﻿<?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 6  » Using lists to randomize trial types and trial parameters</title><generator>InstantForum 2017-1 Final</generator><description>Millisecond Forums</description><link>https://forums.millisecond.com/</link><webMaster>Millisecond Forums</webMaster><lastBuildDate>Tue, 26 May 2026 03:21:09 GMT</lastBuildDate><ttl>20</ttl><item><title>Using lists to randomize trial types and trial parameters</title><link>https://forums.millisecond.com/Topic41301.aspx</link><description>Hello,&lt;br/&gt;&lt;br/&gt;I'm trying to use lists to randomize trial type and--within each trial type--its parameters, inspired by the post below (made to the Inquisit 5 forum -- I'm assuming the sample-code works in Inquisit 6 but of course could be wrong!).&lt;br/&gt;&lt;a href="https://forums.millisecond.com/Topic40813.aspx?Keywords=randomize%20list"&gt;https://forums.millisecond.com/Topic40813.aspx?Keywords=randomize%20list&lt;/a&gt;&lt;br/&gt;&lt;br/&gt;Because there are two trial types: YOU and OTHER, and there will be 12 trials of each type, within onBlockBegin I sampled from another list with items "YOU" and "OTHER" which has &amp;nbsp;poolsize=24, selectionmode=random, selectionrate=always (see the code below).&lt;br/&gt;&lt;br/&gt;Additionally, within each trial type, trials can be assigned one out of 12 different parameters. To do that, within onBlockBegin, I sampled from two other lists containing the parameters (poolsize=12, replace=false, selectionmode=random, selectionrate=always).&lt;br/&gt;&lt;br/&gt;However, when I try to run the code I'm getting the following message:&lt;br/&gt;&lt;br/&gt;Object 'list.you_eff_rewlist.appenditem(list.eff_rew_you.nextvalue)list.you_eff_rewlist' was not found.&lt;br/&gt;&lt;br/&gt;I suspect it has something to do with the way I sample values from the list (e.g., 'list.YOU_eff_rewList.appendItem(list.eff_rew_YOU.nextValue')?&lt;br/&gt;&lt;br/&gt;Any help will be greatly appreciated. On a related topic, I wonder if there are better --less brittle--ways randomize trial types (and its parameters) by sampling without replacement from a list, other than appending N values sampled from a random list like I'm doing below?&lt;br/&gt;&lt;br/&gt;Thanks a lot in advance,&lt;br/&gt;EN&lt;br/&gt;&lt;br/&gt;--&lt;br/&gt;&lt;br/&gt;// Global variables -- these will all be eventually defined in emot_induction.iqx&lt;br/&gt;&lt;br/&gt;&amp;lt;values&amp;gt;&lt;br/&gt;/ boxesChecked1 = 0 //Number of boxes clicked in the 1st attempt&lt;br/&gt;/ boxesChecked2 = 0 //Number of boxes clicked in the 2nd attempt&lt;br/&gt;/ sup_boxesChecked = 0 //The larger of the two numbers&lt;br/&gt;/ minimumThreshold = 13 //Arbitrary minimum number of boxes &amp;lt;&amp;lt;-- ADJUSTABLE need to check what is usually achieved&lt;br/&gt;&lt;br/&gt;//Thresholds specified by the procedure defined in calibration_time_boxcount.iqx&lt;br/&gt;/ easyThresh50 = 8 //The number of boxes at the 50% level&lt;br/&gt;/ mediumThresh65 = 10 //The number of boxes at the 65% level&lt;br/&gt;/ mediumThresh80 = 13 //The number of boxes at the 80% level&lt;br/&gt;/ hardThresh95 = 15 //The number of boxes at the 95% level&lt;br/&gt;//Color of trial type -- in fact, red (YOU) or blue (OTHER) &lt;br/&gt;/ c_colorname = black&lt;br/&gt;//Stores the selected combination of effort and reward -- e.g., e1_r1 means the lowest effort and the lowest reward&lt;br/&gt;/ eff_rew = "e0_r0" // for filling purposes, this condition DOES NOT EXIST&lt;br/&gt;&lt;br/&gt;// Position of the two choices&lt;br/&gt;/ effortX = 0%&lt;br/&gt;/ restX = 0%&lt;br/&gt;&lt;br/&gt;// Trial type&lt;br/&gt;/ trialType = null&lt;br/&gt;&lt;br/&gt;// Strings to be displayed at every trial&lt;br/&gt;/ centerLabel = null // will be either YOU or OTHER&lt;br/&gt;/ effortCredits = 0&lt;br/&gt;/ effortBoxes = 0&lt;br/&gt;&amp;lt;/values&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;parameters&amp;gt; // &lt;br/&gt;//These are the rewards (fixed for all participants)&lt;br/&gt;/ low_pay = 2 //Low reward&lt;br/&gt;/ medium_pay = 6 //Medium reward&lt;br/&gt;/ high_pay = 10 //High reward&lt;br/&gt;&lt;br/&gt;/ num_trials = 24 // total number of trials&lt;br/&gt;&amp;lt;/parameters&amp;gt;&lt;br/&gt;&lt;br/&gt;// These checkboxes will also be defined in emot_induction.iqx -- must comment them when transferring!&lt;br/&gt;&amp;lt;checkboxes row1&amp;gt;&lt;br/&gt;/ caption=" "&lt;br/&gt;/ options=("1", "2", "3", "4", "5", "6", "7", "8", "9", "10")&lt;br/&gt;/ optionvalues = ("1", "2", "3", "4","5", "6", "7", "8", "9", "10")&lt;br/&gt;/ required = true&lt;br/&gt;/ orientation = horizontalequal&lt;br/&gt;&amp;lt;/checkboxes&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;checkboxes row2&amp;gt;&lt;br/&gt;/ caption=" "&lt;br/&gt;/ options=("11", "12", "13", "14", "15", "16", "17", "18", "19", "20")&lt;br/&gt;/ optionvalues = ("11", "12", "13", "14","15", "16", "17", "18", "19", "20")&lt;br/&gt;/ required = true&lt;br/&gt;/ orientation = horizontalequal&lt;br/&gt;&amp;lt;/checkboxes&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;checkboxes row3&amp;gt;&lt;br/&gt;/ caption=" "&lt;br/&gt;/ options=("21", "22", "23", "24", "25", "26", "27", "28", "29", "30")&lt;br/&gt;/ optionvalues = ("21", "22", "23", "24","25", "26", "27", "28", "29", "30")&lt;br/&gt;/ required = true&lt;br/&gt;/ orientation = horizontalequal&lt;br/&gt;&amp;lt;/checkboxes&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;checkboxes row4&amp;gt;&lt;br/&gt;/ caption=" "&lt;br/&gt;/ options=("31", "32", "33", "34", "35", "36", "37", "38", "39", "40")&lt;br/&gt;/ optionvalues = ("31", "32", "33", "34","35", "36", "37", "38", "39", "40")&lt;br/&gt;/ required = true&lt;br/&gt;/ orientation = horizontalequal&lt;br/&gt;&amp;lt;/checkboxes&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;checkboxes row5&amp;gt;&lt;br/&gt;/ caption=" "&lt;br/&gt;/ options=("41", "42", "43", "44", "45", "46", "47", "48", "49", "50")&lt;br/&gt;/ optionvalues = ("41", "42", "43", "44","45", "46", "47", "48", "49", "50")&lt;br/&gt;/ required = true&lt;br/&gt;/ orientation = horizontalequal&lt;br/&gt;&amp;lt;/checkboxes&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;surveypage box_clicking&amp;gt;&lt;br/&gt;/caption = "&amp;lt;font color='green'&amp;gt;You must click at least &amp;lt;b&amp;gt;&amp;lt;u&amp;gt;&amp;lt;%values.effortBoxes%&amp;gt;&amp;lt;/u&amp;gt;&amp;lt;/b&amp;gt; boxes"&lt;br/&gt;/questions = [1-5=sequence(row1, row2, row3, row4, row5)]&lt;br/&gt;/ itemfontstyle = ("Arial", 2.00%, false, false, false, false, 5, 1)&lt;br/&gt;/ itemspacing = 0.0%&lt;br/&gt;/ showpagenumbers = false&lt;br/&gt;/ showbackbutton = false&lt;br/&gt;/ showquestionnumbers = false&lt;br/&gt;/ responsefontstyle = ("Arial", 2.0%, false, false, false, false, 5, 1)&lt;br/&gt;/ timeout = 10000 // 10 seconds&lt;br/&gt;/ showNextButton = false&lt;br/&gt;&amp;lt;/surveypage&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;block SocialEffort_Block&amp;gt;&lt;br/&gt;/ onBlockBegin = [&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;// Randomized trial type order -- 24 