Context-dependent semantic priming in number naming.
Bibliographic record
Abstract
Previous research has shown that time to name single-digit Arabic numbers is about 15 ms slower when naming trials are interleaved with simple multiplication (e.g., state product of 2 x 3) than when naming digits is interleaved with magnitude comparison (e.g., state larger; 2 upward arrow 3). To explain this phenomenon, J. I. D. Campbell and A. W. S. Metcalfe (2008) proposed that the comparison context enables both semantic and asemantic pathways for digit naming but that number-fact retrieval inhibits the semantic route and slows digit naming relative to the comparison context. To test this hypothesis, the authors modified the naming context paradigm by introducing a semantic priming manipulation. They replicated the digit-naming response time advantage for comparison relative to the multiplication context and observed semantic priming only in the comparison context. In comparison blocks, digit naming was 8 ms faster immediately after naming near digit primes (+/-1) compared to far primes (>or=3), but in multiplication blocks there was no priming. The results reinforce the theory that number-fact retrieval can inhibit the semantic route for digit naming (L. Cohen & S. Dehaene, 1995) and thereby reconfigure the cognitive architecture for naming digits.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".