Beyond the brain localization of complex traits: Distributed white matter markers of personality
Bibliographic record
Abstract
Extensive work in personality neuroscience has shown mixed results in the ability to localize reliable relationships between personality traits and neuroimaging measures. However, recent work in translational neuroimaging has recognized that multifaceted psychological dispositions are not represented in discrete, highly localized brain areas. As such, standard univariate neuroimaging analyses may not be well-suited for capturing broad personality traits supported by distributed networks. The present study uses an out-of-sample predictive modeling approach to identify multivariate signatures of Big Five personality traits within the structural integrity of the brain’s white matter pathways using diffusion magnetic resonance imaging. In Study 1 (N = 491), we trained a ridge regression model to predict each of the Big Five traits and tested these models in an independent hold-out subsample. We found that models for both Neuroticism and Openness were significantly related to predictive accuracy in the hold-out sample. Study 2 (N = 108) applied the predictive models from Study 1 to an independent set of data collected on a different scanner and using a different Big Five scale. Here, we found that the model for Neuroticism remained a significant predictor of individual difference, providing evidence that this white matter signature of Neuroticism generalizes across differences in measurement and samples.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".