Bibliographic record
Abstract
Participants are faster to decide that two stimuli are identical than to decide that they are different. Opposing theories suggested that this fast-same effect is either due (a) to a response bias toward similarity or (b) to facilitation caused by the repetition of the stimuli attributes. Although both theories predict the fast-same effect in a conventional same-different task, they make distinct predictions for tasks in which response bias is removed. In such tasks, the bias theory predicts that the fast-same would disappear whereas the facilitation theory predicts that the fast-same would remain. We tested those hypotheses using a same-different task in which participants had to indicate if all the attributes of the stimuli were matching or all were mismatching by pressing one response key, or if some attributes were matching and some were mismatching, by pressing another response key. We call this an exclusive-OR same-different task. Results show that participants were much faster in the "all-matching" condition compared with the "all-mismatching" condition, therefore supporting the facilitation theory. A fit of the linear ballistic accumulator model to the observed data provide additional supports that the fast-same effect is not caused by bias, but by a faster accumulation rate of evidence in the "all-matching" condition. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
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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 | 0.003 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".