It doesn't matter how you feel. The facial identity aftereffect is invariant to changes in facial expression
Bibliographic record
Abstract
Previous studies have shown that facial expression aftereffects are modulated by the identity of the adapting face, suggesting both identity-dependent and identity-independent representations of facial expression. In this study, we asked whether facial identity aftereffects were similarly modulated by expression. In Experiment 1, the congruency of expression between adapting and test faces did not affect the identity aftereffect for novel faces, suggesting that the neural representations activated by novel identities are independent of expression. In Experiment 2, we examined whether expression dependency might be found with more familiar faces but still did not find any modulation of identity aftereffects by the congruency of expression. In Experiment 3, we measured the similarity between faces used to probe expression and identity adaptation, using both an ideal observer and human subjects, to determine if the discrepancy between the results of these two studies is related to greater similarity between faces from the same person with different expressions than between faces of different people with the same expression. However, the contrast thresholds required to discriminate between faces of differing expression were similar to those for faces with differing identity. We conclude that, in contrast to the significant identity-dependent component seen in representations of expression, representations of facial identity are independent of variations in expression.
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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 | 0.000 | 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".