﻿<?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  » Posttrialpause setup</title><generator>InstantForum 2017-1 Final</generator><description>Millisecond Forums</description><link>https://forums.millisecond.com/</link><webMaster>Millisecond Forums</webMaster><lastBuildDate>Sat, 03 Oct 2026 10:11:27 GMT</lastBuildDate><ttl>20</ttl><item><title>RE: Posttrialpause setup</title><link>https://forums.millisecond.com/Topic35304.aspx</link><description>&lt;blockquote data-id="35303" class="if-quote-wrapper" unselectable="on" data-guid="1681347214966" id="if_insertedNode_1681347213442" contenteditable="false"&gt;&lt;a class="quote-para" unselectable="on" style="display: none;" href="#" data-id="35303" 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="35303" 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="35303" title=" "&gt;&amp;nbsp;&lt;/a&gt;&lt;/div&gt;&lt;span unselectable="on" class="quote-markup"&gt;[b]&lt;/span&gt;Libra - 4/13/2023&lt;span unselectable="on" class="quote-markup"&gt;[/b]&lt;/span&gt;&lt;/div&gt;&lt;div class="if-quote-message if-quote-message-35303"&gt;&lt;div class="if-quote-message-margin" contenteditable="true"&gt;Hi Dave,&amp;nbsp;&lt;br/&gt;Thank you so much! this makes a lot of sense. It seems that /trialduration&amp;nbsp;is exactly what I would want.&amp;nbsp;&lt;br/&gt;"/trialduration&amp;nbsp;Specifies the absolute duration of a trial, from beginning to end, including the posttrialpause. If the subject responds quickly, the posttrialpause interval is lengthened to fill out the remaining time in the duration. If the subject does not respond before the duration, the trial is terminated and the next trial begins."&amp;nbsp;&lt;br/&gt;&lt;br/&gt;I updated my trial to be like this.&amp;nbsp;&lt;br/&gt;&lt;br/&gt;&amp;lt;trial POSO_posBG_shrt_ITI_1pt6&amp;gt;  &lt;br/&gt;/ validresponse = ("1", "4")&lt;br/&gt;/ correctresponse = ("4")&lt;br/&gt;/ stimulustimes = [0 = fixation; 800=noreplace (POSO); &lt;br/&gt;1000 = blanks; &lt;br/&gt;1100 = poso_goodtarget]&lt;br/&gt;/ trialdata = [POSO poso_goodtarget]&lt;br/&gt;/ beginresponsetime = 1100&lt;br/&gt;/ trialduration = 4800&lt;br/&gt;/ responseinterrupt=frames&lt;br/&gt;/ posttrialpause = 1600&lt;br/&gt;&amp;lt;/trial&amp;gt;&lt;br/&gt;&lt;br/&gt;Could I check my understanding with you? Please bear with me, just want to make sure! &lt;br/&gt;In this trial, the maximum span for poso_goodtarget to be presented on the screen is from 1100 to 3200 (4800-1600) from trial onset (and this happens if participants do not make a response). If participants respond, say at 1200ms from trial onset, the latency of this trial will be 100ms. A blank screen immediately starts when participants make the response at 1200ms. So from 1200 to 4800 of this trial, participants will continue to see the blank screen. Then, the next trial begins. &lt;br/&gt;&lt;br/&gt;Ideally, I would want to verify this with saved data. Right now a single trial's data tells me the latency, each stimulus onset, and the fixed posttrialpause (for this trial, always 1600ms). So I can't make sure that the entire trial is absolutely 4800ms, no matter how fast participants make a response and whether they make a response or not. Is there a way to save the total block time in the data file? Essentially, if a block only contains trials like these, then the total block duration should be the same for each monkey run. [I tried block.name.timestamp] but it only seems to record the block starting time, right?&lt;br/&gt;&lt;br/&gt;Thank you again!&amp;nbsp;&lt;br/&gt;&lt;a class="if-quote-goto quote-link" href="#" data-id="35303"&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;&amp;gt; In this trial, the maximum span for poso_goodtarget to be presented on the screen is from 1100 to 3200 (4800-1600) from trial onset (and this happens if participants do not make a response). &lt;br/&gt;&lt;br/&gt;Correct.