Beyond ‘Interaction’: How to Understand Social Effects on Social Cognition
Bibliographic record
Abstract
In recent years, a number of philosophers and cognitive scientists have advocated for an ‘interactive turn’ in the methodology of social cognition research: to become more ecologically valid, we must design experiments that are interactive rather than merely observational. While the practical aim of improving ecological validity in the study of social cognition is laudable, we think that the notion of ‘interaction’ is not suitable for this task. As it is currently deployed in the social cognition literature, this notion leads to serious conceptual and methodological confusion. In this article, we tackle this confusion on three fronts: (i) we revise the ‘interactionist’ definition of interaction; (ii) we demonstrate a number of potential methodological confounds that arise in interactive experimental designs; and (iii) we show that ersatz interactivity works just as well as the real thing. We conclude that the notion of ‘interaction’, as it is currently being deployed in this literature, obscures an accurate understanding of human social cognition. 1 Introduction2 Defining ‘Interaction’3 The Constituents of Interaction 3.1 The social Simon effect 3.2 Level-2 perspective-taking 3.3 Interaction effects on infant learning 3.4 Conversational alignment4 How Much Does ‘Real’ Interaction Matter?5 Conclusion
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.040 |
| Scholarly communication | 0.008 | 0.025 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".