Visual Statistical Learning task - making triplets out of 16 image files

Hi everyone,

I’m currently implementing a visual statistical learning task for a project. I have a folder containing 16 shapes, and for each participant (so each run of the experiment) I want to do the following:

Create 8 triplets out of the 16 shapes, which obviously have to stay fixed throughout the experiment as participants will be tested on triplet recognition later in the experiment. The TPs (transitional probabilities) of the triplets will have to differ: 0.33 for half of the triplets (4) and 1.0 for the other 4 (thus measuring sensitivity on multiple levels, so to speak).

0.33 TP triplets will have to come from the first 4 shapes, so for instance:


The rest of the triplets (with a TP of 1), come from the remaining shapes, e.g.:


Right now I’m working on the familiarization phase (basically a 10 minute non-stop stream of the shapes with triplets embedded), in which each triplet occurs 24 times (the timing etc. are no problem to set correctly), with the constraint that no triplet can occur twice in a row.

As I’m relatively new to PsychoPy, this seems like a daunting task at this point.

I’m struggling with figuring out which of these facets I should include in condition files, and which of the randomisations I can use the loop randomising options for.

Any help in getting started would be immensely appreciated, and I can provide further info for anyone who’s curious.

Best wishes,


I would like to know if u sucsses to create VSL. I also want to try.