Add files using upload-large-folder tool

.gitattributes

@@ -57,3 +57,19 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+data/914_Session_1_PDDys_VV_withcueinfo.mat filter=lfs diff=lfs merge=lfs -text
+data/913_Session_1_PDDys_VV_withcueinfo.mat filter=lfs diff=lfs merge=lfs -text
+data/801_Session_2_PDDys_VV_withcueinfo.mat filter=lfs diff=lfs merge=lfs -text
+data/802_Session_1_PDDys_VV_withcueinfo.mat filter=lfs diff=lfs merge=lfs -text
+data/8010_Session_1_PDDys_VV_withcueinfo.mat filter=lfs diff=lfs merge=lfs -text
+data/802_Session_2_PDDys_VV_withcueinfo.mat filter=lfs diff=lfs merge=lfs -text
+data/803_Session_1_PDDys_VV_withcueinfo.mat filter=lfs diff=lfs merge=lfs -text
+data/801_Session_1_PDDys_VV_withcueinfo.mat filter=lfs diff=lfs merge=lfs -text
+data/803_Session_2_PDDys_VV_withcueinfo.mat filter=lfs diff=lfs merge=lfs -text
+data/804_Session_1_PDDys_VV_withcueinfo.mat filter=lfs diff=lfs merge=lfs -text
+data/804_Session_2_PDDys_VV_withcueinfo.mat filter=lfs diff=lfs merge=lfs -text
+data/805_Session_1_PDDys_VV_withcueinfo.mat filter=lfs diff=lfs merge=lfs -text
+data/805_Session_2_PDDys_VV_withcueinfo.mat filter=lfs diff=lfs merge=lfs -text
+data/8060_Session_1_PDDys_VV_withcueinfo.mat filter=lfs diff=lfs merge=lfs -text
+data/806_Session_1_PDDys_VV_withcueinfo.mat filter=lfs diff=lfs merge=lfs -text
+data/8070_Session_1_PDDys_VV_withcueinfo.mat filter=lfs diff=lfs merge=lfs -text

LICENSE.txt

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PD_RewP_Script.m

@@ -0,0 +1,301 @@
+%%
+
+% N=28 Parkinson's patients and N=28 matched controls
+% PD patients came in for 2 sessions 1 week apart: ON or OFF meds (counterbalanced).  
+% EEG files are labeled with session #, see ONOFF.mat for which session was ON or OFF.  
+% Note that controls DID NOT have 2 sessions.
+
+
+clear all; clc
+homedir='Y:\EEG_Data\PDDys\PD 4 PREDICT\';
+datalocation=[homedir,'\PROCESSED EEG DATA\'];   % Data are here
+savepath = [datalocation,'CLEAN\'];
+cd(datalocation);
+
+load([homedir,'VV_Behavior.mat']); % Aggregate behavior files output from Matlab Psychtoolbox
+load([homedir,'ONOFF.mat']);  % 3 columns: subject, session, [ON=1 OFF=0]
+load([homedir,'BV_Chanlocs_60.mat']);
+%*************************************
+
+MEASURES = xlsread([homedir,'MEASURES']);  % Subj symptom measures taken in ON session
+%************************
+%   COLUMN LABELS
+%   MEASURES(:,1) = PD IDx
+%   MEASURES(:,2) = NAART Scores
+%   MEASURES(:,3) = BDI Ratings
+%   MEASURES(:,4) = MMSE Scores
+%   MEASURES(:,5) = UPDRS Ratings
+%   MEASURES(:,6) = Years Since Diagnosis (Rank Ordered)
+%   MEASURES(:,7) = Levadopa Equivalent Dose (LED)
+%   MEASURES(:,8) = Accelerometer hand placement: 1 = Left Hand // 2 = Right hand
+%************************
+
+% Subject Numbers
+PDsx=[801:811,813:829];  
+CTLsx=[8010,8070,8060,890:914];
+
+
+%% MAKE ERPs
+for subj=[PDsx,CTLsx]
+    for session=1:2;
+        if (subj>850 && session==1) || subj<850  % If not CTL, do session 2  (CTL did not have a session 2)
+                
+                load([num2str(subj),'_Session_',num2str(session),'_PDDys_VV_withcueinfo.mat'],'EEG','bad_chans','bad_epochs','bad_ICAs');
+                
+                for ai=1:size(EEG.epoch,2)
+                    VECTOR(ai,1)=EEG.epoch(ai).FB;
+                    VECTOR(ai,2)=EEG.epoch(ai).Resp;
+                    VECTOR(ai,3)=EEG.epoch(ai).Resptime;
+                    VECTOR(ai,4)=EEG.epoch(ai).Stim;
+                    VECTOR(ai,5)=EEG.epoch(ai).Stimtime;
+                    VECTOR(ai,6)=EEG.epoch(ai).Cie;
+                    VECTOR(ai,7)=EEG.epoch(ai).Cuetime;
+                    VECTOR(ai,8)=EEG.epoch(ai).RT;
+                    VECTOR(ai,9)=EEG.epoch(ai).BEHCondi;
+                    VECTOR(ai,10)=EEG.epoch(ai).BEHOptimal;
+                    VECTOR(ai,11)=EEG.epoch(ai).BEHRT;
+                    VECTOR(ai,12)=EEG.epoch(ai).BEHFB;
+                end
+                % Remove practice trials
+                VECTOR(isnan(VECTOR(:,9)),:)=NaN;
+                % Add this for later: FB-parsed by condi
+                for vvi=1:length(VECTOR),
+                    if VECTOR(vvi,1)==0, VECTOR(vvi,13)=VECTOR(vvi,9);
+                    elseif VECTOR(vvi,1)==1, VECTOR(vvi,13)=4+VECTOR(vvi,9);
+                    end
+                end
+                
+                % Remove the bad ICAs identified by APPLE:
+                bad_ICAs_To_Remove=bad_ICAs{2};
+                EEG = pop_subcomp( EEG, bad_ICAs_To_Remove, 0);
+                
+                % low-pass filter for display
+                dims=size(EEG.data);
+                EEG.data=eegfilt(EEG.data,500,[],20);
+                EEG.data=reshape(EEG.data,dims(1),dims(2),dims(3));
+                
+                % Set times
+                tx=-6000:2:1998;
+                b1=find(tx==-200);  b2=find(tx==0);
+                t1=find(tx==-500);  t2=find(tx==1000); % For ERPs
+                r1=find(tx==250);  r2=find(tx==450); % For Topos
+                tx2disp=-500:2:1000;
+                
+                % Accelerometer was worn on the non-dominant hand
+                % Aggregate accelerometer data
+                EEG.X=EEG.X-repmat(mean(EEG.X),4000,1);
+                EEG.Y=EEG.Y-repmat(mean(EEG.Y),4000,1);
+                EEG.Z=EEG.Z-repmat(mean(EEG.Z),4000,1);
+                % Add to EEG.data as 61st channel - but not the rejected trials
+                EEG.data(61,:,:)=(EEG.X(:,bad_epochs{1}~=1).^2)+(EEG.Y(:,bad_epochs{1}~=1).^2)+(EEG.Z(:,bad_epochs{1}~=1).^2);
+                dims=size(EEG.data);
+
+                % Basecor your ERPs here so they are pretty.
+                BASE1=squeeze(  mean(EEG.data(:,b1:b2,:),2)  );
+                for chani=1:dims(1)-1   % don't basecor the tremor data
+                    DATA(chani,:,:)=squeeze(EEG.data(chani,:,:))-repmat( BASE1(chani,:),dims(2),1 );
+                end
+                
+                % Parse by condition
+                    %************************
+                    %   CONDITIONS 
+                    %       1 = CHOOSE  EASY  LOSE
+                    %       2 = CHOOSE  HARD  LOSE
+                    %       3 = MATCH   EASY  LOSE
+                    %       4 = MATCH   HARD  LOSE
+                    %       5 = CHOOSE  EASY  WIN
+                    %       6 = CHOOSE  HARD  WIN
+                    %       7 = MATCH   EASY  WIN
+                    %       8 = MATCH   HARD  WIN   
+                    %************************                
+                for ai=1:8 % all FB
+                    ERP(:,ai,:)=mean(DATA(:,t1:t2,VECTOR(:,13)==ai),3); % DATA(ELECTRODE, TIME , CONDITION)
+                    TOPO(:,ai) = squeeze(mean(mean(DATA(:,r1:r2,VECTOR(:,13)==ai),2),3));                    
+                end
+
+                % Save and move on to next
+                save([savepath,num2str(subj),'_Session_',num2str(session),'_PDDys_VV_withcueinfo_ALL_THE_GOODS.mat'],'ERP','TOPO','VECTOR');
+                
+                clc;
+                disp(['AND PARTICPANT ',num2str(subj),' HAS BEEN SAVED']);
+                
+                clearvars -except datalocation ONOFF VV_Behavior BV_Chanlocs_60 PDsx CTLsx session subj savepath;
+                close all;
+                
+        end
+    end
+end
+
+%% COMBINE ERPS
+
+site = [21,36];%  Cz FCz 
+tx=-500:2:1000;
+time1 = 250;    time2 = 450;
+t1=find(tx==time1);  t2=find(tx==time2); % FOR REW-P
+TIME = time2-time1;
+tx2disp=-500:2:1000;
+COLS={'r','b','g','k'};
+
+BigN=size(ONOFF,1)./2;
+
+row=1;
+for mi=1:size(ONOFF,1)
+    disp([num2str(ONOFF(mi,1)),'_Session_',num2str(ONOFF(mi,2)),'_PDDys_VV_withcueinfo_ALL_THE_GOODS.mat']);
+    load([savepath,num2str(ONOFF(mi,1)),'_Session_',num2str(ONOFF(mi,2)),'_PDDys_VV_withcueinfo_ALL_THE_GOODS.mat']);
+    
+    if ONOFF(mi,3)==1 % ON
+        ON.ID(floor(row))=ONOFF(mi,1);
+        ON.Session(floor(row))=ONOFF(mi,2);
+        ON.VECTOR=VECTOR;
+        ON.ERPs(floor(row),:,:)=squeeze(mean(ERP(site,:,:),1));
+        ON.Topos(floor(row),:,:)=TOPO(:,:);
+    elseif ONOFF(mi,3)==0 % OFF
+        OFF.ID(floor(row))=ONOFF(mi,1);
+        OFF.Session(floor(row))=ONOFF(mi,2);
+        OFF.VECTOR=VECTOR;
+        OFF.ERPs(floor(row),:,:)=squeeze(mean(ERP(site,:,:),1));
+        OFF.Topos(floor(row),:,:)=TOPO(:,:);
+    end
+    row=row+.5;
+    clear ERPs VECTOR;
+end
+
+row=1;
+for CTLi=[8010,8060,8070,890:914];
+    disp([num2str(CTLi),'_Session_1_PDDys_VV_withcueinfo_ALL_THE_GOODS.mat']);
+    load([savepath,num2str(CTLi),'_Session_1_PDDys_VV_withcueinfo_ALL_THE_GOODS.mat']);
+    CTL.ID(floor(row))=CTLi;
+    CTL.Session(floor(row))=1;
+    CTL.VECTOR=VECTOR;
+    CTL.ERPs(floor(row),:,:)=squeeze(mean(ERP(site,:,:),1)); 
+    CTL.Topos(floor(row),:,:)=TOPO(:,:);
+    
+    row=row+1;
+    clear ERPs VECTOR;
+end
+CtlN=row-1;
+
+%************************
+%         TOPOS 
+%************************
+TOPO_CON = (CTL.Topos(:,:,5)+CTL.Topos(:,:,6)+CTL.Topos(:,:,7)+CTL.Topos(:,:,8)   )/4;
+TOPO_ON = (ON.Topos(:,:,5)+ON.Topos(:,:,6)+ON.Topos(:,:,7)+ON.Topos(:,:,8)   )/4;
+TOPO_OFF = (OFF.Topos(:,:,5)+OFF.Topos(:,:,6)+OFF.Topos(:,:,7)+OFF.Topos(:,:,8)   )/4;
+
+figure; hold on
+
+% CONTROL TOPO
+subplot(1,3,1)
+topoplot(mean(TOPO_CON,1),BV_Chanlocs_60);
+title('CONTROL');
+set(gca,'clim',[-3 3]);
+
+% ON TOPO
+subplot(1,3,2)
+topoplot(mean(TOPO_ON,1),BV_Chanlocs_60);
+title('ON');
+set(gca,'clim',[-3 3]);
+
+% OFF TOPO
+subplot(1,3,3)
+topoplot(mean(TOPO_OFF,1),BV_Chanlocs_60);
+title('OFF');
+set(gca,'clim',[-3 3]);
+cbar
+
+      
+%************************
+%   ERP 
+%************************
+win_CON = (CTL.ERPs(:,5,:)+CTL.ERPs(:,6,:)+CTL.ERPs(:,7,:)+CTL.ERPs(:,8,:))/4;
+win_ON = (ON.ERPs(:,5,:)+ON.ERPs(:,6,:)+ON.ERPs(:,7,:)+ON.ERPs(:,8,:))/4;
+win_OFF = (OFF.ERPs(:,5,:)+OFF.ERPs(:,6,:)+OFF.ERPs(:,7,:)+OFF.ERPs(:,8,:))/4; 
+
+lose_CON = (CTL.ERPs(:,:,1)+CTL.ERPs(:,:,2)+CTL.ERPs(:,:,3)+CTL.ERPs(:,:,4))/4;
+lose_ON = (ON.ERPs(:,:,1)+ON.ERPs(:,:,2)+ON.ERPs(:,:,3)+ON.ERPs(:,:,4))/4;
+lose_OFF = (OFF.ERPs(:,:,1)+OFF.ERPs(:,:,2)+OFF.ERPs(:,:,3)+OFF.ERPs(:,:,4))/4;
+
+figure;hold on;
+rectangle('Position',[time1,0,TIME,3],'Curvature',0.1,'FaceColor',[.9 .9 .9])% ON WIN
+plot(tx2disp,squeeze(nanmean(win_CON,1)),COLS{1});
+plot(tx2disp,squeeze(nanmean(win_ON,1)),COLS{2});
+plot(tx2disp,squeeze(nanmean(win_OFF,1)),COLS{3});
+shadedErrorBar(tx2disp, squeeze(nanmean(win_CON,1)),  nanstd(squeeze(win_CON))   ./sqrt(28),COLS{1})
+shadedErrorBar(tx2disp, squeeze(nanmean(win_ON,1)),  nanstd(squeeze(win_ON))   ./sqrt(28),COLS{2})
+shadedErrorBar(tx2disp, squeeze(nanmean(win_OFF,1)),  nanstd(squeeze(win_OFF))   ./sqrt(28),COLS{3})
+plot(tx2disp,squeeze(nanmean(win_CON,1)),COLS{1},'LineWidth',4);
+plot(tx2disp,squeeze(nanmean(win_ON,1)),COLS{2},'LineWidth',4);
+plot(tx2disp,squeeze(nanmean(win_OFF,1)),COLS{3},'LineWidth',4);
+
+title('ERPs FOR WINS');
+h_legend=legend({'HC','ON','OFF'});
+set(h_legend,'FontSize',12);
+plot([0 0],[-6 6],'k:');
+set(gca,'ylim',[-1 4],'xlim',[-100 1000])
+pcrit=.05;
+[H,P,CI,STATS]=ttest(win_CON,win_ON);
+P(P>pcrit)=NaN;  P(P<=pcrit)=1;
+plot(tx2disp,-.5*squeeze(P),'k','linewidth',3);  clear H P CI STATS;
+[H,P,CI,STATS]=ttest(win_CON,win_OFF);
+P(P>pcrit)=NaN;  P(P<=pcrit)=1;
+plot(tx2disp,-.7*squeeze(P),'r','linewidth',3);  clear H P CI STATS;
+
+CONTROL_ERP = squeeze(mean(win_CON(:,:,t1:t2),3));
+ON_ERP = squeeze(mean(win_ON(:,:,t1:t2),3));
+OFF_ERP = squeeze(mean(win_OFF(:,:,t1:t2),3));
+
+[H,P,CI,STATS]=ttest(CONTROL_ERP,ON_ERP)
+text(.7,3.5,['CONTROL v. ON  t= ',num2str(STATS.tstat),'   p= ',num2str(P)])
+[H,P,CI,STATS]=ttest(CONTROL_ERP,OFF_ERP)
+text(.7,3.3,['CONTROL v. OFF  t= ',num2str(STATS.tstat),'   p= ',num2str(P)])
+[H,P,CI,STATS]=ttest(ON_ERP,OFF_ERP)
+text(.7,3.1,['ON v. OFF  t= ',num2str(STATS.tstat),'   p= ',num2str(P)])
+
+%************************
+%   FOR ANALYSIS 
+%************************
+% Run these in SPSS to determine that there's no interaction between group
+% and volition / difficulty
+for condi=1:8   
+    SPSS_CONT(:,condi)= squeeze(nanmean(CTL.ERPs(:,condi,t1:t2),3));
+    SPSS_ON(:,condi)= squeeze(nanmean(ON.ERPs(:,condi,t1:t2),3));
+    SPSS_OFF(:,condi)= squeeze(nanmean(OFF.ERPs(:,condi,t1:t2),3));
+end
+
+% So then combine all rewards across volition and difficulty conditions
+REWP_ON = [SPSS_ON(:,5),SPSS_ON(:,6),SPSS_ON(:,7),SPSS_ON(:,8)];
+REWP_OFF = [SPSS_OFF(:,5),SPSS_OFF(:,6),SPSS_OFF(:,7),SPSS_OFF(:,8)];
+
+ALL_REWP_ON = mean(REWP_ON,2);
+ALL_REWP_OFF = mean(REWP_OFF,2);
+
+%************************
+%   CORRELATIONS 
+%************************       
+YrsDx=tiedrank(MEASURES(:,6),1);
+
+figure;
+hold on;
+subplot(2,1,1)
+scatter(YrsDx,ALL_REWP_ON,'MarkerEdgeColor',[0 .5 .5],...
+              'MarkerFaceColor',[0 .7 .7],...
+              'LineWidth',1.5)
+lsline
+title('ON: REW-P v. YRS DIAGNOSED ')
+set(gca,'ylim',[-2 5])
+[RHO,PVAL] = corr(ALL_REWP_ON,MEASURES(:,6),'TYPE','Spearman');
+text(.7,4,['r= ',num2str(RHO),'   p= ',num2str(PVAL)])
+
+subplot(2,1,2)
+hold on;
+scatter(YrsDx,ALL_REWP_OFF,'MarkerEdgeColor',[0 .5 .5],...
+              'MarkerFaceColor',[0 .7 .7],...
+              'LineWidth',1.5)
+lsline
+title('OFF: REW-P v. YRS DIAGNOSED ')
+set(gca,'ylim',[-2 5])
+[RHO,PVAL] = corr(ALL_REWP_OFF,MEASURES(:,6),'TYPE','Spearman');
+text(.7,4,['r= ',num2str(RHO),'   p= ',num2str(PVAL)])
+
+%% 
+

