Dorsal striatum mediates cognitive control, not cognitive effort per se, in decision-making: an event-related fMRI study
Bibliographic record
Abstract
Background: Whether the dorsal striatum (DS) mediates cognitive control or cognitive effort per se in decision-making is unclear because as cognitive control requirements of a task intensify, cognitive effort requirements increase proportionately. We implemented a task that disentangled cognitive control and cognitive effort to specify the function DS mediates in decision-making. Methods: Sixteen healthy young adults completed a number Stroop task with simultaneous blood-oxygenation-level-dependent response (BOLD) measurement. Participants selected the physically larger number of a pair. Discriminating smaller physical size differences increases cognitive effort, but does not demand greater cognitive control. We also investigated the effect of interdimensional conflict between physical size and numerical magnitude. Selections in this incongruent case are more cognitively effortful and require greater cognitive control to suppress responding to the irrelevant dimension. Enhancing cognitive effort or cognitive control requirements increases response times and error rates. Results: Behavioural interference occurred for both conditions; however, DS BOLD signal only correlated with interference due to increased cognitive control requirements. DS was not preferentially activated for discriminations of smaller relative to larger physical size differences between number pairs, even when using liberal statistical criteria. Conclusions: Our findings support the increasingly accepted notion that DS mediates cognitive control specifically and does not index cognitive effort per se.
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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) | 0.001 | 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".