Trait absorption is not reliably associated with brain structure or resting-state functional connectivity
Bibliographic record
Abstract
Trait 'absorption' is a psychological construct with a rich history that was initially born from early work on hypnotic suggestibility. Absorption characterizes an individual's tendency to become effortlessly engrossed in the contents of experience, whether in terms of external sensory phenomena or internal imagery and fantasy, and is reliably associated with a constellation of psychological, cognitive, and behavioral traits. Here, we conducted a comprehensive neuroimaging investigation of associations between trait absorption and the brain. In particular, we assessed multivariate relationships between absorption scores and neuroimaging measures of grey matter density, as well as static and dynamic resting-state functional connectivity. We investigated these relationships using partial least squares in a discovery dataset (n = 201) and then attempted to reproduce results in an independent replication dataset (n = 68). Results revealed a lack of significant associations between absorption and grey matter density across both datasets, and a significant association between absorption and static resting-state functional connectivity in the discovery dataset which was not replicated in the replication dataset. Additional control analyses further indicated the lack of a reliable brain-absorption relationship, whereas we found a replicable association between the closely related trait of 'openness to experience' and resting-state functional connectivity. We conclude that absorption is not reliably associated with brain structure or function in the present datasets and discuss factors that may have contributed to this result. This study serves as the first comprehensive and adequately powered investigation of the neural correlates of absorption and motivates future studies to refine the conceptualization of this perplexing trait.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".