Antisaccade Tasks

Fig 1. Anti-saccade task, taken from Luna et al, NeuroImage, 2001.

Scoring by Automatic Eye Scoring, see github readme citation section lab papers methods quotes. See Eye Tracking for camera hardware.

The breakdown of antisaccade tasks is below. For all Eye Tracking tasks, see EyeTracking Task Table (includes Memory Guided Saccade (MGS) Task).

1. Fewer than 25 viable trials (this means they have to have 25 (n) that are not dropped/scored as -1 (can be 0,1,2)) - what we consider the minimum of trials where we believe the ppt actually understood the task
2. Less than 50% 'on task' trials (coded as 1,2)
3. Statistical outliers by the residuals of the model; ppt whose residuals are ±2 SD from the mean across any measure

-in progress

RAW FILES FROM EYE TRACKER ARE EDF OR EYD—– NEED TO BE CONVERTED TO ASC FOR SCRIPT TO WORK

To score, you need to

1. Identify data location
2. Source dollarreward.R script in order to create score_all_anti function [https://github.com/LabNeuroCogDevel/autoeyescore/tree/master/EyeLink]
3. Run all data through function [if Habit; alldollarreward_data ← score_all_anti(“/Volumes/L/bea_res/Data/Temporary Raw Data/lab_eyetracker/subj_info/sub-1*/ses*/*_DollarReward/sub_*.asc*”)]

Data should have a row for every trial (repeating lunaid) and saccade information per column (ex: dot position, trial type, latency, number of saccades, and computed event outcome)

1. Clean data by extracting lunaid, visit date, neutral vs reward trials, mutate variables you want like mean latency, correct response rate (accuracy), percent of error corrected trials (error rate) see https://github.com/LabNeuroCogDevel/Antisaccade-impulsive-control/blob/main/EyeLink/Dollarreward_cleaning.Rmd
2. Turn to wide format so each row represents a single participant

Coding outcome from Automatic Eye Scoring:

• -1, dropped event or bad eye tracking
• 0, incorrect- the participant looked directly at the stimulus
• 1, correct- the participant looked in the opposite direction of the stimulus
• 2, error corrected- the participant first looked at the stimulus then looked in the opposite direction

percent of error corrected trials is computed as trials scored 2/0+1+2, can be computed as 2/1+2.

see Automatic Eye Scoring

Raw files from EEG are .bdf and can be read immediately into MATLAB script

1. Open MATLAB scoring script [/Volumes/Hera/Projects/7TBrainMech/scripts/eeg/eog_cal]
2. Identify data location [if Habit: /Volumes/Hera/Raw/EEG/Habit]
3. Grab subject*_anti.bdf and subject*_eyecal.bdf
4. Information on stimulus channel output, see dollarreward.py
5. Run script? Should export long-format csv with all variables of interest that can be pulled into R

Processed csv read into R

STEPS REPEATED AS ABOVE FOR BEHAVIORAL DATA

1. Data should have a row for every trial (repeating lunaid) and saccade information per column (ex: dot position, trial type, latency, number of saccades, and computed event outcome)
2. Clean data by extracting lunaid, visit date, neutral vs reward trials, mutate variables you want like mean latency of correct trials, correct response rate (percent of correct trials; accuracy), percent of error corrected trials (error rate), latency variability for correct trials
3. Turn to wide format so each row represents a single participant [see code /Volumes/Hera/Victoria/Antisaccade-impulsive-control/EOG_Antisaccade_cleaning.Rmd]

**percent of error corrected trials is computed as trials scored 2/1+2.

1. Analyze variables of interest [/Volumes/Hera/Victoria/Antisaccade-impulsive-control/Analysis_EOG_as_data.Rmd]

For 7T EEG

 
[micromed_time, mark]=make_photodiodevector(EEG);

iti = mode(mark); 

mark = mark - iti + 254;
       
% 101-105: anti cue 
% 151-155: target (dot on, look away)
% 254 = back to fixation

simple = nan(size(mark));
simple(mark == 254)= 1; % (New ITI)
simple(mark>=100 & mark<110)= 2; % (new Anti cue - red fixation cross, prepatory)
simple(mark>=150 & mark<= 155)= 3; % (new dot on, look away) 

• Hallett, P. E. (1978). Primary and secondary saccades to goals defined by instructions. Vision research, 18(10), 1279-1296.
• Luna, B., Thulborn, K. R., Munoz, D. P., Merriam, E. P., Garver, K. E., Minshew, N. J., Keshavan, M. S., Genovese, C. R., Eddy, W. F., & Sweeney, J. A. (2001). Maturation of Widely Distributed Brain Function Subserves Cognitive Development. NeuroImage, 13(5), 786–793.