On what ground do we mentalize? Characteristics of current tasks and sources of information that contribute to mentalizing judgments.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it