Biology subjects

Mankel, K.

Publications and source records attributed to Mankel, K..

7 recordsLinked to original sources

bioRxiv2026

Predicting Speech in Noise Perception using Measures of Neural Entrainment

Speech perception ability in a cocktail party environment is highly variable, even for people with clinically healthy hearing. The mechanisms of these differences are poorly understood. A possible reason for these differences could be due to the neural mechanisms of auditory attention. This investigation aims to quantify how well a person is able to neurally entrain to a continuous narrative using a temporal response function (TRF), then evaluate if that TRF measure is correlated with an individual's performance on a selective attention and narrative comprehension task. This was accomplished using a cohort of twenty-five young and healthy listeners who completed speech-in-noise (SIN) perception tasks while their neural activity was recorded with EEG. This EEG data and regressors (speech envelope and gammatone spectrogram) of an auditory stimulus was used to train TRF models. TRF cross validation correlation coefficients were then used in multiple regression models to predict a participant's SIN performance. Task performance had a significant positive relationship with the neural entrainment to an attended stimulus and a negative relationship with the neural entrainment to an unattended stimulus. This was observed with both the selective attention and narrative comprehension measures. The difference of the attended and unattended correlation coefficients were also significantly related to increased task performance. These measures could be a potential biomarker to predict an individual's speech perception in a noisy environment.

neuroscience↗

bioRxiv2026

Chirped Speech (Cheech) Enables Rapid Assessment of Multi-Level Auditory Evoked Potentials During Speech-in-Noise Recognition

Difficulties understanding speech in noise remain a common complaint even among listeners with normal hearing sensitivity, highlighting the need for objective, more effective measures of real-world listening. The goal of this study was to validate the use of a novel, chirped-speech (Cheech) stimulus - continuous, naturally-spoken speech fused with chirps designed to elicit robust auditory evoked potentials - to characterize relationships between speech recognition, listening effort, and auditory neural encoding. Twenty-five normal-hearing adults completed a sentence-recognition task using both original (unmodified) and Cheech-modified AzBio sentence lists in quiet, +3 dB, and -3 dB signal-to-noise ratio (SNR) conditions while neural responses from the brainstem through cortex were recorded simultaneously. Speech recognition remained near ceiling in quiet but declined with decreasing SNR for both original and Cheech stimuli. Compared with clean speech, Cheech-modified speech showed slightly poorer recognition performance as SNR decreased and somewhat higher perceived effort overall. Yet, Cheech was highly effective at evoking auditory responses from the brainstem (auditory brainstem response, ABR) through the cortex (including middle- and late-latency responses, MLR and LLR) even with <5 minutes listening time per condition. Neural responses showed reduced amplitudes and prolonged latencies as SNR decreased. In general, ABR latencies and wave I amplitudes were associated with speech-in-noise recognition performance, whereas cortical responses (MLR Na, Nb, and LLR P1) were associated with subjective workload. These findings show that Cheech-modified speech preserves intelligibility while yielding robust, multilevel neural recordings during sentence perception, offering a promising approach to examine hierarchical auditory processing under ecologically relevant speech-in-noise conditions.

neuroscience↗

bioRxiv2026

Neural microstates underlying categorical speech perception using Bayesian nonparametrics

Categorical perception (CP) reflects the human auditory systems ability to map continuous acoustic signals onto discrete categories. Understanding the relationship between neural dynamics and perceptual decisions is central to speech-language processing. Here, we implemented a data-driven approach using Bayesian nonparametrics and machine learning to characterize the relationship between auditory cortical responses and speech categorization behaviors. By applying a hierarchical Dirichlet process hidden Markov Model (HDP-HMM) to source-reconstructed event-related potential (ERP) data, we identified temporally distinct neural microstates that capture the evolving stages of speech categorization without imposing predefined temporal windows. Machine learning classifiers, including extreme gradient boosting (XGBoost), support vector machines, and random forests, were applied to decode prototypical (Tk1/5) versus ambiguous (Tk3) speech sound tokens from the microstate data. Using whole-brain activity, the XGBoost classifier achieved the highest decoding accuracy of 94.1% with an area under the curve (AUC) 94.1% within a specific encoding microstate occurring approximately 200-250 ms after stimulus onset. A reduced set of 15 informative brain regions identified via Shapley additive explanations (SHAP) yielded comparable classification performance (90.3% accuracy; AUC 90.0%), with many regions localized to frontal, temporal, and parietal regions in the left hemisphere. Furthermore, neural activity within these regions robustly predicted listeners behavioral identification slopes (R2 = 0.92, p < 0.00001), linking microstate-specific cortical dynamics to individual differences in perceptual gradiency of speech perception. These findings demonstrate that speech categorization emerges within temporally discrete neural microstates during early sensory-perceptual encoding and is supported by a selective, distributed cortical network with clear behavioral relevance.

neuroscience↗

bioRxiv2024

Auditory brainstem responses to speech-in-noise reflect selective attention, comprehension, and subjective listening effort