trials&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.trialTypeList.appendItem(list.trialType.nextValue); //1&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.trialTypeList.appendItem(list.trialType.nextValue); //2&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.trialTypeList.appendItem(list.trialType.nextValue); //3&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.trialTypeList.appendItem(list.trialType.nextValue); //4&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.trialTypeList.appendItem(list.trialType.nextValue); //5&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.trialTypeList.appendItem(list.trialType.nextValue); //6&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.trialTypeList.appendItem(list.trialType.nextValue); //7&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.trialTypeList.appendItem(list.trialType.nextValue); //8&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.trialTypeList.appendItem(list.trialType.nextValue); //9&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.trialTypeList.appendItem(list.trialType.nextValue); //10&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.trialTypeList.appendItem(list.trialType.nextValue); //11&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.trialTypeList.appendItem(list.trialType.nextValue); //12&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.trialTypeList.appendItem(list.trialType.nextValue); //13&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.trialTypeList.appendItem(list.trialType.nextValue); //14&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.trialTypeList.appendItem(list.trialType.nextValue); //15&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.trialTypeList.appendItem(list.trialType.nextValue); //16&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.trialTypeList.appendItem(list.trialType.nextValue); //17&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.trialTypeList.appendItem(list.trialType.nextValue); //18&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.trialTypeList.appendItem(list.trialType.nextValue); //19&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.trialTypeList.appendItem(list.trialType.nextValue); //20&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.trialTypeList.appendItem(list.trialType.nextValue); //21&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.trialTypeList.appendItem(list.trialType.nextValue); //22&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.trialTypeList.appendItem(list.trialType.nextValue); //23&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.trialTypeList.appendItem(list.trialType.nextValue); //24&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;// Randomized YOU effort-reward 12 pairs&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.YOU_eff_rewList.appendItem(list.eff_rew_YOU.nextValue) //1&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.YOU_eff_rewList.appendItem(list.eff_rew_YOU.nextValue) //2&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.YOU_eff_rewList.appendItem(list.eff_rew_YOU.nextValue) //3&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.YOU_eff_rewList.appendItem(list.eff_rew_YOU.nextValue) //4&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.YOU_eff_rewList.appendItem(list.eff_rew_YOU.nextValue) //5&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.YOU_eff_rewList.appendItem(list.eff_rew_YOU.nextValue) //6&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.YOU_eff_rewList.appendItem(list.eff_rew_YOU.nextValue) //7&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.YOU_eff_rewList.appendItem(list.eff_rew_YOU.nextValue) //8&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.YOU_eff_rewList.appendItem(list.eff_rew_YOU.nextValue) //9&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.YOU_eff_rewList.appendItem(list.eff_rew_YOU.nextValue) //10&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.YOU_eff_rewList.appendItem(list.eff_rew_YOU.nextValue) //11&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.YOU_eff_rewList.appendItem(list.eff_rew_YOU.nextValue) //12&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;// Randomized OTHER effort-reward 12 pairs&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.OTHER_eff_rewList.appendItem(list.eff_rew_OTHER.nextValue) //1&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.OTHER_eff_rewList.appendItem(list.eff_rew_OTHER.nextValue) //2&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.OTHER_eff_rewList.appendItem(list.eff_rew_OTHER.nextValue) //3&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.OTHER_eff_rewList.appendItem(list.eff_rew_OTHER.nextValue) //4&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.OTHER_eff_rewList.appendItem(list.eff_rew_OTHER.nextValue) //5&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.OTHER_eff_rewList.appendItem(list.eff_rew_OTHER.nextValue) //6&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.OTHER_eff_rewList.appendItem(list.eff_rew_OTHER.nextValue) //7&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.OTHER_eff_rewList.appendItem(list.eff_rew_OTHER.nextValue) //8&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.OTHER_eff_rewList.appendItem(list.eff_rew_OTHER.nextValue) //9&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.OTHER_eff_rewList.appendItem(list.eff_rew_OTHER.nextValue) //10&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.OTHER_eff_rewList.appendItem(list.eff_rew_OTHER.nextValue) //11&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.OTHER_eff_rewList.appendItem(list.eff_rew_OTHER.nextValue) //12&lt;br/&gt;]&lt;br/&gt;// trials = [1-parameters.num_trials = BoxClick_Trial]&lt;br/&gt;/ trials = [1-24 = BoxClick_Trial] // ATTENTION: defining the number of trials manually....&lt;br/&gt;&lt;br/&gt;&amp;lt;/block&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;trial BoxClick_Trial&amp;gt;&lt;br/&gt;/ onTrialBegin = [&lt;br/&gt;&amp;nbsp; // pick random postion for effort_option&lt;br/&gt;&amp;nbsp; values.effortX = list.x.nextValue;&lt;br/&gt;&amp;nbsp; // pick position for rest_option&lt;br/&gt;&amp;nbsp; values.restX = list.x.nextValue;&lt;br/&gt;&amp;nbsp; // set trial type&lt;br/&gt;&amp;nbsp; //values.trialType = list.trialType.nextValue;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;values.trialType = list.trialTypeList.nextValue;&lt;br/&gt;&amp;nbsp; // set center label according to trial type&lt;br/&gt;&amp;nbsp; if (values.trialType == "YOU") {&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;values.c_colorname = red;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;//values.eff_rew = list.eff_rew_YOU.nextValue;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;values.eff_rew = list.YOU_eff_rewList.nextValue;&lt;br/&gt;&amp;nbsp; } else { // OTHER&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;values.c_colorname = blue;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;//values.eff_rew = list.eff_rew_OTHER.nextValue;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;values.eff_rew = list.OTHER_eff_rewList.nextValue;&lt;br/&gt;&amp;nbsp; }&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;// set winnings and effort according to the value in eff_rew&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;if (values.eff_rew == "e1_r1") {&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;values.effortBoxes = values.easyThresh50;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;values.effortCredits = parameters.low_pay;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;} else if (values.eff_rew == "e1_r2") {&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;values.effortBoxes = values.easyThresh50;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;values.effortCredits = parameters.medium_pay;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;} else if (values.eff_rew == "e1_r3") {&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;values.effortBoxes = values.easyThresh50;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;values.effortCredits = parameters.high_pay;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;} else if (values.eff_rew == "e2_r1") {&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;values.effortBoxes = values.mediumThresh65;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;values.effortCredits = parameters.low_pay;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;} else if (values.eff_rew == "e2_r2") {&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;values.effortBoxes = values.mediumThresh65;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;values.effortCredits = parameters.medium_pay;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;} else if (values.eff_rew = "e2_r3") {&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;values.effortBoxes = values.mediumThresh65;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;values.effortCredits = parameters.high_pay;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;} else if (values.eff_rew = "e3_r1") {&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;values.effortBoxes = values.mediumThresh80;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;values.effortCredits = parameters.low_pay;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;} else if (values.eff_rew = "e3_r2") {&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;values.effortBoxes = values.mediumThresh80;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;values.effortCredits = parameters.medium_pay;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;} else if (values.eff_rew = "e3_r3") {&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;values.effortBoxes = values.mediumThresh80;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;values.effortCredits = parameters.high_pay;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;} else if (values.eff_rew = "e4_r1") {&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;values.effortBoxes = values.hardThresh95;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;values.effortCredits = parameters.low_pay;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;} else if (values.eff_rew = "e4_r2") {&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;values.effortBoxes = values.hardThresh95;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;values.effortCredits = parameters.medium_pay;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;} else if (values.eff_rew = "e4_r3") {&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;values.effortBoxes = values.hardThresh95;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;values.effortCredits = parameters.high_pay;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;}&lt;br/&gt;]&lt;br/&gt;/ stimulusframes = [1=effort_option, rest_option, center_label]&lt;br/&gt;/ inputdevice = mouse&lt;br/&gt;/ validresponse = (effort_option, rest_option)&lt;br/&gt;&lt;br/&gt;/ branch = [&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;if (trial.BoxClick_Trial.response=="effort_option") { // if subj chose the effortful option&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;// run the clicking box survey&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;return surveyPage.box_clicking&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;}&lt;br/&gt;]&lt;br/&gt;&amp;lt;/trial&amp;gt;&lt;br/&gt;&lt;br/&gt;// Stores the order of the 24 trial types -- YOU or OTHER&lt;br/&gt;&amp;lt;list trialTypeList&amp;gt;&lt;br/&gt;/ selectionMode = sequence&lt;br/&gt;&amp;lt;/list&amp;gt;&lt;br/&gt;&lt;br/&gt;// Stores the order in which the effort/reward pairs will be presented in the 12 YOU trials&lt;br/&gt;&amp;lt;list YOU_eff_rewList&amp;gt;&lt;br/&gt;/ selectionMode = sequence&lt;br/&gt;&amp;lt;/list&amp;gt;&lt;br/&gt;&lt;br/&gt;// Stores the order in which the effort/reward pairs will be presented in the 12 OTHER trials&lt;br/&gt;&amp;lt;list OTHER_eff_rewList&amp;gt;&lt;br/&gt;/ selectionMode = sequence&lt;br/&gt;&amp;lt;/list&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;text effort_option&amp;gt;&lt;br/&gt;/ items = ("&amp;lt;%values.effortCredits%&amp;gt; credits for &amp;lt;%values.effortBoxes%&amp;gt; boxes")&lt;br/&gt;/ hPosition = values.effortX&lt;br/&gt;/ vPosition = 40%&lt;br/&gt;/ fontStyle = ("arial", 12pt)&lt;br/&gt;/ size = (15%, 10%)&lt;br/&gt;/ vJustify = center&lt;br/&gt;/ txBGColor = lightGray&lt;br/&gt;&amp;lt;/text&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;text rest_option&amp;gt;&lt;br/&gt;/ items = ("1 credit for Rest")&lt;br/&gt;/ hPosition = values.restX&lt;br/&gt;/ vPosition = 40%&lt;br/&gt;/ fontStyle = ("arial", 12pt)&lt;br/&gt;/ size = (15%, 10%)&lt;br/&gt;/ vJustify = center&lt;br/&gt;/ txBGColor = lightGray&lt;br/&gt;&amp;lt;/text&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;text center_label&amp;gt;&lt;br/&gt;// items = ("&amp;lt;%values.trialType%&amp;gt;") // this is the label that should be used in the exp!&lt;br/&gt;/ items = ("&amp;lt;%values.eff_rew%&amp;gt;") // testing the list selection!&lt;br/&gt;/ position = (50%, 30%)&lt;br/&gt;/ txColor = values.c_colorname&lt;br/&gt;/ fontStyle = ("arial", 16pt, true)&lt;br/&gt;&amp;lt;/text&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;list trialType&amp;gt;&lt;br/&gt;/ items = ("YOU", "OTHER")&lt;br/&gt;/ poolsize = parameters.num_trials&lt;br/&gt;/ selectionmode = random&lt;br/&gt;/ selectionrate = always&lt;br/&gt;&amp;lt;/list&amp;gt;&lt;br/&gt;&lt;br/&gt;// This list contains all effort (4) x reward (3) = 12 combinations for the YOU trials&lt;br/&gt;// It will be used to to sample --once-- all the 12 combinations&lt;br/&gt;&amp;lt;list eff_rew_YOU&amp;gt;&lt;br/&gt;/ items = ("e1_r1", "e1_r2", "e1_r3", "e2_r1", "e2_r2", "e2_r3", "e3_r1", "e3_r2", "e3_r3", "e4_r1", "e4_r2", "e4_r3")&lt;br/&gt;/ poolsize = parameters.num_trials/2&lt;br/&gt;/ replace = false&lt;br/&gt;/ selectionmode = random&lt;br/&gt;/ selectionrate = always&lt;br/&gt;&amp;lt;/list&amp;gt;&lt;br/&gt;&lt;br/&gt;// Likewise for the OTHER trials&lt;br/&gt;&amp;lt;list eff_rew_OTHER&amp;gt;&lt;br/&gt;/ items = ("e1_r1", "e1_r2", "e1_r3", "e2_r1", "e2_r2", "e2_r3", "e3_r1", "e3_r2", "e3_r3", "e4_r1", "e4_r2", "e4_r3")&lt;br/&gt;/ poolsize = parameters.num_trials/2&lt;br/&gt;/ replace = false&lt;br/&gt;/ selectionmode = random&lt;br/&gt;/ selectionrate = always&lt;br/&gt;&amp;lt;/list&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;list x&amp;gt;&lt;br/&gt;/ items = (35%, 65%)&lt;br/&gt;/ selectionrate = always&lt;br/&gt;&amp;lt;/list&amp;gt;&lt;br/&gt;&lt;br/&gt;&lt;br/&gt;</description><pubDate>Sat, 05 Jul 2025 07:15:24 GMT</pubDate><dc:creator>EN</dc:creator></item><item><title>RE: Using lists to randomize trial types and trial parameters</title><link>https://forums.millisecond.com/Topic41305.aspx</link><description>&lt;blockquote data-id="41304" class="if-quote-wrapper" unselectable="on" data-guid="1751699662865" contenteditable="false" id="if_insertedNode_1751699661811"&gt;&lt;a class="quote-para" unselectable="on" style="display: