pseudorandomizing a LDT in OSWeb

sophiie

Hi everyone,

I am preparing a Lexical Decision Task to be run online and I want to make sure that there will never be more than 2 words from the same valence condition (there are 4 with the non-words) presented in a row. Does anyone know how to do that in OSWeb?

Thanks in advance!

Sophie :)

Fab

Hi @sophiie,

The easiest way to deal with if your experiement was not running in a browser would be to use the constrain command in the loop's script. However, This function is not currently supported by OSWeb.

So I can think of two options in your case:

Option 1: You prepare your stimuli files offline using some outside programming or whatever else method you see fit to generate semi-randomized stimuli list files (for example 100 of them) that meet your criteria, you upload them to the pool, and then you set your loop to use a file source. You use some Javascript code to pick one of the 100 stimuli list files at random and you get your task to use that one to populate the trials. This method is simple from the OSWeb perspective, but it requires you to prerare your stimuli files outside Open Sesame.

Option 2. You can generate your stimuli list within the program, at the start, using arrays in Javascript, implementing your semi-randomization there. Then once your stimuli list is ready and you start presenting your trials, you'd have to use Javascript coding to keep track of trials by creating a counter that should be incremented on every trial, and use that counter to populate the trial with the relevant stimulus information read from the arrays you created earlier. So, for this solution, you'll need to learn to program a little in Javascript.

Here's an exampele of Javascript code that generates an array of 100 elements, each coded as 0 to 4 (which would represented for example 4 conditions) and randomizes them while making sure that the same condition is not repeated in more then 2 consecutive trials.

function generateTrials(numTrials = 100, numConditions = 4, maxRepeats = 2) {
  let trials = [];
  let conditionCounts = Array(numConditions).fill(0);
  let lastCondition = null;
  let repeatCount = 0;

  for (let i = 0; i < numTrials; i++) {
    let condition = Math.floor(Math.random() * numConditions);

    // Check for max repeats and condition count
    while (condition === lastCondition && repeatCount >= maxRepeats || conditionCounts[condition] >= numTrials / numConditions) {
      condition = Math.floor(Math.random() * numConditions);
    }

    // Update last condition and repeat count
    if (condition === lastCondition) {
      repeatCount++;
    } else {
      repeatCount = 1;
      lastCondition = condition;
    }

    trials.push(condition);
    conditionCounts[condition]++;
  }

  // Final check to ensure all conditions are met equally
  if (!conditionCounts.every(count => count === numTrials / numConditions)) {
    console.log("Adjusting, please rerun.");
    return generateTrials(); // Rerun if the distribution is not perfect
  }

  return trials;
}

let trials = generateTrials();
console.log(trials);

This is just to give you an idea of what kind of coding you'd need to write. You's have to adapt it to your own requirements, of course.

You can find a lot of helpers about Javascript on the internet.

Hope this helps,

Fabrice.