﻿<?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  » standardize the display size of images with different original dimensions</title><generator>InstantForum 2017-1 Final</generator><description>Millisecond Forums</description><link>https://forums.millisecond.com/</link><webMaster>Millisecond Forums</webMaster><lastBuildDate>Sat, 26 Sep 2026 15:53:46 GMT</lastBuildDate><ttl>20</ttl><item><title>standardize the display size of images with different original dimensions</title><link>https://forums.millisecond.com/Topic35723.aspx</link><description>Dear Dave,&lt;br/&gt;&lt;br/&gt;I hope all is well. I was wondering if there is a way to standardize the display size of images that have different original dimensions. I attempted to use something like "/size (300px, 300px)," but the displayed size still varies for different images. If there is no method available, I will manually adjust the size of the images to achieve uniformity. Thank you in advance for your assistance.&lt;br/&gt;&lt;br/&gt;Best regards,&lt;br/&gt;M</description><pubDate>Wed, 08 Nov 2023 01:57:24 GMT</pubDate><dc:creator>ttyelnv</dc:creator></item><item><title>RE: standardize the display size of images with different original dimensions</title><link>https://forums.millisecond.com/Topic35757.aspx</link><description>&lt;blockquote data-id="35756" class="if-quote-wrapper" unselectable="on" data-guid="1699408254600" contenteditable="false" id="if_insertedNode_1699408253656"&gt;&lt;a class="quote-para" unselectable="on" style="display: none;" href="#" data-id="35756" 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="35756" 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="35756" title=" "&gt;&amp;nbsp;&lt;/a&gt;&lt;/div&gt;&lt;span unselectable="on" class="quote-markup"&gt;[b]&lt;/span&gt;Dave - 11/8/2023&lt;span unselectable="on" class="quote-markup"&gt;[/b]&lt;/span&gt;&lt;/div&gt;&lt;div class="if-quote-message if-quote-message-35756"&gt;&lt;div class="if-quote-message-margin" contenteditable="true"&gt;&lt;blockquote data-id="35755" class="if-quote-wrapper" unselectable="on" data-guid="1699408254600" contenteditable="false" id="if_insertedNode_1699407212881"&gt;&lt;a class="quote-para" unselectable="on" style="display: none;" href="#" data-id="35755" 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="35755" 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="35755" title=" "&gt;&amp;nbsp;&lt;/a&gt;&lt;/div&gt;&lt;span unselectable="on" class="quote-markup"&gt;[b]&lt;/span&gt;nuttymenkk - 11/8/2023&lt;span unselectable="on" class="quote-markup"&gt;[/b]&lt;/span&gt;&lt;/div&gt;&lt;div class="if-quote-message if-quote-message-35755"&gt;&lt;div class="if-quote-message-margin" contenteditable="true"&gt;&lt;blockquote data-id="35754" class="if-quote-wrapper" unselectable="on" data-guid="1699408254600" contenteditable="false" id="if_insertedNode_1699406020112"&gt;&lt;a class="quote-para" unselectable="on" style="display: none;" href="#" data-id="35754" 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="35754" 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="35754" title=" "&gt;&amp;nbsp;&lt;/a&gt;&lt;/div&gt;&lt;span unselectable="on" class="quote-markup"&gt;[b]&lt;/span&gt;Dave - 11/7/2023&lt;span unselectable="on" class="quote-markup"&gt;[/b]&lt;/span&gt;&lt;/div&gt;&lt;div class="if-quote-message if-quote-message-35754"&gt;&lt;div class="if-quote-message-margin" contenteditable="true"&gt;&lt;blockquote data-id="35753" class="if-quote-wrapper" unselectable="on" data-guid="1699408254600" contenteditable="false" id="if_insertedNode_1699361389052"&gt;&lt;a class="quote-para" unselectable="on" style="display: none;" href="#" data-id="35753" 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="35753" 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="35753" title=" "&gt;&amp;nbsp;&lt;/a&gt;&lt;/div&gt;&lt;span unselectable="on" class="quote-markup"&gt;[b]&lt;/span&gt;nuttymenkk - 11/7/2023&lt;span unselectable="on" class="quote-markup"&gt;[/b]&lt;/span&gt;&lt;/div&gt;&lt;div class="if-quote-message if-quote-message-35753"&gt;&lt;div class="if-quote-message-margin" contenteditable="true"&gt;&lt;blockquote data-id="35752" class="if-quote-wrapper" unselectable="on" data-guid="1699408254600" contenteditable="false" id="if_insertedNode_1699333712673"&gt;&lt;a class="quote-para" unselectable="on" style="display: none;" href="#" data-id="35752" 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="35752" 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="35752" title=" "&gt;&amp;nbsp;&lt;/a&gt;&lt;/div&gt;&lt;span unselectable="on" class="quote-markup"&gt;[b]&lt;/span&gt;nuttymenkk - 11/7/2023&lt;span unselectable="on" class="quote-markup"&gt;[/b]&lt;/span&gt;&lt;/div&gt;&lt;div class="if-quote-message if-quote-message-35752"&gt;&lt;div class="if-quote-message-margin" contenteditable="true"&gt;I see, thank you very much Dave&lt;a class="if-quote-goto quote-link" href="#" data-id="35752"&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;I have an additional question regarding the randomization of a set of pictures. Currently, I have two groups of pictures: one falls under the "true picture" category, while the other belongs to the "false picture" category. My objective is to record the correctness of subjects' answers based on these pictures.&lt;br/&gt;&lt;br/&gt;To achieve this, I created two trials, one for the true category and another for the false category. I have developed the following script. However, it appears that there are repetitions in the displayed pictures. Upon further investigation, I found that Inquisit alternates between the "F_image" and "T_image" categories, ensuring no repetition within this alternation. However, my code does not account for the specific picture names.