mrg4
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Group: Forum Members
Posts: 19,
Visits: 218
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Hi, I am currently working on creating templates for some standard procedures which only vary in their stimulus material and a few parameters. One of those parameters is the number of trials within a specific block. Is there any chance of using values within a block's trial-attribute? Here is a example of how I imagine the ideal sultion:
<block Test> /trials = [1-values.NumTrials = noreplace(trialA, trialB) /... </block>
Is this possible somehow? Thanks in advance!
Dominik
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Dave
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Group: Administrators
Posts: 13K,
Visits: 105K
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+xHi, I am currently working on creating templates for some standard procedures which only vary in their stimulus material and a few parameters. One of those parameters is the number of trials within a specific block. Is there any chance of using values within a block's trial-attribute? Here is a example of how I imagine the ideal sultion: <block Test> /trials = [1-values.NumTrials = noreplace(trialA, trialB) /... </block> Is this possible somehow? Thanks in advance! Dominik You can achieve this by performing trial selection via a <list> and setting the <list>'s /poolsize via <parameters>: <parameters> / ntrials = 10</parameters> <block myblock> / stop = [ block.myblock.trialcount >= parameters.ntrials ]/ trials = [1=list.triallist]</block> <list triallist> / items = (trial.a, trial.b) / poolsize = parameters.ntrials</list> <trial a> / validresponse = (0) / trialduration = 10 / branch = [ list.triallist.nextvalue; ]</trial> <trial b> / validresponse = (0) / trialduration = 10 / branch = [ list.triallist.nextvalue; ]</trial> The above will run 10 trials (5 x a; 5 x b). Change the ntrials parameter to 20, and you'll get 10 x a and 10 x b: <parameters> / ntrials = 20 </parameters> <block myblock> / stop = [ block.myblock.trialcount >= parameters.ntrials ] / trials = [1=list.triallist] </block> <list triallist> / items = (trial.a, trial.b) / poolsize = parameters.ntrials </list> <trial a> / validresponse = (0) / trialduration = 10 / branch = [ list.triallist.nextvalue; ] </trial> <trial b> / validresponse = (0) / trialduration = 10 / branch = [ list.triallist.nextvalue; ] </trial> Hope this helps.
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mrg4
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Group: Forum Members
Posts: 19,
Visits: 218
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+x+xHi, I am currently working on creating templates for some standard procedures which only vary in their stimulus material and a few parameters. One of those parameters is the number of trials within a specific block. Is there any chance of using values within a block's trial-attribute? Here is a example of how I imagine the ideal sultion: <block Test> /trials = [1-values.NumTrials = noreplace(trialA, trialB) /... </block> Is this possible somehow? Thanks in advance! Dominik You can achieve this by performing trial selection via a <list> and setting the <list>'s /poolsize via <parameters>: <parameters> / ntrials = 10</parameters> <block myblock> / stop = [ block.myblock.trialcount >= parameters.ntrials ]/ trials = [1=list.triallist]</block> <list triallist> / items = (trial.a, trial.b) / poolsize = parameters.ntrials</list> <trial a> / validresponse = (0) / trialduration = 10 / branch = [ list.triallist.nextvalue; ]</trial> <trial b> / validresponse = (0) / trialduration = 10 / branch = [ list.triallist.nextvalue; ]</trial> The above will run 10 trials (5 x a; 5 x b). Change the ntrials parameter to 20, and you'll get 10 x a and 10 x b: <parameters> / ntrials = 20 </parameters> <block myblock> / stop = [ block.myblock.trialcount >= parameters.ntrials ] / trials = [1=list.triallist] </block> <list triallist> / items = (trial.a, trial.b) / poolsize = parameters.ntrials </list> <trial a> / validresponse = (0) / trialduration = 10 / branch = [ list.triallist.nextvalue; ] </trial> <trial b> / validresponse = (0) / trialduration = 10 / branch = [ list.triallist.nextvalue; ] </trial> Hope this helps. Thank you so much for your quick response! That's the solution I was looking for! :) Best regards.
