Caudate nucleus-dependent navigation strategies are associated with increased risk-taking and set-shifting behavior
Bibliographic record
Abstract
When people navigate, they use strategies dependent on one of two memory systems. The hippocampus-based spatial strategy consists of using multiple landmarks to create a cognitive map of the environment. In contrast, the caudate nucleus-based response strategy is based on the memorization of a series of turns. Importantly, response learners display more gray matter and functional activity in the caudate nucleus and less gray matter in the hippocampus. In parallel, the caudate nucleus is involved in decision-making by mediating attention toward rewards and in set-shifting by mediating preparatory actions. The present study, therefore, examined the link between navigational strategy use, that are associated with gray matter differences in the caudate nucleus and hippocampus, and decision-making and set-shifting performance. Fifty-three participants completed the 4 on 8 virtual maze, the Iowa Gambling Task (IGT), the Wisconsin Card Sorting Test-64 (WCST-64), and a task-switching test. The results revealed that people who use response strategies displayed increased risk-taking behavior in the IGT compared to the people using hippocampus-dependent spatial strategies. Response strategy was also associated with enhanced set-shifting performance in the WCST-64 and task-switching test. These results confirm that risk-taking and set-shifting behavior, that are differentially impacted by the caudate nucleus and hippocampus memory systems, can be predicted by navigational strategy.
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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 | 0.001 |
| 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".