| Title: | Datasets from the SAMPLING Project |
| Version: | 1.0.0 |
| Maintainer: | Lucas Castillo <lucas.castillo-marti@warwick.ac.uk> |
| Description: | Contains human behaviour datasets collected by the SAMPLING project (https://sampling.warwick.ac.uk). |
| License: | CC BY 4.0 |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.3.1 |
| URL: | https://github.com/lucas-castillo/samplrData, https://lucas-castillo.github.io/samplrData/ |
| BugReports: | https://github.com/lucas-castillo/samplrData/issues |
| Depends: | R (≥ 2.10) |
| LazyData: | true |
| Imports: | Rdpack |
| RdMacros: | Rdpack |
| NeedsCompilation: | no |
| Packaged: | 2024-06-13 09:54:15 UTC; Lucas |
| Author: | Lucas Castillo 
[aut, cre, cph],
Yun-Xiao Li [aut,
cph],
Adam N Sanborn
[aut, cph],
European Research Council (ERC) [fnd] |
| Repository: | CRAN |
| Date/Publication: | 2024-06-13 18:00:06 UTC |
samplrData: Datasets from the SAMPLING Project
Description
Contains human behaviour datasets collected by the SAMPLING project (https://sampling.warwick.ac.uk).
Author(s)
Maintainer: Lucas Castillo lucas.castillo-marti@warwick.ac.uk (ORCID) [copyright holder]
Authors:
Yun-Xiao Li yunxiao.li@warwick.ac.uk (ORCID) [copyright holder]
Adam N Sanborn a.n.sanborn@warwick.ac.uk (ORCID) [copyright holder]
Other contributors:
European Research Council (ERC) [funder]
See Also
Useful links:
Report bugs at https://github.com/lucas-castillo/samplrData/issues
Data from Experiment 1 in Castillo et al. (2024)
Description
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.
- id
participant id
- part_Gender
participant's gender (self-reported)
- part_Height
participant's own height (self-reported)
- part_Home
participant's home country (self-reported)
- RQ_Rep
percentage of correct responses in Randomness Questionnaire, for coin toss pairs where one sequence had too many repetitions
- RQ_Alt
percentage of correct responses in Randomness Questionnaire, for coin toss pairs where one sequence had too many alternations
- RQ_GFM
percentage of correct responses in Randomness Questionnaire, Gambling Fallacies Measure section
- minHeight
height participant reports to be the shortest adult in the UK (from target gender)
- maxHeight
height participant reports to be the tallest adult in the UK (from target gender)
- condition
whether the participant did the uniform condition first (UN) or not (NU)
- target_gender
gender they had to generate heights from, either male (M) or female (F)
- index
position of the item in the sequence, 0 indexed
- block
whether the item belongs to the first sequence the participant uttered (A) or the second (B)
- target_dist
whether the instructions asked for heights as distributed in the population (N) or uniformly distributed (U)
- label
what the participant uttered
- unit
height unit, either centimetres (cm) or feet and inches (f_in).
- value
value in cms of the height uttered.
- value_in_units
value of the height uttered depending on the value of
unit(either in inches or in centimetres). Used to calculate adjacencies, distances, etc.- 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])- R
whether the item is a repetition of the last
- A
whether the item is adjacent to the last (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.e1
Format
An object of class data.frame with 5836 rows and 29 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.
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.
Data from Experiment 1 in Spicer et al. (2022)
Description
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.
Usage
spicer2022.anchoringrepulsion.e1
Format
An object of class data.frame with 9600 rows and 11 columns.
Details
These data are licensed under CC BY 4.0, reproduced from materials in OSF.
- Timestamp
Date and time of the experimental session
- Pt
Participant ID
- Trial
Trial ID based on order of presentation
- Boundary
Comparison value for that trial
- DotCount
Number of dots shown on that trial
- Region
Region for that dot count, being either high or low
- Decision
Decision made by the participant on whether dot count was higher or lower than the boundary for that trial
- Dec_RT
Response time for the decision
- Accuracy
Accuracy of the selected decision
- Estimate
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
- Est_RT
Response time for the estimate
Source
References
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.
Data from Experiment 2 in Spicer et al. (2022)
Description
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.
Usage
spicer2022.anchoringrepulsion.e2
Format
An object of class data.frame with 2960 rows and 13 columns.
Details
These data are licensed under CC BY 4.0, reproduced from materials in OSF.
- Timestamp
Date and time of the experimental session
- Pt
Participant ID
- Trial
Trial ID based on order of presentation
- QID
ID for the target question of that trial
- Question
Question text
- Region
Expected region for that question, being either high or low
- Answer
Unbiased answer for that question from calibration data
- Boundary
Comparison value for that trial
- Decision
Decision made by the participant on whether answer to the question was higher or lower than the boundary
- Dec_RT
Response time for the decision
- Accuracy
Accuracy of the selected decision based on calibration data
- Estimate
Direct estimate of the answer to the question for that trial made by the participant
- Est_RT
Response time for the estimate
Source
References
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.
Data from Experiment 2a in Spicer et al. (2022)
Description
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.
Usage
spicer2022.anchoringrepulsion.e2a
Format
An object of class data.frame with 9920 rows and 13 columns.
Details
This experiment is described in the supplementary materials. These data are licensed under CC BY 4.0, reproduced from materials in OSF.