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Andrew Papale
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Group: Forum Members
Posts: 67,
Visits: 239
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+x+x+xHi, I am currently working on creating templates for some standard procedures which only vary in their stimulus material and a few parameters. One of those parameters is the number of trials within a specific block. Is there any chance of using values within a block's trial-attribute? Here is a example of how I imagine the ideal sultion: <block Test> /trials = [1-values.NumTrials = noreplace(trialA, trialB) /... </block> Is this possible somehow? Thanks in advance! Dominik You can achieve this by performing trial selection via a <list> and setting the <list>'s /poolsize via <parameters>: <parameters> / ntrials = 10</parameters> <block myblock> / stop = [ block.myblock.trialcount >= parameters.ntrials ]/ trials = [1=list.triallist]</block> <list triallist> / items = (trial.a, trial.b) / poolsize = parameters.ntrials</list> <trial a> / validresponse = (0) / trialduration = 10 / branch = [ list.triallist.nextvalue; ]</trial> <trial b> / validresponse = (0) / trialduration = 10 / branch = [ list.triallist.nextvalue; ]</trial> The above will run 10 trials (5 x a; 5 x b). Change the ntrials parameter to 20, and you'll get 10 x a and 10 x b: <parameters> / ntrials = 20 </parameters> <block myblock> / stop = [ block.myblock.trialcount >= parameters.ntrials ] / trials = [1=list.triallist] </block> <list triallist> / items = (trial.a, trial.b) / poolsize = parameters.ntrials </list> <trial a> / validresponse = (0) / trialduration = 10 / branch = [ list.triallist.nextvalue; ] </trial> <trial b> / validresponse = (0) / trialduration = 10 / branch = [ list.triallist.nextvalue; ] </trial> Hope this helps. Thank you so much for your quick response! That's the solution I was looking for! :) Best regards. I am trying to implement this to randomize total trial numbers, but it is not working correctly. e.g. in the code I specified, I wanted it to run 5 trials, and Inquisit is instead running exactly 3 trials of my task. The only difference appears to be that I branch from my trial in trial list to a feedback screen, and then from that feedback screen to list.triallist.nextvalue. Any help would be appreciated! <block experiment> / onblockbegin = [ values.scrfunc = noreplace("IEV","DEV","CEV","CEVR","IEV","DEV","IEV","DEV"); parameters.ntrials = 5; values.blockCount = values.blockCount + 1; values.trialCount = values.trialCount + parameters.ntrials; ] / stop = [ block.experiment.trialcount >= parameters.ntrials ] / trials = [1=list.triallist] </block>
<list triallist> /items = (trial.experiment) / poolsize = parameters.ntrials </list>
<trial experiment> / branch = [ return trial.dispFeedback; ] </trial>
<trial dispFeedback> / branch = [ list.triallist.nextvalue ] </trial>
<expt main1> / blocks = [ 1=instructions; 2=experiment; 3=experiment; 4=endscreen; ] </expt>
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Dave
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Group: Administrators
Posts: 13K,
Visits: 105K
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+x+x+x+xHi, I am currently working on creating templates for some standard procedures which only vary in their stimulus material and a few parameters. One of those parameters is the number of trials within a specific block. Is there any chance of using values within a block's trial-attribute? Here is a example of how I imagine the ideal sultion: <block Test> /trials = [1-values.NumTrials = noreplace(trialA, trialB) /... </block> Is this possible somehow? Thanks in advance! Dominik You can achieve this by performing trial selection via a <list> and