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This type of data (repeated measures on a group of subjects) lends itself perfectly for a linear mixed model (LMM) analysis. I'm into psychophysics myself and I have mostly abandoned simple ANOVAs and switched to LMMs almost completely, especially because it can handle missing data, and because LMMs allow for inclusion of trial and session number to correct ...


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Short answer It all depends on what you wish to measure; if you are after measurements where a few milliseconds matter, response boxes are the way to go. Background RT measurements are finicky; USB peripherals add polling latencies to your recorded response times. This can add unacceptable latencies (8 ms or so) to RT estimates. Apart from this effect on ...


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Regardless of what the chance level exactly was, as alluded to in the comments, the scores leap from 0.1 to 0.8. This results in nearly vertical slopes that are prone to convergence errors and hence lead to suboptimal threshold estimates in terms of accuracy. MATLAB always warns me when that happens. I have no experience with PsychoPy unfortunately. If you ...


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LMM is a good suggestion, second that. Consider also fitting a log-normal to the RT's as recommended by Haines & friends https://psyarxiv.com/xr7y3/download/?format=pdf You will have more or less uncertainty depending on how many trials you got, but it won't matter that people did different numbers of trials. I find the arguments in this paper for 'one ...


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