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CCC_exported.m
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CCC_exported.m
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function app = CCC_exported(schedule, mode, initial_alpha, app_custom)
%% Classical Conditioning Closet Core
% Description :
% The Core script of the Classical Conditioning Closet App for testing purpose
% Author :
% 2021 Knowblesse
%
%% Parameters
app = struct();
app.alpha_A.Value = initial_alpha(1);
app.alpha_B.Value = initial_alpha(2);
app.alpha_C.Value = initial_alpha(3);
% Rescorla-Wager Model
app.paramRW_lr_acq.Value = 0.1;
app.paramRW_lr_ext.Value = 0.05;
% Mackintosh Model
app.paramM_lr_acq.Value = 0.08;
app.paramM_lr_ext.Value = 0.04;
app.paramM_k.Value = 0.05;
app.paramM_epsilon.Value = 0.02;
% Pearce-Hall Model
app.paramPH_SA.Value = 0.04;
app.paramPH_SB.Value = 0.04;
app.paramPH_SC.Value = 0.04;
% Esber Haselgrove Model
app.paramEH_lr1_acq.Value = 0.05; % lr1 : when delta V >= 0
app.paramEH_lr2_acq.Value = 0.03; % product of two acq lr > product of two ext lr
app.paramEH_lr1_ext.Value = 0.04;
app.paramEH_lr2_ext.Value = 0.02;
app.paramEH_k.Value = 0.2;
app.paramEH_lr_pre.Value = 0.02;
app.paramEH_limitV.Value = false;
% Schmajuk-Pearson-Hall Model
app.paramSPH_SA.Value = 0.3;
app.paramSPH_SB.Value = 0.3;
app.paramSPH_SC.Value = 0.3;
app.paramSPH_beta_ex.Value = 0.1;
app.paramSPH_beta_in.Value = 0.09;
app.paramSPH_gamma.Value = 0.2;
% TD Model
app.paramTD_table.Data = table(1,4,1,4,1,4,9,10,50);
app.paramTD_beta.Value = 0.875;
app.paramTD_c.Value = 0.08;
app.paramTD_gamma.Value = 0.95;
% Jeong Model
app.paramJ_SA.Value = 1;
app.paramJ_SB.Value = 1;
app.paramJ_SC.Value = 1;
app.paramJ_beta_ex.Value = 0.1;
app.paramJ_beta_in.Value = 0.09;
app.paramJ_gamma.Value = 0.5; % Effect of J to alpha
app.paramJ_baseline.Value = 0.2; % Minimum alpha
app.paramJ_minimum_p.Value = 0.01; % minimum prob of event
if exist('app_custom','var')
app = app_custom;
end
%% Experiement Variables
app.V = zeros(1000,3);
app.V_pos = zeros(1000,3); % V value for S-P-H model, V_dot(=V_pos - V_bar) is assigned to V instead
app.V_bar = zeros(1000,3);
app.alpha = zeros(1000,3);
app.numTrial = 1;
%% Meaningless code snippet for smooth importing appdesigner-based GUI code to CUI code
app.Tab_RW = [];
app.Tab_M = [];
app.Tab_PH = [];
app.Tab_EH = [];
app.Tab_SPH = [];
app.Tab_TD = [];
app.Tab_J = [];
app.ModelButtonGroup = struct();
%% Helper Function
app.ModelButtonGroup.SelectedObject = struct();
switch(mode)
case(1)
app.ModelButtonGroup.SelectedObject.Text = 'Rescorla-Wagner';
case(2)
app.ModelButtonGroup.SelectedObject.Text = 'Mackintosh';
case(3)
app.ModelButtonGroup.SelectedObject.Text = 'Pearce-Hall';
case(4)
app.ModelButtonGroup.SelectedObject.Text = 'Esber-Haselgrove';
