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 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".