Decreased risk‐taking and loss‐chasing after subthalamic nucleus lesion in rats
Bibliographic record
Abstract
The subthalamic nucleus (STN) is known to play a role in the control of impulsivity of action and in impulsivity of choice under certain conditions. In order to assess its influence on decision-making under uncertainty, we have tested here the effects of bilateral STN lesions in rats performing a probability discounting task (PDT) and a "loss-chasing" task, both tasks assessing risky decision under uncertainty, but one in a positive context (probability to obtain a larger reward) and the other in a negative context (risk for a larger loss). The PDT measures the choice between a small certain and a large uncertain reward. Conversely, in the "loss-chasing" task, animals choose between accepting a small certain loss versus risking a larger but uncertain penalty. The results show that STN lesions reduce risk-taking in both the PDT and the loss-chasing task, suggesting that STN inactivation could decrease risky decision-making whatever the nature of the outcome in an ambiguous context. Interestingly, opposite results were found in a small number of animals for which the lesions extended to the area dorsal to the STN (in the zona incerta), such that these animals increased choice of the uncertain option in the PDT. These results confirm the specificity of STN involvement in these processes and may provide explanations for some side-effects reported in patients when STN manipulations extend to the Zona Incerta. They also support the choice of the STN as a target for the treatment of impulse control disorders in Parkinson's disease and in obsessive compulsive disorders.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".