none;" href="#" data-id="41304" title="Move Cursor Below" contenteditable="false"&gt;&lt;span unselectable="on"&gt;+&lt;/span&gt;&lt;/a&gt;&lt;a class="quote-delete" unselectable="on" style="display: none;" href="#" data-id="41304" title="Delete Quote" contenteditable="false"&gt;&lt;span unselectable="on"&gt;x&lt;/span&gt;&lt;/a&gt;&lt;span unselectable="on" class="quote-markup"&gt;[quote]&lt;/span&gt;&lt;div unselectable="on" class="if-quote-header" contenteditable="false"&gt;&lt;div unselectable="on" class="if-quote-toggle-wrapper"&gt;&lt;a class="if-quote-toggle quote-link" href="#" data-id="41304" title=" "&gt;&amp;nbsp;&lt;/a&gt;&lt;/div&gt;&lt;span unselectable="on" class="quote-markup"&gt;[b]&lt;/span&gt;Dave - 7/5/2025&lt;span unselectable="on" class="quote-markup"&gt;[/b]&lt;/span&gt;&lt;/div&gt;&lt;div class="if-quote-message if-quote-message-41304"&gt;&lt;div class="if-quote-message-margin" contenteditable="true"&gt;&lt;blockquote data-id="41303" class="if-quote-wrapper" unselectable="on" data-guid="1751699662865" contenteditable="false" id="if_insertedNode_1751679063062"&gt;&lt;a class="quote-para" unselectable="on" style="display: none;" href="#" data-id="41303" title="Move Cursor Below" contenteditable="false"&gt;&lt;span unselectable="on"&gt;+&lt;/span&gt;&lt;/a&gt;&lt;a class="quote-delete" unselectable="on" style="display: none;" href="#" data-id="41303" title="Delete Quote" contenteditable="false"&gt;&lt;span unselectable="on"&gt;x&lt;/span&gt;&lt;/a&gt;&lt;span unselectable="on" class="quote-markup"&gt;[quote]&lt;/span&gt;&lt;div unselectable="on" class="if-quote-header" contenteditable="false"&gt;&lt;div unselectable="on" class="if-quote-toggle-wrapper"&gt;&lt;a class="if-quote-toggle quote-link" href="#" data-id="41303" title=" "&gt;&amp;nbsp;&lt;/a&gt;&lt;/div&gt;&lt;span unselectable="on" class="quote-markup"&gt;[b]&lt;/span&gt;EN - 7/5/2025&lt;span unselectable="on" class="quote-markup"&gt;[/b]&lt;/span&gt;&lt;/div&gt;&lt;div class="if-quote-message if-quote-message-41303"&gt;&lt;div class="if-quote-message-margin" contenteditable="true"&gt;Thanks, Dave!&lt;br/&gt;I should have noticed that.&lt;br/&gt;&lt;br/&gt;However, even after including the separators, that code still repeatedly shows the same parameters.&lt;br/&gt;What I want to achieve is the following:&lt;br/&gt;&lt;br/&gt;--12 YOU trials and12 OTHER trials randomized across 24 trials&lt;br/&gt;--For the 12 YOU trials, I want the parameters e1_r1, e1_r2, e1_r3, e2_r1, e2_r2, e2_r3, e3_r1, e3_r2, e3_r3, e4_r1, e4_r2, e4_r3 to appear once and only once.&lt;br/&gt;--Similarly for the 12 OTHER trials.&lt;br/&gt;&lt;br/&gt;However, when I run that code I get the same parameters multiple times (for some reason, e2_r3 shows up very often for both YOU and OTHER), no idea why.&lt;br/&gt;Any help would be much appreciated.&lt;br/&gt;Thanks a lot in advance!&lt;br/&gt;EN&lt;a class="if-quote-goto quote-link" href="#" data-id="41303"&gt;&lt;span class="goto"&gt;&lt;/span&gt;&lt;/a&gt;&lt;/div&gt;&lt;/div&gt;&lt;span unselectable="on" class="quote-markup"&gt;[/quote]&lt;/span&gt;&lt;/blockquote&gt;&lt;br/&gt;You are using the assingment operator ("=") in places where you ought to be using logical operator "==" instead.&lt;br/&gt;&lt;br/&gt;[code]/ onTrialBegin = [&lt;br/&gt;  // pick random postion for effort_option&lt;br/&gt;  values.effortX = list.x.nextValue;&lt;br/&gt;  // pick position for rest_option&lt;br/&gt;  values.restX = list.x.nextValue;&lt;br/&gt;  // set trial type&lt;br/&gt;  //values.trialType = list.trialType.nextValue;&lt;br/&gt;    values.trialType = list.trialTypeList.nextValue;&lt;br/&gt;  // set center label according to trial type&lt;br/&gt;  if (values.trialType == "YOU") {&lt;br/&gt;        values.c_colorname = red;&lt;br/&gt;        //values.eff_rew = list.eff_rew_YOU.nextValue;&lt;br/&gt;        values.eff_rew = list.YOU_eff_rewList.nextValue;&lt;br/&gt;  } else { // OTHER&lt;br/&gt;        values.c_colorname = blue;&lt;br/&gt;        //values.eff_rew = list.eff_rew_OTHER.nextValue;&lt;br/&gt;        values.eff_rew = list.OTHER_eff_rewList.nextValue;&lt;br/&gt;  }    &lt;br/&gt;    // set winnings and effort according to the value in eff_rew&lt;br/&gt;    if (values.eff_rew == "e1_r1") {&lt;br/&gt;        values.effortBoxes = values.easyThresh50;&lt;br/&gt;        values.effortCredits = parameters.low_pay;&lt;br/&gt;    } else if (values.eff_rew == "e1_r2") {&lt;br/&gt;        values.effortBoxes = values.easyThresh50;&lt;br/&gt;        values.effortCredits = parameters.medium_pay;&lt;br/&gt;    } else if (values.eff_rew == "e1_r3") {&lt;br/&gt;        values.effortBoxes = values.easyThresh50;&lt;br/&gt;        values.effortCredits = parameters.high_pay;    &lt;br/&gt;    } else if (values.eff_rew == "e2_r1") {&lt;br/&gt;        values.effortBoxes = values.mediumThresh65;&lt;br/&gt;        values.effortCredits = parameters.low_pay;    &lt;br/&gt;    } else if (values.eff_rew == "e2_r2") {&lt;br/&gt;        values.effortBoxes = values.mediumThresh65;&lt;br/&gt;        values.effortCredits = parameters.medium_pay;&lt;br/&gt;    } else if (values.eff_rew = "e2_r3") {// wrong operator&lt;br/&gt;        values.effortBoxes = values.mediumThresh65;&lt;br/&gt;        values.effortCredits = parameters.high_pay;&lt;br/&gt;    } else if (values.eff_rew = "e3_r1") {// wrong operator&lt;br/&gt;        values.effortBoxes = values.mediumThresh80;&lt;br/&gt;        values.effortCredits = parameters.low_pay;&lt;br/&gt;    } else if (values.eff_rew = "e3_r2") {// wrong operator&lt;br/&gt;        values.effortBoxes = values.mediumThresh80;&lt;br/&gt;        values.effortCredits = parameters.medium_pay;        &lt;br/&gt;    } else if (values.eff_rew = "e3_r3") {// wrong operator&lt;br/&gt;        values.effortBoxes = values.mediumThresh80;&lt;br/&gt;        values.effortCredits = parameters.high_pay;&lt;br/&gt;    } else if (values.eff_rew = "e4_r1") {// wrong operator&lt;br/&gt;        values.effortBoxes = values.hardThresh95;&lt;br/&gt;        values.effortCredits = parameters.low_pay;&lt;br/&gt;    } else if (values.eff_rew = "e4_r2") {// wrong operator&lt;br/&gt;        values.effortBoxes = values.hardThresh95;&lt;br/&gt;        values.effortCredits = parameters.medium_pay;&lt;br/&gt;    } else if (values.eff_rew = "e4_r3") {// wrong operator&lt;br/&gt;        values.effortBoxes = values.hardThresh95;&lt;br/&gt;        values.effortCredits = parameters.high_pay;&lt;br/&gt;    }&lt;br/&gt;][/code]&lt;br/&gt;&lt;br/&gt;&lt;a class="if-quote-goto quote-link" href="#" data-id="41304"&gt;&lt;span class="goto"&gt;&lt;/span&gt;&lt;/a&gt;&lt;/div&gt;&lt;/div&gt;&lt;span unselectable="on" class="quote-markup"&gt;[/quote]&lt;/span&gt;&lt;/blockquote&gt;&lt;br/&gt;Thanks, Dave!&lt;br/&gt;I should have noticed that TOO.