,
Yun-Xiao Li [aut, cph] ,
Adam N Sanborn [aut, cph] ,
European Research Council (ERC) [fnd]| Title: | Datasets from the SAMPLING Project |
|---|---|
| Description: | Contains human behaviour datasets collected by the SAMPLING project (<https://sampling.warwick.ac.uk>). |
| Authors: | Lucas Castillo [aut, cre, cph]
,
Yun-Xiao Li [aut, cph] ,
Adam N Sanborn [aut, cph] ,
European Research Council (ERC) [fnd] |
| Maintainer: | Lucas Castillo <[email protected]> |
| License: | CC BY 4.0 |
| Version: | 1.0.0.9000 |
| Built: | 2025-01-15 03:08:16 UTC |
| Source: | https://github.com/lucas-castillo/samplrdata |
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.e1castillo2024.rgmomentum.e1
An 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.
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.
participant id
participant's gender (self-reported)
participant's age (self-reported)
position of the item in the sequence, 0 indexed
unique identifier for the participant
whether the item belongs to the first sequence the participant uttered (A) or the second (B)
syllable uttered
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 participant was allocated to the one-dimensional or two-dimensional condition
Which of five possible configurations the participant learned
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)
whether the item is a repetition of the last
whether the item is adjacent to the last in the display (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.e2castillo2024.rgmomentum.e2
An object of class data.frame with 28483 rows and 20 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.
Perceptual judgments. Participants made judgments of numerosity against comparison values or absolute estimates. Comparison values (boundaries) were either similar or dissimilar to the true answer.
spicer2022.anchoringrepulsion.e1spicer2022.anchoringrepulsion.e1
An object of class data.frame with 9600 rows and 11 columns.
These data are licensed under CC BY 4.0, reproduced from materials in OSF.
Date and time of the experimental session
Participant ID
Trial ID based on order of presentation
Comparison value for that trial
Number of dots shown on that trial
Region for that dot count, being either high or low
Decision made by the participant on whether dot count was higher or lower than the boundary for that trial
Response time for the decision
Accuracy of the selected decision
Direct estimate of the number of dots on that trial made by the participant. NaN is used for trials in which no estimate was requested
Response time for the estimate
Spicer J, Zhu J, Chater N, Sanborn AN (2022). “Perceptual and Cognitive Judgments Show Both Anchoring and Repulsion.” Psychological Science, 33(9), 1395–1407. doi:10.1177/09567976221089599.
Cognitive judgments. Participants answered questions about commonly experienced values. judgments of numerosity against comparison values or absolute estimates. Comparison values (boundaries) were either similar or dissimilar to the true answer.
spicer2022.anchoringrepulsion.e2spicer2022.anchoringrepulsion.e2
An object of class data.frame with 2960 rows and 13 columns.
These data are licensed under CC BY 4.0, reproduced from materials in OSF.
Date and time of the experimental session
Participant ID
Trial ID based on order of presentation
ID for the target question of that trial
Question text
Expected region for that question, being either high or low
Unbiased answer for that question from calibration data
Comparison value for that trial
Decision made by the participant on whether answer to the question was higher or lower than the boundary
Response time for the decision
Accuracy of the selected decision based on calibration data
Direct estimate of the answer to the question for that trial made by the participant
Response time for the estimate
Spicer J, Zhu J, Chater N, Sanborn AN (2022). “Perceptual and Cognitive Judgments Show Both Anchoring and Repulsion.” Psychological Science, 33(9), 1395–1407. doi:10.1177/09567976221089599.
Cognitive judgments. Participants answered questions about commonly experienced values. Unlike in Experiment 2, participants viewed each question multiple times, comparing each against both a low (25.5) and high (75.5) comparison value to create 40 trial cases. As in Experiment 1, decisions were requested on all trials, but only 30% of trials were randomly selected to include a direct estimate.
spicer2022.anchoringrepulsion.e2aspicer2022.anchoringrepulsion.e2a
An object of class data.frame with 9920 rows and 13 columns.
This experiment is described in the supplementary materials. These data are licensed under CC BY 4.0, reproduced from materials in OSF.