- Timestamp
Date and time of the experimental session
- Pt
Unique ID for that participant
- Trial
Trial ID based on order of presentation
- QID
ID for the target question of that trial. Note that these IDs match those of the calibration data.
- Question
Question text for that trial
- Region
Expected region for that question, being either high or low
- Answer
Unbiased answer for that question from calibration data
- Boundary
Comparison value for that trial
- Decision
Decision made by the participant on whether answer to the question was higher or lower than the boundary for that trial
- Dec_RT
Response time for the decision
- Accuracy
Accuracy of the selected decision based on calibration data
- Estimate
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
- Est_RT
Response time for the estimate
Source
References
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.
Data from Experiment 3 in Sundh et al. (2023)
Description
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.
Usage
sundh2023.meanvariance.e3
Format
An object of class data.frame with 12420 rows and 10 columns.
Details
These data are licensed under CC BY 4.0, reproduced from materials in OSF.
- ID
- block
3 blocks in total
- trial
Trial Number within a block
- query, querydetail
Verbal descriptions of the query
- querytype
Type of query: e.g. notBgA = p(¬B|A)
- Estimate
Estimated probability, in percentages
- starttime, endtime
- RT
Source
References
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.
Data from Experiment 4 in Sundh et al. (2023)
Description
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.
Usage
sundh2023.meanvariance.e4
Format
An object of class data.frame with 13320 rows and 7 columns.
Details
These data are licensed under CC BY 4.0, reproduced from materials in OSF.
- ID
- block
3 blocks in total
- query, querydetail
Verbal descriptions of the query
- querytype
Type of query: e.g. not B given A = p(¬B|A)
- queryset
Whether the query is about biden and 2050 climate goals or UK election and economic recovery
- Estimate
Estimated probability, in percentages
Source
References
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.
Data from Experiment 1 in Zhu et al. (2020)
Description
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.
Usage
zhu2020.bayesiansampler.e1
Format
An object of class data.frame with 7080 rows and 10 columns.
Details
These data are licensed under CC BY 4.0, reproduced from materials in OSF.
- ID
- block
3 blocks in total
- trial
Trial Number within a block
- query, querydetail
Verbal descriptions of the query
- querytype
Type of query: e.g. notBgA = p(¬B|A)
- Estimate
Estimated probability, in percentages
- starttime, endtime
- RT
Source
References
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.
Data from Experiment 2 in Zhu et al. (2020)
Description
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.
Usage
zhu2020.bayesiansampler.e2
Format
An object of class data.frame with 22380 rows and 10 columns.
Details
These data are licensed under CC BY 4.0, reproduced from materials in OSF.
- ID
- block
3 blocks in total
- trial
Trial Number within a block
- query, querydetail
Verbal descriptions of the query
- querytype
Type of query: e.g. notBgA = p(¬B|A)
- Estimate
Estimated probability, in percentages
- starttime, endtime
- RT
Source
References
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.
Data from Experiment 1 in Zhu et al. (2022)
Description
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.
Usage
zhu2022.coherenceaccuracy.e1
Format
An object of class data.frame with 82 rows and 23 columns.
Details
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.
- group
Self-reported response on whether they have played poker before
- q1-q9
Answers to the poker questions
- mq1-mq9
Answers to the ball questions
- gfs
number of correct answers in gambler's fallacy questionnaire
- cs
Inferred poker playing time in the last 12 months
- RT
- taskEqual
judged similarity between the Card and Ball task (0=all equal, 1=all differ, 0.5=answers differ but urn and deck were equal)
Source
References
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.
Data from Experiment 2 in Zhu et al. (2022)
Description
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.
Usage
zhu2022.coherenceaccuracy.e2
Format
An object of class data.frame with 186 rows and 23 columns.
Details
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.
- group
value here is always professional (in contrast to Experiment 1)
- q1-q9
Answers to the poker questions
- mq1-mq9
Answers to the ball questions
- gfs
number of correct answers in gambler's fallacy questionnaire
- cs
Inferred poker playing time in the last 12 months
- RT
- taskEqual
judged similarity between the Card and Ball task (0=all equal, 1=all differ, 0.5=answers differ but urn and deck were equal)
Source
References
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.
Data from Animal Experiment in Zhu et al. (2022)
Description
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.
Usage
zhu2022.structurenoise.animals
Format
An object of class data.frame with 4967 rows and 7 columns.
Details
These data are licensed under CC BY 4.0, reproduced from materials in OSF.
- ID
Participant ID
- Order
Index of the response
- Responses
Transcribed response
- Animal
Category the response was allocated to
- StartType,EndType
Absolute time of starting and ending to type the response
- IRI
Time between last response's EndType and this response's StartType
Source
References
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.
Data from Time Experiment in Zhu et al. (2022)
Description
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).
Usage
zhu2022.structurenoise.time
Format
An object of class data.frame with 29822 rows and 6 columns.
Details
These data are licensed under CC BY 4.0, reproduced from materials in OSF.
- ID
Participant ID
- Order
Index of the response
- StartType,EndType
Absolute time of starting and ending to type the response
- IRI
Time between last response's EndType and this response's StartType
- Target
Whether the participant had to reproduce a 1/3s, 1s or 3s interval
Source
References
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.