Intertrial Variability in the Premotor Cortex Accounts for Individual Differences in Peripersonal Space
Bibliographic record
Abstract
We live in a dynamic environment, constantly confronted with approaching objects that we may either avoid or be forced to address. A multisensory and sensorimotor interface, the peripersonal space (PPS), mediates every physical interaction between our body and the environment. Behavioral investigations show high variability in the extension of PPS across individuals, but there is a lack of evidence on the neural underpinnings of these large individual differences. Here, we used approaching auditory stimuli and fMRI to capture the individual boundary of PPS and examine its neural underpinnings. Precisely, we tested the hypothesis that intertrial variability (ITV) in brain regions coding PPS predicts individual differences of its boundary at the behavioral level. Selectively in the premotor cortex, we found that ITV, rather than trial-averaged amplitude, of BOLD responses to far rather than near dynamic stimuli predicts the individual extension of PPS. Our results provide the first empirical support for the relevance of ITV of brain responses for individual differences in human behavior. SIGNIFICANCE STATEMENT: Peripersonal space (PPS) is a multisensory and sensorimotor interface mediating every physical interaction between the body and the environment. A major characteristic of the boundary of PPS in humans is the extremely high variability of its location across individuals. We show that interindividual differences in the extension of the PPS are predicted by variability of BOLD responses in the premotor cortex to far stimuli approaching our body. Our results provide the first empirical support to the relevance of variability of evoked responses for human behavior and its variance across individuals.
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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 | 0.001 | 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".