Cross-modal interactions of faces, voices and names in person familiarity
Bibliographic record
Abstract
Person recognition often involves integration of several cues. We asked if familiarity judgments for one cue were influenced by the congruency of pairings with other cues. In a learning phase, subjects studied audiovisual clips of faces, voices and names. A test phase presented uni-modal and bi-modal stimuli. For 10 subjects the bi-modal test stimuli were faces and voices, for 10 faces and names, and for 10 voices and names. In one set of blocks the target was the first modality, and in the other set it was the second. Targets in bi-modal stimuli were paired with either the same or a different identity in the second modality. Face/voice combinations showed congruency effects in reaction time but face/name and voice/name combinations did not. There was no difference between faces modulating target voices and voices modulating target faces. This is consistent with interactions between sensory representations before amodal stages of person recognition.
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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 | 0.004 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".