a key parameter of experimental design, the quantity of acquired data, influences TRF analyses fit to either individual data (subject-specific analyses), or group data (generic analyses). We show that although model performance monotonically increases with data quantity, the amount of data required to achieve significant prediction accuracies can vary substantially based on whether the fitted model contains densely (e.g., acoustic envelope) or sparsely (e.g., lexical surprisal) spaced features, especially when the goal of the analyses is to capture the aspect of neural responses uniquely explained by specific features. Moreover, we demonstrate that generic models can exhibit high performance on small amounts of test data (2-8 min), if they are trained on a sufficiently large data set. As such, they may be particularly useful for clinical and multi-task study designs with limited recording time. Finally, we show that the regularization procedure used in fitting TRF models can interact with the quantity of data used to fit the models, with larger training quantities resulting in systematically larger TRF amplitudes. Together, demonstrations in this work should aid the learning process of new users of TRF analyses, and in combination with other tools, such as piloting and power analyses, may serve as a detailed reference for choosing acquisition duration in future studies.
4 citations
TL;DR: The authors investigated how continuous speech is represented in auditory cortex in the presence of interfering speech, in younger and older adults, and found that older adults needed a substantially longer integration time window to achieve their better reconstruction of the speech envelope.
Abstract: Understanding speech in a noisy environment is crucial in day-to-day interactions, and yet becomes more challenging with age, even for healthy aging. Age-related changes in the neural mechanisms that enable speech-in-noise listening have been investigated previously; however, the extent to which age affects the timing and fidelity of encoding of target and interfering speech streams are not well understood. Using magnetoencephalography (MEG), we investigated how continuous speech is represented in auditory cortex in the presence of interfering speech, in younger and older adults. Cortical representations were obtained from neural responses that time-locked to the speech envelopes using speech envelope reconstruction and temporal response functions (TRFs). TRFs showed three prominent peaks corresponding to auditory cortical processing stages: early (∼50 ms), middle (∼100 ms) and late (∼200 ms). Older adults showed exaggerated speech envelope representations compared to younger adults. Temporal analysis revealed both that the age-related exaggeration starts as early as ∼50 ms, and that older adults needed a substantially longer integration time window to achieve their better reconstruction of the speech envelope. As expected, with increased speech masking, envelope reconstruction for the attended talker decreased and all three TRF peaks were delayed, with aging contributing additionally to the reduction. Interestingly, for older adults the late peak was delayed, suggesting that this late peak may receive contributions from multiple sources. Together these results suggest that there are several mechanisms at play compensating for age-related temporal processing deficits at several stages, but which are not able to fully reestablish unimpaired speech perception. NEW & NOTEWORTHY We observed age-related changes in cortical temporal processing of continuous speech that may be related to older adults’ difficulty understanding speech in noise. These changes occur in both timing and strength of the speech representations at different cortical processing stages, and depend on both noise condition and selective attention. Critically, their dependency on noise condition changes dramatically among the early, middle, and late cortical processing stages, underscoring how aging differentially affects these stages.
3 citations
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TL;DR: In this paper, a different approach to problems of multiple significance testing is presented, which calls for controlling the expected proportion of falsely rejected hypotheses -the false discovery rate, which is equivalent to the FWER when all hypotheses are true but is smaller otherwise.
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