case(5)
app.ModelButtonGroup.SelectedObject.Text = 'Temporal-Difference';
case(6)
app.ModelButtonGroup.SelectedObject.Text = 'Schmajuk-P-H';
case(7)
app.ModelButtonGroup.SelectedObject.Text = 'Jeong';
end
%% Run
app.V = zeros(size(schedule,1)+1,3);
app.V_pos = zeros(size(schedule,1)+1,3); % V value for S-P-H model, V_dot(=V_pos - V_bar) is assigned to V instead
app.V_bar = zeros(size(schedule,1)+1,3);
app.alpha = zeros(size(schedule,1)+1,3);
app.numTrial = 1;
%%%%%%%%%%%%%%%%%%%%%%%%%%CCC_exported Start %%%%%%%%%%%%%%%%%%%%%%%%%%
% Schedule
% +------+------+------+------+--------+
% | Col1 | Col2 | Col3 | Col4 | Col5 |
% +------+------+------+------+--------+
% | CS A | CS B | CS C | US | lambda |
% +------+------+------+------+--------+
if isempty(schedule)
msgbox('Empty Schedule');
else
selectedButton = app.ModelButtonGroup.SelectedObject;
switch(selectedButton.Text)
case('Rescorla-Wagner')
app.TabGroup.SelectedTab = app.Tab_RW;
app.alpha(1,:) = [app.alpha_A.Value, app.alpha_B.Value, app.alpha_C.Value];
% Do Simulation
for t = 1 : size(schedule,1)
CS = schedule(t,1:3);
US = schedule(t,4);
Vtot = sum(app.V(t,:) .* CS);
Lambda = US .* schedule(t,5);
if schedule(t,4) ~= 0 % US
lr = app.paramRW_lr_acq.Value;
else
lr = app.paramRW_lr_ext.Value;
end
deltaV = app.alpha(t,:) .* CS .* (lr .* (Lambda - Vtot));
app.V(t + 1,:) = app.V(t,:) + deltaV;
app.alpha(t + 1, :) = app.alpha(t,:);
end
case('Mackintosh')
app.TabGroup.SelectedTab = app.Tab_M;
% Do Simulation
app.alpha(1,:) = [app.alpha_A.Value, app.alpha_B.Value, app.alpha_C.Value];
for t = 1 : size(schedule,1)
CS = schedule(t,1:3);
US = schedule(t,4);
Vtot = sum(app.V(t,:) .* CS);
Lambda = US .* schedule(t,5);
if schedule(t,4) ~= 0 % US
lr = app.paramM_lr_acq.Value;
else
lr = app.paramM_lr_ext.Value;
end
deltaV = app.alpha(t,:) .* CS .* lr .* (Lambda - app.V(t,:)); % not Vtot
% alpha change
deltaAlpha = [0, 0, 0];
for s = 1 : 3
D_x = abs(Lambda - (Vtot - app.V(t,s)));
D_s = abs(Lambda - app.V(t,s));
if D_s < D_x
deltaAlpha(s) = CS(s) * app.paramM_k.Value * (1-app.alpha(t,s)) * (D_x - D_s) / 2;
elseif D_s == D_x
deltaAlpha(s) = -1 * CS(s) * app.paramM_k.Value * app.paramM_epsilon.Value;
else
deltaAlpha(s) = CS(s) * app.paramM_k.Value * app.alpha(t,s) * (D_x - D_s) / 2;
end
end
app.V(t + 1,:) = app.V(t,:) + deltaV;
app.alpha(t + 1, :) = app.alpha(t,:) + deltaAlpha;
end
case('Pearce-Hall')
app.TabGroup.SelectedTab = app.Tab_PH;
% Do Simulation
app.alpha(1,:) = [app.alpha_A.Value, app.alpha_B.Value, app.alpha_C.Value];
for t = 1 : size(schedule,1)
CS = schedule(t,1:3);
US = schedule(t,4);
S = [app.paramPH_SA.Value, app.paramPH_SB.Value, app.paramPH_SC.Value];
V_pos_tot = sum(app.V_pos(t,:) .* CS);
V_bar_tot = sum(app.V_bar(t,:) .* CS);
