A Common Cortical Metric for Spatial, Temporal, and Social Distance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Distance describes more than physical space: we speak of close friends and distant relatives, and of the near future and distant past. Did these ubiquitous spatial metaphors arise in language coincidentally or did they arise because they are rooted in a common neural computation? To address this question, we used statistical pattern recognition techniques to analyze human fMRI data. First, a machine learning algorithm was trained to discriminate patterns of fMRI responses based on relative egocentric distance within trials from one distance domain (e.g., photographs of objects relatively close to or far away from the viewer in spatial distance trials). Next, we tested whether the decision boundary generated from this training could distinguish brain responses according to relative egocentric distance within each of two separate distance domains (e.g., phrases referring to the immediate or more remote future within temporal distance trials; photographs of participants' friends or acquaintances within social distance trials). This procedure was repeated using all possible combinations of distance domains for training and testing the classifier. In all cases, above-chance decoding across distance domains was possible in the right inferior parietal lobule (IPL). Furthermore, the representational similarity structure within this brain area reflected participants' own judgments of spatial distance, temporal soon-ness, and social familiarity. Thus, the right IPL may contain a parsimonious encoding of proximity to self in spatial, temporal, and social frames of reference.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 it