In Search of a Neural Mechanism for Domain-General Value Comparison in Decision Making
Bibliographic record
Abstract
Decision making requires comparing the costs and benefits associated with available options. Decision costs include the expected effort to be exerted, whether cognitive or physical. Benefits, on the other hand, include the anticipated reward or positive outcome of the decision (Kurzban et al., 2013). For example, if you feel hungry during the day, you might opt for a quick snack to satisfy your hunger temporarily, or you could invest the additional effort to prepare a well-rounded meal. Despite the importance of effort-based decision making in everyday life, it remains unclear how the brain resolves the tension between maximizing reward and minimizing costs to come to a decision. Designing experiments to study decision making in the brain requires consideration of factors such as valuation comparison (i.e., the relative value of the options) and decision difficulty. Studying decision making as an effort–reward trade-off is further complicated by phenomena such as effort discounting (i.e., the devaluation of rewards as cognitive effort increases) and the subjective value of rewards (Apps et al., 2015). Considering all these factors, it is perhaps unsurprising that the neural mechanisms involved in effort-based decision making remain an area of active investigation, as well as a topic of broad interest in daily life. From a mechanistic perspective, cost–benefit decision making requires dynamic assessment of estimated effort expenditure and expected reward, both of which are subjective and can fluctuate over time. The dorsal anterior cingulate cortex (dACC) has been identified as a candidate area for encoding aspects of effort-based decision making in humans and rodents (Westbrook et al., 2019). dACC is located in the midcingulate cortex in the medial surface of the frontal lobe and is connected to regions involved in cognitive control, executive function, and decision making (e.g., dorsolateral prefrontal cortex, parietal cortex, and … Correspondence should be addressed to Ahmad Samara at asamara1{at}student.ubc.ca.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".