Novelty and uncertainty interact to regulate the balance between exploration and exploitation in the human brain
Bibliographic record
Abstract
Abstract Recent evidence suggests that both novelty and uncertainty act as potent features guiding exploration. However, these variables are often conflated with each other experimentally, and an understanding of how these attributes interact to regulate the balance between exploration and exploitation has proved elusive. Using a novel task designed to decouple stimulus novelty and estimation uncertainty, we identify separable behavioral and neural mechanisms by which exploration is colored. We show that uncertainty was avoided except when the information gained through exploration could be reliably exploited in the future. In contrast, and contrary to existing theory, novel options grew increasingly attractive relative to familiar counterparts irrespective of the opportunity to leverage their consequences and despite the uncertainty inherent to novel options. These findings led us to develop a formal computational framework in which uncertainty directed choice adapts to the prospective utility of exploration, while novel stimuli persistently draw favor as a result of inflated reward expectations biasing an exploitative strategy. Crucially, novelty is proposed to actively modulate uncertainty processing, effectively blunting the influence of uncertainty in shaping the subjective utility ascribed to novel stimuli. Both behavioral data and fMRI activity sampled from the ventromedial prefrontal cortex, frontopolar cortex and ventral striatum validate this model, thereby establishing a computational account that can not only explain behavior but also shed light on the functional contribution of these key brain regions to the exploration/exploitation trade-off. Our results point to multiple strategies and neural substrates charged with balancing the explore/exploit dilemma, with each targeting distinct aspects of the decision problem to foster a manageable decomposition of an otherwise intractable task.
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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 | 0.002 |
| 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".