Bibliographic record
Abstract
One hypothesized component of speech production is “efference copy”; a signal carrying the predicted sensory-consequences of the motor-system’s actions [Wolpert and Ghahramani (2000)]. While brain-imaging studies [e.g., Aliu et al. (2009); Numminen et al. (1999)] have shown dampened auditory-cortex response to self-generated sounds (the predicted effect of efference copy), there are few behavioral demonstrations. This experiment will examine whether a context-effect (a common behavioral measure) is also dampened by efference copy. A context effect is a shift in categorization caused by surrounding sounds. For example, a syllable ambiguous between /da/ and /ga/ is perceived as more /da/-like when preceded by /ar/, but more /ga/-like when preceded by /al/ [Mann (1980)]. In this experiment, participants will silently mouth /ar/ or /al/ in time to a recording (it is assumed that mouthing engages efference copy). In one condition the recording will match what participants are mouthing; in another condition it will mismatch; in a third condition participants will hear the sounds without mouthing. After each mouthing, they will categorize a target syllable as /da/ or /ga/. The prediction is that the context effect will be dampened in the matching condition (due to efference copy), but not in the mismatching condition.
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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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".