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README.md

Placebo Intervention Enhances Reward Learning in Healthy Individuals

DOI

This repository contains data and analyses for the paper "Placebo Intervention Enhances Reward Learning in Healthy Individuals".

If you want to use this data/analysis in a research publication, please cite our paper.

Turi, Z., Mittner, M., Paulus, W. & Antal, A. (2017). Placebo Intervention Enhances Reward Learning in Healthy Individuals. Scientific Reports. 7: 41028. doi:10.1038/srep41028

@article{Turi_placebo2016,
  author={Turi, Z. and Mittner, M. and Paulus, W. and Antal, A.},
  title={Placebo Intervention Enhances Reward Learning in Healthy Individuals},
  year=2017,
  journal={Scientific Reports},
  volume=7,
  number=41028,
  doi=10.1038/srep41028
}

Requirements

Analysis are coded in R and stan. Quite a lot R-packages and the Stan are required. It is easiest to set up the R-packages using conda. We provide an environment.yml file which allows to set up R with all needed packages with very few commands.

  1. download anaconda or miniconda

e.g. on linux:

wget https://repo.continuum.io/miniconda/Miniconda-latest-Linux-x86_64.sh
bash Miniconda-latest-Linux-x86_64.sh
conda update conda
conda install anaconda-client anaconda-build conda-build
  1. clone this repository
git clone
https://github.com/ihrke/2016-placebo-tdcs-study
cd 2016-2016-placebo-tdcs-study
  1. re-create the environment used for creating these analyses:
conda env create

If you are not willing to do this, you will have to look at the environment.yml file to check all dependencies.

Setup

This repository uses the ProjectTemplate directory layout. It also provides an environment.yml which allows to set up R with all needed packages with very few commands.

Data

Raw data is located in data/raw and is provided in .csv format.

The .R scripts located in data load the raw files into R workspace under the name of the R-file (without the .R extension).

NOTE: there are also pre-processed exports of all the variables discussed next; those are located under data/export. These files have been created by the script src/export_data.R.

The data is structured as follows (refer to the paper for details).

Subjectively reported anticipation/experiences

stored in variable antexp

> summary(antexp)

      PID        AAntDirection   AAntAmount     AExpDirection   AExpAmount
 Min.   : 1.00   -1: 2         Min.   :-30.00   0   :15       Min.   :-15.000
 1st Qu.: 8.00   0 :10         1st Qu.:  0.00   1   :12       1st Qu.:  0.000
 Median :15.00   1 :17         Median : 10.00   -1  : 1       Median :  0.000
 Mean   :15.34                 Mean   : 13.58   NA's: 1       Mean   :  9.125
 3rd Qu.:23.00                 3rd Qu.: 30.00                 3rd Qu.: 15.625
 Max.   :30.00                 Max.   : 60.00                 Max.   : 60.000
                                                              NA's   :1
 BAntDirection   BAntAmount     BExpDirection   BExpAmount
 -1: 4         Min.   :-20.00   -1: 8         Min.   :-30.0000
 0 :10         1st Qu.:  0.00   0 :15         1st Qu.: -5.0000
 1 :15         Median : 10.00   1 : 6         Median :  0.0000
               Mean   : 10.62                 Mean   : -0.1724
               3rd Qu.: 15.00                 3rd Qu.:  0.0000
               Max.   : 60.00                 Max.   : 60.0000
               NA's   :1

Variables are coded as follows:

  • PID - number of the participant
  • AAntDirection - anticipated direction of change in condition "A" (low-uncertainty) before the experiment; one of (-1, 0, 1) where
    • -1 means that subjects expected to get worse,
    • 0 means no anticipated change,
    • 1 means subjects expected to have improved performance
  • AAntAmount - anticipated amount of change in condition "A" (low-uncertainty) on a scale from -100 to +100
  • AExpDirection - experienced amount of change in condition "A" (low-uncertainty) after the experiment; one of (-1, 0, 1) where
    • -1 means that subjects experienced a decline in performance
    • 0 means no experienced change,
    • 1 means subjects experienced to have improved performance
  • AExpAmount - experienced amount of change in condition "A" (low-uncertainty) on a scale from -100 to +100
  • all those variables exist also prefixed with "B" for condition "B" (high-uncertainty)