README.md

@@ -0,0 +1,67 @@
+---
+license: pddl
+tags:
+- eeg
+- medical
+- clinical
+- classification
+- parkinson
+- reward processing
+---
+# Brown2020: EEG Parkinson's Classification Dataset with Reward Processing Task
+The Brown2020 dataset comprises EEG recordings from a reinforcement learning task aimed at assessing reward processing in individuals with Parkinson's disease (PD) and healthy controls. A total of 56 participants took part: 28 individuals diagnosed with PD and 28 age- and sex-matched control participants. Each PD participant completed two sessions (ON and OFF dopaminergic medication), spaced one week apart. Control participants completed a single session.
+
+Participants performed a reinforcement learning task involving probabilistic feedback. On each trial, a pair of colored stimuli was presented, with each stimulus associated with a predefined probability of reward. Conditions were manipulated along two dimensions: difficulty (90/10% vs. 70/30% reward probability) and volition (free choice vs. instructed choice). The EEG was time-locked to the feedback screen, allowing for the measurement of reward-related event-related potentials (ERPs).
+
+EEG data were recorded using a 64-channel Brain Vision system at a sampling rate of 500 Hz.
+## Paper
+Brown, D. R., Richardson, S. P., & Cavanagh, J. F. (2020). **An EEG marker of reward processing is diminished in Parkinson’s disease**. _Brain research_, 1727, 146541.
+
+DISCLAIMER: We (DISCO) are NOT the owners or creators of this dataset, but we merely uploaded it here, to support our's ("EEG-Bench") and other's work on EEG benchmarking.
+## Dataset Structure
+- `data/` contains the annotated experiment EEG data.
+- `MEASURES.xlsx` and `ONOFF.mat` contain subject-specific information like NAART test results, BDI and whether they were ON or OFF medication at their first visit (`ONOFF.mat`). See `PD_RewP_Script.m` for information on how to decode these files.
+- `scripts/` contains the MATLAB files used to execute the experiment.
+- `images/` contains the stimuli and visuals presented to the patients.
+
+### Filename Format
+```
+[PID]_Session_[SESSION]_PDDys_VV_withcueinfo.mat
+```
+PID is the patient ID (e.g. `801`), while SESSION distinguishes different days of recording (can be `1` or `2` for patients with PD and is always `1` for patients without PD). All patients with PID <= 829 have Parkinson's Disease and all patients with PID >= 890 do NOT have Parkinson's Disease and hence belong to the control group.
+
+### Fields in each File
+A `.mat` file can be read in python as follows:
+```python
+from scipy.io import loadmat
+filename = "801_Session_2_PDDys_VV_withcueinfo.mat"
+mat = loadmat(filename, simplify_cells=True)
+```
+(A field "fieldname" can be read from `mat` as `mat["fieldname"]`.)
+
+Then `mat` contains (among others) the following fields and subfields
+- `EEG`
+    - `data`: EEG data of shape `(#channels, trial_len, #trials)`. E.g. a recording of 119 trials/epochs with 60 channels, each trial having a duration of 8 seconds and a sampling rate of 500 Hz will have shape `(60, 4000, 119)`.
+    - `event`: Contains a list of dictionaries, each entry (each event) having the following description:
+        - `latency`: The onset of the event, measured as the index in the merged time-dimension `#trials x trial_len` (note `#trials` being the _outer_ and `trial_len` being the _inner_ array when merging).
+        - `type`: The type of event. It can be either:
+            - `"S  1"`: An instruction to freely choose a stimulus is shown
+            - `"S  2"`: An instruction to select the stimulus with a box around it is shown
+            - `"S  3"`: A stimulus pair is shown on the screen
+            - `"S  4"`: The patient presses the left button
+            - `"S  5"`: The patient presses the right button
+            - `"S  6"`: A message is shown that the button pressed by the patient did not match the stimulus with a box around it (perhaps this event is also shown when the button is pressed too early)
+            - `"S  7"`: A message is shown that the patient did not press any button in the required time-interval (4 seconds)
+            - `"S 10"`: A red `0` (representing "no reward") is shown on the screen
+            - `"S 11"`: A green `1` (representing "a reward of 1 point") is shown on the screen
+
+                Typically, a trial starts with an instruction (`S  1` or `S  2`), followed by a pair of stimuli shown on the screen (`S  3`), a button being pressed by the patient (`S  4` or `S  5`) and a reward being displayed (`S 10` or `S 11`).
+    - `chanlocs`: A list of channel descriptors
+    - `nbchan`: Number of channels
+    - `trials`: Number of trials/epochs in this recording
+    - `srate`: Sampling Rate (Hz)
+
+Additionally, the field and `bad_chans` lists bad channels of this recording.
+
+## License
+By the original authors of this work, this work has been licensed under the PDDL v1.0 license (see LICENSE.txt).

