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Here is the screenshot of the stimuli program code:

It means, all the response times are 100 milliseconds less than the real. The histogram of response times proved this:

So, we need to add 100 to all the response times data.

Based on our previous studies, data cleaning includes:

• Remove unmotivated participants (e.g., too many errors by binomial test)
• Remove outlying participants

It is extremely complicated to set the rule to label a participant as unmotivated. Nevertheless, it is a necessary thing. For now, we only did the outlier detection part.

Formerly, the clustering of factor distributions were performed on the averaged results across different n_fact parameters, which might obscure the results. It might be better to search the results with best silhouette score across different n_fact parameters.