﻿<?xml version="1.0" encoding="UTF-8"?><rss version="2.0" xmlns:dc="http://purl.org/dc/elements/1.1/"><channel><title>Millisecond Forums » Millisecond Forums » Inquisit 4  » "Inquisit no longer works" -</title><generator>InstantForum 2017-1 Final</generator><description>Millisecond Forums</description><link>https://forums.millisecond.com/</link><webMaster>Millisecond Forums</webMaster><lastBuildDate>Mon, 06 Apr 2026 17:04:08 GMT</lastBuildDate><ttl>20</ttl><item><title>"Inquisit no longer works" -</title><link>https://forums.millisecond.com/Topic17765.aspx</link><description>Hello, &lt;br/&gt;I scripted my own version of the Spatial N-Back task. Inquisit even runs the entire script. After presenting the end instructions, the program closes, shuts down and I get a note from windows Explorer: "Inquisit no longer works".&lt;br/&gt;The funny thing is, Inquisit still gives me the data sheet for the raw data. But the summary sheet I don't get. And like I said the entire program shuts down... It shows in the background that it is trying to store results and seconds later i get the information that it just broke down...&lt;br/&gt;&lt;img 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" alt="" height="198" width="307"&gt;&lt;br/&gt;&lt;br/&gt;Maybe someone can tell me what I am doing to Inquisit? Did I script something wrong for the summary data sheet?&lt;br/&gt;The following is the script. For a better understanding: 2 practice blocks (1 easy, 1 hard), 4 experiment blocks (easy1, hard1, easy2, hard2). And yes I need to know the single data of every trial.&lt;br/&gt;I really hope someone can find the bug in it. And especially why Inquisit is just shutting down like that? Am I asking too many parameters and it can't calculate it anymore?&lt;br/&gt;&lt;br/&gt;I am reallly looking forward to an answer.&lt;br/&gt;&lt;br/&gt;Kind regards,&lt;br/&gt;Ulrike Nestler&lt;br/&gt;&lt;br/&gt;************************************************************************************************************************************************************************&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; DEFAULTS&lt;br/&gt;&lt;br/&gt;&amp;lt;defaults&amp;gt;&lt;br/&gt;/ minimumversion = "4.0.0.0"&lt;br/&gt;/ fontstyle = ("Arial", 16pt)&lt;br/&gt;/ screencolor = (255, 255, 255)&lt;br/&gt;/ txcolor = (0, 0, 0)&lt;br/&gt;/ txbgcolor = (255, 255, 255)&lt;br/&gt;&amp;lt;/defaults&amp;gt;&lt;br/&gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;values&amp;gt;&lt;br/&gt;/StartN = 1&lt;br/&gt;/LastN = 3&lt;br/&gt;/debugmode = 0&lt;br/&gt;&amp;lt;/values&amp;gt;&lt;br/&gt;&lt;br/&gt;******************************************&lt;br/&gt;experimental parameters updated at runtime&lt;br/&gt;******************************************&lt;br/&gt;/completed:&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; 0 = script was not completed (script was prematurely aborted); 1 = script was completed (all conditions run)&lt;br/&gt;&lt;br/&gt;minus1:&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; contains the stimulusitemnumber of the stimulus in the trial preceding the current one &lt;br/&gt;minus2:&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; contains the stimulusitemnumber of the stimulus shown 2 trials before the current one&lt;br/&gt;minus3:&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; contains the stimulusitemnumber of the stimulus shown 3 trials before the current one&lt;br/&gt;minus4:&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; contains the stimulusitemnumber of the stimulus shown 4 trials before the current one&lt;br/&gt;stim:&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; contains the current stimulusitem &lt;br/&gt;stimnumber:&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; contains the current stimulusitemnumber&lt;br/&gt;N:&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; the lag between a target and the stimulus it repeats&lt;br/&gt;currenttarget:&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; contains the stimulusnumber of the current target&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; (a target in N = 0 trials is a stimulus with the same stimulus number as stored in target0)&lt;br/&gt;Hits:&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; codes the number of correctly identifiying a target&lt;br/&gt;FalseA:&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; codes the number of times a participant identifies a non-target as a target&lt;br/&gt;Misses:&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; codes the number of times a participant misses to identify a target&lt;br/&gt;CorrReject:&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; codes the number of times a participant correctly identifies a non-target (and does nothing)&lt;br/&gt;values.TotalHits:&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; the number of total hits across all experimental blocks&lt;br/&gt;values.TotalFA:&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; the number of total false alarms across all experimental blocks&lt;br/&gt;values.TotalBlocks:&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; the total number of experimental blocks run&lt;br/&gt;starttrialcounter:&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; keeps track of how many start trials have been run&lt;br/&gt;repetitioncounter:&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp; keeps track of how many times an experimental block has been run&lt;br/&gt;trial_Hit:&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; codes for each trial: trial_Hit = 1 (it's a hit), trial_Hit = 0 (it's not a hit)&lt;br/&gt;trial_Miss:&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; codes for each trial: trial_Miss = 1 (it's a Miss), trial_Miss = 0 (it's not a Miss)&lt;br/&gt;trial_CR:&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; codes for each trial: trial_CR = 1 (it's a correct rejection), trial_CR = 0 (it's not a correct rejection)&lt;br/&gt;trial_FA:&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; codes for each trial: trial_FA = 1 (it's a false alarm), trial_FA = 0 (it's not a a false alarm)&lt;br/&gt;&lt;br/&gt;&amp;lt;values &amp;gt;&lt;br/&gt;/completed = 0&lt;br/&gt;/minus1 = 0&lt;br/&gt;/minus2 = 0&lt;br/&gt;/minus3 = 0&lt;br/&gt;/minus4 = 0&lt;br/&gt;/N = 0&lt;br/&gt;/currenttarget = 0&lt;br/&gt;&lt;br/&gt;/Hits = 0&lt;br/&gt;/FalseA = 0&lt;br/&gt;/Misses = 0&lt;br/&gt;/CorrReject = 0&lt;br/&gt;/TotalHits = 0&lt;br/&gt;/TotalFA = 0&lt;br/&gt;/TotalMisses = 0&lt;br/&gt;/TotalCR = 0&lt;br/&gt;&lt;br/&gt;/Hits_easy1 = 0&lt;br/&gt;/FalseA_easy1 = 0&lt;br/&gt;/Misses_easy1 = 0&lt;br/&gt;/CorrReject_easy1 = 0&lt;br/&gt;/TotalHits_easy1 = 0&lt;br/&gt;/TotalFA_easy1 = 0&lt;br/&gt;/TotalMisses_easy1 = 0&lt;br/&gt;/TotalCR_easy1 = 0&lt;br/&gt;&lt;br/&gt;/Hits_easy2 = 0&lt;br/&gt;/FalseA_easy2 = 0&lt;br/&gt;/Misses_easy2 = 0&lt;br/&gt;/CorrReject_easy2 = 0&lt;br/&gt;/TotalHits_easy2 = 0&lt;br/&gt;/TotalFA_easy2 = 0&lt;br/&gt;/TotalMisses_easy2 = 0&lt;br/&gt;/TotalCR_easy2 = 0&lt;br/&gt;&lt;br/&gt;/Hits_hard1 = 0&lt;br/&gt;/FalseA_hard1 = 0&lt;br/&gt;/Misses_hard1 = 0&lt;br/&gt;/CorrReject_hard1 = 0&lt;br/&gt;/TotalHits_hard1 = 0&lt;br/&gt;/TotalFA_hard1 = 0&lt;br/&gt;/TotalMisses_hard1 = 0&lt;br/&gt;/TotalCR_hard1 = 0&lt;br/&gt;&lt;br/&gt;/Hits_hard2 = 0&lt;br/&gt;/FalseA_hard2 = 0&lt;br/&gt;/Misses_hard2 = 0&lt;br/&gt;/CorrReject_hard2 = 0&lt;br/&gt;/TotalHits_hard2 = 0&lt;br/&gt;/TotalFA_hard2 = 0&lt;br/&gt;/TotalMisses_hard2 = 0&lt;br/&gt;/TotalCR_hard2 = 0&lt;br/&gt;&lt;br/&gt;/TotalBlocks = 0&lt;br/&gt;/starttrialcounter = 0&lt;br/&gt;/repetitioncounter = 0&lt;br/&gt;&lt;br/&gt;/trial_Hit = 0&lt;br/&gt;/trial_Miss = 0&lt;br/&gt;/trial_CR = 0&lt;br/&gt;/trial_FA = 0&lt;br/&gt;&lt;br/&gt;/trialcount_easy1 = 0 &lt;br/&gt;/count_target_easy1 = 0&lt;br/&gt;/count_nontarget_easy1 = 0&lt;br/&gt;&lt;br/&gt;/trialcount_easy2 = 0 &lt;br/&gt;/count_target_easy2 = 0&lt;br/&gt;/count_nontarget_easy2 = 0&lt;br/&gt;&lt;br/&gt;/trialcount_hard1 = 0 &lt;br/&gt;/count_target_hard1 = 0&lt;br/&gt;/count_nontarget_hard1 = 0&lt;br/&gt;&lt;br/&gt;/trialcount_hard2 = 0 &lt;br/&gt;/count_target_hard2 = 0&lt;br/&gt;/count_nontarget_hard2 = 0&lt;br/&gt;&lt;br/&gt;/sumrt_target_easy1 = 0&lt;br/&gt;/sumrt_target_hard1 = 0 &lt;br/&gt;/sumrt_nontarget_easy1 = 0&lt;br/&gt;/sumrt_nontarget_hard1 = 0 &lt;br/&gt;&lt;br/&gt;/sumrt_target_easy2 = 0&lt;br/&gt;/sumrt_target_hard2 = 0 &lt;br/&gt;/sumrt_nontarget_easy2 = 0&lt;br/&gt;/sumrt_nontarget_hard2 = 0 &lt;br/&gt;&lt;br/&gt;&amp;lt;/values&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;expressions&amp;gt;&lt;br/&gt;/errors_easy= expressions.fa_easy+ expressions.oe_easy&lt;br/&gt;/errors_hard= expressions.errors_hard+ expressions.oe_hard&lt;br/&gt;/FA_easy = values.TotalFA_easy1 + values.TotalFA_easy2&lt;br/&gt;/FA_hard = values.TotalFA_hard1 + values.TotalFA_hard2&lt;br/&gt;/OE_easy = values.TotalMisses_easy1 + values.Misses_easy2&lt;br/&gt;/OE_hard = values.TotalMisses_hard1 + values.Misses_hard2&lt;br/&gt;&lt;br/&gt;/correctresponse_easy= expressions.hits_easy+expressions.cr_easy&lt;br/&gt;/correctresponse_hard=expressions.hits_hard+expressions.cr_hard&lt;br/&gt;/Hits_easy =values.TotalHits_easy1 + values.TotalHits_easy2&lt;br/&gt;/Hits_hard=values.TotalHits_hard1 + values.TotalHits_hard2&lt;br/&gt;/CR_easy=values.corrreject_easy1 + values.corrreject_easy2&lt;br/&gt;/CR_hard=values.corrreject_hard1+values.corrreject_hard2&lt;br/&gt;&lt;br/&gt;/ meanlatency.easy = meanlatency(trial.target_easy1, trial.target_easy2, trial.nontarget_easy1, trial.nontarget_easy2)&lt;br/&gt;/ meanlatency.hard = meanlatency(trial.target_hard1, trial.target_hard2, trial.nontarget_hard1, trial.nontarget_hard2)&lt;br/&gt;&lt;br/&gt;/sumrt_target_easy= values.sumrt_target_easy1 +values.sumrt_target_easy2&lt;br/&gt;/sumrt_target_hard=values.sumrt_target_hard1+values.sumrt_target_hard2&lt;br/&gt;/sumrt_nontarget_easy= values.sumrt_nontarget_easy1+values.sumrt_nontarget_easy2&lt;br/&gt;/sumrt_nontarget_hard=values.sumrt_nontarget_hard1+values.sumrt_nontarget_hard2&lt;br/&gt;&lt;br/&gt;/meanrt_target_easy=expressions.sumrt_target_easy/expressions.Hits_easy&lt;br/&gt;/meanrt_target_hard=expressions.sumrt_target_hard/expressions.Hits_hard&lt;br/&gt;&lt;br/&gt;&amp;lt;/expressions&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;data&amp;gt;&lt;br/&gt;/ file = "NBack_rawdata.iqdat"&lt;br/&gt;/ columns = [date, time, subject, values.N, blockcode, values.TotalBlocks, trialcode, trialnum, stimulusitem, stimulusnumber, &lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.currenttarget, response, latency, correct, values.trial_Hit, values.trial_FA, values.trial_Miss, values.trial_CR,&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.Hits_easy1, values.FalseA_easy1, values.Misses_easy1, values.CorrReject_easy1, values.Hits_easy2, values.FalseA_easy2, values.Misses_easy2, values.CorrReject_easy2, values.Hits_hard1, values.FalseA_hard1,values.Misses_hard1, values.CorrReject_hard1,values.Hits_hard2, values.FalseA_hard2, values.Misses_hard2, values.CorrReject_hard2, values.TotalHits, values.TotalFA, values.TotalMisses, values.TotalCR, latency, values.trial.misses, values.sumrt_target_easy1, values.sumrt_target_easy2,values.sumrt_target_hard1, values.sumrt_target_hard2,values.sumrt_nontarget_easy1, values.sumrt_nontarget_easy2, values.sumrt_nontarget_hard1, values.sumrt_nontarget_hard2 ]&lt;br/&gt;/ separatefiles = false&lt;br/&gt;&amp;lt;/data&amp;gt;&lt;br/&gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;summarydata &amp;gt;&lt;br/&gt;/ file = "NBack_summary.iqdat"&lt;br/&gt;/ columns = [script.startdate, script.starttime, script.subjectid, script.groupid, script.elapsedtime, values.completed, values.TotalHits, values.TotalFA, values.TotalMisses, values.TotalCR, expressions.errors_easy, expressions.fa_easy, expressions.oe_easy, expressions.errors_hard, expressions.fa_hard, expressions.oe_hard, expressions.correctresponse_easy, expressions.hits_easy, expressions.cr_easy, expressions.correctresponse_hard, expressions.hits_hard, expressions.cr_hard, expressions.sumrt_target_easy, expressions.sumrt_target_hard, expressions.sumrt_nontarget_easy, expressions.sumrt_nontarget_hard, expressions.meanrt_target_easy, expressions.meanrt_target_hard]&lt;br/&gt;&amp;lt;/summarydata&amp;gt;&lt;br/&gt;************************************************************************************************************************************************************************&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; DATA COLLECTION&lt;br/&gt;&lt;br/&gt;this implementation suggests to collect the following information:&lt;br/&gt;date:&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; Date of Experiment&lt;br/&gt;time:&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; Time of Day&lt;br/&gt;subject:&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; Subjectnumber&lt;br/&gt;values.N:&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; the type of N-back trial&amp;nbsp; &lt;br/&gt;blockcode:&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; the name of the current block&lt;br/&gt;values.TotalBlocks:&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; the total number of experimental blocks run&lt;br/&gt;trialcode:&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; the