The effect of visual familiarity on the implicit learning of prototype and eigenfaces
Bibliographic record
Abstract
The human visual system implicitly learns statistical regularities from the environment. Our previous study demonstrated that central tendency (prototype) and at least the first two principal components (eigenfaces) are more efficiently learned from a group of newly encountered faces than the actually studied faces (Gao & Wilson, 2013), which provides an efficient mechanism for encoding new facial identities at an individual level. However, it is not clear whether the prototype and eigenfaces also play an essential role in encoding familiar faces. In the current study, we investigate the effect of visual familiarity on the learning of the prototype and eigenfaces. Adult participants (N = 31, mean age = 20 ± 2.6 years, 13 males) studied 16 synthetic faces in four successive learning sessions. In each session, each face was studied for 20 seconds over four presentations. We measured participants memory performance after each learning session using an old/new recognition paradigm with the new faces sampled from an orthogonal volume of the face space relative to the studied faces. We also measured participants false memory for the unseen prototype face and eigenfaces of the first principal component of the studied faces after the first and the fourth learning sessions. Participants memory for studied faces improved from a moderate level (Hit = 0.60, FA = 0.22) after the first learning session to ceiling (Hit = 0.91, FA = 0.06) after the third learning session. However, the false recognition rates for the unseen prototype face and eigenfaces did not change between the first and the fourth learning sessions, and in both cases were higher than the recognition rates of the studied faces (ps<0.05). The results suggest that even with increased familiarity of the studied faces, prototype and principal components still play a crucial role in encoding individual facial identities. Meeting abstract presented at VSS 2014
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".