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Sleep Restriction and Reinforcement Learning - Data Analysis

dataset
posted on 01.12.2020, 09:46 by Andreas Gerhardsson, Danja Porada, Johan Lundström, John Axelsson, Johanna Schwarz
Project description
In this study we investigated the effect of two nights of sleep restriction on reinforcement learning using a probabilistic selection task. The current data set includes data from 32 participants measured before normal sleep and after two nights of sleep restriction, collected during the summer of 2016. Besides data from the probabilistic selection task there is sleepiness and stress ratings assessed before the task.

The analysis was done using R, and the data set includes analysis scripts for all analyses performed.

Recommended citation for this dataset:
Gerhardsson A, Porada K D, Lundström N J, Axelsson J & Schwarz J (2020) Data
from: Does insufficient sleep affect how you learn from reward or punishment? –
Reinforcement learning after two nights of sleep restriction.
10.17045/sthlmuni.11955939

File List
Sleep Restriction and Reinforcement Learning - Data Analysis
| - data/
| - pst_full_data.txt
| - scripts/
| - pst_cm_1_fit.R
| - pst_cm_2_preanalysis.R
| - pst_cm_3_analysis.R
| - pst_kss.R
| - pst_lp_rt.R
| - pst_lp_winstay_loseshift.R
| - pst_plot_fnc.R
| - pst_supplementary.R
| - pst_test_phase_rt.R
| - pst_test_phase.R
| - RL_regressors_1a.stan
| - RL_regressors.stan

Models, plots and tables are produced by the scripts

Variable List for pst_full_data.txt (Variable // Description)

Code // subject + sleep condition + order
subject // Subject ID
sleep // sleep condition character
sr // sleep restriction (1 = yes, =, no)
BaselineFirst // order of sleep condition (1 = normal sleep first, 0 = Sleep
restriction first)
female // gender (1 = female, 0 = male)
age // Age in years
night // not relevant
days_between_tests // days between tests
testtime // time of test HH:MM:SS default origin
blockcode // block code of PST (learning phase or test phase)
blocknum // block number of PST (first block = 4)
trialcode // trial code of PST (symbol + order + phase)
trialnum // trial number, originally including all events (responses etc.)
stimulusitem1 // experiment path to symbol 1, not relevant for analysis, see
Figur 1 in manuscript.
stimulusitem2 // experiment path to symbol 2, not relevant for analysis, see
Figur 1 in manuscript.
values.winletter // which symbol to win
response_key // response key number on keyboard
values.selectedletter // symbol chosen
correct // correct during learning phase = positive feedback, during test phase
= best option
response_time_ms // response time in milliseconds
expressions.percA_ab // cumulative proportion correct for symbol pair AB
expressions.percC_CD // cumulative proportion correct for symbol pair CD
expressions.percE_EF // cumulative proportion correct for symbol pair EF
Bed time // Bedtime according to actigraph
Get up time // get up time according to actigraph
Time in bed // Time in bed according to actigraph
Sleep start // Sleep start according to actigraph
Sleep end // Sleep end according to actigraph
Assumed sleep // Assumed sleep according to actigraph
Actual sleep time // Actual sleep according to actigraph (H:M:S)
Actual sleep (%) // Actual sleep percent according to actigraph
Actual wake time // Actual wake according to actigraph (H:M:S)
Actual wake (%) // Actual wake percent according to actigraph
Sleep efficiency // Sleep efficiency percent according to actigraph
Sleep latency // Sleep latency according to actigraph (H:M:S)
get_up_easy // sleep diary easy to get up (5 = very easy, 1 = very difficult)
well_rested // well rested after sleep (5 = fully, 1 = not at all)
KSS // Karolinska sleepiness scale
SUSS // Subjective stress scale
kss_rt_ms // Karolinska sleepiness scale, response time in milliseconds
stress_rt_ms // Subjective stress scale, response time in milliseconds

METHODOLOGICAL INFORMATION

1. Description of methods used for collection/generation of data:
See published article and supplementary material

2. Methods for processing the data:

win-stay and lose-shift was calculated for each participant by sleep condition
and symbol pair.

win-stay = 1 if stay = 1 and feedback = positive, else win-stay = 0.
lose-shift = 1 if stay = 0 and feedback = negative, else lose-shift = 0.

3. Instrument- or software-specific information needed to interpret the data:
Software and packages required to run analyses, install may also include other
package dependencies
R (https://www.r-project.org/)
Packages:
bayesplot - Gabry J, Mahr T (2019). “bayesplot: Plotting for Bayesian
Models.” Rpackage version 1.7.0 package version 1.7.0
bayestestR - Makowski, D., Ben-Shachar, M., & Lüdecke, D. (2019). bayestestR:
Describing Effects and their Uncertainty, Existence and Significance
within the Bayesian Framework. Journal of Open Source Software,
4(40), 1541. doi:10.21105/joss.01541
brms - Paul-Christian Bürkner (2017). brms: An R Package for Bayesian Multilevel Models
Using Stan. Journal of Statistical Software, 80(1), 1-28.
doi:10.18637/jss.v080.i01
ggplot2 - H. Wickham. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York,
2016.
ggpubr - Alboukadel Kassambara (2019). ggpubr: 'ggplot2' Based Publication Ready Plots. R
package version 0.2.4. https://CRAN.R-project.org/package=ggpubr
gridExtra - Baptiste Auguie (2017). gridExtra: Miscellaneous Functions for
"Grid" Graphics. R Graphics. R
package version 2.3. https://CRAN.R-project.org/package=gridExtra
plyr - Hadley Wickham (2011). The Split-Apply-Combine Strategy for Data Analysis. Journal
of Statistical Software, 40(1), 1-29. URL http://www.jstatsoft.org/v40/i01/.
rstanarm - Goodrich B, Gabry J, Ali I & Brilleman S. (2018). rstanarm: Bayesian applied
regression modeling via Stan. R package version 2.17.4. http://mc-stan.org/
see - Daniel Lüdecke, Dominique Makowski, Philip Waggoner and Mattan S. Ben-Shachar
(2019). see: Visualisation Toolbox for 'easystats' and Extra Geoms, Themes and
Color Palettes for 'ggplot2'. R package version 0.3.0.
https://CRAN.R-project.org/package=see
shinystan - Jonah Gabry (2018). shinystan: Interactive Visual and Numerical Diagnostics and
Posterior Analysis for Bayesian Models. R package version 2.5.0.
https://CRAN.R-project.org/package=shinystan
wesanderson - Karthik Ram and Hadley Wickham (2018). wesanderson: A Wes Anderson Palette
Generator. R package version 0.3.6.
https://CRAN.R-project.org/package=wesanderson

Software used to perform Probalisitic Selection Task
Inquisit 4 (www.millisecond.com)

History

Original title

Does insufficient sleep affect how you learn from reward or punishment? Reinforcement learning after 2 nights of sleep restriction

Original language

English

Associated Publication

Gerhardsson, A, Porada, DK, Lundström, JN, Axelsson, J, Schwarz, J. Does insufficient sleep affect how you learn from reward or punishment? Reinforcement learning after 2 nights of sleep restriction. J Sleep Res. 2020; 00:e13236. https://doi.org/10.1111/jsr.13236

Affiliation (institution of first SU-affiliated author)

308 Psykologiska institutionen | Department of Psychology

Licence

Exports