Date and time of the experimental session
Unique ID for that participant
Trial ID based on order of presentation
ID for the target question of that trial. Note that these IDs match those of the calibration data.
Question text for that trial
Expected region for that question, being either high or low
Unbiased answer for that question from calibration data
Comparison value for that trial
Decision made by the participant on whether answer to the question was higher or lower than the boundary for that trial
Response time for the decision
Accuracy of the selected decision based on calibration data
Direct estimate of the answer to the question for that trial made by the participant. NaN is used for trials in which no estimate was requested
Response time for the estimate
Spicer J, Zhu J, Chater N, Sanborn AN (2022). “Perceptual and Cognitive Judgments Show Both Anchoring and Repulsion.” Psychological Science, 33(9), 1395–1407. doi:10.1177/09567976221089599.
Participants made probability judgments of the format: “What is the probability that the weather is [X] on a random day in England?". Various weather events were used, and the queries included both marginal events, conditional events, conjunctions, and disjunctions. The total set of 20 unique queries formed a block within which the presentation order was randomized for each participant. The experiment consisted of three blocks, so that all participants responded to each unique query three times.
sundh2023.meanvariance.e3sundh2023.meanvariance.e3
An object of class data.frame with 12420 rows and 10 columns.
These data are licensed under CC BY 4.0, reproduced from materials in OSF.
3 blocks in total
Trial Number within a block
Verbal descriptions of the query
Type of query: e.g. notBgA = p(¬B|A)
Estimated probability, in percentages
Sundh J, Zhu J, Chater N, Sanborn A (2023). “A Unified Explanation of Variability and Bias in Human Probability Judgments: How Computational Noise Explains the Mean Variance Signature.” Journal of Experimental Psychology: General, 152(10), 2842–2860. doi:10.1037/xge0001414.
Participants made probability judgments about future hypothetical events, of the format: “What is the probability that there will be an early UK general election AND the UK economy will recover this year?". The experiment consisted of three blocks, so that all participants responded to each unique query three times.
sundh2023.meanvariance.e4sundh2023.meanvariance.e4
An object of class data.frame with 13320 rows and 7 columns.
These data are licensed under CC BY 4.0, reproduced from materials in OSF.
3 blocks in total
Verbal descriptions of the query
Type of query: e.g. not B given A = p(¬B|A)
Whether the query is about biden and 2050 climate goals or UK election and economic recovery
Estimated probability, in percentages
Sundh J, Zhu J, Chater N, Sanborn A (2023). “A Unified Explanation of Variability and Bias in Human Probability Judgments: How Computational Noise Explains the Mean Variance Signature.” Journal of Experimental Psychology: General, 152(10), 2842–2860. doi:10.1037/xge0001414.
Participants made probability judgments of the format: “What is the probability that the weather is [X] on a random day in England?". Various weather events were used, and the queries included both marginal events, conditional events, conjunctions, and disjunctions. The total set of 20 unique queries formed a block within which the presentation order was randomized for each participant. The experiment consisted of three blocks, so that all participants responded to each unique query three times.
zhu2020.bayesiansampler.e1zhu2020.bayesiansampler.e1
An object of class data.frame with 7080 rows and 10 columns.
These data are licensed under CC BY 4.0, reproduced from materials in OSF.
3 blocks in total
Trial Number within a block
Verbal descriptions of the query
Type of query: e.g. notBgA = p(¬B|A)
Estimated probability, in percentages
Zhu J, Sanborn AN, Chater N (2020). “The Bayesian Sampler: Generic Bayesian Inference Causes Incoherence in Human Probability Judgments.” Psychological Review, 127(5), 719–748. doi:10.1037/rev0000190.
Participants made probability judgments of the format: “What is the probability that the weather is [X] on a random day in England?". Various weather events were used, and the queries included both marginal events, conditional events, conjunctions, and disjunctions. The total set of 20 unique queries formed a block within which the presentation order was randomized for each participant. The experiment consisted of three blocks, so that all participants responded to each unique query three times.
zhu2020.bayesiansampler.e2zhu2020.bayesiansampler.e2
An object of class data.frame with 22380 rows and 10 columns.
These data are licensed under CC BY 4.0, reproduced from materials in OSF.