&lt;br/&gt;&lt;br/&gt;[code]&amp;lt;item F_pictureitems&amp;gt;&lt;br/&gt;/1 = "F1.PNG"&lt;br/&gt;/2 = "F2.PNG"&lt;br/&gt;/3 = "F3.PNG"&lt;br/&gt;/4 = "F4.PNG"&lt;br/&gt;/5 = "F5.PNG"&lt;br/&gt;/6 = "F6.PNG"&lt;br/&gt;...&lt;br/&gt;/90 = "F90.PNG"&lt;br/&gt;&amp;lt;/item&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;item T_pictureitems&amp;gt;&lt;br/&gt;/1 = "T1.PNG"&lt;br/&gt;/2 = "T2.PNG"&lt;br/&gt;/3 = "T3.PNG"&lt;br/&gt;/4 = "T4.PNG"&lt;br/&gt;/5 = "T5.PNG"&lt;br/&gt;/6 = "T6.PNG"&lt;br/&gt;...&lt;br/&gt;/90 = "T90.PNG"&lt;br/&gt;&amp;lt;/item&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;picture T_image&amp;gt;&lt;br/&gt;/ items = T_pictureitems&lt;br/&gt;/ position = (50%, 50%)&lt;br/&gt;/ halign = center&lt;br/&gt;/ size = (40%, 40%)&lt;br/&gt;&amp;lt;/picture&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;picture F_image&amp;gt;&lt;br/&gt;/ items = F_pictureitems&lt;br/&gt;/ position = (50%, 50%)&lt;br/&gt;/ halign = center&lt;br/&gt;/ size = (40%, 40%)&lt;br/&gt;&amp;lt;/picture&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;trial T_image&amp;gt;&lt;br/&gt;/ stimulusframes = [1=T_image]&lt;br/&gt;/ response = timeout(60000)&lt;br/&gt;/ validresponse = ("e","i")&lt;br/&gt;/ correctresponse = ("e")&lt;br/&gt;&amp;lt;/trial&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;trial F_image&amp;gt;&lt;br/&gt;/ stimulusframes = [1=F_image]&lt;br/&gt;/ response = timeout(60000)&lt;br/&gt;/ validresponse = ("e","i")&lt;br/&gt;/ correctresponse = ("i")&lt;br/&gt;&amp;lt;/trial&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;block visual&amp;gt;&lt;br/&gt;/ screencolor = (128,128,128)&lt;br/&gt;/ trials = [1-180 = noreplacenorepeat(F_image,T_image)]&lt;br/&gt;&amp;lt;/block&amp;gt;[/code]&lt;br/&gt;&lt;br/&gt;To address this problem and avoid repetitions, I am considering implementing a &amp;lt;list&amp;gt; that specifies the pool size and selection rate. I intend to amend the code into something like this:&lt;br/&gt;&lt;br/&gt;[code]&amp;lt;picture T_image&amp;gt;&lt;br/&gt;/ items = T_pictureitems&lt;br/&gt;/ position = (50%, 50%)&lt;br/&gt;/ halign = center&lt;br/&gt;/ size = (40%, 40%)&lt;br/&gt;/ select = list.filleritems_T.nextindex&lt;br/&gt;&amp;lt;/picture&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;picture F_image&amp;gt;&lt;br/&gt;/ items = F_pictureitems&lt;br/&gt;/ position = (50%, 50%)&lt;br/&gt;/ halign = center&lt;br/&gt;/ size = (40%, 40%)&lt;br/&gt;/ select = list.filleritems_F.nextindex&lt;br/&gt;&amp;lt;/picture&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;list filleritems_F&amp;gt;&lt;br/&gt;/ poolsize = 90&lt;br/&gt;/ selectionrate = always&lt;br/&gt;&amp;lt;/list&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;list filleritems_T&amp;gt;&lt;br/&gt;/ poolsize = 90&lt;br/&gt;/ selectionrate = always&lt;br/&gt;&amp;lt;/list&amp;gt;[/code]&lt;br/&gt;&lt;br/&gt;Would this be helpful in your opinion? Thank you so much!&lt;a class="if-quote-goto quote-link" href="#" data-id="35753"&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;The alternation between F and T is because that is how you sample trials:&lt;br/&gt;&lt;br/&gt;/ trials = [1-180 = &lt;strong&gt;noreplacenorepeat&lt;/strong&gt;(F_image,T_image)]&lt;br/&gt;&lt;br/&gt;You've told Inquisit to run 180 trials, F_image and T_image&amp;nbsp; in equal proportion (that's 90 each), sampling from those *trials* without replacement and without repeating the same *trial*; the only way to do that is to go F -&amp;gt; T -&amp;gt; F -&amp;gt; ... or T -&amp;gt; F -&amp;gt; T -&amp;gt; ....&lt;br/&gt;&lt;br/&gt;If you simply want 90 F_image trials and 90 T_image trials in random order, then you ought to specify&lt;br/&gt;&lt;br/&gt;/ trials = [1-180 = &lt;strong&gt;noreplace&lt;/strong&gt;(F_image,T_image)]&lt;br/&gt;&lt;br/&gt;Trial selection has nothing to do with item selection in your stimuli. Your picture elements sample from their items randomly without replacement by default.&lt;br/&gt;&lt;br/&gt;You do not need any lists.&lt;br/&gt;&lt;a class="if-quote-goto quote-link" href="#" data-id="35754"&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;Thank you so much Dave for your reply. I have actually tried using &lt;strong&gt;/noreplace &lt;/strong&gt;instead of &lt;strong&gt;/noreplacenorepeat&lt;/strong&gt;&amp;nbsp;before that post but I still noticed some repetitions in the pictures displayed on the screen. Some pictures from F_image and T_image trials are played more than once.&amp;nbsp;&lt;br/&gt;&lt;br/&gt;I have also noticed that if I want to record correctness of participants' responses to pictures under F_image and T_image, inquisit does not return the correctness of a specific picture, but just correctness (in 0 or 1) of F_image and T_image trials. Are&lt;strong&gt; /responsemessage &lt;/strong&gt;an appropriate function in this case?&lt;br/&gt;&lt;br/&gt;Thank you so so much&lt;a class="if-quote-goto quote-link" href="#" data-id="35755"&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;No images will repeat if you have 90 unique items for either &amp;lt;picture&amp;gt; element and you aren't taking additional samples from these same &amp;lt;picture&amp;gt; elements elsewhere.