Breaking down prefixed words is unaffected by morphological boundary opacity: Evidence from behavioral and MEG experiments
Bibliographic record
Abstract
Previous experiments support an initial stage of early, form-based visual word recognition, where morphologically complex words like adorable are segmented into morphemes {adore}+{-able}, despite an orthographic change in the stem. However, most experiments have focused on words with clear boundaries between the affix and stem, making decomposition more straightforward. We investigate whether obscured boundaries between the prefix and stem affect morphological decomposition. Using Tagalog as a test case, we compare the processing of prefixed words [1] without morphophonological changes (e.g., {mang}+{hila} becomes manghila "to pull"), [2] with nasal assimilation obscuring prefix identity (e.g., {mang}+{bulag} becomes mambulag "to blind"), and [3] with nasal substitution obscuring both prefix and stem identities and their morphological boundary at orthographic and phonological levels (e.g., {mang}+{tulak} becomes manulak "to push"). Crucially, these morphophonological changes exhibit variability: nasal substitution is more likely than assimilation for voiceless-initial stems, while the opposite holds for voiced-initial stems. Experiment 1 presents behavioral masked priming data that prefixed words are decomposed into morphemes, even with obscured {prefix}+{stem} boundaries. Experiment 2 further supports these results with data from magnetoencephalography showing neural activity is modulated by stem:whole word transition probability, which indicates morphological decomposition. Findings from both experiments unambiguously show that early, form-based decomposition is robust and flexible enough to recognize morphemes, despite morphophonological changes obscuring the {prefix}+{stem} boundary.
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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 | 0.001 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".