The distributional properties of prefixes influence lexical decision latencies
Bibliographic record
Abstract
Abstract Morphological processing has been extensively studied in English and European languages, but there is a growing interest in extending the research to other languages. Here we examined Malay, an Austronesian language that is morphologically rich. We investigated the effects of morphological constituents on lexical decisions for prefixed words. Specifically, we explored whether readers are sensitive to any distributional properties of the prefix and root morphemes. Variables investigated included length and family size for both prefixes and roots, as well as number of allomorphs, consistency, and productivity for prefixes. Decision latencies were collected for 1,280 Malay words of various morphological structures. Data from the 640 prefixed words were analyzed in a series of GAMM models. We observed a facilitative effect of root family size and an effect of several distributional properties of prefixes on decision latencies after accounting for word frequency and length. Furthermore, a larger interaction between frequency and several distributional properties of prefixes was found for words with three-letter prefixes than for those with two-letter prefixes. These findings provide insight into the types of distributional properties to which Malay readers are sensitive in multimorphemic words.
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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 | 0.000 | 0.000 |
| 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 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".