Opticka’s Two Experiment Modes

1. Behavioural Task – a behavioural task uses a stateMachine to construct a series of experiment states and the transitions between them. It uses StateInfo.m files and userFunctions.m files to specify the states and any customised functions required. It can optionally use taskSequence, metaStimulus, eyeTracker, IO, and other manager classes to control stimuli, variables, and hardware interaction.
2. Method of Constants (MOC) Task – a MOC task is a task where no behavioural control is required. It uses taskSequence and metaStimulus to define a set of stimuli and the variables that will drive unique trials repeated over blocks.

Basic Settings (see below for more details):

• Repeat Blocks: number of blocks to repeat.
• Random Algorithm: the randomisation algorithm used by MATLAB’s rand functions.
• MOC Trial time: the time the stimulus is presented for.
• MOC IT time: the time between trials.
• MOC IB time: the time between blocks.
• REAL Time: We can use two timing methods, when REAL Time=ON then we use tthe PTB method GetSecs to measure time stamps and task time is based on these real time values. When when REAL Time=OFF then we calculate how many frames a time interval should contain and use ticks to measure time as each frame is shown. REAL Time is essential for real experiments, ticks are more useful for debugging where you can stop and step through carefully.

Task Parameters

Opticka uses the taskSequence class to build the stimulus randomisation. This class takes a series of independent variables with values and builds the randomisation into repeated blocks. The GUI does this using the Ind. Variable List editor, but the underlying code looks like this (nBlocks is Task Options > Repeat Blocks in the GUI):

% task sequence with 5 blocks
task = taskSequence('nBlocks', 2);

% first variable: 3 angles that will be applied to stimuli 1 & 2
task.nVar(1).name = 'angle';
task.nVar(1).values = [-25 0 25];
task.nVar(1).stimulus = [1 2];

% second variable: 3 colours that will be applied to stimulus 3
task.nVar(2).name = 'colour';
task.nVar(2).values = {[1 0 0], [0 1 0]};
task.nVar(2).stimulus = 3;

randomiseTask(task);

This will generate a randomisation table like so (3 angles x 2 colours per block = 6 conditions; 2 block = 12 total trials):

Block-Level and Trial-Level Factors

In addition, you can specify independent block and trial factors. For example if we set

task.blockVar.values={'A','B'};
task.blockVar.probability = [0.6 0.4];

Then for each block we assign either A or B with a 60:40% probability. Trial-level factors are specified in the same way, but for each trial independently from the blocks.

task.trialVar.values={'Y','Z'};
task.trialVar.probability = [0.5 0.5];

Trial- and Block-factors can be used to drive state machine functions if you need it.