Individual Differences in Policy Precision: Links to Suicidal Ideation and Network Dynamics
Bibliographic record
Abstract
Abstract Behavioural modelling of decision-making processes has advanced our understanding of impairments associated with various psychiatric conditions. However, as research increasingly prioritises the development of models that best explain observed behaviour, the question of whether these behaviours stem from biologically plausible brain functions has often been overlooked. To address this gap, we developed a probabilistic two-armed bandit task model based on the active inference framework and compared its performance to established reinforcement learning (RL) models. Our model demonstrated superior explanatory power in capturing individual variability in choice behaviour. A key parameter in our model, policy precision—analogous to the temperature parameter in RL models—is also optimised based on previous outcomes. This optimisation accounts for the balance between model-free (MF) and model-based (MB) decision-making strategies. Notably, incorporating the rate of change in policy precision enhanced the model’s ability to explain brain network dynamics and their inter-subject correlations. Specifically, we observed a positive correlation with default mode network dominance and a negative correlation with dorsal attention and frontoparietal network-dominant states. These opposing network patterns suggest a cooperative relationship, as evidenced by correlations between state transitions and behavioural parameters. This transition may represent a neural mechanism underlying MB-MF arbitration, which appears to be disrupted by prolonged activation of another state characterised by heightened ventral attention network activity and increased inter-network connectivity. Finally, we found that reduced prior policy precision in loss-related context is associated with suicidal ideation in individuals with major depressive disorders. Highlights The AIF model explains pronounced individual behavioural variability. Neural signals are better explained by changes in policy precision. The anti-correlation can be explained from the perspective of the MB-MF arbitration. The AIF model better explains the HAM-D score. The AIF model can discriminate suicidal ideation in MDD with a loss task.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".