Behavioral Analyses - Revision history

Chris at 17:51, 6 October 2016

2016-10-06T17:51:21Z

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 [[Category: MATLAB functions]]

Chris: /* getPTBData.m */

2016-10-06T17:26:52Z

getPTBData.m

← Older revision		Revision as of 13:26, 6 October 2016
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	==getPTBData.m==		==getPTBData.m==
	A MATLAB script, getPTBData.m has been written to facilitate the extraction of RT and accuracy data from the .mat files generated by our PTB3 scripts. This script should generally function on our experimental data collected at the MRI scanner or in-lab, unless the experimental paradigm necessitates a vastly different organization of the recorded behavioral data. It is assumed that the behavioral data is stored in an <code>expinfo.data</code> structure field within the .mat file, and within the data field can be found the fields ''conditon'' (note the typo -- this should probably be fixed at some point), ''response'' and ''rt''. Because of the possibility that participants might enter reverse-coded responses (e.g., holding the MRI response keypad upside-down), we take the modal response for each condition as the correct response, under the assumption that participants are not responding by chance. For tasks where high accuracy is expected, this seems reasonable. However, if it seems equally plausible that a participant might get 40% vs. 60% accuracy for a condition, this approach may require some modification to also accept an answer key (and the fMRI setup procedure would need to verify correct response pad orientation). This function takes a list of filenames as an optional parameter, however it is probably more convenient to leave the parameter blank and simply select the .mat files of interest when prompted by the program. RTs and accuracy values are returned as cell arrays of vectors, and <code>cellfun</code> can be used as described in the <code>help</code> for this function to compute the mean RT and task accuracy for each of the selected files.		A MATLAB script, getPTBData.m has been written to facilitate the extraction of RT and accuracy data from the .mat files generated by our PTB3 scripts. This script should generally function on our experimental data collected at the MRI scanner or in-lab, unless the experimental paradigm necessitates a vastly different organization of the recorded behavioral data. It is assumed that the behavioral data is stored in an <code>expinfo.data</code> structure field within the .mat file, and within the data field can be found the fields ''conditon'' (note the typo -- this should probably be fixed at some point), ''response'' and ''rt''. Because of the possibility that participants might enter reverse-coded responses (e.g., holding the MRI response keypad upside-down), we take the modal response for each condition as the correct response, under the assumption that participants are not responding by chance. For tasks where high accuracy is expected, this seems reasonable. However, if it seems equally plausible that a participant might get 40% vs. 60% accuracy for a condition, this approach may require some modification to also accept an answer key (and the fMRI setup procedure would need to verify correct response pad orientation). This function takes a list of filenames as an optional parameter, however it is probably more convenient to leave the parameter blank and simply select the .mat files of interest when prompted by the program. RTs and accuracy values are returned as cell arrays of vectors, and <code>cellfun</code> can be used as described in the <code>help</code> for this function to compute the mean RT and task accuracy for each of the selected files.

			=== Getting the Script ===
			The script is in the usual spot on the UBFS network share. Copy it to your local MATLAB folder, which will put it in your path:
			cp ~/ubfs/cpmcnorg/Scripts/Matlab/getPTBData.m ./

	function [rt, acc, filenames]=getPTBData(filenames)		function [rt, acc, filenames]=getPTBData(filenames)

Chris at 15:46, 6 October 2016

2016-10-06T15:46:49Z

← Older revision		Revision as of 11:46, 6 October 2016
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	end		end

	[[Category: MATLAB ~~Functions~~]]		[[Category: MATLAB functions]]

Chris: /* getPTBData.m */

2016-10-06T15:43:38Z

getPTBData.m

← Older revision		Revision as of 11:43, 6 October 2016
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	end		end

	[rt,~~a cc~~]=cellfun(@(x) extractData(x), filenames, 'UniformOutput', false);		[rt,acc]=cellfun(@(x) extractData(x), filenames, 'UniformOutput', false);
	filenames=filenames';		filenames=filenames';
	%helper functions		%helper functions

Chris at 15:42, 6 October 2016

2016-10-06T15:42:50Z

← Older revision		Revision as of 11:42, 6 October 2016
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	end		end

	[[Category: ~~Matlab functions~~]]		[[Category: MATLAB Functions]]

Chris: Created page with "Behavioral analyses are obviously necessary in a conventional cognitive behavioral study using reaction time and/or accuracy as primary measures of interest. However these dat..."

2016-10-06T15:41:57Z

Created page with "Behavioral analyses are obviously necessary in a conventional cognitive behavioral study using reaction time and/or accuracy as primary measures of interest. However these dat..."

