Individual differences in sequential decision-making
Bibliographic record
Abstract
People differ widely in how they make decisions in uncertain environments. While many studies leverage this variability to measure differences in specific cognitive processes and parameters, the key dimension(s) of individual variability in uncertain decision-making tasks has not been identified. Here, we analyzed behavioral data from 1001 participants performing a restless three-armed bandit task, where reward probabilities fluctuated unpredictably over time. Using a novel analytical approach that controlled for the stochasticity in this tasks, we identified a dominant nonlinear axis of individual variability. We found that this primary axis of variability was strongly and selectively correlated with the probability of exploration, as inferred by latent state modeling. This suggests that the major factor shaping individual differences in bandit task performance is the tendency to explore (versus exploit), rather than personality characteristics, reinforcement learning model parameters, or low-level strategies. Certain demographic characteristics also predicted variance along this principle axis: participants at the exploratory end tended to be younger than participants at the exploitative end, and self-identified men were overrepresented at both extremes. Together, these findings offer a principled framework for understanding individual differences in task behavior while highlighting the cognitive and demographic factors that shape individual differences in decision-making under uncertainty.
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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 | 0.002 | 0.011 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".