An investigation of the time course of category congruence and priming distance effects in number classification tasks.
Bibliographic record
Abstract
The issue investigated in the present research is the nature of the information that is responsible for producing masked priming effects (e.g., semantic information or stimulus-response [S-R] associations) when responding to number stimuli. This issue was addressed by assessing both the magnitude of the category congruence (priming) effect and the nature of the priming distance effect across trials using single-digit primes and targets. Participants made either magnitude (i.e., whether the number presented was larger or smaller than 5) or identification (i.e., press the left button if the number was either a 1, 2, 3, or 4 or the right button if the number was either a 6, 7, 8, or 9) judgments. The results indicated that, regardless of task instruction, there was a clear priming distance effect and a significantly increasing category congruence effect. These results indicated that both semantic activation and S-R associations play important roles in producing masked priming effects.
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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 | 0.010 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".