In the file section you find the following files/folders: **/data** folder that contains: - allsubs_df.pickle : file with the preprocessed data for all subjects - /raw_data : folder with the raw data for all subjects **/plots** folder where the plots created by the plotting.py file are saved **/exported_data** files used during the analysis will be saved here **/experiment** foler than contains opensesame file of the experiment **plotting.py** script that creates all the plots shown in the paper **functions.py** script with functions used by plotting.py **SMARTfuncs.py** functions from the SMART method **SMARTClass.py** Class from the SMART method When trying to recreate the plots from the figure, the easiest thing is to download all files as a zip. Then (after unzipping) run the plotting.py script. All folders listed above should also be present in the same directory, or the code will throw an error. Running the permutation analyses takes a decent amount of time. You can also just run it first without doing that. In the funcs.py script, change nPerm (number of permutations performed) to 1 to accomplish this. When running the plotting.py file a pandas dataframe is loaded (called df), which has the following columns: - **subject**: subject number - **condition**: which eccentricities are the singletons presented on in this trial. First digit refers to the target, second to the distractor. For example '2_6' means that the target was presented at eccentricity 2 while the distractor was presented at eccentricity 6. Note 2 = near, 4 = middle and 6 = far. - **onsets** : saccade latency on this trial - **to_target** : did saccade go to target 1: yes, 0 : no - **to_dist** : did saccade go to distractor 1: yes, 0 = no - **ecc1**: target eccentricity. Note 2 = near, 4 = middle and 6 = far. - **ecc2** : distractor eccentricity. Note 2 = near, 4 = middle and 6 = far. - **saliency:** saliency of the target, 1: target salient, 0 : target non-salient - **remove_trial**: old unused variable, always False - **trial** trial number - **locs**: location of target and dist in pixels - **to_salient**: to salient item, 1: yes, 0 = no - **to_closest**: to closest item: 1: yes, 0 = no - **selected_ecc**: eccentricity of selected item. Note 2 = near, 4 = middle and 6 = far. - **selected_item**: identity of selected item. Note sd = salient distractor, st = salient target, nst = non salient target, nsd = non salient distractor - **distance** : absolute difference between eccentricities, same as ecc_diff - **close_salient**: was the closest element salient, 1 : yes, 0 : no - **salient_ecc** : eccentricity of salient item - **non_salient_ecc**: eccentricity of non-salient item - **ecc_diff**: absolute difference between eccentricities, same as distance