Neural and computational mechanisms of effort under the pressure of a deadline.

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[1] § Results › Deadline pressure leads to seeking effort ↔ Code/analyse_population.m, lines 2267–2296 · score 0.60 · logistic regression, low pressure blocks, high pressure blocks, predicting choices, coefficient, model

Paper

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The authors' code

MATLAB · 2,330 lines · 117 KB · no license · 1 match

%% for groups of offline subjects split by precise parameters
% subjects = {'SH_200519';'DC_200519';'SS_200519';'GE_230519';'TZ_230519';'MS_230519';'LGW_280519';'HB_280519';'CS_280519';'KD_290519';'CJ_290519';'EA_290519';'KR_050619';'ML_050619';'LA_050619';'YC_060619';'AT_060619';'CK_070619';'CL_190619';'LX_190619'};
% subjects = {'SW_100619';'SM_100619';'VK_100619';'BS_110619';'BR_130619';'CY_140619';'RG_140619';'RD_140619';'LM_170619';'AL_170619';'AS_170619';'DR_170619';'FH_170619';'FS_170619';'HS_180619';'ACV_180619';'VC_180619'};%;'JC_200619'};
% subjects = {'TS_250619';'WK_260619';'MZ_260619';'EN_270619';'HD_030719';'AK_300719';'MR_300719';'RP_020819';'VS_020819'}; % discount = 1
% subjects = {'AD_030719';'FT_040719';'LI_040719';'MW_120719';'AR_120719';'ND_300719'}; % discount factor = .6
cd('D:\Data - fmri dataset')
dataset_flag = 1; %1=fMRI 2=Offline 3=Offline without pressure modulation
if dataset_flag == 1 %fMRI
subjects = {'RR_270220';'HR_090320';'KL_100320';'MB_120320';'MJ_230921';'TK_230921';'BL_300921';'AJ_131021';'MR_251021';'FB_151121';'CS_291121';'TK_291121';'JJ_011221';'JM_031221';'LR_061221';'HD_061221';'AD_131221';'AE_161221';'NP_171221';'PD_201221';'AF_201221';'AK_221221';'NA_231221';'CS_231221';'KY_231221';'IK_110322';'VS_110322';'HG_300921_combined';'SJ_111021_combined';'OH_011121_combined'};
elseif dataset_flag == 2 %Offline change in pressure across blocks
subjects = {'TS_250619';'WK_260619';'MZ_260619';'EN_270619';'HD_030719';'AK_300719';'MR_300719';'RP_020819';'VS_020819';'AD_030719';'FT_040719';'LI_040719';'MW_120719';'AR_120719';'ND_300719'};
elseif dataset_flag == 3 %Daniele dataset1 no change in pressure across blocks
subjects = {'SH_200519';'DC_200519';'SS_200519';'GE_230519';'TZ_230519';'MS_230519';'LGW_280519';'HB_280519';'CS_280519';'KD_290519';'CJ_290519';'EA_290519';'KR_050619';'ML_050619';'LA_050619';'YC_060619';'AT_060619';'CK_070619';'CL_190619';'LX_190619';'SW_100619';'SM_100619';'VK_100619';'BS_110619';'BR_130619';'CY_140619';'RG_140619';'RD_140619';'LM_170619';'AL_170619';'AS_170619';'DR_170619';'FH_170619';'FS_170619';'HS_180619';'ACV_180619';'VC_180619';};%'JC_200619'};
end
%% load model parameters
if dataset_flag == 1 %fMRI
load('C:\Users\apisauro\Documents\MATLAB\Goal Pressure\Parameters used for the paper\PAR_fMRI.mat')
load('C:\Users\apisauro\Documents\MATLAB\Goal Pressure\Parameters used for the paper\k_pre_fMRI')
bad_subj = [];
best_model_id = 3;
elseif dataset_flag == 2
load('C:\Users\apisauro\Documents\MATLAB\Goal Pressure\Parameters used for the paper\PAR_offline.mat')
load('C:\Users\apisauro\Documents\MATLAB\Goal Pressure\Parameters used for the paper\k_pre_offline')
bad_subj = [3 12];
best_model_id = 3;
end
%% IMPORTANT FLAGS/PARS
skip_bad_trial = 1; %to only use trials from good blocks (goal reached)
threshold = 1; %trials above this advancement threshold are excluded
n_choices = 3; %including time outs
%less important
pressure0_flag = 0; %don't subtract pressure
reward_max_flag = 2; % =1 assuming Max = Max(Rewards) =2 Max = Mean(Rewards) =3 Max = Mean(Rewards(3),Rewards(4)) for homeostasis
%% GRAPHICS STUFF
trasp = 0.7; %for figures 1 for saving for illustrator 0.7 for plotting
MeanWidth = 0.05; %for jittering points
n_sub = length(subjects);
nrow = ceil(n_sub/5);
ncol = 6;
%% LOAD DATA
block_completed = zeros(n_sub,1);
for i_sub = 1:n_sub
[result{i_sub},choices{i_sub},reward{i_sub},reward0{i_sub},reward1{i_sub},reward2{i_sub},effort{i_sub},effort1{i_sub},effort2{i_sub},RT{i_sub},tiredness{i_sub}] = analyse_results(subjects{i_sub},0);
block_completed(i_sub) = sum(result{i_sub}.block_completed);
end
bad_subj = find(block_completed<8);
good_subjects = [1:n_sub];
good_subjects(bad_subj) = [];
n_good_sub = length(good_subjects);
ForceLevels = result{1}.params.ForceLevel;
Rewards = result{1}.params.Reward;
if isfield(result{i_sub}.params,'blocks_pressure')
variable_pressure_flag = 1;
else
variable_pressure_flag = 0;
end
n_blocks = result{1}.params.blocks;
n_trials = result{1}.params.blockLen-1;
n_efforts = length(ForceLevels);
n_rewards = length(Rewards);
%% indeces and initialitations
all_trials = (1:n_blocks*n_trials);
all_trials_pop = (1:n_blocks*n_trials*n_good_sub);
% 1D index
early_blocks = (1:n_blocks/2);
late_blocks = n_blocks/2+1:n_blocks;
early_trials = (1:n_trials/2);
late_trials = n_trials/2+1:n_trials;
% 2D indeces for n_blocks x n_trials
early_trials_ind = (1:round(n_blocks*n_trials/2));
late_trials_ind = all_trials;
late_trials_ind(early_trials_ind) = [];
early_blocks_ind = early_blocks; %first 15 blocks = first 15 rows
for i_trial = 2:n_trials
early_blocks_ind = [early_blocks_ind early_blocks+n_blocks*(i_trial-1)];
end
late_blocks_ind = all_trials;
late_blocks_ind(early_blocks_ind) = [];
% 3D indeces for n_blocks x n_trials x n_good_sub
early_blocks_indeces = early_blocks_ind;
for i_sub = 2:n_good_sub
early_blocks_indeces = [early_blocks_indeces early_blocks_ind+n_blocks*n_trials*(i_sub-1)];
end
late_blocks_indeces = all_trials_pop;
late_blocks_indeces(early_blocks_indeces)=[];
if variable_pressure_flag
% hard = high pressure easy = low_pressure
hard_blocks_indeces = [];
easy_blocks_indeces = [];
hard_blocks_ind = {n_good_sub,1};
easy_blocks_ind = {n_good_sub,1};
for i_sub = 1:n_good_sub
i = good_subjects(i_sub);
% 1D index
easy_blocks = find(result{i}.params.blocks_pressure==2)';
hard_blocks = find(result{i}.params.blocks_pressure==1)';
% 2D index
hard_blocks_ind{i_sub} = hard_blocks;
easy_blocks_ind{i_sub} = easy_blocks;
for i_trial = 2:n_trials
easy_blocks_ind{i_sub} = [easy_blocks_ind{i_sub} easy_blocks+n_blocks*(i_trial-1)];
hard_blocks_ind{i_sub} = [hard_blocks_ind{i_sub} hard_blocks+n_blocks*(i_trial-1)];
end
% 3D indeces for n_blocks x n_trials x n_good_sub
easy_blocks_indeces = [easy_blocks_indeces easy_blocks_ind{i_sub}+n_blocks*n_trials*(i_sub-1)];
hard_blocks_indeces = [hard_blocks_indeces hard_blocks_ind{i_sub}+n_blocks*n_trials*(i_sub-1)];
end
population_choices_sortedbyblockpressure = zeros(n_blocks,n_trials,n_good_sub); %first 15 rows are easy and the latter 15 are hard
population_simulated_choices_sortedbyblockpressure = zeros(n_blocks,n_trials,n_good_sub); %first 15 rows are easy and the latter 15 are hard
population_delta_tiredness_sortedbyblockpressure = zeros(n_blocks,n_good_sub); %first 15 rows are easy and the latter 15 are hard
end
% trail based data structure
population_choices = zeros(n_blocks,n_trials,n_good_sub);
population_reward = zeros(n_blocks,n_trials,n_good_sub);
population_reward_tot = zeros(n_blocks,n_trials,n_good_sub);
population_effort = zeros(n_blocks,n_trials,n_good_sub);
population_vigor = zeros(n_blocks,n_trials,n_good_sub);
population_max_vigor = zeros(n_blocks,n_trials,n_good_sub);
population_RT = zeros(n_blocks,n_trials,n_good_sub);
population_drew = zeros(n_blocks,n_trials,n_good_sub);
population_deff = zeros(n_blocks,n_trials,n_good_sub);
population_rew1 = zeros(n_blocks,n_trials,n_good_sub);
population_rew2 = zeros(n_blocks,n_trials,n_good_sub);
population_eff1 = zeros(n_blocks,n_trials,n_good_sub);
population_eff2 = zeros(n_blocks,n_trials,n_good_sub);
population_advancement = zeros(n_blocks,n_trials,n_good_sub);
population_reward_advancement_homeostasis = zeros(n_blocks,n_trials,n_good_sub);
population_pressure = zeros(n_blocks,n_trials,n_good_sub);
population_step_factor = zeros(n_blocks,n_trials,n_good_sub);
% block based data structure
population_tiredness = zeros(n_blocks,n_good_sub);
population_delta_tiredness = zeros(n_blocks,n_good_sub);
population_goals = zeros(n_blocks,n_good_sub); %1/0 whether or not goal was reached
population_goals_trials = NaN(n_blocks,n_good_sub); %number of the trial in which goal reached
population_block_pressure = zeros(n_blocks,n_good_sub); %1/2 high/low pressure block
% derived data structure
population_choices_percentage = zeros(5,n_choices,n_good_sub); %alltrials/earlytrials/latetrials/earlyblocks/lateblocks x n_choices x n_good_sub
population_efforts_percentage = zeros(5,n_efforts,n_good_sub);
population_rewards_percentage = zeros(5,n_rewards,n_good_sub);
population_choices_efforts = zeros(5,n_choices,n_good_sub); %alltrials/earlytrials/latetrials/earlyblocks/lateblocks x n_choices x n_good_sub
population_choices_rewards = zeros(5,n_choices,n_good_sub);
population_efforts_rewards = zeros(5,n_rewards,n_good_sub);
population_rewards_efforts = zeros(5,n_efforts,n_good_sub);
max_pressure = zeros(n_good_sub,1);
max_reward = zeros(n_good_sub,1);
%% pressure0 trajectory
advancement_target_experiment = 0.9697; %target advancement before last trial as experimented by subjects
minimum_effort_step = min(result{end}.params.ForceLevel)/mean(result{end}.params.ForceLevel); %how much I advance if I chose minimum effort
advancement_target_theory = 1-minimum_effort_step*1/n_trials; %target advancement before last trial so that one wins with minimum effort
advancement0 = zeros(n_trials,1);
pressure00 = zeros(n_trials,1);
prudency_factor = (1:0.05:1.5); %mean advancement (on first data set) before last trial is 0.9697
h_pressure0 = figure('Name','Pressure 0 trajectory');
for i_prudency = 1: length(prudency_factor)
for i_trial = 1:n_trials
advancement0(i_prudency,i_trial) = (i_trial-1)/(n_trials)*prudency_factor(i_prudency);
advancement_exp(i_trial) = (i_trial-1)/(n_trials-1)*advancement_target_experiment;
avancement_theory(i_trial) = (i_trial-1)/(n_trials-1)*advancement_target_theory;
pressure00(i_prudency,i_trial) = (1-advancement0(i_prudency,i_trial))/(n_trials+1-i_trial);
pressure00_exp(i_trial) = (1-advancement_exp(i_trial))/(n_trials+1-i_trial);
pressure00_theory(i_trial) = (1-avancement_theory(i_trial))/(n_trials+1-i_trial);
end
subplot(2,2,1)
plot(1:n_trials,advancement0(i_prudency,:))
ylabel('advancement')
hold on
xlim([1 n_trials])
subplot(2,2,3)
hold on
plot(1:n_trials,pressure00(i_prudency,:))
ylabel('pressure')
xlim([1 n_trials])
xlabel('trial')
end
subplot(2,2,2)
hold on
plot(1:n_trials,advancement_exp,'b')
hold on
plot(1:n_trials,avancement_theory,'g')
xlim([1 n_trials])
subplot(2,2,4)
hold on
plot(1:n_trials,pressure00_exp,'b')
hold on
plot(1:n_trials,pressure00_theory,'g')
xlim([1 n_trials])
xlabel('trial')
legend('exp','theory')
if pressure0_flag == 1
pressure0 = pressure00_theory;
else
pressure0 = zeros(n_trials,1);
end
%% store data
for i_sub = 1:n_good_sub
i = good_subjects(i_sub);
% max_pressure(i_sub) = max(ForceLevels)/result{i}.params.effort_step;
if reward_max_flag == 1
max_reward(i_sub) = max(Rewards)*n_trials;
elseif reward_max_flag == 2
max_reward(i_sub) = mean(Rewards)*n_trials;
elseif reward_max_flag == 3
max_reward(i_sub) = mean(Rewards(3),Rewards(4))*n_trials;
end
for i_block =1:n_blocks
population_tiredness(i_block,i_sub) = tiredness{i}(i_block);
if i_block >1
population_delta_tiredness(i_block,i_sub) = population_tiredness(i_block,i_sub)-population_tiredness(i_block-1,i_sub);
else
population_delta_tiredness(1,i_sub) = population_tiredness(1,i_sub)-result{i}.data(1).fatigueRating;
end
for i_trial =1:n_trials
try
if choices{i}(i_block,i_trial)== 0 %it's a time out
population_choices(i_block,i_trial,i_sub) = NaN;
else %it's any other trial
population_choices(i_block,i_trial,i_sub) = choices{i}(i_block,i_trial);
end
population_reward(i_block,i_trial,i_sub) = reward{i}(i_block,i_trial);
population_reward_tot(i_block,i_trial,i_sub) = sum(population_reward(i_block,1:i_trial-1,i_sub),2);
population_effort(i_block,i_trial,i_sub) = effort{i}(i_block,i_trial);
population_vigor(i_block,i_trial,i_sub) = nanmean(result{i}.data(i_trial+(i_block-1)*(n_trials+1)).data1);
population_max_vigor(i_block,i_trial,i_sub) = result{i}.data(i_trial+(i_block-1)*(n_trials+1)).maximumForce;
population_RT(i_block,i_trial,i_sub) = RT{i}(i_block,i_trial);
population_advancement(i_block,i_trial,i_sub) = result{i}.data(i_trial+(i_block-1)*(n_trials+1)).advancement;
if i_trial>1
population_reward_advancement_homeostasis(i_block,i_trial,i_sub)= population_reward_tot(i_block,i_trial,i_sub)/max_reward(i_sub) - population_advancement(i_block,i_trial-1,i_sub);
else
population_reward_advancement_homeostasis(i_block,i_trial,i_sub)= 0;
end
population_rew1(i_block,i_trial,i_sub) = reward1{i}(i_block,i_trial);
population_rew2(i_block,i_trial,i_sub) = reward2{i}(i_block,i_trial);
population_eff1(i_block,i_trial,i_sub) = effort1{i}(i_block,i_trial);
population_eff2(i_block,i_trial,i_sub) = effort2{i}(i_block,i_trial);
population_drew(i_block,i_trial,i_sub) = reward1{i}(i_block,i_trial)-reward2{i}(i_block,i_trial);
population_deff(i_block,i_trial,i_sub) = effort1{i}(i_block,i_trial)-effort2{i}(i_block,i_trial);
if i_trial >1
population_pressure(i_block,i_trial,i_sub) = ((1-population_advancement(i_block,i_trial-1,i_sub))/(n_trials-(i_trial-1)))-pressure0(i_trial); %take the advancement/trial left of the previous trial
else
population_pressure(i_block,i_trial,i_sub) = 1/n_trials-pressure0(i_trial); %first trial have fixed pressure
end
if variable_pressure_flag
population_step_factor(i_block,i_trial,i_sub) = result{i}.data(i_trial+(i_block-1)*(n_trials+1)).pressure;
end
catch
fprintf('skipped trial %d block %d subject %d\n',i_trial,i_block,i_sub)
end
end
if (result{i}.data(i_block*(n_trials+1)-1).advancement >=1) %% && i_sub>1) || result{i}.data(i_block*(n_trials+1)).advancement >= 1 %advancement after each block last trial advancemen
population_goals(i_block,i_sub) = 1;
trial_n = find(squeeze(population_advancement(i_block,:,i_sub))>=1,1); %trial in which goal is reached
if ~isempty(trial_n)
population_goals_trials(i_block,i_sub) = trial_n;
else
fprintf('problem with advancement in block %d subject %d\n',i_block,i_sub);
end
end
end
if variable_pressure_flag
population_block_pressure(:,i_sub) = result{i}.params.blocks_pressure; %2 = easy 1 = hard
ind_tosortbyblockpressure = [find(population_block_pressure(:,i_sub)==2)' find(population_block_pressure(:,i_sub)==1)' find(isnan(population_block_pressure(:,i_sub)))'];
population_choices_sortedbyblockpressure(:,:,i_sub) = population_choices(ind_tosortbyblockpressure,:,i_sub);
population_delta_tiredness_sortedbyblockpressure(:,i_sub) = population_delta_tiredness(ind_tosortbyblockpressure,i_sub);
end
end
%% SIMULATE CHOICE DATA FOR WINNING MODEL
population_simulated_choices = zeros(size(population_choices));
for i_sub = 1:n_good_sub
i = i_sub;
beta = PAR{i}{3}(1);
pressure_weight = PAR{i}{3}(2);
base = 1;
for i_block =1:n_blocks
for i_trial =1:n_trials
R1 = population_rew1(i_block,i_trial,i_sub);
R2 = population_rew2(i_block,i_trial,i_sub);
E1 = population_eff1(i_block,i_trial,i_sub);
E2 = population_eff2(i_block,i_trial,i_sub);
PR = population_pressure(i_block,i_trial,i_sub);
V1 = R1+pressure_weight*E1.*PR;
V2 = R2+pressure_weight*E2.*PR;
prob_V1_winning = exp((V1-V2).*beta)./(exp(base*beta) + exp(beta.*(V1-V2)));
if prob_V1_winning > 0.5 %it's V1 winning it's a 1
population_simulated_choices(i_block,i_trial,i_sub) = 1;
else %it's V2 winning it's a 2
population_simulated_choices(i_block,i_trial,i_sub) = 2;
end
end
end
if variable_pressure_flag
population_block_pressure(:,i_sub) = result{i}.params.blocks_pressure; %2 = easy 1 = hard
ind_tosortbyblockpressure = [find(population_block_pressure(:,i_sub)==2)' find(population_block_pressure(:,i_sub)==1)' find(isnan(population_block_pressure(:,i_sub)))'];
population_simulated_choices_sortedbyblockpressure(:,:,i_sub) = population_simulated_choices(ind_tosortbyblockpressure,:,i_sub);
end
end
%% to compute number of successful trials
population_choices_made = ~isnan(population_choices); %1 = trials in which a choice was made
population_reward_choice_made = population_reward; % reward for trials in which a choice was made
population_reward_choice_made(population_choices_made==0)=NaN; %set to NaN reward for trials were no choice was made
population_success_trials_choice_made = population_reward_choice_made;
population_success_trials_choice_made(population_reward_choice_made>0)=1; %succesful trials is when a choice is made and a reward obtained
population_success_trials_choice_made_good_blocks = population_success_trials_choice_made;
% to skip trials in failed blocks
if skip_bad_trial
for i_sub = 1:n_good_sub
for i_block = 1:n_blocks
if population_goals(i_block,i_sub)==0
population_choices(i_block,:,i_sub) = NaN;
population_reward(i_block,:,i_sub) = NaN;
population_reward_tot(i_block,:,i_sub) = NaN;
population_effort(i_block,:,i_sub) = NaN;
population_RT(i_block,:,i_sub) = NaN;
population_drew (i_block,:,i_sub) = NaN;
population_deff(i_block,:,i_sub) = NaN;
population_advancement(i_block,:,i_sub) = NaN;
population_pressure(i_block,:,i_sub)= NaN;
population_reward_advancement_homeostasis(i_block,:,i_sub)= NaN;
population_success_trials_choice_made_good_blocks(i_block,:,i_sub) = NaN; %in bad blocks all trials are a failure
end
for i_trial = 4:n_trials
if population_advancement(i_block,i_trial-1,i_sub)>threshold %skip if it's a trial done after crossing the line
population_choices(i_block,i_trial,i_sub) = NaN;
population_RT(i_block,i_trial,i_sub) = NaN;
population_drew (i_block,i_trial,i_sub) = NaN;
population_deff(i_block,i_trial,i_sub) = NaN;
population_pressure(i_block,i_trial,i_sub)= NaN;
population_reward_advancement_homeostasis(i_block,i_trial,i_sub)= NaN;
end
end
end
end
end
%% population averages/percentages
mean_population_choices = nanmean(population_choices,3);
std_population_choices = nanstd(population_choices,[],3);
mean_population_simulated_choices = nanmean(population_simulated_choices,3);
std_population_simulated_choices = nanstd(population_simulated_choices,[],3);
mean_population_reward = nanmean(population_reward,3);
std_population_reward = nanstd(population_reward,[],3);
mean_population_reward_tot = nanmean(population_reward_tot,3);
std_population_reward_tot = nanstd(population_reward_tot,[],3);
mean_population_effort = nanmean(population_effort,3);
std_population_effort = nanstd(population_effort,[],3);
mean_population_RT = nanmean(population_RT,3);
std_population_RT = nanstd(population_RT,[],3);
mean_population_advancement = nanmean(population_advancement,3);
std_population_advancement = nanstd(population_advancement,[],3);
mean_population_reward_advancement_homeostasis = nanmean(population_reward_advancement_homeostasis,3);
std_population_reward_advancement_homeostasis = nanstd(population_reward_advancement_homeostasis,[],3);
mean_population_pressure = nanmean(population_pressure,3);
std_population_pressure = nanstd(population_pressure,[],3);
X_rew = unique(population_drew(~isnan(population_drew(:))));
X_eff = unique(population_deff(~isnan(population_deff(:))));
mean_population_choices_sortedbyblockpressure = nanmean(population_choices_sortedbyblockpressure,3);
std_population_choices_sortedbyblockpressure = nanstd(population_choices_sortedbyblockpressure,[],3);
mean_population_simulated_choices_sortedbyblockpressure = nanmean(population_simulated_choices_sortedbyblockpressure,3);
std_population_simulated_choices_sortedbyblockpressure = nanstd(population_simulated_choices_sortedbyblockpressure,[],3);
for i_sub=1:n_good_sub
sub_choices = population_choices(:,:,i_sub);
sub_rewards = population_reward(:,:,i_sub);
sub_efforts = population_effort(:,:,i_sub);
% percentage choices
population_choices_percentage(1,1,i_sub) = nansum(sub_choices(:)==1)/length(sub_choices(:)); %all trials
population_choices_percentage(1,2,i_sub) = nansum(sub_choices(:)==2)/length(sub_choices(:));
population_choices_percentage(1,3,i_sub) = nansum(isnan(sub_choices(:)))/length(sub_choices(:));
population_choices_percentage(2,1,i_sub) = nansum(sub_choices(early_trials_ind)==1)/length(sub_choices(early_trials_ind)); %early_trials
population_choices_percentage(2,2,i_sub) = nansum(sub_choices(early_trials_ind)==2)/length(sub_choices(early_trials_ind));
population_choices_percentage(2,3,i_sub) = nansum(isnan(sub_choices(early_trials_ind)))/length(sub_choices(early_trials_ind));
population_choices_percentage(3,1,i_sub) = nansum(sub_choices(late_trials_ind)==1)/length(sub_choices(late_trials_ind)); %late_trials
population_choices_percentage(3,2,i_sub) = nansum(sub_choices(late_trials_ind)==2)/length(sub_choices(late_trials_ind));
population_choices_percentage(3,3,i_sub) = nansum(isnan(sub_choices(late_trials_ind)))/length(sub_choices(late_trials_ind));
population_choices_percentage(4,1,i_sub) = nansum(sub_choices(early_blocks_ind)==1)/length(sub_choices(early_blocks_ind)); %early_blocks
population_choices_percentage(4,2,i_sub) = nansum(sub_choices(early_blocks_ind)==2)/length(sub_choices(early_blocks_ind));
population_choices_percentage(4,3,i_sub) = nansum(isnan(sub_choices(early_blocks_ind)))/length(sub_choices(early_blocks_ind));
population_choices_percentage(5,1,i_sub) = nansum(sub_choices(late_blocks_ind)==1)/length(sub_choices(late_blocks_ind)); %late_blocks
population_choices_percentage(5,2,i_sub) = nansum(sub_choices(late_blocks_ind)==2)/length(sub_choices(late_blocks_ind));
population_choices_percentage(5,3,i_sub) = nansum(isnan(sub_choices(late_blocks_ind)))/length(sub_choices(late_blocks_ind));
% average reward choices
population_choices_rewards(1,1,i_sub) = nanmean(sub_rewards(sub_choices(:)==1)); %all trials
population_choices_rewards(1,2,i_sub) = nanmean(sub_rewards(sub_choices(:)==2));
population_choices_rewards(1,3,i_sub) = nanmean(sub_rewards(isnan(sub_choices(:))));
population_choices_rewards(2,1,i_sub) = nanmean(sub_rewards(intersect(find(sub_choices(:)==1),early_trials_ind))); %early_trials
population_choices_rewards(2,2,i_sub) = nanmean(sub_rewards(intersect(find(sub_choices(:)==2),early_trials_ind)));
population_choices_rewards(2,3,i_sub) = nanmean(sub_rewards(intersect(find(isnan(sub_choices(:))),early_trials_ind)));
population_choices_rewards(3,1,i_sub) = nanmean(sub_rewards(intersect(find(sub_choices(:)==1),late_trials_ind)));
population_choices_rewards(3,2,i_sub) = nanmean(sub_rewards(intersect(find(sub_choices(:)==2),late_trials_ind)));
population_choices_rewards(3,3,i_sub) = nanmean(sub_rewards(intersect(find(isnan(sub_choices(:))),late_trials_ind)));
population_choices_rewards(4,1,i_sub) = nanmean(sub_rewards(intersect(find(sub_choices(:)==1),early_blocks_ind)));
population_choices_rewards(4,2,i_sub) = nanmean(sub_rewards(intersect(find(sub_choices(:)==2),early_blocks_ind)));
population_choices_rewards(4,3,i_sub) = nanmean(sub_rewards(intersect(find(isnan(sub_choices(:))),early_blocks_ind)));
population_choices_rewards(5,1,i_sub) = nanmean(sub_rewards(intersect(find(sub_choices(:)==1),late_blocks_ind)));
population_choices_rewards(5,2,i_sub) = nanmean(sub_rewards(intersect(find(sub_choices(:)==2),late_blocks_ind)));
population_choices_rewards(5,3,i_sub) = nanmean(sub_rewards(intersect(find(isnan(sub_choices(:))),late_blocks_ind)));
% average effort choices
population_choices_efforts(1,1,i_sub) = nanmean(sub_efforts(sub_choices(:)==1)); %all trials
population_choices_efforts(1,2,i_sub) = nanmean(sub_efforts(sub_choices(:)==2));
population_choices_efforts(1,3,i_sub) = nanmean(sub_efforts(isnan(sub_choices(:))));
population_choices_efforts(2,1,i_sub) = nanmean(sub_efforts(intersect(find(sub_choices(:)==1),early_trials_ind))); %early_trials
population_choices_efforts(2,2,i_sub) = nanmean(sub_efforts(intersect(find(sub_choices(:)==2),early_trials_ind)));
population_choices_efforts(2,3,i_sub) = nanmean(sub_efforts(intersect(find(isnan(sub_choices(:))),early_trials_ind)));
population_choices_efforts(3,1,i_sub) = nanmean(sub_efforts(intersect(find(sub_choices(:)==1),late_trials_ind)));
population_choices_efforts(3,2,i_sub) = nanmean(sub_efforts(intersect(find(sub_choices(:)==2),late_trials_ind)));
population_choices_efforts(3,3,i_sub) = nanmean(sub_efforts(intersect(find(isnan(sub_choices(:))),late_trials_ind)));
population_choices_efforts(4,1,i_sub) = nanmean(sub_efforts(intersect(find(sub_choices(:)==1),early_blocks_ind)));
population_choices_efforts(4,2,i_sub) = nanmean(sub_efforts(intersect(find(sub_choices(:)==2),early_blocks_ind)));
population_choices_efforts(4,3,i_sub) = nanmean(sub_efforts(intersect(find(isnan(sub_choices(:))),early_blocks_ind)));
population_choices_efforts(5,1,i_sub) = nanmean(sub_efforts(intersect(find(sub_choices(:)==1),late_blocks_ind)));
population_choices_efforts(5,2,i_sub) = nanmean(sub_efforts(intersect(find(sub_choices(:)==2),late_blocks_ind)));
population_choices_efforts(5,3,i_sub) = nanmean(sub_efforts(intersect(find(isnan(sub_choices(:))),late_blocks_ind)));
for i_rew=1:n_rewards
population_rewards_percentage(1,i_rew,i_sub)=nansum(sub_rewards(:)==Rewards(i_rew))/length(sub_rewards(:));
population_rewards_percentage(2,i_rew,i_sub)=nansum(sub_rewards(early_trials_ind)==Rewards(i_rew))/length(sub_rewards(early_trials_ind));
population_rewards_percentage(3,i_rew,i_sub)=nansum(sub_rewards(late_trials_ind)==Rewards(i_rew))/length(sub_rewards(late_trials_ind));
population_rewards_percentage(4,i_rew,i_sub)=nansum(sub_rewards(early_blocks_ind)==Rewards(i_rew))/length(sub_rewards(early_blocks_ind));
population_rewards_percentage(5,i_rew,i_sub)=nansum(sub_rewards(late_blocks_ind)==Rewards(i_rew))/length(sub_rewards(late_blocks_ind));
population_efforts_rewards(1,i_rew,i_sub)=nanmean(sub_efforts(sub_rewards(:)==Rewards(i_rew)));
population_efforts_rewards(2,i_rew,i_sub)=nanmean(sub_efforts(intersect(find(sub_rewards(:)==Rewards(i_rew)),early_trials_ind)));
population_efforts_rewards(3,i_rew,i_sub)=nanmean(sub_efforts(intersect(find(sub_rewards(:)==Rewards(i_rew)),late_trials_ind)));
population_efforts_rewards(4,i_rew,i_sub)=nanmean(sub_efforts(intersect(find(sub_rewards(:)==Rewards(i_rew)),early_blocks_ind)));
population_efforts_rewards(5,i_rew,i_sub)=nanmean(sub_efforts(intersect(find(sub_rewards(:)==Rewards(i_rew)),late_blocks_ind)));
end
for i_level=1:n_efforts
population_efforts_percentage(1,i_level,i_sub)=nansum(sub_efforts(:)==ForceLevels(i_level))/length(sub_efforts(:));
population_efforts_percentage(2,i_level,i_sub)=nansum(sub_efforts(early_trials_ind)==ForceLevels(i_level))/length(sub_efforts(early_trials_ind));
population_efforts_percentage(3,i_level,i_sub)=nansum(sub_efforts(late_trials_ind)==ForceLevels(i_level))/length(sub_efforts(late_trials_ind));
population_efforts_percentage(4,i_level,i_sub)=nansum(sub_efforts(early_blocks_ind)==ForceLevels(i_level))/length(sub_efforts(early_blocks_ind));
population_efforts_percentage(5,i_level,i_sub)=nansum(sub_efforts(late_blocks_ind)==ForceLevels(i_level))/length(sub_efforts(late_blocks_ind));
population_rewards_efforts(1,i_level,i_sub)=nanmean(sub_rewards(sub_efforts(:)==ForceLevels(i_level)));
population_rewards_efforts(2,i_level,i_sub)=nanmean(sub_rewards(intersect(find(sub_efforts(:)==ForceLevels(i_level)),early_trials_ind)));
population_rewards_efforts(3,i_level,i_sub)=nanmean(sub_rewards(intersect(find(sub_efforts(:)==ForceLevels(i_level)),late_trials_ind)));
population_rewards_efforts(4,i_level,i_sub)=nanmean(sub_rewards(intersect(find(sub_efforts(:)==ForceLevels(i_level)),early_blocks_ind)));
population_rewards_efforts(5,i_level,i_sub)=nanmean(sub_rewards(intersect(find(sub_efforts(:)==ForceLevels(i_level)),late_blocks_ind)));
end
end
%% saving figures in
if dataset_flag == 1 %fMRI
cd('C:\Users\apisauro\Pictures\Goal Pressure project\Results\Behavioural results\Task\fMRI experiment\Latest plots')
elseif dataset_flag == 2 %Offline change in pressure across blocks
cd('C:\Users\apisauro\Pictures\Goal Pressure project\Results\Behavioural results\Task\Offline Experiment (Daniele)\Latest plots')
elseif dataset_flag == 3 %Daniele dataset1 no change in pressure across blocks
cd('C:\Users\apisauro\Pictures\Goal Pressure project\Results\Behavioural results\Task\Offline Experiment (Daniele)\No pressure modulation across blocks')
end
%% subject performance
h_goal = figure('Name','Goal and Reward','units','normalized','outerposition',[0 0 1 1]);
subplot(4,3,1:2)
imagesc([population_goals' nanmean(population_goals,1)'])
ylabel('subjects')
title('goal completed')
colormap parula
colorbar
subplot(4,3,4:5)
population_goals_trials2 = 9-population_goals_trials;
population_goals_trials2(isnan(population_goals_trials)) = -1;
imagesc([population_goals_trials2' nanmean(population_goals_trials2,1)'])
ylabel('subjects')
title('n trials before deadline goal reached')
colorbar
subplot(4,3,7:8)
block_rewards = squeeze(sum(population_reward,2))';
imagesc([block_rewards nanmean(block_rewards,2)]); %average across trials
title('reward collected')
colorbar
ylabel('subjects')
subplot(4,3,10:11)
population_success_trials = population_reward>0;
imagesc(squeeze(mean(population_success_trials,2))')
title('trials successful')
colorbar
xlabel('blocks')
ylabel('subjects')
%avg goal reached
subplot(4,3,3)
bar(1,nanmean(population_goals(:)),'b')
hold on
plot(ones(n_good_sub,1)+MeanWidth*rand(n_good_sub,1),mean(population_goals,1),'.k')
%jitterplot(mean(population_goals,1),ones(n_good_sub,1),1)
if variable_pressure_flag
ind_high_pressure = find(population_block_pressure==1);
ind_low_pressure = find(population_block_pressure==2);
hold on
bar(2,nanmean(population_goals(ind_high_pressure)),'r')
hold on
bar(3,nanmean(population_goals(ind_low_pressure)),'g')
for i_sub=1:n_good_sub
hold on
plot(2+MeanWidth*rand,nanmean(population_goals(population_block_pressure(:,i_sub)==1,i_sub),1),'.k')
hold on
plot(3+MeanWidth*rand,nanmean(population_goals(population_block_pressure(:,i_sub)==2,i_sub),1),'.k')
end
end
ylim([0 1])
ylabel('% goals')
%avg trial at which goal reached
subplot(4,3,6)
bar(1,nanmean(population_goals_trials(:)),'b')
hold on
plot(ones(n_good_sub,1)+MeanWidth*rand(n_good_sub,1),nanmean(population_goals_trials,1),'.k')
ylim([5 8])
ylabel('trial')
if variable_pressure_flag
hold on
bar(2,nanmean(population_goals_trials(ind_high_pressure)),'r')
hold on
bar(3,nanmean(population_goals_trials(ind_low_pressure)),'g')
for i_sub=1:n_good_sub
hold on
plot(2+MeanWidth*rand,nanmean(population_goals_trials(population_block_pressure(:,i_sub)==1,i_sub),1),'.k')
hold on
plot(3+MeanWidth*rand,nanmean(population_goals_trials(population_block_pressure(:,i_sub)==2,i_sub),1),'.k')
end
end
% rewards
subplot(4,3,9)
bar(1,nanmean(block_rewards(:)),'b')
hold on
%jitterplot(nanmean(block_rewards,2),ones(n_good_sub,1),1)
plot(ones(n_good_sub,1)+MeanWidth*rand(n_good_sub,1),nanmean(block_rewards,2),'.k')
ylabel('reward')
if variable_pressure_flag
hold on
bar(2,nanmean(block_rewards(ind_high_pressure)),'r')
hold on
bar(3,nanmean(block_rewards(ind_low_pressure)),'g')
for i_sub=1:n_good_sub
hold on
plot(2+MeanWidth*rand,nanmean(block_rewards(i_sub,population_block_pressure(:,i_sub)==1),2),'.k')
hold on
plot(3+MeanWidth*rand,nanmean(block_rewards(i_sub,population_block_pressure(:,i_sub)==2),2),'.k')
end
set(gca,'XTick',[1 2 3],'XTicklabel',{'all blocks';'high p blocks';'low p blocks'});
end
%different failure rates
subplot(4,3,12)
bar(1,1-nanmean(population_choices_made(:)),'b') %time outs
hold on
plot(ones(n_good_sub,1)+MeanWidth*rand(n_good_sub,1),1-squeeze(nanmean(mean(population_choices_made,2),1)),'.k')
hold on
bar(2,1-nanmean(population_success_trials_choice_made(:)),'b') %choice made effort failed
hold on
plot(2*ones(n_good_sub,1)+MeanWidth*rand(n_good_sub,1),1-squeeze(nanmean(mean(population_success_trials_choice_made,2),1)),'.k')
hold on
bar(3,1-nanmean(population_success_trials_choice_made_good_blocks(:)),'b') %choice made effort failed in good blocks
hold on
plot(3*ones(n_good_sub,1)+MeanWidth*rand(n_good_sub,1),1-squeeze(nanmean(mean(population_success_trials_choice_made_good_blocks,2),1)),'.k')
set(gca,'XTick',[1 2 3],'XTicklabel',{'time outs';'fail';'fail(goal)'},'YTick',[0 0.5 1],'YTicklabel',[0 50 100]);
ylim([0 1])
ylabel('%')
% if variable_pressure_flag
% hold on
% bar(2,nanmean(block_rewards(ind_high_pressure)),'w')
% hold on
% bar(3,nanmean(block_rewards(ind_low_pressure)),'w')
