Articulatory gestures influence the perception of speech.
Bibliographic record
Abstract
A central claim of the motor theory of speech perception [Liberman et al. (1967)] is that speech perception involves motor representations. We report evidence that articulatory movements influence perception. Subjects made forced-choice identifications of naturally recorded /aba/ and /ava/ tokens, while silently making articulatory gestures in time with presentation of the auditory tokens. These articulatory gestures either agreed with the auditory token (e.g., articulating “aba” while hearing /aba/) or disagreed (e.g., articulating “ava” while hearing /aba/). Subjects more frequently misidentified auditory /aba/ as /ava/ when articulated gestures disagreed, as compared to a base line condition (i.e., simply listening). Two further conditions suggest that simple priming of /ava/-percepts is unlikely. First, subjects articulated “afa” instead of ava while hearing /aba/. If error rates are specifically related to articulatory gestures, then the similarity in gestural movements between /f/ and /v/ should result in similar error rates. A priming account would not make such a prediction. In line with the motor explanation, subjects did have an equivalent error rate in afa and ava conditions. Second, minimal interference was observed when subjects only imagined themselves saying /ava/. These results support the notion that activation of motor movements can influence the perception of speech.
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".