&lt;br/&gt;&lt;br/&gt;&amp;gt; If participants respond, say at 1200ms from trial onset, the latency of this trial will be 100ms. &lt;br/&gt;&lt;br/&gt;Correct.&lt;br/&gt;&lt;br/&gt;&amp;gt; A blank screen immediately starts when participants make the response at 1200ms. &lt;br/&gt;&amp;gt; So from 1200 to 4800 of this trial, participants will continue to see the blank screen. Then, the next trial begins. &lt;br/&gt;&lt;br/&gt;Also correct.&lt;br/&gt;&lt;br/&gt;To verify a trial's duration:&lt;br/&gt;- Take the trial's timestamp property in trial N.&lt;br/&gt;- Take the trial's timestamp property in trial N + 1.&lt;br/&gt;- Calculate the absolute difference to verify the duration of trial N.&lt;br/&gt;&lt;br/&gt;Example below:&lt;br/&gt;&lt;br/&gt;[code]&lt;br/&gt;&lt;br/&gt;&amp;lt;data&amp;gt;&lt;br/&gt;/ columns = [date time build subject blocknum blockcode trialnum trialcode response correct latency stimulusitem stimulusitem stimulusitem stimulusitem stimulusonset stimulusonset stimulusonset stimulusonset posttrialpause &lt;br/&gt;values.effectiveBlankPeriod, values.timestampPrevioustrial, values.timestampCurrenttrial, values.previousTrialDuration]&lt;br/&gt;/ labels = true&lt;br/&gt;/ separatefiles = true&lt;br/&gt;&amp;lt;/data&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;expt 1&amp;gt;&lt;br/&gt;/ blocks = [1=data_BG1]&lt;br/&gt;&amp;lt;/expt&amp;gt;&lt;br/&gt;&lt;br/&gt;------------------------------------------------------------------------------------&lt;br/&gt;*** SIGNIFICANT POSITIVE PERSON'S NAME SURVEY ******&lt;br/&gt;------------------------------------------------------------------------------------&lt;br/&gt;&amp;lt;block get_ready_BG&amp;gt;&lt;br/&gt;/ trials = [1=get_ready]&lt;br/&gt;&amp;lt;/block&amp;gt;&lt;br/&gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;block get_ready&amp;gt;&lt;br/&gt;/ trials = [1=get_ready]&lt;br/&gt;&amp;lt;/block&amp;gt;&lt;br/&gt; &lt;br/&gt; &amp;lt;trial get_ready&amp;gt;&lt;br/&gt;/ stimulusframes = [1 = getready]&lt;br/&gt;/ validresponse = ("5")&lt;br/&gt;&amp;lt;/trial&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;text getready&amp;gt;&lt;br/&gt;/items=("Get ready!")&lt;br/&gt;/ position = (50%, 50%)&lt;br/&gt;&amp;lt;/text&amp;gt;&lt;br/&gt;&lt;br/&gt; &amp;lt;trial block_end&amp;gt;&lt;br/&gt;/ stimulustimes = []&lt;br/&gt;/ responsemode = noresponse&lt;br/&gt; &amp;lt;/trial&amp;gt;&lt;br/&gt;&lt;br/&gt; &amp;lt;text goodreminder_rightBG&amp;gt;  &lt;br/&gt;/ numitems = 1&lt;br/&gt;/ items = ("GOOD")&lt;br/&gt;/ position = (75, 25)&lt;br/&gt;&amp;lt;/text&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;text badreminder_leftBG&amp;gt;  &lt;br/&gt;/ numitems = 1&lt;br/&gt;/ items = ("BAD") &lt;br/&gt;/ position = (25, 25)&lt;br/&gt;&amp;lt;/text&amp;gt;&lt;br/&gt;&lt;br/&gt;------------------------------------------------------------------------------------&lt;br/&gt;*** TEXT PRIMES AND TARGETS *****&lt;br/&gt;------------------------------------------------------------------------------------&lt;br/&gt;&lt;br/&gt;&lt;br/&gt;&lt;br/&gt;********** NEW PRIMES ****************&lt;br/&gt;&lt;br/&gt;&amp;lt;text responses&amp;gt;&lt;br/&gt;/ txcolor = (0, 0, 0)&lt;br/&gt;/ position = (50,25)&lt;br/&gt;/ fontstyle = ("Verdana", -20, true, true, false, false, 5, 0)&lt;br/&gt;/ items = responseitems&lt;br/&gt;&amp;lt;/text&amp;gt; &lt;br/&gt;&lt;br/&gt;&amp;lt;item responseitems&amp;gt;&lt;br/&gt;&amp;lt;/item&amp;gt;&lt;br/&gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;text POSO&amp;gt;&lt;br/&gt;/ numitems = 1&lt;br/&gt;/ items = ("POSO")&lt;br/&gt;/ select = noreplace&lt;br/&gt;&amp;lt;/text&amp;gt;&lt;br/&gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;text poso_goodtarget&amp;gt;&lt;br/&gt;/ items=good&lt;br/&gt;/ select = list.poso_goodtargetnumbers.nextvalue&lt;br/&gt;&amp;lt;/text&amp;gt;&lt;br/&gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;item good&amp;gt;&lt;br/&gt;/ 1 = "honor"&lt;br/&gt;/ 2 = "lucky"&lt;br/&gt;/ 3 = "diamond"&lt;br/&gt;/ 4 = "loyal"&lt;br/&gt;/ 5 = "freedom"&lt;br/&gt;/ 6 = "rainbow"&lt;br/&gt;/ 7 = "love"&lt;br/&gt;/ 8 = "honest"&lt;br/&gt;&amp;lt;/item&amp;gt;&lt;br/&gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;list poso_goodtargetnumbers&amp;gt;&lt;br/&gt;/ items = (1,2,3,4,5,6,7,8)&lt;br/&gt;/ poolsize=48&lt;br/&gt;/ selectionmode = random&lt;br/&gt;/ replace = false&lt;br/&gt;&amp;lt;/list&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;text blanks&amp;gt;  &lt;br/&gt;/ numitems = 3&lt;br/&gt;/ items = ("                                             ", "                                             ", "                                             ")&lt;br/&gt;/ select = noreplace&lt;br/&gt;&amp;lt;/text&amp;gt;&lt;br/&gt;&lt;br/&gt;----------------------------&lt;br/&gt;&amp;lt;block data_BG1&amp;gt;&lt;br/&gt;/ bgstim = (badreminder_leftBG, goodreminder_rightBG)&lt;br/&gt;/ trials = [1-10 = POSO_posBG_shrt_ITI_1pt6]&lt;br/&gt;&amp;lt;/block&amp;gt;&lt;br/&gt;&lt;br/&gt;*****&lt;br/&gt;&amp;lt;text fixation&amp;gt;&lt;br/&gt;/ items = ("+")&lt;br/&gt;/ position = (50%, 50%)&lt;br/&gt;&amp;lt;/text&amp;gt; &lt;br/&gt;******&lt;br/&gt;&lt;br/&gt;&lt;br/&gt;**** Positive Object prime *** positive target and negative target *** BG *** short SOA&lt;br/&gt;&lt;br/&gt;&amp;lt;values&amp;gt;&lt;br/&gt;/ effectiveBlankPeriod = -1 // calculates effective duration of the blank period&lt;br/&gt;/ timestampCurrenttrial = -1&lt;br/&gt;/ timestampPrevioustrial = -1&lt;br/&gt;/ previousTrialDuration = -1 // calculates the previous trial's duration&lt;br/&gt;&amp;lt;/values&amp;gt;&lt;br/&gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;trial POSO_posBG_shrt_ITI_1pt6&amp;gt;&lt;br/&gt;/ ontrialbegin = [&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;values.timestampPrevioustrial = values.timestampCurrenttrial;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;values.timestampCurrenttrial = trial.POSO_posBG_shrt_ITI_1pt6.timestamp;&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;values.previousTrialDuration = values.timestampCurrenttrial - values.timestampPrevioustrial;&lt;br/&gt;]&lt;br/&gt;&lt;br/&gt;/ ontrialend = [&lt;br/&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;values.effectiveBlankPeriod = 1600 + (2100 - trial.POSO_posBG_shrt_ITI_1pt6.latency);&lt;br/&gt;]&lt;br/&gt;/ validresponse = ("1", "4")&lt;br/&gt;/ correctresponse = ("4")&lt;br/&gt;/ stimulustimes = [0 = fixation; 800=noreplace (POSO); &lt;br/&gt;1000 = blanks; &lt;br/&gt;1100 = poso_goodtarget]&lt;br/&gt;/ trialdata = [POSO poso_goodtarget]&lt;br/&gt;/ beginresponsetime = 1100&lt;br/&gt;/ posttrialpause = 1600&lt;br/&gt;/ trialduration = 4800&lt;br/&gt;/ responseinterrupt=frames&lt;br/&gt;&amp;lt;/trial&amp;gt;&lt;br/&gt;[/code]&lt;br/&gt;&lt;br/&gt;&lt;img 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" alt=""&gt;&lt;br/&gt;</description><pubDate>Thu, 13 Apr 2023 01:21:22 GMT</pubDate><dc:creator>Dave</dc:creator></item><item><title>RE: Posttrialpause setup</title><link>https://forums.millisecond.com/Topic35305.aspx</link><description>Awesome! Thank you so much for helping!&amp;nbsp;&lt;br/&gt;&lt;br/&gt;</description><pubDate>Thu, 13 Apr 2023 01:15:08 GMT</pubDate><dc:creator>Libra</dc:creator></item><item><title>Posttrialpause setup</title><link>https://forums.millisecond.com/Topic35301.aspx</link><description>Hi,&amp;nbsp;&lt;br/&gt;I have a question about my inquisit code.