&lt;br/&gt;Best wishes,&lt;br/&gt;EN&lt;br/&gt;</description><pubDate>Sat, 05 Jul 2025 07:15:24 GMT</pubDate><dc:creator>EN</dc:creator></item><item><title>RE: Using lists to randomize trial types and trial parameters</title><link>https://forums.millisecond.com/Topic41304.aspx</link><description>&lt;blockquote data-id="41303" class="if-quote-wrapper" unselectable="on" data-guid="1751679064716" contenteditable="false" id="if_insertedNode_1751679063062"&gt;&lt;a class="quote-para" unselectable="on" style="display: none;" href="#" data-id="41303" title="Move Cursor Below" contenteditable="false"&gt;&lt;span unselectable="on"&gt;+&lt;/span&gt;&lt;/a&gt;&lt;a class="quote-delete" unselectable="on" style="display: none;" href="#" data-id="41303" title="Delete Quote" contenteditable="false"&gt;&lt;span unselectable="on"&gt;x&lt;/span&gt;&lt;/a&gt;&lt;span unselectable="on" class="quote-markup"&gt;[quote]&lt;/span&gt;&lt;div unselectable="on" class="if-quote-header" contenteditable="false"&gt;&lt;div unselectable="on" class="if-quote-toggle-wrapper"&gt;&lt;a class="if-quote-toggle quote-link" href="#" data-id="41303" title=" "&gt;&amp;nbsp;&lt;/a&gt;&lt;/div&gt;&lt;span unselectable="on" class="quote-markup"&gt;[b]&lt;/span&gt;EN - 7/5/2025&lt;span unselectable="on" class="quote-markup"&gt;[/b]&lt;/span&gt;&lt;/div&gt;&lt;div class="if-quote-message if-quote-message-41303"&gt;&lt;div class="if-quote-message-margin" contenteditable="true"&gt;Thanks, Dave!&lt;br/&gt;I should have noticed that.&lt;br/&gt;&lt;br/&gt;However, even after including the separators, that code still repeatedly shows the same parameters.&lt;br/&gt;What I want to achieve is the following:&lt;br/&gt;&lt;br/&gt;--12 YOU trials and12 OTHER trials randomized across 24 trials&lt;br/&gt;--For the 12 YOU trials, I want the parameters e1_r1, e1_r2, e1_r3, e2_r1, e2_r2, e2_r3, e3_r1, e3_r2, e3_r3, e4_r1, e4_r2, e4_r3 to appear once and only once.&lt;br/&gt;--Similarly for the 12 OTHER trials.&lt;br/&gt;&lt;br/&gt;However, when I run that code I get the same parameters multiple times (for some reason, e2_r3 shows up very often for both YOU and OTHER), no idea why.&lt;br/&gt;Any help would be much appreciated.&lt;br/&gt;Thanks a lot in advance!&lt;br/&gt;EN&lt;a class="if-quote-goto quote-link" href="#" data-id="41303"&gt;&lt;span class="goto"&gt;&lt;/span&gt;&lt;/a&gt;&lt;/div&gt;&lt;/div&gt;&lt;span unselectable="on" class="quote-markup"&gt;[/quote]&lt;/span&gt;&lt;/blockquote&gt;&lt;br/&gt;You are using the assingment operator ("=") in places where you ought to be using logical operator "==" instead.&lt;br/&gt;&lt;br/&gt;[code]/ onTrialBegin = [&lt;br/&gt;  // pick random postion for effort_option&lt;br/&gt;  values.effortX = list.x.nextValue;&lt;br/&gt;  // pick position for rest_option&lt;br/&gt;  values.restX = list.x.nextValue;&lt;br/&gt;  // set trial type&lt;br/&gt;  //values.trialType = list.trialType.nextValue;&lt;br/&gt;    values.trialType = list.trialTypeList.nextValue;&lt;br/&gt;  // set center label according to trial type&lt;br/&gt;  if (values.trialType == "YOU") {&lt;br/&gt;        values.c_colorname = red;&lt;br/&gt;        //values.eff_rew = list.eff_rew_YOU.nextValue;&lt;br/&gt;        values.eff_rew = list.YOU_eff_rewList.nextValue;&lt;br/&gt;  } else { // OTHER&lt;br/&gt;        values.c_colorname = blue;&lt;br/&gt;        //values.eff_rew = list.eff_rew_OTHER.nextValue;&lt;br/&gt;        values.eff_rew = list.OTHER_eff_rewList.nextValue;&lt;br/&gt;  }    &lt;br/&gt;    // set winnings and effort according to the value in eff_rew&lt;br/&gt;    if (values.eff_rew == "e1_r1") {&lt;br/&gt;        values.effortBoxes = values.easyThresh50;&lt;br/&gt;        values.effortCredits = parameters.low_pay;&lt;br/&gt;    } else if (values.eff_rew == "e1_r2") {&lt;br/&gt;        values.effortBoxes = values.easyThresh50;&lt;br/&gt;        values.effortCredits = parameters.medium_pay;&lt;br/&gt;    } else if (values.eff_rew == "e1_r3") {&lt;br/&gt;        values.effortBoxes = values.easyThresh50;&lt;br/&gt;        values.effortCredits = parameters.high_pay;    &lt;br/&gt;    } else if (values.eff_rew == "e2_r1") {&lt;br/&gt;        values.effortBoxes = values.mediumThresh65;&lt;br/&gt;        values.effortCredits = parameters.low_pay;    &lt;br/&gt;    } else if (values.eff_rew == "e2_r2") {&lt;br/&gt;        values.effortBoxes = values.mediumThresh65;&lt;br/&gt;        values.effortCredits = parameters.medium_pay;&lt;br/&gt;    } else if (values.eff_rew = "e2_r3") {// wrong operator&lt;br/&gt;        values.effortBoxes = values.mediumThresh65;&lt;br/&gt;        values.effortCredits = parameters.high_pay;&lt;br/&gt;    } else if (values.eff_rew = "e3_r1") {// wrong operator&lt;br/&gt;        values.effortBoxes = values.mediumThresh80;&lt;br/&gt;        values.effortCredits = parameters.low_pay;&lt;br/&gt;    } else if (values.eff_rew = "e3_r2") {// wrong operator&lt;br/&gt;        values.effortBoxes = values.mediumThresh80;&lt;br/&gt;        values.effortCredits = parameters.medium_pay;        &lt;br/&gt;    } else if (values.eff_rew = "e3_r3") {// wrong operator&lt;br/&gt;        values.effortBoxes = values.mediumThresh80;&lt;br/&gt;        values.effortCredits = parameters.high_pay;&lt;br/&gt;    } else if (values.eff_rew = "e4_r1") {// wrong operator&lt;br/&gt;        values.effortBoxes = values.hardThresh95;&lt;br/&gt;        values.effortCredits = parameters.low_pay;&lt;br/&gt;    } else if (values.eff_rew = "e4_r2") {// wrong operator&lt;br/&gt;        values.effortBoxes = values.hardThresh95;&lt;br/&gt;        values.effortCredits = parameters.medium_pay;&lt;br/&gt;    } else if (values.eff_rew = "e4_r3") {// wrong operator&lt;br/&gt;        values.effortBoxes = values.hardThresh95;&lt;br/&gt;        values.effortCredits = parameters.high_pay;&lt;br/&gt;    }&lt;br/&gt;][/code]&lt;br/&gt;&lt;br/&gt;</description><pubDate>Sat, 05 Jul 2025 01:36:16 GMT</pubDate><dc:creator>Dave</dc:creator></item><item><title>RE: Using lists to randomize trial types and trial parameters</title><link>https://forums.millisecond.com/Topic41303.aspx</link><description>Thanks, Dave!&lt;br/&gt;I should have noticed that.&lt;br/&gt;&lt;br/&gt;However, even after including the separators, that code still repeatedly shows the same parameters.&lt;br/&gt;What I want to achieve is the following:&lt;br/&gt;&lt;br/&gt;--12 YOU trials and12 OTHER trials randomized across 24 trials&lt;br/&gt;--For the 12 YOU trials, I want the parameters e1_r1, e1_r2, e1_r3, e2_r1, e2_r2, e2_r3, e3_r1, e3_r2, e3_r3, e4_r1, e4_r2, e4_r3 to appear once and only once.&lt;br/&gt;--Similarly for the 12 OTHER trials.&lt;br/&gt;&lt;br/&gt;However, when I run that code I get the same parameters multiple times (for some reason, e2_r3 shows up very often for both YOU and OTHER), no idea why.&lt;br/&gt;Any help would be much appreciated.&lt;br/&gt;Thanks a lot in advance!&lt;br/&gt;EN</description><pubDate>Sat, 05 Jul 2025 00:45:36 GMT</pubDate><dc:creator>EN</dc:creator></item><item><title>RE: Using lists to randomize trial types and trial parameters</title><link>https://forums.millisecond.com/Topic41302.aspx</link><description>&lt;blockquote data-id="41301" class="if-quote-wrapper" unselectable="on" data-guid="1751627994914" contenteditable="false" id="if_insertedNode_1751627994179"&gt;&lt;a class="quote-para" unselectable="on" style="display: none;" href="#" data-id="41301" title="Move Cursor Below" contenteditable="false"&gt;&lt;span unselectable="on"&gt;+&lt;/span&gt;&lt;/a&gt;&lt;a class="quote-delete" unselectable="on" style="display: none;" href="#" data-id="41301" title="Delete Quote" contenteditable="false"&gt;&lt;span unselectable="on"&gt;x&lt;/span&gt;&lt;/a&gt;&lt;span unselectable="on" class="quote-markup"&gt;[quote]&lt;/span&gt;&lt;div unselectable="on" class="if-quote-header" contenteditable="false"&gt;&lt;div unselectable="on" class="if-quote-toggle-wrapper"&gt;&lt;a class="if-quote-toggle quote-link" href="#" data-id="41301" title=" "&gt;&amp;nbsp;&lt;/a&gt;&lt;/div&gt;&lt;span unselectable="on" class="quote-markup"&gt;[b]&lt;/span&gt;EN - 7/4/2025&lt;span unselectable="on" class="quote-markup"&gt;[/b]&lt;/span&gt;&lt;/div&gt;&lt;div class="if-quote-message if-quote-message-41301"&gt;&lt;div class="if-quote-message-margin" contenteditable="true"&gt;Hello,&lt;br/&gt;&lt;br/&gt;I'm trying to use lists to randomize trial type and--within each trial type--its parameters, inspired by the post below (made to the Inquisit 5 forum -- I'm assuming the sample-code works in Inquisit 6 but of course could be wrong!).