Theory of mind affects the interpretation of another person's focus of attention
Bibliographic record
Abstract
People are drawn to social, animate things more than inanimate objects. Previous research has also shown gaze following in humans, a process that has been linked to theory of mind (ToM). In three experiments, we investigated whether animacy and ToM are involved when making judgements about the location of a cursor in a scene. In Experiment 1, participants were told that this cursor represented the gaze of an observer and were asked to decide whether the observer was looking at a target object. This task is similar to that carried out by researchers manually coding eye-tracking data. The results showed that participants were biased to perceive the gaze cursor as directed towards animate objects (faces) compared to inanimate objects. In Experiments 2 and 3 we tested the role of ToM, by presenting the same scenes to new participants but now with the statement that the cursor was generated by a 'random' computer system or by a computer system designed to seek targets. The bias to report that the cursor was directed toward faces was abolished in Experiment 2, and minimised in Experiment 3. Together, the results indicate that people attach minds to the mere representation of an individual's gaze, and this attribution of mind influences what people believe an individual is looking at.
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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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".