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Running a free-viewing experiment with the TRACKPixx3

This demo requires TRACKPixx Revision 18 or later. You can check for recent firmware updates at

This demo uses the TRACKPixx3 eye tracker to replicate the findings of Yarbus (1967) which showed that viewing patterns change as a function of the task demand. In this demo, we present a painting three times and collect 8 seconds of free viewing for each presentation. Before each viewing we pose a different question to the participant relative to the painting. After collecting gaze data we plot gaze paths in MATLAB, overlaid over the images, for initial inspection. Finally we save gaze data and some metadata into a .csv file for easy transfer to our analysis software of choice.

At the beginning of the demo there is an optional calibration step. This step calls TPxTrackpixx3CalibrationTesting, which implements a standard MATLAB TRACKPixx3 calibration script. The tracker must be calibrated for every new participant. We also recommend calibrating after a participant moves away from the chinrest.

Following calibration the demo wakes the TRACKPixx3, sets up a tracking schedule with SetupTPxSchedule, and starts this schedule with StartTpxSchedule at the onset of each painting. This schedule controls recording of eye data, which is stored on a data buffer on the DATAPixx3 controller. We use SetMarker to store and retrieve the time the TRACKPixx3 began recording.

When the maximum viewing time has been reached, we send StopTPxSchedule to the tracker to stop recording. We read eye data from the trial using GetTPxStatus and ReadTPxData, and store this data before moving on to the next trial.


function TPxYarbusTask(initRequired)
% This demo recreates the viewing task used by Yarbus, which demonstrated
% that the pattern of free-viewing of an image changes as a function of the
% task required of the viewer. 
% We present the same image three times, for 8 seconds each. Each time we
% pose a different question to the participant (question order is
% randomized). Gaze scan paths for the three trials are then plotted side
% by side for comparison.
% If initRequired is set to 1, the function first calls
% TPxTrackpixx3CalibrationTesting to connect to the TRACKPixx3 and
% calibrate the tracker.

% Most recently tested with:
% -- TRACKPixx3 firmware revision 18 
% -- DATAPixx3 firmware revision 19 
% -- MATLAB version (R2019a) 
% -- Psychtoolbox verison 3.0.15 
% -- Datapixx Toolbox version 3.7.5735
% -- Windows 10 version 1903, 64bit

% DeAngelus, M., & Pelz, J. B. (2009). Top-down control of eye movements: Yarbus revisited. Visual Cognition, 17(6-7), 790-811.
% Yarbus, A. L. (1967). Eye movements during perception of complex objects. In Eye movements and vision (pp. 171-211). Springer, Boston, MA.

% Oct 15, 2019  lef     Written
% Mar 26  2020  lef     Updated

%% Step 1 - Initialize (if needed)

if nargin==0

% Get some user input
fileName= input('Enter participant name: ', 's');
fileID = [fileName '.mat'];

%If a calibration is needed, call the calibration script
if initRequired
    fprintf('\nInitialization required\n\nCalibrating the device...');

%Connect to TRACKPixx3

%% Step 2 - Set up the TRACKPixx recording schedule

%write all commands to the DATAPixx device register

%% Step 3 - Show our image and record eye position

%set our ideal viewing time in seconds
viewingTime = 8;

%create a structure to store our data. This format allows us to easily add
%more trials and images if we want. For now we stick to 3
data = struct('Trial', [],...
              'Question', {'In the image, what time of day is it?',...
              'In the image, what is the average age of the group?',...
              'In the image, what is the overall mood of the event?'},...
              'Image', {'Renoir_Boating.jpg','Renoir_Boating.jpg', 'Renoir_Boating.jpg'},...
              'ViewingTime', [],...
              'EyeData', []);
%shuffle order of presentation
order = randperm(numel(data));

%open window
Screen('Preference', 'SkipSyncTests', 1 );
screenID = 2;                                              %change to switch display
[windowPtr, rect]=Screen('OpenWindow', screenID, [0,0,0]);
Screen('BlendFunction', windowPtr, 'GL_SRC_ALPHA', 'GL_ONE_MINUS_SRC_ALPHA');

%show instructions to participant
text_to_draw = ['FREE VIEWING DEMO:\n\nYou will be asked a question about a painting. \nYou will have 8 seconds to view the painting and decide your answer.\n\nPress any key to start.'];
DrawFormattedText(windowPtr, text_to_draw, 'center', 700, 255);
Screen('Flip', windowPtr);