data/8010_Session_1_PDDys_VV_withcueinfo.mat

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scripts/BEH_VV.m

@@ -0,0 +1,702 @@
+%% Calculate Data
+clear all; clc
+datapath=('Y:\EEG_Data\PDDys\BEH\');
+cd(datapath);
+
+SUBJS=[801:811,813:829];  % Only include here if they have BOTH sessions done!
+
+for subno=SUBJS
+    for session=1:2  
+        
+        disp(['VV Beh --- Subno: ',num2str(subno),'  Session: ',num2str(session)]); disp(' ');
+        
+        % TRAIN
+        fileID=dir([num2str(subno),'_S',num2str(session),'*']);
+        load(fileID.name);  clear fileID
+        
+        % ----- Columns are: -----
+        % 1 = block
+        % 2 = trial
+        % 3 =              (cTrialID)
+        % 4 = Forced choice
+        % 6 = S1
+        % 8 = S2
+        % 10 = S1 is left
+        % 11 = Stim Selected
+        % 13 = Reward (1 or 0)
+        % 14 = RT
+        
+        % ----------- Re-make With More Simpleness
+        ABchoose=[1,2]; CDchoose=[3,4]; WXmatch=[5,6]; YZmatch=[7,8];
+        for ai=1:length(task_struct.trainTrials)
+            if     any(task_struct.trainTrials(ai,6)==ABchoose)
+                CONDI=1;
+            elseif any(task_struct.trainTrials(ai,6)==CDchoose)
+                CONDI=2;
+            elseif any(task_struct.trainTrials(ai,6)==WXmatch)
+                CONDI=3;
+            elseif any(task_struct.trainTrials(ai,6)==YZmatch)
+                CONDI=4;
+            end
+            TRAIN(ai,1)=CONDI;      clear CONDI;                 % Condi: 1=ABchoose  2=CDchoose  3=ABmatch   4=CDmatch
+            TRAIN(ai,2)=mod(task_struct.trainTrials(ai,11),2);   % Optimal choice (odd num are optimal: 1,3,5,7)
+            TRAIN(ai,3)=task_struct.trainTrials(ai,14);  % RT
+            TRAIN(ai,4)=task_struct.trainTrials(ai,13);  % was rewarded or not
+        end
+        
+        % ----------- Re-make With More Simpleness
+        
+        for ai=1:length(task_struct.testTrials)
+            ThisSet=task_struct.testTrials(ai,[6,8]);
+            ThisSet=str2num(cat(2,num2str(ThisSet(1)),num2str(ThisSet(2))));
+            ThisChoice=task_struct.testTrials(ai,11);
+            
+            if     any(ThisSet==[12,21]),  CONDI='AB';
+            elseif any(ThisSet==[13,31]),  CONDI='AC';
+            elseif any(ThisSet==[14,41]),  CONDI='AD';
+            elseif any(ThisSet==[15,51]),  CONDI='AW';
+            elseif any(ThisSet==[16,61]),  CONDI='AX';
+            elseif any(ThisSet==[17,71]),  CONDI='AY';
+            elseif any(ThisSet==[18,81]),  CONDI='AZ';
+            elseif any(ThisSet==[23,32]),  CONDI='BC';
+            elseif any(ThisSet==[24,42]),  CONDI='BD';
+            elseif any(ThisSet==[25,52]),  CONDI='BW';
+            elseif any(ThisSet==[26,62]),  CONDI='BX';
+            elseif any(ThisSet==[27,72]),  CONDI='BY';
+            elseif any(ThisSet==[28,82]),  CONDI='BZ';
+            elseif any(ThisSet==[34,43]),  CONDI='CD';
+            elseif any(ThisSet==[35,53]),  CONDI='CW';
+            elseif any(ThisSet==[36,63]),  CONDI='CX';
+            elseif any(ThisSet==[37,73]),  CONDI='CY';
+            elseif any(ThisSet==[38,83]),  CONDI='CZ';
+            elseif any(ThisSet==[45,54]),  CONDI='DW';
+            elseif any(ThisSet==[46,64]),  CONDI='DX';
+            elseif any(ThisSet==[47,74]),  CONDI='DY';
+            elseif any(ThisSet==[48,84]),  CONDI='DZ';
+            elseif any(ThisSet==[56,65]),  CONDI='WX';
+            elseif any(ThisSet==[57,75]),  CONDI='WY';
+            elseif any(ThisSet==[58,85]),  CONDI='WZ';
+            elseif any(ThisSet==[67,76]),  CONDI='XY';
+            elseif any(ThisSet==[68,86]),  CONDI='XZ';
+            elseif any(ThisSet==[78,87]),  CONDI='YZ';
+            end
+            
+            TEST(ai).condi=CONDI;                       % Condi
+            TEST(ai).choice=ThisChoice;                 % This Choice
+            TEST(ai).RT=task_struct.testTrials(ai,14);  % RT
+            clear ThisSet ThisChoice CONDI;
+        end
+        
+        save([num2str(subno),'_sess',num2str(session),'_VVbeh.mat'],'TRAIN','TEST');
+        
+        clear task_struct disp_struct AB* CD* TRAIN TEST
+    end
+end
+
+load('Y:\EEG_Data\PDDys\ONOFF.mat','ONOFF')
+
+row=0;
+for subno=SUBJS
+    for session=1:2;
+        row=row+1;
+        
+        if subno~=ONOFF(row,1) || session~=ONOFF(row,2), BOOM; end  % Kills it if mismatch in ON/OFF Matrix!
+        
+        load([num2str(subno),'_sess',num2str(session),'_VVbeh.mat'],'TRAIN','TEST');
+        
+        MEGA(row).ID=subno;
+        MEGA(row).session=session;
+        MEGA(row).ONOFF=ONOFF(row,3);
+        MEGA(row).TRN_blocks=size(TRAIN,1)./40;
+        MEGA(row).TRN_ACC=mean(TRAIN(:,2));
+        MEGA(row).TRN_RT=mean(TRAIN(:,3));
+        for bi=1:4 % Condi: 1=ABchoose  2=CDchoose  3=ABmatch   4=CDmatch
+            MINISET=TRAIN(TRAIN(:,1)==bi,:);
+            MINISET(:,[5,6])=[MINISET(2:end,[2,3]);[NaN,NaN]];
+            MINISET(:,6)=MINISET(:,6)-MINISET(:,3);  % RT diff
+            % --------
+            WINS=MINISET(MINISET(:,4)==1,[2,5,6]);
+            LOSSES=MINISET(MINISET(:,4)==0,[2,5,6]);
+            % --------
+            WinStay(bi)=mean(WINS(:,1)==WINS(:,2));
+            LoseSwitch(bi)=mean(LOSSES(:,1)~=LOSSES(:,2));
+            WinSpeed(bi)=mean(WINS(WINS(:,1)==WINS(:,2),3));
+            % --------
+            clear MINISET WINS LOSSES
+        end
+        
+        MEGA(row).WinStay=WinStay;
+        MEGA(row).LoseSwitch=LoseSwitch;
+        MEGA(row).WinSpeed=WinSpeed;
+        
+        % ********************************************************
+        for ci=1:128
+            RT(ci,1)=TEST(ci).RT;
+            % ^^^^ General Accuracy     A,B,C,D  ==   W,X,Y,Z           - 4 of each set
+            ACC=NaN; PARSE=NaN;
+            if     strmatch(TEST(ci).condi,'AB');
+               if TEST(ci).choice==1, ACC=1; elseif TEST(ci).choice==2, ACC=0; end; PARSE=1;
+            elseif strmatch(TEST(ci).condi,'WX');
+               if TEST(ci).choice==5, ACC=1; elseif TEST(ci).choice==6, ACC=0; end; PARSE=2;
+            elseif strmatch(TEST(ci).condi,'CD');
+               if TEST(ci).choice==3, ACC=1; elseif TEST(ci).choice==4, ACC=0; end; PARSE=3; 
+            elseif strmatch(TEST(ci).condi,'YZ');
+               if TEST(ci).choice==7, ACC=1; elseif TEST(ci).choice==8, ACC=0; end; PARSE=4; 
+            end
+            % ^^^^ free vs. forced   A,B,C,D  ==   W,X,Y,Z             - 8 of each set
+            BIAS=NaN;
+            if     strmatch(TEST(ci).condi,'AW');
+               if TEST(ci).choice==1, BIAS=1; elseif TEST(ci).choice==5, BIAS=0; end; PARSE=5;
+            elseif strmatch(TEST(ci).condi,'CY');
+               if TEST(ci).choice==3, BIAS=1; elseif TEST(ci).choice==7, BIAS=0; end; PARSE=6; 
+            elseif strmatch(TEST(ci).condi,'DZ');
+               if TEST(ci).choice==4, BIAS=1; elseif TEST(ci).choice==8, BIAS=0; end; PARSE=7;
+            elseif strmatch(TEST(ci).condi,'BX');
+               if TEST(ci).choice==2, BIAS=1; elseif TEST(ci).choice==6, BIAS=0; end; PARSE=8; 
+            end
+            % ^^^^ 
+            WITHINSET=NaN;
+            if     strmatch(TEST(ci).condi,'AC');
+               if TEST(ci).choice==1, WITHINSET=1; elseif TEST(ci).choice==3, WITHINSET=0; end; PARSE=9;
+            elseif strmatch(TEST(ci).condi,'AD');
+               if TEST(ci).choice==1, WITHINSET=1; elseif TEST(ci).choice==4, WITHINSET=0; end; PARSE=10; 
+            elseif strmatch(TEST(ci).condi,'BC');
+               if TEST(ci).choice==3, WITHINSET=1; elseif TEST(ci).choice==2, WITHINSET=0; end; PARSE=11;
+            elseif strmatch(TEST(ci).condi,'BD');
+               if TEST(ci).choice==4, WITHINSET=1; elseif TEST(ci).choice==2, WITHINSET=0; end; PARSE=12; 
+            elseif strmatch(TEST(ci).condi,'WY');
+               if TEST(ci).choice==5, WITHINSET=1; elseif TEST(ci).choice==7, WITHINSET=0; end; PARSE=13;
+            elseif strmatch(TEST(ci).condi,'WZ');
+               if TEST(ci).choice==5, WITHINSET=1; elseif TEST(ci).choice==8, WITHINSET=0; end; PARSE=14; 
+            elseif strmatch(TEST(ci).condi,'XY');
+               if TEST(ci).choice==7, WITHINSET=1; elseif TEST(ci).choice==6, WITHINSET=0; end; PARSE=15;
+            elseif strmatch(TEST(ci).condi,'XZ');
+               if TEST(ci).choice==8, WITHINSET=1; elseif TEST(ci).choice==6, WITHINSET=0; end; PARSE=16; 
+            end
+            % ^^^^
+            EASY=NaN;
+            if     strmatch(TEST(ci).condi,'AX');
+                if TEST(ci).choice==1, EASY=1; elseif TEST(ci).choice==5, EASY=0; end; PARSE=17;
+            elseif strmatch(TEST(ci).condi,'AY');
+                if TEST(ci).choice==1, EASY=1; elseif TEST(ci).choice==7, EASY=0; end; PARSE=18;
+            elseif strmatch(TEST(ci).condi,'AZ');
+                if TEST(ci).choice==1, EASY=1; elseif TEST(ci).choice==8, EASY=0; end; PARSE=19;
+            elseif strmatch(TEST(ci).condi,'BW');
+                if TEST(ci).choice==5, EASY=1; elseif TEST(ci).choice==2, EASY=0; end; PARSE=20;
+            elseif strmatch(TEST(ci).condi,'CW');
+                if TEST(ci).choice==5, EASY=1; elseif TEST(ci).choice==3, EASY=0; end; PARSE=21;
+            elseif strmatch(TEST(ci).condi,'DW');
+                if TEST(ci).choice==5, EASY=1; elseif TEST(ci).choice==4, EASY=0; end; PARSE=22;
+            end
+            % ^^^^
+            MEDIUM=NaN;
+            if     strmatch(TEST(ci).condi,'CX');
+                if TEST(ci).choice==3, MEDIUM=1; elseif TEST(ci).choice==6, MEDIUM=0; end; PARSE=23;
+            elseif strmatch(TEST(ci).condi,'CZ');
+                if TEST(ci).choice==3, MEDIUM=1; elseif TEST(ci).choice==8, MEDIUM=0; end; PARSE=24;
+            elseif strmatch(TEST(ci).condi,'BY');
+                if TEST(ci).choice==7, MEDIUM=1; elseif TEST(ci).choice==2, MEDIUM=0; end; PARSE=25;
+            elseif strmatch(TEST(ci).condi,'DY');
+                if TEST(ci).choice==7, MEDIUM=1; elseif TEST(ci).choice==4, MEDIUM=0; end; PARSE=26;
+            end
+            % ^^^^
+            HARD=NaN;
+            if     strmatch(TEST(ci).condi,'BZ');
+                if TEST(ci).choice==8, HARD=1; elseif TEST(ci).choice==2, HARD=0; end; PARSE=27;
+            elseif strmatch(TEST(ci).condi,'DX');
+                if TEST(ci).choice==4, HARD=1; elseif TEST(ci).choice==6, HARD=0; end; PARSE=28;
+            end
+            % ^^^^ 
+            TST_ACC(ci,1)=ACC;
+            TST_BIAS(ci,1)=BIAS;
+            TST_WITHINSET(ci,1)=WITHINSET;
+            TST_EASY(ci,1)=EASY;
+            TST_MEDIUM(ci,1)=MEDIUM;
+            TST_HARD(ci,1)=HARD;
+            TST_PARSE(ci,1)=PARSE;
+            clear ACC BIAS WITHINSET EASY MEDIUM HARD PARSE;
+        end
+        
+        for di=1:4
+            ACCURACIES(di)=nanmean(TST_ACC(TST_PARSE==di));
+        end
+        for di=5:8
+            BIASES(di-4)=nanmean(TST_BIAS(TST_PARSE==di));
+        end
+        for di=9:16
+            WITHINSETS(di-8)=nanmean(TST_WITHINSET(TST_PARSE==di));
+        end
+        for di=17:22
+            EASYS(di-16)=nanmean(TST_EASY(TST_PARSE==di));
+        end
+        for di=23:26
+            MEDIUMS(di-22)=nanmean(TST_MEDIUM(TST_PARSE==di));
+        end
+        for di=27:28
+            HARDS(di-26)=nanmean(TST_HARD(TST_PARSE==di));
+        end
+        
+        
+        MEGA(row).TST_ACC=ACCURACIES;
+        MEGA(row).TST_BIAS=BIASES;
+        MEGA(row).TST_WITHINSET=WITHINSETS;
+        MEGA(row).TST_EASY=EASYS;
+        MEGA(row).TST_MEDIUM=MEDIUMS;
+        MEGA(row).TST_HARD=HARDS;
+        MEGA(row).TST_RT=mean(RT);
+        
+        clearvars -except MEGA subjcount subno session RT row ONOFF SUBJS;
+    end
+end
+save('VV_Behavior.mat','MEGA');
+
+clear ONOFF RT row session subno
+
+%%
+clear all; clc
+datapath=('Y:\EEG_Data\PDDys\BEH\');
+cd(datapath);
+SUBJS=[801:811,813:829];  % Only include here if they have BOTH sessions done!
+
+load('Y:\EEG_Data\PDDys\PD_Moderators.mat','Mods','Mods_Hdr')
+
+load('VV_Behavior.mat','MEGA');
+
+row=0; 
+for subno=SUBJS
+    row=row+1;
+    for mi=1:size(MEGA,2)
+       if MEGA(mi).ID==subno && MEGA(mi).ONOFF==1
+           ON.ID(row,:)=MEGA(mi).ID;
+           ON.session(row,:)=MEGA(mi).session;
+           ON.TRN_ACC(row,:)=MEGA(mi).TRN_ACC;
+           ON.TRN_RT(row,:)=MEGA(mi).TRN_RT;
+           ON.WinStay(row,:)=MEGA(mi).WinStay;
+           ON.LoseSwitch(row,:)=MEGA(mi).LoseSwitch;
+           ON.WinSpeed(row,:)=MEGA(mi).WinSpeed;
+           ON.TST_ACC(row,:)=MEGA(mi).TST_ACC;
+           ON.TST_BIAS(row,:)=MEGA(mi).TST_BIAS;
+           ON.TST_WITHINSET(row,:)=MEGA(mi).TST_WITHINSET;
+           ON.TST_EASY(row,:)=MEGA(mi).TST_EASY;
+           ON.TST_MEDIUM(row,:)=MEGA(mi).TST_MEDIUM;
+           ON.TST_HARD(row,:)=MEGA(mi).TST_HARD;
+           ON.TST_RT(row,:)=MEGA(mi).TST_RT;
+           ON.Blocks(row,:)=MEGA(mi).TRN_blocks;
+       elseif MEGA(mi).ID==subno && MEGA(mi).ONOFF==0
+           OFF.ID(row,:)=MEGA(mi).ID;
+           OFF.session(row,:)=MEGA(mi).session;
+           OFF.TRN_ACC(row,:)=MEGA(mi).TRN_ACC;
+           OFF.TRN_RT(row,:)=MEGA(mi).TRN_RT;
+           OFF.WinStay(row,:)=MEGA(mi).WinStay;
+           OFF.LoseSwitch(row,:)=MEGA(mi).LoseSwitch;
+           OFF.WinSpeed(row,:)=MEGA(mi).WinSpeed;
+           OFF.TST_ACC(row,:)=MEGA(mi).TST_ACC;
+           OFF.TST_BIAS(row,:)=MEGA(mi).TST_BIAS;
+           OFF.TST_WITHINSET(row,:)=MEGA(mi).TST_WITHINSET;
+           OFF.TST_EASY(row,:)=MEGA(mi).TST_EASY;
+           OFF.TST_MEDIUM(row,:)=MEGA(mi).TST_MEDIUM;
+           OFF.TST_HARD(row,:)=MEGA(mi).TST_HARD;
+           OFF.TST_RT(row,:)=MEGA(mi).TST_RT;
+           OFF.Blocks(row,:)=MEGA(mi).TRN_blocks;
+       end
+    end
+end
+
+save('BEH_VV_PD','ON','OFF');
+
+BigN=length(SUBJS);
+jitter=rand(1,BigN)./2.5; jitter=jitter-mean(jitter);
+
+
+% % SOMESX=double(repmat((Mods(:,9)>nanmedian(Mods(:,9))),1,4));
+% % SOMESX(SOMESX==0)=NaN;
+% % ON.TST_BIAS=ON.TST_BIAS.*SOMESX;
+% % OFF.TST_BIAS=OFF.TST_BIAS.*SOMESX;
+
+
+%%
+figure;
+subplot(1,3,1); hold on
+bar(1:4,mean(ON.WinStay),.25,'w');
+bar(1.25:4.25,mean(OFF.WinStay),.25,'r');
+errorbar(1:4,mean(ON.WinStay),std(ON.WinStay)./sqrt(BigN),'k.');
+errorbar(1.25:4.25,mean(OFF.WinStay),std(OFF.WinStay)./sqrt(BigN),'k.');
+set(gca,'xlim',[0 5],'xtick',[1:1:4],'xticklabel',{'AB','CD','WY','XZ'},'ylim',[.5 1]);
+title('TRN WinStay');
+subplot(1,3,2); hold on
+bar(1:4,nanmean(ON.LoseSwitch),.25,'w');
+bar(1.25:4.25,nanmean(OFF.LoseSwitch),.25,'r');
+errorbar(1:4,nanmean(ON.LoseSwitch),nanstd(ON.LoseSwitch)./sqrt(BigN),'k.');
+errorbar(1.25:4.25,nanmean(OFF.LoseSwitch),nanstd(OFF.LoseSwitch)./sqrt(BigN),'k.');
+set(gca,'xlim',[0 5],'xtick',[1:1:4],'xticklabel',{'AB','CD','WY','XZ'});
+title('TRN LoseSwitch');
+subplot(1,3,3); hold on
+bar(1:4,nanmean(ON.WinSpeed),.25,'w');
+bar(1.25:4.25,nanmean(OFF.WinSpeed),.25,'r');
+errorbar(1:4,nanmean(ON.WinSpeed),nanstd(ON.WinSpeed)./sqrt(BigN),'k.');
