Shapeception: Unravelling Brain Activity during Animated Shape Perception and Mentalization
Bibliographic record
Abstract
In this paper, we investigate the brain activity elicited during perception of animated shapes as stimuli, which have been found to evoke mental state attributions. Contrary to a previous study, we incorporated the participants' responses in the analysis, and observed robust activations in mPFC, which has been found to play an important role in understanding other's and one's own nature. From our analyses, TPOj was observed showing robust activation during the task as well as functionally connected to AA and LTC, which lead to speculation that empathy might co-occur with mentalizing in the task and that humans might be able to empathize with these interacting shapes, in spite of lacking human features. Along with this, in one of our analyses, we were able to localize a region close to the pSTS, where the activation depicted the participants' 'ability to mentalize'. Based on our observations, we modelled the prediction of mentalization and propose our model as an approach towards developing a brain-activity based model to detect ToM (Theory of Mind) difficulties, which could be useful in research about disorders like Autism Spectrum Disorder (ASD) as well as assessment of mentalization-based treatments. Additionally, we use our findings to reiterate how the Resting State might not always act as a good control condition and that control conditions should be task-specific.
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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 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.000 | 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".