Bibliographic record
Abstract
The visual system is efficient at detecting regularities in the environment. When two objects reliably co-occur, changes in one object are automatically transferred to its co-occurring partner. It is unknown how such updating can transpire across categorical boundaries. In Experiment 1, participants viewed a random temporal stream of objects, which came from two distinct categories based on texture (i.e., objects in Category A had stripes vs. objects in Category B had dots). Each object had a unique shape. After exposure, one object in Category A (e.g., A1) increased in size, and participants recalled the size of another object in the same category (e.g., A2) or in the different category (e.g., B1). We found that objects in the same category were recalled to be reliably larger than objects in the different category, suggesting that changes in one object are more likely to be transferred to another in the same category than in an object in a different category. To elucidate if the cross-category transfer can be facilitated by statistical regularities, we conducted Experiment 2, where participants viewed the same objects, except now objects in the two categories were temporally paired (i.e., A1 reliably appeared before B1). After exposure, one object in Category A (e.g., A1) increased in size, and participants recalled the size of the cross-category paired partner (B1), the within-category random object (A2), or cross-category random object (B2). We found that the within-category object (A2) was recalled to be reliably larger than any cross-category object (B1 or B2). This suggests that changes in one object were more strongly transferred to other objects in the same category any objects of a different category, regardless of statistical regularities. These results reveal a within-category advantage of updating of feature changes, that they are more readily transferred within the same category than across categories. Meeting abstract presented at VSS 2017
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".