Lambda = US .* schedule(t,5);
Lambda_bar = (V_pos_tot - V_bar_tot) - Lambda;
deltaV_pos = CS .* S .* app.alpha(t,:) .* Lambda;
deltaV_bar = CS .* S .* app.alpha(t,:) .* Lambda_bar;
% alpha change
newAlpha = repmat(abs(Lambda - (V_pos_tot - V_bar_tot)),1,3);
app.V(t,:) = app.V_pos(t,:) - app.V_bar(t,:);
app.V_pos(t+1,:) = app.V_pos(t,:) + deltaV_pos;
app.V_bar(t+1,:) = app.V_bar(t,:) + deltaV_bar;
app.alpha(t+1,:) = newAlpha;
end
case('Esber-Haselgrove')
app.TabGroup.SelectedTab = app.Tab_EH;
% Do Simulation
app.alpha(1,:) = [app.alpha_A.Value, app.alpha_B.Value, app.alpha_C.Value];
phi = [app.alpha_A.Value, app.alpha_B.Value, app.alpha_C.Value];
V_pre = [0, 0, 0];
for t = 1 : size(schedule,1)
CS = schedule(t,1:3);
US = schedule(t,4);
V_pos_tot = sum(app.V_pos(t,:) .* CS);
V_bar_tot = sum(app.V_bar(t,:) .* CS);
Lambda = US .* schedule(t,5);
newAlpha = phi + (app.V_pos(t,:) + app.V_bar(t,:)) - app.paramEH_k.Value * V_pre;
if((Lambda - (V_pos_tot - V_bar_tot)) > 0)
beta_pos = app.paramEH_lr1_acq.Value;
beta_bar = app.paramEH_lr2_acq.Value;
else
beta_pos = app.paramEH_lr1_ext.Value;
beta_bar = app.paramEH_lr2_ext.Value;
end
deltaV_pos = CS .* newAlpha .* beta_pos .* (Lambda - (V_pos_tot - V_bar_tot));
deltaV_bar = CS .* newAlpha .* beta_bar .* ((V_pos_tot - V_bar_tot) - Lambda);
app.V(t,:) = app.V_pos(t,:) - app.V_bar(t,:);
if app.paramEH_limitV.Value
app.V_pos(t+1,:) = min(max(app.V_pos(t,:) + deltaV_pos,[0,0,0]),[1,1,1]);
app.V_bar(t+1,:) = min(max(app.V_bar(t,:) + deltaV_bar,[0,0,0]),[1,1,1]);
else
app.V_pos(t+1,:) = app.V_pos(t,:) + deltaV_pos;
app.V_bar(t+1,:) = app.V_bar(t,:) + deltaV_bar;
end
app.alpha(t,:) = newAlpha;
V_pre = V_pre + app.paramEH_lr_pre.Value * (CS - V_pre);
end
case('Schmajuk-P-H')
app.TabGroup.SelectedTab = app.Tab_SPH;
% Do Simulation
app.alpha(1,:) = [app.alpha_A.Value, app.alpha_B.Value, app.alpha_C.Value];
S = [app.paramSPH_SA.Value, app.paramSPH_SB.Value, app.paramSPH_SC.Value];
for t = 1 : size(schedule,1)
CS = schedule(t,1:3);
US = schedule(t,4);
% In the original paper, V_dot is used as exert
% conditioned response and V_dot = V - N.
% In this program these variable is translated into
% below
% V_dot => V
% V => V_pos
% N => V_bar
V = app.V_pos(t,:) - app.V_bar(t,:);
Lambda = US .* schedule(t,5);
Lambda_bar = sum(CS .* V) - Lambda;
% alpha change
if(t == 1) % if the first trial, use the initial value, not t-1
newAlpha = app.paramSPH_gamma.Value * abs(Lambda) + (1-app.paramSPH_gamma.Value) * app.alpha(1,:);
oldAlpha = app.alpha(1,:);
else
newAlpha = app.paramSPH_gamma.Value * abs(Lambda - sum(CS .* app.V(t-1,:))) + (1-app.paramSPH_gamma.Value) * app.alpha(t-1,:);
oldAlpha = app.alpha(t-1,:);
end
newAlpha = min(max(...
newAlpha,[0,0,0]),[1,1,1]); % alpha value should be in range [0,1]. // Really??
