Neuroanatomical correlates of auditory and visual statistical learning: Cortical and subcortical volume predictors
Bibliographic record
Abstract
Statistical Learning (SL) is the ability to implicitly extract and learn statistical regularities from the environment. SL processes are ubiquitous across the lifespan through their proven role in language acquisition and adaptive behaviors. While recent neuroimaging research has identified networks of active brain regions during SL processes, less is known about whether structural differences in these regions may contribute to SL abilities. Here, we explore the extent to which visual SL (VSL) and auditory SL (ASL) performance are linked to cortical vs. subcortical brain volume. 61 Participants (43 Female) completed ASL and VSL tasks, each involving a familiarization and a testing phase. During familiarization, participants observed a sequence of structured triplets of unfamiliar and non-linguistic shapes (for VSL), and environmental sounds (for ASL). During testing, participants identified correct triplets/pairs on a series of alternative-forced-choice questions for both VSL and ASL. High-resolution T1-weighted Magnetic Resonance Imaging (MRI) scans were taken of all participants, from which volumes were extracted for each cortical (planum temporale, fusiform gyrus, middle temporal gyrus, inferior frontal gyrus) and subcortical (caudate, nucleus accumbens, putamen, globus pallidus) regions of interest. Hierarchical regression analyses revealed that performance during the ASL task was significantly related to the volume of the IFG, whereas VSL performance was significantly associated with volumes of all examined subcortical ROIs (p < 0.05). These findings suggest that differences in the IFG and subcortical volumes are differentially associated with SL abilities across visual and auditory modalities, further advancing our understanding of the domain-general and domain-specific aspects of SL processes.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".