Influence of the constituent morpheme boundary on compound word access
Bibliographic record
Abstract
Embedded morphemes are thought to become available during the processing of multi-morphemic words, and impact access to the whole word. According to the edge-aligned embedded word activation theory Grainger & Beyersmann, (2017), embedded morphemes receive activation when the whole word can be decomposed into constituent morphemes. Thus, interfering with morphological decomposition also interferes with access to the embedded morphemes. Numerous studies have examined the effects of interfering with boundary and constituent-internal letters on morphological decomposition by comparing the effect of transposing letters at the morphemic boundary to constituent-internal letters. These studies, which report inconsistent findings, have typically used derived multi-morphemic words (e.g., cleaner), and sometimes use a control replacement letter condition that is not matched to the transposed letter conditions in terms of location. Across five experiments, we test the edge-aligned activation theory by examining the effects of replacing and transposing boundary and constituent-internal letters of compounds. Our findings suggest that replacing boundary letters interferes with access to both embedded constituents, while replacing constituent-internal letters still allows for access to the unaltered constituent, thus compensating for the interference in the altered constituent. Our findings are consistent with the edge-aligned theory with respect to letter replacement, and also imply that letter replacement must match the position of letter transposition when it is used as a control condition.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".