The relatedness-of-meaning effect for ambiguous words in lexical-decision tasks: When does relatedness matter?
Bibliographic record
Abstract
Effects of the number of meanings (NOM) and the relatedness of those meanings (ROM) were examined for Japanese Katakana words using a lexical-decision task. In Experiment 1, only a NOM advantage was observed. In Experiment 2, the same Katakana words produced a ROM advantage when Kanji words and nonwords were added. Because the Kanji nonwords consisted of unrelated characters whereas the Kanji words consisted of related characters, participants may have used the relatedness of activated meanings as a cue in making lexical decisions in this experiment, artificially creating a ROM advantage for Katakana words. Consistent with this explanation, no ROM effect for Katakana words was observed in Experiment 3 when the Kanji nonwords consisted of characters with similar (i.e., related) meanings. These results pose a further challenge to the position that the speed of semantic coding is modulated by ROM for ambiguous words.
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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 | 0.005 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".