+errorbar(1.25:4.25,nanmean(OFF.WinSpeed),nanstd(OFF.WinSpeed)./sqrt(BigN),'k.');
+set(gca,'xlim',[0 5],'xtick',[1:1:4],'xticklabel',{'AB','CD','WY','XZ'});
+title('TRN WinSpeed');
+
+
+%%
+figure;
+subplot(2,5,1); hold on
+bar(1,mean(ON.TRN_ACC),'w');
+bar(2,mean(OFF.TRN_ACC),'r');
+errorbar(1,mean(ON.TRN_ACC),std(ON.TRN_ACC)./sqrt(BigN),'k.');
+errorbar(2,mean(OFF.TRN_ACC),std(OFF.TRN_ACC)./sqrt(BigN),'k.');
+% plot(1,ON.TRN_ACC,'b.'); plot(2,OFF.TRN_ACC,'b.');
+% plot([1 2],[ON.TRN_ACC OFF.TRN_ACC],'b-');
+set(gca,'xlim',[0 3],'xtick',[1:1:2],'xticklabel',{'ON','OFF'},'ylim',[.5 1]);
+title('TRN Acc');
+
+subplot(2,5,2:3); hold on
+bar(1:4,mean(ON.TST_ACC),.25,'w');
+bar(1.25:1:4.25,mean(OFF.TST_ACC),.25,'r');
+errorbar(1:4,mean(ON.TST_ACC),std(ON.TST_ACC)./sqrt(BigN),'k.');
+errorbar(1.25:1:4.25,mean(OFF.TST_ACC),std(OFF.TST_ACC)./sqrt(BigN),'k.');
+% plot(1:4,ON.TST_ACC,'b.'); plot(1.25:1:4.25,OFF.TST_ACC,'b.');
+% plot([1 1.25],[ON.TST_ACC(:,1) OFF.TST_ACC(:,1)],'b-');
+% plot([2 2.25],[ON.TST_ACC(:,2) OFF.TST_ACC(:,2)],'b-');
+% plot([3 3.25],[ON.TST_ACC(:,3) OFF.TST_ACC(:,3)],'b-');
+% plot([4 4.25],[ON.TST_ACC(:,4) OFF.TST_ACC(:,4)],'b-');
+set(gca,'xlim',[0 5],'xtick',[1:1:4],'xticklabel',{'AB','WX','CD','YZ'});
+title('TST Acc');
+
+subplot(2,5,4:5); hold on
+bar(1:4,nanmean(ON.TST_BIAS),.25,'w');
+bar(1.25:1:4.25,nanmean(OFF.TST_BIAS),.25,'r');
+errorbar(1:4,nanmean(ON.TST_BIAS),nanstd(ON.TST_BIAS)./sqrt(BigN),'k.');
+errorbar(1.25:1:4.25,nanmean(OFF.TST_BIAS),nanstd(OFF.TST_BIAS)./sqrt(BigN),'k.');
+% plot(1:4,ON.TST_BIAS,'b.'); plot(1.25:1:4.25,OFF.TST_BIAS,'b.');
+% plot([1 1.25],[ON.TST_BIAS(:,1) OFF.TST_BIAS(:,1)],'b-');
+% plot([2 2.25],[ON.TST_BIAS(:,2) OFF.TST_BIAS(:,2)],'b-');
+% plot([3 3.25],[ON.TST_BIAS(:,3) OFF.TST_BIAS(:,3)],'b-');
+% plot([4 4.25],[ON.TST_BIAS(:,4) OFF.TST_BIAS(:,4)],'b-');
+set(gca,'xlim',[0 5],'xtick',[1:1:4],'xticklabel',{'AW','CY','DZ','BX'});
+title('TST BIAS');
+
+
+
+subplot(2,5,6); hold on
+bar(1,mean(ON.TRN_ACC-OFF.TRN_ACC),'b');
+errorbar(1,mean(ON.TRN_ACC-OFF.TRN_ACC),std(ON.TRN_ACC-OFF.TRN_ACC)./sqrt(BigN),'k.');
+set(gca,'xlim',[0 2],'xtick',[1:1:1]);
+title('TRN Acc DIFF');
+
+subplot(2,5,7:8); hold on
+bar(1:4,mean(ON.TST_ACC-OFF.TST_ACC),.25,'b');
+errorbar(1:4,mean(ON.TST_ACC-OFF.TST_ACC),std(ON.TST_ACC-OFF.TST_ACC)./sqrt(BigN),'k.');
+set(gca,'xlim',[0 5],'xtick',[1:1:4],'xticklabel',{'AB','WX','CD','YZ'});
+title('TST Acc');
+
+
+subplot(2,5,9:10); hold on
+bar(1:4,nanmean( ON.TST_BIAS-OFF.TST_BIAS ),.25,'w');
+errorbar(1:4,nanmean(ON.TST_BIAS-OFF.TST_BIAS ),std(ON.TST_BIAS-OFF.TST_BIAS )./sqrt(BigN),'k.');
+% for plotdiffi=1:4
+%     plot(plotdiffi+jitter',(ON.TST_BIAS(:,plotdiffi)-OFF.TST_BIAS(:,plotdiffi)),'mo');
+% end
+set(gca,'xlim',[0 5],'xtick',[1:1:4],'xticklabel',{'AW','CY','DZ','BX'});
+title('TST BIAS');
+
+%%
+MODS=9;
+
+BigDiff=ON.TST_BIAS-OFF.TST_BIAS;
+for polyi=1:BigN
+    POLY{1}(polyi)=corr(ON.TST_BIAS(polyi,:)',(1:4)');
+    POLY{2}(polyi)=corr(OFF.TST_BIAS(polyi,:)',(1:4)');
+    POLY{3}(polyi)=corr(BigDiff(polyi,:)',(1:4)');
+end
+
+dispidx=3;
+figure;
+subplot(1,2,1); hold on
+bar(mean(POLY{dispidx}),'w');
+plot(.86:.01:1.13,POLY{dispidx},'bd');
+errorbar(mean(POLY{dispidx}),std(POLY{dispidx})./sqrt(BigN),'k.')
+set(gca,'xlim',[0 2],'xtick',[1:1:2])
+ylabel('Slope in Accuracy Difference');
+[H,P,CI,STATS]=ttest(POLY{dispidx})
+title(['BIAS t=',num2str(STATS.tstat),' p=',num2str(P)]);
+subplot(1,2,2); hold on
+scatter( Mods(:,MODS) , POLY{dispidx}' ,'k'); lsline
+[rho,p]=corr( Mods(:,MODS) , POLY{dispidx}' ,'type','Spearman','rows','complete' );
+text(.4,.1,['r=',num2str(rho),' p=',num2str(p)],'sc')
+title(['POLY & ',Mods_Hdr{MODS}])
+
+FORSPSS=[BigDiff,POLY{3}'];
+
+for slopei=1:4
+    if slopei==1,     X=ON.TST_BIAS; 
+    elseif slopei==2, X=OFF.TST_BIAS; 
+    elseif slopei==3,  X=CTL.TST_BIAS;   
+    elseif slopei==4,  X=ON.TST_BIAS-OFF.TST_BIAS;
+    end
+    for polyi=1:length(X)
+        SLOPES{slopei}(polyi)=corr(X(polyi,:)',(1:4)');
+    end
+end
+
+figure; 
+subplot(2,2,1);  hold on
+bar(1,mean(SLOPES{1}),'w');
+bar(2,mean(SLOPES{2}),'r');
+bar(3,mean(SLOPES{3}),'g');
+errorbar(1,mean(SLOPES{1}),std(SLOPES{1})./sqrt(BigN),'k.');
+errorbar(2,mean(SLOPES{2}),std(SLOPES{2})./sqrt(BigN),'k.');
+errorbar(3,mean(SLOPES{3}),std(SLOPES{3})./sqrt(BigN_ctl),'k.');
+ylabel('Slope in Accuracy Difference');
+subplot(2,2,2);  hold on
+scatter( Mods(:,MODS) , SLOPES{4} ,'b'); lsline
+[rho,p]=corr( Mods(:,MODS) , SLOPES{4}' ,'type','Spearman','rows','complete' );
+text(.4,.1,['r=',num2str(rho),' p=',num2str(p)],'sc')
+title(['ON-OFF & ',Mods_Hdr{MODS}])
+subplot(2,2,3);  hold on
+scatter( Mods(:,MODS) , SLOPES{1} ,'k'); lsline
+[rho,p]=corr( Mods(:,MODS) , SLOPES{1}' ,'type','Spearman','rows','complete' );
+text(.4,.1,['r=',num2str(rho),' p=',num2str(p)],'sc')
+title(['ON & ',Mods_Hdr{MODS}])
+subplot(2,2,4);  hold on
+scatter( Mods(:,MODS) , SLOPES{2} ,'r'); lsline
+[rho,p]=corr( Mods(:,MODS) , SLOPES{2}' ,'type','Spearman','rows','complete' );
+text(.4,.1,['r=',num2str(rho),' p=',num2str(p)],'sc')
+title(['OFF & ',Mods_Hdr{MODS}])
+
+
+%%
+
+% LED & win-speed diff
+% LED & BX bias diff
+
+MOD_IDX=2;
+
+A1=sum(OFF.TST_BIAS(:,1),2)
+A2=sum(ON.TST_BIAS(:,1),2)
+A3=A1-A2;
+
+figure; 
+subplot(1,3,1); hold on
+scatter( Mods(:,MOD_IDX) , A1 ,'k'); lsline
+[rho,p]=corr( Mods(:,MOD_IDX) , A1 ,'type','Spearman','rows','complete' );
+text(.4,.1,['r=',num2str(rho),' p=',num2str(p)],'sc')
+title(['OFF & ',Mods_Hdr{MOD_IDX}])
+subplot(1,3,2); hold on
+scatter( Mods(:,MOD_IDX) ,A2 ,'k' ); lsline
+[rho,p]=corr( Mods(:,MOD_IDX) , A2 ,'type','Spearman','rows','complete' );
+text(.4,.1,['r=',num2str(rho),' p=',num2str(p)],'sc')
+title(['ON & ',Mods_Hdr{MOD_IDX}])
+subplot(1,3,3); hold on
+scatter( Mods(:,MOD_IDX) , A3 ,'k'  ); lsline
+[rho,p]=corr( Mods(:,MOD_IDX) , A3 ,'type','Spearman','rows','complete' );
+text(.4,.1,['r=',num2str(rho),' p=',num2str(p)],'sc')
+title(['OFF-ON & ',Mods_Hdr{MOD_IDX}])
+
+%%
+load('VV_Behavior_CTL.mat','CTL');
+
+BigN=length(SUBJS);
+jitter=rand(1,BigN)./2.5; jitter=jitter-mean(jitter);
+BigN_ctl=size(CTL.ID,1);
+noise_ctl=rand(1,BigN_ctl)./100;
+
+figure;
+subplot(2,5,1); hold on
+bar(1,mean(ON.TRN_ACC),'w');
+bar(2,mean(OFF.TRN_ACC),'r');
+bar(3,mean(CTL.TRN_ACC),'g');
+errorbar(1,mean(ON.TRN_ACC),std(ON.TRN_ACC)./sqrt(BigN),'k.');
+errorbar(2,mean(OFF.TRN_ACC),std(OFF.TRN_ACC)./sqrt(BigN),'k.');
+errorbar(3,mean(CTL.TRN_ACC),std(CTL.TRN_ACC)./sqrt(BigN_ctl),'k.');
+set(gca,'xlim',[0 4],'xtick',[1:1:3],'xticklabel',{'ON','OFF','CTL'},'ylim',[.5 1]);
+title('TRN Acc');
+
+subplot(2,5,2:3); hold on
+bar(1-.25:4-.25,mean(ON.TST_ACC),.25,'w');
+bar(1:1:4,mean(OFF.TST_ACC),.25,'r');
+bar(1.25:1:4.25,mean(CTL.TST_ACC),.25,'g');
+errorbar(1-.25:4-.25,mean(ON.TST_ACC),std(ON.TST_ACC)./sqrt(BigN),'k.');
+errorbar(1:1:4,mean(OFF.TST_ACC),std(OFF.TST_ACC)./sqrt(BigN),'k.');
+errorbar(1.25:1:4.25,mean(CTL.TST_ACC),std(CTL.TST_ACC)./sqrt(BigN_ctl),'k.');
+set(gca,'xlim',[0 5],'xtick',[1:1:4],'xticklabel',{'AB','WX','CD','YZ'});
+title('TST Acc');
+
+subplot(2,5,4:5); hold on
+bar(1-.25:4-.25,mean(ON.TST_BIAS),.25,'w');
+bar(1:1:4,mean(OFF.TST_BIAS),.25,'r');
+bar(1.25:1:4.25,mean(CTL.TST_BIAS),.25,'g');
+errorbar(1-.25:4-.25,mean(ON.TST_BIAS),std(ON.TST_BIAS)./sqrt(BigN),'k.');
+errorbar(1:1:4,mean(OFF.TST_BIAS),std(OFF.TST_BIAS)./sqrt(BigN),'k.');
+errorbar(1.25:1:4.25,mean(CTL.TST_BIAS),std(CTL.TST_BIAS)./sqrt(BigN_ctl),'k.');
+set(gca,'xlim',[0 5],'xtick',[1:1:4],'xticklabel',{'AW','CY','DZ','BX'});
+title('TST BIAS');
+
+[H,P,CI,STATS]=ttest2(ON.TST_BIAS(:,1),CTL.TST_BIAS(:,1))
+[H,P,CI,STATS]=ttest2(OFF.TST_BIAS(:,1),CTL.TST_BIAS(:,1))
+
+subplot(2,5,6); hold on
+bar(1,mean(ON.TRN_ACC-OFF.TRN_ACC),'b');
+errorbar(1,mean(ON.TRN_ACC-OFF.TRN_ACC),std(ON.TRN_ACC-OFF.TRN_ACC)./sqrt(BigN),'k.');
+set(gca,'xlim',[0 2],'xtick',[1:1:1]);
+title('TRN Acc DIFF');
+
+subplot(2,5,7:8); hold on
+bar(1:4,mean(ON.TST_ACC-OFF.TST_ACC),.25,'b');
+errorbar(1:4,mean(ON.TST_ACC-OFF.TST_ACC),std(ON.TST_ACC-OFF.TST_ACC)./sqrt(BigN),'k.');
+set(gca,'xlim',[0 5],'xtick',[1:1:4],'xticklabel',{'AB','WX','CD','YZ'});
+title('TST Acc DIFF');
+
+subplot(2,5,9:10); hold on
+bar(1:4,mean(ON.TST_BIAS-OFF.TST_BIAS),.25,'w');
+errorbar(1:4,mean(ON.TST_BIAS-OFF.TST_BIAS),std(ON.TST_BIAS-OFF.TST_BIAS)./sqrt(BigN),'k.');
+% % for plotdiffi=1:4
+% %     plot(plotdiffi+jitter',(ON.TST_BIAS(:,plotdiffi)-OFF.TST_BIAS(:,plotdiffi)),'mo');
+% % end
+set(gca,'xlim',[0 5],'xtick',[1:1:4],'xticklabel',{'AW','CY','DZ','BX'});
+title('TST BIAS DIFF');
+
+%%
+
+%%
+
+
+figure; 
+subplot(3,2,1:2)
+hold on
+bar(1-.25:1:8-.25,mean(VV.CTL.TST_WITHINSET),.25,'g');
+bar(1:1:8,mean(VV.ON.TST_WITHINSET),.25,'w');
+bar(1+.25:1:8+.25,mean(VV.OFF.TST_WITHINSET),.25,'r');
+errorbar(1-.25:1:8-.25,mean(VV.CTL.TST_WITHINSET),std(VV.CTL.TST_WITHINSET)./sqrt(BigN_ctl),'k.');
+errorbar(1:1:8,mean(VV.ON.TST_WITHINSET),std(VV.ON.TST_WITHINSET)./sqrt(BigN),'k.');
+errorbar(1+.25:1:8+.25,mean(VV.OFF.TST_WITHINSET),std(VV.OFF.TST_WITHINSET)./sqrt(BigN),'k.');
+plot([0 9],[.5 .5],'b:')
+set(gca,'xlim',[0 9],'xtick',[1:1:8],'xticklabel',{'AC','AD','CB','BD','WY','WZ','YX','XZ'},'ytick',[0:.25:1]);
+title('TST w/in set Acc');
+
+subplot(3,2,3:4)
+hold on
+bar(1-.25:1:6-.25,nanmean(VV.CTL.TST_EASY),.25,'g');
+bar(1:1:6,nanmean(VV.ON.TST_EASY),.25,'w');
+bar(1+.25:1:6+.25,nanmean(VV.OFF.TST_EASY),.25,'r');
+errorbar(1-.25:1:6-.25,nanmean(VV.CTL.TST_EASY),nanstd(VV.CTL.TST_EASY)./sqrt(BigN_ctl),'k.');
+errorbar(1:1:6,nanmean(VV.ON.TST_EASY),nanstd(VV.ON.TST_EASY)./sqrt(BigN),'k.');
+errorbar(1+.25:1:6+.25,nanmean(VV.OFF.TST_EASY),nanstd(VV.OFF.TST_EASY)./sqrt(BigN),'k.');
+plot([0 7],[.5 .5],'b:')
+set(gca,'xlim',[0 7],'xtick',[1:1:6],'xticklabel',{'AX','AY','AZ','WB','WC','WD'},'ytick',[0:.25:1]);
+title('TST EASY Acc');
+
+subplot(3,2,5)
+hold on
+bar(1-.25:1:4-.25,nanmean(VV.CTL.TST_MEDIUM),.25,'g');
+bar(1:1:4,nanmean(VV.ON.TST_MEDIUM),.25,'w');
+bar(1+.25:1:4+.25,nanmean(VV.OFF.TST_MEDIUM),.25,'r');
+errorbar(1-.25:1:4-.25,nanmean(VV.CTL.TST_MEDIUM),nanstd(VV.CTL.TST_MEDIUM)./sqrt(BigN_ctl),'k.');
+errorbar(1:1:4,nanmean(VV.ON.TST_MEDIUM),nanstd(VV.ON.TST_MEDIUM)./sqrt(BigN),'k.');
+errorbar(1+.25:1:4+.25,nanmean(VV.OFF.TST_MEDIUM),nanstd(VV.OFF.TST_MEDIUM)./sqrt(BigN),'k.');
+plot([0 5],[.5 .5],'b:')
+set(gca,'xlim',[0 5],'xtick',[1:1:4],'xticklabel',{'CX','CZ','YB','YD'},'ytick',[0:.25:1]);
+title('TST MEDIUM Acc');
+
+subplot(3,2,6)
+hold on
+bar(1-.25:1:2-.25,nanmean(VV.CTL.TST_HARD),.25,'g');
+bar(1:1:2,nanmean(VV.ON.TST_HARD),.25,'w');
+bar(1+.25:1:2+.25,nanmean(VV.OFF.TST_HARD),.25,'r');
+errorbar(1-.25:1:2-.25,nanmean(VV.CTL.TST_HARD),nanstd(VV.CTL.TST_HARD)./sqrt(BigN_ctl),'k.');
+errorbar(1:1:2,nanmean(VV.ON.TST_HARD),nanstd(VV.ON.TST_HARD)./sqrt(BigN),'k.');
+errorbar(1+.25:1:2+.25,nanmean(VV.OFF.TST_HARD),nanstd(VV.OFF.TST_HARD)./sqrt(BigN),'k.');
+plot([0 3],[.5 .5],'b:')
+set(gca,'xlim',[0 3],'xtick',[1:1:2],'xticklabel',{'ZB','DX'},'ytick',[0:.25:1]);
+title('TST HARD Acc');
+
+
+
+
+% ^^^^^^
+
+
+
+figure; 
+subplot(3,2,1:2)
+hold on
+bar(1-.3:1:8-.3,nanmean(VV.ON.TST_WITHINSET(Esx,:)),.15,'w');
+bar(1-.1:1:8-.1,nanmean(VV.OFF.TST_WITHINSET(Esx,:)),.15,'r');
+bar(1+.1:1:8+.1,nanmean(VV.ON.TST_WITHINSET(Lsx,:)),.15,'w');
+bar(1+.3:1:8+.3,nanmean(VV.OFF.TST_WITHINSET(Lsx,:)),.15,'r');
+errorbar(1-.3:1:8-.3,mean(VV.ON.TST_WITHINSET(Esx,:)),std(VV.ON.TST_WITHINSET(Esx,:))./sqrt(EarlyN),'k.');
+errorbar(1-.1:1:8-.1,mean(VV.OFF.TST_WITHINSET(Esx,:)),std(VV.OFF.TST_WITHINSET(Esx,:))./sqrt(EarlyN),'k.');
+errorbar(1+.1:1:8+.1,mean(VV.ON.TST_WITHINSET(Lsx,:)),std(VV.ON.TST_WITHINSET(Lsx,:))./sqrt(LateN),'k.');
+errorbar(1+.3:1:8+.3,mean(VV.OFF.TST_WITHINSET(Lsx,:)),std(VV.OFF.TST_WITHINSET(Lsx,:))./sqrt(LateN),'k.');
+plot([0 9],[.5 .5],'b:')
+set(gca,'xlim',[0 9],'xtick',[1:1:8],'xticklabel',{'AC','AD','CB','BD','WY','WZ','YX','XZ'},'ytick',[0:.25:1]);
+title('TST w/in set Acc');
+
+subplot(3,2,3:4)
+hold on
+bar(1-.3:1:6-.3,nanmean(VV.ON.TST_EASY(Esx,:)),.15,'w');
+bar(1-.1:1:6-.1,nanmean(VV.OFF.TST_EASY(Esx,:)),.15,'r');
+bar(1+.1:1:6+.1,nanmean(VV.ON.TST_EASY(Lsx,:)),.15,'w');
+bar(1+.3:1:6+.3,nanmean(VV.OFF.TST_EASY(Lsx,:)),.15,'r');
+errorbar(1-.3:1:6-.3,mean(VV.ON.TST_EASY(Esx,:)),std(VV.ON.TST_EASY(Esx,:))./sqrt(EarlyN),'k.');
+errorbar(1-.1:1:6-.1,mean(VV.OFF.TST_EASY(Esx,:)),std(VV.OFF.TST_EASY(Esx,:))./sqrt(EarlyN),'k.');
+errorbar(1+.1:1:6+.1,mean(VV.ON.TST_EASY(Lsx,:)),std(VV.ON.TST_EASY(Lsx,:))./sqrt(LateN),'k.');
+errorbar(1+.3:1:6+.3,mean(VV.OFF.TST_EASY(Lsx,:)),std(VV.OFF.TST_EASY(Lsx,:))./sqrt(LateN),'k.');
+plot([0 7],[.5 .5],'b:')
+set(gca,'xlim',[0 7],'xtick',[1:1:6],'xticklabel',{'AX','AY','AZ','WB','WC','WD'},'ytick',[0:.25:1]);
+title('TST EASY Acc');
+
+subplot(3,2,5)
+hold on
+bar(1-.3:1:4-.3,nanmean(VV.ON.TST_MEDIUM(Esx,:)),.15,'w');
+bar(1-.1:1:4-.1,nanmean(VV.OFF.TST_MEDIUM(Esx,:)),.15,'r');
+bar(1+.1:1:4+.1,nanmean(VV.ON.TST_MEDIUM(Lsx,:)),.15,'w');
+bar(1+.3:1:4+.3,nanmean(VV.OFF.TST_MEDIUM(Lsx,:)),.15,'r');
+errorbar(1-.3:1:4-.3,mean(VV.ON.TST_MEDIUM(Esx,:)),std(VV.ON.TST_MEDIUM(Esx,:))./sqrt(EarlyN),'k.');
+errorbar(1-.1:1:4-.1,mean(VV.OFF.TST_MEDIUM(Esx,:)),std(VV.OFF.TST_MEDIUM(Esx,:))./sqrt(EarlyN),'k.');
+errorbar(1+.1:1:4+.1,mean(VV.ON.TST_MEDIUM(Lsx,:)),std(VV.ON.TST_MEDIUM(Lsx,:))./sqrt(LateN),'k.');
+errorbar(1+.3:1:4+.3,mean(VV.OFF.TST_MEDIUM(Lsx,:)),std(VV.OFF.TST_MEDIUM(Lsx,:))./sqrt(LateN),'k.');
+plot([0 5],[.5 .5],'b:')
+set(gca,'xlim',[0 5],'xtick',[1:1:4],'xticklabel',{'CX','CZ','YB','YD'},'ytick',[0:.25:1]);
+title('TST MEDIUM Acc');
+
+subplot(3,2,6)
+hold on
+bar(1-.3:1:2-.3,nanmean(VV.ON.TST_HARD(Esx,:)),.15,'w');
+bar(1-.1:1:2-.1,nanmean(VV.OFF.TST_HARD(Esx,:)),.15,'r');
+bar(1+.1:1:2+.1,nanmean(VV.ON.TST_HARD(Lsx,:)),.15,'w');
+bar(1+.3:1:2+.3,nanmean(VV.OFF.TST_HARD(Lsx,:)),.15,'r');
+errorbar(1-.3:1:2-.3,mean(VV.ON.TST_HARD(Esx,:)),std(VV.ON.TST_HARD(Esx,:))./sqrt(EarlyN),'k.');
+errorbar(1-.1:1:2-.1,mean(VV.OFF.TST_HARD(Esx,:)),std(VV.OFF.TST_HARD(Esx,:))./sqrt(EarlyN),'k.');
+errorbar(1+.1:1:2+.1,mean(VV.ON.TST_HARD(Lsx,:)),std(VV.ON.TST_HARD(Lsx,:))./sqrt(LateN),'k.');
+errorbar(1+.3:1:2+.3,mean(VV.OFF.TST_HARD(Lsx,:)),std(VV.OFF.TST_HARD(Lsx,:))./sqrt(LateN),'k.');
+plot([0 3],[.5 .5],'b:')
+set(gca,'xlim',[0 3],'xtick',[1:1:2],'xticklabel',{'ZB','DX'},'ytick',[0:.25:1]);
+title('TST HARD Acc');
+
+
+
+
+
+
+%%
+% % % clc;
+% % % disp([num2str(subno),'_sess',num2str(session),'_VVbeh'])
+% % % disp(' ')
+% % % disp('Accuracy: >.5 shows that they learned optimal choice')
+% % % disp(['  choose: AB (90/10)','   match: WX (90/10)','   choose: CD (70/30)','   match: YZ (70/30)'])
+% % % disp(['Test Acc: ',num2str(MEGA(row).TST_ACC)])
+% % % disp(' ')
+% % % disp('BIAS: >.5 is prefer Choose over Match  (may only happen for first 2)')
+% % % disp(['  AW (90/90)','   CY (70/70)','   DZ (30/30)','   BX (30/30)'])
+% % % disp(['Test BIAS: ',num2str(MEGA(row).TST_BIAS)])
+
+
+
+%%
+
+
+
+
+