New page

Behavioral analyses are obviously necessary in a conventional cognitive behavioral study using reaction time and/or accuracy as primary measures of interest. However these data are generally reported for fMRI studies as well, as they are reassurances that participants were performing the task as expected, and can additionally be used to filter out fMRI data associated with periods of inattentiveness or identify participants who may not have been performing the task as directed. Behavioral data generated by our PsychToolBox3 scripts are saved in .mat files. Though the format of these files can be modified, it is a near certainty that any such files will contain fields recording trial-by-trial responses and response latencies which can be analyzed to compute overall accuracy and response time values.

==getPTBData.m==
A MATLAB script, getPTBData.m has been written to facilitate the extraction of RT and accuracy data from the .mat files generated by our PTB3 scripts. This script should generally function on our experimental data collected at the MRI scanner or in-lab, unless the experimental paradigm necessitates a vastly different organization of the recorded behavioral data. It is assumed that the behavioral data is stored in an <code>expinfo.data</code> structure field within the .mat file, and within the data field can be found the fields ''conditon'' (note the typo -- this should probably be fixed at some point), ''response'' and ''rt''. Because of the possibility that participants might enter reverse-coded responses (e.g., holding the MRI response keypad upside-down), we take the modal response for each condition as the correct response, under the assumption that participants are not responding by chance. For tasks where high accuracy is expected, this seems reasonable. However, if it seems equally plausible that a participant might get 40% vs. 60% accuracy for a condition, this approach may require some modification to also accept an answer key (and the fMRI setup procedure would need to verify correct response pad orientation). This function takes a list of filenames as an optional parameter, however it is probably more convenient to leave the parameter blank and simply select the .mat files of interest when prompted by the program. RTs and accuracy values are returned as cell arrays of vectors, and <code>cellfun</code> can be used as described in the <code>help</code> for this function to compute the mean RT and task accuracy for each of the selected files.

function [rt, acc, filenames]=getPTBData(filenames)
%% function [RT, ACC, FILENAMES]=getPTBData(FILENAMES)
% Optional arguments:
% FILENAMES (string): FILENAMES is a string or a cell array of strings
% that are filenames of .mat files generated by our PsychToolBox3
% scripts. If FILENAMES are not supplied, the user will be prompted to
% select files from their filesystem.
%
% Returns:
% RT: a cell array of trial-by-trial RT vectors (1 for each file in
% FILENAMES), where the RT for incorrect trials has been replaced with
% NaN. When computing the mean RT, the user should use nanmean(RT{i})
% rather than mean(RT{i})because of the possibility that there are NaN
% values among the RTs.
%
% ACC: a cell array of trial-by-trial accuracy vectors (1 for each file in
% FILENAMES).
%
% FILENAMES: a cell array of filenames that are inspected by the function.
% If FILENAMES was provided as a parameter, then this return value will
% be redundant. However if the filenames parameter was omitted, then this
% return value will be useful in disambiguating which sets of RT/ACC
% vectors correspond to which data file, in the even that multiple data
% files were processed simultaneously.
%
% SAMPLE USAGE:
% [rt, acc, fnames]=getPTBData(); %no filenames provided; prompt to select
% cellfun(@(x) nanmean(x), rt) %compute mean RTs for each data file
% cellfun(@(x) nanmean(x), acc) %compute accuracy for each data file
%
if(~exist('filenames', 'var') || isempty(filenames))
[f,p]=uigetfile('*.mat', 'Select a PTB Data File', 'MultiSelect', 'on');
filenames=fullfile(p,f);
end

[rt,a cc]=cellfun(@(x) extractData(x), filenames, 'UniformOutput', false);
filenames=filenames';
%helper functions
function [RT,ACC]=extractData(matfilename)
matfile=load(matfilename);
data=matfile.expinfo.data;
condition=cell2mat({data.conditon}');%per-trial condition
response=cell2mat({data.response}');%per-trial responses
conditions=unique(condition);%all unique experimental conditions
responsecodes=int8(response); %convert text response codes to int
responsecodes=responsecodes(:,1);%we only need the first column if responses are 2-character eprime '1!', '2@' codes
%use modal response for each condition to infer the correct
%response (on the assumption the participant has better than chance
%accuracy)
conditioncodes=nan(1,length(conditions));
for i=1:length(conditions)
conditioncodes(i)=mode(responsecodes(condition==conditions(i)));
end
%Use the conditioncodes to generate an answer key
answerkey=condition;
for i=1:length(answerkey)
answerkey(i)=conditioncodes(conditions==answerkey(i));
end
ACC=responsecodes==answerkey; %accuracy vector has a TRUE/1 if the response matches the corresponding answerkey, FALSE/0 otherwise
RT=cell2mat({data.rt}'); %extract the RT data
RT(ACC==0)=nan; %remove all RTs where the response was incorrect (accuracy=0 for the trial)
end
end

[[Category: Matlab functions]]