&lt;br/&gt;&lt;br/&gt;I do not understand what you mean by "correctness of a specifc picture". What image was displayed is logged to the data file by default, just like correctness of the response in the given trial, unless you opted not to record that information. /responsemessage has absolutely nothing to do with any of this&lt;a class="if-quote-goto quote-link" href="#" data-id="35756"&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;Here's a downscaled example, using only 9 (not 90) items per &amp;lt;picture&amp;gt;. If you run this, the result will be that across the 18 trials (9&amp;nbsp; F_image trials, 9 T_image trials, in random order), you'll see each image item exactly once.&lt;br/&gt;&lt;br/&gt;[code]&amp;lt;item F_pictureitems&amp;gt;&lt;br/&gt;/1 = "F1.PNG"&lt;br/&gt;/2 = "F2.PNG"&lt;br/&gt;/3 = "F3.PNG"&lt;br/&gt;/4 = "F4.PNG"&lt;br/&gt;/5 = "F5.PNG"&lt;br/&gt;/6 = "F6.PNG"&lt;br/&gt;/7 = "F7.PNG"&lt;br/&gt;/8 = "F8.PNG"&lt;br/&gt;/9 = "F9.PNG"&lt;br/&gt;&amp;lt;/item&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;item T_pictureitems&amp;gt;&lt;br/&gt;/1 = "T1.PNG"&lt;br/&gt;/2 = "T2.PNG"&lt;br/&gt;/3 = "T3.PNG"&lt;br/&gt;/4 = "T4.PNG"&lt;br/&gt;/5 = "T5.PNG"&lt;br/&gt;/6 = "T6.PNG"&lt;br/&gt;/7 = "T7.PNG"&lt;br/&gt;/8 = "T8.PNG"&lt;br/&gt;/9 = "T9.PNG"&lt;br/&gt;&amp;lt;/item&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;picture T_image&amp;gt;&lt;br/&gt;/ items = T_pictureitems&lt;br/&gt;/ position = (50%, 50%)&lt;br/&gt;/ halign = center&lt;br/&gt;/ size = (40%, 40%)&lt;br/&gt;&amp;lt;/picture&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;picture F_image&amp;gt;&lt;br/&gt;/ items = F_pictureitems&lt;br/&gt;/ position = (50%, 50%)&lt;br/&gt;/ halign = center&lt;br/&gt;/ size = (40%, 40%)&lt;br/&gt;&amp;lt;/picture&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;trial T_image&amp;gt;&lt;br/&gt;/ stimulusframes = [1=T_image]&lt;br/&gt;/ response = timeout(60000)&lt;br/&gt;/ validresponse = ("e","i")&lt;br/&gt;/ correctresponse = ("e")&lt;br/&gt;&amp;lt;/trial&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;trial F_image&amp;gt;&lt;br/&gt;/ stimulusframes = [1=F_image]&lt;br/&gt;/ response = timeout(60000)&lt;br/&gt;/ validresponse = ("e","i")&lt;br/&gt;/ correctresponse = ("i")&lt;br/&gt;&amp;lt;/trial&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;block visual&amp;gt;&lt;br/&gt;/ screencolor = (128,128,128)&lt;br/&gt;/ trials = [1-18 = noreplace(F_image,T_image)]&lt;br/&gt;&amp;lt;/block&amp;gt;[/code]&lt;br/&gt;&lt;br/&gt;Here's the data output of the above, sorted by trial type and stimulus item displayed: The data file is also attached below.&lt;br/&gt;&lt;br/&gt;&lt;img src="data:image/png;base64,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" alt=""&gt;&lt;br/&gt;</description><pubDate>Wed, 08 Nov 2023 01:57:24 GMT</pubDate><dc:creator>Dave</dc:creator></item><item><title>RE: standardize the display size of images with different original dimensions</title><link>https://forums.millisecond.com/Topic35756.aspx</link><description>&lt;blockquote data-id="35755" class="if-quote-wrapper" unselectable="on" data-guid="1699407213873" contenteditable="false" id="if_insertedNode_1699407212881"&gt;&lt;a class="quote-para" unselectable="on" style="display: none;" href="#" data-id="35755" 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="35755" 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="35755" title=" "&gt;&amp;nbsp;&lt;/a&gt;&lt;/div&gt;&lt;span unselectable="on" class="quote-markup"&gt;[b]&lt;/span&gt;nuttymenkk - 11/8/2023&lt;span unselectable="on" class="quote-markup"&gt;[/b]&lt;/span&gt;&lt;/div&gt;&lt;div class="if-quote-message if-quote-message-35755"&gt;&lt;div class="if-quote-message-margin" contenteditable="true"&gt;&lt;blockquote data-id="35754" class="if-quote-wrapper" unselectable="on" data-guid="1699407213873" contenteditable="false" id="if_insertedNode_1699406020112"&gt;&lt;a class="quote-para" unselectable="on" style="display: none;" href="#" data-id="35754" 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="35754" 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="35754" title=" "&gt;&amp;nbsp;&lt;/a&gt;&lt;/div&gt;&lt;span unselectable="on" class="quote-markup"&gt;[b]&lt;/span&gt;Dave - 11/7/2023&lt;span unselectable="on" class="quote-markup"&gt;[/b]&lt;/span&gt;&lt;/div&gt;&lt;div class="if-quote-message if-quote-message-35754"&gt;&lt;div class="if-quote-message-margin" contenteditable="true"&gt;&lt;blockquote data-id="35753" class="if-quote-wrapper" unselectable="on" data-guid="1699407213873" contenteditable="false" id="if_insertedNode_1699361389052"&gt;&lt;a class="quote-para" unselectable="on" style="display: none;" href="#" data-id="35753" 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="35753" 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="35753" title=" "&gt;&amp;nbsp;&lt;/a&gt;&lt;/div&gt;&lt;span unselectable="on" class="quote-markup"&gt;[b]&lt;/span&gt;nuttymenkk - 11/7/2023&lt;span unselectable="on" class="quote-markup"&gt;[/b]&lt;/span&gt;&lt;/div&gt;&lt;div class="if-quote-message if-quote-message-35753"&gt;&lt;div class="if-quote-message-margin" contenteditable="true"&gt;&lt;blockquote data-id="35752" class="if-quote-wrapper" unselectable="on" data-guid="1699407213873" contenteditable="false" id="if_insertedNode_1699333712673"&gt;&lt;a class="quote-para" unselectable="on" style="display: none;" href="#" data-id="35752" 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="35752" 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="35752" title=" "&gt;&amp;nbsp;&lt;/a&gt;&lt;/div&gt;&lt;span unselectable="on" class="quote-markup"&gt;[b]&lt;/span&gt;nuttymenkk - 11/7/2023&lt;span unselectable="on" class="quote-markup"&gt;[/b]&lt;/span&gt;&lt;/div&gt;&lt;div class="if-quote-message if-quote-message-35752"&gt;&lt;div class="if-quote-message-margin" contenteditable="true"&gt;I see, thank you very much Dave&lt;a class="if-quote-goto quote-link" href="#" data-id="35752"&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;I have an additional question regarding the randomization of a set of pictures. Currently, I have two groups of pictures: one falls under the "true picture" category, while the other belongs to the "false picture" category. My objective is to record the correctness of subjects' answers based on these pictures.