Explaining the flaws in human random generation as local sampling with momentum.

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

SUBMITTER: Castillo L 

PROVIDER: S-EPMC10796055 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature