Adaptive Prediction: The Brain Trades Phonemic for Semantic Expectations Under Acoustic Uncertainty
Bibliographic record
Abstract
Abstract Listeners daily adapt to new talkers, yet how acoustic variability challenges the brain’s predictive mechanisms during speech processing remains unclear. Here, we used EEG and Temporal Response Function to examine neural responses to continuous speech narrated by a single talker (Single), enabling stable acoustic model formation, or multiple talkers (Multi), introducing acoustic uncertainty. We assessed whether talker variability influences phoneme recognition and predictive processing, indexed by neural responses to phonemes, phonemic and semantic surprisal. In the Multi condition, responses to phonemes increased but responses to phonemic surprisal decreased, indicating greater speech perception demands and weaker phonemic predictions. Conversely, semantic surprisal responses were stronger, suggesting increased reliance on lexical-semantic predictions. These findings reveal a trade-off in the brain’s predictive mechanisms, where acoustic uncertainty reduces lower-level phonemic anticipation but promotes higher-level semantic prediction. Adaptive processing underscored the brain’s ability to dynamically adjust predictions across linguistic levels, promoting speech comprehension in variable environments. Significance Humans attend to many different talkers daily, switching between them apparently without any effort. This stems from the brain’s ability to construct adaptive, probabilistic models of speech. In this study, we provide novel evidence that predictions in language comprehension are sensitive to acoustic uncertainty. Specifically, under conditions of increased acoustic uncertainty, listeners rely more on bottom-up information in phoneme recognition and reduce the anticipation of phonemic information. This is accompanied by enhanced semantic prediction, suggesting that listeners compensate for acoustic uncertainty by increasing reliance on higher-level contextual representations. This flexible approach allows for robust comprehension, highlighting the brain’s capacity to dynamically adjust its predictive processing to accommodate varying acoustic environments. Teaser Talker-driven acoustic uncertainty reduces phoneme predictions but boosts semantic inference.
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 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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".