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+Code and Figures for Section B: How to develop a computational model
+How should we choose?
+clear; close all; clc
+dir ='C:\Users\dream\OneDrive - University College London\Lab\FLUX\2021FluxCompModellingWorkshop-main'
+cd(dir);
+addpath('.\general_functions\') % add softmax function here
+
+
+
+outcome = 1; V = .5; alpha = .6;
+PE = outcome - V
+V = V + alpha * PE
+When reward probabilities are fixed, what is the best learning rate? 
+% 80:20 rewarding outcomes
+outcomes = [zeros(20,1); ones(80,1)]; outcomes = outcomes(randperm(length(outcomes)));
+
+p_reward =[];
+
+p_reward(1:100)= 0.8;
+
+
+% prior at 0
+V = 0.5; 
+% learning rate
+params = [];
+params.alpha = 1; %play with this paramenter: try .3, .1 
+PE = [];
+for t = 1:length(outcomes)
+    [V(t+1), PE(t)] = learn_RescWagn(V(t),outcomes(t),params);
+end
+
+figure()
+plot(V,'DisplayName','value','LineWidth',5)
+hold on
+
+plot(outcomes,'o','DisplayName','outcomes','MarkerSize',48,'Marker','.',...
+    'LineStyle','none',...
+    'Color',[0.929411764705882 0.694117647058824 0.125490196078431])
+hold on
+plot(p_reward,'DisplayName','p(reward)','LineWidth',5,'Color', [0.6350, 0.0780, 0.1840])
+
+xlabel('trial','FontSize', 24);
+legend('FontSize', 22)
+
+When reward probabilities are alternating, what is the best learning rate? 
+
+% alternating 80:20 rewarding outcomes
+outcomes1 = [zeros(5,1); ones(20,1)]; outcomes1 = outcomes1(randperm(length(outcomes1)));
+outcomes2 = [zeros(20,1); ones(5,1)]; outcomes2 = outcomes2(randperm(length(outcomes2)));
+
+outcomes=[]
+outcomes(1:25)=outcomes1
+outcomes(26:50)=outcomes2
+outcomes(51:75)=outcomes1
+outcomes(76:100)=outcomes2
+
+
+p_reward =[];
+
+p_reward(1:25)= 0.8;
+p_reward(26:50)= 0.2;
+p_reward(51:75)= 0.8;
+p_reward(76:100)= 0.2;
+
+
+% learning rate 
+params = [];
+params.alpha = .3; % try .1 
+PE = [];
+for t = 1:length(outcomes)
+    [V(t+1), PE(t)] = learn_RescWagn(V(t),outcomes(t),params);
+end
+
+figure()
+plot(V,'DisplayName','value','LineWidth',5)
+hold on
+plot(outcomes,'o','DisplayName','outcomes','MarkerSize',48,'Marker','.',...
+    'LineStyle','none',...
+    'Color',[0.929411764705882 0.694117647058824 0.125490196078431])
+hold on
+plot(p_reward,'DisplayName','p(reward)','LineWidth',5,'Color', [0.6350, 0.0780, 0.1840])
+
+xlabel('trial','FontSize', 24);
+legend('FontSize', 22)
+
+
+
+
+
+How does the softmax work?
+% Value of both purple (1) and orange (2) slot machines ranging from 0 and 100
+values = [0:100; 100:-1:0];
+
+Explotiation behaviour - where we choose the slot machine whenever its value is better than the other
+Temperature is LOW, choices are LESS NOISY, and more DETERMINSITIC
+taus = 0.0001;
+
+figure()
+for i = 1:length(taus)
+    params = []; params.tau = taus(i);
+    for v = 1:length(values)
+        policy(i,v,:) = softmax(values(:,v),params);
+    end
+    plot(values(1,:)-values(2,:),squeeze(policy(i,:,1)),'DisplayName',['tau = ' num2str(taus(i))],'LineWidth',2)
+    hold on
+end
+
+legend
+ax = gca;
+ax.FontSize = 16; 
+xlabel('Value (Purple - Orange)','FontSize',20)
+ylabel('P(Choose Purple)','FontSize',20)
+xlim([-100 100])
+ylim([0 1])
+
+
+Exploration behaviour - where we choose the slot machines randomly
+Temperature is HIGH, choices are MORE NOISY, and more RANDOM
+taus = [10000];
+
+figure()
+for i = 1:length(taus)
+    params = []; params.tau = taus(i);
+    for v = 1:length(values)
+        policy(i,v,:) = softmax(values(:,v),params);
+    end
+    plot(values(1,:)-values(2,:),squeeze(policy(i,:,1)),'DisplayName',['tau = ' num2str(taus(i))],'LineWidth',2)
+    hold on
+end
+legend
+ax = gca;
+ax.FontSize = 16; 
+xlabel('Value (Purple - Orange)','FontSize',20)
+ylabel('P(Choose Purple)','FontSize',20)
+xlim([-100 100])
+ylim([0 1])
+
+
+
+Varying temperature leads to different choice curves....
+taus = [0.01; 15; 1000];
+
+figure()
+for i = 1:length(taus)
+    params = []; params.tau = taus(i);
+    for v = 1:length(values)
+        policy(i,v,:) = softmax(values(:,v),params);
+    end
+    plot(values(1,:)-values(2,:),squeeze(policy(i,:,1)),'DisplayName',['tau = ' num2str(taus(i))],'LineWidth',2)
+    hold on
+end
+legend
+ax = gca;
+ax.FontSize = 16; 
+xlabel('Value (Purple - Orange)','FontSize',20)
+ylabel('P(Choose Purple)','FontSize',20)
+xlim([-100 100])
+ylim([0 1])
+
+
+
+When temperature = 1,
+taus = [1];
+
+figure()
+for i = 1:length(taus)
+    params = []; params.tau = taus(i);
+    for v = 1:length(values)
+        policy(i,v,:) = softmax(values(:,v),params);
+    end
+    plot(values(1,:)-values(2,:),squeeze(policy(i,:,1)),'DisplayName',['tau = ' num2str(taus(i))],'LineWidth',2, 'Color','b')
+    hold on
+end
+legend
+ax = gca;
+ax.FontSize = 16; 
+xlabel('Value (Purple - Orange)','FontSize',20)
+ylabel('P(Choose Purple)','FontSize',20)
+xlim([-100 100])
+ylim([0 1])
+
+
+When temperature = 15,
+taus = [15];
+
+figure()
+for i = 1:length(taus)
+    params = []; params.tau = taus(i);
+    for v = 1:length(values)
+        policy(i,v,:) = softmax(values(:,v),params);
+    end
+    plot(values(1,:)-values(2,:),squeeze(policy(i,:,1)),'DisplayName',['tau = ' num2str(taus(i))],'LineWidth',2, 'Color','r')
+    hold on
+end
+legend
+ax = gca;
+ax.FontSize = 16; 
+xlabel('Value (Purple - Orange)','FontSize',20)
+ylabel('P(Choose Purple)','FontSize',20)
+xlim([-100 100])
+ylim([0 1])