name of the current trial&lt;br/&gt;stimulusitem:&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; the file of the stimulus shown during a trial&lt;br/&gt;stimulusnumber:&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; the item number of the stimulus shown during a trial&lt;br/&gt;values.currenttarget:&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; the item number of the current target&lt;br/&gt;response:&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; the response of the participant (30 = "A", 38 = "L", 0 = noresponse &amp;amp; invalid keys)&lt;br/&gt;latency:&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; how fast a participant responded within the 3000ms timeframe, if at all&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; Note: if noresponse or invalid keys, latency = 3000&lt;br/&gt;correct:&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; the correctness of the response&lt;br/&gt;values.trial_Hit:&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; whether it was a Hit (=1)&lt;br/&gt;values.trial_FA:&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; whether it was a False Alarm (=1)&lt;br/&gt;values.trial_Miss:&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; whether it was&amp;nbsp; Miss (=1)&lt;br/&gt;values.trial_CR:&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; whether it was a Correct Rejection ( = 1)&lt;br/&gt;values.Hits:&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; the sum of Hits in a block&lt;br/&gt;values.FalseA:&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; the sum of False Alarms in a block&lt;br/&gt;values.Misses:&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; the sum of Misses in a block&lt;br/&gt;values.CorrReject:&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; the sum of Correct Rejections in a block&lt;br/&gt;values.TotalHits:&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; the sum of total hits across all experimental blocks&lt;br/&gt;values.TotalFA:&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; the number of total false alarms across all experimental blocks&lt;br/&gt;&lt;br/&gt;&lt;br/&gt;************************&lt;br/&gt;raw data&lt;br/&gt;************************&lt;br/&gt;&lt;br/&gt;&lt;br/&gt;************************&lt;br/&gt;summary data&lt;br/&gt;************************&lt;br/&gt;&lt;br/&gt;script.startdate:&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; date script was run&lt;br/&gt;script.starttime:&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; time script was started&lt;br/&gt;script.subjectid:&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; subject id number&lt;br/&gt;script.groupid:&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; group id number&lt;br/&gt;script.elapsedtime:&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; time it took to run script (in ms)&lt;br/&gt;/completed:&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; 0 = script was not completed (script was prematurely aborted); 1 = script was completed (all conditions run)&lt;br/&gt;&lt;br/&gt;values.TotalHits:&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; the sum of total hits across all experimental blocks&lt;br/&gt;values.TotalFA:&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; the number of total false alarms across all experimental blocks&lt;br/&gt;values.TotalMisses:&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; the sum of Misses in a block&lt;br/&gt;values.TotalCR:&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; the sum of Correct Rejections in a block&lt;br/&gt;&lt;br/&gt;&lt;br/&gt;&lt;br/&gt;+++++++++++++++++++++++++++&lt;br/&gt;Expressions&lt;br/&gt;+++++++++++++++++++++++++++&lt;br/&gt;errors_easy:&amp;nbsp;&amp;nbsp;&amp;nbsp; total of false alarms and omission errors in the easy condition&lt;br/&gt;errors_hard:&amp;nbsp;&amp;nbsp;&amp;nbsp; total of false alarms and omission errors in the hard condition&lt;br/&gt;FA_easy:&amp;nbsp;&amp;nbsp;&amp;nbsp; total of false alarms in the easy condition&lt;br/&gt;OE_easy:&amp;nbsp;&amp;nbsp;&amp;nbsp; total of omission errors in the hard condition&lt;br/&gt;FA_hard:&amp;nbsp;&amp;nbsp;&amp;nbsp; total of false alarms in the hard condition&lt;br/&gt;OE_hard:&amp;nbsp;&amp;nbsp;&amp;nbsp; total of omission errors in the hard condition&lt;br/&gt;&lt;br/&gt;correctresponse_easy: &amp;nbsp;&amp;nbsp;&amp;nbsp; total of hits and correct rejections in the easy condition&lt;br/&gt;correctresponse_hard:&amp;nbsp;&amp;nbsp;&amp;nbsp; total of hits and correct rejections in the hard condition&lt;br/&gt;Hits_easy:&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; total of hits in the easy condition&lt;br/&gt;Hits_hard: &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; total of hits in the hard condition&lt;br/&gt;CR_easy:&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; total of correct rejections in the easy condition&lt;br/&gt;CR_hard:&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; total of correct rejections in the hard condition&lt;br/&gt;&lt;br/&gt;&lt;br/&gt;&lt;br/&gt;&lt;br/&gt;++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++&lt;br/&gt;&lt;br/&gt;EXPERIMENT &lt;br/&gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;expt&amp;gt;&lt;br/&gt;/ onexptbegin = [if (values.debugmode == 1) text.targetalert.textcolor = (red)]&lt;br/&gt;/ preinstructions = (intro1, intro2)&lt;br/&gt;/ blocks = [1 = practice_easy; 2 = intro3; 3 = practice_hard; 4= inter1; 5= nback_easy1; 6= inter2; 7=nback_hard1; 8= inter3; 9=nback_easy2; 10=inter4; 11= nback_hard2]&lt;br/&gt;/ onexptend = [values.completed = 1]&lt;br/&gt;/ postinstructions = (end)&lt;br/&gt;&amp;lt;/expt&amp;gt;&lt;br/&gt;&lt;br/&gt;&lt;br/&gt;PRACTICE-BLOCKS&lt;br/&gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;block practice_easy&amp;gt;&lt;br/&gt;/ onblockbegin = [values.N = 1]&lt;br/&gt;/ onblockbegin = [&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.currenttarget = 0; values.minus1 = 0; values.minus2 = 0; values.minus3 = 0; values.minus4 = 0;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.TotalBlocks += 1;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.starttrialcounter = 0;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.repetitioncounter += 1;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; ]&lt;br/&gt;/ trials = [1 = start; 2-25 = noreplace(nontarget, nontarget, nontarget, target)]&lt;br/&gt;/ errormessage = true(ErrorFeedback, 500)&lt;br/&gt;/ correctmessage = true(CorrectFeedback, 500)&lt;br/&gt;/ screencolor = (255, 255, 255)&lt;br/&gt;/ recorddata = false&lt;br/&gt;&amp;lt;/block&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;block practice_hard&amp;gt;&lt;br/&gt;/ onblockbegin = [values.N = 3]&lt;br/&gt;/ onblockbegin = [&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.currenttarget = 0; values.minus1 = 0; values.minus2 = 0; values.minus3 = 0; values.minus4 = 0;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.TotalBlocks += 1;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.starttrialcounter = 0;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.repetitioncounter += 1;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; ]&lt;br/&gt;/ trials = [1 = start; 2-25 = noreplace(nontarget, nontarget, nontarget, target)]&lt;br/&gt;/ errormessage = true(ErrorFeedback, 500)&lt;br/&gt;/ correctmessage = true(CorrectFeedback, 500)&lt;br/&gt;/ screencolor = (255, 255, 255)&lt;br/&gt;/ recorddata = false&lt;br/&gt;&amp;lt;/block&amp;gt;&lt;br/&gt;&lt;br/&gt;EXPERIMENTAL-BLOCKS &lt;br/&gt;&lt;br/&gt;&amp;lt;block nback_easy1&amp;gt;&lt;br/&gt;/ onblockbegin = [values.N = 1; values.TotalHits_easy1 = 0; values.TotalFA_easy1 = 0; values.TotalBlocks = 0; values.TotalMisses_easy1 = 0; values.TotalCR_easy1 = 0]&lt;br/&gt;/ onblockbegin = [&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.currenttarget = 0; values.minus1 = 0; values.minus2 = 0; values.minus3 = 0; values.minus4 = 0;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.Hits_easy1 = 0; values.FalseA_easy1 = 0; values.Misses_easy1 = 0; values.CorrReject_easy1 = 0;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; ]&lt;br/&gt;/ trials = [1 = start; 2 - 25 = noreplace(nontarget_easy1, nontarget_easy1, nontarget_easy1, target_easy1)]&lt;br/&gt;/ errormessage = true(Eraser, 500)&lt;br/&gt;/ correctmessage = true(Eraser, 500)&lt;br/&gt;/ screencolor = (255, 255, 255)&lt;br/&gt;&amp;lt;/block&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;block nback_hard1&amp;gt;&lt;br/&gt;/ onblockbegin = [values.N = 3; values.TotalHits_hard1 = 0; values.TotalFA_hard1 = 0; values.TotalBlocks = 0; values.TotalMisses_hard1 = 0; values.TotalCR_hard1 = 0]&lt;br/&gt;/ onblockbegin = [&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.currenttarget = 0; values.minus1 = 0; values.minus2 = 0; values.minus3 = 0; values.minus4 = 0;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.Hits_hard1 = 0; values.FalseA_hard1 = 0; values.Misses_hard1 = 0; values.CorrReject_hard1 = 0;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.TotalBlocks += 1; &lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.starttrialcounter = 0;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.repetitioncounter += 1;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; ]&lt;br/&gt;/ trials = [1 = start; 2 - 25 = noreplace(nontarget_hard1, nontarget_hard1, nontarget_hard1, target_hard1)]&lt;br/&gt;/ errormessage = true(Eraser, 500)&lt;br/&gt;/ correctmessage = true(Eraser, 500)&lt;br/&gt;/ screencolor = (255, 255, 255)&lt;br/&gt;&amp;lt;/block&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;block nback_easy2&amp;gt;&lt;br/&gt;/ onblockbegin = [values.N = 1; values.TotalHits_easy2 = 0; values.TotalFA_easy2 = 0; values.TotalBlocks = 0; values.TotalMisses_easy2 = 0; values.TotalCR_easy2 = 0]&lt;br/&gt;/ onblockbegin = [&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.currenttarget = 0; values.minus1 = 0; values.minus2 = 0; values.minus3 = 0; values.minus4 = 0;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.Hits_easy2 = 0; values.FalseA_easy2 = 0; values.Misses_easy2 = 0; values.CorrReject_easy2 = 0;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.TotalBlocks += 1; &lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.starttrialcounter = 0;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.repetitioncounter += 1;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; ]&lt;br/&gt;/ trials = [1 = start; 2 - 25 = noreplace(nontarget_easy2, nontarget_easy2, nontarget_easy2, target_easy2)]&lt;br/&gt;/ errormessage = true(Eraser, 500)&lt;br/&gt;/ correctmessage = true(Eraser, 500)&lt;br/&gt;/ screencolor = (255, 255, 255)&lt;br/&gt;&amp;lt;/block&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;block nback_hard2&amp;gt;&lt;br/&gt;/ onblockbegin = [values.N = 3; values.TotalHits_hard2 = 0; values.TotalFA_hard2 = 0; values.TotalBlocks = 0; values.TotalMisses_hard2 = 0; values.TotalCR_hard2 = 0]&lt;br/&gt;/ onblockbegin = [&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.currenttarget = 0; values.minus1 = 0; values.minus2 = 0; values.minus3 = 0; values.minus4 = 0;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.Hits_hard2 = 0; values.FalseA_hard2 = 0; values.Misses_hard2 = 0; values.CorrReject_hard2 = 0;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.TotalBlocks += 1; &lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.starttrialcounter = 0;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.repetitioncounter += 1;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; ]&lt;br/&gt;/ trials = [1 = start; 2 - 25 = noreplace(nontarget_hard2, nontarget_hard2, nontarget_hard2, target_hard2)]&lt;br/&gt;/ errormessage = true(Eraser, 500)&lt;br/&gt;/ correctmessage = true(Eraser, 500)&lt;br/&gt;/ screencolor = (255, 255, 255)&lt;br/&gt;&amp;lt;/block&amp;gt;&lt;br/&gt;&lt;br/&gt;&lt;br/&gt;&lt;br/&gt;INSTRUCTIONS&lt;br/&gt;&amp;lt;instruct&amp;gt;&lt;br/&gt;/ windowsize = (1000,700)&lt;br/&gt;/ inputdevice = keyboard&lt;br/&gt;/ prevkey = (28)&lt;br/&gt;/ nextkey = (57)&lt;br/&gt;/ prevlabel = "Zurück [Enter]"&lt;br/&gt;/ nextlabel = "Weiter [Leertaste]"&lt;br/&gt;/ finishlabel = "Aufgabe starten [Leertaste]"&lt;br/&gt;&amp;lt;/instruct&amp;gt;&lt;br/&gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;htmlpage intro1&amp;gt;&lt;br/&gt;/ file = "nbackintro1.htm"&lt;br/&gt;&amp;lt;/htmlpage&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;htmlpage intro2&amp;gt;&lt;br/&gt;/ file = "nbackintro2easy.htm"&lt;br/&gt;&amp;lt;/htmlpage&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;htmlpage intro3&amp;gt;&lt;br/&gt;/ file = "nbackintro2hard.htm"&lt;br/&gt;&amp;lt;/htmlpage&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;htmlpage inter1&amp;gt;&lt;br/&gt;/ file = "nbackinter1easy.htm"&lt;br/&gt;&amp;lt;/htmlpage&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;htmlpage inter2&amp;gt;&lt;br/&gt;/ file = "nbackinter2hard.htm"&lt;br/&gt;&amp;lt;/htmlpage&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;htmlpage inter3&amp;gt;&lt;br/&gt;/ file = "nbackinter3easy.htm"&lt;br/&gt;&amp;lt;/htmlpage&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;htmlpage inter4&amp;gt;&lt;br/&gt;/ file = "nbackinter4hard.htm"&lt;br/&gt;&amp;lt;/htmlpage&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;htmlpage