How do we make social decisions? Gaze strategies used to predict and optimize social information during conversation.
Bibliographic record
Abstract
Our ability to make quick social decisions during everyday interactions is vital to effective communication. However, in social situations, we have access to an abundance of information and the cognitive challenge to rapidly select optimal strategies to gather information necessary for the social decision-making process. In this study, we investigated how modifying information availability influences observers’ decisions and the gaze strategies selected to make affiliation judgment (friends vs. strangers) for silent videos of two interacting individuals. We demonstrated that eliminating information (full-body vs. head-only cues) resulted in a reduced ability to distinguish friends from strangers and the use of different eye-movement strategies to perform the same social task. Observers made more fixations towards the talkers’ eyes than other locations in both conditions. However, when information was restricted to a head-only view, participants switched gaze between eyes and mouth more frequently than with full information. Further, availability of full-body cues resulted in observers switching gaze between talkers more frequently to discriminate friends from strangers. When examining how gaze strategy predicts overall accuracy of a social decision given full-body information, we demonstrated that individuals who fixated more on the mouth were likely to be more accurate in affiliation discrimination. We suggest that gaze contributes to social decisions during conversation because we are able to predict the conversational structure of a conversation. For example, we demonstrated that observers are able to predict the point of a turn exchange between talkers by fixating on the talker who is about to speak up to 250 ms prior to them speaking. These results demonstrate that observers select gaze strategies to optimize all available information and that greater optimization predicts increased accuracy of our decisions. We further conclude that human social abilities rely on versatile decision-making and prediction strategies to handle the complexity of our social world. Meeting abstract presented at VSS 2015
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".