You can’t Stroop a lexical decision: Is semantic processing fundamentally facilitative?
Bibliographic record
Abstract
It is well documented that related prime words facilitate target processing in lexical decision (e.g., doctor facilitates nurse), but interfere with target processing in the Stroop task (e.g., the word blue slows the time to name the colour red). Five experiments explored several potential explanations for these differences. In Experiments 1 and 2, all stimuli were novel (as in a typical lexical decision design). Participants were faster both to make lexical decisions and to read colour words aloud that were primed by incongruent associates (e.g., banana) relative to a neutral prime (e.g., knot). Experiments 3 and 4 used a small set of repeatedly presented stimuli (as in a typical Stroop design). Incongruent colour words facilitated lexical decisions to target colour words, but interfered with identification (reading aloud). Experiment 5 further showed that interference is still observed in identification when the distractor set size is large but the target/response set size is small. These findings suggest that semantic connections are solely facilitative and that response competition only occurs when there is a small set of repeated responses and identification (rather than lexical decision) is required. The more general problem of research fragmentation is briefly 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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 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".