Transition from feature-search to singleton-detection strategies in visual search: The role of number of target-defining options.
Bibliographic record
Abstract
When searching for a uniquely colored target in an RSVP stream of homogeneously colored nontarget items, observers can use singleton-detection and/or feature-search modes. Using an attentional-capture paradigm, we varied systematically (a) the number of possible target colors from 1 to 4 and (b) the presence or absence of a colored ring surrounding the nontarget item displayed 200 ms before the target. When present, the ring was either the same color as 1 of the possible targets (color-match), or an irrelevant color (color-mismatch). Capture was measured as the impairment in target identification accuracy when the ring was present relative to when it was absent. Greater capture in the color-match than in the color-mismatch condition was regarded as evidence of feature-search mode. Capture in the color-mismatch condition was regarded as evidence for singleton-detection mode. We show that, as the number of target colors is increased, the relative prominence of feature-search mode decreases, and that of singleton-detection mode increases correspondingly. This novel finding shows that, when both feature-search and singleton-detection modes are possible, at least some degree of feature-search mode is used until the number of possible target-defining colors reaches about 4. This suggests that the weight assigned to singleton-detection mode increases, and that assigned to feature-search mode decreases correspondingly, as the difficulty of maintaining the target-defining features in mind is increased. (PsycINFO Database Record
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".