end&amp;gt;&lt;br/&gt;/ file = "nback_end.htm"&lt;br/&gt;&amp;lt;/htmlpage&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;block intro3&amp;gt;&lt;br/&gt;/ preinstructions = (intro3)&lt;br/&gt;/ recorddata = false&lt;br/&gt;&amp;lt;/block&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;block inter1&amp;gt;&lt;br/&gt;/ preinstructions = (inter1)&lt;br/&gt;/ recorddata = false&lt;br/&gt;&amp;lt;/block&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;block inter2&amp;gt;&lt;br/&gt;/ preinstructions = (inter2)&lt;br/&gt;/ recorddata = false&lt;br/&gt;&amp;lt;/block&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;block inter3&amp;gt;&lt;br/&gt;/ preinstructions = (inter3)&lt;br/&gt;/ recorddata = false&lt;br/&gt;&amp;lt;/block&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;block inter4&amp;gt;&lt;br/&gt;/ preinstructions = (inter4)&lt;br/&gt;/ recorddata = false&lt;br/&gt;&amp;lt;/block&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;block end&amp;gt;&lt;br/&gt;/ preinstructions = (end)&lt;br/&gt;/ recorddata = false&lt;br/&gt;&amp;lt;/block&amp;gt;&lt;br/&gt;&lt;br/&gt;&lt;br/&gt;SHAPES&lt;br/&gt;&amp;lt;item shapes&amp;gt;&lt;br/&gt;/ 1 = "flash1.png"&lt;br/&gt;/ 2 = "flash2.png"&lt;br/&gt;/ 3 = "flash3.png"&lt;br/&gt;/ 4 = "flash4.png"&lt;br/&gt;/ 5 = "flash5.png"&lt;br/&gt;/ 6 = "flash6.png"&lt;br/&gt;/ 7 = "flash7.png"&lt;br/&gt;/ 8 = "flash8.png"&lt;br/&gt;&amp;lt;/item&amp;gt;&lt;br/&gt;&lt;br/&gt;************************************************************************************************************************************************************************&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; STIMULI-SELECTION&lt;br/&gt;&lt;br/&gt;****List Variable to select notargetvalue&lt;br/&gt;&lt;br/&gt;*list.notargetvalue selects any of the 8 numbers (item indices) but the one selected for values.currenttarget&lt;br/&gt;&amp;lt;list notargetvalue&amp;gt;&lt;br/&gt;/ items = (1, 2, 3, 4, 5, 6, 7, 8)&lt;br/&gt;/ not = (values.currenttarget)&lt;br/&gt;/ replace = true&lt;br/&gt;&amp;lt;/list&amp;gt;&lt;br/&gt;&lt;br/&gt;&lt;br/&gt;***randomly selects one of the shapes &lt;br/&gt;&amp;lt;picture startshape&amp;gt;&lt;br/&gt;/ items = shapes&lt;br/&gt;/ select = replace&lt;br/&gt;/ size = (30%, 30%)&lt;br/&gt;&amp;lt;/picture&amp;gt;&lt;br/&gt;&lt;br/&gt;***selects any shape but the&amp;nbsp; established target &lt;br/&gt;&amp;lt;picture nontargetshape&amp;gt;&lt;br/&gt;/ items = shapes&lt;br/&gt;/ select = notargetvalue&lt;br/&gt;/ size = (30%, 30%)&lt;br/&gt;&amp;lt;/picture&amp;gt;&lt;br/&gt;&lt;br/&gt;***selects the item with the same item number as the established target&lt;br/&gt;&amp;lt;picture targetshape&amp;gt;&lt;br/&gt;/ items = shapes&lt;br/&gt;/ select = values.currenttarget&lt;br/&gt;/ size = (30%, 30%)&lt;br/&gt;&amp;lt;/picture&amp;gt;&lt;br/&gt;&lt;br/&gt;************************************************************************************************************************************************************************&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; FEEDBACK MESSAGES FOR PRACTICE TRIALS ONLY&lt;br/&gt;&amp;lt;picture CorrectFeedback&amp;gt;&lt;br/&gt;/ items = ("happyc.png")&lt;br/&gt;/ position = (50, 50)&lt;br/&gt;/ size = (30%,30%)&lt;br/&gt;&amp;lt;/picture&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;picture ErrorFeedback&amp;gt;&lt;br/&gt;/ items = ("sadc.png")&lt;br/&gt;/ position = (50, 50)&lt;br/&gt;/ size = (30%,30%)&lt;br/&gt;&amp;lt;/picture&amp;gt;&lt;br/&gt;&lt;br/&gt;************************************************************************************************************************************************************************&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; ASSISTANT STIMULI&lt;br/&gt;&lt;br/&gt;*****acts as an eraser after showing the key stimuli for 500ms, staying on for the remainder of the time dedicated to each trial&lt;br/&gt;&amp;lt;picture eraser&amp;gt;&lt;br/&gt;/ items = ("flash9.png")&lt;br/&gt;/ position = (50, 50)&lt;br/&gt;/ size = (30%,30%)&lt;br/&gt;&amp;lt;/picture&amp;gt;&lt;br/&gt;&lt;br/&gt;*****Debug Code:&lt;br/&gt;&amp;lt;text targetalert&amp;gt;&lt;br/&gt;/ items = ("target")&lt;br/&gt;/ position = (50%, 80%)&lt;br/&gt;/ txcolor = (255, 255, 255)&lt;br/&gt;/ txbgcolor = (255, 255, 255)&lt;br/&gt;/ fontstyle = ("Arial", 3%, true, false, false, false, 5, 1)&lt;br/&gt;&amp;lt;/text&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;text fixcross&amp;gt;&lt;br/&gt;/ items = ("+")&lt;br/&gt;/ fontstyle = ("Arial", 34pt)&lt;br/&gt;&amp;lt;/text&amp;gt;&lt;br/&gt;&lt;br/&gt;************************************************************************************************************************************************************************&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; TRIALS&lt;br/&gt;&lt;br/&gt;&amp;lt;trial start&amp;gt;&lt;br/&gt;/ stimulustimes = [0=fixcross; 300= startshape; 2500 = Eraser]&lt;br/&gt;/ validresponse = (44, 50)&lt;br/&gt;/ correctresponse = (44)&lt;br/&gt;/ ontrialbegin = [&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; {if (values.N == 1) values.currenttarget = values.minus1};&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; {if (values.N == 2) values.currenttarget = values.minus2};&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; {if (values.N == 3) values.currenttarget = values.minus3};&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.starttrialcounter += 1;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; ]&lt;br/&gt;/ ontrialend = [&amp;nbsp;&amp;nbsp;&amp;nbsp; &lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; {if (values.N == 0) values.currenttarget = picture.startshape.currentitemnumber};&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.minus3 = values.minus2;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.minus2 = values.minus1;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.minus1 = picture.startshape.currentitemnumber&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; ]&lt;br/&gt;/ beginresponsetime = 300&lt;br/&gt;/ responseinterrupt = immediate&lt;br/&gt;/ timeout = 2500&lt;br/&gt;/ recorddata = false&lt;br/&gt;/ branch = [if (values.starttrialcounter &amp;lt; values.N) trial.start]&lt;br/&gt;&amp;lt;/trial&amp;gt;&lt;br/&gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;trial nontarget&amp;gt;&lt;br/&gt;/ stimulustimes = [0=fixcross; 300 = nontargetshape; 2500 = eraser]&lt;br/&gt;/ validresponse = (44, 50)&lt;br/&gt;/ correctresponse = (44)&lt;br/&gt;/ ontrialbegin = [&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; {if (values.N == 