The role of visual and auditory information in social event segmentation
Bibliographic record
Abstract
Humans organize their social worlds into discrete social and nonsocial events. Social event segmentation refers to the ability to parse the environmental content into social and nonsocial units. Here we investigated the role that perceptual information from visual and auditory modalities, in isolation and in conjunction, played in social event segmentation. Participants viewed a video clip depicting an interaction between two actors and marked the boundaries of social and nonsocial events. Depending on the condition, the clip at first contained only auditory or only visual information. Then, the clip was shown containing both auditory and visual information. Higher overall group consensus and response consistency in parsing the clip was found for social segmentation and when both auditory and visual information was available. Presenting the clip in the visual domain only benefitted group agreement in social segmentation while the inclusion of auditory information (in the audiovisual condition) also improved response consistency in nonsocial segmentation. Thus, social segmentation appears to depend mostly on the information from the visual modality, with auditory information contributing in ambiguous or uncertain conditions as well as during nonsocial segmentation.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".