-
Notifications
You must be signed in to change notification settings - Fork 0
/
DepressionSim_COGED_ControlEfficacyExp.m
138 lines (99 loc) · 5.05 KB
/
DepressionSim_COGED_ControlEfficacyExp.m
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
function DepressionSim_COGED_ControlEfficacyExp()
%% RUN SIMULATION
clear all;
close all;
clc;
import Simulations.*;
import EVC.*;
plotOffline = 0;
% simulation settings
nSubj = 1; % 54
nTrials = 210;
probedControlEfficacyExp = [0.5 1 1.5];
baselineAutomaticity = 1;
taskAAutomaticityRange = [linspace(0.2, 0.4, 4) baselineAutomaticity ];%[ 0.1 0.11 0.12 0.15 0.9 1]; %linspace(0.01, 1, 6); 0.2:0.2:1
taskAAutomaticityRange(end) = [];
taskAAutomaticityRange = fliplr(taskAAutomaticityRange); % easy to difficult
% plot settings
plotSEM = 0;
showLegend = 0;
fixYLimit = 1;
% limits
ylimitSV = [0 1.75];
% RUN REGRESSION
probedControlEfficacyExp = [1 3 5];
numCostParams = length(probedControlEfficacyExp);
% regressor = repmat(probedControlEfficacyExp, nSubj, 1);
subjectiveValueLog = nan(length(probedControlEfficacyExp), length(taskAAutomaticityRange));
for parameterCondition = 1:numCostParams
disp(['*************************TESTED CONTRLOL EFFICACY EXP: ' num2str(probedControlEfficacyExp(parameterCondition))]);
subjectiveValue = nan(1, length(taskAAutomaticityRange));
controlSignalLog = zeros(1, length(taskAAutomaticityRange));
for taskAAutomaticityIdx = 1:length(taskAAutomaticityRange);
EVCSim = DDM_WestbrookBraver2015();
Simulation6_WestbrookBraver2015_params;
EVCSim.nSubj = nSubj;
EVCSim.nTrials = nTrials;
EVCSim.taskAAutomaticity = taskAAutomaticityRange(taskAAutomaticityIdx);
EVCSim.taskBAutomaticity = baselineAutomaticity;
EVCSim.rewardMinimum = 1;
temp.rewardFnc.params{1} = 1;
temp.rewardFnc.params{2} = 0;
temp.rewardFnc.params{3} = probedControlEfficacyExp(parameterCondition);
temp.rewardFnc.params{4} = 0;
temp.rewardFnc.type = EVCFnc.REWRATE_EXT;
EVCSim.rewardFnc = EVCFnc(temp.rewardFnc.type, temp.rewardFnc.params);
EVCSim.run();
% find equilibrium trial
t_eq = nan;
SV = nan;
for t = 1:length(EVCSim.subjData.Log.ExpectedState)
if(round(EVCSim.subjData.Log.ExpectedState(t).descr == 'taskB'))
SV = EVCSim.subjData.Log.ExpectedState(t).outcomeValues(2);
t_eq = t;
break;
end
end
subjectiveValue(taskAAutomaticityIdx) = SV;
controlSignalLog(taskAAutomaticityIdx) = EVCSim.subjData.Log.CtrlIntensities(t_eq, 2);
disp(['task A automaticity ' num2str(taskAAutomaticityIdx) '/' num2str(length(taskAAutomaticityRange)) '.']);
end
subjectiveValueLog(parameterCondition, :) = subjectiveValue;
end
save(['logfiles/DepressionSim_COGED_nSubj' num2str(nSubj) '_ControlEfficacyExp_' num2str(min(probedControlEfficacyExp)) ...
'_' num2str(max(probedControlEfficacyExp)) ...
'_' num2str(probedControlEfficacyExp(2)-probedControlEfficacyExp(1)) ...
'.mat']);
%% perform regression and plot
% load('logfiles/DepressionSim_Padmala_ControlEfficacyExp_0.1_0.2_0.01.mat');
EVCPlotSettings;
DepressionSim_ylimits;
ylimitSV = [0.6 1];
fig1 = figure(1);
set(fig1, 'Position', [100 100 250 230]);
if(plotOffline)
set(fig1, 'visible','off');
end
colorGradient = getAlphaGradient([0.7 0.7 0.7], [0 0 0], size(subjectiveValueLog,1));
legendText = {};
for parameterCondition = 1:size(subjectiveValueLog,1)
subjectiveValue = subjectiveValueLog(parameterCondition,:);
plot([1:length(subjectiveValue)]+1,subjectiveValue/EVCSim.taskAReward, 'LineWidth', lineWidth.line1, 'Color', colorGradient(parameterCondition,:)); hold on;
legendText{parameterCondition} = ['Control Efficacy \epsilon = ' num2str(probedControlEfficacyExp(parameterCondition))];
end
hold off;
xlabel('Task Difficulty', 'fontSize', fontSize.xlabel);
ylabel({'Subjective Value'}, 'fontSize', fontSize.ylabel);
leg = legend(legendText, 'Location', 'southwest');
set(leg, 'FontSize', fontSize.xlabel-3);
set(gca, 'fontSize', fontSize.xlabel);
if(fixYLimit)
ylim(ylimitSV);
end
saveas(fig1,['figures/DepressionSim_COGED_ControlEfficacyExp_Full_nSubj' num2str(nSubj) '_ControlEfficacyExp_' num2str(min(probedControlEfficacyExp)) ...
'_' num2str(max(probedControlEfficacyExp)) ...
'_' num2str(probedControlEfficacyExp(2)-probedControlEfficacyExp(1)) '.fig'],'fig');
%% PRINT PARAMETERS
printSimulationParameters(EVCSim);
% openfig('figures/DepressionSim_Padmala_ControlEfficacyExp_Subset_nSubj100_ControlEfficacyExp_0.1_0.2_0.05.fig','new','visible')
end