Visual, auditory and bimodal recognition of people and cars
Bibliographic record
Abstract
We have an impressive ability to quickly recognize people from seeing their face and can also recognize identity, but not as well, from a person's voice. When both a voice and face are paired together we show an intermediate level of performance. Here we asked whether this dominance of visual information over auditory information was specific to face-voice pairs or whether this was also the case for recognition of other auditory visual associations. We specifically asked whether visual and auditory information interact differently between face-voice pairs compared to car-car horn pairs. In two separate experiments, participants learned a set of 10 visual/auditory identities—face-voice pairs and car-car horn pairs. Subsequently, participants were tested for recognition of the learned identities in three different stimulus conditions: (1) unimodal visual, (2) unimodal auditory and (3) bimodal. We then repeated the bimodal condition but instructed participants to attend to either the auditory or visual modality. Identity recognition was best for unimodal visual, followed by bimodal, which was followed by unimodal auditory conditions, for both face-voice and car-car horn pairs. Surprisingly, voice identity recognition was far worse than car horn identity recognition. In the bimodal condition where attention was directed to the visual modality there was no effect of the presence of the auditory information. When attention was directed, however, to the auditory modality the presence of visual images slowed participant responses and even more so than in the unimodal auditory condition. This effect was greater for face-voice than car-car horn pairs. Despite our vast experience with voices this yields no benefit for voice recognition over car horn recognition. These results suggest that, although visual and auditory modalities interact similarly across different classes of stimuli, the bimodal association is stronger for face-voice pairs where the bias is toward visual information.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".