Providing a taxonomy for social cognition: how to bridge the gap between expert opinion, empirical data, and theoretical models
Bibliographic record
Abstract
The terminology used to describe components of social cognition lacks clarity and specificity. Recent studies have tried to reach consensus on definitions of social cognition based on expert opinion. These efforts resulted in semantically well-defined terms and distinct concepts, but it remains unclear whether these terms also align with empirical data and existing theoretical models of social cognition. In this commentary, we examine whether the proposed definitions for social cognition are supported by clinical observations and the extant knowledge base on the underlying neural substrates of these skills. In addition, we consider how the proposed definitions align with existing theoretical models of social cognition. We argue that consensus should not be based solely on expert opinion. Therefore, we propose an updated biopsychosocial model of social cognition that integrates proposed expert definitions with a theoretical model of social cognition based on empirical data: the Hierarchical Interdependent Taxonomy of Social cognition (HITS) model. The HITS model guides future research, helps to address the poor construct validity that has been revealed for several tests of social cognition, and provides a framework for the assessment of social cognition.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".