% for i_sub=1:n_good_sub
% hold on
% plot(2,nanmean(block_rewards(population_block_pressure(:,i_sub)==1,i_sub),1),'.k')
% hold on
% plot(3,nanmean(block_rewards(population_block_pressure(:,i_sub)==2,i_sub),1),'.k')
% end
% set(gca,'XTick',[1 2 3],'XTicklabel',{'all';'high p';'low p'});
% end
%% figure %high progress
h_choice_high_progress = figure('Name','Population choices high progress','units','normalized','outerposition',[0 0 1 1]);
bar(1,nanmean(population_choices(:))-1,'b')
hold on
plot(ones(n_good_sub,1)+MeanWidth*rand(n_good_sub,1),nanmean(squeeze(nanmean(population_choices,2)),1)-1,'.k')
ylim([0 1])
ylabel('% high progress')
%% percentage behaviour
h_choice_reward_effort = figure('Name','Population choices','units','normalized','outerposition',[0 0 1 1]);
subplot(3,5,1)
for i_sub=1:n_good_sub
plot([1 2 3],100*population_choices_percentage(1,:,i_sub),'-o','Color',[(i_sub)/n_good_sub 0.5 (n_good_sub-i_sub)/n_good_sub])
hold on
end
hold on
plot([1 2 3],100*mean(population_choices_percentage(1,:,:),3),'sk','MarkerSize',10,'MarkerFaceColor',[0 0 0])
ylim([0 100])
set(gca,'xtick',[1 2 3],'xticklabel',{'left', 'right', 'timeout'})
title('all trials')
ylabel('choices')
subplot(3,5,2)
for i_sub=1:n_good_sub
plot([1 2 3],100*population_choices_percentage(2,:,i_sub),'-o','Color',[(i_sub)/n_good_sub 0.5 (n_good_sub-i_sub)/n_good_sub])
hold on
end
hold on
plot([1 2 3],100*mean(population_choices_percentage(2,:,:),3),'sk','MarkerSize',10,'MarkerFaceColor',[0 0 0])
set(gca,'xtick',[1 2 3],'xticklabel',{'left', 'right', 'timeout'})
ylim([0 100])
title('early trials')
subplot(3,5,3)
for i_sub=1:n_good_sub
plot([1 2 3],100*population_choices_percentage(3,:,i_sub),'-o','Color',[(i_sub)/n_good_sub 0.5 (n_good_sub-i_sub)/n_good_sub])
hold on
end
hold on
plot([1 2 3],100*mean(population_choices_percentage(3,:,:),3),'sk','MarkerSize',10,'MarkerFaceColor',[0 0 0])
set(gca,'xtick',[1 2 3],'xticklabel',{'left', 'right', 'timeout'})
ylim([0 100])
title('late trials')
subplot(3,5,4)
for i_sub=1:n_good_sub
plot([1 2 3],100*population_choices_percentage(4,:,i_sub),'-o','Color',[(i_sub)/n_good_sub 0.5 (n_good_sub-i_sub)/n_good_sub])
hold on
end
hold on
plot([1 2 3],100*mean(population_choices_percentage(4,:,:),3),'sk','MarkerSize',10,'MarkerFaceColor',[0 0 0])
set(gca,'xtick',[1 2 3],'xticklabel',{'left', 'right', 'timeout'})
ylim([0 100])
title('early blocks')
subplot(3,5,5)
for i_sub=1:n_good_sub
plot([1 2 3],100*population_choices_percentage(5,:,i_sub),'-o','Color',[(i_sub)/n_good_sub 0.5 (n_good_sub-i_sub)/n_good_sub])
hold on
end
hold on
plot([1 2 3],100*mean(population_choices_percentage(5,:,:),3),'sk','MarkerSize',10,'MarkerFaceColor',[0 0 0])
set(gca,'xtick',[1 2 3],'xticklabel',{'left', 'right', 'timeout'})
ylim([0 100])
title('late blocks')
subplot(3,5,6)
for i_sub=1:n_good_sub
plot([1 2 3 4],100*population_rewards_percentage(1,:,i_sub),'-o','Color',[(i_sub)/n_good_sub 0.5 (n_good_sub-i_sub)/n_good_sub])
hold on
end
hold on
plot([1 2 3 4],100*mean(population_rewards_percentage(1,:,:),3),'sk','MarkerSize',10,'MarkerFaceColor',[0 0 0])
set(gca,'xtick',[1 2 3 4],'xticklabel',{num2str(Rewards(:))})
ylim([0 100])
xlim([1 4])
ylabel('reward')
subplot(3,5,7)
for i_sub=1:n_good_sub
plot([1 2 3 4],100*population_rewards_percentage(2,:,i_sub),'-o','Color',[(i_sub)/n_good_sub 0.5 (n_good_sub-i_sub)/n_good_sub])
hold on
end
hold on
plot(100*mean(population_rewards_percentage(2,:,:),3),'sk','MarkerSize',10,'MarkerFaceColor',[0 0 0])
set(gca,'xtick',[1 2 3 4],'xticklabel',{num2str(Rewards(:))})
ylim([0 100])
xlim([1 4])
subplot(3,5,8)
for i_sub=1:n_good_sub
plot([1 2 3 4],100*population_rewards_percentage(3,:,i_sub),'-o','Color',[(i_sub)/n_good_sub 0.5 (n_good_sub-i_sub)/n_good_sub])
hold on
end
hold on
plot(100*mean(population_rewards_percentage(3,:,:),3),'sk','MarkerSize',10,'MarkerFaceColor',[0 0 0])
set(gca,'xtick',[1 2 3 4],'xticklabel',{num2str(Rewards(:))})
ylim([0 100])
xlim([1 4])
subplot(3,5,9)
for i_sub=1:n_good_sub
plot([1 2 3 4],100*population_rewards_percentage(4,:,i_sub),'-o','Color',[(i_sub)/n_good_sub 0.5 (n_good_sub-i_sub)/n_good_sub])
hold on
end
hold on
plot(100*mean(population_rewards_percentage(4,:,:),3),'sk','MarkerSize',10,'MarkerFaceColor',[0 0 0])
set(gca,'xtick',[1 2 3 4],'xticklabel',{num2str(Rewards(:))})
ylim([0 100])
xlim([1 4])
subplot(3,5,10)
for i_sub=1:n_good_sub
plot([1 2 3 4],100*population_rewards_percentage(5,:,i_sub),'-o','Color',[(i_sub)/n_good_sub 0.5 (n_good_sub-i_sub)/n_good_sub])
hold on
end
hold on
plot(100*mean(population_rewards_percentage(5,:,:),3),'sk','MarkerSize',10,'MarkerFaceColor',[0 0 0])
set(gca,'xtick',[1 2 3 4],'xticklabel',{num2str(Rewards(:))})
ylim([0 100])
xlim([1 4])
subplot(3,5,11)
for i_sub=1:n_good_sub
plot([1 2 3 4],100*population_efforts_percentage(1,:,i_sub),'-o','Color',[(i_sub)/n_good_sub 0.5 (n_good_sub-i_sub)/n_good_sub])
hold on
end
hold on
plot(100*mean(population_efforts_percentage(1,:,:),3),'sk','MarkerSize',10,'MarkerFaceColor',[0 0 0])
set(gca,'xtick',[1 2 3 4],'xticklabel',{num2str(ForceLevels(:))})
ylabel('effort')
ylim([0 100])
xlim([1 4])
subplot(3,5,12)
for i_sub=1:n_good_sub
plot([1 2 3 4],100*population_efforts_percentage(2,:,i_sub),'-o','Color',[(i_sub)/n_good_sub 0.5 (n_good_sub-i_sub)/n_good_sub])
hold on
end
hold on
plot(100*mean(population_efforts_percentage(2,:,:),3),'sk','MarkerSize',10,'MarkerFaceColor',[0 0 0])
set(gca,'xtick',[1 2 3 4],'xticklabel',{num2str(ForceLevels(:))})
ylim([0 100])
xlim([1 4])
subplot(3,5,13)
for i_sub=1:n_good_sub
plot([1 2 3 4],100*population_efforts_percentage(3,:,i_sub),'-o','Color',[(i_sub)/n_good_sub 0.5 (n_good_sub-i_sub)/n_good_sub])
hold on
end
hold on
plot(100*mean(population_efforts_percentage(3,:,:),3),'sk','MarkerSize',10,'MarkerFaceColor',[0 0 0])
set(gca,'xtick',[1 2 3 4],'xticklabel',{num2str(ForceLevels(:))})
ylim([0 100])
xlim([1 4])
subplot(3,5,14)
for i_sub=1:n_good_sub
plot([1 2 3 4],100*population_efforts_percentage(4,:,i_sub),'-o','Color',[(i_sub)/n_good_sub 0.5 (n_good_sub-i_sub)/n_good_sub])
hold on
end
hold on
plot(100*mean(population_efforts_percentage(4,:,:),3),'sk','MarkerSize',10,'MarkerFaceColor',[0 0 0])
set(gca,'xtick',[1 2 3 4],'xticklabel',{num2str(ForceLevels(:))})
ylim([0 100])
xlim([1 4])
subplot(3,5,15)
for i_sub=1:n_good_sub
plot([1 2 3 4],100*population_efforts_percentage(5,:,i_sub),'-o','Color',[(i_sub)/n_good_sub 0.5 (n_good_sub-i_sub)/n_good_sub])
hold on
end
hold on
plot(100*mean(population_efforts_percentage(5,:,:),3),'sk','MarkerSize',10,'MarkerFaceColor',[0 0 0])
set(gca,'xtick',[1 2 3 4],'xticklabel',{num2str(ForceLevels(:))})
ylim([0 100])
xlim([1 4])
%% avg reward/effort for choice type
h_choice_reward_effort2 = figure('Name','Population average reward-effort','units','normalized','outerposition',[0 0 1 1]);
subplot(4,5,1)
for i_sub=1:n_good_sub
plot([1 2 3],population_choices_efforts(1,:,i_sub),'-o','Color',[(i_sub)/n_good_sub 0.5 (n_good_sub-i_sub)/n_good_sub])
hold on
end
hold on
plot([1 2 3],nanmean(population_choices_efforts(1,:,:),3),'sk','MarkerSize',10,'MarkerFaceColor',[0 0 0])
ylim([0 1])
set(gca,'xtick',[1 2 3],'xticklabel',{'left', 'right', 'timeout'})
title('all trials')
ylabel('efforts')
subplot(4,5,2)
for i_sub=1:n_good_sub
plot([1 2 3],population_choices_efforts(2,:,i_sub),'-o','Color',[(i_sub)/n_good_sub 0.5 (n_good_sub-i_sub)/n_good_sub])
hold on
end
hold on
plot([1 2 3],nanmean(population_choices_efforts(2,:,:),3),'sk','MarkerSize',10,'MarkerFaceColor',[0 0 0])
set(gca,'xtick',[1 2 3],'xticklabel',{'left', 'right', 'timeout'})
ylim([0 1])
title('early trials')
subplot(4,5,3)
for i_sub=1:n_good_sub
plot([1 2 3],population_choices_efforts(3,:,i_sub),'-o','Color',[(i_sub)/n_good_sub 0.5 (n_good_sub-i_sub)/n_good_sub])
hold on
end
hold on
plot([1 2 3],nanmean(population_choices_efforts(3,:,:),3),'sk','MarkerSize',10,'MarkerFaceColor',[0 0 0])
set(gca,'xtick',[1 2 3],'xticklabel',{'left', 'right', 'timeout'})
ylim([0 1])
title('late trials')
subplot(4,5,4)
for i_sub=1:n_good_sub
plot([1 2 3],population_choices_efforts(4,:,i_sub),'-o','Color',[(i_sub)/n_good_sub 0.5 (n_good_sub-i_sub)/n_good_sub])
hold on
end
hold on
plot([1 2 3],nanmean(population_choices_efforts(4,:,:),3),'sk','MarkerSize',10,'MarkerFaceColor',[0 0 0])
set(gca,'xtick',[1 2 3],'xticklabel',{'left', 'right', 'timeout'})
ylim([0 1])
title('early blocks')
subplot(4,5,5)
for i_sub=1:n_good_sub
plot([1 2 3],population_choices_efforts(5,:,i_sub),'-o','Color',[(i_sub)/n_good_sub 0.5 (n_good_sub-i_sub)/n_good_sub])
hold on
end
hold on
plot([1 2 3],nanmean(population_choices_efforts(5,:,:),3),'sk','MarkerSize',10,'MarkerFaceColor',[0 0 0])
set(gca,'xtick',[1 2 3],'xticklabel',{'left', 'right', 'timeout'})
ylim([0 1])
title('late blocks')
subplot(4,5,6)
for i_sub=1:n_good_sub
plot([1 2 3],population_choices_rewards(1,:,i_sub),'-o','Color',[(i_sub)/n_good_sub 0.5 (n_good_sub-i_sub)/n_good_sub])
hold on
end
hold on
plot([1 2 3],nanmean(population_choices_rewards(1,:,:),3),'sk','MarkerSize',10,'MarkerFaceColor',[0 0 0])
ylim([0 10])
set(gca,'xtick',[1 2 3],'xticklabel',{'left', 'right', 'timeout'})
title('all trials')
ylabel('rewards')
subplot(4,5,7)
for i_sub=1:n_good_sub
plot([1 2 3],population_choices_rewards(2,:,i_sub),'-o','Color',[(i_sub)/n_good_sub 0.5 (n_good_sub-i_sub)/n_good_sub])
hold on
end
hold on
plot([1 2 3],nanmean(population_choices_rewards(2,:,:),3),'sk','MarkerSize',10,'MarkerFaceColor',[0 0 0])
set(gca,'xtick',[1 2 3],'xticklabel',{'left', 'right', 'timeout'})
ylim([0 10])
title('early trials')
subplot(4,5,8)
for i_sub=1:n_good_sub
plot([1 2 3],population_choices_rewards(3,:,i_sub),'-o','Color',[(i_sub)/n_good_sub 0.5 (n_good_sub-i_sub)/n_good_sub])
hold on
end
hold on
plot([1 2 3],nanmean(population_choices_rewards(3,:,:),3),'sk','MarkerSize',10,'MarkerFaceColor',[0 0 0])
set(gca,'xtick',[1 2 3],'xticklabel',{'left', 'right', 'timeout'})
ylim([0 10])
title('late trials')
subplot(4,5,9)
for i_sub=1:n_good_sub
plot([1 2 3],population_choices_rewards(4,:,i_sub),'-o','Color',[(i_sub)/n_good_sub 0.5 (n_good_sub-i_sub)/n_good_sub])
hold on
end
hold on
plot([1 2 3],nanmean(population_choices_rewards(4,:,:),3),'sk','MarkerSize',10,'MarkerFaceColor',[0 0 0])
set(gca,'xtick',[1 2 3],'xticklabel',{'left', 'right', 'timeout'})
ylim([0 10])
title('early blocks')
subplot(4,5,10)
for i_sub=1:n_good_sub
plot([1 2 3],population_choices_rewards(5,:,i_sub),'-o','Color',[(i_sub)/n_good_sub 0.5 (n_good_sub-i_sub)/n_good_sub])
hold on
end
hold on
plot([1 2 3],nanmean(population_choices_rewards(5,:,:),3),'sk','MarkerSize',10,'MarkerFaceColor',[0 0 0])
set(gca,'xtick',[1 2 3],'xticklabel',{'left', 'right', 'timeout'})
ylim([0 10])
title('late blocks')
subplot(4,5,11)
for i_sub=1:n_good_sub
plot([1 2 3 4],population_efforts_rewards(1,:,i_sub),'-o','Color',[(i_sub)/n_good_sub 0.5 (n_good_sub-i_sub)/n_good_sub])
hold on
end
hold on
plot([1 2 3 4],mean(population_efforts_rewards(1,:,:),3),'sk','MarkerSize',10,'MarkerFaceColor',[0 0 0])
set(gca,'xtick',[1 2 3 4],'xticklabel',{num2str(Rewards(:))})
ylim([0 1])
xlim([1 4])
ylabel('effort')
subplot(4,5,12)
for i_sub=1:n_good_sub
plot([1 2 3 4],population_efforts_rewards(2,:,i_sub),'-o','Color',[(i_sub)/n_good_sub 0.5 (n_good_sub-i_sub)/n_good_sub])
hold on
end
hold on
plot(nanmean(population_efforts_rewards(2,:,:),3),'sk','MarkerSize',10,'MarkerFaceColor',[0 0 0])
set(gca,'xtick',[1 2 3 4],'xticklabel',{num2str(Rewards(:))})
ylim([0 1])
xlim([1 4])
subplot(4,5,13)
for i_sub=1:n_good_sub
plot([1 2 3 4],population_efforts_rewards(3,:,i_sub),'-o','Color',[(i_sub)/n_good_sub 0.5 (n_good_sub-i_sub)/n_good_sub])
hold on
end
hold on
plot(nanmean(population_efforts_rewards(3,:,:),3),'sk','MarkerSize',10,'MarkerFaceColor',[0 0 0])
set(gca,'xtick',[1 2 3 4],'xticklabel',{num2str(Rewards(:))})
ylim([0 1])
xlim([1 4])
subplot(4,5,14)
for i_sub=1:n_good_sub
plot([1 2 3 4],population_efforts_rewards(4,:,i_sub),'-o','Color',[(i_sub)/n_good_sub 0.5 (n_good_sub-i_sub)/n_good_sub])
hold on
end
hold on
plot(nanmean(population_efforts_rewards(4,:,:),3),'sk','MarkerSize',10,'MarkerFaceColor',[0 0 0])
set(gca,'xtick',[1 2 3 4],'xticklabel',{num2str(Rewards(:))})
ylim([0 1])
xlim([1 4])
subplot(4,5,15)
for i_sub=1:n_good_sub
plot([1 2 3 4],population_efforts_rewards(5,:,i_sub),'-o','Color',[(i_sub)/n_good_sub 0.5 (n_good_sub-i_sub)/n_good_sub])
hold on
end
hold on
plot(nanmean(population_efforts_rewards(5,:,:),3),'sk','MarkerSize',10,'MarkerFaceColor',[0 0 0])
set(gca,'xtick',[1 2 3 4],'xticklabel',{num2str(Rewards(:))})
ylim([0 1])
xlim([1 4])
subplot(4,5,16)
for i_sub=1:n_good_sub
plot([1 2 3 4],population_rewards_efforts(1,:,i_sub),'-o','Color',[(i_sub)/n_good_sub 0.5 (n_good_sub-i_sub)/n_good_sub])
hold on
end
hold on
plot(nanmean(population_rewards_efforts(1,:,:),3),'sk','MarkerSize',10,'MarkerFaceColor',[0 0 0])
set(gca,'xtick',[1 2 3 4],'xticklabel',{num2str(ForceLevels(:))})
ylabel('rewards')
ylim([0 10])
xlim([1 4])
subplot(4,5,17)
for i_sub=1:n_good_sub
plot([1 2 3 4],population_rewards_efforts(2,:,i_sub),'-o','Color',[(i_sub)/n_good_sub 0.5 (n_good_sub-i_sub)/n_good_sub])
hold on
end
hold on
plot(nanmean(population_rewards_efforts(2,:,:),3),'sk','MarkerSize',10,'MarkerFaceColor',[0 0 0])
set(gca,'xtick',[1 2 3 4],'xticklabel',{num2str(ForceLevels(:))})
ylim([0 10])
xlim([1 4])
subplot(4,5,18)
for i_sub=1:n_good_sub
plot([1 2 3 4],population_rewards_efforts(3,:,i_sub),'-o','Color',[(i_sub)/n_good_sub 0.5 (n_good_sub-i_sub)/n_good_sub])
hold on
end
hold on
plot(nanmean(population_rewards_efforts(3,:,:),3),'sk','MarkerSize',10,'MarkerFaceColor',[0 0 0])
set(gca,'xtick',[1 2 3 4],'xticklabel',{num2str(ForceLevels(:))})
ylim([0 10])
xlim([1 4])
subplot(4,5,19)
for i_sub=1:n_good_sub
plot([1 2 3 4],population_rewards_efforts(4,:,i_sub),'-o','Color',[(i_sub)/n_good_sub 0.5 (n_good_sub-i_sub)/n_good_sub])
hold on
end
hold on
plot(nanmean(population_rewards_efforts(4,:,:),3),'sk','MarkerSize',10,'MarkerFaceColor',[0 0 0])
set(gca,'xtick',[1 2 3 4],'xticklabel',{num2str(ForceLevels(:))})
ylim([0 10])
xlim([1 4])
subplot(4,5,20)
for i_sub=1:n_good_sub
plot([1 2 3 4],population_rewards_efforts(5,:,i_sub),'-o','Color',[(i_sub)/n_good_sub 0.5 (n_good_sub-i_sub)/n_good_sub])
hold on
end
hold on
plot(nanmean(population_rewards_efforts(5,:,:),3),'sk','MarkerSize',10,'MarkerFaceColor',[0 0 0])
set(gca,'xtick',[1 2 3 4],'xticklabel',{num2str(ForceLevels(:))})
ylim([0 10])
xlim([1 4])
%% distance analysis
D1 = 0;
D2 = prctile(1-population_advancement(:),90);
X_distance = [D1:abs(D2-D1)/10:D2];
[N_distance,X_distance] = hist(1-population_advancement(:),X_distance); %10 bins between the 10th and 90th perctile of population_pressure
for i_distance = 1:length(X_distance)
if i_distance==1
indeces_this_distance{i_distance} = find((1-population_advancement(:))<X_distance(i_distance));
elseif i_distance<length(X_distance)
indeces_this_distance{i_distance} = find((1-population_advancement(:))<X_distance(i_distance+1)&(1-population_advancement(:))>X_distance(i_distance));
else
indeces_this_distance{i_distance} = find((1-population_advancement(:))>X_distance(i_distance));
end
indeces_this_distance_early_blocks{i_distance} = intersect(indeces_this_distance{i_distance},early_blocks_indeces);
indeces_this_distance_late_blocks{i_distance} = intersect(indeces_this_distance{i_distance},late_blocks_indeces);
indeces_this_distance_hard_blocks{i_distance} = intersect(indeces_this_distance{i_distance},hard_blocks_indeces);
indeces_this_distance_easy_blocks{i_distance} = intersect(indeces_this_distance{i_distance},easy_blocks_indeces);
population_choices_vs_distance(i_distance)= nanmean(population_choices(indeces_this_distance{i_distance}));
population_choices_vs_distance_se(i_distance) = nanstd(population_choices(indeces_this_distance{i_distance}))/sqrt(length(~isnan(population_choices(indeces_this_distance{i_distance})))/n_good_sub);
population_choices_vs_distance_early_blocks(i_distance)= nanmean(population_choices(indeces_this_distance_early_blocks{i_distance}));
population_choices_vs_distance_late_blocks(i_distance)= nanmean(population_choices(indeces_this_distance_late_blocks{i_distance}));