&lt;br/&gt;&lt;br/&gt;To achieve this, I created two trials, one for the true category and another for the false category. I have developed the following script. However, it appears that there are repetitions in the displayed pictures. Upon further investigation, I found that Inquisit alternates between the "F_image" and "T_image" categories, ensuring no repetition within this alternation. However, my code does not account for the specific picture names.&lt;br/&gt;&lt;br/&gt;[code]&amp;lt;item F_pictureitems&amp;gt;&lt;br/&gt;/1 = "F1.PNG"&lt;br/&gt;/2 = "F2.PNG"&lt;br/&gt;/3 = "F3.PNG"&lt;br/&gt;/4 = "F4.PNG"&lt;br/&gt;/5 = "F5.PNG"&lt;br/&gt;/6 = "F6.PNG"&lt;br/&gt;...&lt;br/&gt;/90 = "F90.PNG"&lt;br/&gt;&amp;lt;/item&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;item T_pictureitems&amp;gt;&lt;br/&gt;/1 = "T1.PNG"&lt;br/&gt;/2 = "T2.PNG"&lt;br/&gt;/3 = "T3.PNG"&lt;br/&gt;/4 = "T4.PNG"&lt;br/&gt;/5 = "T5.PNG"&lt;br/&gt;/6 = "T6.PNG"&lt;br/&gt;...&lt;br/&gt;/90 = "T90.PNG"&lt;br/&gt;&amp;lt;/item&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;picture T_image&amp;gt;&lt;br/&gt;/ items = T_pictureitems&lt;br/&gt;/ position = (50%, 50%)&lt;br/&gt;/ halign = center&lt;br/&gt;/ size = (40%, 40%)&lt;br/&gt;&amp;lt;/picture&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;picture F_image&amp;gt;&lt;br/&gt;/ items = F_pictureitems&lt;br/&gt;/ position = (50%, 50%)&lt;br/&gt;/ halign = center&lt;br/&gt;/ size = (40%, 40%)&lt;br/&gt;&amp;lt;/picture&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;trial T_image&amp;gt;&lt;br/&gt;/ stimulusframes = [1=T_image]&lt;br/&gt;/ response = timeout(60000)&lt;br/&gt;/ validresponse = ("e","i")&lt;br/&gt;/ correctresponse = ("e")&lt;br/&gt;&amp;lt;/trial&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;trial F_image&amp;gt;&lt;br/&gt;/ stimulusframes = [1=F_image]&lt;br/&gt;/ response = timeout(60000)&lt;br/&gt;/ validresponse = ("e","i")&lt;br/&gt;/ correctresponse = ("i")&lt;br/&gt;&amp;lt;/trial&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;block visual&amp;gt;&lt;br/&gt;/ screencolor = (128,128,128)&lt;br/&gt;/ trials = [1-180 = noreplacenorepeat(F_image,T_image)]&lt;br/&gt;&amp;lt;/block&amp;gt;[/code]&lt;br/&gt;&lt;br/&gt;To address this problem and avoid repetitions, I am considering implementing a &amp;lt;list&amp;gt; that specifies the pool size and selection rate. I intend to amend the code into something like this:&lt;br/&gt;&lt;br/&gt;[code]&amp;lt;picture T_image&amp;gt;&lt;br/&gt;/ items = T_pictureitems&lt;br/&gt;/ position = (50%, 50%)&lt;br/&gt;/ halign = center&lt;br/&gt;/ size = (40%, 40%)&lt;br/&gt;/ select = list.filleritems_T.nextindex&lt;br/&gt;&amp;lt;/picture&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;picture F_image&amp;gt;&lt;br/&gt;/ items = F_pictureitems&lt;br/&gt;/ position = (50%, 50%)&lt;br/&gt;/ halign = center&lt;br/&gt;/ size = (40%, 40%)&lt;br/&gt;/ select = list.filleritems_F.nextindex&lt;br/&gt;&amp;lt;/picture&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;list filleritems_F&amp;gt;&lt;br/&gt;/ poolsize = 90&lt;br/&gt;/ selectionrate = always&lt;br/&gt;&amp;lt;/list&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;list filleritems_T&amp;gt;&lt;br/&gt;/ poolsize = 90&lt;br/&gt;/ selectionrate = always&lt;br/&gt;&amp;lt;/list&amp;gt;[/code]&lt;br/&gt;&lt;br/&gt;Would this be helpful in your opinion? Thank you so much!&lt;a class="if-quote-goto quote-link" href="#" data-id="35753"&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;The alternation between F and T is because that is how you sample trials:&lt;br/&gt;&lt;br/&gt;/ trials = [1-180 = &lt;strong&gt;noreplacenorepeat&lt;/strong&gt;(F_image,T_image)]&lt;br/&gt;&lt;br/&gt;You've told Inquisit to run 180 trials, F_image and T_image&amp;nbsp; in equal proportion (that's 90 each), sampling from those *trials* without replacement and without repeating the same *trial*; the only way to do that is to go F -&amp;gt; T -&amp;gt; F -&amp;gt; ... or T -&amp;gt; F -&amp;gt; T -&amp;gt; ....&lt;br/&gt;&lt;br/&gt;If you simply want 90 F_image trials and 90 T_image trials in random order, then you ought to specify&lt;br/&gt;&lt;br/&gt;/ trials = [1-180 = &lt;strong&gt;noreplace&lt;/strong&gt;(F_image,T_image)]&lt;br/&gt;&lt;br/&gt;Trial selection has nothing to do with item selection in your stimuli. Your picture elements sample from their items randomly without replacement by default.&lt;br/&gt;&lt;br/&gt;You do not need any lists.&lt;br/&gt;&lt;a class="if-quote-goto quote-link" href="#" data-id="35754"&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;Thank you so much Dave for your reply. I have actually tried using &lt;strong&gt;/noreplace &lt;/strong&gt;instead of &lt;strong&gt;/noreplacenorepeat&lt;/strong&gt;&amp;nbsp;before that post but I still noticed some repetitions in the pictures displayed on the screen. Some pictures from F_image and T_image trials are played more than once.&amp;nbsp;&lt;br/&gt;&lt;br/&gt;I have also noticed that if I want to record correctness of participants' responses to pictures under F_image and T_image, inquisit does not return the correctness of a specific picture, but just correctness (in 0 or 1) of F_image and T_image trials. Are&lt;strong&gt; /responsemessage &lt;/strong&gt;an appropriate function in this case?