&amp;nbsp;&lt;br/&gt;&lt;br/&gt;I set a trial to be something like below.&amp;nbsp;&lt;br/&gt;&lt;br/&gt;&amp;lt;trial POSO_posBG_shrt_ITI_1pt6&amp;gt;  &lt;br/&gt;/ validresponse = ("1", "4")&lt;br/&gt;/ correctresponse = ("4")&lt;br/&gt;/ stimulustimes = [0 = fixation; 800=noreplace (POSO); &lt;br/&gt;1000 = blanks; &lt;br/&gt;1100 = poso_goodtarget]&lt;br/&gt;/ trialdata = [POSO poso_goodtarget]&lt;br/&gt;/ beginresponsetime = 1100&lt;br/&gt;/ timeout = 3200&lt;br/&gt;/ responseinterrupt=frames&lt;br/&gt;/ posttrialpause = 1600+(500-trial.POSO_posBG_shrt_ITI_1pt6.latency)&lt;br/&gt;&amp;lt;/trial&amp;gt;&lt;br/&gt;&lt;br/&gt;I wrote the trial in this way with the following goals in mind:&amp;nbsp;&lt;br/&gt;(1) Participants can&amp;nbsp;make a response between the time window of 1100 to 3200ms from a trial onset. Their response latency in the trial could range between 0 to 2100ms.&amp;nbsp;&lt;br/&gt;(2)&amp;nbsp;Once they make a response within this window, the poso_goodtarget word will disappear (blank screen starts).&amp;nbsp;&lt;br/&gt;(3) No matter how fast participants make a response or whether they make a response or not, the overall duration of a trial plus the immediately following posttrial pause will always be a set value of 3200 + 1600 = 4800.&amp;nbsp;&lt;br/&gt;- If the participant make a response within 500ms (1600ms post trial onset), they will see the blank screen for a longer time (1600 + 500 - latency).&amp;nbsp;&lt;br/&gt;- If the participant make a response between 500 to 2100ms, they will see the blank screen for a longer time.&amp;nbsp;&lt;br/&gt;&lt;br/&gt;However, when looking at the saved data file, it doesn't seem like the post-trial pause duration is taking the trial response latency into account.&amp;nbsp;&lt;br/&gt;&lt;br/&gt;I attached a simplified version of the experiment and the saved monkey data file. Could you help me understand why the script is not working in the way I intend it to be? Thank you!&amp;nbsp;&lt;br/&gt;&lt;br/&gt;&lt;br/&gt;&lt;br/&gt;</description><pubDate>Thu, 13 Apr 2023 01:15:08 GMT</pubDate><dc:creator>Libra</dc:creator></item><item><title>RE: Posttrialpause setup</title><link>https://forums.millisecond.com/Topic35303.aspx</link><description>Hi Dave,&amp;nbsp;&lt;br/&gt;Thank you so much! this makes a lot of sense. It seems that /trialduration&amp;nbsp;is exactly what I would want.&amp;nbsp;&lt;br/&gt;"/trialduration&amp;nbsp;Specifies the absolute duration of a trial, from beginning to end, including the posttrialpause. If the subject responds quickly, the posttrialpause interval is lengthened to fill out the remaining time in the duration. If the subject does not respond before the duration, the trial is terminated and the next trial begins."&amp;nbsp;&lt;br/&gt;&lt;br/&gt;I updated my trial to be like this.&amp;nbsp;&lt;br/&gt;&lt;br/&gt;&amp;lt;trial POSO_posBG_shrt_ITI_1pt6&amp;gt;  &lt;br/&gt;/ validresponse = ("1", "4")&lt;br/&gt;/ correctresponse = ("4")&lt;br/&gt;/ stimulustimes = [0 = fixation; 800=noreplace (POSO); &lt;br/&gt;1000 = blanks; &lt;br/&gt;1100 = poso_goodtarget]&lt;br/&gt;/ trialdata = [POSO poso_goodtarget]&lt;br/&gt;/ beginresponsetime = 1100&lt;br/&gt;/ trialduration = 4800&lt;br/&gt;/ responseinterrupt=frames&lt;br/&gt;/ posttrialpause = 1600&lt;br/&gt;&amp;lt;/trial&amp;gt;&lt;br/&gt;&lt;br/&gt;Could I check my understanding with you? Please bear with me, just want to make sure! &lt;br/&gt;In this trial, the maximum span for poso_goodtarget to be presented on the screen is from 1100 to 3200 (4800-1600) from trial onset (and this happens if participants do not make a response). If participants respond, say