Decomposing the neural pathways mediating value-based choice
Bibliographic record
Abstract
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, disagreement persists regarding precise regional functions and how value information flows between value-based choice regions. In the current study, we isolate neural activity related to decision-making using a gambling task where expected gains and losses are dissociated from the received outcomes of choices. We apply multilevel modelling and mediation analysis to formally test whether brain regions identified as part of the value-based choice network mediate between perceptions of expected value and choices to take or pass a gamble. A critical function in decision-making is accruing and representing value information to drive choice. Several regions have been assigned this role, including ventromedial prefrontal (vmPFC) and posterior parietal cortex (PPC), and the ventral striatum (VStr). The implied chain of events is one where regions that support the process of gathering relevant information mediate the relationship between choice and representations of value in other brain regions. Here, we formally test whether distinct brain regions express interregional mediation consistent with this chain of processes. We observe that activity in vmPFC does not predict choice, but rather is highly associated with outcome evaluation. By contrast, both PPC and VStr (bilaterally) mediate between expected value and choice. Interregional mediation analyses reveal that VStr fully mediates between PPC and choice. Together these results suggest that VStr, and not vmPFC nor PPC, functions as an important driver of late stage choice. Significance Statement Making choices that maximize gain and minimize loss is critical for success. Our paradigm and analytic approach allowed isolation of choice-related neural signals from outcome-related signals. The vmPFC is involved at outcome rather than at choice. Isolating choice-related neural activity, we formally demonstrate that VStr and PPC mediate between expected value and choice. Our approach adds significant innovation by using generalized multilevel modelling to predict behavior with concurrent neural activity and formally testing the fully mediated pathway from stimulus through neural activity to behavior. Applying interregional multilevel mediation analysis, we demonstrate that ventral striatum comprises a final, critical step in processing value-based choice, mediating the relationship between value representation and 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".