Set Randomization?

I can’t seem to figure out if you can use any of the randomization options to have a set randomization. I have say 100 stimuli (used in various ways for each of 6 different trials) but I would like them to appear in the same random pattern each time this test is administered. Is this possible??? :confused:

You have 100 stimuli and you have six different trial definitions that use them, and you want them to be presented in the same order with each of the six types of trial. Is that correct?

I don’t think you can do this directly in Superlab, but I’m just a fellow user and I may be wrong.

I think you could set up a stimulus list in an external file, and randomize that file several ways, saving each randomization in a different file, naming them, for example, order1-orderR. Then you can set up a experiment with six blocks, each using a different trial definitions, but which all refer to the unrandomized stimulus list. Make R copies of this experiment, naming them (e.g.) exp1 - expR. Then work your way through the R experiments, going into the Superlab stimulus list editor, and use copy/paste to replace all of the stimuli in the unsorted list with the appropriate randomized list. For example, replace the stimuli in exp3 with the items in the file order3.

This won’t give you a different randomization each time you run, but if you use a reasonable number for R, it should be pretty comparable.

Another way to do it, if you don’t mind an even bigger kludge, would be to set up your stimuli as external files, for example, image files, all in a certain folder. The location of this folder is not known by Superlab. Now write a script to randomly order the names of these image files and copy (or link) them into a second folder which will be known by Superlab, with names all of the form “stim001.jpg” or whatever. In other words, you will write the Superlab experiment so that it refers to files in the second folder, stim001.jgp … stim100.jpg via a stimulus list. Do not have Superlab randomize the stimulus list. Now, before each run, use the script to re-randomize the stimulus files. You will want to keep a record of the output of the randomization runs so that you will be able to reconstruct which stimuli were actually presented to the subject. One simple way to do this is to make the seed of the random number generator part of the subject ID or the output data file name. If you use numeric subject IDs, then it’s even simpler: just use the subject ID (or the subject ID plus a constant) as the seed of the random number generator.

This method will give you a different randomization with each run, but is more complicated and requires running an external script before starting each experiment. If you are on a Mac, you can eliminate the run-time complexity by using Automator.

Greg Shenaut