Explore-exploit instability reveals computational decision-making heterogeneity in early psychosis
Bibliographic record
Abstract
Abstract Background and Hypothesis Psychosis spectrum illnesses are characterized by impaired goal-directed behavior and significant clinical and neurophysiological heterogeneity. This study investigated cognitive heterogeneity by applying computational modeling to trial-wise decision making task behavior. Study Design 75 participants with Early Psychosis (EP) and 68 controls completed a dynamic decision-making task during two baseline sessions as part of a larger longitudinal fMRI study. Study Results Consistent with prior studies, EP exhibited more choice switching. However, this was not explained by reward learning deficits as no group difference in reward acquisition was found. Instead, a Hidden Markov model fit to choice sequences revealed increased exploration as a result of higher probability of transition from exploitation to exploration in EP, leaving a favorable option too soon. A Bayesian learner model that estimates both value and uncertainty characterized EP behavior better than traditional RL models with fixed learning rates. Results of computational modeling implicated elevated uncertainty sensitivity and decision noise as independent contributors to suboptimal transition into exploration among EP. Task strategy yielded three computational subtypes (normative, uncertainty-sensitive, high decision-noise) with unique cognitive and symptom profiles. Conclusions These specific microcognitive disruptions underlying the distinct neurocomputational subtypes are individually measurable and may have the potential for targeted interventions.
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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 | 0.006 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".