Attentional Disengagement during External and Internal Distractions Reduces Neural Speech Tracking in Background Noise
Bibliographic record
Abstract
Abstract Within-situation disengagement – the mental withdrawal during conversations in acoustically challenging environments – is a common experience of older people with hearing difficulties. Yet, most research on the neural mechanisms of attentional disengagement from speech listening has focused on the distraction by one competing speaker, whereas within-situation disengagement is often characterized by distraction towards external visual stimuli or internal thoughts and occurs in situations with ambient, multi-talker background masking. Across three electroencephalography (EEG) experiments in human participants of either sex, the current study examined how disengagement due to external and internal distractions affect the neural tracking of speech masked by different levels of multi-talker babble (speech in quiet, +6 dB, and −3 dB SNR). We observed enhanced early neural responses (<0.2 s) to the speech envelope for speech masked by background babble compared to speech in quiet (Experiments 1-3), suggesting stochastic facilitation. Importantly, neural tracking of the speech envelope was reduced when individuals were distracted by a visual-stimulus stream (Experiment 2) and by internal thought and imagination (Experiment 3). There were some indices suggesting the greatest disengagement-related decline in neural speech tracking occurs for the most difficult speech-masking condition, but this was not consistent across all measures. The current data show that disengagement due to external and internal distractions yield decreases in neural speech tracking, potentially suggesting converging neural pathways through which gain is downregulated in auditory cortex. These results indicate that disengagement from listening can be identified through non-invasive neural measures. Significance Many older adults with hearing difficulties mentally “tune out” during conversations in noisy environments, yet the neural mechanisms underlying this within-situation disengagement remain poorly understood. Across three electroencephalography experiments, we examined how external (visual) and internal (thought-based) distractions influence neural tracking of speech masked by multi-talker babble. We observed that attentional disengagement – whether induced by external stimuli or internal thoughts – reduced the brain’s tracking of the speech envelope. These findings demonstrate that listening disengagement can be objectively identified through neural measures and suggest a convergence in neural pathways through which both external and internal distractions down-regulate auditory gain, providing new insight into attentional control in challenging listening conditions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".