Ventromedial Frontal Cortex Is Critical for Guiding Attention to Reward-Predictive Visual Features in Humans
Bibliographic record
Abstract
Adaptively interacting with our environment requires extracting information that will allow us to successfully predict reward. This can be a challenge, particularly when there are many candidate cues, and when rewards are probabilistic. Recent work has demonstrated that visual attention is allocated to stimulus features that have been associated with reward on previous trials. The ventromedial frontal lobe (VMF) has been implicated in learning in dynamic environments of this kind, but the mechanism by which this region influences this process is not clear. Here, we hypothesized that the VMF plays a critical role in guiding attention to reward-predictive stimulus features based on feedback. We tested the effects of VMF damage in human subjects on a visual search task in which subjects were primed to attend to task-irrelevant colors associated with different levels of reward, incidental to the search task. Consistent with previous work, we found that distractors had a greater influence on reaction time when they appeared in colors associated with high reward in the previous trial compared with colors associated with low reward in healthy control subjects and patients with prefrontal damage sparing the VMF. However, this reward modulation of attentional priming was absent in patients with VMF damage. Thus, an intact VMF is necessary for directing attention based on experience with cue-reward associations. We suggest that this region plays a role in selecting reward-predictive cues to facilitate future learning. SIGNIFICANCE STATEMENT: There has been a swell of interest recently in the ventromedial frontal cortex (VMF), a brain region critical to associative learning. However, the underlying mechanism by which this region guides learning is not well understood. Here, we tested the effects of damage to this region in humans on a task in which rewards were linked incidentally to visual features, resulting in trial-by-trial attentional priming. Controls and subjects with prefrontal damage sparing the VMF showed normal reward priming, but VMF-damaged patients did not. This work sheds light on a potential mechanism through which this region influences behavior. We suggest that the VMF is necessary for directing attention to reward-predictive visual features based on feedback, facilitating future learning and decision-making.
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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 | 0.000 |
| 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".