population_choices_vs_distance_hard_blocks(i_distance)= nanmean(population_choices(indeces_this_distance_hard_blocks{i_distance}));
population_choices_vs_distance_easy_blocks(i_distance)= nanmean(population_choices(indeces_this_distance_easy_blocks{i_distance}));
end
%% choice vs goal pressure analysis
P1 = prctile(population_pressure(:),10);
P2 = prctile(population_pressure(:),90);
X_pressure = [P1:abs(P2-P1)/10:P2];
[N_pressure,X_pressure] = hist(population_pressure(:),X_pressure); %10 bins between the 10th and 90th perctile of population_pressure
for i_pressure = 1:length(X_pressure)
if i_pressure==1
indeces_this_pressure{i_pressure} = find(population_pressure(:)<X_pressure(i_pressure));
elseif i_pressure<length(X_pressure)
indeces_this_pressure{i_pressure} = find((population_pressure(:)<X_pressure(i_pressure+1))&population_pressure(:)>X_pressure(i_pressure));
else
indeces_this_pressure{i_pressure} = find(population_pressure(:)>X_pressure(i_pressure));
end
indeces_this_pressure_early_blocks{i_pressure} = intersect(indeces_this_pressure{i_pressure},early_blocks_indeces);
indeces_this_pressure_late_blocks{i_pressure} = intersect(indeces_this_pressure{i_pressure},late_blocks_indeces);
indeces_this_pressure_hard_blocks{i_pressure} = intersect(indeces_this_pressure{i_pressure},hard_blocks_indeces);
indeces_this_pressure_easy_blocks{i_pressure} = intersect(indeces_this_pressure{i_pressure},easy_blocks_indeces);
population_choices_vs_pressure(i_pressure)= nanmean(population_choices(indeces_this_pressure{i_pressure}));
population_choices_vs_pressure_se(i_pressure) = nanstd(population_choices(indeces_this_pressure{i_pressure}))/sqrt(length(~isnan(population_choices(indeces_this_pressure{i_pressure})))/n_good_sub);
population_choices_vs_pressure_std(i_pressure) = nanstd(population_choices(indeces_this_pressure{i_pressure}));
population_choices_vs_pressure_early_blocks(i_pressure)= nanmean(population_choices(indeces_this_pressure_early_blocks{i_pressure}));
population_choices_vs_pressure_late_blocks(i_pressure)= nanmean(population_choices(indeces_this_pressure_late_blocks{i_pressure}));
population_choices_vs_pressure_hard_blocks(i_pressure)= nanmean(population_choices(indeces_this_pressure_hard_blocks{i_pressure}));
population_choices_vs_pressure_easy_blocks(i_pressure)= nanmean(population_choices(indeces_this_pressure_easy_blocks{i_pressure}));
population_choices_vs_pressure_hard_blocks_se(i_pressure)= nanstd(population_choices(indeces_this_pressure_hard_blocks{i_pressure}))/sqrt(length(~isnan(population_choices(indeces_this_pressure_hard_blocks{i_pressure})))/n_good_sub);
population_choices_vs_pressure_easy_blocks_se(i_pressure)= nanstd(population_choices(indeces_this_pressure_easy_blocks{i_pressure}))/sqrt(length(~isnan(population_choices(indeces_this_pressure_easy_blocks{i_pressure})))/n_good_sub);
population_choices_vs_pressure_hard_blocks_std(i_pressure)= nanstd(population_choices(indeces_this_pressure_hard_blocks{i_pressure}));
population_choices_vs_pressure_easy_blocks_std(i_pressure)= nanstd(population_choices(indeces_this_pressure_easy_blocks{i_pressure}));
end
%% simulated choice vs goal pressure analysis
for i_pressure = 1:length(X_pressure)
population_sim_choices_vs_pressure(i_pressure)= nanmean(population_simulated_choices(indeces_this_pressure{i_pressure}));
population_sim_choices_vs_pressure_se(i_pressure) = nanstd(population_simulated_choices(indeces_this_pressure{i_pressure}))/sqrt(length(~isnan(population_simulated_choices(indeces_this_pressure{i_pressure})))/n_good_sub);
population_sim_choices_vs_pressure_std(i_pressure) = nanstd(population_simulated_choices(indeces_this_pressure{i_pressure}));
population_sim_choices_vs_pressure_early_blocks(i_pressure)= nanmean(population_simulated_choices(indeces_this_pressure_early_blocks{i_pressure}));
population_sim_choices_vs_pressure_late_blocks(i_pressure)= nanmean(population_simulated_choices(indeces_this_pressure_late_blocks{i_pressure}));
population_sim_choices_vs_pressure_hard_blocks(i_pressure)= nanmean(population_simulated_choices(indeces_this_pressure_hard_blocks{i_pressure}));
population_sim_choices_vs_pressure_easy_blocks(i_pressure)= nanmean(population_simulated_choices(indeces_this_pressure_easy_blocks{i_pressure}));
population_sim_choices_vs_pressure_hard_blocks_se(i_pressure)= nanstd(population_simulated_choices(indeces_this_pressure_hard_blocks{i_pressure}))/sqrt(length(~isnan(population_simulated_choices(indeces_this_pressure_hard_blocks{i_pressure})))/n_good_sub);
population_sim_choices_vs_pressure_easy_blocks_se(i_pressure)= nanstd(population_simulated_choices(indeces_this_pressure_easy_blocks{i_pressure}))/sqrt(length(~isnan(population_simulated_choices(indeces_this_pressure_easy_blocks{i_pressure})))/n_good_sub);
population_sim_choices_vs_pressure_hard_blocks_std(i_pressure)= nanstd(population_simulated_choices(indeces_this_pressure_hard_blocks{i_pressure}));
population_sim_choices_vs_pressure_easy_blocks_std(i_pressure)= nanstd(population_simulated_choices(indeces_this_pressure_easy_blocks{i_pressure}));
end
%% choice vs reaction time analysis
RT1 = prctile(population_RT(:),5);
RT2 = prctile(population_RT(:),95);
logRT1 = prctile(log(population_RT(:)),5);
logRT2 = prctile(log(population_RT(:)),95);
X_RT = [RT1:abs(RT2-RT1)/20:RT2];
X_log_RT = [logRT1:abs(logRT2-logRT1)/20:logRT2];
[N_RT,X_RT] = hist(population_RT(:),X_RT); %10 bins between the 10th and 90th perctile of population_RT
[N_log_RT,X_log_RT] = hist(log(population_RT(:)),X_log_RT); %10 bins between the 10th and 90th perctile of population_RT
for i_RT = 1:length(X_RT)
if i_RT==1
indeces_this_RT{i_RT} = find(population_RT(:)<X_RT(i_RT));
indeces_this_log_RT{i_RT} = find(log(population_RT(:))<X_log_RT(i_RT));
elseif i_RT<length(X_RT)
indeces_this_RT{i_RT} = find((population_RT(:)<X_RT(i_RT+1))&population_RT(:)>X_RT(i_RT));
indeces_this_log_RT{i_RT} = find((log(population_RT(:))<X_log_RT(i_RT+1))&log(population_RT(:))>X_log_RT(i_RT));
else
indeces_this_RT{i_RT} = find(population_RT(:)>X_RT(i_RT));
indeces_this_log_RT{i_RT} = find(log(population_RT(:))>X_log_RT(i_RT));
end
indeces_this_RT_early_blocks{i_RT} = intersect(indeces_this_RT{i_RT},early_blocks_indeces);
indeces_this_RT_late_blocks{i_RT} = intersect(indeces_this_RT{i_RT},late_blocks_indeces);
indeces_this_RT_hard_blocks{i_RT} = intersect(indeces_this_RT{i_RT},hard_blocks_indeces);
indeces_this_RT_easy_blocks{i_RT} = intersect(indeces_this_RT{i_RT},easy_blocks_indeces);
population_choices_vs_RT(i_RT)= nanmean(population_choices(indeces_this_RT{i_RT}));
population_choices_vs_RT_se(i_RT) = nanstd(population_choices(indeces_this_RT{i_RT}))/sqrt(length(indeces_this_RT{i_RT})/n_good_sub);
population_choices_vs_RT_std(i_RT) = nanstd(population_choices(indeces_this_RT{i_RT}));
population_choices_vs_log_RT(i_RT)= nanmean(population_choices(indeces_this_log_RT{i_RT}));
population_choices_vs_log_RT_se(i_RT) = nanstd(population_choices(indeces_this_log_RT{i_RT}))/sqrt(length(indeces_this_log_RT{i_RT})/n_good_sub);
population_choices_vs_log_RT_std(i_RT) = nanstd(population_choices(indeces_this_log_RT{i_RT}));
population_choices_vs_RT_early_blocks(i_RT)= nanmean(population_choices(indeces_this_RT_early_blocks{i_RT}));
population_choices_vs_RT_late_blocks(i_RT)= nanmean(population_choices(indeces_this_RT_late_blocks{i_RT}));
population_choices_vs_RT_hard_blocks(i_RT)= nanmean(population_choices(indeces_this_RT_hard_blocks{i_RT}));
population_choices_vs_RT_easy_blocks(i_RT)= nanmean(population_choices(indeces_this_RT_easy_blocks{i_RT}));
end
%% RT vs goal pressure analysis
P1 = prctile(population_pressure(:),5);
P2 = prctile(population_pressure(:),95);
X_pressure = [P1:abs(P2-P1)/10:P2];
[N_pressure,X_pressure] = hist(population_pressure(:),X_pressure); %10 bins between the 10th and 90th perctile of population_pressure
for i_pressure = 1:length(X_pressure)
if i_pressure==1
indeces_this_pressure{i_pressure} = find(population_pressure(:)<X_pressure(i_pressure));
elseif i_pressure<length(X_pressure)
indeces_this_pressure{i_pressure} = find((population_pressure(:)<X_pressure(i_pressure+1))&population_pressure(:)>X_pressure(i_pressure));
else
indeces_this_pressure{i_pressure} = find(population_pressure(:)>X_pressure(i_pressure));
end
indeces_this_pressure_early_blocks{i_pressure} = intersect(indeces_this_pressure{i_pressure},early_blocks_indeces);
indeces_this_pressure_late_blocks{i_pressure} = intersect(indeces_this_pressure{i_pressure},late_blocks_indeces);
indeces_this_pressure_hard_blocks{i_pressure} = intersect(indeces_this_pressure{i_pressure},hard_blocks_indeces);
indeces_this_pressure_easy_blocks{i_pressure} = intersect(indeces_this_pressure{i_pressure},easy_blocks_indeces);
population_RT_vs_pressure(i_pressure)= nanmean(population_RT(indeces_this_pressure{i_pressure}));
population_RT_vs_pressure_se(i_pressure) = nanstd(population_RT(indeces_this_pressure{i_pressure}))/sqrt(length(indeces_this_pressure{i_pressure})/n_good_sub);
population_RT_vs_pressure_std(i_pressure) = nanstd(population_RT(indeces_this_pressure{i_pressure}));
population_log_RT_vs_pressure(i_pressure)= nanmean(log(population_RT(indeces_this_pressure{i_pressure})));
population_log_RT_vs_pressure_se(i_pressure) = nanstd(log(population_RT(indeces_this_pressure{i_pressure})))/sqrt(length(indeces_this_pressure{i_pressure})/n_good_sub);
population_log_RT_vs_pressure_std(i_pressure) = nanstd(log(population_RT(indeces_this_pressure{i_pressure})));
population_RT_vs_pressure_early_blocks(i_pressure)= nanmean(population_RT(indeces_this_pressure_early_blocks{i_pressure}));
population_RT_vs_pressure_late_blocks(i_pressure)= nanmean(population_RT(indeces_this_pressure_late_blocks{i_pressure}));
population_RT_vs_pressure_hard_blocks(i_pressure)= nanmean(population_RT(indeces_this_pressure_hard_blocks{i_pressure}));
population_RT_vs_pressure_easy_blocks(i_pressure)= nanmean(population_RT(indeces_this_pressure_easy_blocks{i_pressure}));
end
%% choice vs homeostatic analysis
H1 = prctile(population_reward_advancement_homeostasis(:),5);
H2 = prctile(population_reward_advancement_homeostasis(:),95);
X_homeostasis = [H1:abs(H2-H1)/10:H2];
[N_homeostasis,X_homeostasis] = hist(population_reward_advancement_homeostasis(:),X_homeostasis); %10 bins between the 10th and 90th perctile of population_RT
for i_homeostasis = 1:length(X_homeostasis)
if i_homeostasis==1
indeces_this_homeostasis{i_homeostasis} = find(population_reward_advancement_homeostasis(:)<X_homeostasis(i_homeostasis));
elseif i_homeostasis<length(X_homeostasis)
indeces_this_homeostasis{i_homeostasis} = find((population_reward_advancement_homeostasis(:)<X_homeostasis(i_homeostasis+1))&population_reward_advancement_homeostasis(:)>X_homeostasis(i_homeostasis));
else
indeces_this_homeostasis{i_homeostasis} = find(population_reward_advancement_homeostasis(:)>X_homeostasis(i_homeostasis));
end
indeces_this_homeostasis_early_blocks{i_homeostasis} = intersect(indeces_this_homeostasis{i_homeostasis},early_blocks_indeces);
indeces_this_homeostasis_late_blocks{i_homeostasis} = intersect(indeces_this_homeostasis{i_homeostasis},late_blocks_indeces);
indeces_this_homeostasis_hard_blocks{i_homeostasis} = intersect(indeces_this_homeostasis{i_homeostasis},hard_blocks_indeces);
indeces_this_homeostasis_easy_blocks{i_homeostasis} = intersect(indeces_this_homeostasis{i_homeostasis},easy_blocks_indeces);
population_choices_vs_homeostasis(i_homeostasis)= nanmean(population_choices(indeces_this_homeostasis{i_homeostasis}));
population_choices_vs_homeostasis_se(i_homeostasis) = nanstd(population_choices(indeces_this_homeostasis{i_homeostasis}))/sqrt(length(indeces_this_homeostasis{i_homeostasis})/n_good_sub);
population_choices_vs_homeostasis_early_blocks(i_homeostasis)= nanmean(population_choices(indeces_this_homeostasis_early_blocks{i_homeostasis}));
population_choices_vs_homeostasis_late_blocks(i_homeostasis)= nanmean(population_choices(indeces_this_homeostasis_late_blocks{i_homeostasis}));
population_choices_vs_homeostasis_hard_blocks(i_homeostasis)= nanmean(population_choices(indeces_this_homeostasis_hard_blocks{i_homeostasis}));
population_choices_vs_homeostasis_easy_blocks(i_homeostasis)= nanmean(population_choices(indeces_this_homeostasis_easy_blocks{i_homeostasis}));
end
%% pressure distance distribution
h_pressure_distance = figure('Name',['Population pressure distance distribution '],'units','normalized','outerposition',[0 0 1 1]);
subplot(2,3,1)
boundedline(1:n_trials,1-mean(nanmean(mean_population_advancement(:,1:n_trials,:),3),1), nanstd(squeeze(nanmean(mean_population_advancement(:,1:n_trials,:),1)),[],2),'alpha','cmap',[.1 .1 .1],'transparency', trasp);
hold on
plot(1:n_trials,1-mean(nanmean(mean_population_advancement(:,1:n_trials,:)),3),'LineWidth',5,'Color',[0.1 0.1 0.1])
title('distance')
xlim([1 n_trials])
ylim([0 1])
xlabel('trials')
subplot(2,3,4)
hist(1-population_advancement(:),20)
subplot(2,3,2)
boundedline(1:n_trials,mean(nanmean(mean_population_pressure(:,1:n_trials,:),3),1), nanstd(squeeze(nanmean(mean_population_pressure(:,1:n_trials,:),1)),[],2),'alpha','cmap',[.1 .1 .1],'transparency', trasp);
hold on
plot(1:n_trials,mean(nanmean(mean_population_pressure(:,1:n_trials,:)),3),'LineWidth',5,'Color',[0.1 0.1 0.1])
hold on
% plot([1 n_trials],[max_pressure(1) max_pressure(1)],'--r')
title('pressure')
xlim([1 n_trials])
xlabel('trials')
subplot(2,3,5)
hist(population_pressure(:),20)
subplot(2,3,3)
boundedline(1:n_trials,mean(nanmean(mean_population_reward_advancement_homeostasis(:,1:n_trials,:),3),1), nanstd(squeeze(nanmean(mean_population_reward_advancement_homeostasis(:,1:n_trials,:),1)),[],2),'alpha','cmap',[.1 .1 .1],'transparency', trasp);
hold on
plot(1:n_trials,mean(nanmean(mean_population_reward_advancement_homeostasis(:,1:n_trials,:)),3),'LineWidth',5,'Color',[0.1 0.1 0.1])
hold on
% plot([1 n_trials],[max_pressure(1) max_pressure(1)],'--r')
title('homeostasis')
xlim([1 n_trials])
xlabel('trials')
subplot(2,3,6)
hist(population_reward_advancement_homeostasis(:),20)
%% pressure distance distribution
h_pressure_homeostasis_distance = figure('Name',['Population pressure distance distribution ']);
subplot(3,1,1)
plot(population_pressure(:),population_reward_advancement_homeostasis(:),'o')
xlabel('pressure')
ylabel('homeostasis')
subplot(3,1,2)
plot(1-population_advancement(:),population_reward_advancement_homeostasis(:),'o')
xlabel('distance')
ylabel('homeostasis')
subplot(3,1,3)
plot(population_pressure(:),1-population_advancement(:),'o')
xlabel('pressure')
ylabel('distance')
%% simulated choices vs trials/pressure FIG 2 A/B for simulated data
h_pop0_sim_name = 'Population simulated choices vs trials pressure ';
h_pop0_sim = figure('Name',h_pop0_sim_name,'units','normalized','outerposition',[0 0 1 1]);
subplot(2,2,1)
boundedline(1:n_trials,nanmean(mean_population_simulated_choices,1), mean(std_population_simulated_choices(:,1:n_trials),1)/sqrt(n_blocks),'alpha','cmap',[.5 .5 .5],'transparency', trasp);
hold on
plot(1:n_trials,nanmean(mean_population_simulated_choices,1),'LineWidth',5,'Color',[0.1 0.1 0.1])
hold on
ylim([0.9 2.1])
xlim([1 n_trials])
hold on
plot([1 n_trials],[1 1],'--r')
hold on
plot([1 n_trials],[2 2],'--r')
set(gca,'YTick',1:2);
set(gca,'YTickLabel',{'0','100%'});
ylabel('% high effort sim choice')
title('N = 30');
subplot(2,2,2)
boundedline(X_pressure,population_sim_choices_vs_pressure, population_sim_choices_vs_pressure_se,'alpha','cmap',[.5 .5 .5],'transparency', trasp);
hold on
plot(X_pressure,population_sim_choices_vs_pressure,'LineWidth',5,'Color',[0.1 0.1 0.1])
plot([P1 P2],[1 1],'--r')
hold on
plot([P1 P2],[2 2],'--r')
set(gca,'YTick',1:2);
set(gca,'YTickLabel',{'0','100%'});
xlim([P1 P2])
ylim([0.9 2.1])
title('Binned pressure');
fprintf('Correlation sim choice pressure is %f \n',corr(X_pressure',population_sim_choices_vs_pressure'))
if variable_pressure_flag
subplot(2,2,3)
boundedline(1:n_trials,nanmean(mean_population_simulated_choices_sortedbyblockpressure(1:15,:),1), mean(std_population_simulated_choices_sortedbyblockpressure(1:15,1:n_trials),1)/sqrt(n_blocks),'alpha','cmap',[.5 .5 .5],'transparency', trasp);
