Adaptation to Faces and Voices
Bibliographic record
Abstract
Exposure, or adaptation, to faces or voices biases perceptions of subsequent stimuli, for example, causing faces to appear more normal than they would be otherwise if they are similar to the previously presented stimuli. Studies also suggest that there may be cross-modal adaptation between sound and vision, although the evidence is inconsistent. We examined adaptation effects within and across voices and faces and also tested whether adaptation crosses between male and female stimuli. We exposed participants to sex-typical or sex-atypical stimuli and measured the perceived normality of subsequent stimuli. Exposure to female faces or voices altered perceptions of subsequent female stimuli, and these adaptation effects crossed modality; exposure to voices influenced judgments of faces, and vice versa. We also found that exposure to female stimuli did not influence perception of subsequent male stimuli. Our data demonstrate that recent experience of faces and voices changes subsequent perception and that mental representations of faces and voices may not be modality dependent. Both unimodal and cross-modal adaptation effects appear to be relatively sex-specific.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.011 | 0.006 |
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; both teacher heads agree on what is shown here.
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".