setting the <list>'s /poolsize via <parameters>: <parameters> / ntrials = 10</parameters> <block myblock> / stop = [ block.myblock.trialcount >= parameters.ntrials ]/ trials = [1=list.triallist]</block> <list triallist> / items = (trial.a, trial.b) / poolsize = parameters.ntrials</list> <trial a> / validresponse = (0) / trialduration = 10 / branch = [ list.triallist.nextvalue; ]</trial> <trial b> / validresponse = (0) / trialduration = 10 / branch = [ list.triallist.nextvalue; ]</trial> The above will run 10 trials (5 x a; 5 x b). Change the ntrials parameter to 20, and you'll get 10 x a and 10 x b: <parameters> / ntrials = 20 </parameters> <block myblock> / stop = [ block.myblock.trialcount >= parameters.ntrials ] / trials = [1=list.triallist] </block> <list triallist> / items = (trial.a, trial.b) / poolsize = parameters.ntrials </list> <trial a> / validresponse = (0) / trialduration = 10 / branch = [ list.triallist.nextvalue; ] </trial> <trial b> / validresponse = (0) / trialduration = 10 / branch = [ list.triallist.nextvalue; ] </trial> Hope this helps. Thank you so much for your quick response! That's the solution I was looking for! :) Best regards. I am trying to implement this to randomize total trial numbers, but it is not working correctly. e.g. in the code I specified, I wanted it to run 5 trials, and Inquisit is instead running exactly 3 trials of my task. The only difference appears to be that I branch from my trial in trial list to a feedback screen, and then from that feedback screen to list.triallist.nextvalue. Any help would be appreciated! <block experiment> / onblockbegin = [ values.scrfunc = noreplace("IEV","DEV","CEV","CEVR","IEV","DEV","IEV","DEV"); parameters.ntrials = 5; values.blockCount = values.blockCount + 1; values.trialCount = values.trialCount + parameters.ntrials; ] / stop = [ block.experiment.trialcount >= parameters.ntrials ] / trials = [1=list.triallist] </block>
<list triallist> /items = (trial.experiment) / poolsize = parameters.ntrials </list>
<trial experiment> / branch = [ return trial.dispFeedback; ] </trial>
<trial dispFeedback> / branch = [ list.triallist.nextvalue ] </trial>
<expt main1> / blocks = [ 1=instructions; 2=experiment; 3=experiment; 4=endscreen; ] </expt> Feedback trials obviously go towards the trial count, so what you get is the expected resutl:
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Dave
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Group: Administrators
Posts: 13K,
Visits: 105K
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+x+x+x+x+xHi, I am currently working on creating templates for some standard procedures which only vary in their stimulus material and a few parameters. One of those parameters is the number of trials within a specific block. Is there any chance of using values within a block's trial-attribute? Here is a example of how I imagine the ideal sultion: <block Test> /trials = [1-values.NumTrials = noreplace(trialA, trialB) /... </block> Is this possible somehow? Thanks in advance! Dominik You can achieve this by performing trial selection via a <list> and setting the <list>'s /poolsize via <parameters>: <parameters> / ntrials = 10</parameters> <block myblock> / stop = [ block.myblock.trialcount >= parameters.ntrials ]/ trials = [1=list.triallist]</block> <list triallist> / items = (trial.a, trial.b) / poolsize = parameters.ntrials</list> <trial a> / validresponse = (0) / trialduration = 10 / branch = [ list.triallist.nextvalue; ]</trial> <trial b> / validresponse = (0) / trialduration = 10 / branch = [ list.triallist.nextvalue; ]</trial> The above will run 10 trials (5 x a; 5 x b). Change the ntrials parameter to 20, and you'll get 10 x a and 10 x b: <parameters> / ntrials = 20 </parameters> <block myblock> / stop = [ block.myblock.trialcount >= parameters.ntrials ] / trials = [1=list.triallist] </block> <list triallist> / items = (trial.a, trial.b) / poolsize = parameters.ntrials </list> <trial a> / validresponse = (0) / trialduration = 10 / branch = [ list.triallist.nextvalue; ] </trial> <trial b> / validresponse = (0) / trialduration = 10 / branch = [ list.triallist.nextvalue; ] </trial> Hope this helps. Thank you so much for your quick response! That's the solution I was looking for! :) Best regards. I am trying to implement this to randomize total trial numbers, but it is not working correctly. e.g. in the code I specified, I wanted it to run 5 trials, and Inquisit is instead running exactly 3 trials of my task. The only difference appears to be that I branch from my trial in trial list to a feedback screen, and then from that feedback screen to list.triallist.nextvalue. Any help would be appreciated! <block experiment> / onblockbegin = [ values.scrfunc = noreplace("IEV","DEV","CEV","CEVR","IEV","DEV","IEV","DEV"); parameters.ntrials = 5; values.blockCount = values.blockCount + 1; values.trialCount = values.trialCount + parameters.ntrials; ] / stop = [ block.experiment.trialcount >= parameters.ntrials ] / trials = [1=list.triallist] </block>
<list triallist> /items = (trial.experiment) / poolsize = parameters.ntrials </list>
<trial experiment> / branch = [ return trial.dispFeedback; ] </trial>
<trial dispFeedback> / branch = [ list.triallist.nextvalue ] </trial>
<expt main1> / blocks = [ 1=instructions; 2=experiment; 3=experiment; 4=endscreen; ] </expt> Feedback trials obviously go towards the trial count, so what you get is the expected resutl: ![](data:image/png;base64,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) To do this properly, i.e. 5 "experiment" trials, each followed by a feedback trial per block, you'll want to do something like this: <values> / scrfunc = "" / blockcount = 0 / trialcount = 0 </values>
<parameters> / ntrials = 0 </parameters>
<block experiment> / onblockbegin = [ values.scrfunc = noreplace("IEV","DEV","CEV","CEVR","IEV","DEV","IEV","DEV"); parameters.ntrials = 5; list.triallist.poolsize = parameters.ntrials; values.trialcount = 0; values.blockCount = values.blockCount + 1; ] / stop = [ values.trialcount >= parameters.ntrials ] / trials = [1=list.triallist] </block>
<list triallist> /items = (trial.experiment) / poolsize = parameters.ntrials </list>
<trial experiment> / trialduration = 100
/ branch = [ return trial.dispFeedback; ] </trial>
<trial dispFeedback> / ontrialbegin = [ values.trialcount += 1; ] / trialduration = 100 / branch = [ list.triallist.nextvalue ] </trial>
<expt main1> / blocks = [ 1=instructions; 2=experiment; 3=experiment; 4=endscreen; ] </expt>
<block instructions> / preinstructions = (intro) </block>
<block endscreen> / preinstructions = (end) </block>
<page intro> ^intro page </page>
<page end> ^end page </page>
<data> / columns = (date time subject group blocknum blockcode trialnum trialcode block.experiment.trialcount values.trialcount) </data>
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Andrew Papale
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Group: Forum Members
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+x+x+x+x+x+xHi, I am currently working on creating templates for some standard procedures which only vary in their stimulus material and a few parameters. One of those parameters is the number of trials within a specific block. Is there any chance of using values within a block's trial-attribute? Here is a example of how I imagine the ideal sultion: <block Test> /trials = [1-values.NumTrials = noreplace(trialA, trialB) /... </block> Is this possible somehow? Thanks in advance! Dominik You can achieve this by performing trial selection