&lt;br/&gt;&lt;br/&gt;Thank you so so much&lt;a class="if-quote-goto quote-link" href="#" data-id="35755"&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;No images will repeat if you have 90 unique items for either &amp;lt;picture&amp;gt; element and you aren't taking additional samples from these same &amp;lt;picture&amp;gt; elements elsewhere.&lt;br/&gt;&lt;br/&gt;I do not understand what you mean by "correctness of a specifc picture". What image was displayed is logged to the data file by default, just like correctness of the response in the given trial, unless you opted not to record that information. /responsemessage has absolutely nothing to do with any of this</description><pubDate>Wed, 08 Nov 2023 01:41:33 GMT</pubDate><dc:creator>Dave</dc:creator></item><item><title>RE: standardize the display size of images with different original dimensions</title><link>https://forums.millisecond.com/Topic35755.aspx</link><description>&lt;blockquote data-id="35754" class="if-quote-wrapper" unselectable="on" data-guid="1699406021481" contenteditable="false" id="if_insertedNode_1699406020112"&gt;&lt;a class="quote-para" unselectable="on" style="display: none;" href="#" data-id="35754" 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="35754" 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="35754" title=" "&gt;&amp;nbsp;&lt;/a&gt;&lt;/div&gt;&lt;span unselectable="on" class="quote-markup"&gt;[b]&lt;/span&gt;Dave - 11/7/2023&lt;span unselectable="on" class="quote-markup"&gt;[/b]&lt;/span&gt;&lt;/div&gt;&lt;div class="if-quote-message if-quote-message-35754"&gt;&lt;div class="if-quote-message-margin" contenteditable="true"&gt;&lt;blockquote data-id="35753" class="if-quote-wrapper" unselectable="on" data-guid="1699406021481" contenteditable="false" id="if_insertedNode_1699361389052"&gt;&lt;a class="quote-para" unselectable="on" style="display: none;" href="#" data-id="35753" 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="35753" 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="35753" title=" "&gt;&amp;nbsp;&lt;/a&gt;&lt;/div&gt;&lt;span unselectable="on" class="quote-markup"&gt;[b]&lt;/span&gt;nuttymenkk - 11/7/2023&lt;span unselectable="on" class="quote-markup"&gt;[/b]&lt;/span&gt;&lt;/div&gt;&lt;div class="if-quote-message if-quote-message-35753"&gt;&lt;div class="if-quote-message-margin" contenteditable="true"&gt;&lt;blockquote data-id="35752" class="if-quote-wrapper" unselectable="on" data-guid="1699406021481" contenteditable="false" id="if_insertedNode_1699333712673"&gt;&lt;a class="quote-para" unselectable="on" style="display: none;" href="#" data-id="35752" 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="35752" 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="35752" title=" "&gt;&amp;nbsp;&lt;/a&gt;&lt;/div&gt;&lt;span unselectable="on" class="quote-markup"&gt;[b]&lt;/span&gt;nuttymenkk - 11/7/2023&lt;span unselectable="on" class="quote-markup"&gt;[/b]&lt;/span&gt;&lt;/div&gt;&lt;div class="if-quote-message if-quote-message-35752"&gt;&lt;div class="if-quote-message-margin" contenteditable="true"&gt;I see, thank you very much Dave&lt;a class="if-quote-goto quote-link" href="#" data-id="35752"&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;I have an additional question regarding the randomization of a set of pictures. Currently, I have two groups of pictures: one falls under the "true picture" category, while the other belongs to the "false picture" category. My objective is to record the correctness of subjects' answers based on these pictures.&lt;br/&gt;&lt;br/&gt;To achieve this, I created two trials, one for the true category and another for the false category. I have developed the following script. However, it appears that there are repetitions in the displayed pictures. Upon further investigation, I found that Inquisit alternates between the "F_image" and "T_image" categories, ensuring no repetition within this alternation. However, my code does not account for the specific picture names.&lt;br/&gt;&lt;br/&gt;[code]&amp;lt;item F_pictureitems&amp;gt;&lt;br/&gt;/1 = "F1.PNG"&lt;br/&gt;/2 = "F2.PNG"&lt;br/&gt;/3 = "F3.PNG"&lt;br/&gt;/4 = "F4.PNG"&lt;br/&gt;/5 = "F5.PNG"&lt;br/&gt;/6 = "F6.PNG"&lt;br/&gt;...&lt;br/&gt;/90 = "F90.PNG"&lt;br/&gt;&amp;lt;/item&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;item T_pictureitems&amp;gt;&lt;br/&gt;/1 = "T1.PNG"&lt;br/&gt;/2 = "T2.PNG"&lt;br/&gt;/3 = "T3.PNG"&lt;br/&gt;/4 = "T4.PNG"&lt;br/&gt;/5 = "T5.PNG"&lt;br/&gt;/6 = "T6.PNG"&lt;br/&gt;...&lt;br/&gt;/90 = "T90.PNG"&lt;br/&gt;&amp;lt;/item&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;picture T_image&amp;gt;&lt;br/&gt;/ items = T_pictureitems&lt;br/&gt;/ position = (50%, 50%)&lt;br/&gt;/ halign = center&lt;br/&gt;/ size = (40%, 40%)&lt;br/&gt;&amp;lt;/picture&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;picture F_image&amp;gt;&lt;br/&gt;/ items = F_pictureitems&lt;br/&gt;/ position = (50%, 50%)&lt;br/&gt;/ halign = center&lt;br/&gt;/ size = (40%, 40%)&lt;br/&gt;&amp;lt;/picture&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;trial T_image&amp;gt;&lt;br/&gt;/ stimulusframes = [1=T_image]&lt;br/&gt;/ response = timeout(60000)&lt;br/&gt;/ validresponse = ("e","i")&lt;br/&gt;/ correctresponse = ("e")&lt;br/&gt;&amp;lt;/trial&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;trial F_image&amp;gt;&lt;br/&gt;/ stimulusframes = [1=F_image]&lt;br/&gt;/ response = timeout(60000)&lt;br/&gt;/ validresponse = ("e","i")&lt;br/&gt;/ correctresponse = ("i")&lt;br/&gt;&amp;lt;/trial&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;block