Novel insular cortex and claustrum activation observed during a visuomotor adaptation task using a viewing window paradigm
Bibliographic record
Abstract
Previous literature has reported a wide range of anatomical correlates when participants are required to perform a visuomotor adaptation task. However, traditional adaptation tasks suffer a number of inherent limitations that may, in part, give rise to this variability. The overt nature of the required visuomotor transformation and sparse visual environment do not map well onto conditions in which a visuomotor transformation would normally be required in everyday life. For instance, when one must utilize the relationship between a vehicle's steering wheel and the resultant movement to drive down the street, the nature of the required transformation is most likely encompassed in the higher order goal of driving down the road. To further clarify these neural underpinnings, functional magnetic resonance imaging (fMRI) was performed on twelve (5M, age range 20 – 45 years old; mean age = 27) naive participants performing a viewing window task in which a visuomotor transformation was created by varying the relationship between the participant's movement and the resultant movement of the viewing window. The viewing window task removes the focus of the experiment away from the required visuomotor transformation and more naturally replicates scenarios in which haptic and visual information would be combined to achieve a higher-level goal. Activity related to visuomotor adaptation was found within previously reported regions of the parietal lobes, frontal lobes, and occipital lobes. In addition, previously unreported activation was observed within the claustrum and insular cortex, regions well-established as multi-modal convergence zones. These results confirm the diverse nature of the systems recruited to perform a required visuomotor adaptation, and provides the first evidence of participation of the claustrum and insular cortex to overcome a visuomotor transformation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".