The role of perceptual and contextual information in social event segmentation
Bibliographic record
Abstract
Social event segmentation, or parsing the ongoing dynamic content into discrete social events, is thought to represent an underlying mechanism that supports the expert human ability to navigate complex social environments quickly and seamlessly. Here we examined whether social event segmentation is influenced by the appropriate social context. To do so, we created two video clips, one in which several events unfolded in a contextually consistent manner (Contextual condition), and the other, in which the order of these events was scrambled using a random sequence (Non-contextual condition). Participants viewed each clip and were asked to mark social and non-social events. Results demonstrated that the same information was identified as constituting event breakpoints within each contextual and non-contextual clip. However, increased group response agreement for social relative to non-social event boundaries was observed in the Contextual relative to the Non-contextual condition. Thus, while perceptual information appears to underlie the identification of social and non-social events, contextual information acts to reduce the uncertainty regarding event boundaries, specifically while parsing social information. Meeting abstract presented at VSS 2018
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".