&lt;br/&gt;&lt;a href="https://forums.millisecond.com/Topic40813.aspx?Keywords=randomize%20list"&gt;&lt;a href="https://forums.millisecond.com/Topic40813.aspx?Keywords=randomize%20list"&gt;https://forums.millisecond.com/Topic40813.aspx?Keywords=randomize%20list&lt;/a&gt;&lt;/a&gt;&lt;br/&gt;&lt;br/&gt;Because there are two trial types: YOU and OTHER, and there will be 12 trials of each type, within onBlockBegin I sampled from another list with items "YOU" and "OTHER" which has &amp;nbsp;poolsize=24, selectionmode=random, selectionrate=always (see the code below).&lt;br/&gt;&lt;br/&gt;Additionally, within each trial type, trials can be assigned one out of 12 different parameters. To do that, within onBlockBegin, I sampled from two other lists containing the parameters (poolsize=12, replace=false, selectionmode=random, selectionrate=always).&lt;br/&gt;&lt;br/&gt;However, when I try to run the code I'm getting the following message:&lt;br/&gt;&lt;br/&gt;Object 'list.you_eff_rewlist.appenditem(list.eff_rew_you.nextvalue)list.you_eff_rewlist' was not found.&lt;br/&gt;&lt;br/&gt;I suspect it has something to do with the way I sample values from the list (e.g., 'list.YOU_eff_rewList.appendItem(list.eff_rew_YOU.nextValue')?&lt;br/&gt;&lt;br/&gt;Any help will be greatly appreciated. On a related topic, I wonder if there are better --less brittle--ways randomize trial types (and its parameters) by sampling without replacement from a list, other than appending N values sampled from a random list like I'm doing below?&lt;br/&gt;&lt;br/&gt;Thanks a lot in advance,&lt;br/&gt;EN&lt;br/&gt;&lt;br/&gt;--&lt;br/&gt;&lt;br/&gt;// Global variables -- these will all be eventually defined in emot_induction.iqx&lt;br/&gt;&lt;br/&gt;&amp;lt;values&amp;gt;&lt;br/&gt;/ boxesChecked1 = 0 //Number of boxes clicked in the 1st attempt&lt;br/&gt;/ boxesChecked2 = 0 //Number of boxes clicked in the 2nd attempt&lt;br/&gt;/ sup_boxesChecked = 0 //The larger of the two numbers&lt;br/&gt;/ minimumThreshold = 13 //Arbitrary minimum number of boxes &amp;lt;&amp;lt;-- ADJUSTABLE need to check what is usually achieved&lt;br/&gt;&lt;br/&gt;//Thresholds specified by the procedure defined in calibration_time_boxcount.iqx&lt;br/&gt;/ easyThresh50 = 8 //The number of boxes at the 50% level&lt;br/&gt;/ mediumThresh65 = 10 //The number of boxes at the 65% level&lt;br/&gt;/ mediumThresh80 = 13 //The number of boxes at the 80% level&lt;br/&gt;/ hardThresh95 = 15 //The number of boxes at the 95% level&lt;br/&gt;//Color of trial type -- in fact, red (YOU) or blue (OTHER) &lt;br/&gt;/ c_colorname = black&lt;br/&gt;//Stores the selected combination of effort and reward -- e.g., e1_r1 means the lowest effort and the lowest reward&lt;br/&gt;/ eff_rew = "e0_r0" // for filling purposes, this condition DOES NOT EXIST&lt;br/&gt;&lt;br/&gt;// Position of the two choices&lt;br/&gt;/ effortX = 0%&lt;br/&gt;/ restX = 0%&lt;br/&gt;&lt;br/&gt;// Trial type&lt;br/&gt;/ trialType = null&lt;br/&gt;&lt;br/&gt;// Strings to be displayed at every trial&lt;br/&gt;/ centerLabel = null // will be either YOU or OTHER&lt;br/&gt;/ effortCredits = 0&lt;br/&gt;/ effortBoxes = 0&lt;br/&gt;&amp;lt;/values&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;parameters&amp;gt; // &lt;br/&gt;//These are the rewards (fixed for all participants)&lt;br/&gt;/ low_pay = 2 //Low reward&lt;br/&gt;/ medium_pay = 6 //Medium reward&lt;br/&gt;/ high_pay = 10 //High reward&lt;br/&gt;&lt;br/&gt;/ num_trials = 24 // total number of trials&lt;br/&gt;&amp;lt;/parameters&amp;gt;&lt;br/&gt;&lt;br/&gt;// These checkboxes will also be defined in emot_induction.iqx -- must comment them when transferring!&lt;br/&gt;&amp;lt;checkboxes row1&amp;gt;&lt;br/&gt;/ caption=" "&lt;br/&gt;/ options=("1", "2", "3", "4", "5", "6", "7", "8", "9", "10")&lt;br/&gt;/ optionvalues = ("1", "2", "3", "4","5", "6", "7", "8", "9", "10")&lt;br/&gt;/ required = true&lt;br/&gt;/ orientation = horizontalequal&lt;br/&gt;&amp;lt;/checkboxes&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;checkboxes row2&amp;gt;&lt;br/&gt;/ caption=" "&lt;br/&gt;/ options=("11", "12", "13", "14", "15", "16", "17", "18", "19", "20")&lt;br/&gt;/ optionvalues = ("11", "12", "13", "14","15", "16", "17", "18", "19", "20")&lt;br/&gt;/ required = true&lt;br/&gt;/ orientation = horizontalequal&lt;br/&gt;&amp;lt;/checkboxes&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;checkboxes row3&amp;gt;&lt;br/&gt;/ caption=" "&lt;br/&gt;/ options=("21", "22", "23", "24", "25", "26", "27", "28", "29", "30")&lt;br/&gt;/ optionvalues = ("21", "22", "23", "24","25", "26", "27", "28", "29", "30")&lt;br/&gt;/ required = true&lt;br/&gt;/ orientation = horizontalequal&lt;br/&gt;&amp;lt;/checkboxes&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;checkboxes row4&amp;gt;&lt;br/&gt;/ caption=" "&lt;br/&gt;/ options=("31", "32", "33", "34", "35", "36", "37", "38", "39", "40")&lt;br/&gt;/ optionvalues = ("31", "32", "33", "34","35", "36", "37", "38", "39", "40")&lt;br/&gt;/ required = true&lt;br/&gt;/ orientation = horizontalequal&lt;br/&gt;&amp;lt;/checkboxes&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;checkboxes row5&amp;gt;&lt;br/&gt;/ caption=" "&lt;br/&gt;/ options=("41", "42", "43", "44", "45", "46", "47", "48", "49", "50")&lt;br/&gt;/ optionvalues = ("41", "42", "43", "44","45", "46", "47", "48", "49", "50")&lt;br/&gt;/ required = true&lt;br/&gt;/ orientation = horizontalequal&lt;br/&gt;&amp;lt;/checkboxes&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;surveypage box_clicking&amp;gt;&lt;br/&gt;/caption = "&amp;lt;font color='green'&amp;gt;You must click at least &amp;lt;b&amp;gt;&amp;lt;u&amp;gt;&amp;lt;%values.effortBoxes%&amp;gt;&amp;lt;/u&amp;gt;&amp;lt;/b&amp;gt; boxes"&lt;br/&gt;/questions = [1-5=sequence(row1, row2, row3, row4, row5)]&lt;br/&gt;/ itemfontstyle = ("Arial", 2.00%, false, false, false, false, 5, 1)&lt;br/&gt;/ itemspacing = 0.0%&lt;br/&gt;/ showpagenumbers = false&lt;br/&gt;/ showbackbutton = false&lt;br/&gt;/ showquestionnumbers = false&lt;br/&gt;/ responsefontstyle = ("Arial", 2.0%, false, false, false, false, 5, 1)&lt;br/&gt;/ timeout = 10000 // 10 seconds&lt;br/&gt;/ showNextButton = false&lt;br/&gt;&amp;lt;/surveypage&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;block SocialEffort_Block&amp;gt;&lt;br/&gt;/ onBlockBegin = [&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;// Randomized trial type order -- 24 