The role of long-distance phonological processes in spoken word recognition: A preliminary investigation
Why this work is in the frame
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Bibliographic record
Abstract
Previous work has demonstrated that during spoken word recognition, listeners can use a variety of cues to anticipate an upcoming sound before the sound is encountered. However, this vein of research has largely focused on local phenomena that hold between adjacent sounds. In order to fill this gap, we combine the Visual World Paradigm with an Artificial Language Learning methodology to investigate whether knowledge of a long-distance pattern of sibilant harmony can be utilized during spoken word recognition. The hypothesis was that participants trained on sibilant harmony could more quickly identify a target word from among a set of competitors when that target contained a prefix which had undergone regressive sibilant harmony. Participants tended to behave as expected for the subset of items that they saw during training, but the effect did not reach statistical significance and did not extend to novel items. This suggests that participants did not learn the rule of sibilant harmony and may have been memorizing which base went with which alternant. Failure to learn the pattern may have been due to certain aspects of the design, which will be addressed in future iterations of the experiment.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it