Effects of synthetic face adaptation: An fMRI study
Bibliographic record
Abstract
Previous psychophysical evidence suggests that face processing mechanisms can be adapted in an identity-specific manner. Moreover, the effect of adaptation critically depends on the identity strength of the test face, with faces closer to the mean being more affected than faces further from the mean (Anderson & Wilson, 2005). Here, we have studied the underlying neural processes of this identity-specific adaptation effect using an event-related fMRI adaptation paradigm. BOLD responses in the fusiform face area (FFA) were measured for synthetic faces with the same identity but at different distances from the mean using a 3T MRI system. Signals were assessed either without adaptation or after adapting to a strong (12%) anti-face (on the opposite side of the mean). As a control, responses were also measured for an irrelevant task (i.e. contrast judgements for a Gabor patch) using the same adaptation protocol as for the face-processing task. This allowed us to tease apart identity-specific adaptation effects on the BOLD signal from non-specific effects of the adaptation protocol. With no adaptation, the BOLD signal from the FFA showed no marked difference between the faces with different identity strengths. After adaptation, the BOLD responses to faces with identities closer to the mean face were lower than responses to faces with stronger identities. This pattern of responses is qualitatively similar to the pattern of results observed with different identity strengths using psychophysical methods, and may reflect the operation of a gain control mechanism subserving face perception.
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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 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".