3 blocks in total
Trial Number within a block
Verbal descriptions of the query
Type of query: e.g. notBgA = p(¬B|A)
Estimated probability, in percentages
Zhu J, Sanborn AN, Chater N (2020). “The Bayesian Sampler: Generic Bayesian Inference Causes Incoherence in Human Probability Judgments.” Psychological Review, 127(5), 719–748. doi:10.1037/rev0000190.
Participants (from Prolific) estimated the frequencies of different 3-card combinations in a 52 card deck and 3-ball combinations in a 52 ball urn (mathematically identical questions). They also answered surveys on poker playing habits and gamblers fallacy questionnaire.
zhu2022.coherenceaccuracy.e1zhu2022.coherenceaccuracy.e1
An object of class data.frame with 82 rows and 23 columns.
See exact questions in original paper's supplementary materials (Appendix B). These data are licensed under CC BY 4.0, reproduced from materials in OSF.
Self-reported response on whether they have played poker before
Answers to the poker questions
Answers to the ball questions
number of correct answers in gambler's fallacy questionnaire
Inferred poker playing time in the last 12 months
judged similarity between the Card and Ball task (0=all equal, 1=all differ, 0.5=answers differ but urn and deck were equal)
Zhu J, Newall PW, Sundh J, Chater N, Sanborn AN (2022). “Clarifying the Relationship between Coherence and Accuracy in Probability Judgments.” Cognition, 223, 105022. doi:10.1016/j.cognition.2022.105022.
Participants (professional players recruited from twoplustwo.com) estimated the frequencies of different 3-card combinations in a 52 card deck and 3-ball combinations in a 52 ball urn (mathematically identical questions). They also answered surveys on poker playing habits and gamblers fallacy questionnaire.
zhu2022.coherenceaccuracy.e2zhu2022.coherenceaccuracy.e2
An object of class data.frame with 186 rows and 23 columns.
See exact questions in original paper's supplementary materials (Appendix B). These data are licensed under CC BY 4.0, reproduced from materials in OSF.
value here is always professional (in contrast to Experiment 1)
Answers to the poker questions
Answers to the ball questions
number of correct answers in gambler's fallacy questionnaire
Inferred poker playing time in the last 12 months
judged similarity between the Card and Ball task (0=all equal, 1=all differ, 0.5=answers differ but urn and deck were equal)
Zhu J, Newall PW, Sundh J, Chater N, Sanborn AN (2022). “Clarifying the Relationship between Coherence and Accuracy in Probability Judgments.” Cognition, 223, 105022. doi:10.1016/j.cognition.2022.105022.
Participants were asked to type animal names as they came to mind and were explicitly instructed that they could resubmit previous animals, though not consecutively.
zhu2022.structurenoise.animalszhu2022.structurenoise.animals
An object of class data.frame with 4967 rows and 7 columns.
These data are licensed under CC BY 4.0, reproduced from materials in OSF.
Participant ID
Index of the response
Transcribed response
Category the response was allocated to
Absolute time of starting and ending to type the response
Time between last response's EndType and this response's StartType
Zhu J, León-Villagrá P, Chater N, Sanborn AN (2022). “Understanding the Structure of Cognitive Noise.” PLoS Computational Biology, 18(8), e1010312. doi:10.1371/journal.pcbi.1010312.
Participants first listened to a sample of the target temporal interval for 60 seconds. Participants were instructed to reproduce the target by pressing the spacebar when they believed the target interval had elapsed (i.e. perfect performance in the task would mean IRI == Target).
zhu2022.structurenoise.timezhu2022.structurenoise.time
An object of class data.frame with 29822 rows and 6 columns.
These data are licensed under CC BY 4.0, reproduced from materials in OSF.
Participant ID
Index of the response
Absolute time of starting and ending to type the response
Time between last response's EndType and this response's StartType
Whether the participant had to reproduce a 1/3s, 1s or 3s interval
Zhu J, León-Villagrá P, Chater N, Sanborn AN (2022). “Understanding the Structure of Cognitive Noise.” PLoS Computational Biology, 18(8), e1010312. doi:10.1371/journal.pcbi.1010312.