Detecting agency from the biological motion of veridical<i>vs</i>animated agents
Bibliographic record
Abstract
The ability to detect agency is fundamental for understanding the social world. Underlying this capacity are neural circuits that respond to patterns of intentional biological motion in the superior temporal sulcus and temporoparietal junction. Here we show that the brain's blood oxygenation level dependent (BOLD) response to such motion is modulated by the representation of the actor. Dynamic social interactions were portrayed by either live-action agents or computer-animated agents, enacting the exact same patterns of biological motion. Using an event-related design, we found that the BOLD response associated with the perception and interpretation of agency was greater when identical physical movements were performed by real rather than animated agents. This finding has important implications for previous work on biological motion that has relied upon computer-animated stimuli and demonstrates that the neural substrates of social perception are finely tuned toward real-world agents. In addition, the response in lateral temporal areas was observed in the absence of instructions to make mental inferences, thus demonstrating the spontaneous implementation of the intentional stance.
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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 | 0.002 |
| 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) | 0.001 | 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".