ArticlePLoS computational biology2024
Explaining the flaws in human random generation as local sampling with momentum.
Article in PLoS computational biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- The samplr package: A tool for modeling human cognition with sampling algorithms.Behavior research methods · 2026Article
- Generated outcomes in risky choice reveal biased sampling and sequential dependencies.Communications psychology · 2026Article
- A large-scale comparison of divergent creativity in humans and large language models.Nature human behaviour · 2026Article
- Random behavior is stable across tasks and time.Journal of experimental psychology. General · 2025Article
- Noise in Cognition: Bug or Feature?Perspectives on psychological science : a journal of the Association for Psychological Science · 2025Article
- The sign of exploration during reward-based motor learning is not independent from trial to trial.Experimental brain research · 2025Article
- Active reinforcement learning versus action bias and hysteresis: control with a mixture of experts and nonexperts.PLoS computational biology · 2024Article
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Authors and funding
4 authors.
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Abstract
In many tasks, human behavior is far noisier than is optimal. Yet when asked to behave randomly, people are typically too predictable. We argue that these apparently contrasting observations have the same origin: the operation of a general-purpose local sampling algorithm for probabilistic inference. This account makes distinctive predictions regarding random sequence generation, not predicted by previous accounts-which suggests that randomness is produced by inhibition of habitual behavior, striving for unpredictability. We verify these predictions in two experiments: people show the same deviations from randomness when randomly generating from non-uniform or recently-learned distributions. In addition, our data show a novel signature behavior, that people's sequences have too few changes of trajectory, which argues against the specific local sampling algorithms that have been proposed in past work with other tasks. Using computational modeling, we show that local sampling where direction is maintained across trials best explains our data, which suggests it may be used in other tasks too. While local sampling has previously explained why people are unpredictable in standard cognitive tasks, here it also explains why human random sequences are not unpredictable enough.
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