Neural correlates of social and non-social personal space intrusions: Role of defensive and peripersonal space systems in interpersonal distance regulation
Bibliographic record
Abstract
Personal space regulation ensures the maintenance of a margin of safety between the individual and the surrounding world. However, little is known about the specific neural mechanisms implicated in regulating the distance from conspecifics versus non-social stimuli. Here, we investigated the neural correlates of personal space intrusions by social versus non-social stimuli. Thirty volunteers underwent fMRI scanning while viewing approaching or withdrawing faces (Social) and insects/arachnids (Non-social). Preferred distance to the stimuli was assessed behaviourally in a computerized task, and in real life. Results showed that approaching social and non-social stimuli of varying threat levels elicited activation of frontoparietal regions previously linked to peripersonal space, as well as of the midbrain periaqueductal gray, suggesting the engagement of defensive mechanisms by personal space intrusions. However, functional connectivity patterns of the midbrain differed for social and non-social stimuli, with enhanced coupling with the premotor cortex to approaching social stimuli. Additionally, connectivity strength between the midbrain and the premotor cortex was associated with preferred interpersonal distance. These findings highlight a common defensive architecture implicated in personal space regulation to social and non-social stimuli, and the specific neural mechanisms involved in regulating the distance from conspecifics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".