Statistical Word Segmentation in Unfamiliar Speech
Bibliographic record
Abstract
Statistical learning, the ability to detect patterns in sensory input, allows listeners to segment words from continuous speech by tracking transitional probabilities. While this mechanism is robust in familiar contexts, its adaptability to unfamiliar speech with distinct phonological properties remains less understood. This study investigates whether English-speaking adults can use TPs to segment an artificial language modeled on Cantonese. Participants identified words where syllables consistently occurred together (statistical words) and syllables that partially co-occurred (part-words) compared to those that never did (non-words). However, they struggled to distinguish statistical words from part-words when frequency was controlled. Pupillometry results showed participants dilated more to part-words and non-words at test, compared to frequency-controlled statistical words. Pupillary responses during familiarization also predicted test performance, demonstrating the potential of pupillometry to track learning in real time. These findings highlight the flexibility of statistical learning in adapting to novel linguistic contexts while revealing its limitations.
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How this classification was reachedexpand
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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.023 |
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; both teacher heads agree on what is shown here.
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".