The auditory brainstem plays a crucial role in speech-in-noise listening, refining numerous acoustic features such as pitch and spatial location under continual descending influence from cortex. However, the difficulty of characterizing brainstem activity during continuous speech listening has obscured its functional role in ecologically valid contexts--not only the effects of selective attention on neural responses, but also their impact on comprehension and listening effort. Here, we evaluate the role of the brainstem on speech-in-noise perception and selective attention using a continuous, speech-based stimulus with embedded chirps (Cheech) that rapidly and effectively evokes auditory brainstem responses (ABRs) while participants listen to short story narratives. The Cheech-modified stories were presented alone or in the presence of another spatially separated talker, and neural responses were measured throughout by EEG. Both word-level detection performance and narrative-level comprehension were evaluated, as well as subjective reports of listening effort. ABR wave V was modulated by the presence of a competing talker, selective attention for the target versus masker, and the talker sex. Additionally, faster wave V peak latencies and larger amplitudes were associated with identification of the target words embedded within the stories, while faster latencies additionally related to better comprehension question accuracy and lower subjective listening effort. Collectively, our results provide evidence for the influence of brainstem encoding processes on individual speech-listening behaviors, including the ability to selectively attend to and comprehend target speech in the presence of a competing talker. Significance statementThis study highlights the crucial role of the brainstem in speech-in-noise perception and higher-level language abilities like comprehension and subjective listening effort. Through the use of a novel speech stimulus blended with embedded chirps (Cheech) to evoke brainstem responses while listening to short story narratives, we show how selective attention influences subcortical processes. Faster and more robust subcortical processes, in turn, were associated with more successful speech-listening performance across multiple behavioral metrics. These findings advance our understanding of how the brainstem supports selective attention and speech perception in challenging listening environments.

neuroscience↗

bioRxiv2024

Individual Differences in Cognition and Perception Predict Neural Processing of Speech in Noise for Audiometrically Normal Listeners.

Individuals with normal hearing exhibit considerable variability in their capacity to understand speech in noisy environments. Previous research suggests the cause of this variance may be due to individual differences in cognition and auditory perception. To investigate the impact of cognitive and perceptual differences on speech comprehension, 25 adult human participants with normal hearing completed numerous cognitive and psychoacoustic tasks including the Flanker, Stroop, Trail Making, Reading Span, and temporal fine structure (TFS) tests. They also completed a continuous multi-talker spatial attention task while neural activity was recorded using electroencephalography (EEG). The auditory cortical N1 response was extracted as a measure of neural speech encoding during continuous speech listening using an engineered "chirped-speech" (Cheech) stimulus. We compared N1 component morphologies of target and masker speech stimuli to assess neural correlates of attentional gains while listening to concurrently played short story narratives. Performance on cognitive and psychoacoustic tasks were used to predict N1 component amplitude differences between attended and unattended speech using multiple regression. Results show inhibitory control and working memory abilities can predict N1 amplitude differences between the target and masker stories. Interestingly, none of the cognitive and psychoacoustic predictors correlated with behavioral speech-in-noise listening performance in the attention task, suggesting that neural measures may be more sensitive measures of cognitive and auditory processing as compared to behavioral measures alone. SIGNIFICANCE STATEMENTThese findings contribute to our understanding of how cognition affects the neural mechanisms of speech perception. Specifically, our results highlight the complex interplay between cognitive abilities and neural encoding of speech in challenging listening environments with multiple speakers. By incorporating these additional measures of cognition, we can achieve a more comprehensive understanding of an individuals speech perception abilities, even in individuals with normal hearing. This approach could lead to earlier detection of hearing issues and more personalized interventions, ultimately enhancing communication outcomes for those with hearing difficulty.

neuroscience↗

bioRxiv2021

Functional plasticity coupled with structural predispositions in auditory cortex shape successful music category learning

Categorizing sounds into meaningful groups helps listeners more efficiently process the auditory scene and is a foundational skill for speech perception and language development. Yet, how auditory categories develop in the brain through learning, particularly for nonspeech sounds, is not well understood. Here, we asked musically naive listeners to complete a brief ([~]20 min) training session where they learned to identify sounds from a nonspeech continuum (minor-major 3rd musical intervals). We used multichannel EEG to track behaviorally relevant neuroplastic changes in the auditory event-related potentials (ERPs) pre- to post-training. To rule out mere exposure-induced changes, neural effects were evaluated against a control group of 14 nonmusicians who did not undergo training. We also compared individual categorization performance with structural volumetrics of bilateral primary auditory cortex (PAC) from MRI to evaluate neuroanatomical substrates of learning. Behavioral performance revealed steeper (i.e., more categorical) identification functions in the posttest that correlated with better training accuracy. At the neural level, improvement in learners behavioral identification was characterized by smaller P2 amplitudes at posttest, particularly over right hemisphere. Critically, learning-related changes in the ERPs were not observed in control listeners, ruling out mere exposure effects. Learners also showed smaller and thinner PAC bilaterally, indicating superior categorization was associated with structural differences in primary auditory brain regions. Collectively, our data suggest successful auditory categorical learning of nonspeech sounds is characterized by short-term functional changes (i.e., greater post-training efficiency) in sensory coding processes superimposed on preexisting structural differences in bilateral auditory cortex.

neuroscience↗

bioRxiv2020

Single-trial neural dynamics influence auditory category learning

Percepts are naturally grouped into meaningful categories to process continuous stimulus variations in the environment. Theories of category acquisition have existed for decades, but how they arise in the brain due to learning is not well understood. Here, advanced computational modeling techniques borrowed from educational data mining and cognitive psychology were used to trace the development of auditory categories within a short-term training session. Nonmusicians were rapidly trained for 20 min on musical interval identification (i.e., minor and major 3rd interval dyads) while their brain activity was recorded via EEG. Categorization performance and neural responses were then assessed for the trained (3rds) and novel untrained (major/minor 6ths) continua. Computational modeling was used to predict behavioral identification responses and whether the inclusion of single-trial features of the neural data could predict successful learning performance. Model results revealed meaningful brain-behavior relationships in auditory category learning detectible on the single-trial level; smaller P2 amplitudes were associated with a greater probability of correct interval categorization after learning. These findings highlight the nuanced dynamics of brain-behavior coupling that help explain the temporal emergence of auditory categorical learning in the brain.

neuroscience↗