via a <list> and setting the <list>'s /poolsize via <parameters>: <parameters> / ntrials = 10</parameters> <block myblock> / stop = [ block.myblock.trialcount >= parameters.ntrials ]/ trials = [1=list.triallist]</block> <list triallist> / items = (trial.a, trial.b) / poolsize = parameters.ntrials</list> <trial a> / validresponse = (0) / trialduration = 10 / branch = [ list.triallist.nextvalue; ]</trial> <trial b> / validresponse = (0) / trialduration = 10 / branch = [ list.triallist.nextvalue; ]</trial> The above will run 10 trials (5 x a; 5 x b). Change the ntrials parameter to 20, and you'll get 10 x a and 10 x b: <parameters> / ntrials = 20 </parameters> <block myblock> / stop = [ block.myblock.trialcount >= parameters.ntrials ] / trials = [1=list.triallist] </block> <list triallist> / items = (trial.a, trial.b) / poolsize = parameters.ntrials </list> <trial a> / validresponse = (0) / trialduration = 10 / branch = [ list.triallist.nextvalue; ] </trial> <trial b> / validresponse = (0) / trialduration = 10 / branch = [ list.triallist.nextvalue; ] </trial> Hope this helps. Thank you so much for your quick response! That's the solution I was looking for! :) Best regards. I am trying to implement this to randomize total trial numbers, but it is not working correctly. e.g. in the code I specified, I wanted it to run 5 trials, and Inquisit is instead running exactly 3 trials of my task. The only difference appears to be that I branch from my trial in trial list to a feedback screen, and then from that feedback screen to list.triallist.nextvalue. Any help would be appreciated! <block experiment> / onblockbegin = [ values.scrfunc = noreplace("IEV","DEV","CEV","CEVR","IEV","DEV","IEV","DEV"); parameters.ntrials = 5; values.blockCount = values.blockCount + 1; values.trialCount = values.trialCount + parameters.ntrials; ] / stop = [ block.experiment.trialcount >= parameters.ntrials ] / trials = [1=list.triallist] </block>
<list triallist> /items = (trial.experiment) / poolsize = parameters.ntrials </list>
<trial experiment> / branch = [ return trial.dispFeedback; ] </trial>
<trial dispFeedback> / branch = [ list.triallist.nextvalue ] </trial>
<expt main1> / blocks = [ 1=instructions; 2=experiment; 3=experiment; 4=endscreen; ] </expt> Feedback trials obviously go towards the trial count, so what you get is the expected resutl: ![](data:image/png;base64,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) To do this properly, i.e. 5 "experiment" trials, each followed by a feedback trial per block, you'll want to do something like this: <values> / scrfunc = "" / blockcount = 0 / trialcount = 0 </values>
<parameters> / ntrials = 0 </parameters>
<block experiment> / onblockbegin = [ values.scrfunc = noreplace("IEV","DEV","CEV","CEVR","IEV","DEV","IEV","DEV"); parameters.ntrials = 5; list.triallist.poolsize = parameters.ntrials; values.trialcount = 0; values.blockCount = values.blockCount + 1; ] / stop = [ values.trialcount >= parameters.ntrials ] / trials = [1=list.triallist] </block>
<list triallist> /items = (trial.experiment) / poolsize = parameters.ntrials </list>
<trial experiment> / trialduration = 100
/ branch = [ return trial.dispFeedback; ] </trial>
<trial dispFeedback> / ontrialbegin = [ values.trialcount += 1; ] / trialduration = 100 / branch = [ list.triallist.nextvalue ] </trial>
<expt main1> / blocks = [ 1=instructions; 2=experiment; 3=experiment; 4=endscreen; ] </expt>
<block instructions> / preinstructions = (intro) </block>
<block endscreen> / preinstructions = (end) </block>
<page intro> ^intro page </page>
<page end> ^end page </page>
<data> / columns = (date time subject group blocknum blockcode trialnum trialcode block.experiment.trialcount values.trialcount) </data>
Thank you, this works wonderfully.
