AN ECOLOGICAL APPROACH TO THEORY-OF-MIND MEASUREMENT: CREATION OF THE EV-TOMI FROM OPEN-ENDED REPORTS
Bibliographic record
Abstract
Theory-of-Mind (ToM), or mentalizing about what other people might be thinking about, is an important part of conscious experience that facilitates social cognition and navigation of our perceived worlds (e.g., predicting other people's thoughts and behaviours).In a 2023 study, our research group began exploring Theory-of-Mind in relation to inner speech, and we found that the selected ToM measures left something to be desired.For example, measures claiming to capture ToM had items that seemed to describe understanding of one's own time perception or one's own episodic memory rather than thinking about other people's mentalizations.Furthermore, existing ToM questionnaires are typically based on a priori notions of what researchers think ToM is, for example, as informed by literature reviews and judged by a panel of experts.To fill this gap, and in seeking ecological validity for ToM measurement, our team took an open-format approach to ask Canadian students, "if you are trying to infer what other people are thinking or experiencing, what comes into your mind?"We have used these responses to create the "Ecologically Valid Theory-of-Mind Inventory" (EV-ToMI).Here we present preliminary results of endorsement, reliability, and validity of this measure, and relationships with other self-processes.
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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 | 0.099 | 0.193 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".