Nonlinear modulation of human exploration by distinct sources of uncertainty
Bibliographic record
Abstract
Abstract Decision-making in uncertain environments requires balancing exploration and exploitation, with exploration typically assumed to increase monotonically with uncertainty. Challenging this prevailing assumption, we demonstrate a more complex relationship by decomposing environmental uncertainty into volatility (systematic change in reward contingencies, learnable) and stochasticity (random noise in observations, unlearnable). Across two behavioral experiments (N=1001, N=747) using a probabilistic reward task, we find a robust U-shaped relationship between the volatility-to-stochasticity ( v / s ) ratio and exploratory behavior, with participants exploring more when either stochasticity or volatility dominates. Remarkably, this pattern extends to real-world financial behavior, as demonstrated through analysis of five years of S&P 500 stock market data, where portfolio diversity (a proxy for exploration) shows the same U-shaped relationship with market volatility (systematic price movements driven by fundamental factors, e.g., economic shifts) relative to trading noise (random fluctuations from trading activity unrelated to fundamentals). These findings reveal how humans adaptively modulate exploration strategies based on the qualitative composition of uncertainty, with optimal performance occurring at intermediate uncertainty ratios. This nonlinear relationship has important implications for understanding decision-making across domains where uncertainty arises from multiple sources.
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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 | 0.008 |
| 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".