Humans make an excessive number of indecisions under time constraints
Bibliographic record
Abstract
Failing to decide when acting under time constraints can be detrimental, such as a driver failing to decide where to steer a car and causing a crash. Decision-making research has explored how humans select motor plans to maximize reward given sensorimotor delays and uncertainties. Yet studies imposing time constraints have not examined indecisive behaviour, where humans fail to decide before a deadline. Here we test the idea that optimal motor planning that accounts for sensorimotor delays and uncertainties will result in indecisions. Participants were shown two targets and were required to reach the same target as a computer agent. They received one point for selecting the same target as the agent, zero points for selecting the opposite target, or zero points if they were indecisive and failed to reach a target by 1500 ms. Agent movement onset time was drawn from a normal distribution. In a repeated measures design, we manipulated the mean (1000, 1100, or 1200 ms) and standard deviation (50 or 150 ms) of the normal distribution that determined the agent movement onset time. We developed a model that finds the decision time that maximizes expected reward. The model predicts less indecisions in the 1200 ms conditions than the 1000 ms conditions. However, participants made more indecisions in the 1200 ms condition than the 1000 ms condition. Further, participants made more indecisions in the 1200 ms condition compared to the model. Our results suggest that humans suboptimally account for sensorimotor uncertainties, leading to an excessive number of indecisions.
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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 | 0.003 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".