scripts/BEH_VV_CTL.m

@@ -0,0 +1,320 @@
+%% Calculate Data
+clear all; clc
+datapath=('Y:\EEG_Data\PDDys\BEH\');
+cd(datapath);
+
+SUBJS=[8010,8070,8060,890:914];
+
+for subno=SUBJS
+    for session=1
+        
+        disp(['VV Beh --- Subno: ',num2str(subno),'  Session: ',num2str(session)]); disp(' ');
+        
+        % TRAIN
+        fileID=dir([num2str(subno),'_S',num2str(session),'*']);
+        load(fileID.name);  clear fileID
+        
+        % ----- Columns are: -----
+        % 1 = block
+        % 2 = trial
+        % 3 =              (cTrialID)
+        % 4 = Forced choice
+        % 6 = S1
+        % 8 = S2
+        % 10 = S1 is left
+        % 11 = Stim Selected
+        % 13 = Reward (1 or 0)
+        % 14 = RT
+        
+        % ----------- Re-make With More Simpleness
+        ABchoose=[1,2]; CDchoose=[3,4]; WXmatch=[5,6]; YZmatch=[7,8];
+        for ai=1:length(task_struct.trainTrials)
+            if     any(task_struct.trainTrials(ai,6)==ABchoose)
+                CONDI=1;
+            elseif any(task_struct.trainTrials(ai,6)==CDchoose)
+                CONDI=2;
+            elseif any(task_struct.trainTrials(ai,6)==WXmatch)
+                CONDI=3;
+            elseif any(task_struct.trainTrials(ai,6)==YZmatch)
+                CONDI=4;
+            end
+            TRAIN(ai,1)=CONDI;      clear CONDI;                 % Condi: 1=ABchoose  2=CDchoose  3=ABmatch   4=CDmatch
+            TRAIN(ai,2)=mod(task_struct.trainTrials(ai,11),2);   % Optimal choice (odd num are optimal: 1,3,5,7)
+            TRAIN(ai,3)=task_struct.trainTrials(ai,14);  % RT
+            TRAIN(ai,4)=task_struct.trainTrials(ai,13);  % was rewarded or not
+        end
+        
+        % ----------- Re-make With More Simpleness
+        
+        for ai=1:length(task_struct.testTrials)
+            ThisSet=task_struct.testTrials(ai,[6,8]);
+            ThisSet=str2num(cat(2,num2str(ThisSet(1)),num2str(ThisSet(2))));
+            ThisChoice=task_struct.testTrials(ai,11);
+            
+            if     any(ThisSet==[12,21]),  CONDI='AB';
+            elseif any(ThisSet==[13,31]),  CONDI='AC';
+            elseif any(ThisSet==[14,41]),  CONDI='AD';
+            elseif any(ThisSet==[15,51]),  CONDI='AW';
+            elseif any(ThisSet==[16,61]),  CONDI='AX';
+            elseif any(ThisSet==[17,71]),  CONDI='AY';
+            elseif any(ThisSet==[18,81]),  CONDI='AZ';
+            elseif any(ThisSet==[23,32]),  CONDI='BC';
+            elseif any(ThisSet==[24,42]),  CONDI='BD';
+            elseif any(ThisSet==[25,52]),  CONDI='BW';
+            elseif any(ThisSet==[26,62]),  CONDI='BX';
+            elseif any(ThisSet==[27,72]),  CONDI='BY';
+            elseif any(ThisSet==[28,82]),  CONDI='BZ';
+            elseif any(ThisSet==[34,43]),  CONDI='CD';
+            elseif any(ThisSet==[35,53]),  CONDI='CW';
+            elseif any(ThisSet==[36,63]),  CONDI='CX';
+            elseif any(ThisSet==[37,73]),  CONDI='CY';
+            elseif any(ThisSet==[38,83]),  CONDI='CZ';
+            elseif any(ThisSet==[45,54]),  CONDI='DW';
+            elseif any(ThisSet==[46,64]),  CONDI='DX';
+            elseif any(ThisSet==[47,74]),  CONDI='DY';
+            elseif any(ThisSet==[48,84]),  CONDI='DZ';
+            elseif any(ThisSet==[56,65]),  CONDI='WX';
+            elseif any(ThisSet==[57,75]),  CONDI='WY';
+            elseif any(ThisSet==[58,85]),  CONDI='WZ';
+            elseif any(ThisSet==[67,76]),  CONDI='XY';
+            elseif any(ThisSet==[68,86]),  CONDI='XZ';
+            elseif any(ThisSet==[78,87]),  CONDI='YZ';
+            end
+            
+            TEST(ai).condi=CONDI;                           % Condi
+            TEST(ai).choice=ThisChoice;                      % This Choice
+            TEST(ai).RT=task_struct.testTrials(ai,14);  % RT
+            clear ThisSet ThisChoice CONDI;
+        end
+        
+        save([num2str(subno),'_sess',num2str(session),'_VVbeh.mat'],'TRAIN','TEST');
+        
+        clear task_struct disp_struct AB* CD* TRAIN TEST
+    end
+end
+
+row=0;
+for subno=SUBJS
+    for session=1;
+        row=row+1;
+        
+        load([num2str(subno),'_sess',num2str(session),'_VVbeh.mat'],'TRAIN','TEST');
+        
+        MEGA(row).ID=subno;
+        MEGA(row).session=session;
+        MEGA(row).TRN_blocks=size(TRAIN,1)./40;
+        MEGA(row).TRN_ACC=mean(TRAIN(:,2));
+        MEGA(row).TRN_RT=mean(TRAIN(:,3));
+        for bi=1:4 % Condi: 1=ABchoose  2=CDchoose  3=ABmatch   4=CDmatch
+            MINISET=TRAIN(TRAIN(:,1)==bi,:);
+            MINISET(:,[5,6])=[MINISET(2:end,[2,3]);[NaN,NaN]];
+            MINISET(:,6)=MINISET(:,6)-MINISET(:,3);  % RT diff
+            % --------
+            WINS=MINISET(MINISET(:,4)==1,[2,5,6]);
+            LOSSES=MINISET(MINISET(:,4)==0,[2,5,6]);
+            % --------
+            WinStay(bi)=mean(WINS(:,1)==WINS(:,2));
+            LoseSwitch(bi)=mean(LOSSES(:,1)~=LOSSES(:,2));
+            WinSpeed(bi)=mean(WINS(WINS(:,1)==WINS(:,2),3));
+            % --------
+            clear MINISET WINS LOSSES
+        end
+        
+        MEGA(row).WinStay=WinStay;
+        MEGA(row).LoseSwitch=LoseSwitch;
+        MEGA(row).WinSpeed=WinSpeed;
+        
+        % ********************************************************
+        for ci=1:128
+            RT(ci,1)=TEST(ci).RT;
+            % ^^^^ General Accuracy     A,B,C,D  ==   W,X,Y,Z           - 4 of each set
+            ACC=NaN; PARSE=NaN;
+            if     strmatch(TEST(ci).condi,'AB');
+               if TEST(ci).choice==1, ACC=1; elseif TEST(ci).choice==2, ACC=0; end; PARSE=1;
+            elseif strmatch(TEST(ci).condi,'WX');
+               if TEST(ci).choice==5, ACC=1; elseif TEST(ci).choice==6, ACC=0; end; PARSE=2;
+            elseif strmatch(TEST(ci).condi,'CD');
+               if TEST(ci).choice==3, ACC=1; elseif TEST(ci).choice==4, ACC=0; end; PARSE=3; 
+            elseif strmatch(TEST(ci).condi,'YZ');
+               if TEST(ci).choice==7, ACC=1; elseif TEST(ci).choice==8, ACC=0; end; PARSE=4; 
+            end
+            % ^^^^ free vs. forced   A,B,C,D  ==   W,X,Y,Z             - 8 of each set
+            BIAS=NaN;
+            if     strmatch(TEST(ci).condi,'AW');
+               if TEST(ci).choice==1, BIAS=1; elseif TEST(ci).choice==5, BIAS=0; end; PARSE=5;
+            elseif strmatch(TEST(ci).condi,'CY');
+               if TEST(ci).choice==3, BIAS=1; elseif TEST(ci).choice==7, BIAS=0; end; PARSE=6; 
+            elseif strmatch(TEST(ci).condi,'DZ');
+               if TEST(ci).choice==4, BIAS=1; elseif TEST(ci).choice==8, BIAS=0; end; PARSE=7;
+            elseif strmatch(TEST(ci).condi,'BX');
+               if TEST(ci).choice==2, BIAS=1; elseif TEST(ci).choice==6, BIAS=0; end; PARSE=8; 
+            end
+            % ^^^^ 
+            WITHINSET=NaN;
+            if     strmatch(TEST(ci).condi,'AC');
+               if TEST(ci).choice==1, WITHINSET=1; elseif TEST(ci).choice==3, WITHINSET=0; end; PARSE=9;
+            elseif strmatch(TEST(ci).condi,'AD');
+               if TEST(ci).choice==1, WITHINSET=1; elseif TEST(ci).choice==4, WITHINSET=0; end; PARSE=10; 
+            elseif strmatch(TEST(ci).condi,'BC');
+               if TEST(ci).choice==3, WITHINSET=1; elseif TEST(ci).choice==2, WITHINSET=0; end; PARSE=11;
+            elseif strmatch(TEST(ci).condi,'BD');
+               if TEST(ci).choice==4, WITHINSET=1; elseif TEST(ci).choice==2, WITHINSET=0; end; PARSE=12; 
+            elseif strmatch(TEST(ci).condi,'WY');
+               if TEST(ci).choice==5, WITHINSET=1; elseif TEST(ci).choice==7, WITHINSET=0; end; PARSE=13;
+            elseif strmatch(TEST(ci).condi,'WZ');
+               if TEST(ci).choice==5, WITHINSET=1; elseif TEST(ci).choice==8, WITHINSET=0; end; PARSE=14; 
+            elseif strmatch(TEST(ci).condi,'XY');
+               if TEST(ci).choice==7, WITHINSET=1; elseif TEST(ci).choice==6, WITHINSET=0; end; PARSE=15;
+            elseif strmatch(TEST(ci).condi,'XZ');
+               if TEST(ci).choice==8, WITHINSET=1; elseif TEST(ci).choice==6, WITHINSET=0; end; PARSE=16; 
+            end
+            % ^^^^
+            EASY=NaN;
+            if     strmatch(TEST(ci).condi,'AX');
+                if TEST(ci).choice==1, EASY=1; elseif TEST(ci).choice==5, EASY=0; end; PARSE=17;
+            elseif strmatch(TEST(ci).condi,'AY');
+                if TEST(ci).choice==1, EASY=1; elseif TEST(ci).choice==7, EASY=0; end; PARSE=18;
+            elseif strmatch(TEST(ci).condi,'AZ');
+                if TEST(ci).choice==1, EASY=1; elseif TEST(ci).choice==8, EASY=0; end; PARSE=19;
+            elseif strmatch(TEST(ci).condi,'BW');
+                if TEST(ci).choice==5, EASY=1; elseif TEST(ci).choice==2, EASY=0; end; PARSE=20;
+            elseif strmatch(TEST(ci).condi,'CW');
+                if TEST(ci).choice==5, EASY=1; elseif TEST(ci).choice==3, EASY=0; end; PARSE=21;
+            elseif strmatch(TEST(ci).condi,'DW');
+                if TEST(ci).choice==5, EASY=1; elseif TEST(ci).choice==4, EASY=0; end; PARSE=22;
+            end
+            % ^^^^
+            MEDIUM=NaN;
+            if     strmatch(TEST(ci).condi,'CX');
+                if TEST(ci).choice==3, MEDIUM=1; elseif TEST(ci).choice==6, MEDIUM=0; end; PARSE=23;
+            elseif strmatch(TEST(ci).condi,'CZ');
+                if TEST(ci).choice==3, MEDIUM=1; elseif TEST(ci).choice==8, MEDIUM=0; end; PARSE=24;
+            elseif strmatch(TEST(ci).condi,'BY');
+                if TEST(ci).choice==7, MEDIUM=1; elseif TEST(ci).choice==2, MEDIUM=0; end; PARSE=25;
+            elseif strmatch(TEST(ci).condi,'DY');
+                if TEST(ci).choice==7, MEDIUM=1; elseif TEST(ci).choice==4, MEDIUM=0; end; PARSE=26;
+            end
+            % ^^^^
+            HARD=NaN;
+            if     strmatch(TEST(ci).condi,'BZ');
+                if TEST(ci).choice==8, HARD=1; elseif TEST(ci).choice==2, HARD=0; end; PARSE=27;
+            elseif strmatch(TEST(ci).condi,'DX');
+                if TEST(ci).choice==4, HARD=1; elseif TEST(ci).choice==6, HARD=0; end; PARSE=28;
+            end
+            % ^^^^ 
+            TST_ACC(ci,1)=ACC;
+            TST_BIAS(ci,1)=BIAS;
+            TST_WITHINSET(ci,1)=WITHINSET;
+            TST_EASY(ci,1)=EASY;
+            TST_MEDIUM(ci,1)=MEDIUM;
+            TST_HARD(ci,1)=HARD;
+            TST_PARSE(ci,1)=PARSE;
+            clear ACC BIAS WITHINSET EASY MEDIUM HARD PARSE;
+        end
+        
+        for di=1:4
+            ACCURACIES(di)=nanmean(TST_ACC(TST_PARSE==di));
+        end
+        for di=5:8
+            BIASES(di-4)=nanmean(TST_BIAS(TST_PARSE==di));
+        end
+        for di=9:16
+            WITHINSETS(di-8)=nanmean(TST_WITHINSET(TST_PARSE==di));
+        end
+        for di=17:22
+            EASYS(di-16)=nanmean(TST_EASY(TST_PARSE==di));
+        end
+        for di=23:26
+            MEDIUMS(di-22)=nanmean(TST_MEDIUM(TST_PARSE==di));
+        end
+        for di=27:28
+            HARDS(di-26)=nanmean(TST_HARD(TST_PARSE==di));
+        end
+        
+        
+        MEGA(row).TST_ACC=ACCURACIES;
+        MEGA(row).TST_BIAS=BIASES;
+        MEGA(row).TST_WITHINSET=WITHINSETS;
+        MEGA(row).TST_EASY=EASYS;
+        MEGA(row).TST_MEDIUM=MEDIUMS;
+        MEGA(row).TST_HARD=HARDS;
+        MEGA(row).TST_RT=mean(RT);
+        
+        clearvars -except MEGA subjcount subno session RT row ONOFF SUBJS;
+    end
+end
+save('VV_Behavior_CTL.mat','MEGA');
+
+clear RT row session subno
+
+%%
+
+row=0;
+for subno=SUBJS
+    row=row+1;
+    CTL.ID(row,:)=MEGA(row).ID;
+    CTL.session(row,:)=MEGA(row).session;
+    CTL.TRN_ACC(row,:)=MEGA(row).TRN_ACC;
+    CTL.TRN_RT(row,:)=MEGA(row).TRN_RT;
+    CTL.WinStay(row,:)=MEGA(row).WinStay;
+    CTL.LoseSwitch(row,:)=MEGA(row).LoseSwitch;
+    CTL.WinSpeed(row,:)=MEGA(row).WinSpeed;
+    CTL.TST_ACC(row,:)=MEGA(row).TST_ACC;
+    CTL.TST_BIAS(row,:)=MEGA(row).TST_BIAS;
+    CTL.TST_WITHINSET(row,:)=MEGA(row).TST_WITHINSET;
+    CTL.TST_EASY(row,:)=MEGA(row).TST_EASY;
+    CTL.TST_MEDIUM(row,:)=MEGA(row).TST_MEDIUM;
+    CTL.TST_HARD(row,:)=MEGA(row).TST_HARD;
+    CTL.TST_RT(row,:)=MEGA(row).TST_RT;
+    CTL.Blocks(row,:)=MEGA(mi).TRN_blocks;
+end
+save('VV_Behavior_CTL.mat','MEGA','CTL');
+
+BigN=length(SUBJS);
+jitter=rand(1,BigN)./2.5; jitter=jitter-mean(jitter);
+%%
+figure;
+subplot(1,5,1); hold on
+bar(1,mean(CTL.TRN_ACC),'g');
+errorbar(1,mean(CTL.TRN_ACC),std(CTL.TRN_ACC)./sqrt(BigN),'k.');
+set(gca,'xlim',[0 2],'xtick',[1:1:1],'xticklabel',{'CTL'},'ylim',[.5 1]);
+title('TRN Acc');
+
+subplot(1,5,2:3); hold on
+bar(1:4,mean(CTL.TST_ACC),.4,'g');
+errorbar(1:4,mean(CTL.TST_ACC),std(CTL.TST_ACC)./sqrt(BigN),'k.');
+plot(1:4,CTL.TST_ACC,'b.'); 
+set(gca,'xlim',[0 5],'xtick',[1:1:4],'xticklabel',{'AB','WX','CD','YZ'});
+title('TST Acc');
+
+subplot(1,5,4:5); hold on
+bar(1:4,mean(CTL.TST_BIAS),.4,'g');
+errorbar(1:4,mean(CTL.TST_BIAS),std(CTL.TST_BIAS)./sqrt(BigN),'k.');
+plot(1:4,CTL.TST_BIAS,'b.'); 
+set(gca,'xlim',[0 5],'xtick',[1:1:4],'xticklabel',{'AW','CY','DZ','BX'});
+title('TST BIAS');
+
+
+
+
+
+%%
+% % % clc;
+% % % disp([num2str(subno),'_sess',num2str(session),'_VVbeh'])
+% % % disp(' ')
+% % % disp('Accuracy: >.5 shows that they learned optimal choice')
+% % % disp(['  choose: AB (90/10)','   match: WX (90/10)','   choose: CD (70/30)','   match: YZ (70/30)'])
+% % % disp(['Test Acc: ',num2str(MEGA(row).TST_ACC)])
+% % % disp(' ')
+% % % disp('BIAS: >.5 is prefer Choose over Match  (may only happen for first 2)')
+% % % disp(['  AW (90/90)','   CY (70/70)','   DZ (30/30)','   BX (30/30)'])
+% % % disp(['Test BIAS: ',num2str(MEGA(row).TST_BIAS)])
+
+
+
+%%
+
+
+
+
+

