Neural networks underlying magnitude perception: a specific meta-analysis of fMRI studies
Bibliographic record
Abstract
Daily life requires simultaneously processing spatial, temporal, and numerical inputs to form a valid mental representation of the environment. The interrelation between these perceptions has been a subject of theoretical debate. For instance, a theory of magnitude (ATOM) asserts that magnitude perceptions are processed in overlapping brain areas, which has been tested in behavioral and neuroimaging studies. We aimed to combine functional magnetic resonance imaging (fMRI) results using a coordinate-based meta-analysis to test this primary assumption of ATOM regarding overlapping brain areas. We conducted separate literature searches for space, time, and number perception following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The analysis was based on 19 articles regarding space, 38 regarding time, and 31 regarding number perception. Coordinates were analyzed using the "Activation Likelihood Estimation" method, which focused on conjunction analysis. Double conjunction analyses revealed activations mainly in the fronto-parietal areas and insular cortex. The triple conjunction analysis revealed activations in the right hemisphere, specifically in the inferior parietal and inferior frontal areas (previously linked to magnitude perception) and the anterior insular cortex (implicated in interoception and salience). In support of the ATOM theory, these findings suggest that overlapping neural networks may underlie space, time, and number perceptions.
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.017 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".