visual&amp;gt;&lt;br/&gt;/ screencolor = (128,128,128)&lt;br/&gt;/ trials = [1-180 = noreplacenorepeat(F_image,T_image)]&lt;br/&gt;&amp;lt;/block&amp;gt;[/code]&lt;br/&gt;&lt;br/&gt;To address this problem and avoid repetitions, I am considering implementing a &amp;lt;list&amp;gt; that specifies the pool size and selection rate. I intend to amend the code into something like this:&lt;br/&gt;&lt;br/&gt;[code]&amp;lt;picture T_image&amp;gt;&lt;br/&gt;/ items = T_pictureitems&lt;br/&gt;/ position = (50%, 50%)&lt;br/&gt;/ halign = center&lt;br/&gt;/ size = (40%, 40%)&lt;br/&gt;/ select = list.filleritems_T.nextindex&lt;br/&gt;&amp;lt;/picture&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;picture F_image&amp;gt;&lt;br/&gt;/ items = F_pictureitems&lt;br/&gt;/ position = (50%, 50%)&lt;br/&gt;/ halign = center&lt;br/&gt;/ size = (40%, 40%)&lt;br/&gt;/ select = list.filleritems_F.nextindex&lt;br/&gt;&amp;lt;/picture&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;list filleritems_F&amp;gt;&lt;br/&gt;/ poolsize = 90&lt;br/&gt;/ selectionrate = always&lt;br/&gt;&amp;lt;/list&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;list filleritems_T&amp;gt;&lt;br/&gt;/ poolsize = 90&lt;br/&gt;/ selectionrate = always&lt;br/&gt;&amp;lt;/list&amp;gt;[/code]&lt;br/&gt;&lt;br/&gt;Would this be helpful in your opinion? Thank you so much!&lt;a class="if-quote-goto quote-link" href="#" data-id="35753"&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;The alternation between F and T is because that is how you sample trials:&lt;br/&gt;&lt;br/&gt;/ trials = [1-180 = &lt;strong&gt;noreplacenorepeat&lt;/strong&gt;(F_image,T_image)]&lt;br/&gt;&lt;br/&gt;You've told Inquisit to run 180 trials, F_image and T_image&amp;nbsp; in equal proportion (that's 90 each), sampling from those *trials* without replacement and without repeating the same *trial*; the only way to do that is to go F -&amp;gt; T -&amp;gt; F -&amp;gt; ... or T -&amp;gt; F -&amp;gt; T -&amp;gt; ....&lt;br/&gt;&lt;br/&gt;If you simply want 90 F_image trials and 90 T_image trials in random order, then you ought to specify&lt;br/&gt;&lt;br/&gt;/ trials = [1-180 = &lt;strong&gt;noreplace&lt;/strong&gt;(F_image,T_image)]&lt;br/&gt;&lt;br/&gt;Trial selection has nothing to do with item selection in your stimuli. Your picture elements sample from their items randomly without replacement by default.&lt;br/&gt;&lt;br/&gt;You do not need any lists.&lt;br/&gt;&lt;a class="if-quote-goto quote-link" href="#" data-id="35754"&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;Thank you so much Dave for your reply. I have actually tried using &lt;strong&gt;/noreplace &lt;/strong&gt;instead of &lt;strong&gt;/noreplacenorepeat&lt;/strong&gt;&amp;nbsp;before that post but I still noticed some repetitions in the pictures displayed on the screen. Some pictures from F_image and T_image trials are played more than once.&amp;nbsp;&lt;br/&gt;&lt;br/&gt;I have also noticed that if I want to record correctness of participants' responses to pictures under F_image and T_image, inquisit does not return the correctness of a specific picture, but just correctness (in 0 or 1) of F_image and T_image trials. Are&lt;strong&gt; /responsemessage &lt;/strong&gt;an appropriate function in this case?&lt;br/&gt;&lt;br/&gt;Thank you so so much</description><pubDate>Wed, 08 Nov 2023 01:22:54 GMT</pubDate><dc:creator>ttyelnv</dc:creator></item><item><title>RE: standardize the display size of images with different original dimensions</title><link>https://forums.millisecond.com/Topic35754.aspx</link><description>&lt;blockquote data-id="35753" class="if-quote-wrapper" unselectable="on" data-guid="1699361389563" contenteditable="false" id="if_insertedNode_1699361389052"&gt;&lt;a class="quote-para" unselectable="on" style="display: none;" href="#" data-id="35753" 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="35753" 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="35753" title=" "&gt;&amp;nbsp;&lt;/a&gt;&lt;/div&gt;&lt;span unselectable="on" class="quote-markup"&gt;[b]&lt;/span&gt;nuttymenkk - 11/7/2023&lt;span unselectable="on" class="quote-markup"&gt;[/b]&lt;/span&gt;&lt;/div&gt;&lt;div class="if-quote-message if-quote-message-35753"&gt;&lt;div class="if-quote-message-margin" contenteditable="true"&gt;&lt;blockquote data-id="35752" class="if-quote-wrapper" unselectable="on" data-guid="1699361389563" contenteditable="false" id="if_insertedNode_1699333712673"&gt;&lt;a class="quote-para" unselectable="on" style="display: none;" href="#" data-id="35752" 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="35752" 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="35752" title=" "&gt;&amp;nbsp;&lt;/a&gt;&lt;/div&gt;&lt;span unselectable="on" class="quote-markup"&gt;[b]&lt;/span&gt;nuttymenkk - 11/7/2023&lt;span unselectable="on" class="quote-markup"&gt;[/b]&lt;/span&gt;&lt;/div&gt;&lt;div class="if-quote-message if-quote-message-35752"&gt;&lt;div class="if-quote-message-margin" contenteditable="true"&gt;I see, thank you very much Dave&lt;a class="if-quote-goto quote-link" href="#" data-id="35752"&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;I have an additional question regarding the randomization of a set of pictures. Currently, I have two groups of pictures: one falls under the "true picture" category, while the other belongs to the "false picture" category. My objective is to record the correctness of subjects' answers based on these pictures.