scripts/VV_Main.m

@@ -0,0 +1,177 @@
+%% Choice Bias AKA Value of Volition Task - 4 stims ---------------- JOYSTICK + EEG
+% Adapted from Jeff Cockburn's script by JFC 02/18/2015
+
+% clear screen
+clc;
+% clear memory
+clear all;
+close all;
+
+%%%%%%%%%%%%%%%%%%
+% struct holding info need to present and track data
+disp_struct = struct();
+% struct to hold experiment parameters
+task_struct = struct();
+
+% will store all subject trials
+allTrainTrials = [];
+allTestTrials = [];
+% start time for the experiment
+task_struct.startTime = GetSecs();
+
+% set the randome seed
+RandStream.setGlobalStream(RandStream('mt19937ar','seed',sum(100*clock)));
+
+% track the start time for the experiment
+experiemtn_start = GetSecs();
+
+% get the subject number for data storage
+subject_number = input('Enter the subject number :\n','s');
+session = input('Is this the first or second visit? (enter 1 or 2):\n','s'); session=str2num(session);
+% Pick 1 or 2 screens  (if 1, put 0, if 2, put 1)
+SCREENS=2;
+% build the file name
+file_name = [num2str(subject_number) '_S' num2str(session) '_' datestr(now, 'mm-dd-yyyy_HH-MM-SS')];
+
+% *********% *********% *********% *********% *********% *********% *********
+% Initialize joystick
+addpath(genpath('C:\Users\GA217B\Desktop\PDDys Suite\JoyMEX'));
+JoyMEX('init',0);
+% ********* Call JoyTest to get these trigger numbers for each device
+LEFTKEY=5;
+RIGHTKEY=6;
+% Initialize EEG triggers
+ioObject = io64;
+LTP1address = hex2dec('C050'); 
+status = io64(ioObject);
+% *********% *********% *********% *********% *********% *********% *********
+
+% hide the cursor
+HideCursor();
+
+% skip the screen test for now
+Screen('Preference', 'SkipSyncTests', 1);
+% disable user responses from being displayed
+ListenChar(2);
+% ensure similar keyboard capture across platforms
+KbName('UnifyKeyNames');
+
+% initialize
+[disp_struct, task_struct] = taskInit(disp_struct, task_struct, SCREENS, session, LEFTKEY, RIGHTKEY);
+
+%%%%%%%%%%%%%%%%%%%%%%%%%
+% INSTRUCT
+train_Inst1(disp_struct);
+train_Inst2(disp_struct);
+%
+instruct_slide = Screen('MakeTexture', disp_struct.wPtr, imread(['../images/Slide1.jpg']));
+Screen('DrawTexture', disp_struct.wPtr, instruct_slide);
+Screen(disp_struct.wPtr, 'Flip');KbWait([],3); %Waits for keyboard(any) press
+%
+train_Inst3(disp_struct);
+%
+instruct_slide = Screen('MakeTexture', disp_struct.wPtr, imread(['../images/Slide2.jpg']));
+Screen('DrawTexture', disp_struct.wPtr, instruct_slide);
+Screen(disp_struct.wPtr, 'Flip');KbWait([],3); %Waits for keyboard(any) press
+instruct_slide = Screen('MakeTexture', disp_struct.wPtr, imread(['../images/Slide3.jpg']));
+Screen('DrawTexture', disp_struct.wPtr, instruct_slide);
+Screen(disp_struct.wPtr, 'Flip');KbWait([],3); %Waits for keyboard(any) press
+%
+train_Inst4(disp_struct);
+%
+instruct_slide = Screen('MakeTexture', disp_struct.wPtr, imread(['../images/Slide4.jpg']));
+Screen('DrawTexture', disp_struct.wPtr, instruct_slide);
+Screen(disp_struct.wPtr, 'Flip');KbWait([],3); %Waits for keyboard(any) press
+%
+train_Inst5(disp_struct);
+runPractice(disp_struct, task_struct, ioObject, LTP1address);
+train_Inst_Xtra(disp_struct);
+train_Inst_Xtra2(disp_struct);
+train_Inst6(disp_struct);
+
+%%%%%%%%%%%%%%%%%%%
+% TRAIN
+trainDone = false;
+trainBlock = 0;
+while ~trainDone
+    trainBlock = trainBlock + 1;
+    
+    % build the training trials
+    trainTrials = buildTrainTrials(disp_struct, task_struct);
+    trainTrials(:, task_struct.cBlock) = trainBlock;
+    trainTrials(:, task_struct.cTrialType) = task_struct.TRAIN;
+    
+    % loop through each trial
+    tI = 1;
+    while tI <= size(trainTrials,1)
+        trainTrials(tI,:) = runTrainTrial(disp_struct, task_struct, trainTrials(tI,:), ioObject, LTP1address);
+        
+        % was this a free-choice trial?
+        if trainTrials(tI, task_struct.cTrialCond) == task_struct.FC
+            % find it's no-choice pair, and set the resp/feedback
+            ncI = find( trainTrials(:, task_struct.cTrialID) == trainTrials(tI, task_struct.cTrialID) & trainTrials(:, task_struct.cTrialCond) == task_struct.NC );
+            trainTrials(ncI, task_struct.cRespAct) = trainTrials(tI, task_struct.cRespAct) + task_struct.ncAdjust;
+            trainTrials(ncI, task_struct.cRespRew) = trainTrials(tI, task_struct.cRespRew);
+        end
+        
+        % move trial if FC or match under RT limit - otherwise repeat
+        if trainTrials(tI, task_struct.cRespAct) ~= disp_struct.RESP_SLOW && (trainTrials(tI, task_struct.cTrialCond) == task_struct.FC || trainTrials(tI, task_struct.cMatch))
+            tI = tI + 1;
+        end
+    end
+    
+    % store all training from the current block
+    allTrainTrials = [allTrainTrials; trainTrials];
+    
+    % check to see if we've exceeded the max # of blocks
+    if trainBlock >= task_struct.maxTrain
+        trainDone = true;
+    elseif trainBlock >= task_struct.minTrain
+        % check performance for each stim pair
+        perfAB = mean(trainTrials(trainTrials(:, task_struct.cS1) == task_struct.sCodes.Afc, task_struct.cRespAct) == task_struct.sCodes.Afc);
+        perfCD = mean(trainTrials(trainTrials(:, task_struct.cS1) == task_struct.sCodes.Cfc, task_struct.cRespAct) == task_struct.sCodes.Cfc);
+        
+        % did they meet performance threshold on all stim pairs
+        trainDone = perfAB >= task_struct.minPerf(1) && perfCD >= task_struct.minPerf(2);
+    end
+    
+    % take a break
+    if ~trainDone
+        trainBreak(disp_struct);
+    end
+end % while still training
+
+
+%%%%%%%%%%%%%%%%%%%
+% TEST
+test_Inst1(disp_struct);
+testTrials = buildTestTrials(disp_struct, task_struct);
+tI = 1;
+% run through all the test trials
+while tI <= size(testTrials,1)
+    testTrials(tI,:) = runTestTrial(disp_struct, task_struct, testTrials(tI,:), ioObject, LTP1address);
+    
+    % move to the next trial if valid response
+    if testTrials(tI, task_struct.cRespAct) ~= disp_struct.RESP_SLOW
+        tI = tI + 1;
+    end
+end % while not done test trials
+% store all test trials
+allTestTrials = testTrials;
+
+% final note to the subject
+taskDone(disp_struct);
+
+% store task data
+task_struct.endTime = GetSecs();
+task_struct.expTime = task_struct.endTime - task_struct.startTime;
+task_struct.trainTrials = allTrainTrials;
+task_struct.testTrials = allTestTrials;
+% save to file
+save( fullfile('..', 'Data', file_name), 'task_struct', 'disp_struct');
+
+
+% clean up
+sca;
+ListenChar();
+ShowCursor();

scripts/buildTestTrials.m

@@ -0,0 +1,24 @@
+function trials = buildTestTrials(disp_struct, task_struct)
+    
+    % full cross-combination of all stimuli
+    [p,q] = meshgrid(1:length(task_struct.pWin), 1:length(task_struct.pWin));
+    pairs = repmat([p(:) q(:)], 2, 1);
+    % remove stimuli paired with themselves
+    pairs( pairs(:,1) == pairs(:,2), : ) = [];
+    % double the bias pairs
+    pairs = [pairs; repmat([1 2 3 4; 5 6 7 8]', 4, 1)];
+    
+    % define the trials
+    trials = nan(size(pairs, 1), task_struct.cRT);
+    trials(:, task_struct.cBlock) = 0;
+    trials(:, task_struct.cTrialNum) = 1:size(trials, 1);
+    trials(:, task_struct.cTrialID) = 1:size(trials, 1);
+    trials(:, task_struct.cTrialCond) = task_struct.FC;
+    trials(:, task_struct.cTrialType) = task_struct.TEST;
+    trials(:, task_struct.cS1) = pairs(:, 1);
+    trials(:, task_struct.cS2) = pairs(:, 2);
+    trials(:, task_struct.cIsS1Left) = rand(size(trials, 1), 1) > 0.5;
+    
+    % randomize
+    trials = trials(randperm(size(trials,1)), :);
+end % function

scripts/buildTrainTrials.m

@@ -0,0 +1,71 @@
+function trials = buildTrainTrials(disp_struct, task_struct)
+
+    % define each fc stimulus pair trial
+    stimPairs = [1,2; 3,4;];
+    
+    % will hold all the trials
+    fcTrials = [];
+    
+    % loop through each pair
+    for pI = 1 : size(stimPairs, 1)
+        % will hold the stimulus trials
+        pairTrials = nan(task_struct.numTrainReps, task_struct.cRT);
+        pairTrials(:, task_struct.cTrialCond) = task_struct.FC;
+        startID = 1 + 10*(pI - 1);
+        pairTrials(:, task_struct.cTrialID) = startID:(startID+task_struct.numTrainReps-1);
+        
+        % specs for card 1
+        pairTrials(:,task_struct.cS1) = stimPairs(pI, 1);
+        stim1Win = rand(task_struct.numTrainReps, 1) <= task_struct.pWin(stimPairs(pI, 1));
+        pairTrials(stim1Win,task_struct.cS1Rew) = task_struct.WIN;
+        pairTrials(~stim1Win,task_struct.cS1Rew) = task_struct.LOSS;
+        % specs for card 2
+        pairTrials(:,task_struct.cS2) = stimPairs(pI, 2);
+        stim2Win = rand(task_struct.numTrainReps, 1) <= task_struct.pWin(stimPairs(pI, 2));
+        pairTrials(stim2Win,task_struct.cS2Rew) = task_struct.WIN;
+        pairTrials(~stim2Win,task_struct.cS2Rew) = task_struct.LOSS;
+        
+        % compile paired trials
+        fcTrials = [fcTrials; pairTrials];
+        
+    end % for each stim pair
+    
+    % get all AB, CD pairs
+    ABfc = fcTrials(fcTrials(:, task_struct.cS1) == task_struct.sCodes.Afc,:);
+    CDfc = fcTrials(fcTrials(:, task_struct.cS1) == task_struct.sCodes.Cfc,:);
+    
+    % build matching nc trials
+    ABnc = ABfc;
+    ABnc(:, task_struct.cTrialCond) = task_struct.NC;
+    ABnc(:, task_struct.cS1) = ABnc(:, task_struct.cS1) + task_struct.ncAdjust;
+    ABnc(:, task_struct.cS2) = ABnc(:, task_struct.cS2) + task_struct.ncAdjust;
+    % for CD
+    CDnc = CDfc;
+    CDnc(:, task_struct.cTrialCond) = task_struct.NC;
+    CDnc(:, task_struct.cS1) = CDnc(:, task_struct.cS1) + task_struct.ncAdjust;
+    CDnc(:, task_struct.cS2) = CDnc(:, task_struct.cS2) + task_struct.ncAdjust;
+    
+    % knit the fc and nc trials together
+    AB = nan(2*size(ABfc,1), size(ABfc,2));
+    AB(1:2:end) = ABfc;
+    AB(2:2:end) = ABnc;
+    % CD trials
+    CD = nan(2*size(CDfc,1), size(CDfc,2));
+    CD(1:2:end) = CDfc;
+    CD(2:2:end) = CDnc;
+    
+    % now knit all AB, CD,trials toghether
+    % maintaining the order within each AB, CD and EF set
+    trials = nan(2*size(AB,1), size(AB,2));
+    slots = reshape(randperm(size(trials,1)), size(AB,1), 2);
+    % assign AB slots
+    trials(sort(slots(:,1)),:) = AB;
+    trials(sort(slots(:,2)),:) = CD;
+    
+    
+    trials(:, task_struct.cTrialNum) = 1:size(trials,1);
+    % stim ordering for each trial
+    trials(:,task_struct.cIsS1Left) = rand(size(trials,1), 1) >= 0.5;
+    trials(:,task_struct.cTrialType) = task_struct.TRAIN;
+    
+end % function

scripts/runChoice.m

@@ -0,0 +1,210 @@
+function trial = runChoice(disp_struct, task_struct, trial, TRAINTEST, ioObject, LTP1address)
+
+% Fixation
+JIT=Shuffle(.3:.001:.5);
+fixation_text = '+';
+DrawFormattedText(disp_struct.wPtr,fixation_text,'center','center');
+Screen(disp_struct.wPtr, 'Flip');
+WaitSecs(JIT(1));
+
+% get the center for the screen
+center_x = round(disp_struct.wPtr_rect(3)/2);
+center_y = round(disp_struct.wPtr_rect(4)/2);
+
+% define the text font
+Screen('TextSize', disp_struct.wPtr, 40);
+Screen('TextFont', disp_struct.wPtr, 'Times');
+Screen('TextStyle', disp_struct.wPtr, 0);
+Screen('TextColor', disp_struct.wPtr, [255 255 255]);
+Screen('FillRect', disp_struct.wPtr,[0 0 0]);
+
+% stimuli bounds
+sWidth = 300;
+sHeight = 300;
+sTop = center_y - sHeight/2;
+sL_left = center_x - 80 - sWidth;
+sR_left = center_x + 80;
+sL_rect = [sL_left, sTop, sL_left + sWidth, sTop + sHeight];
+sR_rect = [sR_left, sTop, sR_left + sWidth, sTop + sHeight];
+
+% define the left and right stimuli
+if trial(task_struct.cIsS1Left)
+    respL = trial(task_struct.cS1);
+    respR = trial(task_struct.cS2);
+    s1Rect = sL_rect;
+    s2Rect = sR_rect;
+else
+    respL = trial(task_struct.cS2);
+    respR = trial(task_struct.cS1);
+    s1Rect = sR_rect;
+    s2Rect = sL_rect;
+end
+
+% show the stimuli
+Screen('DrawTexture', disp_struct.wPtr, disp_struct.cards(trial(task_struct.cS1)), [], s1Rect);
+Screen('DrawTexture', disp_struct.wPtr, disp_struct.cards(trial(task_struct.cS2)), [], s2Rect);
+io64(ioObject,LTP1address,3); WaitSecs(.05); io64(ioObject,LTP1address,0);
+
+% see if we need to frame the nc stim
+if trial(task_struct.cTrialCond) == task_struct.NC
+    if trial(task_struct.cRespAct) == trial(task_struct.cS1)
+        ncFrameRect = s1Rect;
+    else
+        ncFrameRect = s2Rect;
+    end
+    Screen('FrameRect', disp_struct.wPtr, [0 0 255], ncFrameRect, 5);
+end % frame the fc selected stim
+
+% limit input
+RestrictKeysForKbCheck([disp_struct.RESP_L, disp_struct.RESP_R]);
+KbReleaseWait();
+
+% flip to show the stims
+[VBLTimestamp StimulusOnsetTime FlipTimestamp Missed Beampos] = Screen(disp_struct.wPtr, 'Flip', 0, 1);
+% %     % wait for response
+% %     [secs, keyCode, deltaSecs] = KbWait([], 2, GetSecs()+task_struct.maxRT);
+wait_stamp=GetSecs;
+while 1
+    [a ab] = JoyMEX(0);
+    if find(ab) ~= 0
+        if ab(1,disp_struct.RESP_L) == 1
+            stim_resp = disp_struct.RESP_L;
+            io64(ioObject,LTP1address,4); WaitSecs(.05); io64(ioObject,LTP1address,0);
+            break;
+        elseif ab(1,disp_struct.RESP_R) == 1
+            stim_resp = disp_struct.RESP_R;
+            io64(ioObject,LTP1address,5); WaitSecs(.05); io64(ioObject,LTP1address,0);
+            break;
+        end
+    elseif(GetSecs-wait_stamp) > task_struct.maxRT,
+        stim_resp=999;
+        break;
+    end
+end
+% compute the RT
+RT = GetSecs - StimulusOnsetTime;
+
+if stim_resp == disp_struct.RESP_L
+    % left-most action
+    choice = respL;
+    % hide the non-selected stim
+    Screen('FillRect', disp_struct.wPtr, [0 0 0], sR_rect);
+    % hide selection
+    Screen(disp_struct.wPtr, 'Flip');
+    WaitSecs(0.75);
+    
+elseif stim_resp == disp_struct.RESP_R
+    % right-most action
+    choice = respR;
+    % hide the non-selected stim
+    Screen('FillRect', disp_struct.wPtr, [0 0 0], sL_rect);
+    % hide selection
+    Screen(disp_struct.wPtr, 'Flip');
+    WaitSecs(0.75);
+else
+    % they didn't respond in time
+    choice = disp_struct.RESP_SLOW;
+end
+
+
+% make sure they matched if this was a no-choice trial
+if trial(task_struct.cTrialCond) == task_struct.NC && choice == trial(task_struct.cRespAct)
+    trial(task_struct.cMatch) = true;
+else
+    trial(task_struct.cMatch) = false;
+end
+
+% do not update the response if they didn't match
+if trial(task_struct.cTrialCond) == task_struct.FC || trial(task_struct.cMatch)
+    % store response
+    trial(task_struct.cRespAct) = choice;
+    trial(task_struct.cRT) = RT;
+end
+
+
+% --------------------------------------- FB
+if TRAINTEST==1
+    % define the text font
+    Screen('TextSize', disp_struct.wPtr, 60);
+    Screen('TextFont', disp_struct.wPtr, 'Times');
+    Screen('TextStyle', disp_struct.wPtr, 0);
+    Screen('TextColor', disp_struct.wPtr, [255 255 255]);
+    Screen('FillRect', disp_struct.wPtr,[0 0 0]);
+    
+    % get feedback for the selected stimulus
+    if trial(task_struct.cTrialCond) == task_struct.NC && ~trial(task_struct.cMatch)
+        fbText = 'You must match the framed symbol';
+        respRew = NaN;
+        SIMPLEFB=1;
+        FBTRIGGER=6;
+    elseif trial(task_struct.cRespAct) == trial(task_struct.cS1)
+        % they picked stim 1
+        respRew = trial(task_struct.cS1Rew);
+        fbText = num2str(respRew);
+        SIMPLEFB=0;
+        FBTRIGGER=10+respRew;
+    elseif trial(task_struct.cRespAct) == trial(task_struct.cS2)
+        % they picked stim 2
+        respRew = trial(task_struct.cS2Rew);
+        fbText = num2str(respRew);
+        SIMPLEFB=0;
+        FBTRIGGER=10+respRew;
+    else
+        % they responded too slow
+        fbText = ['Too Slow!\nYou must respond in under ' num2str(task_struct.maxRT) ' seconds'];
+        respRew = NaN;
+        SIMPLEFB=1;
+        FBTRIGGER=7;
+    end
+    
+    if respRew==0
+        Screen('TextSize', disp_struct.wPtr, 100);
+        Screen('TextColor', disp_struct.wPtr, [255 0 0]);
+    elseif  respRew==1
+        Screen('TextSize', disp_struct.wPtr, 100);
+        Screen('TextColor', disp_struct.wPtr, [0 255 0]);
+    end
+    
+    
+    % show the feedback
+    if SIMPLEFB==1
+        io64(ioObject,LTP1address,FBTRIGGER); WaitSecs(.05); io64(ioObject,LTP1address,0);
+        DrawFormattedText(disp_struct.wPtr, fbText, 'center', 'center');
+        Screen(disp_struct.wPtr, 'Flip');
+        WaitSecs(0.75);
+        Screen(disp_struct.wPtr, 'Flip');
+    else
+        % FIRST show the selected stim
+        Screen('DrawTexture', disp_struct.wPtr, disp_struct.cards(trial(task_struct.cS1)), [], s1Rect);
+        Screen('DrawTexture', disp_struct.wPtr, disp_struct.cards(trial(task_struct.cS2)), [], s2Rect);
+        % Blank out the alternative
+        if trial(task_struct.cRespAct) == respL
+            Screen('FillRect', disp_struct.wPtr, [0 0 0], sR_rect);
+        elseif trial(task_struct.cRespAct) == respR
+            Screen('FillRect', disp_struct.wPtr, [0 0 0], sL_rect);
+        end
+        % If forced, show the box
+        if trial(task_struct.cTrialCond) == task_struct.NC
+            if trial(task_struct.cRespAct) == trial(task_struct.cS1)
+                ncFrameRect = s1Rect;
+            else
+                ncFrameRect = s2Rect;
+            end
+            Screen('FrameRect', disp_struct.wPtr, [0 0 255], ncFrameRect, 5);
+        end
+        % Next show the FB
+        io64(ioObject,LTP1address,FBTRIGGER); WaitSecs(.05); io64(ioObject,LTP1address,0);
+        DrawFormattedText(disp_struct.wPtr, fbText, 'center', 'center');
+        Screen(disp_struct.wPtr, 'Flip');
+        WaitSecs(1.25);
+        Screen(disp_struct.wPtr, 'Flip');
+    end
+    clear FBTRIGGER;
+    Screen('TextSize', disp_struct.wPtr, 60);
+    Screen('TextColor', disp_struct.wPtr, [255 255 255]);
+    
+    % store the feedback
+    trial(task_struct.cRespRew) = respRew;
+end
+end % function
+

