Effects of Voice and Biographic Data on Face Encoding
Bibliographic record
Abstract
There are various perceptual and informational cues for recognizing people. How these interact in the recognition process is of interest. Our goal was to determine if the encoding of faces was enhanced by the concurrent presence of a voice, biographic data, or both. Using a between-subject design, four groups of 10 subjects learned the identities of 24 faces seen in video-clips. Half of the faces were seen only with their names, while the other half had additional information. For the first group this was the person's voice, for the second, it was biographic data, and for the third, both voice and biographic data. In a fourth control group, the additional information was the voice of a generic narrator relating non-biographic information. In the retrieval phase, subjects performed a familiarity task and then a face-to-name identification task with dynamic faces alone. Our results consistently showed no benefit to face encoding with additional information, for either the familiarity or identification task. Tests for equivalency indicated that facilitative effects of a voice or biographic data on face encoding were not likely to exceed 3% in accuracy. We conclude that face encoding is minimally influenced by cross-modal information from voices or biographic data.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".