Subjectively reported arousal

stored in variable arousal

> summary(arousal)
Participant   BL_before         BL_after        A_before         A_after
1      : 1   Min.   : 4.000   Min.   : 3.00   Min.   : 2.000   Min.   :3.000
2      : 1   1st Qu.: 6.000   1st Qu.: 5.00   1st Qu.: 7.000   1st Qu.:5.000
3      : 1   Median : 8.000   Median : 7.00   Median : 7.000   Median :5.500
4      : 1   Mean   : 7.241   Mean   : 6.31   Mean   : 6.897   Mean   :6.071
5      : 1   3rd Qu.: 8.000   3rd Qu.: 8.00   3rd Qu.: 8.000   3rd Qu.:8.000
6      : 1   Max.   :10.000   Max.   :10.00   Max.   :10.000   Max.   :9.000
(Other):23                                                     NA's   :1
  B_before         B_after
Min.   : 4.000   Min.   :3.000
1st Qu.: 6.000   1st Qu.:5.000
Median : 7.000   Median :6.000
Mean   : 7.034   Mean   :6.036
3rd Qu.: 8.000   3rd Qu.:7.000
Max.   :10.000   Max.   :9.000
                NA's   :1

Variables are coded as follows (all arousal ratings on a scale from 1-10):

  • Participant - number of the participant
  • BL_before - arousal rating in the baseline session ("N") before the experiment
  • BL_after - arousal rating in the baseline session ("N") after the experiment
  • A_before - arousal rating in the low-uncertainty session ("A") before the experiment
  • A_after - arousal rating in the low-uncertainty session ("A") after the experiment
  • B_before - arousal rating in the high-uncertainty session ("B") before the experiment
  • B_after - arousal rating in the high-uncertainty session ("B") after the experiment

Data from Reinforcement learning task (learning phase)

data from the three different sessions are stored in three variables

  • learn.N - baseline
  • learn.A - low-uncertainty condition
  • learn.B - high-uncertainty condition

and there is also a single data.frame learn combining the three where the factor condition specifies the condition

> summary(learn.N)
  Participant        pair   condition      ACC                RT
 P01    : 360   Min.   :1   N:10440   Min.   :-1.0000   Min.   :0.01671
 P02    : 360   1st Qu.:1             1st Qu.: 0.0000   1st Qu.:0.69982
 P03    : 360   Median :2             Median : 1.0000   Median :0.88307
 P04    : 360   Mean   :2             Mean   : 0.7006   Mean   :0.90199
 P05    : 360   3rd Qu.:3             3rd Qu.: 1.0000   3rd Qu.:1.08305
 P06    : 360   Max.   :3             Max.   : 1.0000   Max.   :1.68286
 (Other):8280                                           NA's   :165
     reward
 Min.   :0.0000
 1st Qu.:0.0000
 Median :1.0000
 Mean   :0.5875
 3rd Qu.:1.0000
 Max.   :1.0000

Variables are coded as follows:

  • Participant - number of the participant (consistent across the three conditions)
  • pair - pair number (1,2,3) with (60/40, 70/30 or 80/20 % coherence)
  • condition - one of N, A, B
  • ACC - accuracy: 1 correct, 0 incorrect, -1 no response
  • RT - reaction time in s
  • reward - 1: reward was received, 0: no reward

Data from Reinforcement learning task (transfer phase)

data from the three different sessions are stored in three variables

  • transfer.N - baseline
  • transfer.A - low uncertainty condition
  • transfer.B - high uncertainty condition

and there is also a single data.frame transfer combining the three where the factor condition specifies the condition

> summary(transfer.N)
  Participant         RT          symb1   symb2    choice
 P01    : 180   Min.   :0.01675   A:870   A:870   A   :1258
 P02    : 180   1st Qu.:0.68321   B:870   B:870   B   : 457
 P03    : 180   Median :0.86648   C:870   C:870   C   :1268
 P04    : 180   Mean   :0.89940   D:870   D:870   D   : 565
 P05    : 180   3rd Qu.:1.08307   E:870   E:870   E   :1004
 P06    : 180   Max.   :1.68285   F:870   F:870   F   : 600
 (Other):4140   NA's   :68                        NA's:  68

Variables are coded as follows:

  • Participant - number of the participant (consistent across the three conditions)
  • RT - reaction time in s
  • symb1, symb2 - the two presented symbols (one of A,B,C,D,E,F)
  • choice - which symbol was picked or NA in case of no response

Analyses

All analyses are located in src/. To run the scripts, you need to have the ProjecTemplate package and various other packages installed.

The first two lines in each file

library(ProjectTemplate)
load.project()

convert the raw data into a more convenient format by

  1. running the data/<dataset>.R file
  2. running the preprocessing scripts in munge
  3. loading the convenience functions in lib