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Open Access Publications from the University of California

Local Sampling with Momentum Accounts for Human Random Sequence Generation


Many models of cognition assume that people can generate independent samples, yet people fail to do so in random generation tasks. One prominent explanation for this behavior is that people use learned schemas. Instead, we propose that deviations from randomness arise from people sampling locally rather than independently. To test these explanations, we teach people one- and two-dimensional arrangements of syllables and ask them to generate random sequences from them. Although our results reproduce characteristic features of human random generation, such as a preference for adjacent items and an avoidance of repetitions, we also find an effect of dimensionality on the patterns people produce. Furthermore, model comparisons revealed that local sampling accounted better for participants' sequences than a schema account. Finally, evaluating the importance of each models' constituents, we show that the local sampling model proposed new states based on its current trajectory, rather than an inhibition-of-return-like principle.

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