%wait for participant to continue
[~, ~, ~] = KbPressWait;
Screen('Flip', windowPtr);

start_time = Datapixx('GetTime');

for k = 1:numel(data)
    index = order(k);
    data(k).Trial = index;
    currentQuestion = data(index).Question;
    im = imread(data(index).Image);
    imTexture = Screen('MakeTexture', windowPtr, im); 
    text_to_draw = [currentQuestion '\n\nPress any key to start.'];
    DrawFormattedText(windowPtr, text_to_draw, 'center', 700, 255);
    Screen('Flip', windowPtr);
    %wait for a keypress
    [~, ~, ~] = KbPressWait;
    %set up recording to start on the same frame flip that shows the image.
    %We also get the time of the flip using a Marker which indcates the
    %frame flip on the DATAPixx clock
    %draw our image and flip
    Screen('DrawTexture', windowPtr, imTexture, [], rect);
    Screen('Flip', windowPtr);
    time1 = Datapixx('GetMarker');
    time2 = time1;
    %repeatedly check the device for current time, and break loop when our
    %8 seconds are done.
    while (time2 - time1) < viewingTime
        time2 = Datapixx('GetTime');
    %stop recording
    %read in eye data
    status = Datapixx('GetTPxStatus');
    toRead = status.newBufferFrames;
    [bufferData, ~, ~] = Datapixx('ReadTPxData', toRead);
    %bufferData is formatted as follows:
    %1      --- Timetag (in seconds)
    %2      --- Left Eye X (in pixels) 
    %3      --- Left Eye Y (in pixels)
    %4      --- Left Pupil Diameter (in pixels)
    %5      --- Right Eye X (in pixels)
    %6      --- Right Eye Y (in pixels)
    %7      --- Right Pupil Diameter (in pixels)
    %8      --- Digital Input Values (24 bits)
    %9      --- Left Blink Detection (0=no, 1=yes)
    %10     --- Right Blink Detection (0=no, 1=yes) 
    %11     --- Digital Output Values (24 bits)
    %12     --- Left Eye Fixation Flag (0=no, 1=yes) 
    %13     --- Right Eye Fixation Flag (0=no, 1=yes)  
    %14     --- Left Eye Saccade Flag (0=no, 1=yes) 
    %15     --- Right Eye Saccade Flag (0=no, 1=yes)  
    %16     --- Message code (integer) 
    %17     --- Left Eye Raw X (in pixels) 
    %18     --- Left Eye Raw Y (in pixels)  
    %19     --- Right Eye Raw X (in pixels)  
    %20     --- Right Eye Raw Y (in pixels) 
    %IMPORTANT: "RIGHT" and "LEFT" refer to the right and left eyes shown
    %in the console overlay. In tabletop and MEG setups, this view is
    %inverted. This means "RIGHT" in our labelling convention corresponds
    %to the participant's left eye. Similarly "LEFT" in our convention
    %refers to left on the screen, which corresponds to the participant's
    %right eye.

    %If you are using an MRI setup with an inverting mirror, "RIGHT" will
    %correspond to the participant's right eye.
    %save eye data from trial as a table in the trial structure
    data(index).EyeData = array2table(bufferData, 'VariableNames', {'TimeTag', 'LeftEyeX', 'LeftEyeY', 'LeftPupilDiameter', 'RightEyeX', 'RightEyeY', 'RightPupilDiameter',...
                                    'DigitalIn', 'LeftBlink', 'RightBlink', 'DigitalOut', 'LeftEyeFixationFlag', 'RightEyeFixationFlag', 'LeftEyeSaccadeFlag', 'RightEyeSaccadeFlag',...
                                    'MessageCode', 'LeftEyeRawX', 'LeftEyeRawY', 'RightEyeRawX', 'RightEyeRawY'});

    %get some other trial data
    data(index).ViewingTime = time2 - time1;
    %interim save
    save(fileID, 'data');    

%Close everything
finish_time = Datapixx('GetTime');

%% Step 4 - Plot some gaze paths

numCols = 3;
numRows = ceil(numel(data)/numCols); 

for k = 1:numel(data)
    subplot(numRows, numCols, k);
    x = data(k).EyeData.LeftEyeX;
    y = data(k).EyeData.LeftEyeY;
    plot(x,y, 'ob', 'linewidth', 1, 'markersize', 1); 
    hold on
    x = data(k).EyeData.RightEyeX;
    y = data(k).EyeData.RightEyeY;
    plot(x,y, 'or', 'linewidth', 1, 'markersize', 1); 
    xlim([-rect(3)/2, rect(3)/2]);
    ylim([-rect(4)/2, rect(4)/2]);
    im = imread(data(k).Image); 
    h = image(xlim, -ylim,im);
    set(h,'alphadata', .5);
    legend({'Left eye', 'Right Eye'});
    xlabel('X position (pixels)');
    ylabel('Y position (pixels)');

%% Step 5 - Write data to csv for subsequent analysis

rawResults = table();
for k = 1:numel(data)
    newTrial = data(k).EyeData;
    newTrial.TrialNumber = repmat(data(k).Trial, [height(newTrial), 1]);
    newTrial.Question = repmat({data(k).Question}, [height(newTrial), 1]);
    newTrial.ViewingTime = repmat(data(k).ViewingTime, [height(newTrial), 1]);
    newTrial = newTrial(:, [21:end, 1:20]);
    rawResults = [rawResults; newTrial];

csvFileID = [fileName '_Results.csv'];
writetable(rawResults, csvFileID);


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