function [rdm] = sim_readdata_exp2(datapath,savefile) % read and save Meadows multiple arrangement data from json files - Experiment 2 % datapath: data directory % savefile: data file to be saved % DC Dima 2020 (diana.c.dima@gmail.com) nstim = 65; %hard-coded %find the datafile in the directory files = dir(datapath); files = {files(:).name}; files = files{contains(files, '.json')}; data = jsondecode(fileread(fullfile(datapath,files))); %append datasets in which people didn't hit 'submit' data = sim_appenddata(data, fullfile(datapath,'additional')); %save all data as a .mat file save(fullfile(datapath,'rep_data.mat'),'data') subnames = fieldnames(data); nsub = numel(subnames); incompl_idx = false(nsub,1); %mark incomplete participants exclude_idx = false(nsub,1); %mark participants excluded after QC rdm = nan(nsub,nstim,nstim); rdm_qc = nan(nsub,8,8); %8 stimuli per training catch_answers = cell(nsub,3); feedback = cell(nsub,1); %mturk_id = cell(nsub,1); gender = nan(nsub,1); age = nan(nsub,1); for isub = 1:nsub %check if MA task was finished datasub = getfield(data,subnames{isub}); % mturk_id{isub} = datasub.tasks{1}.mTurkID; %first check that they finished the MA task if ~strcmp(datasub.tasks{9}.status, 'finished') incompl_idx(isub) = 1; else if isub==1 %get stimulus list in order stimlist = datasub.tasks{1}.stimuli; stimlist = {stimlist(:).name}; stimlist = sort(stimlist); end %get age and gender pinfo = datasub.tasks{2}; gender(isub) = strcmp(pinfo.gender,'female'); age(isub) = str2double(pinfo.age); %display catch trials and select participants based on them ct = datasub.tasks{8}; catch_answers{isub,1} = ct.Video1; catch_answers{isub,2} = ct.Video2; catch_answers{isub,3} = ct.Video3; feedback{isub} = datasub.tasks{10}.Feedback; fprintf('Catch answers for sub %d\n, %s\n,%s\n,%s\n', isub, ct.Video1, ct.Video2, ct.Video3); fprintf('\nFeedback: %s\n', feedback{isub}) x = input('Exclude? Y/N: ', 's'); %no point extracting data for excluded subjects if strcmp(x,'Y') exclude_idx(isub) = 1; else %training matrix qc = datasub.tasks{6}; qcstim = {qc.stimuli(:).name}; [stimlist_qc,idx] = sort(qcstim); rdmqcsub = squareform(qc.rdm); rdm_qc(isub,:,:) = rdmqcsub(idx,idx); %full matrix - sort & normalize df = datasub.tasks{9}; rdm(isub,:,:) = sim_assignrdm(df,stimlist); end end end %remove participants who did not complete or were excluded idx = incompl_idx|exclude_idx; rdm_qc(idx,:,:) = []; rdm(idx,:,:) = []; %save training & MA data qc = []; qc.stimlist = stimlist_qc; qc.rdm = rdm_qc; save(savefile, 'qc', 'rdm', 'exclude_idx', 'incompl_idx','age','gender', 'stimlist','catch_answers', 'feedback');%,'mturk_id') end