Face Aftereffect formation is influenced by the diversity of the training set
Bibliographic record
Abstract
Introduction. Although the face aftereffect phenomenon is known for face identity, age and emotion, it is unknown how the diversity of the face training set influences aftereffect formation. Observers saw a homogeneous set of distorted faces (middle-aged Caucasian males) and a diverse set of distorted faces (male and female; Caucasian, Asian, Latino and African American) during adaptation. Evidence of aftereffects was tested. Methods. 33 participants underwent an opposing aftereffects paradigm viewing a set of diverse and a set of Caucasian male faces. Participants viewed manipulated face images in one of two adaptation conditions: 1) the diverse face set with features contracted, and the homogeneous face set with features expanded or 2) the opposite. During pretest, participants viewed 64 face pairs, one contracted by 10% and one expanded by 10% and selected which face they found more attractive. During adaptation, faces were distorted by 60%. Post-adaptation testing was identical to pre-adaptation testing. Results. Using a mean change score (contracted faces selected as more attractive before vs. after adaptation) as the dependent variable, the two adaptation conditions were significantly different (F(1,31)=13.072, p= <.05). Adaptation to the contracted diverse face set led to evidence of aftereffects in the expected directions (t(31)=2.685, p= <0.05), but the significant differences observed after adaptation to the contracted homogeneous face set was opposite of the expect direction for both conditions (t(31)=3.061, p= <0.05). Aftereffects were created only by adapting to the diverse face set, not the homogeneous face set. These results indicate that adapting to the diverse face set after a featural manipulation led to aftereffects for all faces (the diverse and the homogeneous face sets) in the same direction. This result suggests that diversity within a face set influenced aftereffect formation.
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 | 0.000 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".