&lt;br/&gt;&lt;br/&gt;To achieve this, I created two trials, one for the true category and another for the false category. I have developed the following script. However, it appears that there are repetitions in the displayed pictures. Upon further investigation, I found that Inquisit alternates between the "F_image" and "T_image" categories, ensuring no repetition within this alternation. However, my code does not account for the specific picture names.&lt;br/&gt;&lt;br/&gt;[code]&amp;lt;item F_pictureitems&amp;gt;&lt;br/&gt;/1 = "F1.PNG"&lt;br/&gt;/2 = "F2.PNG"&lt;br/&gt;/3 = "F3.PNG"&lt;br/&gt;/4 = "F4.PNG"&lt;br/&gt;/5 = "F5.PNG"&lt;br/&gt;/6 = "F6.PNG"&lt;br/&gt;...&lt;br/&gt;/90 = "F90.PNG"&lt;br/&gt;&amp;lt;/item&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;item T_pictureitems&amp;gt;&lt;br/&gt;/1 = "T1.PNG"&lt;br/&gt;/2 = "T2.PNG"&lt;br/&gt;/3 = "T3.PNG"&lt;br/&gt;/4 = "T4.PNG"&lt;br/&gt;/5 = "T5.PNG"&lt;br/&gt;/6 = "T6.PNG"&lt;br/&gt;...&lt;br/&gt;/90 = "T90.PNG"&lt;br/&gt;&amp;lt;/item&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;picture T_image&amp;gt;&lt;br/&gt;/ items = T_pictureitems&lt;br/&gt;/ position = (50%, 50%)&lt;br/&gt;/ halign = center&lt;br/&gt;/ size = (40%, 40%)&lt;br/&gt;&amp;lt;/picture&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;picture F_image&amp;gt;&lt;br/&gt;/ items = F_pictureitems&lt;br/&gt;/ position = (50%, 50%)&lt;br/&gt;/ halign = center&lt;br/&gt;/ size = (40%, 40%)&lt;br/&gt;&amp;lt;/picture&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;trial T_image&amp;gt;&lt;br/&gt;/ stimulusframes = [1=T_image]&lt;br/&gt;/ response = timeout(60000)&lt;br/&gt;/ validresponse = ("e","i")&lt;br/&gt;/ correctresponse = ("e")&lt;br/&gt;&amp;lt;/trial&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;trial F_image&amp;gt;&lt;br/&gt;/ stimulusframes = [1=F_image]&lt;br/&gt;/ response = timeout(60000)&lt;br/&gt;/ validresponse = ("e","i")&lt;br/&gt;/ correctresponse = ("i")&lt;br/&gt;&amp;lt;/trial&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;block visual&amp;gt;&lt;br/&gt;/ screencolor = (128,128,128)&lt;br/&gt;/ trials = [1-180 = noreplacenorepeat(F_image,T_image)]&lt;br/&gt;&amp;lt;/block&amp;gt;[/code]&lt;br/&gt;&lt;br/&gt;To address this problem and avoid repetitions, I am considering implementing a &amp;lt;list&amp;gt; that specifies the pool size and selection rate. I intend to amend the code into something like this:&lt;br/&gt;&lt;br/&gt;[code]&amp;lt;picture T_image&amp;gt;&lt;br/&gt;/ items = T_pictureitems&lt;br/&gt;/ position = (50%, 50%)&lt;br/&gt;/ halign = center&lt;br/&gt;/ size = (40%, 40%)&lt;br/&gt;/ select = list.filleritems_T.nextindex&lt;br/&gt;&amp;lt;/picture&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;picture F_image&amp;gt;&lt;br/&gt;/ items = F_pictureitems&lt;br/&gt;/ position = (50%, 50%)&lt;br/&gt;/ halign = center&lt;br/&gt;/ size = (40%, 40%)&lt;br/&gt;/ select = list.filleritems_F.nextindex&lt;br/&gt;&amp;lt;/picture&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;list filleritems_F&amp;gt;&lt;br/&gt;/ poolsize = 90&lt;br/&gt;/ selectionrate = always&lt;br/&gt;&amp;lt;/list&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;list filleritems_T&amp;gt;&lt;br/&gt;/ poolsize = 90&lt;br/&gt;/ selectionrate = always&lt;br/&gt;&amp;lt;/list&amp;gt;[/code]&lt;br/&gt;&lt;br/&gt;Would this be helpful in your opinion? Thank you so much!&lt;a class="if-quote-goto quote-link" href="#" data-id="35753"&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;The alternation between F and T is because that is how you sample trials:&lt;br/&gt;&lt;br/&gt;/ trials = [1-180 = &lt;strong&gt;noreplacenorepeat&lt;/strong&gt;(F_image,T_image)]&lt;br/&gt;&lt;br/&gt;You've told Inquisit to run 180 trials, F_image and T_image&amp;nbsp; in equal proportion (that's 90 each), sampling from those *trials* without replacement and without repeating the same *trial*; the only way to do that is to go F -&amp;gt; T -&amp;gt; F -&amp;gt; ... or T -&amp;gt; F -&amp;gt; T -&amp;gt; ....&lt;br/&gt;&lt;br/&gt;If you simply want 90 F_image trials and 90 T_image trials in random order, then you ought to specify&lt;br/&gt;&lt;br/&gt;/ trials = [1-180 = &lt;strong&gt;noreplace&lt;/strong&gt;(F_image,T_image)]&lt;br/&gt;&lt;br/&gt;Trial selection has nothing to do with item selection in your stimuli. Your picture elements sample from their items randomly without replacement by default.&lt;br/&gt;&lt;br/&gt;You do not need any lists.&lt;br/&gt;</description><pubDate>Tue, 07 Nov 2023 12:56:20 GMT</pubDate><dc:creator>Dave</dc:creator></item><item><title>RE: standardize the display size of images with different original dimensions</title><link>https://forums.millisecond.com/Topic35753.aspx</link><description>&lt;blockquote data-id="35752" class="if-quote-wrapper" unselectable="on" data-guid="1699333713701" contenteditable="false" id="if_insertedNode_1699333712673"&gt;&lt;a class="quote-para" unselectable="on" style="display: none;" href="#" data-id="35752" 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="35752" 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="35752" title=" "&gt;&amp;nbsp;&lt;/a&gt;&lt;/div&gt;&lt;span unselectable="on" class="quote-markup"&gt;[b]&lt;/span&gt;nuttymenkk - 11/7/2023&lt;span unselectable="on" class="quote-markup"&gt;[/b]&lt;/span&gt;&lt;/div&gt;&lt;div class="if-quote-message if-quote-message-35752"&gt;&lt;div class="if-quote-message-margin" contenteditable="true"&gt;I see, thank you very much Dave&lt;a class="if-quote-goto quote-link" href="#" data-id="35752"&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;I have an additional question regarding the randomization of a set of pictures. Currently, I have two groups of pictures: one falls under the "true picture" category, while the other belongs to the "false picture" category. My objective is to record the correctness of subjects' answers based on these pictures.