at 1200ms from trial onset, the latency of this trial will be 100ms. A blank screen immediately starts when participants make the response at 1200ms. So from 1200 to 4800 of this trial, participants will continue to see the blank screen. Then, the next trial begins. &lt;br/&gt;&lt;br/&gt;Ideally, I would want to verify this with saved data. Right now a single trial's data tells me the latency, each stimulus onset, and the fixed posttrialpause (for this trial, always 1600ms). So I can't make sure that the entire trial is absolutely 4800ms, no matter how fast participants make a response and whether they make a response or not. Is there a way to save the total block time in the data file? Essentially, if a block only contains trials like these, then the total block duration should be the same for each monkey run. [I tried block.name.timestamp] but it only seems to record the block starting time, right?&lt;br/&gt;&lt;br/&gt;Thank you again!&amp;nbsp;&lt;br/&gt;</description><pubDate>Thu, 13 Apr 2023 00:36:32 GMT</pubDate><dc:creator>Libra</dc:creator></item><item><title>RE: Posttrialpause setup</title><link>https://forums.millisecond.com/Topic35302.aspx</link><description>&lt;blockquote data-id="35301" class="if-quote-wrapper" unselectable="on" data-guid="1681339054108" id="if_insertedNode_1681339053550" contenteditable="false"&gt;&lt;a class="quote-para" unselectable="on" style="display: none;" href="#" data-id="35301" 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="35301" 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="35301" title=" "&gt;&amp;nbsp;&lt;/a&gt;&lt;/div&gt;&lt;span unselectable="on" class="quote-markup"&gt;[b]&lt;/span&gt;Libra - 4/12/2023&lt;span unselectable="on" class="quote-markup"&gt;[/b]&lt;/span&gt;&lt;/div&gt;&lt;div class="if-quote-message if-quote-message-35301"&gt;&lt;div class="if-quote-message-margin" contenteditable="true"&gt;Hi,&amp;nbsp;&lt;br/&gt;I have a question about my inquisit code.&amp;nbsp;&lt;br/&gt;&lt;br/&gt;I set a trial to be something like below.&amp;nbsp;&lt;br/&gt;&lt;br/&gt;&amp;lt;trial POSO_posBG_shrt_ITI_1pt6&amp;gt;  &lt;br/&gt;/ validresponse = ("1", "4")&lt;br/&gt;/ correctresponse = ("4")&lt;br/&gt;/ stimulustimes = [0 = fixation; 800=noreplace (POSO); &lt;br/&gt;1000 = blanks; &lt;br/&gt;1100 = poso_goodtarget]&lt;br/&gt;/ trialdata = [POSO poso_goodtarget]&lt;br/&gt;/ beginresponsetime = 1100&lt;br/&gt;/ timeout = 3200&lt;br/&gt;/ responseinterrupt=frames&lt;br/&gt;/ posttrialpause = 1600+(500-trial.POSO_posBG_shrt_ITI_1pt6.latency)&lt;br/&gt;&amp;lt;/trial&amp;gt;&lt;br/&gt;&lt;br/&gt;I wrote the trial in this way with the following goals in mind:&amp;nbsp;&lt;br/&gt;(1) Participants can&amp;nbsp;make a response between the time window of 1100 to 3200ms from a trial onset. Their response latency in the trial could range between 0 to 2100ms.&amp;nbsp;&lt;br/&gt;(2)&amp;nbsp;Once they make a response within this window, the poso_goodtarget word will disappear (blank screen starts).&amp;nbsp;&lt;br/&gt;(3) No matter how fast participants make a response or whether they make a response or not, the overall duration of a trial plus the immediately following posttrial pause will always be a set value of 3200 + 1600 = 4800.&amp;nbsp;&lt;br/&gt;- If the participant make a response within 500ms (1600ms post trial onset), they will see the blank screen for a longer time (1600 + 500 - latency).&amp;nbsp;&lt;br/&gt;- If the participant make a response between 500 to 2100ms, they will see the blank screen for a longer time.&amp;nbsp;&lt;br/&gt;&lt;br/&gt;However, when looking at the saved data file, it doesn't seem like the post-trial pause duration is taking the trial response latency into account.