EFFECTS OF ACOUSTIC VARIABILITY ON SECOND LANGUAGE VOCABULARY LEARNING
Bibliographic record
Abstract
This study examined the effects of acoustic variability on second language vocabulary learning. English native speakers learned new words in Spanish. Exposure frequency to the words was constant. Dependent measures were accuracy and latency of picture-to-Spanish and Spanish-to-English recall. Experiment 1 compared presentation formats of neutral (conversational) voice only, three voice types, and six voice types. No significant differences emerged. Experiment 2 compared presentation formats of one speaker, three speakers, and six speakers. Vocabulary learning was superior in the higher-variability conditions. Experiment 3 partially replicated Experiment 1 while rotating voice types across subjects in moderate and no-variability conditions. Vocabulary learning was superior in the higher variability conditions. These results are consistent with an exemplar-based theory of initial lexical learning and representation.Portions of these data were presented at the 143rd meeting of the Acoustical Society of America in Cancun, Mexico and at the Fourth International Conference on the Mental Lexicon in Windsor, Canada. The authors would like to thank Paola Rijos for help in data collection and scoring and the anonymous SSLA reviewers.
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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 | 0.013 |
| 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 | 0.001 |
| 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".