Neural decoding reveals the functional anatomy of auditory integration and competition in speech perception
Bibliographic record
Abstract
At the beginning of a word, many continuations are possible – for example, when hearing packet, listeners may consider pass and package. Cognitive science shows that listeners in this situation immediately consider multiple words and narrow down the set of candidates via competition. However, cognitive neuroscience has not yet been able to identify the cortical locus of this competition. One potential source of this difficulty is the hypothesis (tested here) that representations of unfolding auditory stimuli take the form of distributed patterns of activity, rather than the overall quantity of activity. We investigated competition dynamics by combining direct recordings from the cortex of humans listening to spoken words with machine learning to decode those distributed patterns. Two critical first steps in investigating the neural basis of these competition dynamics were to identify regions a) whose pattern of activity supports multiple candidates; and b) whose pattern of activity persists over time (after stimulus offset). We characterized both properties in six language regions by using machine learning to assess which words were under consideration in each area every 25 ms during listening. Auditory areas – early in the processing stream – exhibited competition dynamics lasting even after stimulus offset. This pattern was not seen in higher-level language areas. Thus, competition among distributed patterns of activity in auditory sensory areas may underlie human word recognition.
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 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".