Elucidating medial temporal and frontal lobe contributions to approach-avoidance conflict decision-making using functional MRI and the hierarchical drift diffusion model
Bibliographic record
Abstract
The prefrontal cortex (PFC) has long been associated with arbitrating between approach and avoidance in the face of conflicting and uncertain motivational information, but recent work has also highlighted medial temporal lobe (MTL) involvement. It remains unclear, however, how the contributions of these regions differ in their resolution of conflict information and uncertainty. We designed an fMRI paradigm in which participants approached or avoided object pairs that differed by motivational conflict and outcome uncertainty (complete certainty vs. complete uncertainty). Behavioral data and decision-making parameters estimated using the hierarchical drift diffusion model revealed that participants' responding was driven by conflict rather than uncertainty. Our neural data suggest that PFC areas contribute to cognitive control during approach-avoidance conflict by potentially adjusting response caution and the strength of evidence generated towards either choice, with differential involvement of anterior cingulate cortex and dorsolateral prefrontal cortex. The MTL, on the other hand, appears to contribute to evidence generation, with the hippocampus linked to evidence accumulation for stimuli. Although findings within perirhinal cortex were comparatively equivocal, some evidence suggests contributions to perceptual representations, particularly under conditions of threat. Our findings provide evidence that MTL and PFC regions may contribute uniquely to arbitrating approach-avoidance conflict.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".