Bibliographic record
Abstract
Change blindness is influenced by factors such as: set size (the number of items in the scene), change size (the degree of change), and familiarity (whether or not the change occurs in a familiar stimulus).The objectives of this research were: (a) to investigate the role of familiarity in detecting changes in human faces and (b) to establish the temporal locus of the face familiarity effect within Tovey & Herdman's (2014) stage model for change blindness.Chinese and Caucasian participants detected changes in images of same-race (familiar) and other-race (unfamiliar) faces in a flicker paradigm.Familiarity, set size, and change size were jointly manipulated to determine the locus of the face familiarity effect using Sternberg's additive factors logic.Caucasian (but not Chinese) participants were faster and more accurate in detecting changes in Caucasian faces than in Chinese faces, and a 3-way interaction in the Caucasian participants' accuracy data was observed.iii v Image editing/morphing software .....
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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 | 0.013 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".