The involvement of the striatum in decision making
Bibliographic record
Abstract
Decision making has been extensively studied in the context of economics and from a group perspective, but still little is known on individual decision making. Here we discuss the different cognitive processes involved in decision making and its associated neural substrates. The putative conductors in decision making appear to be the prefrontal cortex and the striatum. Impaired decision-making skills in various clinical populations have been associated with activity in the prefrontal cortex and in the striatum. We highlight the importance of strengthening the degree of integration of both cognitive and neural substrates in order to further our understanding of decision-making skills. In terms of cognitive paradigms, there is a need to improve the ecological value of experimental tasks that assess decision making in various contexts and with rewards; this would help translate laboratory learnings into real-life benefits. In terms of neural substrates, the use of neuroimaging techniques helps characterize the neural networks associated with decision making; more recently, ways to modulate brain activity, such as in the prefrontal cortex and connected regions (eg, striatum), with noninvasive brain stimulation have also shed light on the neural and cognitive substrates of decision making. Together, these cognitive and neural approaches might be useful for patients with impaired decision-making skills. The drive behind this line of work is that decision-making abilities underlie important aspects of wellness, health, security, and financial and social choices in our daily lives.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".