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Record W4294700288 · doi:10.1101/2022.09.02.506424

Neural responses to movie naturalistic stimuli are related to listening demand in cochlear implant users

2022· preprint· en· W4294700288 on OpenAlexafffund
Bowen Xiu, Brandon T. Paul, Joseph Chen, Trung Le, Vincent Lin, Andrew Dimitrijevic

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2022
Typepreprint
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsUniversity of TorontoHealth Sciences CentreToronto Metropolitan UniversitySunnybrook Health Science Centre
FundersUniversity of Toronto
KeywordsActive listeningConversationPerceptionPsychologySpeech perceptionAudiologyNoise (video)Speech recognitionCochlear implantContext (archaeology)Computer scienceCommunicationMedicineArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract There is a weak relationship between clinical and self-reported speech perception outcomes in cochlear implant (CI) listeners. Such poor correspondence may be due to differences in clinical and “real-world” listening environments and stimuli. Speech sounds in the real world are often accompanied by visual cues, background environmental noise and is generally in the context of a connected conversation. The aims of this study were to determine if brain responses to naturalistic speech could index speech perception and listening demand in CI users. Accordingly, we recorded high density EEG while CI users listened/watched a naturalistic stimulus (i.e., the television show, “The Office”). We used continuous EEG to quantify “speech neural tracking” (i.e., TRFs, temporal response functions) to the television show audio track and additionally 8–12 Hz (alpha) brain rhythms commonly related to listening effort. Background noise at three different signal-to-noise ratios (SNRs), +5, +10, and +15 dB were presented to vary the difficulty of following the television show mimicking a natural noisy environment. The task included an additional condition of audio-only (no video). After each condition, participants subjectively rated listening demand and the degree of words and conversations they felt they could understand. Fifteen CI users reported progressively higher degrees of listening demand and less words and conversation with increasing background noise. Listening demand and conversation understanding in the audio-only condition was comparable to that of the highest noise condition (+5 dB). The addition of the background noise reduced the degree of speech neural tracking. Mixed effect modeling showed that listening demand and conversation understanding were correlated to cortical speech tracking such that high demand and low conversation understanding lower associated with lower amplitude TRFs. In the high noise condition, greater listening demand was negatively correlated to parietal alpha power such that higher demand was related to lower alpha power. No significant correlations were observed between TRF/alpha and clinical speech perception scores. These results are similar to previous findings showing little relationship between speech perception and quality of life in CI users. However, the physiological responses to complex natural speech may anticipate aspects of quality-of-life measures such as self-perceived listening demand.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.030
GPT teacher head0.284
Teacher spread0.254 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2022
Admission routes2
Has abstractyes

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