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Andrew Papale
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Group: Forum Members
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+x+x+x+x+x+x+xHi, I am currently working on creating templates for some standard procedures which only vary in their stimulus material and a few parameters. One of those parameters is the number of trials within a specific block. Is there any chance of using values within a block's trial-attribute? Here is a example of how I imagine the ideal sultion: <block Test> /trials = [1-values.NumTrials = noreplace(trialA, trialB) /... </block> Is this possible somehow? Thanks in advance! Dominik You can achieve this by performing trial selection via a <list> and setting the <list>'s /poolsize via <parameters>: <parameters> / ntrials = 10</parameters> <block myblock> / stop = [ block.myblock.trialcount >= parameters.ntrials ]/ trials = [1=list.triallist]</block> <list triallist> / items = (trial.a, trial.b) / poolsize = parameters.ntrials</list> <trial a> / validresponse = (0) / trialduration = 10 / branch = [ list.triallist.nextvalue; ]</trial> <trial b> / validresponse = (0) / trialduration = 10 / branch = [ list.triallist.nextvalue; ]</trial> The above will run 10 trials (5 x a; 5 x b). Change the ntrials parameter to 20, and you'll get 10 x a and 10 x b: <parameters> / ntrials = 20 </parameters> <block myblock> / stop = [ block.myblock.trialcount >= parameters.ntrials ] / trials = [1=list.triallist] </block> <list triallist> / items = (trial.a, trial.b) / poolsize = parameters.ntrials </list> <trial a> / validresponse = (0) / trialduration = 10 / branch = [ list.triallist.nextvalue; ] </trial> <trial b> / validresponse = (0) / trialduration = 10 / branch = [ list.triallist.nextvalue; ] </trial> Hope this helps. Thank you so much for your quick response! That's the solution I was looking for! :) Best regards. I am trying to implement this to randomize total trial numbers, but it is not working correctly. e.g. in the code I specified, I wanted it to run 5 trials, and Inquisit is instead running exactly 3 trials of my task. The only difference appears to be that I branch from my trial in trial list to a feedback screen, and then from that feedback screen to list.triallist.nextvalue. Any help would be appreciated! <block experiment> / onblockbegin = [ values.scrfunc = noreplace("IEV","DEV","CEV","CEVR","IEV","DEV","IEV","DEV"); parameters.ntrials = 5; values.blockCount = values.blockCount + 1; values.trialCount = values.trialCount + parameters.ntrials; ] / stop = [ block.experiment.trialcount >= parameters.ntrials ] / trials = [1=list.triallist] </block>
<list triallist> /items = (trial.experiment) / poolsize = parameters.ntrials </list>
<trial experiment> / branch = [ return trial.dispFeedback; ] </trial>
<trial dispFeedback> / branch = [ list.triallist.nextvalue ] </trial>
<expt main1> / blocks = [ 1=instructions; 2=experiment; 3=experiment; 4=endscreen; ] </expt> Feedback trials obviously go towards the trial count, so what you get is the expected resutl: ![](data:image/png;base64,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) To do this properly, i.e. 5 "experiment" trials, each followed by a feedback trial per block, you'll want to do something like this: <values> / scrfunc = "" / blockcount = 0 / trialcount = 0 </values>
<parameters> / ntrials = 0 </parameters>
<block experiment> / onblockbegin = [ values.scrfunc = noreplace("IEV","DEV","CEV","CEVR","IEV","DEV","IEV","DEV"); parameters.ntrials = 5; list.triallist.poolsize = parameters.ntrials; values.trialcount = 0; values.blockCount = values.blockCount + 1; ] / stop = [ values.trialcount >= parameters.ntrials ] / trials = [1=list.triallist] </block>
<list triallist> /items = (trial.experiment) / poolsize = parameters.ntrials </list>
<trial experiment> / trialduration = 100
/ branch = [ return trial.dispFeedback; ] </trial>
<trial dispFeedback> / ontrialbegin = [ values.trialcount += 1; ] / trialduration = 100 / branch = [ list.triallist.nextvalue ] </trial>
<expt main1> / blocks = [ 1=instructions; 2=experiment; 3=experiment; 4=endscreen; ] </expt>
<block instructions> / preinstructions = (intro) </block>
<block endscreen> / preinstructions = (end) </block>
<page intro> ^intro page </page>
<page end> ^end page </page>
<data> / columns = (date time subject group blocknum blockcode trialnum trialcode block.experiment.trialcount values.trialcount) </data>
Thank you, this works wonderfully. Can this same logic work to achieve variable block numbers? I assumed it would but am having some trouble implementing. Thanks, - Andre
