Bibliographic record
Abstract
Human life involves and requires joint action. Coordinating our actions with others not only gives rise to cultural products that individuals could not achieve alone, such as the Egyptian pyramids or the performance of a symphony. Rather, everyday life also has us engage in many joint actions, from folding a sheet together to having a conversation. How do people manage to act together in a coordinated way? In this chapter, we consider this question in terms of the cognitive mechanisms underlying joint action, focusing on real-time interactions in dyads or small groups. To illustrate what we are aiming to explore, think of two people who are loading shopping bags into the trunk of a car. At times, they each take a rather light bag from the shopping cart, move towards the trunk and then coordinate who is setting their bag down first, and where. At other moments, they carry heavy bags together, making sure to lift and set these down at the same time. As this shows, performing a joint action often requires adapting one’s own actions to what another person is doing. In this chapter, we first introduce some key concepts that have been highlighted in previous accounts of joint action, briefly addressing shared intentions, commitment and representations of joint goals. The main part focuses on coordination mechanisms – cognitive processes and mental representations that make performing joint actions of the kind described above possible. Specifically, we will review findings from experimental studies that shed light on general coordination strategies, representations of joint abilities and tasks, mechanisms of predicting own and others’ actions, and non-verbal communication through action. We will conclude by discussing ways in which different coordination mechanisms might be combined to allow co-actors to take on complementary roles.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.153 | 0.096 |
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".