Concordant Cues in Faces and Voices
Bibliographic record
Abstract
Information from faces and voices combines to provide multimodal signals about a person. Faces and voices may offer redundant, overlapping (backup signals), or complementary information (multiple messages). This article reports two experiments which investigated the extent to which faces and voices deliver concordant information about dimensions of fitness and quality. In Experiment 1, participants rated faces and voices on scales for masculinity/femininity, age, health, height, and weight. The results showed that people make similar judgments from faces and voices, with particularly strong correlations for masculinity/femininity, health, and height. If, as these results suggest, faces and voices constitute backup signals for various dimensions, it is hypothetically possible that people would be able to accurately match novel faces and voices for identity. However, previous investigations into novel face–voice matching offer contradictory results. In Experiment 2, participants saw a face and heard a voice and were required to decide whether the face and voice belonged to the same person. Matching accuracy was significantly above chance level, suggesting that judgments made independently from faces and voices are sufficiently similar that people can match the two. Both sets of results were analyzed using multilevel modeling and are interpreted as being consistent with the backup signal hypothesis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".