Participants produced a random sequence of heights of either men or women in the United Kingdom. In one sequence, they sampled heights as distributed according to a uniform distribution (Uniform condition); in the other sequence, heights were distributed following their actual distribution (which is roughly Gaussian). These data are licensed under CC BY 4.0, reproduced from materials in OSF.
participant id
participant's gender (self-reported)
participant's own height (self-reported)
participant's home country (self-reported)
percentage of correct responses in Randomness Questionnaire, for coin toss pairs where one sequence had too many repetitions
percentage of correct responses in Randomness Questionnaire, for coin toss pairs where one sequence had too many alternations
percentage of correct responses in Randomness Questionnaire, Gambling Fallacies Measure section
height participant reports to be the shortest adult in the UK (from target gender)
height participant reports to be the tallest adult in the UK (from target gender)
whether the participant did the uniform condition first (UN) or not (NU)
gender they had to generate heights from, either male (M) or female (F)
position of the item in the sequence, 0 indexed
whether the item belongs to the first sequence the participant uttered (A) or the second (B)
whether the instructions asked for heights as distributed in the population (N) or uniformly distributed (U)
what the participant uttered
height unit, either centimetres (cm) or feet and inches (f_in).
value in cms of the height uttered.
value of the height uttered depending on the value of unit (either in inches or in centimetres). Used to calculate adjacencies, distances, etc.
timestamp of when the utterance starts, in seconds.
temporal difference with the start of the previous item (i.e. starts[index] - starts[index - 1])
whether the item is a repetition of the last
whether the item is adjacent to the last (after removing repetitions)
whether the item is a turning point, considering all items (after removing repetitions)
the Euclidean distance to the previous item (after removing repetitions)
a measure of how likely the item is in a uniform or gaussian distribution (see text)
the expectation for measure * derived from reshuffling the participant's sequence 10000 times
castillo2024.rgmomentum.e1An object of class data.frame with 5836 rows and 29 columns.
Castillo L, León-Villagrá P, Chater N, Sanborn AN (2024). “Explaining the Flaws in Human Random Generation as Local Sampling with Momentum.” PLOS Computational Biology, 20(1), 1--24. doi:10.1371/journal.pcbi.1011739 .