What Eye Tracking Reveals in Implicit-Discrete Versus Explicit-Continuous Theory-of-Mind Measures
Bibliographic record
Abstract
Adults can understand others’ mental states (Theory of Mind, ToM), but their private knowledge tends to hinder this ability (ToM errors). Eye-tracking recorded where participants looked in two ToM tasks. In one task, adult participants watched videos where characters held either false or true (inaccurate or accurate) beliefs about an animal’s location. This task was implicit because it did not solicit a response from participants. As predicted, participants looked longer and first looked where characters, with true beliefs, would search for an object; however, participants looked shorter and did not first look where characters, with false beliefs, would search for an object. In another task, adults listened to stories where characters held either false or true belief about an object’s location. This task was explicit because it solicited a response from participants. Contrary to predictions, participants made more ToM errors when indicating where characters, with true beliefs, would search for an object. In comparison, participants made fewer ToM errors when indicating (1) where characters, with false beliefs, would search for an object, and (2) where characters, with false and true beliefs, initially put an object. Methodological issues may account for this discrepancy. Overall, the study found ToM errors in adults.
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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 | 0.001 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".