On what ground do we mentalize? Characteristics of current tasks and sources of information that contribute to mentalizing judgments.
Bibliographic record
Abstract
Mentalizing is an aspect of social cognition that is garnering increased interest. Although a wide variety of experimental tasks are available to measure mentalizing abilities in adults, the most widely used tasks typically focus on specific aspects of mentalizing, and mentalizing judgments are performed based on a limited set of information about the agent and the context. Here, we present the Eight Sources of Information Framework (8-SIF), a model that describes the sources of information that can contribute to mentalizing judgments both in real life and in the context of mentalizing tasks. This model is then used to systematically review and analyze the most classical mentalizing tasks, with a particular focus on the sources of information provided as a basis for mentalizing judgments in these tasks. Next, mentalizing tasks with improved ecological validity are also examined, highlighting the greater richness and diversity of the sources of information provided in such tasks relative to the most classical tasks. We believe that the 8-SIF is an important first step to increase awareness of the sources of information that can contribute to mentalizing judgments and to favor investigations of the potential impact of these sources of information on mentalizing performance in different populations.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".