Psychological Mechanisms in Social Cognition Research: Taking Social Power Area as an Example
Bibliographic record
Abstract
This article discusses the methods and approaches used to examine psychological processes in the field of social cognition research, with a specific focus on the area of social power. Social cognition refers to the mental processes involved in perceiving, interpreting, and understanding social information. Understanding how individuals perceive and respond to power dynamics within social interactions is crucial for comprehending various social phenomena. The article highlights the importance of examining psychological processes in social cognition research and provides insights into the specific example of social power. We introduced three types of methods to examine psychological processes at the level of experiment design proposed by Spencer, Zanna and Fong, and summarized situations of using these three designs in past. It discusses different experimental designs, measures, and paradigms commonly employed to investigate social power and its influence on cognitive processes. Additionally, it emphasizes the need for interdisciplinary collaboration and the integration of various research methods to gain a comprehensive understanding of social cognition and its underlying psychological mechanisms. Overall, this article serves as a guide for researchers interested in studying psychological processes within the context of social cognition, particularly in relation to social power dynamics.
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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 | 0.011 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.006 |
| Science and technology studies | 0.004 | 0.035 |
| Scholarly communication | 0.009 | 0.017 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".