&amp;nbsp;&lt;br/&gt;&lt;br/&gt;I attached a simplified version of the experiment and the saved monkey data file. Could you help me understand why the script is not working in the way I intend it to be? Thank you!&amp;nbsp;&lt;br/&gt;&lt;br/&gt;&lt;br/&gt;&lt;br/&gt;&lt;a class="if-quote-goto quote-link" href="#" data-id="35301"&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;&amp;gt; However, when looking at the saved data file, it doesn't seem like the post-trial pause duration is taking the trial response latency into account. &lt;br/&gt;&lt;br/&gt;At the time the posttrialpause is determined for the 1st instance of the given trial, there is no latency. Meaning this&lt;br/&gt;&lt;br/&gt;/ posttrialpause = 1600+(500-trial.POSO_posBG_shrt_ITI_1pt6.latency)&lt;br/&gt;&lt;br/&gt;works out to&lt;br/&gt;&lt;br/&gt;/ posttrialpause = 1600+(500-nothing)&lt;br/&gt;&lt;br/&gt;i.e. 2100.&lt;br/&gt;&lt;br/&gt;Suppose, then, you don't respond in this instance of the trial, i.e. produce a latency of 2100. Then, when the posttrialpause for the next instance of that same trial is calculated, there is a latency, and its value is 2100, meaning &lt;br/&gt;&lt;br/&gt;/ posttrialpause = 1600+(500-trial.POSO_posBG_shrt_ITI_1pt6.latency)&lt;br/&gt;&lt;br/&gt;works out to&lt;br/&gt;&lt;br/&gt;/ posttrialpause = 1600+(500-2100)&lt;br/&gt;&lt;br/&gt;i.e. 0.&lt;br/&gt;&lt;br/&gt;&lt;img 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" alt=""&gt;&lt;br/&gt;&lt;br/&gt;See in the above how the posttrialpause adjustment is always trailing, i.e. contingent on the latency of the previous instance of the trial.&lt;br/&gt;&lt;br/&gt;&lt;img src="data:image/png;base64,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" alt=""&gt;&lt;br/&gt;&lt;br/&gt;&lt;br/&gt;Your description here&lt;br/&gt;&lt;br/&gt;"(3) No matter how fast participants make a response or whether they make a response or not, the overall duration of a trial plus the immediately following posttrial pause will always be a set value of 3200 + 1600 = 4800. &lt;br/&gt;- If the participant make a response within 500ms (1600ms post trial onset), they will see the blank screen for a longer time (1600 + 500 - latency). &lt;br/&gt;- If the participant make a response between 500 to 2100ms, they will see the blank screen for a longer time."&lt;br/&gt;&lt;br/&gt;doesn't really make sense to me. If you want the trial to have a fixed duration of 4800, you should use /trialduration (not /timeout) and set it to 4800, with /posttrialpause set to 1600.&lt;br/&gt;&lt;br/&gt;&lt;a href="https://www.millisecond.com/support/docs/current/html/howto/howtocontroltiming.htm"&gt;https://www.millisecond.com/support/docs/current/html/howto/howtocontroltiming.htm&lt;/a&gt;&lt;br/&gt;&lt;br/&gt;With respect to&lt;br/&gt;&lt;br/&gt;"- If the participant make a response within 500ms (1600ms post trial onset), they will see the blank screen for a longer time (1600 + 500 - latency).&lt;br/&gt;- If the participant make a response between 500 to 2100ms, they will see the blank screen for a longer time."&lt;br/&gt;&lt;br/&gt;the blank period&amp;nbsp; in your trial begins as soon as there is a response (i..e. a key press between 1100 and 3200 ms measured from the start of the trial's stimulus presentation sequence). The earlier the response, the longer the blank period. The later the response, the shorter the blank period.&lt;br/&gt;&lt;br/&gt;If your approach worked (which it doesn't), then not responding (latency = 2100) in a given trial would result in a posttrialpause of 1600 + (500 - 2100) = 0, virtually no blank period and a trial duration of a mere ~3200 ms, not 4800.&lt;br/&gt;&lt;br/&gt;</description><pubDate>Wed, 12 Apr 2023 23:24:47 GMT</pubDate><dc:creator>Dave</dc:creator></item></channel></rss>