On the Generalizability of Linear and Non-Linear Region of\n Interest-Based Multivariate Regression Models for fMRI Data
Why this work is in the frame
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Bibliographic record
Abstract
In contrast to conventional, univariate analysis, various types of\nmultivariate analysis have been applied to functional magnetic resonance\nimaging (fMRI) data. In this paper, we compare two contemporary approaches for\nmultivariate regression on task-based fMRI data: linear regression with ridge\nregularization and non-linear symbolic regression using genetic programming.\nThe data for this project is representative of a contemporary fMRI experimental\ndesign for visual stimuli. Linear and non-linear models were generated for 10\nsubjects, with another 4 withheld for validation. Model quality is evaluated by\ncomparing $R$ scores (Pearson product-moment correlation) in various contexts,\nincluding single run self-fit, within-subject generalization, and\nbetween-subject generalization. Propensity for modelling strategies to overfit\nis estimated using a separate resting state scan. Results suggest that neither\nmethod is objectively or inherently better than the other.\n
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.003 |
| 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