Random Behavior Is Stable Across Tasks and Time

Tal Boger

doi:10.1037/xge0001755

Random Behavior Is Stable Across Tasks and Time

2025-04-01

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AI

DOI：10.1037/xge0001755

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Whether it's choosing a tennis serve or escaping a predator, the ability to behave randomly provides a range of adaptive benefits. Decades of work explore how people both produce and detect randomness, revealing profound nonrandom biases and heuristics in our mental representations of randomness. But how is randomness realized in the mind? Do individuals have a one-size-fits-all conception of randomness that they employ across different tasks and time points? Or do they instead use simple context-specific strategies? Here, we develop a model that reveals individual differences in how humans attempt to generate random sequences. Then, in three experiments, we reveal that random behavior is stable across both tasks and time. In Experiment 1, participants generated sequences of random numbers and one-dimensional random locations. Behavior was remarkably consistent across the two tasks. In Experiment 2, we gave participants both a random-number-generation and a two-dimensional random-location-generation task, such that the tasks diverged in structure. We again observed stable individual differences across tasks. Finally, in Experiment 3, we collected data from the same participants as in Experiment 2, but 1 year later; we found stable individual differences across that span. Across all experiments, we find idiosyncratic behaviors that are stable across tasks and time. Thus, we suggest that a trait-like randomness generator exists in the mind.

Keyword:

randomness

computational modeling

random generation

statistical biases

Random Behavior Is Stable Across Tasks and Time

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