F9. REDUCED UNCERTAINTY-DRIVEN EXPLORATION AND ASSOCIATED NEURAL REWARD-RELATED SIGNALS RELATE TO MOTIVATIONAL DEFICIT SEVERITY
Bibliographic record
Abstract
People with schizophrenia (PSZ) can suffer from a reduced tendency to engage in goal-directed behavior, with these impairments often being associated with motivational deficit severity (e.g. amotivation, avolition). Previous work suggests that reductions in goal-directed behavior can be driven by an inability to represent the value of rewards (1) or impairments in adaptive instrumental learning, potentially related to abnormal signaling of reward prediction errors (2). Whether a compromised ability to seek out new information under uncertainty may contribute to deficits in goal-directed behavior, however, has received little attention. Here, we investigated uncertainty-driven exploration and associated neural responses during an explore/exploit paradigm. 24 healthy volunteers (HV) and 26 PSZ performed a three-option slot machine task in a 3T MRI environment (based on Daw et al. (3)). During a stable phase (150 trials), reward payout for every machine fluctuated (sd=10), and one machine consistently was the optimal choice (i.e. the highest expected value). During a volatile phase (150 trials), reward payouts for every machine fluctuated (sd=10) and every 25 trials another machine would become the optimal choice. In the latter phase, participants had to actively sample from all three machines to maximize the total amount of accumulated reward. All participants made less optimal choices during the volatile, compared to the stable, phase (t=8.58, p<.001). PSZ compared to HV made less optimal choices during the volatile phase (t=2.13, p=.038), especially following a change in the location of the optimal machine (t=2.99, p=.004). In an MR subsample (n=22 per group) strong correlations between among others orbitofrontal cortex (OFC), dorsomedial prefrontal cortex/anterior cingulate cortex, insula and ventral striatum outcome phase-related BOLD activity and reward size were observed (parametric regression in AFNI, p<.001 voxel-level, p<.05 [FWE-corrected] minimum cluster size=780 voxels). Region-of-interest (ROI) analyses revealed that Scale for the Assessment of Negative Symptoms (SANS) avolition-anhedonia subscale scores were significantly associated with reward tracking-related activity in OFC (Pearson’s r=-.41; p=.05,) and insula (Pearson’s r=.57, p=.01). Further computational modeling analyses and model-based fMRI analyses will be presented at the annual meeting. PSZ show reduced uncertainty-driven exploration in the service of optimizing behavior during a slots machine task. Performance deficits in PSZ were restricted to the volatile phase of the experiment, where sampling of response options is necessary to maximize reward earnings. At the neural level, deficits in uncertainty-driven exploration may be associated with altered activity in brain regions associated with tracking of choice history and reward size, which was especially prominent in PSZ with high motivational deficits. References: 1. Strauss GP, Waltz JA, Gold JM (2014): A review of reward processing and motivational impairment in schizophrenia. Schizophrenia bulletin. 40 Suppl 2:S107-116. 2. Maia TV, Frank MJ (2017): An Integrative Perspective on the Role of Dopamine in Schizophrenia. Biological psychiatry. 81:52–66. 3. Daw ND, O’Doherty JP, Dayan P, Seymour B, Dolan RJ (2006): Cortical substrates for exploratory decisions in humans. Nature. 441:876–879.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".