Human Exploration in Complex Problem-Solving Tasks: More Effortful Interaction Leads to Higher Efficiency
Why this work is in the frame
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Bibliographic record
Abstract
Exploration, a cornerstone of the human ability to solve novel problems, is a complex process. Most studies on human exploration used overly simple tasks that isolate variables but poorly reflect problems humans evolved to solve—limiting the generalizability of the results. To address this limitation, we introduce the Lockbox paradigm, a novel, ecologically valid, and challenging task that requires active exploration and physical interaction. Data from 263 participants interacting with the Lockbox across three different interaction modalities of varying interaction costs, reveal a remarkable ability to adapt and solve problems efficiently in complex scenarios. By comparing the interaction modalities, we demonstrate the critical role of cost variations, such as physical and temporal costs, in driving attentiveness and shaping exploration strategies. These findings provide important insights into human exploration strategies, with potential applications in fields such as robotics and artificial intelligence.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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 it