Homophone effects in visual word recognition depend on homophone type and task demands.
Bibliographic record
Abstract
This experiment examined how the characteristics of homophones and their mates influence homophone effects, as a function of task demands. Two types of homophones were presented: 1) low-frequency homophones with higher-frequency mates that are not animal names (e.g., maid--made), and 2) low-frequency homophones with mates that are, on average, of equivalent frequency and are animal names (e.g., foul--fowl). We observed a double dissociation: In the lexical decision task (LDT), there was a homophone effect for the first type of homophones but not for the second, whereas in the semantic categorization task (SCT) the opposite was true. These results suggest that in these tasks the effects of homophony arise when the homophone's mate creates competition in terms of the type of processing emphasized in the task, namely, orthographic processing in the LDT and semantic processing in the SCT.
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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 | 0.015 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".