You read my mind: Generating and minimizing intention uncertainty under different social contexts in a two-player online game.
Bibliographic record
Abstract
= 48 dyads) completed a two-player online card game, where the goal was to collect cards with a certain feature (e.g., triangles), with participant cursor movements projected to both players. Participants played six games, three cooperatively and three competitively (Social Decision Context). Points were awarded for two decisions: collecting a card matching one's goal (ability to achieve personal goal) and correctly guessing the other player's goal (ability to guess intention). Data revealed: (a) Card scores did not vary with Social Decision Context, (b) Guess scores did vary with Social Decision Context, with more correct guesses when cooperating compared to competing, and (c) Mouse trajectories (durations and mouse distance traveled) decreased when cooperating compared to competing. These results indicate that better guessing during cooperative play is not due to explicit communication (i.e., circling desired cards), but may be due to increased speed and confidence when making decisions in a cooperative context. Additionally, participants could be actively hiding their intention in a competitive context. Thus, social uncertainty when reading another's intentions is both adaptive-affected by the prescribed social context, and automatic-indirectly inferred from the way another moves their mouse when acting with intention. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".