Repetition and the SNARC effect with one- and two-digit numbers.
Bibliographic record
Abstract
The SNARC (Spatial Numerical Association of Response Codes) effect is the finding that small numbers elicit faster left than right responses and large numbers elicit faster right than left responses. This effect suggests that numbers activate left-right magnitude-laterality codes and that these codes interact with the selection of left-right responses. In the present research, subjects made parity decisions for one-digit numbers (in Experiment 1) and two-digit numbers (in Experiment 2), and we examined the effect of stimulus repetition on the SNARC effect. With single-digit stimuli, responses were faster and the SNARC effect was eliminated when stimuli were identical on successive trials. With two-digit stimuli, responses were faster when the ones digit was repeated, but the SNARC effect was found regardless of whether the digit was repeated or not. We argue that magnitude-laterality codes are activated in the process of accessing number information in memory and that this process can be short circuited if the visual stimulus matches that on the previous trial. Thus, no SNARC effect is found in Experiment 1 when identical stimuli are presented on successive trials. However, this result is not found in Experiment 2 because successive stimuli do not match even if the ones digit is repeated.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".