Action-outcome based flexible behavior requires medial prefrontal cortex lead and its enhanced functional connectivity with dorsomedial striatum
Bibliographic record
Abstract
Abstract Cognitive flexibility plays a key role in ensuring an individual’s survival, and its deficit is a key symptom in many mental conditions and neurodegenerative diseases. The prefrontal cortex and striatum are both essential to cognitive flexibility. However, how the prefrontal cortex and striatum communicate with each other to enable flexible decision-making is not well understood. Competing theories are raised, debating on which structure among these two leads the role in detecting and representing the new circumstances for a change, giving largely opposing predictions on neural activities in the prefrontal cortex and striatum during flexible behavior. To address this question, we trained head-restrained mice to perform an action-outcome based dynamic foraging task and simultaneously recorded single-neuron activities in the medial prefrontal cortex (mPFC) and dorsomedial striatum (DMS). In this task, the animal chooses one of two actions to obtain reward. The animal is guided only by previous reward outcomes. We report that mPFC but not DMS activity stores information about prior reward history. A large fraction of both mPFC and DMS neurons’ activity represents the difference in reward probability between two alternative options, namely the perceived reward probability difference (PRPD), a key decision variable that prescribes which subsequent choice to make. We find that mPFC neural activities track the change of PRPD earlier and faster than those in the DMS, and functional connectivity between mPFC and DMS increases with reducing overall reward proportion.
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.
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) | 0.001 | 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".