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Record W4412501729 · doi:10.1101/2025.07.15.664921

Neural signatures of engagement and event segmentation during story listening in background noise

2025· preprint· en· W4412501729 on OpenAlexafffund
Björn Herrmann, Aysha Motala, Ryan A. Panela, Ingrid S. Johnsrude

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2025
Typepreprint
Languageen
FieldNeuroscience
TopicCognitive Science and Education Research
Canadian institutionsBaycrest HospitalUniversity of TorontoWestern University
FundersCanadian Institutes of Health ResearchCanada First Research Excellence FundCanada Research Chairs
KeywordsActive listeningNoise (video)Event (particle physics)SegmentationPsychologySpeech recognitionComputer scienceCommunicationArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract Speech in everyday life is often masked by background noise, making comprehension effortful. Characterizing brain activity patterns when individuals listen to masked speech can help clarify the mechanisms underlying such effort. However, most previous research has focused on neural activity related to short, disconnected sentences that little resemble the more continuous, story-like spoken speech individuals typically encounter. In the current study, we used functional magnetic resonance imaging (fMRI) in humans (both sexes) to investigate how neural signatures of story listening change in the presence of masking by 12-talker babble noise. We show that, as speech masking increases, spatial and temporal activation patterns in auditory regions become more idiosyncratic to each listener. In contrast, spatial (and to some extent temporal) activity patterns in brain networks linked to effort (e.g. cinguloopercular network involving the anterior insula and anterior cingulate) are more similar across listeners when speech is highly masked and less intelligible, suggesting shared neural processes. Moreover, at times during stories when one meaningful event ended and another began, neural activation increased over extensive regions in frontal, parietal, and medial cortices. This event-boundary response appeared little affected by background noise, suggesting that listeners process meaningful units and, in turn, the gist in naturalistic, continuous speech even when it is masked somewhat by background noise. Overall, the current data may indicate that people stay engaged and cognitive processes associated with naturalistic speech processing remain intact under moderate levels of background noise, whereas auditory processing becomes more idiosyncratic to each listener. Significance statement This functional imaging study investigates how neural signatures of story listening change in the presence of masking multi-talker babble noise. We show that, as speech masking increases, auditory activity patterns become more idiosyncratic to each listener. Spatial activation patterns in effort-related regions (anterior insula, cingulate) become more similar across listeners when speech is strongly masked, indicating shared neural representations. Neural activation also increased at times when one meaningful story event ended and another began. This event-boundary response appeared little affected by moderate levels of background noise, suggesting that listeners process meaningful units in continuous speech even as background babble reduces speech intelligibility. The data indicate that many cognitive processes associated with listening to spoken stories remain intact under background noise.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.281
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.045
GPT teacher head0.314
Teacher spread0.269 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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