2.2.3. Scoring and Data Treatment
The data treatment followed the procedure of Cepulic et al. (2018) and was conducted via a Python script, using Numpy and Pandas packages. For obtaining accuracy data, we excluded fast guesses with reaction times (RT) < 200 ms and calculated the average accuracy for each block. For calculating RT data, we excluded incorrect trials (15.04% of trials in the whole dataset). Then, the data was winsorized (i.e., Tukey correction; Tukey, 1977) at the within-person level, that is, for each block, all RTs longer than the third quartile plus 1.5 times the interquartile range were set to this limit value (7.9% of trials in the whole dataset). Based on the winsorized data, average RTs for each condition in each participant were calculated. We also inspected the averaged data across all conditions for interindividual outliers based on the same criterion; no participants fell outside of this limit. Finally, the mean RTs per condition and participant were transformed by multiplying the reciprocal of the average RT (ms) by 1000 (1000/RT) to normalize the distribution. The transformed RT data reflect the number of correct responses per second, that is, larger values indicate greater speed.