Spontaneous Brain Activity Predicts Task-Evoked Activity During Animate Versus Inanimate Touch
Bibliographic record
Abstract
The spontaneous activity of the brain is characterized by an elaborate temporal structure with scale-free properties as indexed by the power law exponent (PLE). We test the hypothesis that spontaneous brain activity modulates task-evoked activity during interactions with animate versus inanimate stimuli. For this purpose, we developed a paradigm requiring participants to actively touch either animate (real hand) or inanimate (mannequin hand) stimuli. Behaviorally, participants perceived the animate target as closer in space, temporally more synchronous with their own self, and more personally relevant, compared with the inanimate. Neuronally, we observed a modulation of task-evoked activity by animate versus inanimate interactions in posterior insula, in medial prefrontal cortex, comprising anterior cingulate cortex, and in medial superior frontal gyrus. Among these regions, an increased functional connectivity was shown between posterior insula and perigenual anterior cingulate cortex (PACC) during animate compared with inanimate interactions and during resting state. Importantly, PLE during spontaneous brain activity in PACC correlated positively with PACC task-evoked activity during animate versus inanimate stimuli. In conclusion, we demonstrate that brain spontaneous activity in PACC can be related to the distinction between animate and inanimate stimuli and thus might be specifically tuned to align our brain with its animate environment.
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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) | 0.001 | 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".