Into the Mind’s Eye: Exploring the Fast-Same Effect in the Same-Different Task
Bibliographic record
Abstract
Abstract The fast-same effect is the observation that “same” responses are much faster than “different” responses in the same-different task. Moreover, identical stimuli are responded to faster than stimuli that are the same in name only (e.g., B and b). We examine Bamber’s (1969) identity reporter model (a two-stage model predicting load effects), Proctor’s (1981) facilitation framework, and Krueger and Shapiro’s (1981) priming framework, proposed to account for these effects. Facilitation and priming are strong for identical and repeated stimuli, for phonological associates, and, in principle, for any form of association. We thus manipulated two types of associations: nominal (changing the letter case, preserving phonology) and learned (matching arbitrary symbols to letters) associations and used extended training to see variations in load effects. We found that overall performance benefits from phonological information and from training, although training did not change load effects. Additionally, the results from a transfer phase show capacity limitation for learned associates, which severely constrains facilitation. This finding is inconsistent with a priming framework. The results are discussed using an expanded version of Bamber’s identity reporter model, which is also compatible with Proctor’s facilitation framework.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".