trials&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.trialTypeList.appendItem(list.trialType.nextValue); //1&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.trialTypeList.appendItem(list.trialType.nextValue); //2&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.trialTypeList.appendItem(list.trialType.nextValue); //3&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.trialTypeList.appendItem(list.trialType.nextValue); //4&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.trialTypeList.appendItem(list.trialType.nextValue); //5&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.trialTypeList.appendItem(list.trialType.nextValue); //6&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.trialTypeList.appendItem(list.trialType.nextValue); //7&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.trialTypeList.appendItem(list.trialType.nextValue); //8&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.trialTypeList.appendItem(list.trialType.nextValue); //9&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.trialTypeList.appendItem(list.trialType.nextValue); //10&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.trialTypeList.appendItem(list.trialType.nextValue); //11&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.trialTypeList.appendItem(list.trialType.nextValue); //12&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.trialTypeList.appendItem(list.trialType.nextValue); //13&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.trialTypeList.appendItem(list.trialType.nextValue); //14&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.trialTypeList.appendItem(list.trialType.nextValue); //15&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.trialTypeList.appendItem(list.trialType.nextValue); //16&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.trialTypeList.appendItem(list.trialType.nextValue); //17&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.trialTypeList.appendItem(list.trialType.nextValue); //18&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.trialTypeList.appendItem(list.trialType.nextValue); //19&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.trialTypeList.appendItem(list.trialType.nextValue); //20&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.trialTypeList.appendItem(list.trialType.nextValue); //21&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.trialTypeList.appendItem(list.trialType.nextValue); //22&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.trialTypeList.appendItem(list.trialType.nextValue); //23&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.trialTypeList.appendItem(list.trialType.nextValue); //24&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;// Randomized YOU effort-reward 12 pairs&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.YOU_eff_rewList.appendItem(list.eff_rew_YOU.nextValue) //1&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.YOU_eff_rewList.appendItem(list.eff_rew_YOU.nextValue) //2&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.YOU_eff_rewList.appendItem(list.eff_rew_YOU.nextValue) //3&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.YOU_eff_rewList.appendItem(list.eff_rew_YOU.nextValue) //4&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.YOU_eff_rewList.appendItem(list.eff_rew_YOU.nextValue) //5&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.YOU_eff_rewList.appendItem(list.eff_rew_YOU.nextValue) //6&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.YOU_eff_rewList.appendItem(list.eff_rew_YOU.nextValue) //7&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.YOU_eff_rewList.appendItem(list.eff_rew_YOU.nextValue) //8&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.YOU_eff_rewList.appendItem(list.eff_rew_YOU.nextValue) //9&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.YOU_eff_rewList.appendItem(list.eff_rew_YOU.nextValue) //10&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.YOU_eff_rewList.appendItem(list.eff_rew_YOU.nextValue) //11&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.YOU_eff_rewList.appendItem(list.eff_rew_YOU.nextValue) //12&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;// Randomized OTHER effort-reward 12 pairs&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.OTHER_eff_rewList.appendItem(list.eff_rew_OTHER.nextValue) //1&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.OTHER_eff_rewList.appendItem(list.eff_rew_OTHER.nextValue) //2&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.OTHER_eff_rewList.appendItem(list.eff_rew_OTHER.nextValue) //3&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.OTHER_eff_rewList.appendItem(list.eff_rew_OTHER.nextValue) //4&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.OTHER_eff_rewList.appendItem(list.eff_rew_OTHER.nextValue) //5&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.OTHER_eff_rewList.appendItem(list.eff_rew_OTHER.nextValue) //6&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.OTHER_eff_rewList.appendItem(list.eff_rew_OTHER.nextValue) //7&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.OTHER_eff_rewList.appendItem(list.eff_rew_OTHER.nextValue) //8&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.OTHER_eff_rewList.appendItem(list.eff_rew_OTHER.nextValue) //9&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.OTHER_eff_rewList.appendItem(list.eff_rew_OTHER.nextValue) //10&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.OTHER_eff_rewList.appendItem(list.eff_rew_OTHER.nextValue) //11&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;list.OTHER_eff_rewList.appendItem(list.eff_rew_OTHER.nextValue) //12&lt;br/&gt;]&lt;br/&gt;// trials = [1-parameters.num_trials = BoxClick_Trial]&lt;br/&gt;/ trials = [1-24 = BoxClick_Trial] // ATTENTION: defining the number of trials manually....&lt;br/&gt;&lt;br/&gt;&amp;lt;/block&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;trial BoxClick_Trial&amp;gt;&lt;br/&gt;/ onTrialBegin = [&lt;br/&gt;&amp;nbsp; // pick random postion for effort_option&lt;br/&gt;&amp;nbsp; values.effortX = list.x.nextValue;&lt;br/&gt;&amp;nbsp; // pick position for rest_option&lt;br/&gt;&amp;nbsp; values.restX = list.x.nextValue;&lt;br/&gt;&amp;nbsp; // set trial type&lt;br/&gt;&amp;nbsp; //values.trialType = list.trialType.nextValue;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;values.trialType = list.trialTypeList.nextValue;&lt;br/&gt;&amp;nbsp; // set center label according to trial type&lt;br/&gt;&amp;nbsp; if (values.trialType == "YOU") {&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;values.c_colorname = red;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;//values.eff_rew = list.eff_rew_YOU.nextValue;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;values.eff_rew = list.YOU_eff_rewList.nextValue;&lt;br/&gt;&amp;nbsp; } else { // OTHER&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;values.c_colorname = blue;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;//values.eff_rew = list.eff_rew_OTHER.nextValue;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;values.eff_rew = list.OTHER_eff_rewList.nextValue;&lt;br/&gt;&amp;nbsp; }&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;// set winnings and effort according to the value in eff_rew&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;if (values.eff_rew == "e1_r1") {&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;values.effortBoxes = values.easyThresh50;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;values.effortCredits = parameters.low_pay;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;} else if (values.eff_rew == "e1_r2") {&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;values.effortBoxes = values.easyThresh50;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;values.effortCredits = parameters.medium_pay;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;} else if (values.eff_rew == "e1_r3") {&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;values.effortBoxes = values.easyThresh50;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;values.effortCredits = parameters.high_pay;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;} else if (values.eff_rew == "e2_r1") {&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;values.effortBoxes = values.mediumThresh65;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;values.effortCredits = parameters.low_pay;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;} else if (values.eff_rew == "e2_r2") {&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;values.effortBoxes = values.mediumThresh65;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;values.effortCredits = parameters.medium_pay;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;} else if (values.eff_rew = "e2_r3") {&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;values.effortBoxes = values.mediumThresh65;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;values.effortCredits = parameters.high_pay;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;} else if (values.eff_rew = "e3_r1") {&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;values.effortBoxes = values.mediumThresh80;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;values.effortCredits = parameters.low_pay;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;} else if (values.eff_rew = "e3_r2") {&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;values.effortBoxes = values.mediumThresh80;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;values.effortCredits = parameters.medium_pay;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;} else if (values.eff_rew = "e3_r3") {&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;values.effortBoxes = values.mediumThresh80;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;values.effortCredits = parameters.high_pay;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;} else if (values.eff_rew = "e4_r1") {&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;values.effortBoxes = values.hardThresh95;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;values.effortCredits = parameters.low_pay;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;} else if (values.eff_rew = "e4_r2") {&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;values.effortBoxes = values.hardThresh95;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;values.effortCredits = parameters.medium_pay;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;} else if (values.eff_rew = "e4_r3") {&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;values.effortBoxes = values.hardThresh95;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;values.effortCredits = parameters.high_pay;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;}&lt;br/&gt;]&lt;br/&gt;/ stimulusframes = [1=effort_option, rest_option, center_label]&lt;br/&gt;/ inputdevice = mouse&lt;br/&gt;/ validresponse = (effort_option, rest_option)&lt;br/&gt;&lt;br/&gt;/ branch = [&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;if (trial.BoxClick_Trial.response=="effort_option") { // if subj chose the effortful option&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;// run the clicking box survey&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;return surveyPage.box_clicking&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;}&lt;br/&gt;]&lt;br/&gt;&amp;lt;/trial&amp;gt;&lt;br/&gt;&lt;br/&gt;// Stores the order of the 24 trial types -- YOU or OTHER&lt;br/&gt;&amp;lt;list trialTypeList&amp;gt;&lt;br/&gt;/ selectionMode = sequence&lt;br/&gt;&amp;lt;/list&amp;gt;&lt;br/&gt;&lt;br/&gt;// Stores the order in which the effort/reward pairs will be presented in the 12 YOU trials&lt;br/&gt;&amp;lt;list YOU_eff_rewList&amp;gt;&lt;br/&gt;/ selectionMode = sequence&lt;br/&gt;&amp;lt;/list&amp;gt;&lt;br/&gt;&lt;br/&gt;// Stores the order in which the effort/reward pairs will be presented in the 12 OTHER trials&lt;br/&gt;&amp;lt;list OTHER_eff_rewList&amp;gt;&lt;br/&gt;/ selectionMode = sequence&lt;br/&gt;&amp;lt;/list&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;text effort_option&amp;gt;&lt;br/&gt;/ items = ("&amp;lt;%values.effortCredits%&amp;gt; credits for &amp;lt;%values.effortBoxes%&amp;gt; boxes")&lt;br/&gt;/ hPosition = values.effortX&lt;br/&gt;/ vPosition = 40%&lt;br/&gt;/ fontStyle = ("arial", 12pt)&lt;br/&gt;/ size = (15%, 10%)&lt;br/&gt;/ vJustify = center&lt;br/&gt;/ txBGColor = lightGray&lt;br/&gt;&amp;lt;/text&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;text rest_option&amp;gt;&lt;br/&gt;/ items = ("1 credit for Rest")&lt;br/&gt;/ hPosition = values.restX&lt;br/&gt;/ vPosition = 40%&lt;br/&gt;/ fontStyle = ("arial", 12pt)&lt;br/&gt;/ size = (15%, 10%)&lt;br/&gt;/ vJustify = center&lt;br/&gt;/ txBGColor = lightGray&lt;br/&gt;&amp;lt;/text&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;text center_label&amp;gt;&lt;br/&gt;// items = ("&amp;lt;%values.trialType%&amp;gt;") // this is the label that should be used in the exp!&lt;br/&gt;/ items = ("&amp;lt;%values.eff_rew%&amp;gt;") // testing the list selection!&lt;br/&gt;/ position = (50%, 30%)&lt;br/&gt;/ txColor = values.c_colorname&lt;br/&gt;/ fontStyle = ("arial", 16pt, true)&lt;br/&gt;&amp;lt;/text&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;list trialType&amp;gt;&lt;br/&gt;/ items = ("YOU", "OTHER")&lt;br/&gt;/ poolsize = parameters.num_trials&lt;br/&gt;/ selectionmode = random&lt;br/&gt;/ selectionrate = always&lt;br/&gt;&amp;lt;/list&amp;gt;&lt;br/&gt;&lt;br/&gt;// This list contains all effort (4) x reward (3) = 12 combinations for the YOU trials&lt;br/&gt;// It will be used to to sample --once-- all the 12 combinations&lt;br/&gt;&amp;lt;list eff_rew_YOU&amp;gt;&lt;br/&gt;/ items = ("e1_r1", "e1_r2", "e1_r3", "e2_r1", "e2_r2", "e2_r3", "e3_r1", "e3_r2", "e3_r3", "e4_r1", "e4_r2", "e4_r3")&lt;br/&gt;/ poolsize = parameters.num_trials/2&lt;br/&gt;/ replace = false&lt;br/&gt;/ selectionmode = random&lt;br/&gt;/ selectionrate = always&lt;br/&gt;&amp;lt;/list&amp;gt;&lt;br/&gt;&lt;br/&gt;// Likewise for the OTHER trials&lt;br/&gt;&amp;lt;list eff_rew_OTHER&amp;gt;&lt;br/&gt;/ items = ("e1_r1", "e1_r2", "e1_r3", "e2_r1", "e2_r2", "e2_r3", "e3_r1", "e3_r2", "e3_r3", "e4_r1", "e4_r2", "e4_r3")&lt;br/&gt;/ poolsize = parameters.num_trials/2&lt;br/&gt;/ replace = false&lt;br/&gt;/ selectionmode = random&lt;br/&gt;/ selectionrate = always&lt;br/&gt;&amp;lt;/list&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;list x&amp;gt;&lt;br/&gt;/ items = (35%, 65%)&lt;br/&gt;/ selectionrate = always&lt;br/&gt;&amp;lt;/list&amp;gt;&lt;br/&gt;&lt;br/&gt;&lt;br/&gt;&lt;a class="if-quote-goto quote-link" href="#" data-id="41301"&gt;&lt;span class="goto"&gt;&lt;/span&gt;&lt;/a&gt;&lt;/div&gt;&lt;/div&gt;&lt;span unselectable="on" class="quote-markup"&gt;[/quote]&lt;/span&gt;&lt;/blockquote&gt;&lt;br/&gt;Your code is missing statement separators.&lt;br/&gt;&lt;br/&gt;&lt;img src="data:image/png;base64,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" alt=""&gt;</description><pubDate>Fri, 04 Jul 2025 11:25:05 GMT</pubDate><dc:creator>Dave</dc:creator></item></channel></rss>