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Sleep Restriction and Reinforcement Learning - Data Analysis

dataset

posted on 2020-12-01, 09:46 authored by Andreas GerhardssonAndreas 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