Lexical access codes in visual word recognition: Are the joint effects of context and stimulus quality diagnostic?
Bibliographic record
Abstract
Is it possible to identify when lexical access in visual word recognition is based on an orthographic code and when it is based on a phonological code? Some researchers have argued that the joint effects of semantic context and stimulus quality in lexical decision are diagnostic. Their argument is that when phonological recoding is employed, it serves to keep the effects of stimulus quality and context from interacting. Semantic context and stimulus quality are therefore predicted to have additive effects on RT. In contrast, when lexical access is not mediated by a phonological recoding stage, then the effect of stimulus quality interacts with context, as commonly seen in the literature. A strong test of these claims was devised in which participants were forced to use phonological recoding for the purpose of lexical access. An interaction between context and stimulus quality was observed. This finding is taken as evidence that the joint effects of semantic context and stimulus quality are not diagnostic with respect to the nature of the lexical access code (i.e., orthographic versus phonological) for readers of English.
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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 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".