Partnering Up: The Social Cognition of Partnered Interaction in Life and Art
Bibliographic record
Abstract
In this article, I present a model of social cognition that is grounded in the interplay between mentalizing and joint action during social interaction. I first propose a psychological distinction between a “character” and a “partner” as two different ways of conceiving of people in social cognition. A character is someone whom we connect with as a spectator. We can mentalize aboutthem, but they cannot mentalize about us at the same time, since there is no direct interaction. A partner, by contrast, is someone with whom we are engaged in a social interaction such that the mentalizing is reciprocal. However, the defining feature of partnered interaction is not mentalizingper sebut instead theadaptivityby which partners make ongoing behavioral adjustments to one another during their interactions. Such adaptivity provides a foundation for forming social bonds with people. I present a Dual Cohesion perspective that focuses on two complementary manners for achieving social cohesion with people during partnered interactions: alignment in conversation and entrainment in joint physical actions. Alignment is based on a cognitive convergence of ideas, whereas entrainment is based on a behavioral coordination of actions. Overall, the model reveals the interplay between mentalizing and joint action in social cognition and partnered interaction.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".