hold on
plot(1:n_trials,nanmean(mean_population_simulated_choices_sortedbyblockpressure(1:15,:),1),'LineWidth',5,'Color',[0 1 0])
hold on
boundedline(1:n_trials,nanmean(mean_population_simulated_choices_sortedbyblockpressure(16:30,:),1), mean(std_population_simulated_choices_sortedbyblockpressure(16:30,1:n_trials),1)/sqrt(n_blocks),'alpha','cmap',[.5 .5 .5],'transparency', trasp);
hold on
plot(1:n_trials,nanmean(mean_population_simulated_choices_sortedbyblockpressure(16:30,:),1),'LineWidth',5,'Color',[1 0 0])
plot([1 n_trials],[1 1],'--r')
hold on
plot([1 n_trials],[2 2],'--r')
set(gca,'YTick',1:2);
set(gca,'YTickLabel',{'0','100%'});
xlim([1 n_trials])
ylim([0.9 2.1])
xlabel('trial')
ylabel('% high effort choice')
subplot(2,2,4)
boundedline(X_pressure,population_sim_choices_vs_pressure_hard_blocks,population_sim_choices_vs_pressure_hard_blocks_se,'alpha','cmap',[.5 .5 .5],'transparency', trasp);
hold on
plot(X_pressure,population_sim_choices_vs_pressure_hard_blocks,'LineWidth',5,'Color',[0 1 0])
hold on
boundedline(X_pressure,population_sim_choices_vs_pressure_easy_blocks,population_sim_choices_vs_pressure_easy_blocks_se,'alpha','cmap',[.5 .5 .5],'transparency', trasp);
hold on
plot(X_pressure,population_sim_choices_vs_pressure_easy_blocks,'LineWidth',5,'Color',[[1 0 0]])
plot([P1 P2],[1 1],'--r')
hold on
plot([P1 P2],[2 2],'--r')
xlim([P1 P2])
ylim([0.9 2.1])
set(gca,'YTick',1:2);
set(gca,'YTickLabel',{'0','100%'});
xlabel('pressure = distance/time')
fprintf('Correlation sim choice pressure in high pr blocks is %f \n',corr(X_pressure',population_sim_choices_vs_pressure_hard_blocks'))
fprintf('Correlation sim choice pressure in low pr blocks is %f \n',corr(X_pressure',population_sim_choices_vs_pressure_easy_blocks'))
end
%% choices vs trials/pressure FIG 2 A/B
h_pop0_name = 'Population choices vs trials pressure ';
h_pop0 = figure('Name',h_pop0_name,'units','normalized','outerposition',[0 0 1 1]);
subplot(2,2,1)
boundedline(1:n_trials,nanmean(mean_population_choices,1), mean(std_population_choices(:,1:n_trials),1)/sqrt(n_blocks),'alpha','cmap',[.5 .5 .5],'transparency', trasp);
% boundedline(1:n_trials,nanmean(mean_population_choices,1), mean(std_population_choices(:,1:n_trials),1),'alpha','cmap',[.5 .5 .5],'transparency', trasp);
hold on
plot(1:n_trials,nanmean(mean_population_choices,1),'LineWidth',5,'Color',[0.1 0.1 0.1])
hold on
ylim([0.9 2.1])
xlim([1 n_trials])
hold on
plot([1 n_trials],[1 1],'--r')
hold on
plot([1 n_trials],[2 2],'--r')
set(gca,'YTick',1:2);
set(gca,'YTickLabel',{'0','100%'});
ylabel('% high effort choice')
title('N = 30');
subplot(2,2,2)
boundedline(X_pressure,population_choices_vs_pressure, population_choices_vs_pressure_se,'alpha','cmap',[.5 .5 .5],'transparency', trasp);
%boundedline(X_pressure,population_choices_vs_pressure, population_choices_vs_pressure_std,'alpha','cmap',[.5 .5 .5],'transparency', trasp);
hold on
plot(X_pressure,population_choices_vs_pressure,'LineWidth',5,'Color',[0.1 0.1 0.1])
plot([P1 P2],[1 1],'--r')
hold on
plot([P1 P2],[2 2],'--r')
set(gca,'YTick',1:2);
set(gca,'YTickLabel',{'0','100%'});
xlim([P1 P2])
ylim([0.9 2.1])
title('Binned pressure');
fprintf('Correlation choice pressure is %f \n',corr(X_pressure',population_choices_vs_pressure'))
if variable_pressure_flag
subplot(2,2,3)
boundedline(1:n_trials,nanmean(mean_population_choices_sortedbyblockpressure(1:15,:),1), mean(std_population_choices_sortedbyblockpressure(1:15,1:n_trials),1)/sqrt(n_blocks),'alpha','cmap',[.5 .5 .5],'transparency', trasp);
hold on
plot(1:n_trials,nanmean(mean_population_choices_sortedbyblockpressure(1:15,:),1),'LineWidth',5,'Color',[0 1 0])
hold on
boundedline(1:n_trials,nanmean(mean_population_choices_sortedbyblockpressure(16:30,:),1), mean(std_population_choices_sortedbyblockpressure(16:30,1:n_trials),1)/sqrt(n_blocks),'alpha','cmap',[.5 .5 .5],'transparency', trasp);
hold on
plot(1:n_trials,nanmean(mean_population_choices_sortedbyblockpressure(16:30,:),1),'LineWidth',5,'Color',[1 0 0])
plot([1 n_trials],[1 1],'--r')
hold on
plot([1 n_trials],[2 2],'--r')
set(gca,'YTick',1:2);
set(gca,'YTickLabel',{'0','100%'});
xlim([1 n_trials])
ylim([0.9 2.1])
xlabel('trial')
ylabel('% high effort choice')
subplot(2,2,4)
boundedline(X_pressure,population_choices_vs_pressure_hard_blocks,population_choices_vs_pressure_hard_blocks_se,'alpha','cmap',[.5 .5 .5],'transparency', trasp);
hold on
plot(X_pressure,population_choices_vs_pressure_hard_blocks,'LineWidth',5,'Color',[0 1 0])
hold on
boundedline(X_pressure,population_choices_vs_pressure_easy_blocks,population_choices_vs_pressure_easy_blocks_se,'alpha','cmap',[.5 .5 .5],'transparency', trasp);
hold on
plot(X_pressure,population_choices_vs_pressure_easy_blocks,'LineWidth',5,'Color',[[1 0 0]])
plot([P1 P2],[1 1],'--r')
hold on
plot([P1 P2],[2 2],'--r')
xlim([P1 P2])
ylim([0.9 2.1])
set(gca,'YTick',1:2);
set(gca,'YTickLabel',{'0','100%'});
xlabel('pressure = distance/time')
fprintf('Correlation choice pressure in high pr blocks is %f \n',corr(X_pressure',population_choices_vs_pressure_hard_blocks'))
fprintf('Correlation choice pressure in low pr blocks is %f \n',corr(X_pressure',population_choices_vs_pressure_easy_blocks'))
end
%% CHOICES VS TRIALS BLOCKS DISTANCE PRESSURE HOMEOSTASIS
h_choice = figure('Name',['Population choices vs other things ']);
subplot(3,2,1)
boundedline(1:n_trials,nanmean(mean_population_choices(:,1:n_trials),1),std(mean_population_choices(:,1:n_trials),1)/sqrt(n_blocks),'alpha','cmap',[.5 .5 .5],'transparency', trasp);
hold on
plot(1:n_trials,nanmean(mean_population_choices(:,1:n_trials),1),'LineWidth',3,'Color',[0.1 0.1 0.1])
plot([1 n_trials],[1 1],'--r')
hold on
plot([1 n_trials],[2 2],'--r')
ylim([0.9 2.1])
xlim([1 n_trials])
xlabel('trials')
set(gca,'YTick',1:2);
set(gca,'YTickLabel',{'0','100%'});
ylabel('% high effort choice')
subplot(3,2,3)
boundedline(1:n_blocks,nanmean(mean_population_choices(:,1:n_trials),2),std(mean_population_choices(:,1:n_trials),[],2)/sqrt(n_trials),'alpha','cmap',[.5 .5 .5],'transparency', trasp);
hold on
plot(1:n_blocks,nanmean(mean_population_choices(:,1:n_trials),2),'LineWidth',3,'Color',[0.1 0.1 0.1])
hold on
plot([1 n_blocks],[1 1],'--r')
hold on
plot([1 n_blocks],[2 2],'--r')
ylim([0.9 2.1])
set(gca,'YTick',1:2);
set(gca,'YTickLabel',{'0','100%'});
ylabel('% high effort choice')
xlabel('blocks')
subplot(3,2,2)
boundedline(X_distance,population_choices_vs_distance,population_choices_vs_distance_se,'alpha','cmap',[.5 .5 .5],'transparency', trasp);
hold on
plot(X_distance,population_choices_vs_distance,'LineWidth',3,'Color',[0.1 0.1 0.1])
plot([D1 D2],[1 1],'--r')
hold on
plot([D1 D2],[2 2],'--r')
set(gca, 'XDir','reverse')
xlim([D1 D2])
ylim([0.9 2.1])
xlabel('distance')
set(gca,'YTick',1:2);
set(gca,'YTickLabel',{'0','100%'});
subplot(3,2,4)
boundedline(X_pressure,population_choices_vs_pressure,population_choices_vs_pressure_se,'alpha','cmap',[.5 .5 .5],'transparency', trasp);
hold on
plot(X_pressure,population_choices_vs_pressure,'LineWidth',3,'Color',[0.1 0.1 0.1])
plot([P1 P2],[1 1],'--r')
hold on
plot([P1 P2],[2 2],'--r')
xlim([P1 P2])
ylim([0.9 2.1])
xlabel('pressure')
set(gca,'YTick',1:2);
set(gca,'YTickLabel',{'0','100%'});
subplot(3,2,6)
boundedline(X_homeostasis,population_choices_vs_homeostasis,population_choices_vs_homeostasis_se,'alpha','cmap',[.5 .5 .5],'transparency', trasp);
hold on
plot(X_homeostasis,population_choices_vs_homeostasis,'LineWidth',3,'Color',[0.1 0.1 0.1])
plot([H1 H2],[1 1],'--r')
hold on
plot([H1 H2],[2 2],'--r')
xlim([H1 H2])
ylim([0.9 2.1])
xlabel('homeostasis')
set(gca,'YTick',1:2);
set(gca,'YTickLabel',{'0','100%'});
%% choices vs trials/blocks early late blocks
h_choice_early_late_blocks = figure('Name','Population choices vs trials early late blocks','units','normalized','outerposition',[0 0 1 1]);
subplot(3,2,1)
plot(nanmean(mean_population_choices(early_blocks,1:n_trials),1),'g')
hold on
plot(nanmean(mean_population_choices(late_blocks,1:n_trials),1),'r')
plot([1 n_trials],[1 1],'--r')
hold on
plot([1 n_trials],[2 2],'--r')
ylim([0.9 2.1])
xlim([1 n_trials])
set(gca,'YTick',1:2);
set(gca,'YTickLabel',{'0','100%'});
ylabel('% high effort choice')
xlabel('trials')
subplot(3,2,3)
plot(early_blocks,nanmean(mean_population_choices(early_blocks,1:n_trials),2),'g')
hold on
plot(early_blocks,nanmean(mean_population_choices(early_blocks,1:n_trials),2)+nanstd(mean_population_choices(early_blocks,1:n_trials),[],2)/sqrt(n_trials),'--g')
hold on
plot(early_blocks,nanmean(mean_population_choices(early_blocks,1:n_trials),2)-nanstd(mean_population_choices(early_blocks,1:n_trials),[],2)/sqrt(n_trials),'--g')
hold on
subplot(3,2,3)
plot(late_blocks,nanmean(mean_population_choices(late_blocks,1:n_trials),2),'r')
hold on
plot(late_blocks,nanmean(mean_population_choices(late_blocks,1:n_trials),2)+nanstd(mean_population_choices(late_blocks,1:n_trials),[],2)/sqrt(n_trials),'--r')
hold on
plot(late_blocks,nanmean(mean_population_choices(late_blocks,1:n_trials),2)-nanstd(mean_population_choices(late_blocks,1:n_trials),[],2)/sqrt(n_trials),'--r')
hold on
plot([1 n_blocks],[1 1],'--r')
hold on
plot([1 n_blocks],[2 2],'--r')
ylim([0.9 2.1])
set(gca,'YTick',1:2);
set(gca,'YTickLabel',{'0','100%'});
ylabel('% high effort choice')
xlabel('blocks')
subplot(3,2,2)
plot(X_distance,population_choices_vs_distance_early_blocks,'g')
hold on
plot(X_distance,population_choices_vs_distance_late_blocks,'r')
hold on
plot([D1 D2],[1 1],'--r')
hold on
plot([D1 D2],[2 2],'--r')
xlim([D1 D2])
ylim([0.9 2.1])
set(gca, 'XDir','reverse')
set(gca,'YTick',1:2);
set(gca,'YTickLabel',{'0','100%'});
xlabel('distance')
subplot(3,2,4)
plot(X_pressure,population_choices_vs_pressure_early_blocks,'g')
hold on
plot(X_pressure,population_choices_vs_pressure_late_blocks,'r')
plot([P1 P2],[1 1],'--r')
hold on
plot([P1 P2],[2 2],'--r')
xlim([P1 P2])
ylim([0.9 2.1])
xlabel('pressure')
set(gca,'YTick',1:2);
set(gca,'YTickLabel',{'0','100%'});
ylabel('% high effort choice')
subplot(3,2,6)
plot(X_homeostasis,population_choices_vs_homeostasis_early_blocks,'g')
hold on
plot(X_homeostasis,population_choices_vs_homeostasis_late_blocks,'r')
plot([H1 H2],[1 1],'--r')
hold on
plot([H1 H2],[2 2],'--r')
xlim([H1 H2])
ylim([0.9 2.1])
xlabel('homeostasis')
set(gca,'YTick',1:2);
set(gca,'YTickLabel',{'0','100%'});
ylabel('% high effort choice')
%% choices vs trials/blocks hard easy blocks
if variable_pressure_flag
hard_blocks_sorted = (16:30); %this make sense only for population_choices_sortedbyblockpressure
easy_blocks_sorted = (1:15);
h_choice_hard_easy_blocks = figure('Name','Population choices vs trials hard easy blocks','units','normalized','outerposition',[0 0 1 1]);
subplot(3,2,1)
plot(nanmean(mean_population_choices_sortedbyblockpressure(hard_blocks_sorted,1:n_trials),1),'r')
hold on
plot(nanmean(mean_population_choices_sortedbyblockpressure(easy_blocks_sorted,1:n_trials),1),'g')
plot([1 n_trials],[1 1],'--r')
hold on
plot([1 n_trials],[2 2],'--r')
ylim([0.9 2.1])
xlim([1 n_trials])
set(gca,'YTick',1:2);
set(gca,'YTickLabel',{'0','100%'});
ylabel('% high effort choice')
xlabel('trials')
subplot(3,2,3)
plot(hard_blocks_sorted,nanmean(mean_population_choices_sortedbyblockpressure(hard_blocks_sorted,1:n_trials),2),'r')
hold on
plot(hard_blocks_sorted,nanmean(mean_population_choices_sortedbyblockpressure(hard_blocks_sorted,1:n_trials),2)+nanstd(mean_population_choices_sortedbyblockpressure(hard_blocks_sorted,1:n_trials),[],2)/sqrt(n_trials),'--r')
hold on
plot(hard_blocks_sorted,nanmean(mean_population_choices_sortedbyblockpressure(hard_blocks_sorted,1:n_trials),2)-nanstd(mean_population_choices_sortedbyblockpressure(hard_blocks_sorted,1:n_trials),[],2)/sqrt(n_trials),'--r')
hold on
subplot(3,2,3)
plot(easy_blocks_sorted,nanmean(mean_population_choices_sortedbyblockpressure(easy_blocks_sorted,1:n_trials),2),'g')
hold on
plot(easy_blocks_sorted,nanmean(mean_population_choices_sortedbyblockpressure(easy_blocks_sorted,1:n_trials),2)+nanstd(mean_population_choices_sortedbyblockpressure(easy_blocks_sorted,1:n_trials),[],2)/sqrt(n_trials),'--g')
hold on
plot(easy_blocks_sorted,nanmean(mean_population_choices_sortedbyblockpressure(easy_blocks_sorted,1:n_trials),2)-nanstd(mean_population_choices_sortedbyblockpressure(easy_blocks_sorted,1:n_trials),[],2)/sqrt(n_trials),'--g')
hold on
plot([1 n_blocks],[1 1],'--r')
hold on
plot([1 n_blocks],[2 2],'--r')
ylim([0.9 2.1])
set(gca,'YTick',1:2);
set(gca,'YTickLabel',{'0','100%'});
ylabel('% high effort choice')
xlabel('blocks')
subplot(3,2,2)
plot(X_distance,population_choices_vs_distance_hard_blocks,'r')
hold on
plot(X_distance,population_choices_vs_distance_easy_blocks,'g')
hold on
plot([D1 D2],[1 1],'--r')
hold on
plot([D1 D2],[2 2],'--r')
xlim([D1 D2])
ylim([0.9 2.1])
set(gca, 'XDir','reverse')
xlabel('distance')
subplot(3,2,4)
plot(X_pressure,population_choices_vs_pressure_hard_blocks,'r')
hold on
plot(X_pressure,population_choices_vs_pressure_easy_blocks,'g')
plot([P1 P2],[1 1],'--r')
hold on
plot([P1 P2],[2 2],'--r')
xlim([P1 P2])
ylim([0.9 2.1])
xlabel('pressure')
set(gca,'YTick',1:2);
set(gca,'YTickLabel',{'0','100%'});
subplot(3,2,6)
plot(X_homeostasis,population_choices_vs_homeostasis_hard_blocks,'r')
hold on
plot(X_homeostasis,population_choices_vs_homeostasis_easy_blocks,'g')
plot([H1 H2],[1 1],'--r')
hold on
plot([H1 H2],[2 2],'--r')
xlim([H1 H2])
ylim([0.9 2.1])
xlabel('homeostasis')
set(gca,'YTick',1:2);
set(gca,'YTickLabel',{'0','100%'});
end
%% RT vs trials/blocks
h_RT = figure('Name','Population RT vs time ','units','normalized','outerposition',[0 0 1 1]);
subplot(2,2,1)
boundedline(1:n_trials, mean(nanmean(population_RT(:,1:n_trials,:),3),1), nanstd(squeeze(nanmean(population_RT(:,1:n_trials,:),1)),[],2),'alpha','cmap',[.1 .1 .1],'transparency', trasp);
hold on
plot(1:n_trials,mean(nanmean(population_RT(:,1:n_trials,:)),3),'LineWidth',5,'Color',[0.1 0.1 0.1])
ylabel('time(s)')
xlabel('trials')
ylim([0.5 1.75])
subplot(2,2,2)
boundedline(1:n_blocks, mean(nanmean(population_RT(1:n_blocks,:,:),3),2), nanstd(squeeze(nanmean(population_RT(1:n_blocks,:,:),2)),[],2),'alpha','cmap',[.1 .1 .1],'transparency', trasp);
hold on
plot(mean(nanmean(population_RT(:,1:n_trials,:),3),2),'LineWidth',5,'Color',[0.1 0.1 0.1])
ylabel('time(s)')
xlabel('blocks')
ylim([0.5 1.75])
subplot(2,2,3)
plot(X_RT,population_choices_vs_RT,'LineWidth',5,'Color',[0.1 0.1 0.1])
hold on
boundedline(X_RT, population_choices_vs_RT, population_choices_vs_RT_std,'alpha','cmap',[.1 .1 .1],'transparency', trasp);
plot([RT1 RT2],[1 1],'--r')
hold on
plot([RT1 RT2],[2 2],'--r')
xlim([RT1 RT2])
ylim([0.5 2.5])
xlabel('RT')
ylabel('% high effort choice')
set(gca,'YTick',[1 2],'YTickLabel',[0 100]);
set(gca,'XTick',[1 2]);
subplot(2,2,4)
boundedline(X_pressure, population_RT_vs_pressure, population_RT_vs_pressure_std,'alpha','cmap',[.1 .1 .1],'transparency', trasp);
hold on
plot(X_pressure,population_RT_vs_pressure,'LineWidth',5,'Color',[0.1 0.1 0.1])
xlim([P1 P2])
ylim([0.25 1.75])
xlabel('pressure')
ylabel('RT')
h_logRT = figure('Name','Population RT vs time ','units','normalized','outerposition',[0 0 1 1]);
subplot(2,2,1)
plot(mean(nanmean(log(population_RT(:,1:n_trials,:)),3),1),'LineWidth',5,'Color',[0.1 0.1 0.1])
hold on
boundedline(1:8, mean(nanmean(log(population_RT(:,1:n_trials,:)),3),1),nanstd(squeeze(nanmean(log(population_RT(:,1:n_trials,:)),1)),[],2),'alpha','cmap',[.1 .1 .1],'transparency', trasp);
ylabel('log RT')
xlabel('trials')
subplot(2,2,2)
plot(mean(nanmean(log(population_RT(:,1:n_trials,:)),3),2),'LineWidth',5,'Color',[0.1 0.1 0.1])
hold on
boundedline(1:n_blocks, mean(nanmean(log(population_RT(1:n_blocks,:,:)),3),2), nanstd(squeeze(mean(log(population_RT(1:n_blocks,:,:)),2)),[],2),'alpha','cmap',[.1 .1 .1],'transparency', trasp);
subplot(2,2,3)
plot(X_log_RT,population_choices_vs_log_RT,'LineWidth',5,'Color',[0.1 0.1 0.1])
hold on
boundedline(X_log_RT, population_choices_vs_log_RT, population_choices_vs_log_RT_std,'alpha','cmap',[.1 .1 .1],'transparency', trasp);
plot([logRT1 logRT2],[1 1],'--r')
hold on
