Measuring the reliability of binocular rivalry

The dataset holds a Matlab structure m, with all the data published in Acquafredda et al., JoV 2023.m = structure containing average data for generating the analyses and figures reported in the manuscript. It contains separate fields for the four different experiments and two experimental sessions, session1 and session2, where the variables below were measured.Each of these fields has subfields containing the information detailed below, coded with the format (N x M) with N = number of participants and M = 1. Variables reported in the structure:OD: % Ocular dominance defined as the proportion of exclusive right-eye dominance (please see formula in the paper)Mpd: % Mean durations of exclusive dominance phasesMixed_prop: % proportion of the total testing time spent reporting mixed percepts Bootstrapped_SE_ses1: %For each trial of session1 we represented perceptual reports as a list of phases, each linked with its duration and type (left eye, right eye, mixed). This list was resampled 10,000 times with reinsertion; for each resampling, we estimated the three parameters of interest. Finally, we took the standard deviation across these 10,000 values as a measure of standard error and combined standard errors across trials by taking the median. Bootstrapped_SE_ses2: % same as described above, but for each trial of session2. Bootstrapped_SE_diff: % for each of the 10000 resamplings described above, we computed the difference between the parameters of interest across the two session. This was repeated for each trial (trial 1 session 1 minus trial 1 in session 2, trial 2 in session 1 minus trial 2 in session 2, etc...). We took the standard deviation across these 10,000 differences as a measure of standard error and combined standard errors across trials by taking the median. We used this value as indicative of internal consistency.