scripts/runConditionNotice.m

@@ -0,0 +1,28 @@
+function trial = runConditionNotice(disp_struct, task_struct, trial, ioObject, LTP1address)
+    % define the text font
+    Screen('TextSize', disp_struct.wPtr, 60);
+    Screen('TextFont', disp_struct.wPtr, 'Times');
+    Screen('TextStyle', disp_struct.wPtr, 0);
+    Screen('TextColor', disp_struct.wPtr, [255 255 255]);
+    Screen('FillRect', disp_struct.wPtr,[0 0 0]);
+    
+    if trial(task_struct.cTrialCond) == task_struct.FC
+        % free choice trial
+        condText = 'Choose';
+        io64(ioObject,LTP1address,1); WaitSecs(.05); io64(ioObject,LTP1address,0);
+    else
+        % no-choice trial
+        Screen('TextColor', disp_struct.wPtr, [0 0 255]);
+        condText = 'Match';
+        io64(ioObject,LTP1address,2); WaitSecs(.05); io64(ioObject,LTP1address,0);
+    end
+    
+    DrawFormattedText(disp_struct.wPtr, condText, 'center', 'center');
+    Screen(disp_struct.wPtr, 'Flip');
+    WaitSecs(0.5);
+    
+    % clear
+    Screen(disp_struct.wPtr, 'Flip');
+    Screen('TextColor', disp_struct.wPtr, [255 255 255]);
+
+end % function

scripts/runFeedback.m

@@ -0,0 +1,99 @@
+function trial = runFeedback(disp_struct, task_struct, trial, ioObject, LTP1address)
+    % define the text font
+    Screen('TextSize', disp_struct.wPtr, 60);
+    Screen('TextFont', disp_struct.wPtr, 'Times');
+    Screen('TextStyle', disp_struct.wPtr, 0);
+    Screen('TextColor', disp_struct.wPtr, [255 255 255]);
+    Screen('FillRect', disp_struct.wPtr,[0 0 0]);
+    
+    % get feedback for the selected stimulus
+    if trial(task_struct.cTrialCond) == task_struct.NC && ~trial(task_struct.cMatch)
+        fbText = 'You must match the framed symbol';
+        respRew = NaN;
+        SIMPLEFB=1;
+    elseif trial(task_struct.cRespAct) == trial(task_struct.cS1)
+        % they picked stim 1
+        respRew = trial(task_struct.cS1Rew);
+        fbText = num2str(respRew);
+        SIMPLEFB=0;
+    elseif trial(task_struct.cRespAct) == trial(task_struct.cS2)
+        % they picked stim 2
+        respRew = trial(task_struct.cS2Rew);
+        fbText = num2str(respRew);
+        SIMPLEFB=0;
+    else 
+        % they responded too slow
+        fbText = ['Too Slow!\nYou must respond in under ' num2str(task_struct.maxRT) ' seconds'];
+        respRew = NaN;
+        SIMPLEFB=1;
+    end
+    
+    
+    % define the left and right stimuli
+    center_x = round(disp_struct.wPtr_rect(3)/2);
+    center_y = round(disp_struct.wPtr_rect(4)/2);
+    sWidth = 300;
+    sHeight = 300;
+    sTop = center_y - sHeight/2;
+    sL_left = center_x - 80 - sWidth;
+    sR_left = center_x + 80;
+    sL_rect = [sL_left, sTop, sL_left + sWidth, sTop + sHeight];
+    sR_rect = [sR_left, sTop, sR_left + sWidth, sTop + sHeight];
+    if trial(task_struct.cIsS1Left)
+        respL = trial(task_struct.cS1);
+        respR = trial(task_struct.cS2);
+        s1Rect = sL_rect;
+        s2Rect = sR_rect;
+    else
+        respL = trial(task_struct.cS2);
+        respR = trial(task_struct.cS1);
+        s1Rect = sR_rect;
+        s2Rect = sL_rect;
+    end
+    if respRew==0
+        Screen('TextSize', disp_struct.wPtr, 100);
+        Screen('TextColor', disp_struct.wPtr, [255 0 0]);
+    elseif  respRew==1
+        Screen('TextSize', disp_struct.wPtr, 100);
+        Screen('TextColor', disp_struct.wPtr, [0 255 0]);
+    end
+    
+    
+    % show the feedback
+    if SIMPLEFB==1
+        DrawFormattedText(disp_struct.wPtr, fbText, 'center', 'center');
+        Screen(disp_struct.wPtr, 'Flip');
+        WaitSecs(0.75);
+        Screen(disp_struct.wPtr, 'Flip');
+    else
+        % FIRST show the selected stim
+        Screen('DrawTexture', disp_struct.wPtr, disp_struct.cards(trial(task_struct.cS1)), [], s1Rect);
+        Screen('DrawTexture', disp_struct.wPtr, disp_struct.cards(trial(task_struct.cS2)), [], s2Rect);
+        % Blank out the alternative
+        if trial(task_struct.cRespAct) == respL
+            Screen('FillRect', disp_struct.wPtr, [0 0 0], sR_rect);
+        elseif trial(task_struct.cRespAct) == respR
+            Screen('FillRect', disp_struct.wPtr, [0 0 0], sL_rect);
+        end
+        % If forced, show the box
+        if trial(task_struct.cTrialCond) == task_struct.NC
+            if trial(task_struct.cRespAct) == trial(task_struct.cS1)
+                ncFrameRect = s1Rect;
+            else
+                ncFrameRect = s2Rect;
+            end
+            Screen('FrameRect', disp_struct.wPtr, [0 0 255], ncFrameRect, 5);
+        end
+        % Next show the FB
+        DrawFormattedText(disp_struct.wPtr, fbText, 'center', 'center');
+        Screen(disp_struct.wPtr, 'Flip');
+        WaitSecs(1.25);
+        Screen(disp_struct.wPtr, 'Flip');
+    end
+
+    Screen('TextSize', disp_struct.wPtr, 60);
+    Screen('TextColor', disp_struct.wPtr, [255 255 255]);
+    
+    % store the feedback
+    trial(task_struct.cRespRew) = respRew; 
+end % function

scripts/runPractice.m

@@ -0,0 +1,24 @@
+function trials = runPractice(disp_struct, task_struct, ioObject, LTP1address)
+    % use cards [13 14 15 16] for practice trials
+    
+    % set up the practice trials
+    trials = nan(6, task_struct.cRT);
+    trials(:,task_struct.cBlock) = -1;
+    trials(:,task_struct.cTrialNum) = 1:size(trials,1);
+    trials(:,task_struct.cTrialID) = 1:size(trials,1);
+    trials(:,task_struct.cTrialCond) = [task_struct.FC, task_struct.NC, task_struct.FC, task_struct.FC, task_struct.NC, task_struct.NC];
+    trials(:,task_struct.cTrialType) = task_struct.PRAC;
+    trials(:,task_struct.cS1) = [13 15 13 13 15 15];
+    trials(:,task_struct.cS1Rew) = [task_struct.WIN task_struct.WIN task_struct.WIN task_struct.LOSS task_struct.LOSS task_struct.WIN];
+    trials(:,task_struct.cS2) = [14 16 14 14 16 16];
+    trials(:,task_struct.cS2Rew) = [task_struct.WIN task_struct.WIN task_struct.WIN task_struct.LOSS task_struct.LOSS task_struct.WIN];
+    trials(:,task_struct.cIsS1Left) = [1 1 0 1 0 1];
+    trials(:,task_struct.cRespAct) = [nan 15 nan nan 16 15];
+    trials(:,task_struct.cRespRew) = [nan task_struct.WIN nan nan task_struct.LOSS task_struct.WIN];
+    
+    % loop through each practice trial
+    for tI = 1 : size(trials,1)
+        runTrainTrial(disp_struct, task_struct, trials(tI,:), ioObject, LTP1address);
+    end % for each trial
+    
+end % practice

scripts/runTestTrial.m

@@ -0,0 +1,16 @@
+function trial = runTestTrial(disp_struct, task_struct, trial, ioObject, LTP1address)
+
+    % blank screen
+    WaitSecs(0.5);
+    Screen(disp_struct.wPtr, 'Flip');
+
+    % show the stims, and get the choice
+    TRAINTEST=0;
+    trial = runChoice(disp_struct, task_struct, trial, TRAINTEST, ioObject, LTP1address);
+    
+    % show too slow feedback
+    if trial(task_struct.cRespAct) == disp_struct.RESP_SLOW
+        runFeedback(disp_struct, task_struct, trial, ioObject, LTP1address);
+    end
+    
+end % function

scripts/runTrainTrial.m

@@ -0,0 +1,15 @@
+function trial = runTrainTrial(disp_struct, task_struct, trial, ioObject, LTP1address)
+
+    % blank screen
+    WaitSecs(1);
+    Screen(disp_struct.wPtr, 'Flip');
+
+    % show the choice condition
+    trial = runConditionNotice(disp_struct, task_struct, trial, ioObject, LTP1address);
+    WaitSecs(0.5);
+    % show the stims, and get the choice, show the FB
+    TRAINTEST=1;
+    trial = runChoice(disp_struct, task_struct, trial, TRAINTEST, ioObject, LTP1address);
+
+    
+end % function

scripts/taskDone.m

@@ -0,0 +1,33 @@
+function [] = taskDone(disp_struct)
+
+    % the outline task instructions
+    text_0 = '-COMPLETE-\n\n\n';
+    text_1 = 'The task is complete.\n\nPlease let the experimenter know you''re done.\n\n Thank you for your time'; 
+    
+    Screen('TextSize', disp_struct.wPtr, 30);
+    Screen('TextFont', disp_struct.wPtr, 'Times');
+    Screen('TextStyle', disp_struct.wPtr, 0);
+    Screen('TextColor', disp_struct.wPtr, [255 255 255]);
+    Screen('FillRect', disp_struct.wPtr,[0 0 0]);
+    % the number of charaters per line
+    wrap_length = 70;
+    
+    % Print out the initial instructions
+    
+    % Bold the intro
+    Screen('TextStyle', disp_struct.wPtr, 1);
+    [nx, ny, textbounds] = DrawFormattedText(disp_struct.wPtr, text_0, 'center', 50, [], wrap_length, [], [], 1.25 );
+    
+    % reset to normal font
+    Screen('TextStyle', disp_struct.wPtr, 0);
+    [nx, ny, textbounds] = DrawFormattedText(disp_struct.wPtr, text_1, 'center', ny, [], wrap_length, [], [], 1.25 );
+    
+    % flip the screen
+    Screen(disp_struct.wPtr, 'Flip');
+    WaitSecs(0.5);
+    
+    % wait for P
+    RestrictKeysForKbCheck(KbName('SPACE'));
+    % wait for keypress
+    KbWait([],2); % Waits for keyboard(any) press
+end

scripts/taskInit.m

@@ -0,0 +1,83 @@
+% initialize the task structure
+function [disp_struct, task_struct] = taskInit(disp_struct, task_struct, SCREENS, session, LEFTKEY, RIGHTKEY)
+    % structure to hold data pertinant to the task
+    task_data = struct();
+    % set up the matrix columns
+    task_struct.cBlock = 1;
+    task_struct.cTrialNum = 2;
+    task_struct.cTrialID = 3;
+    task_struct.cTrialCond = 4;
+    task_struct.cTrialType = 5;
+    task_struct.cS1 = 6;
+    task_struct.cS1Rew = 7;
+    task_struct.cS2 = 8;
+    task_struct.cS2Rew = 9;
+    task_struct.cIsS1Left = 10;
+    task_struct.cRespAct = 11;
+    task_struct.cMatch = 12;
+    task_struct.cRespRew = 13;
+    task_struct.cRT = 14;
+    
+    
+    % maximum trial gap between free-choice and its no-choice pair
+    task_struct.maxGap = 7;
+    % code for each stimulus
+    task_struct.sCodes = struct('Afc', 1, 'Bfc', 2, 'Cfc', 3, 'Dfc', 4, 'Anc', 5, 'Bnc', 6, 'Cnc', 7, 'Dnc', 8);
+    % the reward probabilities for each stim
+    task_struct.pWin = [0.9 0.1 0.7 0.3 0.9 0.1 0.7 0.3];            % JFC changed to 90/10 and 70/30
+    % number of reps for each stimulus pair per training block
+    task_struct.numTrainReps = 10;
+    task_struct.numTestBiasReps = 10;
+    task_struct.numTestABReps = 4;
+    task_struct.numTestTrainReps = 4;
+    % index to match each free-choice with it's no-choice stim pair
+    task_struct.ncAdjust = 4;
+    
+    % max response time
+    task_struct.maxRT = 4;
+    % training performance thresholds
+    task_struct.minPerf = [0.60 0.60];
+    % min/max number of training blocks
+    task_struct.minTrain = 3;
+    task_struct.maxTrain = 5;
+    
+    % condition flags for each trial type
+    task_struct.NC = 0;
+    task_struct.FC = 1;
+    task_struct.PRAC = -1;
+    task_struct.TRAIN = 0;
+    task_struct.TEST = 1;
+    task_struct.WIN = 1;
+    task_struct.LOSS = 0;
+    
+    % set up the response keys
+    disp_struct.RESP_L = LEFTKEY;
+    disp_struct.RESP_R = RIGHTKEY;
+    disp_struct.RESP_SLOW = -1;
+    
+    % first set up the display window
+    % screenRect_debug = [0,0,1000,800]; % screen for debugging
+    screenRect_task = []; % full screen
+    % open the window
+    [wPtr,rect] = Screen('OpenWindow', SCREENS, [], screenRect_task);
+    % store the display window and it's bounds
+    disp_struct.wPtr = wPtr;
+    disp_struct.wPtr_rect = rect;
+    
+    % load the images
+    if session==1
+        cardNames = {'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'I', 'J', 'K', 'L'}; % Practice stims repeated here b/c of laziness
+    elseif session==2
+        cardNames = {'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'I', 'J', 'K', 'L', 'I', 'J', 'K', 'L'}; % Practice stims repeated here b/c of laziness     
+    else 
+        disp(' '); disp(' '); disp(' '); disp('Enter a 1 or a 2 for session!'); disp(' '); 
+    end
+    disp_struct.cards = nan(length(cardNames), 1);
+    for imgI = 1 : length(cardNames)
+        disp_struct.cards(imgI) = Screen('MakeTexture', disp_struct.wPtr, imread(fullfile('..', 'images', [cardNames{imgI},'.bmp'])));
+    end % for each card
+    % randomize the first 8 (I,J,K,L are for practice)
+    disp_struct.cards(1:length(task_struct.pWin)) = disp_struct.cards(randperm(length(task_struct.pWin)));
+end % function
+
+

