Antisaccade Tasks

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

• DollarReward/RingReward – rewarded and neutral (EPrime)
  • Projects: NCANDA, Habit Study (R37)
  • Code:
    • https://github.com/LabNeuroCogDevel/lncdtask (Psychopy)
    • bea_res/Tasks/Behavorial/RingsRewardBehave_20190920
    • bea_res/Tasks/fromScanner20130219/Rings Reward
    • bea_res/Data/Temporary Raw Data/lab_eyetracker/subj_info
• AntiState (EPrime)
• Anti - no reward (variable iti, 4 sides, EPrime)
  • Brain Mechanisms R01 (7T), cog, pet?
  • bea_res/Tasks/Behavorial/anti-beakid/ANTI.es
• Bars (EPrime)

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 /Volumes/Hera/Victoria/autoeyescore/EyeLink/Dollarreward_cleaning.Rmd)
2. Turn to wide format so each row represents a single participant

Coding outcome:

• -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.

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

1. Open MATLAB scoring script score_anti.m [/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 [https://github.com/LabNeuroCogDevel/lncdtask/blob/main/lncdtask/dollarreward.py]

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.