A Critical Role for Human Ventromedial Frontal Lobe in Value Comparison of Complex Objects Based on Attribute Configuration
Bibliographic record
Abstract
In making decisions, we often choose from among options with multiple value-relevant attributes. Neuroeconomic models propose that the value associated with each attribute is integrated in a global value for each option. However, some evidence from patients with ventromedial frontal lobe (VMF) damage argues against a very general role for this region in value integration, suggesting instead that it contributes critically to a specific value inference or comparison process. Here, we tested value-based decision-making involving artificial multiattribute objects in humans with focal damage to the VMF ( N = 12) compared with a healthy group matched for age and education ( N = 24) and with a group with frontal lobe damage sparing the VMF ( N = 12). In a “configural” condition, overall object value was predicted by the conjunction of two attributes, while in an “elemental” condition, object value could be assessed by combining the independent values of individual attributes. Patients with VMF damage were impaired in making choices when value was uniquely predicted by the configuration of attributes, but intact when choosing based on elemental attribute values. This is evidence that the VMF is critical for inferring the value of whole objects in a multiattribute choice. These findings have implications for models of value-based choice and add to emerging views of how this region may interact with medial temporal lobe systems involved in configural object processing and relational memory. SIGNIFICANCE STATEMENT Neuroeconomic models propose that the ventromedial frontal lobe (VMF) supports multiattribute decisions by integrating the values of attributes. However, researchers have been uncertain about the underlying mechanisms for this process. Patients with VMF damage made multiattribute choices under two conditions: in one, attribute values could be summed to guide choice; in the other, value was predicted by the conjunction of attributes. VMF damage impaired only the latter. This argues that the VMF is critical for inferring value from configural information to guide multiattribute object choice. This region may be key for judging the emergent “value of the forest,” rather than for integrating the individual “value of each tree.”
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".