Does social context influence intention, prediction and motor behavior during a simple in-person card game?
Bibliographic record
Abstract
Recently we showed that 2 players in a simple online card game were better able to predict each other's intentions when cooperating than competing (Ma et al, 2023), which also generated spontaneous differences in mouse movement. However, online interactions using cursor icons have limitations in representing physical presence and conveying social cues. Thus, the current study aims to explore the influence of social contexts (competition and cooperation) on decision-making and intention prediction when two players engage in the same card game in person. Participant dyads played a card game six times under two social contexts: competing or cooperating (3 games each). When competing, only the player with the highest score earns points. When cooperating, participants split points evenly if they reach a combined threshold. Each game consisted of 8 turns, and each turn participants could obtain points by collecting goal-aligned cards and/or correctly guessing the other person’s goal. We tracked card and guess point performance, as well as recorded gaze and hand movements. We predict in-person play will match online play: cooperating dyads will have higher guess scores and move more confidently (less time and hand distance traveled). Preliminary data revealed 1) As designed, card scores remain consistent across games and condition; 2) Guess scores improve across turns as information is acquired; 3) Guess scores are impacted by social context but not in the same way as the online study. Further analysis will reveal if in-person gameplay is genuinely altering the strategies players use to help and interfere with the communication of intention and will consider how gaze and hand movement contribute to this communication. Overall, we believe that cooperation facilitates the communication of intention during gameplay, but may be richer and more nuanced when people are playing in-person than when they played the same game online.
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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 | 0.000 | 0.004 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".