Don’t judge a voice by its cover: Visual interference in vocal age judgments
Bibliographic record
Abstract
Previous studies show that individuals perceive the same face as older when preceded by young relative to old adaptor faces, and the same voice as older when preceded by young relative to old adaptor voices. However, research had not yet addressed whether these adaptation effects can occur cross-modally. Therefore, this study sought to determine whether adaptation to young or old faces influences the perceived age of voices. To do this, 20 participants ages 20-23 years were tested individually over 40 experimental trials. In each trial, participants saw either a young or old face; they then heard a voice and were asked to judge the age of the speaker. It was predicted that voices would be perceived as older when preceded by young adaptor faces. Results in fact showed the opposite trend: voices were consistently judged to be younger when preceded by young relative adaptor faces. Thus, it appears that adaptation evokes the opposite effect on age judgments when the adaptor and test stimulus differ in modality (i.e. one stimulus is visual while the other is auditory). To explain these results, it is proposed that individuals rely more heavily on visual cues than auditory ones when assessing age in their conversational partners, and that sensory cues from different modalities are unconsciously integrated even when a known incongruence exists.
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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 | 0.002 | 0.023 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".