plot([logRT1 logRT2],[2 2],'--r')
xlim([logRT1 logRT2])
ylim([0.5 2.5])
xlabel('log RT')
ylabel('% high effort choice')
set(gca,'YTick',[1 2],'YTickLabel',[0 100]);
subplot(2,2,4)
boundedline(X_pressure, population_log_RT_vs_pressure, population_log_RT_vs_pressure_std,'alpha','cmap',[.1 .1 .1],'transparency', trasp);
hold on
plot(X_pressure,population_log_RT_vs_pressure,'LineWidth',5,'Color',[0.1 0.1 0.1])
xlim([P1 P2])
xlabel('pressure')
ylabel('log RT')
%% Trials goal reach vs fatigue/blocks
T1 = prctile(population_tiredness(:),10);
T2 = prctile(population_tiredness(:),90);
X_tiredness = [T1:abs(T2-T1)/10:T2];
[N_tiredness,X_tiredness] = hist(population_tiredness(:),X_tiredness); %10 bins between the 10th and 90th perctile of population_pressure
for i_tiredness = 1:length(X_tiredness)
if i_tiredness==1
indeces_this_tiredness{i_tiredness} = find(population_tiredness(:)<X_tiredness(i_tiredness));
elseif i_tiredness<length(X_tiredness)
indeces_this_tiredness{i_tiredness} = find((population_tiredness(:)<X_tiredness(i_tiredness+1))&population_tiredness(:)>X_tiredness(i_tiredness));
else
indeces_this_tiredness{i_tiredness} = find(population_tiredness(:)>X_tiredness(i_tiredness));
end
indeces_this_tiredness_early_blocks{i_tiredness} = intersect(indeces_this_tiredness{i_tiredness},early_blocks_indeces);
indeces_this_tiredness_late_blocks{i_tiredness} = intersect(indeces_this_tiredness{i_tiredness},late_blocks_indeces);
population_goals_trials_vs_tiredness(i_tiredness)= nanmean(population_goals_trials(indeces_this_tiredness{i_tiredness}));
population_goals_trials_vs_tiredness_se(i_tiredness) = nanstd(population_goals_trials(indeces_this_tiredness{i_tiredness}))/sqrt(length(indeces_this_tiredness{i_tiredness})/30);
population_goals_trials_vs_tiredness_early_blocks(i_tiredness)= nanmean(population_goals_trials(indeces_this_tiredness_early_blocks{i_tiredness})/30);
population_goals_trials_vs_tiredness_late_blocks(i_tiredness)= nanmean(population_goals_trials(indeces_this_tiredness_late_blocks{i_tiredness})/30);
end
h_fatigue = figure('Name','Population Goal reach vs fatigue ');
subplot(2,1,1)
boundedline(X_tiredness,population_goals_trials_vs_tiredness, population_goals_trials_vs_tiredness_se,'alpha','cmap',[.5 .5 .5],'transparency', trasp);
hold on
plot(X_tiredness,population_goals_trials_vs_tiredness,'LineWidth',5,'Color',[0.1 0.1 0.1])
xlabel('fatigue')
ylabel('trial')
subplot(2,1,2)
boundedline(1:n_blocks,nanmean(population_goals_trials,2), nanstd(population_goals_trials,[],2),'alpha','cmap',[.5 .5 .5],'transparency', trasp);
hold on
plot(1:n_blocks,nanmean(population_goals_trials,2),'LineWidth',5,'Color',[0.1 0.1 0.1])
xlabel('blocks')
xlim([1 n_blocks])
%% vigor vs pressure
h_vigor = figure('Name','Population Vigor vs pressure ','units','normalized','outerposition',[0 0 1 1]);
indEFF{1} = find(population_effort(:)==0.2 & population_reward(:)>0);
indEFF{2} = find(population_effort(:)==0.35 & population_reward(:)>0);
indEFF{3} = find(population_effort(:)==0.5 & population_reward(:)>0);
indEFF{4} = find(population_effort(:)==0.7 & population_reward(:)>0);
for i_eff = 1:4
subplot(3,4,i_eff)
title(sprintf('EFF %d',i_eff));
axis off
subplot(3,4,4+i_eff)
plot(population_pressure(indEFF{i_eff}),population_vigor(indEFF{i_eff} ),'.');
plot_correlation2(population_pressure(indEFF{i_eff}),population_vigor(indEFF{i_eff}),['pressure vs mean vigor'],1);
hold on
plot([min(population_pressure(indEFF{i_eff})) max(population_pressure(indEFF{i_eff}))],[nanmean(population_vigor(indEFF{i_eff})) nanmean(population_vigor(indEFF{i_eff}))],'--r')
ylabel('vigor')
ylim([0 1.5])
subplot(3,4,8+i_eff)
plot(population_pressure(indEFF{i_eff}),population_max_vigor(indEFF{i_eff}),'.');
plot_correlation2(population_pressure(indEFF{i_eff}),population_max_vigor(indEFF{i_eff}),['pressure vs max vigor'],1);
hold on
plot([min(population_pressure(indEFF{i_eff})) max(population_pressure(indEFF{i_eff}))],[nanmean(population_max_vigor(indEFF{i_eff})) nanmean(population_max_vigor(indEFF{i_eff}))],'--r')
ylabel('max vigor')
xlabel('pressure')
ylim([0 1.5])
end
h_vigor2 = figure('Name','Population Vigor vs fatigue ','units','normalized','outerposition',[0 0 1 1]);
blocks_vigor = squeeze(mean(population_vigor,2));
blocks_max_vigor = squeeze(mean(population_max_vigor,2));
subplot(1,2,1)
plot(population_tiredness(:),blocks_vigor(:),'.');
plot_correlation2(population_tiredness(:),blocks_vigor(:),['fatigue vs vigor'],1);
subplot(1,2,2)
plot(population_tiredness(:),blocks_max_vigor(:),'.');
plot_correlation2(population_tiredness(:),blocks_max_vigor(:),['fatigue vs max vigor'],1);
xlabel('fatigue')
subject_vigor = nanmean(blocks_vigor,1);
subject_max_vigor = nanmean(blocks_max_vigor,1);
%% individual subjects
h0 = figure('Name','Population choices vs trial _ind subjects ','units','normalized','outerposition',[0 0 1 1]);
h00 = figure('Name','Population choices vs trial early-late blocks ','units','normalized','outerposition',[0 0 1 1]);
h1 = figure('Name','Population choices vs blocks _ind subjects ','units','normalized','outerposition',[0 0 1 1]);
h0b = figure('Name','Population RT vs trial _ind subjects ','units','normalized','outerposition',[0 0 1 1]);
h1b = figure('Name','Population RT vs blocks _ind subjects ','units','normalized','outerposition',[0 0 1 1]);
h2 = figure('Name','Population tiredness _ind subjects ','units','normalized','outerposition',[0 0 1 1]);
h3 = figure('Name','Population distance-pressure ind subjects ','units','normalized','outerposition',[0 0 1 1]);
h3_2 = figure('Name','Population pressure ind blocks ','units','normalized','outerposition',[0 0 1 1]);
h3_3 = figure('Name','Population distance ind blocks ','units','normalized','outerposition',[0 0 1 1]);
if variable_pressure_flag
h3_4 = figure('Name','Population pressure ind blocks highlowpress','units','normalized','outerposition',[0 0 1 1]);
h3_5 = figure('Name','Population distance ind blocks highlowpress','units','normalized','outerposition',[0 0 1 1]);
end
population_pressure_highlow = NaN(n_trials,1000,2);
index_low = 0;
index_high = 0;
for i_sub = 1:n_good_sub
figure(h0)
subplot(nrow,ncol,i_sub)
try %to deal with subjects with all NaN
boundedline(1:n_trials,nanmean(population_choices(:,1:n_trials,i_sub),1), nanstd(population_choices(:,1:n_trials,i_sub),[],1)/sqrt(n_blocks),'alpha','cmap',[.5 .5 .5],'transparency', trasp);
catch %to deal with subjects with all NaN
boundedline(1:n_trials,zeros(1,8), zeros(1,8),'alpha','cmap',[.5 .5 .5],'transparency', trasp);
end
% boundedline(1:n_trials,nanmean(mean_population_choices,1), mean(std_population_choices(:,1:n_trials),1),'alpha','cmap',[.5 .5 .5],'transparency', trasp);
hold on
plot(1:n_trials,nanmean(population_choices(:,1:n_trials,i_sub),1),'LineWidth',5,'Color',[0.1 0.1 0.1])
hold on
plot([1 n_trials],[1 1],'--r')
hold on
plot([1 n_trials],[2 2],'--r')
ylim([0.9 2.1])
set(gca,'YTick',1:2);
set(gca,'YTickLabel',{'0','100%'});
xlabel('trials')
figure(h00)
subplot(nrow,ncol,i_sub)
plot(nanmean(population_choices(early_blocks,1:n_trials,i_sub),1),'g')
hold on
plot(nanmean(population_choices(late_blocks,1:n_trials,i_sub),1),'r')
hold on
plot([1 n_trials],[1 1],'--r')
hold on
plot([1 n_trials],[2 2],'--r')
ylim([0.9 2.1])
xlabel('trials')
figure(h1)
subplot(nrow,ncol,i_sub)
% boundedline(1:n_blocks,nanmean(population_choices(:,1:n_trials,i_sub),2), nanstd(population_choices(:,1:n_trials,i_sub),[],2)/sqrt(n_trials),'alpha','cmap',[.5 .5 .5],'transparency', trasp);
% hold on
% plot(1:n_blocks,nanmean(population_choices(:,1:n_trials,i_sub),2),'LineWidth',5,'Color',[0.1 0.1 0.1])
plot(nanmean(population_choices(:,1:n_trials,i_sub),2))
hold on
plot(nanmean(population_choices(:,1:n_trials,i_sub),2)+nanstd(population_choices(:,1:n_trials,i_sub),[],2)/sqrt(n_trials),'--b')
hold on
plot(nanmean(population_choices(:,1:n_trials,i_sub),2)-nanstd(population_choices(:,1:n_trials,i_sub),[],2)/sqrt(n_trials),'--b')
hold on
plot([1 n_blocks],[1 1],'--r')
hold on
plot([1 n_blocks],[2 2],'--r')
ylim([0.9 2.1])
set(gca,'YTick',1:2);
set(gca,'YTickLabel',{'0','100%'});
xlabel('blocks')
figure(h0b)
subplot(nrow,ncol,i_sub)
plot(nanmean(population_RT(:,1:n_trials,i_sub),1))
hold on
plot(nanmean(population_RT(:,1:n_trials,i_sub),1)+nanstd(population_RT(:,1:n_trials,i_sub),[],1)/sqrt(n_blocks),'--b')
hold on
plot(nanmean(population_RT(:,1:n_trials,i_sub),1)-nanstd(population_RT(:,1:n_trials,i_sub),[],1)/sqrt(n_blocks),'--b')
ylim([0 3])
xlabel('trials')
figure(h1b)
subplot(nrow,ncol,i_sub)
plot(nanmean(population_RT(:,1:n_trials,i_sub),2))
hold on
plot(nanmean(population_RT(:,1:n_trials,i_sub),2)+nanstd(population_RT(:,1:n_trials,i_sub),[],2)/sqrt(n_trials),'--b')
hold on
plot(nanmean(population_RT(:,1:n_trials,i_sub),2)-nanstd(population_RT(:,1:n_trials,i_sub),[],2)/sqrt(n_trials),'--b')
ylim([0 3])
xlabel('blocks')
figure(h2)
subplot(nrow,ncol,i_sub)
plot(population_tiredness(:,i_sub))
ylim([0 100])
xlabel('blocks')
figure(h3)
subplot(2,3,1)
hold on
plot(nanmean(1-population_advancement(:,1:n_trials,i_sub),1))
hold on
title('distance')
xlim([1 n_trials])
ylim([0 1])
xlabel('trials')
subplot(2,3,4)
sub_advancement = population_advancement(:,:,i_sub);
hist(1-sub_advancement(:),20)
subplot(2,3,2)
hold on
plot(nanmean(population_pressure(:,1:n_trials,i_sub),1))
hold on
% plot([1 n_trials],[max_pressure(1) max_pressure(1)],'--r')
title('pressure')
xlim([1 n_trials])
xlabel('trials')
subplot(2,3,5)
sub_pressure = population_pressure(:,:,i_sub);
hist(sub_pressure(:),20)
subplot(2,3,3)
plot(nanmean(population_reward_advancement_homeostasis(:,1:n_trials,i_sub),1))
hold on
% plot([1 n_trials],[max_pressure(1) max_pressure(1)],'--r')
title('homeostasis')
xlim([1 n_trials])
xlabel('trials')
subplot(2,3,6)
sub_reward_advancement_homeostasis = population_reward_advancement_homeostasis(:,:,i_sub);
hist(sub_reward_advancement_homeostasis(:),20)
figure(h3_2)
subplot(nrow,ncol,i_sub)
for i_block = 1:n_blocks
hold on
plot((1:n_trials),population_pressure(i_block,:,i_sub),'r')
xlim([1 n_trials])
end
figure(h3_3)
subplot(nrow,ncol,i_sub)
for i_block = 1:n_blocks
hold on
plot((1:n_trials),1-population_advancement(i_block,:,i_sub),'g')
xlim([1 n_trials])
ylim([-0.1 1])
end
if variable_pressure_flag
figure(h3_4)
subplot(nrow,ncol,i_sub)
for i_block = 1:n_blocks
hold on
if population_block_pressure(i_block,i_sub) == 1 %high pressure block
plot((1:n_trials),population_pressure(i_block,:,i_sub),'r')
index_high = index_high+1;
population_pressure_highlow(:,index_high,1) = population_pressure(i_block,:,i_sub);
else %low pressure block
plot((1:n_trials),population_pressure(i_block,:,i_sub),'y')
index_low = index_low+1;
population_pressure_highlow(:,index_low,2) = population_pressure(i_block,:,i_sub);
end
xlim([1 n_trials])
ylim([-0.5 0.5])
end
figure(h3_5)
subplot(nrow,ncol,i_sub)
for i_block = 1:n_blocks
hold on
if population_block_pressure(i_block,i_sub) == 1 %high pressure block
plot((1:n_trials),1-population_advancement(i_block,:,i_sub),'g')
else %low pressure block
plot((1:n_trials),1-population_advancement(i_block,:,i_sub),'b')
end
xlim([1 n_trials])
ylim([-0.1 1])
end
end
end
if variable_pressure_flag
figure(h3_4)
subplot(nrow,ncol,i_sub+1)
title('population average')
plot((1:n_trials),nanmean(population_pressure_highlow(:,:,1),2),'r')
hold on
plot((1:n_trials),nanmean(population_pressure_highlow(:,:,2),2),'y')
ylim([-0.5 0.5])
end
%% choice vs DR/DE
h_choice_delta = figure('Name','choice vs DR/DE','units','normalized','outerposition',[0 0 1 1]);
population_choices_drew = zeros(length(X_rew),1);
population_choices_drew_se = zeros(length(X_rew),1);
subplot(2,1,1)
for i_drew = 1:length(X_rew)
population_choices_drew(i_drew) = nanmean(population_choices(population_drew(:)==X_rew(i_drew)));
population_choices_drew_se(i_drew)= nanstd(population_choices(population_drew(:)==X_rew(i_drew)))/sqrt(sum(population_drew(:)==X_rew(i_drew))/30);
end
boundedline(X_rew,population_choices_drew,population_choices_drew_se,'alpha','cmap',[.5 .5 .5],'transparency', trasp);
hold on
plot(X_rew,population_choices_drew,'LineWidth',5,'Color',[0.1 0.1 0.1])
xlabel('delta rew')
ylabel('choice')
ylim([1 2])
ylim([0.9 2.1])
hold on
plot([min(X_rew) max(X_rew)],[1 1],'--r')
hold on
plot([min(X_rew) max(X_rew)],[2 2],'--r')
set(gca,'YTick',1:2);
set(gca,'YTickLabel',{'0','100%'});
ylabel('% high effort choice')
subplot(2,1,2)
population_choices_deff = zeros(length(X_eff),1);
population_choices_deff_se = zeros(length(X_eff),1);
for i_deff = 1:length(X_eff)
population_choices_deff(i_deff) = nanmean(population_choices(population_deff(:)==X_eff(i_deff)));
population_choices_deff_se(i_deff)= nanstd(population_choices(population_deff(:)==X_eff(i_deff)))/sqrt(sum(population_deff(:)==X_eff(i_deff))/30);
end
boundedline(X_eff,population_choices_deff,population_choices_deff_se,'alpha','cmap',[.5 .5 .5],'transparency', trasp);
hold on
plot(X_eff,population_choices_deff,'LineWidth',5,'Color',[0.1 0.1 0.1])
xlabel('delta eff')
ylabel('choice')
ylim([1 2])
ylim([0.9 2.1])
hold on
plot([min(X_eff) max(X_eff)],[1 1],'--r')
hold on
plot([min(X_eff) max(X_eff)],[2 2],'--r')
set(gca,'YTick',1:2);
set(gca,'YTickLabel',{'0','100%'});
ylabel('% high effort choice')
%% RT vs DR/DE
h_RT_delta = figure('Name','RT vs DR/DE','units','normalized','outerposition',[0 0 1 1]);
subplot(2,1,1)
for i_drew = 1:length(X_rew)
errorbar(X_rew(i_drew),mean(population_RT(population_drew(:)==X_rew(i_drew))),std(population_RT(population_drew(:)==X_rew(i_drew)))/sqrt(length(population_RT(:))),'ok')
hold on
end
xlabel('delta rew')
ylabel('RT')
ylim([1 2])
subplot(2,1,2)
for i_deff = 1:length(X_eff)
errorbar(X_eff(i_deff),mean(population_RT(population_deff(:)==X_eff(i_deff))),std(population_RT(population_deff(:)==X_eff(i_deff)))/sqrt(length(population_RT(:))),'ok')
hold on
end
xlabel('delta eff')
ylabel('RT')
ylim([1 2])
%% first trials analysis
subjects_first_trials_choices = squeeze(nanmean(population_choices(:,1,:),1)); %1 x n_subj
subjects_goals = nanmean(population_goals,1); %1/0 whether or not goal was reached
subjects_goals_trials = nanmean(population_goals_trials,1); %number of the trial in which goal reached
subjects_reward_tot = squeeze(nanmean(population_reward_tot(:,end,:),1)); %1 x n_subj
subjects_RT = nanmean(squeeze(nanmean(population_RT,1)),1); %1 x n_subj
subjects_advancement = squeeze(nanmean(population_advancement(:,end,:),1));
subjects_reward_advancement_homeostasis = nanmean(squeeze(nanmean(population_reward_advancement_homeostasis,1)),1);
subjects_pressure = nanmean(squeeze(nanmean(population_pressure,1)),1);
h_first_trials = figure('Name','First trials analysis','units','normalized','outerposition',[0 0 1 1]);
subplot(2,4,1)
plot(subjects_first_trials_choices,subjects_goals,'o')
plot_correlation2(subjects_first_trials_choices,subjects_goals,'fit',1);
axis square
ylabel('fraction goals reached')
xlabel('first trials choices')
set(gca,'XTick',1:2);
set(gca,'XTickLabel',{'0','100%'});
xlim([0.9 2.1])
xlabel('% high effort choice on first trial')
subplot(2,4,2)
plot(subjects_first_trials_choices,subjects_goals_trials,'o')
plot_correlation2(subjects_first_trials_choices,subjects_goals_trials,'fit',1);
axis square
ylabel('avg trial when goal reached')
xlabel('first trials choices')
set(gca,'XTick',1:2);
set(gca,'XTickLabel',{'0','100%'});
xlim([0.9 2.1])
xlabel('% high effort choice on first trial')
subplot(2,4,3)
plot(subjects_first_trials_choices,subjects_reward_tot,'o')
plot_correlation2(subjects_first_trials_choices,subjects_reward_tot,'fit',1);
ylabel('avg reward')
xlabel('first trials choices')
axis square
set(gca,'XTick',1:2);
set(gca,'XTickLabel',{'0','100%'});
xlim([0.9 2.1])
xlabel('% high effort choice on first trial')
subplot(2,4,4)
plot(subjects_first_trials_choices,subjects_pressure,'o')
plot_correlation2(subjects_first_trials_choices,subjects_pressure,'fit',1);
ylabel('avg pressure')
xlabel('first trials choices')
axis square
set(gca,'XTick',1:2);
set(gca,'XTickLabel',{'0','100%'});
xlim([0.9 2.1])
xlabel('% high effort choice on first trial')
subplot(2,4,5)
plot(subjects_first_trials_choices,subjects_RT,'o')
ylabel('avg RT')
