An action-observation method for studying social perception: a mini-review
Bibliographic record
Abstract
An important aspect of any social interaction involves inferring other people’s mental states, intentions, and their likely next actions, by way of facial expression, body posture, eye gaze, and limb movements. An actor’s production of actions during social interactions and the observer’s perception of these actions are thus closely linked. In this review, we outline an action-observation methodology, which not only allows for separate analyses of production and perception, but also promotes the study of the dynamic interaction between these two sides of every social exchange. We review two lines of research that have benefited from its application. The first line focuses on individuals performing tasks alone and the observation of their actions by other individuals in order to make inferences about their attentional states. The second line of study focused on pairs of individuals performing collaborative tasks in naturalistic settings and the observation of these performances by other individuals. We offer several suggestions for how this methodology can be extended to improve on the limitations of the present studies, as well as some suggestions of how to use this methodology to venture into new territory. Our aim is to inspire future research applications of this methodology in order to advance our understanding of social action production and perception.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".