Attention, awareness of contingencies, and control in spatial localization: A qualitative difference approach.
Bibliographic record
Abstract
The qualitative difference method for distinguishing between aware and unaware processes was applied here to a spatial priming task. Participants were asked simply to locate a target stimulus that appeared in one of four locations, and this target stimulus was preceded by a prime in one of the same four locations. The prime location predicted the location of the target with high probability (p = .75), but prime and target mismatched on a task-relevant feature (identity, color). Across 5 experiments, we observed repetition costs in the absence of awareness of the contingency, and repetition benefits in the presence of awareness of the contingency. These results were particularly clear-cut in Experiment 4, in which awareness was defined by reference to self-reported strategy use. Finally, Experiment 5 showed that frequency-based implicit learning effects were present in our experiments but that these implicit learning effects were not strong enough to override repetition costs that pushed performance in the opposite direction. The results of these experiments constitute a novel application of the qualitative difference method to the study of awareness, learning of contingencies, and strategic control.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".