using the psychopy GUI

in the passive condition of the experiment, we draw on an already saved event log of an active condition run.

Errors that happened in the active condition run should be completely disregarded in the passive condition replay, however currently this is not the case.

Example

1. subj-A does active condition
2. in their trial 1, they commit a mistake after the 3rd sample (wait too long), so the trial is reset and they start new (with new reward distributions)
3. thus, they have N + 3 samples in their dataframe for that trial
4. The +3 samples most likely contain outcomes that are not part of the outcomes of the new reward distribution
5. hence, a participant yoked to subj-A can in their passive condition and in the described trial, get more than the usual maximum of two outcomes per option (e.g., 3 or even 4).

This happens rarely enough, but it must be fixed!

When analyzing the behavioral data and plotting the expected value differences in the different conditions (active-fixed: AF, active-variable: AV, yoked-fixed: YF, yoked-variable:YV, description: DESC), I noticed the following:

• EV differences are NOT always the same between the description task, and the corresponding active task by subject (sometimes AF, sometimes AV)

The following figure prompted my investigation of this issue:

• "true" EV diff should always be at 0.9, except for the DESC task ✔️
• "experienced" and "true" EV should be the same for DESC task ✔️
• EV diffs for DESC task should be "a mixture" of EV diffs of the two active tasks (AF, AV)❌

This was due to several small mistakes, but the general error boils down to:

• when preparing the sampled outcomes from an active task to be displayed as a description task, the ordering of magnitudes and probabilities was sometimes different.
• Example: a 1-90%, 4-10% could become a 1-10%, 4-90%
• Example: a 5-100% could become a 1-100%
  • this example needs some explanation: when only a single outcome was encountered in an option, the outcomes of that option were substituted with a code value of 98 (appended) that occurred with a probability of 0. Later in the pipeline, those values with a probability of 0 would not be shown. Now in the case of the example, the magnitude of 5 with 100% was exchanged with the magnitude 1 with a probability 0 (because in this particular example, the 98 code replaced a 1 outcome that never occurred) ... so a 1 is shown with 100% although it was never encountered, and the 5 is not even listed in the description task

Two smaller bug fixes make this error more unlikely to occur:

• 1fa12d2 ... to prevent np.unique from sorting values
• f09dece ... to save outcomes from the active task in order of occurrence

and one bug fix solves this issue completely (even without the two smaller bug fixes above):

• 7704a05 ... to always match a magnitude with its corresponding probability

What does this mean?

This means that each participant did SOME trials of their DESC task with lotteries that did NOT match the lotteries they experienced in their active task version.

Next step is to find out how many trials in the DESC task are affected by this per participant. --> For some trials, we will have had the correct data displayed "by chance" ... i.e., in some cases the bug would have no effect on ordering.

turn the json file into a python file that produces a JSON

... once that is done: import values from the define_ttl_values.py script ... and make that script a single function that returns the values