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Random generation is what comes to mind in naturalistic settings
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DOI：10.1016/j.cognition.2026.106554.png)
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Abstract
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Being unpredictable is useful for creativity, exploration, and good decision-making. Decades of research asking people to generate random sequences of numbers have concluded that people systematically deviate from randomness, but individuals do so in very idiosyncratic ways. However, there is no consensus on the cause of these rich individual differences: most theories postulate that people achieve this by spending cognitive resources monitoring their own output and changing the way they say items accordingly, whereas a Local Sampling account postulates that people draw into a general-purpose ability to produce samples, which they would use to make judgments and choices, and which are inherently somewhat unpredictable. Here we distinguish between these possibilities by asking people to generate sequences both at random and as they come to mind, across two experiments at different production speeds, using a non-uniform distribution. We employ several measures of how sequences deviate from randomness. We find that, consistent with the Local Sampling explanation, people deviate from randomness in virtually identical ways in both sequences, with high individual differences pointing to a common cognitive process. We follow up these findings by computationally modelling human performance using several Local Sampling models. We find many participants had the same model as best-fitting in both sequences; with estimated parameters correlating strongly across tasks showing individual differences in how dependent on previous items sampling is. Overall, we conclude that random generation is better understood as employing a general-purpose faculty, Local Sampling, which is stable across time and tasks, with differences in the sampler’s features resulting in differences in random generation performance.
Keywords:
Random generation
Local sampling
Schema
Network modulation model
MCMC
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