The representational basis of positive and negative repetition effects.
Bibliographic record
Abstract
Repetition of target features in the same spatial location can either benefit or impair performance in perceptual tasks. Moreover, which of these two effects occurs can depend on whether an intervening event is presented temporally between consecutive targets. Here, we explored these effects for color feature repetitions by varying the representational overlap of consecutive targets. The second target on all experimental trials was a simple perceptual color. The task and first target were manipulated to vary the representation produced in response to the first target (perceptual representation of color in Experiment 1; imagined representation of color in Experiments 2 and 5; conceptual representation of color in Experiment 3; color-unrelated representation in Experiment 4). Perceptual and imagined color representations for the first target produced a positive repetition effect when an intervening event did not appear between targets but produced a negative repetition effect when an intervening event did appear between targets. In contrast, conceptual color and color-unrelated representations produced a negative repetition effect both with and without an intervening event. These results suggest that positive repetition effects depend on consecutive targets that share visual representations, whereas negative repetition effects reflect a more complex relationship between stimulus and response features across targets. (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 | 0.001 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".