Decomposing the neural pathways in a simple, value-based choice
Bibliographic record
Abstract
Understanding the neural implementation of value-based choice has been an important focus of neuroscience for several decades. Although a consensus has emerged regarding the brain regions involved, including ventromedial prefrontal cortex (vmPFC), posterior parietal cortex (PPC), and the ventral striatum (vSTR), the multifaceted nature of decision processes is one cause of persistent debate regarding organization of the value-based choice network. In the current study, we isolate neural activity related to valuation and choice selection using a gambling task where expected gains and losses are dissociated from choice outcomes. We apply multilevel mediation analysis to formally test whether brain regions identified as part of the value-based choice network mediate between perceptions of expected value and choice to accept or decline a gamble. Our approach additionally makes predictions regarding interregional relationships to elucidate the chain of processing events within the value-based decision network. Finally, we use dynamic causal modelling (DCM) to compare plausible models of interregional relationships in value-based choice. We observe that activity in vmPFC does not predict take/pass choices, but rather is highly associated with outcome evaluation. By contrast, both PPC and bilateral vSTR (bilaterally) mediate the relationship between expected value and choice. Interregional mediation analyses reveal that vSTR fully mediates between PPC and choice, and this is supported by DCM. Together these results suggest that vSTR, and not vmPFC nor PPC, functions as an important driver of choice.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".