The Lasting Impression of Chairman Mao: Hyperfidelity of Familiar-Face Memory
Bibliographic record
Abstract
We examined the accuracy of a highly-familiar-face representation in memory. In experiment 1, a famous portrait of Chairman Mao was digitally altered in terms of the distance between his two eyes, two pixels at a time. Mainland Chinese adults were shown the original or altered photos, one at a time, and asked to determine whether each was that of Chairman Mao or altered. Eastern Asian and Caucasian participants, who were unfamiliar with Mao's photo, were shown simultaneously the original face paired with the altered ones and asked to determine whether the photos were identical. The Mainland Chinese participants' memory threshold approximated the perceptual discrimination threshold of the Eastern Asian and Caucasian participants. Experiments 2 and 3 ruled out that the result of experiment I was due to artifacts of photographic alteration. The findings of the present study suggest that our memory of a very familiar face is highly accurate, at least in terms of the interocular configuration. The accuracy is perhaps only limited by the perceptual resolution capacity of our visual system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".