1) values.currenttarget = values.minus1};&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; {if (values.N == 2) values.currenttarget = values.minus2};&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; {if (values.N == 3) values.currenttarget = values.minus3}&amp;nbsp;&amp;nbsp;&amp;nbsp; &lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; ]&lt;br/&gt;/ beginresponsetime = 300&lt;br/&gt;/ responseinterrupt = immediate&lt;br/&gt;/ recorddata = false&lt;br/&gt;/ timeout = 2500&lt;br/&gt;&amp;lt;/trial&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;trial nontarget_easy1&amp;gt;&lt;br/&gt;/ stimulustimes = [0=fixcross; 300 = nontargetshape; 2500 = eraser]&lt;br/&gt;/ validresponse = (44, 50)&lt;br/&gt;/ correctresponse = (44)&lt;br/&gt;/ ontrialbegin = [&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; {if (values.N == 1) values.currenttarget = values.minus1};&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; {if (values.N == 2) values.currenttarget = values.minus2};&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; {if (values.N == 3) values.currenttarget = values.minus3}&amp;nbsp;&amp;nbsp;&amp;nbsp; &lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; ]&lt;br/&gt;/ ontrialbegin = [values.trial_Hit = 0; values.trial_Miss = 0; values.trial_CR = 0; values.trial_FA = 0]&lt;br/&gt;/ ontrialbegin = [values.trialcount_easy1 += 1; values.count_nontarget_easy1 += 1]&lt;br/&gt;/ ontrialend = [if (trial.nontarget_easy1.response != 0) {values.sumrt_nontarget_easy1 += trial.nontarget_easy1.latency}]&lt;br/&gt;/ ontrialend = [&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.minus3 = values.minus2;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.minus2 = values.minus1;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.minus1 = picture.nontargetshape.currentitemnumber;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.CorrReject= values.CorrReject+ trial.nontarget.correct;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.trial_CR = trial.nontarget.correct;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.FalseA = values.FalseA + trial.nontarget.error;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.trial_FA = trial.nontarget.error;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.TotalFA = values.TotalFA + trial.nontarget.error;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.TotalCR = values.TotalCR + trial.nontarget.correct;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; ]&lt;br/&gt;/ beginresponsetime = 300&lt;br/&gt;/ responseinterrupt = immediate&lt;br/&gt;/ timeout = 2500&lt;br/&gt;&amp;lt;/trial&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;trial nontarget_easy2&amp;gt;&lt;br/&gt;/ stimulustimes = [0=fixcross; 300 = nontargetshape; 2500 = eraser]&lt;br/&gt;/ validresponse = (44, 50)&lt;br/&gt;/ correctresponse = (44)&lt;br/&gt;/ ontrialbegin = [&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; {if (values.N == 1) values.currenttarget = values.minus1};&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; {if (values.N == 2) values.currenttarget = values.minus2};&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; {if (values.N == 3) values.currenttarget = values.minus3}&amp;nbsp;&amp;nbsp;&amp;nbsp; &lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; ]&lt;br/&gt;/ ontrialbegin = [values.trial_Hit = 0; values.trial_Miss = 0; values.trial_CR = 0; values.trial_FA = 0]&lt;br/&gt;/ ontrialbegin = [values.trialcount_easy2 += 1; values.count_nontarget_easy2 += 1]&lt;br/&gt;/ ontrialend = [if (trial.nontarget_easy2.response != 0) {values.sumrt_nontarget_easy2 += trial.nontarget_easy2.latency}]&lt;br/&gt;/ ontrialend = [&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.minus3 = values.minus2;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.minus2 = values.minus1;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.minus1 = picture.nontargetshape.currentitemnumber;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.CorrReject = values.CorrReject + trial.nontarget.correct;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.trial_CR = trial.nontarget.correct;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.FalseA = values.FalseA+ trial.nontarget.error;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.trial_FA = trial.nontarget.error;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.TotalFA= values.TotalFA + trial.nontarget.error;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.TotalCR = values.TotalCR + trial.nontarget.correct;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; ]&lt;br/&gt;/ beginresponsetime = 300&lt;br/&gt;/ responseinterrupt = immediate&lt;br/&gt;/ timeout = 2500&lt;br/&gt;&amp;lt;/trial&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;trial nontarget_hard1&amp;gt;&lt;br/&gt;/ stimulustimes = [0=fixcross; 300 = nontargetshape; 2500 = eraser]&lt;br/&gt;/ validresponse = (44, 50)&lt;br/&gt;/ correctresponse = (44)&lt;br/&gt;/ ontrialbegin = [&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; {if (values.N == 1) values.currenttarget = values.minus1};&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; {if (values.N == 2) values.currenttarget = values.minus2};&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; {if (values.N == 3) values.currenttarget = values.minus3}&amp;nbsp;&amp;nbsp;&amp;nbsp; &lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; ]&lt;br/&gt;/ ontrialbegin = [values.trial_Hit = 0; values.trial_Miss = 0; values.trial_CR = 0; values.trial_FA = 0]&lt;br/&gt;/ ontrialbegin = [values.trialcount_hard1 += 1; values.count_nontarget_hard1 += 1]&lt;br/&gt;/ ontrialend = [if (trial.nontarget_hard1.response != 0) {values.sumrt_nontarget_hard1 += trial.nontarget_hard1.latency}]&lt;br/&gt;/ ontrialend = [&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.minus3 = values.minus2;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.minus2 = values.minus1;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.minus1 = picture.nontargetshape.currentitemnumber;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.CorrReject = values.CorrReject + trial.nontarget.correct;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.trial_CR = trial.nontarget.correct;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.FalseA = values.FalseA + trial.nontarget.error;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.trial_FA = trial.nontarget.error;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.TotalFA = values.TotalFA + trial.nontarget.error;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.TotalCR= values.TotalCR + trial.nontarget.correct;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; ]&lt;br/&gt;/ beginresponsetime = 300&lt;br/&gt;/ responseinterrupt = immediate&lt;br/&gt;/ timeout = 2500&lt;br/&gt;&amp;lt;/trial&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;trial nontarget_hard2&amp;gt;&lt;br/&gt;/ stimulustimes = [0=fixcross; 300 = nontargetshape; 2500 = eraser]&lt;br/&gt;/ validresponse = (44, 50)&lt;br/&gt;/ correctresponse = (44)&lt;br/&gt;/ ontrialbegin = [&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; {if (values.N == 1) values.currenttarget = values.minus1};&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; {if (values.N == 2) values.currenttarget = values.minus2};&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; {if (values.N == 3) values.currenttarget = values.minus3}&amp;nbsp;&amp;nbsp;&amp;nbsp; &lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; ]&lt;br/&gt;/ ontrialbegin = [values.trial_Hit = 0; values.trial_Miss = 0; values.trial_CR = 0; values.trial_FA = 0]&lt;br/&gt;/ ontrialbegin = [values.trialcount_hard1 += 1; values.count_nontarget_hard2 += 1]&lt;br/&gt;/ ontrialend = [if (trial.nontarget_hard2.response != 0) {values.sumrt_nontarget_hard2 += trial.nontarget_hard2.latency}]&lt;br/&gt;/ ontrialend = [&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.minus3 = values.minus2;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.minus2 = values.minus1;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.minus1 = picture.nontargetshape.currentitemnumber;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.CorrReject = values.CorrReject + trial.nontarget.correct;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.trial_CR = trial.nontarget.correct;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.FalseA = values.FalseA + trial.nontarget.error;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.trial_FA = trial.nontarget.error;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.TotalFA = values.TotalFA + trial.nontarget.error;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.TotalCR = values.TotalCR + trial.nontarget.correct;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; ]&lt;br/&gt;/ beginresponsetime = 300&lt;br/&gt;/ responseinterrupt = immediate&lt;br/&gt;/ timeout = 2500&lt;br/&gt;&amp;lt;/trial&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;trial target&amp;gt;&lt;br/&gt;/ stimulustimes = [0=fixcross; 300= targetshape, targetalert; 2500 = eraser]&lt;br/&gt;/ validresponse = (44, 50)&lt;br/&gt;/ correctresponse = (50)&lt;br/&gt;/ ontrialbegin = [&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; {if (values.N == 1) values.currenttarget = values.minus1};&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; {if (values.N == 2) values.currenttarget = values.minus2};&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; {if (values.N == 3) values.currenttarget = values.minus3};&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; ]&lt;br/&gt;/ ontrialend = [&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.minus3 = values.minus2;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.minus2 = values.minus1;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.minus1 = picture.targetshape.currentitemnumber;&lt;br/&gt;/ beginresponsetime = 300&lt;br/&gt;/ responseinterrupt = immediate&lt;br/&gt;/ recorddata = false&lt;br/&gt;/ timeout = 2500&lt;br/&gt;&amp;lt;/trial&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;trial target_easy1&amp;gt;&lt;br/&gt;/ stimulustimes = [0=fixcross; 300= targetshape, targetalert; 2500 = eraser]&lt;br/&gt;/ validresponse = (44, 50)&lt;br/&gt;/ correctresponse = (50)&lt;br/&gt;/ ontrialbegin = [&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; {if (values.N == 1) values.currenttarget = values.minus1};&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; {if (values.N == 2) values.currenttarget = values.minus2};&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; {if (values.N == 3) values.currenttarget = values.minus3};&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; ]&lt;br/&gt;/ ontrialbegin = [values.trial_Hit = 0; values.trial_Miss = 0; values.trial_CR = 0; values.trial_FA = 0]&lt;br/&gt;/ ontrialbegin = [values.trialcount_easy1 += 1; values.count_target_easy1 += 1]&lt;br/&gt;/ ontrialend = [if (trial.target_easy1.response != 0) {values.sumrt_target_easy1 += trial.target_easy1.latency}]&lt;br/&gt;/ ontrialend = [&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.minus3 = values.minus2;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.minus2 = values.minus1;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.minus1 = picture.targetshape.currentitemnumber;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.Hits = values.Hits+ trial.target.correct;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.trial_Hit =&amp;nbsp; trial.target.correct;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.Misses = values.Misses + trial.target.error;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.trial_Miss =&amp;nbsp; trial.target.error;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.TotalHits = values.TotalHits + trial.target.correct; &lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.TotalMisses = values.TotalMisses + trial.target.error; &lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; ]&lt;br/&gt;/ beginresponsetime = 300&lt;br/&gt;/ responseinterrupt = immediate&lt;br/&gt;/ timeout = 2500&lt;br/&gt;&amp;lt;/trial&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;trial target_easy2&amp;gt;&lt;br/&gt;/ stimulustimes = [0=fixcross; 300= targetshape, targetalert; 2500 = eraser]&lt;br/&gt;/ validresponse = (44, 50)&lt;br/&gt;/ correctresponse = (50)&lt;br/&gt;/ ontrialbegin = [&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; {if (values.N == 1) values.currenttarget = values.minus1};&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; {if (values.N == 2) values.currenttarget = values.minus2};&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; {if (values.N == 3) values.currenttarget = values.minus3};&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; ]&lt;br/&gt;/ ontrialbegin = [values.trial_Hit = 0; values.trial_Miss = 0; values.trial_CR = 0; values.trial_FA = 0]&lt;br/&gt;/ ontrialbegin = [values.trialcount_easy2 += 1; values.count_target_easy2 += 1]&lt;br/&gt;/ ontrialend = [if (trial.target_easy2.response != 0) {values.sumrt_target_easy2 += trial.target_easy2.latency}]&lt;br/&gt;/ ontrialend = [&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.minus3 = values.minus2;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.minus2 = values.minus1;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.minus1 = picture.targetshape.currentitemnumber;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.Hits= values.Hits + trial.target.correct;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.trial_Hit =&amp;nbsp; trial.target.correct;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.Misses= values.Misses + trial.target.error;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.trial_Miss =&amp;nbsp; trial.target.error;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.TotalHits = values.TotalHits + trial.target.correct; &lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.TotalMisses = values.TotalMisses + trial.target.error; &lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; ]&lt;br/&gt;/ beginresponsetime = 300&lt;br/&gt;/ responseinterrupt = immediate&lt;br/&gt;/ timeout = 2500&lt;br/&gt;&amp;lt;/trial&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;trial target_hard1&amp;gt;&lt;br/&gt;/ stimulustimes = [0=fixcross; 300= targetshape, targetalert; 2500 = eraser]&lt;br/&gt;/ validresponse = (44, 50)&lt;br/&gt;/ correctresponse = (50)&lt;br/&gt;/ ontrialbegin = [&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; {if (values.N == 1) values.currenttarget = values.minus1};&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; {if (values.N == 2) values.currenttarget = values.minus2};&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; {if (values.N == 3) values.currenttarget = values.minus3};&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; ]&lt;br/&gt;/ ontrialbegin = [values.trial_Hit = 0; values.trial_Miss = 0; values.trial_CR = 0; values.trial_FA = 0]&lt;br/&gt;/ ontrialbegin = [values.trialcount_hard1 += 1; values.count_target_hard1 += 1]&lt;br/&gt;/ ontrialend = [if (trial.target_hard1.response != 0) {values.sumrt_target_hard1 += trial.target_hard1.latency}]&lt;br/&gt;/ ontrialend = [&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.minus3 = values.minus2;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.minus2 = values.minus1;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.minus1 = picture.targetshape.currentitemnumber;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.Hits = values.Hits + trial.target.correct;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.trial_Hit =&amp;nbsp; trial.target.correct;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.Misses = values.Misses + trial.target.error;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.trial_Miss =&amp;nbsp; trial.target.error;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.TotalHits = values.TotalHits + trial.target.correct; &lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.TotalMisses = values.TotalMisses + trial.target.error; &lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; ]&lt;br/&gt;/ beginresponsetime = 300&lt;br/&gt;/ responseinterrupt = immediate&lt;br/&gt;/ timeout = 2500&lt;br/&gt;&amp;lt;/trial&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;trial target_hard2&amp;gt;&lt;br/&gt;/ stimulustimes = [0=fixcross; 300= targetshape, targetalert; 2500 = eraser]&lt;br/&gt;/ validresponse = (44, 50)&lt;br/&gt;/ correctresponse = (50)&lt;br/&gt;/ ontrialbegin = [&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; {if (values.N == 1) values.currenttarget = values.minus1};&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; {if (values.N == 2) values.currenttarget = values.minus2};&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; {if (values.N == 3) values.currenttarget = values.minus3};&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; ]&lt;br/&gt;/ ontrialbegin = [values.trial_Hit = 0; values.trial_Miss = 0; values.trial_CR = 0; values.trial_FA = 0]&lt;br/&gt;/ ontrialbegin = [values.trialcount_hard2 += 1; values.count_target_hard2 += 1]&lt;br/&gt;/ ontrialend = [if (trial.target_hard2.response != 0) {values.sumrt_target_hard2 += trial.target_hard2.latency}]&lt;br/&gt;/ ontrialend = [&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.minus3 = values.minus2;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.minus2 = values.minus1;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.minus1 = picture.targetshape.currentitemnumber;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.Hits = values.Hits + trial.target.correct;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.trial_Hit =&amp;nbsp; trial.target.correct;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.Misses= values.Misses + trial.target.error;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.trial_Miss =&amp;nbsp; trial.target.error;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.TotalHits= values.TotalHits + trial.target.correct; &lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; values.TotalMisses = values.TotalMisses + trial.target.error; &lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; &amp;nbsp;&amp;nbsp;&amp;nbsp; ]&lt;br/&gt;/ beginresponsetime = 300&lt;br/&gt;/ responseinterrupt = immediate&lt;br/&gt;/ timeout = 2500&lt;br/&gt;&amp;lt;/trial&amp;gt;&lt;br/&gt;&lt;br/&gt;&lt;br/&gt;</description><pubDate>Mon, 23 Nov 2015 08:01:27 GMT</pubDate><dc:creator>Ulrike Nestler</dc:creator></item><item><title>RE: "Inquisit no longer works" -</title><link>https://forums.millisecond.com/Topic17788.aspx</link><description>Hello Dave,&lt;br/&gt;&lt;br/&gt;yes, thanks so much! That was the error! A writing mistake due to lack of concentration I guess... And yes, of course, next time I will use the attachment facilities. Sorry, didn't see it.&lt;br/&gt;&lt;br/&gt;Have a great day!&lt;br/&gt;Kind regards, &lt;br/&gt;Ulrike&lt;br/&gt;</description><pubDate>Mon, 23 Nov 2015 08:01:27 GMT</pubDate><dc:creator>Ulrike Nestler</dc:creator></item><item><title>RE: "Inquisit no longer works" -</title><link>https://forums.millisecond.com/Topic17768.aspx</link><description>The problem is a fatal error in one of your &amp;lt;expressions&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;expressions&amp;gt;&lt;br/&gt;...&lt;br/&gt;/&lt;strong&gt;errors_hard&lt;/strong&gt; = (&lt;strong&gt;expressions.errors_hard&lt;/strong&gt; + expressions.oe_hard)&lt;br/&gt;...&lt;br/&gt;&amp;lt;/expressions&amp;gt;&lt;br/&gt;&lt;br/&gt;Note that the expressions refers to *itself*, i.e., it is recursive. This cannot be sensibly evaluated (i.e., there can be no meaningful end result), leads to an infinite loop (the expression runs itself over and over again) and ultimately a crash.&lt;br/&gt;&lt;br/&gt;A personal request: If possible, please do not paste entire scripts into the message body. Instead use the forum's file attachment facilities to attach the actual script file. Thanks.&lt;br/&gt;</description><pubDate>Fri, 20 Nov 2015 08:01:57 GMT</pubDate><dc:creator>Dave</dc:creator></item></channel></rss>