Exploring Exemplar Bias Effects: Category Influences on Visual Attention
Bibliographic record
Abstract
To successfully perform visual search for object categories, observers are required to draw from prior knowledge to instantiate the appropriate attentional templates. Through encountering category exemplars, long-term categorical representations are developed over time. One account for the way in which exemplars are selected among competitors during encoding is the biased competition theory. Previous research has shown that categorical search templates are biased towards recently encountered perceptual information, but does not acknowledge the potentially separate roles that attentional facilitation and suppression play. In this thesis, I test the idea that competition between exemplars is a necessity when forming attentional templates for categorical search, and that these templates are biased towards information that is attended and away from information that is suppressed. Results from the present experiments suggest that attentional facilitation and suppression both influence how categorical search templates are formed. Furthermore, and contrary to prior work, the process of template formation may rely on non-competitive mechanisms. I discuss how these findings may be situated within a conceptual framework combining attention, categories, and memory.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".