axis square
set(gca,'XTick',1:2);
set(gca,'XTickLabel',{'0','100%'});
xlim([0.9 2.1])
xlabel('% high effort choice on first trial')
subplot(2,4,6)
plot(subjects_first_trials_choices,subjects_reward_advancement_homeostasis,'o')
ylabel('avg homeostasis')
xlabel('first trials choices')
axis square
set(gca,'XTick',1:2);
set(gca,'XTickLabel',{'0','100%'});
xlim([0.9 2.1])
xlabel('% high effort choice on first trial')
subplot(2,4,7)
plot(subjects_first_trials_choices,subjects_advancement,'o')
ylabel('avg advancement')
xlabel('first trials choices')
axis square
set(gca,'XTick',1:2);
set(gca,'XTickLabel',{'0','100%'});
xlim([0.9 2.1])
xlabel('% high effort choice on first trial')
%%
% figure('Name',['RT left/right '])
% % plot([1 2],[population_RT(population_choices==1) population_RT(population_choices==2)],'o');
% % hold on
% errorbar([1 2],[mean(population_RT(population_choices==1)) mean(population_RT(population_choices==2))],[std(population_RT(population_choices==1)) std(population_RT(population_choices==2))],'s');
% title('Reaction Times')
% % ylabel('time(s)')
% h_RT_lr = figure('Name','RT left/right _ind subjects ');
% for i_sub = 1:n_good_sub
% figure(h_RT_lr)
% subplot(nrow,ncol,i_sub)
% errorbar([1 2],[mean(population_RT(population_choices(:,:,i_sub)==1)) mean(population_RT(population_choices(:,:,i_sub)==2))],[std(population_RT(population_choices(:,:,i_sub)==1)) std(population_RT(population_choices(:,:,i_sub)==2))],'s');
% ylabel('time(s)')
% xlim([0.5 2.5])
%
% end
%% save pictures
threshold_tag = sprintf('t %d',threshold);
if skip_bad_trial
trials_tag = ' good trials';
else
trials_tag = ' all trials';
end
saveas(h_pop0,[h_pop0_name threshold_tag trials_tag],'png');
saveas(h_pop0,[h_pop0_name threshold_tag trials_tag],'svg');
saveas(h_pop0_sim,[h_pop0_sim_name threshold_tag trials_tag],'png');
saveas(h_pop0_sim,[h_pop0_sim_name threshold_tag trials_tag],'svg');
saveas(h_pressure_distance,['Population pressure distance distribution ' threshold_tag trials_tag],'png');
saveas(h_pressure_distance,['Population pressure distance distribution ' threshold_tag trials_tag],'svg');
saveas(h_pressure_homeostasis_distance,['Population pressure homeostasis distance scatterplot ' threshold_tag trials_tag],'png');
saveas(h_pressure_homeostasis_distance,['Population pressure homeostasis distance scatterplot ' threshold_tag trials_tag],'svg');
saveas(h_fatigue,['Population Goal reach vs fatigue ' threshold_tag trials_tag],'png');
saveas(h_fatigue,['Population Goal reach vs fatigue ' threshold_tag trials_tag],'svg');
saveas(h3,['Population pressure distance distribution _ind subjects' threshold_tag trials_tag],'png');
saveas(h3,['Population pressure distance distribution _ind subjects' threshold_tag trials_tag],'svg');
saveas(h3_2,['Population pressure ind blocks ' threshold_tag trials_tag],'png');
saveas(h3_2,['Population pressure ind blocks ' threshold_tag trials_tag],'svg');
saveas(h3_3,['Population distance ind blocks ' threshold_tag trials_tag],'png');
saveas(h3_3,['Population distance ind blocks ' threshold_tag trials_tag],'svg');
saveas(h_choice_reward_effort,['Population choices rewards efforts ' threshold_tag trials_tag],'png');
saveas(h_choice_reward_effort,['Population choices rewards efforts ' threshold_tag trials_tag],'svg');
saveas(h_choice_reward_effort2,['Population average reward-effort ' threshold_tag trials_tag],'png');
saveas(h_choice_reward_effort2,['Population average reward-effort ' threshold_tag trials_tag],'svg');
saveas(h_choice,['Population choices vs trials_blocks ' threshold_tag trials_tag],'png');
saveas(h_choice,['Population choices vs trials_blocks ' threshold_tag trials_tag],'svg');
saveas(h_choice_early_late_blocks,['Population choices vs trials early-late blocks ' threshold_tag trials_tag],'png');
saveas(h_choice_early_late_blocks,['Population choices vs trials early-late blocks ' threshold_tag trials_tag],'svg');
saveas(h0,['Population choices vs trials _ind subjects ' threshold_tag trials_tag],'png');
saveas(h0,['Population choices vs trials _ind subjects ' threshold_tag trials_tag],'svg');
saveas(h00,['Population choices vs trials _ind subjects early-late blocks ' threshold_tag trials_tag],'png');
saveas(h00,['Population choices vs trials _ind subjects early-late blocks ' threshold_tag trials_tag],'svg');
saveas(h1,['Population choices vs blocks _ind subjects ' threshold_tag trials_tag],'png');
saveas(h1,['Population choices vs blocks _ind subjects ' threshold_tag trials_tag],'svg');
saveas(h0b,['Population RT vs trial _ind subjects ' threshold_tag trials_tag],'png');
saveas(h0b,['Population RT vs trial _ind subjects ' threshold_tag trials_tag],'svg');
saveas(h1b,['Population RT vs blocks _ind subjects ' threshold_tag trials_tag],'png');
saveas(h1b,['Population RT vs blocks _ind subjects ' threshold_tag trials_tag],'svg');
saveas(h2,['Population tiredness _ind subjects ' threshold_tag trials_tag],'png');
saveas(h2,['Population tiredness _ind subjects ' threshold_tag trials_tag],'svg');
saveas(h_RT,['Population RT vs trials_blocks ' threshold_tag trials_tag],'png');
saveas(h_RT,['Population RT vs trials_blocks ' threshold_tag trials_tag],'svg');
saveas(h_logRT,['Population log RT vs trials_blocks ' threshold_tag trials_tag],'png');
saveas(h_logRT,['Population log RT vs trials_blocks ' threshold_tag trials_tag],'svg');
saveas(h_choice_delta,['Population choices vs delta reward ' threshold_tag trials_tag],'png');
saveas(h_choice_delta,['Population choices vs delta reward ' threshold_tag trials_tag],'svg');
saveas(h_RT_delta,['Population RT vs delta effort ' threshold_tag trials_tag],'png');
saveas(h_RT_delta,['Population RT vs delta effort ' threshold_tag trials_tag],'svg');
saveas(h_goal,['Population Goal Reward ' threshold_tag trials_tag],'png');
saveas(h_goal,['Population Goal Reward ' threshold_tag trials_tag],'svg');
saveas(h_first_trials,['First trials analysis' threshold_tag trials_tag],'png');
saveas(h_first_trials,['First trials analysis' threshold_tag trials_tag],'svg');
saveas(h_vigor,['Vigor pressure analysis' threshold_tag trials_tag],'png');
saveas(h_vigor,['Vigor pressure analysis' threshold_tag trials_tag],'svg');
saveas(h_vigor2,['Vigor fatigue analysis' threshold_tag trials_tag],'png');
saveas(h_vigor2,['Vigor fatigue analysis' threshold_tag trials_tag],'svg');
if variable_pressure_flag
saveas(h_choice_hard_easy_blocks,['Population choices vs trials hard easy blocks' threshold_tag trials_tag],'png');
saveas(h3_4,['Population pressure ind blocks highlow press' threshold_tag trials_tag],'png');
saveas(h3_5,['Population distance ind blocks highlow press' threshold_tag trials_tag],'png');
saveas(h_choice_hard_easy_blocks,['Population choices vs trials hard easy blocks' threshold_tag trials_tag],'svg');
saveas(h3_4,['Population pressure ind blocks highlow press' threshold_tag trials_tag],'svg');
saveas(h3_5,['Population distance ind blocks highlow press' threshold_tag trials_tag],'svg');
hhh = figure('Name','Trials distribution','units','normalized','outerposition',[0 0 1 1]);
for i_sub = 1:n_good_sub
ind_high_press = find(population_block_pressure(:,i_sub)==1);
ind_low_press = find(population_block_pressure(:,i_sub)==2);
subplot(2,n_good_sub,i_sub)
this_sub_trials = population_step_factor(ind_high_press,:,i_sub);
hist(this_sub_trials(:),2);
if i_sub ==1
ylabel('high pressure blocks')
end
subplot(2,n_good_sub,n_good_sub+i_sub)
this_sub_trials = population_step_factor(ind_low_press,:,i_sub);
hist(this_sub_trials(:),2);
if i_sub ==1
ylabel('low pressure blocks')
end
end
saveas(hhh,['Population distribution of easy hard trials highlow press' threshold_tag trials_tag],'png');
saveas(hhh,['Population distribution of easy hard trials highlow press' threshold_tag trials_tag],'svg');
end
% saveas(h_RT_lr,'Population RT left-right choices ','png');
%% print stats
fprintf('avg pop goals is %f +- %f\n',nanmean(population_goals(:)),nanstd(population_goals(:))/sqrt(30*30))
fprintf('avg pop reward is %f +- %f\n',nanmean(sum(squeeze(nansum(population_reward,2)),1)),nanstd(sum(squeeze(nansum(population_reward,2)),1))/sqrt(30))
choices_first_half = population_choices(:,1:4,:);
choices_second_half = population_choices(:,5:8,:);
choices_first_half_sub = squeeze(nanmean(nanmean(choices_first_half,2),1));
choices_second_half_sub = squeeze(nanmean(nanmean(choices_second_half,2),1));
[H,P,CI,STATS] = ttest(choices_first_half_sub-choices_second_half_sub);
fprintf('More high effort choices in the first half %d p = %.4f t = %.4f \n',H,P,STATS.tstat)
effort_first_half = population_effort(:,1:4,:);
effort_second_half = population_effort(:,5:8,:);
effort_first_half_sub = squeeze(nanmean(nanmean(effort_first_half,2),1));
effort_second_half_sub = squeeze(nanmean(nanmean(effort_second_half,2),1));
[H,P,CI,STATS] = ttest(effort_first_half_sub-effort_second_half_sub);
fprintf('More effort in the first half %d p = %.4f t = %.4f \n',H,P,STATS.tstat)
choices_low_pressure = population_choices_sortedbyblockpressure(1:15,:,:);
choices_high_pressure = population_choices_sortedbyblockpressure(16:30,:,:);
choices_low_pressure_sub = squeeze(nanmean(nanmean(choices_low_pressure,2),1));
choices_high_pressure_sub = squeeze(nanmean(nanmean(choices_high_pressure,2),1));
[H,P,CI,STATS] = ttest(choices_high_pressure_sub-choices_low_pressure_sub);
fprintf('More high effort choices in high pressure %d p = %.4f t = %.4f \n',H,P,STATS.tstat)
fatigue_low_pressure = population_delta_tiredness_sortedbyblockpressure(1:15,:,:);
fatigue_high_pressure = population_delta_tiredness_sortedbyblockpressure(16:30,:,:);
fatigue_low_pressure_sub = nanmean(fatigue_low_pressure,2);
fatigue_high_pressure_sub = nanmean(fatigue_high_pressure,2);
[H,P,CI,STATS] = ttest(fatigue_high_pressure_sub-fatigue_low_pressure_sub);
fprintf('Does fatigue increase more in high pressure blocks? %d p = %.4f t = %.4f \n',H,P,STATS.tstat)
population_choice_post_pressure_peplus = NaN(n_blocks,n_trials,n_good_sub);
population_choice_post_pressure_peminus = NaN(n_blocks,n_trials,n_good_sub);
for i_sub=1:n_good_sub
for i_block =1:n_blocks
for i_trial =2:n_trials
if population_step_factor(i_block,i_trial-1,i_sub)==1.25
population_choice_post_pressure_peplus(i_block,i_trial,i_sub) = population_choices(i_block,i_trial,i_sub);
else
population_choice_post_pressure_peminus(i_block,i_trial,i_sub) = population_choices(i_block,i_trial,i_sub);
end
end
end
end
choice_post_pressure_peplus = squeeze(nanmean(nanmean(population_choice_post_pressure_peplus,1),2));
choice_post_pressure_peminus = squeeze(nanmean(nanmean(population_choice_post_pressure_peminus,1),2));
[H,P,CI,STATS] = ttest(choice_post_pressure_peplus(:)-choice_post_pressure_peminus(:));
fprintf('Are high effort choices more likely following a positive pressure PE? %d p = %.4f t = %.4f \n',H,P,STATS.tstat)
%% logistic regression all trials
[mdl,B,p] = log_regress(population_choices(:)-1,population_pressure(:),population_rew1(:),population_rew2(:),population_eff1(:),population_eff2(:));
h_regr = figure('Name','Choice logistic regression','units','normalized','outerposition',[0 0 1 1]);
bar(B)
hold on
errorbar((1:6),table2array(mdl.Coefficients(1:6,1)),table2array(mdl.Coefficients(1:6,2)),'.')
set(gca,'XTick',1:6);
set(gca,'XTickLabel',{'','Pressure','R1','R2','E1','E2'});
ylabel('Likelihood of high effort choice')
title('Predicting choices')
saveas(h_regr,['Choice logistic regression ' threshold_tag trials_tag],'png');
saveas(h_regr,['Choice logistic regression ' threshold_tag trials_tag],'svg');
mdl
regression_matrix.choices = population_choices(:)-1;
regression_matrix.pressure = population_pressure(:);
regression_matrix.rew1 = population_rew1(:);
regression_matrix.rew2 = population_rew2(:);
regression_matrix.eff1 = population_eff1(:);
regression_matrix.eff2 = population_eff2(:);
save regression_matrix regression_matrix
%% HIGH LOW PRESSURE BLOCKS
% HIGH PRESSURE BLOCKS
[mdl_hp,B_hp,p] = log_regress(population_choices(hard_blocks_indeces)'-1,population_pressure(hard_blocks_indeces)',population_rew1(hard_blocks_indeces)',population_rew2(hard_blocks_indeces)',population_eff1(hard_blocks_indeces)',population_eff2(hard_blocks_indeces)');
h_regr_hp = figure('Name','Choice logistic regression','units','normalized','outerposition',[0 0 1 1]);
bar(B_hp)
hold on
errorbar((1:6),table2array(mdl_hp.Coefficients(:,1)),table2array(mdl_hp.Coefficients(:,2)),'.')
set(gca,'XTick',1:6);
set(gca,'XTickLabel',{'','Pressure','R1','R2','E1','E2'});
ylabel('Likelihood of high effort choice')
title('Predicting choices in high pressure blocks')
saveas(h_regr_hp,['Choice logistic regression hp blocks ' threshold_tag trials_tag],'png');
saveas(h_regr_hp,['Choice logistic regression hp blocks ' threshold_tag trials_tag],'svg');
% LOW PRESSURE BLOCKS
[mdl_lp,B_lp,p] = log_regress(population_choices(easy_blocks_indeces)'-1,population_pressure(easy_blocks_indeces)',population_rew1(easy_blocks_indeces)',population_rew2(easy_blocks_indeces)',population_eff1(easy_blocks_indeces)',population_eff2(easy_blocks_indeces)');
h_regr_lp = figure('Name','Choice logistic regression','units','normalized','outerposition',[0 0 1 1]);
bar(B_lp)
hold on
errorbar((1:6),table2array(mdl_lp.Coefficients(:,1)),table2array(mdl_lp.Coefficients(:,2)),'.')
set(gca,'XTick',1:6);
set(gca,'XTickLabel',{'','Pressure','R1','R2','E1','E2'});
ylabel('Likelihood of high effort choice in high pressure blocks')
title('Predicting choices in low pressure blocks')
saveas(h_regr_lp,['Choice logistic regression lp blocks ' threshold_tag trials_tag],'png');
saveas(h_regr_lp,['Choice logistic regression lp blocks ' threshold_tag trials_tag],'svg');
t_statistics = (B_hp(6)-B_lp(6))/(sqrt(table2array(mdl_hp.Coefficients(6,2)).^2+table2array(mdl_lp.Coefficients(6,2)).^2));
fprintf('a higher effect of effort on the higher effort option in hard vs easy blocks Diff = %f t = %.4f \n',B_hp(6)-B_lp(6),t_statistics)
%% HIGH LOW PRESSURE TRIALS
median_pr = prctile(population_pressure(:),50);
hp_trials_indeces = find(population_pressure(:)>median_pr);
lp_trials_indeces = find(population_pressure(:)<=median_pr);
% HIGH PRESSURE TRIALS
[mdl_hpt,B_hpt,p] = log_regress(population_choices(hp_trials_indeces)-1,population_pressure(hp_trials_indeces),population_rew1(hp_trials_indeces),population_rew2(hp_trials_indeces),population_eff1(hp_trials_indeces),population_eff2(hp_trials_indeces));
h_regr_hpt = figure('Name','Choice logistic regression','units','normalized','outerposition',[0 0 1 1]);
bar(B_hpt)
hold on
errorbar((1:6),table2array(mdl_hpt.Coefficients(:,1)),table2array(mdl_hpt.Coefficients(:,2)),'.')
set(gca,'XTick',1:6);
set(gca,'XTickLabel',{'','Pressure','R1','R2','E1','E2'});
ylabel('Likelihood of high effort choice')
title('Predicting choices in high pressure trials')
saveas(h_regr_hpt,['Choice logistic regression hp trials ' threshold_tag trials_tag],'png');
saveas(h_regr_hpt,['Choice logistic regression hp trials ' threshold_tag trials_tag],'svg');
% LOW PRESSURE TRIALS
[mdl_lpt,B_lpt,p] = log_regress(population_choices(lp_trials_indeces)-1,population_pressure(lp_trials_indeces),population_rew1(lp_trials_indeces),population_rew2(lp_trials_indeces),population_eff1(lp_trials_indeces),population_eff2(lp_trials_indeces));
h_regr_lpt = figure('Name','Choice logistic regression','units','normalized','outerposition',[0 0 1 1]);
bar(B_lpt)
hold on
errorbar((1:6),table2array(mdl_lpt.Coefficients(:,1)),table2array(mdl_lpt.Coefficients(:,2)),'.')
set(gca,'XTick',1:6);
set(gca,'XTickLabel',{'','Pressure','R1','R2','E1','E2'});
ylabel('Likelihood of high effort choice in high pressure blocks')
title('Predicting choices in low pressure trials')
saveas(h_regr_lpt,['Choice logistic regression lp trials ' threshold_tag trials_tag],'png');
saveas(h_regr_lpt,['Choice logistic regression lp trials ' threshold_tag trials_tag],'svg');
t_statistics = (B_hpt(6)-B_lpt(6))/(sqrt(table2array(mdl_hpt.Coefficients(6,2)).^2+table2array(mdl_lpt.Coefficients(6,2)).^2));
fprintf('a higher effect of effort on the higher effort option in high vs low pr trials Diff = %f t = %.4f \n',B_hpt(6)-B_lpt(6),t_statistics)

