A three-response task reveals how attention alters decision criteria but not appearance
Bibliographic record
Abstract
Whether attention alters appearance or just changes decision criteria continues to be controversial. When subjects are forced to choose which of two equal targets, one of which has been pre-cued, has a higher contrast, they tend to choose the cued target. This has been interpreted as attention increasing the apparent contrast of the cued target. However, when subjects must decide whether the two targets have equal or unequal contrast, they respond veridically with no apparent effect of attention. The discrepancy between these comparative and equality judgments is explained by attention altering the decision criteria but not appearance. We supposed that when subjects are forced to choose which of two apparently equal targets has the higher contrast, they tend to proportion their uncertainty in favor of the cued target. To test this hypothesis, we employed a three-response task, in which subjects chose which target had the higher contrast but also had the option to report that the targets appeared equal. Across the population, this task disentangled potential attention effects on appearance from those on the decision criteria. We found that as subjects’ narrowed their criteria about what constituted equal contrast, they were more likely to choose the cued target, supporting our uncertainty stealing hypothesis. Across the population. the effects of the attentional cue are explained as changes in the decision criteria and not changes in appearance.
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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 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".