Influencing the time and space of lexical competition: The effect of gradient foreign accentedness.
Bibliographic record
Abstract
This article examines the influence of gradient foreign accentedness on lexical competition during spoken word recognition. Using native and Mandarin-accented English words ranging in degree of foreign accentedness, we investigate the effect of increased accentedness on (a) the size of the competitor space and (b) the strength and duration of competitor activation. Here, we analyze the number of misperceptions in a transcription task, as well as the time course of competitor activation in a Visual World Paradigm eye-tracking task. The transcription data show that as accentedness increases, the number of unique misperceptions increases. This indicates that greater accent strength induces the activation of many additional competitors within the competition space relative to native speech. The eye-tracking data further show that, as accentedness increases, looks to competitors (not produced in the transcription task) increase both in likelihood and duration. This indicates that greater accentedness boosts the strength of competitor activation as well as the duration of the competition process, even when comprehension is ultimately successful, suggesting strong and diffuse competition within the lexicon. The results provide evidence of changes in the underlying dynamics, which lead to the pervasive processing costs associated with foreign-accented speech that are commonly observed in behavioral data. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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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 | 0.005 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".