analyse_population.m, no license · at the source

Overview

Authors: M Andrea Pisauro^1,2,3,4, Daniele Pollicino^3,5, Lucy Fisher¹, Matthew A J Apps^1,2,3

ORCID iDs: M Andrea Pisauro, Lucy Fisher, Matthew A J Apps

Centre for Human Brain Health, School of Psychology, University of Birmingham, Birmingham, UK
Institute for Mental Health, School of Psychology, University of Birmingham, Birmingham, UK
Department of Experimental Psychology, University of Oxford, Oxford, UK
School of Psychology, University of Plymouth, Plymouth, UK
Department of Psychological and Behavioural Science, London School of Economics and Political Science, London, UK

Institutions: University of Oxford (United Kingdom); University of Birmingham (United Kingdom); University of Plymouth (United Kingdom); London School of Economics and Political Science (United Kingdom); University of Liverpool (United Kingdom)

Journal: Nature communications, volume 17, issue 1, article 9169

Dates: received 17 February 2025; accepted 2 July 2026; published online 28 July 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1038/s41467-026-75987-6 · PMID 42660916 · PMCID PMC13522516 · OpenAlex W4394983653

Open access: gold, a free copy (OpenAlex)

Preprint: doi:10.1101/2024.04.17.589910

Status: code verified

Categories: human (organism), cognitive (subfield)

Methods: Machine learning, Evoked potentials, Statistics, fMRI & imaging

Keywords: Motivation, Decision, Human behaviour

MeSH: Decision Making*, Motivation*, Brain Mapping, Computer Simulation, Female, Goals, Gyrus Cinguli, Humans, Magnetic Resonance Imaging, Putamen, Reward (* major topic)

Topic: Neural and Behavioral Psychology Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex

Funding: Biotechnology and Biological Sciences Research Council (BB/R010668/2)

Citations: not cited yet (Europe PMC); 79 references in the paper

Abstract

Deadlines fundamentally shape motivation. Research examining effort-based choices finds effort is an avoided cost. However, this overlooks how making progress on longer-term goals to be reached before deadlines motivate people to exert high effort for little immediate reward. Here, we test a framework where motivation depends on deadline pressure (work remaining / time remaining). Across four studies we use computational modelling examining decisions when physical effort makes progress on goals with deadlines. In support of the hypotheses, deadline pressure significantly impacts decision-making, shifting people from avoiding effort, to seeking the progress it makes. Using ultra-high-field fMRI, we show that functionally connected putamen and midcingulate cortex sub-regions process and update estimates of deadline pressure, with distinct anterior cingulate and putamen sub-regions processing effort both when it is being avoided or sought. These results reveal the neurocomputational mechanisms for how deadline pressure shapes motivation, and keep us on track with goals.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 1 match between paragraphs and lines of code.

OSF 5kjgb

License: none: the authors keep all their rights

State: the link answers, verified on 27 September 2026

Evidence: files inventoried

Languages: MATLAB (5)

Size: 5 files, 5 scripts

Software Heritage: not checked

Found in: “Code availability”

Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation

Tools: Statistics and Machine Learning Toolbox (2 files), boundedline (1 file)

Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)

27 September 2026: the link answers (HTTP 200)

5 files

Code/analyse_population.m, MATLAB, 2,330 lines, 1 match
Code/model_choice_under_pressure.m, MATLAB, 859 lines
Code/model_choice_under_pressure_crossval.m, MATLAB, 895 lines
Code/model_population.m, MATLAB, 711 lines
Code/parameter_recovery.m, MATLAB, 249 lines

At the source:

Code availability

The code to generate the results and the figure, and the experimental paradigm, of this study is available in an Open Science Framework project (https://doi.org/10.17605/OSF.IO/5KJGB).

Reproduced under the paper's license (CC BY), from the paper cited above.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
5 scripts, each with its path and the digest of its content;
1 match between paragraphs of the paper and lines of the code (method lexical-v1);
neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data availability

The behavioural data and the fMRI statistical maps from all analyses have been deposited in an Open Science Framework project (https://doi.org/10.17605/OSF.IO/5KJGB). The raw fMRI data are protected and are not available due to data privacy laws.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 3 keywords, 11 MeSH terms, 1 funder, 67 references.

Cite

This paper

Pisauro, M. A., Pollicino, D., Fisher, L., & Apps, M. A. J. (2026). Neural and computational mechanisms of effort under the pressure of a deadline. Nature communications, 17(1), 9169. https://doi.org/10.1038/s41467-026-75987-6

BibTeX

@article{pisauro2026neural, author = {Pisauro, M Andrea and Pollicino, Daniele and Fisher, Lucy and Apps, Matthew A J}, title = {{Neural and computational mechanisms of effort under the pressure of a deadline}}, journal = {Nature communications}, year = {2026}, month = jul, volume = {17}, number = {1}, pages = {9169}, publisher = {Nature Publishing Group}, issn = {2041-1723}, doi = {10.1038/s41467-026-75987-6}, url = {https://doi.org/10.1038/s41467-026-75987-6}, pmid = {42660916}, pmcid = {PMC13522516} }

RIS

TY - JOUR AU - Pisauro, M Andrea AU - Pollicino, Daniele AU - Fisher, Lucy AU - Apps, Matthew A J TI - Neural and computational mechanisms of effort under the pressure of a deadline T2 - Nature communications J2 - Nat Commun PY - 2026 DA - 2026/07/28 VL - 17 IS - 1 SP - 9169 SN - 2041-1723 PB - Nature Publishing Group DO - 10.1038/s41467-026-75987-6 UR - https://doi.org/10.1038/s41467-026-75987-6 LA - en ER -

CSL-JSON

{ "id": "10.1038/s41467-026-75987-6", "type": "article-journal", "title": "Neural and computational mechanisms of effort under the pressure of a deadline", "container-title": "Nature communications", "author": [ { "family": "Pisauro", "given": "M Andrea" }, { "family": "Pollicino", "given": "Daniele" }, { "family": "Fisher", "given": "Lucy" }, { "family": "Apps", "given": "Matthew A J" } ], "container-title-short": "Nat Commun", "volume": "17", "issue": "1", "page": "9169", "DOI": "10.1038/s41467-026-75987-6", "PMID": "42660916", "PMCID": "PMC13522516", "ISSN": "2041-1723", "publisher": "Nature Publishing Group", "URL": "https://doi.org/10.1038/s41467-026-75987-6", "language": "en", "issued": { "date-parts": [ [ 2026, 7, 28 ] ] } }

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Neural and computational mechanisms of effort under the pressure of a deadline.

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