Exploring the dynamics of the visual word recognition system: Homophone effects in LDT and’naming
Bibliographic record
Abstract
Homophone effects, involving longer response latencies for homophones (e.g., FEAT) than for matched control words (e.g., FLIP) in the lexical decision task (LDT), have now been observed in a number of studies and have provided a useful means of investigating the dynamics of the visual word recognition system. In the present study we furthered that investigation by examining whether homophone effects are observed for homophonic words that differ in morphological structure from their homophone mates (e.g., BILLED/BUILD), and whether homophone effects can be observed in naming tasks. Results showed null homophone effects for morphologically different homophones like BILLED, under conditions where homophone effects for morphologically similar homophones (e.g., FEAT/FEET, WEIGHTED/WAITED) were substantial (Experiments 1 and 3). Results also showed small but significant homophone effects in naming (Experiment 2). Implications for models of visual word recognition are discussed.
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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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".