Rapid category learning in high-level vision: From face instances to person categories
Bibliographic record
Abstract
A face varies in its retinal properties due to fluctuations in orientation, surface lighting and viewing distance. A person's facial appearance can be altered by makeup or changes associated with age and health status. Successful face recognition therefore requires that the range of face instances be correctly categorized as belonging to the same person. However, the perceptual factors governing this visual categorization process are not well understood. We hypothesized that person representations can be abstracted from a rapid and structured stream of face presentations. Using the RSVP method, participants passively viewed a continuous sequence of 160 different grey scale photographs of four Dutch female celebrities (40 photographs per celebrity). The photographs were centrally presented unmasked every 500 ms. In the blocked condition, the photographs were grouped by celebrity (e.g., 40 images of Celebrity A, 40 images of Celebrity B, etc.). In the mixed condition, the photographs were presented in random order. After two rounds of presentations, participants completed a "same/different" test in which two celebrity photographs were sequentially presented for 500 ms, each followed by a visual mask. Participants responded "same" if the photographs depicted the same celebrity or "different" if the photographs depicted two different celebrities. The test faces were novel and not seen during the presentation phase of the experiment. The main finding was that participants in the blocked presentation condition performed reliably better on the same/different task (d' = 2.21) than participants in the mixed presentation condition (d' = 1.74, p < .05) and participants in a no-presentation control condition (d' = 1.41, p < .001). Performance of participants in mixed and control conditions did not reliably differ, p > .10. These results suggest that the rapid presentation of many face instances, grouped by category promotes the efficient formation of person representations. Meeting abstract presented at VSS 2016
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".