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Dave
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+x+x+x+x+x+x+x+xHi, I am currently working on creating templates for some standard procedures which only vary in their stimulus material and a few parameters. One of those parameters is the number of trials within a specific block. Is there any chance of using values within a block's trial-attribute? Here is a example of how I imagine the ideal sultion: <block Test> /trials = [1-values.NumTrials = noreplace(trialA, trialB) /... </block> Is this possible somehow? Thanks in advance! Dominik You can achieve this by performing trial selection via a <list> and setting the <list>'s /poolsize via <parameters>: <parameters> / ntrials = 10</parameters> <block myblock> / stop = [ block.myblock.trialcount >= parameters.ntrials ]/ trials = [1=list.triallist]</block> <list triallist> / items = (trial.a, trial.b) / poolsize = parameters.ntrials</list> <trial a> / validresponse = (0) / trialduration = 10 / branch = [ list.triallist.nextvalue; ]</trial> <trial b> / validresponse = (0) / trialduration = 10 / branch = [ list.triallist.nextvalue; ]</trial> The above will run 10 trials (5 x a; 5 x b). Change the ntrials parameter to 20, and you'll get 10 x a and 10 x b: <parameters> / ntrials = 20 </parameters> <block myblock> / stop = [ block.myblock.trialcount >= parameters.ntrials ] / trials = [1=list.triallist] </block> <list triallist> / items = (trial.a, trial.b) / poolsize = parameters.ntrials </list> <trial a> / validresponse = (0) / trialduration = 10 / branch = [ list.triallist.nextvalue; ] </trial> <trial b> / validresponse = (0) / trialduration = 10 / branch = [ list.triallist.nextvalue; ] </trial> Hope this helps. Thank you so much for your quick response! That's the solution I was looking for! :) Best regards. I am trying to implement this to randomize total trial numbers, but it is not working correctly. e.g. in the code I specified, I wanted it to run 5 trials, and Inquisit is instead running exactly 3 trials of my task. The only difference appears to be that I branch from my trial in trial list to a feedback screen, and then from that feedback screen to list.triallist.nextvalue. Any help would be appreciated! <block experiment> / onblockbegin = [ values.scrfunc = noreplace("IEV","DEV","CEV","CEVR","IEV","DEV","IEV","DEV"); parameters.ntrials = 5; values.blockCount = values.blockCount + 1; values.trialCount = values.trialCount + parameters.ntrials; ] / stop = [ block.experiment.trialcount >= parameters.ntrials ] / trials = [1=list.triallist] </block>
<list triallist> /items = (trial.experiment) / poolsize = parameters.ntrials </list>
<trial experiment> / branch = [ return trial.dispFeedback; ] </trial>
<trial dispFeedback> / branch = [ list.triallist.nextvalue ] </trial>
<expt main1> / blocks = [ 1=instructions; 2=experiment; 3=experiment; 4=endscreen; ] </expt> Feedback trials obviously go towards the trial count, so what you get is the expected resutl: ![](data:image/png;base64,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) To do this properly, i.e. 5 "experiment" trials, each followed by a feedback trial per block, you'll want to do something like this: <values> / scrfunc = "" / blockcount = 0 / trialcount = 0 </values>
<parameters> / ntrials = 0 </parameters>
<block experiment> / onblockbegin = [ values.scrfunc = noreplace("IEV","DEV","CEV","CEVR","IEV","DEV","IEV","DEV"); parameters.ntrials = 5; list.triallist.poolsize = parameters.ntrials; values.trialcount = 0; values.blockCount = values.blockCount + 1; ] / stop = [ values.trialcount >= parameters.ntrials ] / trials = [1=list.triallist] </block>
<list triallist> /items = (trial.experiment) / poolsize = parameters.ntrials </list>
<trial experiment> / trialduration = 100
/ branch = [ return trial.dispFeedback; ] </trial>
<trial dispFeedback> / ontrialbegin = [ values.trialcount += 1; ] / trialduration = 100 / branch = [ list.triallist.nextvalue ] </trial>
<expt main1> / blocks = [ 1=instructions; 2=experiment; 3=experiment; 4=endscreen; ] </expt>
<block instructions> / preinstructions = (intro) </block>
<block endscreen> / preinstructions = (end) </block>
<page intro> ^intro page </page>
<page end> ^end page </page>
<data> / columns = (date time subject group blocknum blockcode trialnum trialcode block.experiment.trialcount values.trialcount) </data>
Thank you, this works wonderfully. Can this same logic work to achieve variable block numbers? I assumed it would but am having some trouble implementing. Thanks, - Andre Sure, you can do something similar for blocks.
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