Emotion Categorization Across Modalities: Representational Similarity and Clustering Patterns from Visual, Auditory, and Audiovisual Stimuli
Bibliographic record
Abstract
Emotion recognition is a fundamental aspect of social communication, yet most research has focused on visual emotion expressions, leaving less understood how auditory and audiovisual cues shape perceptual organization. The present study used representational similarity analysis (RSA) to examine how the structure of emotion recognition differs across modalities. Participants completed an emotion recognition task involving 14 emotions presented in auditory, visual, and audiovisual formats. Confusion matrices were computed for each modality and converted into representational dissimilarity matrices (RDMs). Hierarchical clustering was applied to RDMs using silhouette-based cutoffs to identify emotion clusters. Multidimensional scaling (MDS) was also conducted on confusion profiles to provide two-dimensional visualizations of emotion space. Results revealed that audiovisual recognition produced the highest degree of distinctness, with most emotions forming singleton clusters and only a few small groupings. Visual recognition showed more cohesive clusters organized largely by valence. In contrast, auditory recognition yielded the broadest multi-emotion groupings, where vocal cues blurred categorical boundaries and produced clusters that combined positive and negative states. Together, these findings demonstrate that the perceptual organization of emotion recognition varies by modality. Audiovisual cues enhanced distinctiveness, visual cues reinforced valence-based structure, and auditory cues fostered broader clusters across valence boundaries. By integrating RSA and MDS, this study highlights the importance of moving beyond accuracy to examine the representational structure of emotion perception across sensory modalities.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".