Bibliographic record
Abstract
Numerous studies have examined learners’ ability to track auditory statistical cues (e.g., Saffran, Aslin, and Newport, 1996). It remains unknown, however, how learners manage this feat. This concern is non-trivial: computation of the transitional probabilities in a traditional statistical learning task would involve access to (at least) hundreds of memory traces that have been accumulated over a mere two minutes. The present experiments aim to elucidate the mechanisms underlying statistical learning. Adult participants are exposed to a 2-min continuous speech stream, composed of native phonetic units (study 1), semi-native phonetic units (study 2), or non-native phonetic units (study 3). Participants’ memories for words are then tested through forced-choice comparisons of words, part-words, and phantom words. In study 1, participants successfully segmented the native phonetic speech stream; however, they showed no preference for part-words or phantom words. Furthermore, asymmetries in performance by syllable position (onset, medial, coda) suggest that memory for the segmented words may be more specified/stable in the medial and coda positions. Preliminary results from study 3 (non-native) suggest that participants fail to segment the stream of less familiar sounds. Taken together, these results suggest that statistical learning proceeds via a “chunking”-type mechanism (e.g., Perruchet and Vinter, 1998).
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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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".