### **Included files** - data - processed_csv - raw_ascii - raw_csv - raw_edf - material - preproc.R - TDT_shape.osexp - asc2csv_TDT.py - analTDT_final.R - IO.py - misc.py - variables.json ---------- ### **Running the experiment** The data of the Experiment were collected with [Opensesame](http://osdoc.cogsci.nl/), a Python-based experiment builder [(v3.1.4)](https://github.com/smathot/OpenSesame/releases/tag/release%2F3.1.4) in combination with [Pygaze](http://www.pygaze.org/), (v0.6). Both packages need to be installed (Pygaze does not come with Opensesame by itself). Instructions do integrate them can be found [here](http://osdoc.cogsci.nl/3.1/manual/eyetracking/pygaze/). For eye movement recording we used the Eyelink 1000 (SR Research; Canada). Once everything is installed, the experiment can be opened either by double-clicking on the experimental file (see material) or by first starting Opensesame, then pressing `Ctrl-O` and selecting the experiment. After the experiment was opened, the structure can inspected by clicking through the items in the overview area (on the left). To run the experiment, press the green area in the `menu bar` at the top. Note: If no eyetracker is connected to the experimental computer, eye movements can be simulated with the mouse. To do so, you need to specify the `tracking type` as `advanced dummy` in the item `pygaze_init`. ---------- ### **Data analysis pipeline** ##### *1) Convert EDF files to ASCII files* First, all `edf` files (binary) were converted to `asc` files (plain text) using the `edf2asc` converter, provided by [SR Research](http://download.sr-support.com/dispdoc/index.html). In doing so, we used the `-ns` flag, so that the output file would only contain messages and events (no samples). ##### *2) Combine ASCII files with behavioral data* Based on the raw eye and behavioural data, the script `asc2csv_TDT.py` creates the final dataframes (one per subject) that was used to run the analysis. It writes a new csv file, in which every line represents one saccade. All variables that are included in these data frame are explained in the file `variables.json`. ##### *3) Run preprocessing & analysis* The script `analTDT_final.R` includes some final preprocessing steps (data filtering), data aggregation, and statistics. ---------- ### **Disclaimer & questions** None of the provided code was designed to work out-of-the-box. The code will **not** work without further adjustments to it. Most importantly, the data has to be organized in a suitable folder structure. Once this is fixed, the code *could* work. Feel free to contact me for any suggestions, remarks or help requests.