Bibliographic record
Abstract
Object categories are believed to be organized hierarchically, such that a category’s typical exemplars form the basis of categorical attentional templates that determine how attention is deployed. We ask how encountering category members modifies formation of these templates, specifically through a biased competition mechanism found when voluntarily up- or down-regulating objects. In Experiment 1, we tested whether this form of competition is required in template formation. In each trial, participants were cued to either attend or suppress one of two exemplars of the same category (single cue conditions) or to do both at the same time (double cue). Then, they searched for that category in an array of different objects containing either the attended or suppressed exemplar. Search for suppressed objects was slower than attended objects, but only when competition was present (double cue). This points to the necessity of biased competition at encoding to enhance or suppress exemplars when forming categorical templates. We replicated this effect in Experiment 2, which again either induced biased competition (double cues) or did not (same cue: attend or suppress both objects at the same time). To verify that suppression occurred, Experiment 3 included a baseline condition where participants were instructed not to attend to or suppress either image. Search was slower for suppressed items relative to baseline, suggesting that active suppression is possible under this paradigm. In sum, this study demonstrates the flexibility in categorical template formation, hinting at a potential way in which people develop category representations.
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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 | 0.004 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".