Materials and Data of "Cue Detectability Predicts Instrumental Conditioning"

This repository accompanies the research article Reber, T.P., Samimizad, B., & Mormann F. (2018). Cue Detectability Predicts Instrumental Conditioning. Consciousness & Cognition, in press.

The DOI of this repository is the following:

If you have any questions, please contact treber@live.com.

This repository provides:

• Matlab/Octave code and stimuli used to run the experiment under paradigm directory
• resulting data in the logs directory
• jupyter notebooks (.ipynb files) and Matlab functions to analyze the data in helper_functions directory

Running the Experimental Paradigm

Software requirements to run the paradigm are

• linux, we used Debian 7.2 (Windows and MacOs should also work but may require changes to code and may not be as accurate with timing).
• Octave (Matlab might also work, but again might require small changes to the code)
• Psychtoolbox
• Palamedes Toolbox, we used Version 1.5.0 but newer Versions should work as well
• The scripts, stimuli and further files provided in this repository under the paradigm directory

Once everything is setup and all the scripts and toolboxes are added to the octave path, start octave and run the script paradigm/octave/sc.m, which will take you through the whole experiment as reported in the manuscript.

Further instructions for the experimentator, as well as written Instructions for the subjects are provided under paradigm/manual/.

Analysis of Resulting Data

To document how data is aggregated, analyzed and displayed in the figures in the article, we provide juptyer notebooks. To run these notebooks yourself, you need the install

• Matlab We used 2016a but prior versions may also work. Maybe the code also works with Octave but some changes may become necessary.
• juptyer notebooks
• a Matlab kernel for jupyter
• an R kernel for jupyter

There are a few more dependencies, which are mentioned in the notebook themselves.

There are three jupyter notebooks:

1. To see how we aggregated the data and store them for further processing in R and Matlab, use 1_MATLAB_LoadAndAggregateData.ipynb
2. Analyses performed using R are documented in 2_R_Analyses.ipynb
3. Creation of figures and additional analyses can be inspected in 3_MATLAB_FiguresAndAnalyses.ipynb