An fMRI Study of Risky Decision Making: The Role of Mental Preparation and Conflict.
Bibliographic record
Abstract
INTRODUCTION: The current study aimed to elucidate the role of preparatory cognitive control in decision making and its neural correlates using functional Magnetic Resonance Imaging (fMRI). To this effect, by employing a series of new cognitive tasks, we assessed the role of preparatory cognitive control in monetary (risky) decision making. METHODS: The participants had to decide between a risky and a safe gamble based on their chance of winning (high or low). In the 2-phase gambling task (similar to Cambridge gambling task), the chance and the gamble were presented at the same time (i.e. in a single phase), but in a new 3-phase gambling task, the chance is presented before the gamble. The tasks ended with a feedback phase. RESULTS: In the 3-phase task, holding the chance in memory to guide their decision enabled the participants to have more control on their risk taking behaviors as shown by activation in a network of brain areas involved in the control and conflict, including dorsal Anterior Cingulate Cortex (dACC), indexed by faster reaction times and better performance in the gambling task, and the temporal lobe, which has a role in holding contextual information. DISCUSSION: Holding information in memory to guide decision presumably enables the participants to have more control on their risk taking behaviors. The conflict and uncertainty resulting from this risky decision was indexed by the activation of dACC, known to be activated in conflict and cognitive control.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".