| castillo2024.rgmomentum.e2 {samplrData} | R Documentation |
Data from Experiment 2 in Castillo et al. (2024)
Description
Participants first learned a set of syllables arranged in either a single row (one-dimensional condition) or a grid (two-dimensional condition), then produced two random sequences for the same display. These data are licensed under CC BY 4.0, reproduced from materials in OSF.
- id
participant id
- part_Gender
participant's gender (self-reported)
- part_Age
participant's age (self-reported)
- index
position of the item in the sequence, 0 indexed
- id
unique identifier for the participant
- block
whether the item belongs to the first sequence the participant uttered (A) or the second (B)
- syll
syllable uttered
- starts
timestamp of when the utterance starts, in seconds.
- delays
temporal difference with the start of the previous item (i.e.
starts[index] - starts[index - 1])- dim
whether the participant was allocated to the one-dimensional or two-dimensional condition
- seed
Which of five possible configurations the participant learned
- position
The position of the syllable in the array. For 1D arrays, position is left to right. For 2D arrays positions 1-2 correspond to the top 2 cells; 3-5 to the middle 3 cells; and 6-7 to the bottom three cells (always left to right)
- R
whether the item is a repetition of the last
- A
whether the item is adjacent to the last in the display (after removing repetitions)
- TP_full
whether the item is a turning point, considering all items (after removing repetitions)
- D
the Euclidean distance to the previous item (after removing repetitions)
- S
a measure of how likely the item is in a uniform or gaussian distribution (see text)
- expected_*
the expectation for measure
*derived from reshuffling the participant's sequence 10000 times
Usage
castillo2024.rgmomentum.e2
Format
An object of class data.frame with 28483 rows and 20 columns.
Source
References
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.