scripts/test_Inst1.m

@@ -0,0 +1,39 @@
+function [] = test_Inst1(disp_struct)
+
+    % the outline task instructions
+    text_0 = '-Instructions-\n\n\n';
+    text_1 = 'Great Job!\nIt''s time to test what you''ve learned.\n\n';
+    text_2 = 'Now you''ll be free to choose on every trial,\n but you''ll NO LONGER RECIEVE ANY FEEDBACK!\n\n';
+    text_3 = 'If you see new combinations of symbols, choose the symbol that ''feels'' most likely to award points based on what you''ve learned.\n\n If you''re not sure which one to pick,\n just go with your gut instinct.\n\n';
+    text_4 = 'Press the space bar when you''re ready to begin';
+    
+    Screen('TextSize', disp_struct.wPtr, 30);
+    Screen('TextFont', disp_struct.wPtr, 'Times');
+    Screen('TextStyle', disp_struct.wPtr, 0);
+    Screen('TextColor', disp_struct.wPtr, [255 255 255]);
+    Screen('FillRect', disp_struct.wPtr,[0 0 0]);
+    % the number of charaters per line
+    wrap_length = 70;
+    
+    % Print out the initial instructions
+    
+    % Bold the intro
+    Screen('TextStyle', disp_struct.wPtr, 1);
+    [nx, ny, textbounds] = DrawFormattedText(disp_struct.wPtr, text_0, 'center', 50, [], wrap_length, [], [], 1.25 );
+    
+    % reset to normal font
+    Screen('TextStyle', disp_struct.wPtr, 0);
+    [nx, ny, textbounds] = DrawFormattedText(disp_struct.wPtr, text_1, 'center', ny, [], wrap_length, [], [], 1.25 );
+    [nx, ny, textbounds] = DrawFormattedText(disp_struct.wPtr, text_2, 'center', ny, [], wrap_length, [], [], 1.25 );
+    [nx, ny, textbounds] = DrawFormattedText(disp_struct.wPtr, text_3, 'center', ny, [], wrap_length, [], [], 1.25 );
+    [nx, ny, textbounds] = DrawFormattedText(disp_struct.wPtr, text_4, 'center', ny, [], wrap_length, [], [], 1.25 );
+    
+    % flip the screen
+    Screen(disp_struct.wPtr, 'Flip');
+    WaitSecs(0.5);
+    
+    % wait for P
+    RestrictKeysForKbCheck(KbName('SPACE'));
+    % wait for keypress
+    KbWait([],2); % Waits for keyboard(any) press
+end

scripts/trainBreak.m

@@ -0,0 +1,33 @@
+function [] = trainBreak(disp_struct)
+
+    % the outline task instructions
+    text_0 = '-BREAK-\n\n\n';
+    text_1 = 'Press the space bar when you''re ready to continue'; 
+    
+    Screen('TextSize', disp_struct.wPtr, 30);
+    Screen('TextFont', disp_struct.wPtr, 'Times');
+    Screen('TextStyle', disp_struct.wPtr, 0);
+    Screen('TextColor', disp_struct.wPtr, [255 255 255]);
+    Screen('FillRect', disp_struct.wPtr,[0 0 0]);
+    % the number of charaters per line
+    wrap_length = 70;
+    
+    % Print out the initial instructions
+    
+    % Bold the intro
+    Screen('TextStyle', disp_struct.wPtr, 1);
+    [nx, ny, textbounds] = DrawFormattedText(disp_struct.wPtr, text_0, 'center', 50, [], wrap_length, [], [], 1.25 );
+    
+    % reset to normal font
+    Screen('TextStyle', disp_struct.wPtr, 0);
+    [nx, ny, textbounds] = DrawFormattedText(disp_struct.wPtr, text_1, 'center', ny, [], wrap_length, [], [], 1.25 );
+    
+    % flip the screen
+    Screen(disp_struct.wPtr, 'Flip');
+    WaitSecs(0.5);
+    
+    % wait for P
+    RestrictKeysForKbCheck(KbName('SPACE'));
+    % wait for keypress
+    KbWait([],2); % Waits for keyboard(any) press
+end

scripts/train_Inst1.m

@@ -0,0 +1,35 @@
+function [] = train_Inst1(disp_struct)
+
+    % the outline task instructions
+    text_0 = '-Instructions-\n\n\n';
+    text_1 = 'Your task is to learn about various pictures.\n\n';
+    text_2 = 'Some pictures will award points more reliably than others,\n but you''ll have to learn which ones.\n\n';
+    
+    Screen('TextSize', disp_struct.wPtr, 30);
+    Screen('TextFont', disp_struct.wPtr, 'Times');
+    Screen('TextStyle', disp_struct.wPtr, 0);
+    Screen('TextColor', disp_struct.wPtr, [255 255 255]);
+    Screen('FillRect', disp_struct.wPtr,[0 0 0]);
+    % the number of charaters per line
+    wrap_length = 70;
+    
+    % Print out the initial instructions
+    
+    % Bold the intro
+    Screen('TextStyle', disp_struct.wPtr, 1);
+    [nx, ny, textbounds] = DrawFormattedText(disp_struct.wPtr, text_0, 'center', 50, [], wrap_length, [], [], 1.25 );
+    
+    % reset to normal font
+    Screen('TextStyle', disp_struct.wPtr, 0);
+    [nx, ny, textbounds] = DrawFormattedText(disp_struct.wPtr, text_1, 'center', ny, [], wrap_length, [], [], 1.25 );
+    [nx, ny, textbounds] = DrawFormattedText(disp_struct.wPtr, text_2, 'center', ny, [], wrap_length, [], [], 1.25 );
+    
+    % flip the screen
+    Screen(disp_struct.wPtr, 'Flip');
+    WaitSecs(0.5);
+    
+    % wait for P
+    RestrictKeysForKbCheck(KbName('SPACE'));
+    % wait for keypress
+    KbWait([],2); % Waits for keyboard(any) press
+end

scripts/train_Inst2.m

@@ -0,0 +1,35 @@
+function [] = train_Inst2(disp_struct)
+
+    % the outline task instructions
+    text_0 = '-Instructions-\n\n\n';
+    text_1 = 'On each trial, two pictures will appear on the screen simultaneously.\n\nYou can select either the pictures on the left using the LEFT TRIGGER,\n or the pictures on the right using the RIGHT TRIGGER\n\n\n\n';
+    text_2 = 'Every pictures can appear on the left or the right. This is totally random and does not influence the outcome at all.\n\n\n\n (This is only done to ensure left/right handed people don''t have an advantage.)';
+    
+    Screen('TextSize', disp_struct.wPtr, 30);
+    Screen('TextFont', disp_struct.wPtr, 'Times');
+    Screen('TextStyle', disp_struct.wPtr, 0);
+    Screen('TextColor', disp_struct.wPtr, [255 255 255]);
+    Screen('FillRect', disp_struct.wPtr,[0 0 0]);
+    % the number of charaters per line
+    wrap_length = 70;
+    
+    % Print out the initial instructions
+    
+    % Bold the intro
+    Screen('TextStyle', disp_struct.wPtr, 1);
+    [nx, ny, textbounds] = DrawFormattedText(disp_struct.wPtr, text_0, 'center', 50, [], wrap_length, [], [], 1.25 );
+    
+    % reset to normal font
+    Screen('TextStyle', disp_struct.wPtr, 0);
+    [nx, ny, textbounds] = DrawFormattedText(disp_struct.wPtr, text_1, 'center', ny, [], wrap_length, [], [], 1.25 );
+    [nx, ny, textbounds] = DrawFormattedText(disp_struct.wPtr, text_2, 'center', ny, [], wrap_length, [], [], 1.25 );
+    
+    % flip the screen
+    Screen(disp_struct.wPtr, 'Flip');
+    WaitSecs(0.5);
+    
+    % wait for P
+    RestrictKeysForKbCheck(KbName('SPACE'));
+    % wait for keypress
+    KbWait([],2); % Waits for keyboard(any) press
+end

scripts/train_Inst3.m

@@ -0,0 +1,35 @@
+function [] = train_Inst3(disp_struct)
+
+    % the outline task instructions
+    text_0 = '-Instructions-\n\n\n';
+    text_1 = 'The picture you select will either award you a point (+1) or not (0).\n\n\n\n There''s no *absolute* right answer, but try to pick pictures that have the best chance of awarding points.\n\n\n\n';
+    text_2 = 'At first this might seem difficult, but you''ll get lots of practice.';
+    
+    Screen('TextSize', disp_struct.wPtr, 30);
+    Screen('TextFont', disp_struct.wPtr, 'Times');
+    Screen('TextStyle', disp_struct.wPtr, 0);
+    Screen('TextColor', disp_struct.wPtr, [255 255 255]);
+    Screen('FillRect', disp_struct.wPtr,[0 0 0]);
+    % the number of charaters per line
+    wrap_length = 70;
+    
+    % Print out the initial instructions
+    
+    % Bold the intro
+    Screen('TextStyle', disp_struct.wPtr, 1);
+    [nx, ny, textbounds] = DrawFormattedText(disp_struct.wPtr, text_0, 'center', 50, [], wrap_length, [], [], 1.25 );
+    
+    % reset to normal font
+    Screen('TextStyle', disp_struct.wPtr, 0);
+    [nx, ny, textbounds] = DrawFormattedText(disp_struct.wPtr, text_1, 'center', ny, [], wrap_length, [], [], 1.25 );
+    [nx, ny, textbounds] = DrawFormattedText(disp_struct.wPtr, text_2, 'center', ny, [], wrap_length, [], [], 1.25 );
+    
+    % flip the screen
+    Screen(disp_struct.wPtr, 'Flip');
+    WaitSecs(0.5);
+    
+    % wait for P
+    RestrictKeysForKbCheck(KbName('SPACE'));
+    % wait for keypress
+    KbWait([],2); % Waits for keyboard(any) press
+end

scripts/train_Inst4.m

@@ -0,0 +1,35 @@
+function [] = train_Inst4(disp_struct)
+
+    % the outline task instructions
+    text_0 = '-Instructions-\n\n\n';
+    text_1 = 'OK - here''s an interesting twist: \n\n On some trials, one of the picture will be selected for you and will be framed in blue. These are called ''Match'' trials.\n\n On ''Match'' trials, you must select the framed picture.\n\n\n\n';
+    text_2 = 'On other trials you will be free to choose either picture.\n\n These are called ''Choose'' trials.';
+    
+    Screen('TextSize', disp_struct.wPtr, 30);
+    Screen('TextFont', disp_struct.wPtr, 'Times');
+    Screen('TextStyle', disp_struct.wPtr, 0);
+    Screen('TextColor', disp_struct.wPtr, [255 255 255]);
+    Screen('FillRect', disp_struct.wPtr,[0 0 0]);
+    % the number of charaters per line
+    wrap_length = 70;
+    
+    % Print out the initial instructions
+    
+    % Bold the intro
+    Screen('TextStyle', disp_struct.wPtr, 1);
+    [nx, ny, textbounds] = DrawFormattedText(disp_struct.wPtr, text_0, 'center', 50, [], wrap_length, [], [], 1.25 );
+    
+    % reset to normal font
+    Screen('TextStyle', disp_struct.wPtr, 0);
+    [nx, ny, textbounds] = DrawFormattedText(disp_struct.wPtr, text_1, 'center', ny, [], wrap_length, [], [], 1.25 );
+    [nx, ny, textbounds] = DrawFormattedText(disp_struct.wPtr, text_2, 'center', ny, [], wrap_length, [], [], 1.25 );
+    
+    % flip the screen
+    Screen(disp_struct.wPtr, 'Flip');
+    WaitSecs(0.5);
+    
+    % wait for P
+    RestrictKeysForKbCheck(KbName('SPACE'));
+    % wait for keypress
+    KbWait([],2); % Waits for keyboard(any) press
+end

scripts/train_Inst5.m

@@ -0,0 +1,37 @@
+function [] = train_Inst5(disp_struct)
+
+    % the outline task instructions
+    text_0 = '-Instructions-\n\n\n';
+    text_1 = 'Regardless of whether you Choose or Match on each trial,\n\n your goal is to learn which pictures are more rewarding. \n\n\n\n (Doing so will help you later in the task!)\n\n\n\n';
+    text_2 = 'The faster you learn which pictures are better,\n\n the faster you will finish!\n\n\n\n';
+    text_3 = 'Let''s try a few practice trials.';
+    
+    Screen('TextSize', disp_struct.wPtr, 30);
+    Screen('TextFont', disp_struct.wPtr, 'Times');
+    Screen('TextStyle', disp_struct.wPtr, 0);
+    Screen('TextColor', disp_struct.wPtr, [255 255 255]);
+    Screen('FillRect', disp_struct.wPtr,[0 0 0]);
+    % the number of charaters per line
+    wrap_length = 70;
+    
+    % Print out the initial instructions
+    
+    % Bold the intro
+    Screen('TextStyle', disp_struct.wPtr, 1);
+    [nx, ny, textbounds] = DrawFormattedText(disp_struct.wPtr, text_0, 'center', 50, [], wrap_length, [], [], 1.25 );
+    
+    % reset to normal font
+    Screen('TextStyle', disp_struct.wPtr, 0);
+    [nx, ny, textbounds] = DrawFormattedText(disp_struct.wPtr, text_1, 'center', ny, [], wrap_length, [], [], 1.25 );
+    [nx, ny, textbounds] = DrawFormattedText(disp_struct.wPtr, text_2, 'center', ny, [], wrap_length, [], [], 1.25 );
+    [nx, ny, textbounds] = DrawFormattedText(disp_struct.wPtr, text_3, 'center', ny, [], wrap_length, [], [], 1.25 );
+    
+    % flip the screen
+    Screen(disp_struct.wPtr, 'Flip');
+    WaitSecs(0.5);
+    
+    % wait for P
+    RestrictKeysForKbCheck(KbName('SPACE'));
+    % wait for keypress
+    KbWait([],2); % Waits for keyboard(any) press
+end

scripts/train_Inst6.m

@@ -0,0 +1,35 @@
+function [] = train_Inst6(disp_struct)
+
+    % the outline task instructions
+    text_0 = '-Instructions-\n\n\n';
+    text_1 = 'Please let the experimenter know if you have any questions or don''t fully understand your task\n\n\n';
+    text_2 = 'Press the space bar when you''re ready to begin';
+    
+    Screen('TextSize', disp_struct.wPtr, 30);
+    Screen('TextFont', disp_struct.wPtr, 'Times');
+    Screen('TextStyle', disp_struct.wPtr, 0);
+    Screen('TextColor', disp_struct.wPtr, [255 255 255]);
+    Screen('FillRect', disp_struct.wPtr,[0 0 0]);
+    % the number of charaters per line
+    wrap_length = 70;
+    
+    % Print out the initial instructions
+    
+    % Bold the intro
+    Screen('TextStyle', disp_struct.wPtr, 1);
+    [nx, ny, textbounds] = DrawFormattedText(disp_struct.wPtr, text_0, 'center', 50, [], wrap_length, [], [], 1.25 );
+    
+    % reset to normal font
+    Screen('TextStyle', disp_struct.wPtr, 0);
+    [nx, ny, textbounds] = DrawFormattedText(disp_struct.wPtr, text_1, 'center', ny, [], wrap_length, [], [], 1.25 );
+    [nx, ny, textbounds] = DrawFormattedText(disp_struct.wPtr, text_2, 'center', ny, [], wrap_length, [], [], 1.25 );
+    
+    % flip the screen
+    Screen(disp_struct.wPtr, 'Flip');
+    WaitSecs(0.5);
+    
+    % wait for P
+    RestrictKeysForKbCheck(KbName('SPACE'));
+    % wait for keypress
+    KbWait([],2); % Waits for keyboard(any) press
+end

scripts/train_Inst_Xtra.m

@@ -0,0 +1,35 @@
+function [] = train_Inst_Xtra(disp_struct)
+
+    % the outline task instructions
+    text_0 = '-Instructions-\n\n\n';
+    text_1 = 'OK - here''s something *important* to remember.\n\n\n';
+    text_2 = 'The pictures are RANDOMLY selected for each person, \n\n and are PROBABILISTICALLY associated with the amount of reward.  \n\n\n There is NO RELATIONSHIP between the picture type and probability of reward. \n\n\n    ';
+    
+    Screen('TextSize', disp_struct.wPtr, 30);
+    Screen('TextFont', disp_struct.wPtr, 'Times');
+    Screen('TextStyle', disp_struct.wPtr, 0);
+    Screen('TextColor', disp_struct.wPtr, [255 255 255]);
+    Screen('FillRect', disp_struct.wPtr,[0 0 0]);
+    % the number of charaters per line
+    wrap_length = 70;
+    
+    % Print out the initial instructions
+    
+    % Bold the intro
+    Screen('TextStyle', disp_struct.wPtr, 1);
+    [nx, ny, textbounds] = DrawFormattedText(disp_struct.wPtr, text_0, 'center', 50, [], wrap_length, [], [], 1.25 );
+    
+    % reset to normal font
+    Screen('TextStyle', disp_struct.wPtr, 0);
+    [nx, ny, textbounds] = DrawFormattedText(disp_struct.wPtr, text_1, 'center', ny, [], wrap_length, [], [], 1.25 );
+    [nx, ny, textbounds] = DrawFormattedText(disp_struct.wPtr, text_2, 'center', ny, [], wrap_length, [], [], 1.25 );
+    
+    % flip the screen
+    Screen(disp_struct.wPtr, 'Flip');
+    WaitSecs(0.5);
+    
+    % wait for P
+    RestrictKeysForKbCheck(KbName('SPACE'));
+    % wait for keypress
+    KbWait([],2); % Waits for keyboard(any) press
+end