Dissociable contributions of the prefrontal cortex to hippocampus- and caudate nucleus-dependent virtual navigation strategies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The hippocampus and the caudate nucleus are critical to spatial- and stimulus-response-based navigation strategies, respectively. The hippocampus and caudate nucleus are also known to be anatomically connected to various areas of the prefrontal cortex. However, little is known about the involvement of the prefrontal cortex in these processes. In the current study, we sought to identify the prefrontal areas involved in spatial and response learning. We used functional magnetic resonance imaging (fMRI) and voxel-based morphometry to compare the neural activity and grey matter density of spatial and response strategy users. Twenty-three healthy young adults were scanned in a 1.5 T MRI scanner while they engaged in the Concurrent Spatial Discrimination Learning Task, a virtual navigation task in which either a spatial or response strategy can be used. In addition to increased BOLD activity in the hippocampus, spatial strategy users showed increased BOLD activity and grey matter density in the ventral area of the medial prefrontal cortex, especially in the orbitofrontal cortex. On the other hand, response strategy users exhibited increased BOLD activity and grey matter density in the dorsal area of the medial prefrontal cortex. Given the prefrontal cortex's role in reward-guided decision-making, we discuss the possibility that the ventromedial prefrontal cortex, including the orbitofrontal cortex, supports spatial learning by encoding stimulus-reward associations, while the dorsomedial prefrontal cortex supports response learning by encoding action-reward associations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it