Separating activation and suppression of categorical exemplars
Bibliographic record
Abstract
When performing categorical visual search (e.g., find the bird), we must generate a search template based on our memory representation of that specific category. However, it is unclear the flexibility of category representations, particularly how encountered exemplars modify attentional deployment in category search. In Experiment 1, we examined how categorical representations are shaped by encountered exemplars from long-term memory. Participants first completed an exposure phase, where trials consisted of two real-world objects presented side by side. They were instructed to pay attention to one only side of the screen and to label those objects as natural or artificial. Then, in the search phase, object category labels were presented, and participants had to search for those objects in arrays and make a present/absent responses. Results showed that attended exemplars were faster to search for than ignored exemplars, suggesting that category exemplars learned in the exposure phase shaped the category attentional template used in later search. In Experiment 2, we then tested whether these categorical representations can also be altered by more recently encountered exemplars. Here we cued participants to either attend or suppress one of the two encoded objects belonging to the same category (single cue), or attend one while suppressing the other at the same time (double cue). Immediately after encoding, participants had to search for that category within an array and make a present/absent response. We found that search for suppressed exemplars was slower than attended exemplars, but only when presented with double cues. This suggests that a biased competition mechanism is required during exemplar encoding for successful suppression of category members when deploying search. Overall, our findings suggest that category representations are quite flexible, and can be modified with either immediate or long-term experience.
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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 | 0.000 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".