Typicality Modulates Attentional Capture by Object Categories
Bibliographic record
Abstract
What we pay attention to in the visual environment is often driven by what we know about the world. For example, a number of studies have found that observers can adopt attentional sets for a particular semantic category. However, some objects are more typical members of a category than others. While previous evidence suggests that an object’s typicality can influence the guidance of attention in visual search, it is unclear whether typicality can also influence the capture of attention. To test whether this is the case, participants were given a category of objects at the beginning of each trial. Then, a rapid serial visual presentation (RSVP) stream was presented at fixation, and participants had to indicate whether an object of the given category was present or absent from the stream. Importantly, a single flanker image also appeared above or below the central stream just before the target. This flanker could belong either to the same category as the target or a different category, and could be a typical or atypical exemplar of that category. Participants were less accurate at detecting the target when the flanker belonged to the same category as the target. Moreover, participants were even less accurate when the flanker was a typical exemplar of this category. Similar findings were observed when targets consisted of typical and atypical exemplars. Together, these findings indicate that the extent of attentional capture toward a distractor depends on whether the distractor matches the category and typicality of one’s attentional set.
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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 | 0.007 |
| 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 | 0.000 | 0.001 |
| 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".