Author response: Gaze patterns and brain activations in humans and marmosets in the Frith-Happé theory-of-mind animation task
Bibliographic record
Abstract
In our daily life, we often guess what other people are thinking or intending to do, based on their actions. This ability to ascribe thoughts, intentions or feelings to others is known as Theory of Mind. While we often use our Theory of Mind to understand other humans and interpret social interactions, we can also apply our Theory of Mind to assign feelings and thoughts to animals and even inanimate objects. For example, people watching a movie where the characters are represented by simple shapes, such as triangles, can still see a story unfold, because they infer the triangles’ intentions based on what they see on the screen. While it is clear that humans have a Theory of Mind, how the brain manages this capacity and whether other species have similar abilities remain open questions. Dureux et al. used animations showing abstract shapes engaging in social interactions and advanced brain imaging techniques to compare how humans and marmosets – a type of monkey that is very social and engages in shared childcare – interpret social cues. By comparing the eye movements and brain activity of marmosets to human responses, Dureux et al. wanted to uncover common strategies used by both species to understand social signals, and gain insight into how these strategies have evolved. Dureux et al. found that, like humans, marmosets seem to perceive a difference between shapes interacting socially and moving randomly. Not only did their gaze linger longer on certain shapes in the social scenario, but their brain activity also mirrored that of humans viewing the same scenes. This suggests that, like humans, marmosets possess an inherent ability to interpret social scenarios, even when they are presented in an abstract form, providing a fresh perspective on primates’ abilities to interpret social cues. The findings of Dureux et al. have broad implications for our understanding of human social behavior and could lead to the development of better communication strategies, especially for individuals social cognitive conditions, such as Autism Spectrum Disorder. However, further research will be needed to understand the neural processes underpinning the interpretation of social interactions. Dureux et al.’s research indicates that the marmoset monkey may be the ideal organism to perform this research on.
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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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".