147. Abnormal Frontal Cortical Activity During Rapid Flexible Decision-Making in Schizophrenia: Relationships With Motivational Deficits
Bibliographic record
Abstract
Background: There is a rapidly amassing body of evidence suggesting that reinforcement learning may be impaired in schizophrenia (SZ). Reinforcement learning not only depends on the ability to signal reward prediction errors (RPEs), but also on the ability to modulate the impact that RPEs have on learning, especially in a volatile environment. Here, we investigated the ability of individuals with SZ to use RPEs to rapidly update their decision-making strategies and the neural correlates thereof. Methods: In a 3T MRI environment, 22 SZ and 22 matched controls (HC) performed a monetary probabilistic reversal learning task, during which participants attempted to seek out the most rewarding card deck out of three options (similar to Krugel et al., 2009 PNAS). Reward rates for the three decks were 90%, 50%, and 10%. When participants reached a hidden criterion, another deck became the best deck. Trial-by-trial estimates of learning rate were generated using a model-based approach. Parametric regression analyses were conducted in AFNI (Cox et al., 1995) to identify brain regions that tracked dynamic updates in learning rate. Results: In the entire sample and in accordance with previous work, whole-brain analyses revealed that dynamic estimates of learning rate covaried with activity in multiple brain regions, including posterior parietal cortex, intraparietal sulcus, and orbitofrontal cortex. In frontal pole, controls showed greater learning-rate-associated activity than patients (voxel-wise threshold, P < .005). Analyses performed in regions of interest showed that controls exhibited greater learning rate-associated activity than patients in dorsomedial prefrontal cortex (dmPFC according to Mars et al. Neuroimage 2005; P < .05). In patients, we observed a negative correlation between clinical symptom ratings for anhedonia and learning-rate-associated activity in frontal pole (P < .05). We observed a strong trend toward a significant negative correlation between total score on the Scale for the Assessment of Negative Symptoms and learning-rate-associated activity in dmPFC (P = .06). Conclusion: These results demonstrate that the neural signature of rapid and flexible learning-rate adjustment is altered in SZ. Abnormal learning-rate-related activity in dmPFC and frontal pole could reflect changes in the ability to use RPEs to assess the stability of the environment and adaptively update beliefs. The fact that these abnormalities are predicted by the severity of motivational deficits suggests that alterations in a frontal cortical network may play an important role in the pathophysiology of negative symptoms.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.004 | 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".