&lt;br/&gt;&lt;br/&gt;To achieve this, I created two trials, one for the true category and another for the false category. I have developed the following script. However, it appears that there are repetitions in the displayed pictures. Upon further investigation, I found that Inquisit alternates between the "F_image" and "T_image" categories, ensuring no repetition within this alternation. However, my code does not account for the specific picture names.&lt;br/&gt;&lt;br/&gt;[code]&amp;lt;item F_pictureitems&amp;gt;&lt;br/&gt;/1 = "F1.PNG"&lt;br/&gt;/2 = "F2.PNG"&lt;br/&gt;/3 = "F3.PNG"&lt;br/&gt;/4 = "F4.PNG"&lt;br/&gt;/5 = "F5.PNG"&lt;br/&gt;/6 = "F6.PNG"&lt;br/&gt;...&lt;br/&gt;/90 = "F90.PNG"&lt;br/&gt;&amp;lt;/item&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;item T_pictureitems&amp;gt;&lt;br/&gt;/1 = "T1.PNG"&lt;br/&gt;/2 = "T2.PNG"&lt;br/&gt;/3 = "T3.PNG"&lt;br/&gt;/4 = "T4.PNG"&lt;br/&gt;/5 = "T5.PNG"&lt;br/&gt;/6 = "T6.PNG"&lt;br/&gt;...&lt;br/&gt;/90 = "T90.PNG"&lt;br/&gt;&amp;lt;/item&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;picture T_image&amp;gt;&lt;br/&gt;/ items = T_pictureitems&lt;br/&gt;/ position = (50%, 50%)&lt;br/&gt;/ halign = center&lt;br/&gt;/ size = (40%, 40%)&lt;br/&gt;&amp;lt;/picture&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;picture F_image&amp;gt;&lt;br/&gt;/ items = F_pictureitems&lt;br/&gt;/ position = (50%, 50%)&lt;br/&gt;/ halign = center&lt;br/&gt;/ size = (40%, 40%)&lt;br/&gt;&amp;lt;/picture&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;trial T_image&amp;gt;&lt;br/&gt;/ stimulusframes = [1=T_image]&lt;br/&gt;/ response = timeout(60000)&lt;br/&gt;/ validresponse = ("e","i")&lt;br/&gt;/ correctresponse = ("e")&lt;br/&gt;&amp;lt;/trial&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;trial F_image&amp;gt;&lt;br/&gt;/ stimulusframes = [1=F_image]&lt;br/&gt;/ response = timeout(60000)&lt;br/&gt;/ validresponse = ("e","i")&lt;br/&gt;/ correctresponse = ("i")&lt;br/&gt;&amp;lt;/trial&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;block visual&amp;gt;&lt;br/&gt;/ screencolor = (128,128,128)&lt;br/&gt;/ trials = [1-180 = noreplacenorepeat(F_image,T_image)]&lt;br/&gt;&amp;lt;/block&amp;gt;[/code]&lt;br/&gt;&lt;br/&gt;To address this problem and avoid repetitions, I am considering implementing a &amp;lt;list&amp;gt; that specifies the pool size and selection rate. I intend to amend the code into something like this:&lt;br/&gt;&lt;br/&gt;[code]&amp;lt;picture T_image&amp;gt;&lt;br/&gt;/ items = T_pictureitems&lt;br/&gt;/ position = (50%, 50%)&lt;br/&gt;/ halign = center&lt;br/&gt;/ size = (40%, 40%)&lt;br/&gt;/ select = list.filleritems_T.nextindex&lt;br/&gt;&amp;lt;/picture&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;picture F_image&amp;gt;&lt;br/&gt;/ items = F_pictureitems&lt;br/&gt;/ position = (50%, 50%)&lt;br/&gt;/ halign = center&lt;br/&gt;/ size = (40%, 40%)&lt;br/&gt;/ select = list.filleritems_F.nextindex&lt;br/&gt;&amp;lt;/picture&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;list filleritems_F&amp;gt;&lt;br/&gt;/ poolsize = 90&lt;br/&gt;/ selectionrate = always&lt;br/&gt;&amp;lt;/list&amp;gt;&lt;br/&gt;&lt;br/&gt;&amp;lt;list filleritems_T&amp;gt;&lt;br/&gt;/ poolsize = 90&lt;br/&gt;/ selectionrate = always&lt;br/&gt;&amp;lt;/list&amp;gt;[/code]&lt;br/&gt;&lt;br/&gt;Would this be helpful in your opinion? Thank you so much!</description><pubDate>Tue, 07 Nov 2023 05:22:11 GMT</pubDate><dc:creator>ttyelnv</dc:creator></item><item><title>RE: standardize the display size of images with different original dimensions</title><link>https://forums.millisecond.com/Topic35752.aspx</link><description>I see, thank you very much Dave</description><pubDate>Tue, 07 Nov 2023 05:07:08 GMT</pubDate><dc:creator>ttyelnv</dc:creator></item><item><title>RE: standardize the display size of images with different original dimensions</title><link>https://forums.millisecond.com/Topic35724.aspx</link><description>&lt;blockquote data-id="35723" class="if-quote-wrapper" unselectable="on" data-guid="1698762576368" contenteditable="false" id="if_insertedNode_1698762574939"&gt;&lt;a class="quote-para" unselectable="on" style="display: none;" href="#" data-id="35723" 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="35723" 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="35723" title=" "&gt;&amp;nbsp;&lt;/a&gt;&lt;/div&gt;&lt;span unselectable="on" class="quote-markup"&gt;[b]&lt;/span&gt;nuttymenkk - 10/31/2023&lt;span unselectable="on" class="quote-markup"&gt;[/b]&lt;/span&gt;&lt;/div&gt;&lt;div class="if-quote-message if-quote-message-35723"&gt;&lt;div class="if-quote-message-margin" contenteditable="true"&gt;Dear Dave,&lt;br/&gt;&lt;br/&gt;I hope all is well. I was wondering if there is a way to standardize the display size of images that have different original dimensions. I attempted to use something like "/size (300px, 300px)," but the displayed size still varies for different images. If there is no method available, I will manually adjust the size of the images to achieve uniformity. Thank you in advance for your assistance.&lt;br/&gt;&lt;br/&gt;Best regards,&lt;br/&gt;M&lt;a class="if-quote-goto quote-link" href="#" data-id="35723"&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 will have to make sure that the images all have the same aspect ratio. Inquisit will never distort an image's inherent aspect ratio.</description><pubDate>Tue, 31 Oct 2023 14:30:33 GMT</pubDate><dc:creator>Dave</dc:creator></item></channel></rss>