Morpho-phonological regularities influence the dynamics of real-time word recognition: Evidence from artificial language learning
Bibliographic record
Abstract
Phonological alternations are attested in many of the world’s languages. In production, these robustly generalize to new words and contexts, suggesting that talkers and listeners of a language have internalized them in some form. However, it is unclear whether listeners’ knowledge of phonological alternations is used during real-time spoken word recognition. The present study asks whether listeners use knowledge of phonological alternations to modulate activation of competitor forms during real-time word recognition. In two experiments, listeners learned an artificial language with phonological alternations. We then used eye-tracking in the visual world paradigm to assess real-time spoken word recognition. We examined fixations to competitors that would be a match to the input because of the learned phonological alternation. Results showed that listeners do use phonological alternations in real time. Given a [t] ~ [d] alternation and an auditory stimulus with a surface [d], listeners fixated the [t]-competitor more than one that could not alternate with [d]. They were even able to generalize this to words that had not been learned in their alternated form. However, not all alternations showed the same pattern; listeners did not use a [d] ~ [z] alternation in the same way. Implications for various models of word recognition are discussed.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".