My Color Singleton: Visual Attention to Learned Action-Effects
Bibliographic record
Abstract
We examined the prioritization of a salient feature immediately after an observer performs an action. Previous work suggests that sensory salience is reduced for a feature that results from the observer's own action (i.e., self-caused) compared to a feature that is independent of the observer's action (i.e., externally caused). Similar to how difficult it is to tickle oneself (Blakemore et al., 1998) or to discriminate a self-caused sensory signal from noise (Cardoso-Leite et al., 2010), we expected reduced salience for self-caused visual features. In an initial acquisition phase, participants learned the perceptual outcome of two actions. One key always generated the color red, and the other generated green. By acquiring action-outcome associations, the appearance of red is coded as a self-caused event after performing the corresponding key, whereas it would be coded as an externally caused event after performing the non-corresponding key. In a following test phase, the two colors were presented as salient singletons in otherwise-white search displays. We compared the attentional impact of self-caused and externally-caused singletons, which could be either relevant (the singleton was the target) or irrelevant (the singleton was a distractor). Contrary to previous work, we found that participants were more efficient at both selecting and ignoring a self-caused singleton compared to an externally caused singleton. Specifically, the effective salience of a self-caused singleton can increase when it is relevant (larger cueing effect for the target) or decrease when it is irrelevant (smaller interference effect for a distractor), whereas no such relevance-based modulation was found with externally-caused singletons. These findings demonstrate how performing an actions prepares visual attention for the most optimal strategy toward the predicted action-outcome, discriminating between self-caused and externally caused events, in a task-appropriate manner. Meeting abstract presented at VSS 2015
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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 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".