for s = 1 : 3
if CS(s)
app.alpha(t,s) = newAlpha(s);
else
app.alpha(t,s) = oldAlpha(s);
end
end
% V change
deltaV_pos = [0, 0, 0];
deltaV_bar = [0, 0, 0];
if Lambda - sum(CS .* V) > 0
deltaV_pos = CS .* S .* app.alpha(t,:) .* app.paramSPH_beta_ex.Value .* Lambda;
else
deltaV_bar = CS .* S .* app.alpha(t,:) .* app.paramSPH_beta_in.Value .* Lambda_bar;
end
app.V(t,:) = V;
app.V_pos(t+1,:) = app.V_pos(t,:) + deltaV_pos;
app.V_bar(t+1,:) = app.V_bar(t,:) + deltaV_bar;
end
case('Temporal-Difference')
app.TabGroup.SelectedTab = app.Tab_TD;
%% Parameters
% A Start | A End | B Start | B End | C Start | C End | US Start | US End | ITI
%% Stretch the schedule to include time factor
trial_length = max(app.paramTD_table.Data{1,1:8}) + app.paramTD_table.Data{1,9};% max cs end + iti
newSchedule = zeros(trial_length * size(schedule, 1),5);
for s = 1 : size(schedule,1)
temp = zeros(trial_length, 5);
temp(app.paramTD_table.Data{1,1} : app.paramTD_table.Data{1,2},1) = 1 * schedule(s,1);
temp(app.paramTD_table.Data{1,3} : app.paramTD_table.Data{1,4},2) = 1 * schedule(s,2);
temp(app.paramTD_table.Data{1,5} : app.paramTD_table.Data{1,6},3) = 1 * schedule(s,3);
temp(app.paramTD_table.Data{1,7} : app.paramTD_table.Data{1,8},4) = 1 * schedule(s,4);
temp(:,5) = schedule(s,5);
newSchedule(trial_length*(s-1)+1 : trial_length*s,:) = temp;
end
clearvars temp
%% Initialize
totalTime = size(newSchedule,1);
y = zeros(totalTime,1);
r = zeros(totalTime,1);
x_bar = zeros(totalTime,3);
x = zeros(totalTime,3);
w = zeros(totalTime,3);
%% Do Simulation
trial = 1;
for t = 1 : size(newSchedule,1)
US_weight = newSchedule(t,5);
US_presence = newSchedule(t,4);
x(t,:) = newSchedule(t,1:3);
r(t) = US_weight * US_presence;
y(t) = r(t) + max(w(t,:) * x(t,:)',0);
if t == 1
x_bar(t,:) = 0; %eligibility traces
w(t+1,:) = w(t,:) + app.paramTD_c.Value*(r(t) + app.paramTD_gamma.Value*max(w(t,:) * x(t,:)',0) )*x_bar(t,:);
else
x_bar(t,:) = app.paramTD_beta.Value .* x_bar(t-1,:) + (1-app.paramTD_beta.Value) .* x(t-1,:); %eligibility traces
w(t+1,:) = w(t,:) + app.paramTD_c.Value.*(...
r(t) + app.paramTD_gamma.Value .* max(w(t,:) * x(t,:)',0) - max(w(t,:) * x(t-1,:)',0)...
)*x_bar(t,:); % during the CS presentaion, gamma value makes the delta w negative proportional to it's weight.
end
if rem(t, trial_length) == 0
app.V(trial,:) = w(t,:); % save the last weight value from every trial as V
trial = trial + 1;
end
end
t = trial;
case('Jeong')
app.TabGroup.SelectedTab = app.Tab_J;
% Do Simulation
app.alpha(1,:) = [0,0,0];%[app.alpha_A.Value, app.alpha_B.Value, app.alpha_C.Value];
S = [app.paramJ_SA.Value, app.paramJ_SB.Value, app.paramJ_SC.Value];
J = zeros(size(schedule,1),3);
for t = 1 : size(schedule,1)
CS = schedule(t,1:3);
US = schedule(t,4);
V_dot = app.V_pos(t,:) - app.V_bar(t,:);
Lambda = US .* schedule(t,5);
Lambda_bar = sum(CS .* V_dot) - Lambda;
p = max(1 - abs(min(max(CS .* V_dot,0),1) - Lambda), app.paramJ_minimum_p.Value);
J(t+1,:) = t/(t+1).*J(t,:) - 1/(t+1).*log2(p);
% alpha change
if t == 1
oldAlpha = app.alpha(1,:);
else
oldAlpha = app.alpha(t-1,:);
end
newAlpha = app.paramJ_gamma.Value * J(t,:) + app.paramJ_baseline.Value;
newAlpha = min(max( newAlpha,[0,0,0]),[1,1,1]);
% Update alpha only when CS is present
for s = 1 : 3
if CS(s)
app.alpha(t,s) = newAlpha(s);
else
app.alpha(t,s) = oldAlpha(s);
end
end
% V change
deltaV_pos = CS .* S .* app.alpha(t,:) .* app.paramJ_beta_ex.Value .* Lambda;
deltaV_bar = CS .* S .* app.alpha(t,:) .* app.paramJ_beta_in.Value .* Lambda_bar;
app.V(t,:) = V_dot;
app.V_pos(t+1,:) = app.V_pos(t,:) + deltaV_pos;
app.V_bar(t+1,:) = app.V_bar(t,:) + deltaV_bar;
end
end
app.numTrial = t;
%%%%%%%%%%%%%%%%%%%%%%%%%%CCC_exported End %%%%%%%%%%%%%%%%%%%%%%%%%%
end
end