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Copy pathMcAuley_ideas_1_sim.m
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McAuley_ideas_1_sim.m
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cd /Users/ece.kaya/MATLAB/Githubbed/Misc
stim_ioi = 500;
pdev = .11;
gap_int = 6;
pha_init = 0;
per_init = 600;
stim_comparison = stim_ioi + stim_ioi* pdev;
stim_seq = [repmat(stim_ioi,4,1)' stim_ioi*gap_int stim_comparison];
Wpha = 0.8;
Wper = 0.99;
C_fin = McAuley_freezer (stim_seq, pha_init, per_init, Wpha, Wper);
%% simulate exp & model resp
load /Users/ece.kaya/MATLAB/Githubbed/OscPerc/OPC_matlab/OPC_50.mat
ioilist = OPC_50(1).S(2).condits(:,1).*1000;
pdevlist = OPC_50(1).S(2).condits(:,2);
condits = table(ioilist,pdevlist);
condits = condits(logical(ioilist<700) & logical(ioilist>300),:);
sim = table();
% fixed params
gap_int = 6;
pha_init = 0;
per_init = 500;
% fixed for simulation
Wpha = 0.7;
Wper = 0.99;
for i = 1:height(condits)
stim_ioi = condits.ioilist(i);
pdev = condits.pdevlist(i);
stim_comparison = stim_ioi + stim_ioi* pdev;
stim_seq = [repmat(stim_ioi,4,1)' stim_ioi*gap_int stim_comparison];
C_fin = McAuley_freezer (stim_seq, pha_init, per_init, Wpha, Wper);
sim.ioi(i) = stim_ioi;
sim.dev(i) = pdev;
sim.cfin(i) = C_fin;
sim.discrep(i) = C_fin - pdev;
if sim.cfin(i) > 0 % if Cfinal is positive
sim.resp(i) = 1; % model will think the interval is 'shorter' than predicted (?)
elseif sim.cfin(i) < 0 % if Cfinal negative
sim.resp(i) = -1;
else
sim.resp(i) = NaN;
end
% resp coding
if pdev > 0
sim.devdir(i) = 1;
if sim.resp(i) > 0
sim.hit(i) = 1;
sim.bias(i) = 0;
elseif sim.resp(i) < 0
sim.hit(i) = 0;
sim.bias(i) = -1;
end
elseif pdev < 0
sim.devdir(i) = -1;
if sim.resp(i) > 0
sim.hit(i) = 0;
sim.bias(i) = 1;
elseif sim.resp(i) < 0
sim.hit(i) = 1;
sim.bias(i) = 0;
end
end
end
% ---------------- HIT MEAS ------------------------
ws = 30;
[hitmm, ioimm] = EK_movmean_by(sim.hit,sim.ioi,ws);
[hitmmpos, ioimmpos] = EK_movmean_by(sim.hit(sim.devdir==1), sim.ioi(sim.devdir==1),ws);
[hitmmneg, ioimmneg] = EK_movmean_by(sim.hit(sim.devdir==-1), sim.ioi(sim.devdir==-1),ws);
figure;
sim = sortrows(sim,'ioi','ascend');
plot(sim.ioi, sim.discrep);
figure;
sgtitle({'Model simulation responses', ['initial period = ' num2str(per_init)],...
['phase correction = ' num2str(Wpha)], ['period correction = ' num2str(Wper)]}, 'FontSize', 22);
subplot(3,1,1)
plot(ioimm, hitmm, 'linewidth', 2); hold on;
yline(0.5, '--k', 'linewidth', 2);
EK_plotlabels('IOI', 'hit movmean (ws=30)', 'all resp ', 16);
subplot(3,1,2)
plot(ioimmpos, hitmmpos, 'linewidth', 2); hold on;
plot(ioimmneg, hitmmneg, 'linewidth', 2);
legend('positive dev', 'negative dev');
EK_plotlabels('IOI', 'hit movmean (ws=30)', 'pos-neg resp ', 16);
% ---------------- BIAS MEAS ------------------------
[biasmm, ioimm] = EK_movmean_by(sim.bias, sim.ioi,ws);
absbiasmm = abs(biasmm);
subplot(3,1,3)
plot(ioimm, biasmm, 'linewidth', 2); hold on;
yline(0, '--k', 'linewidth', 2);
EK_plotlabels('IOI', 'bias movmean (ws=30)', 'all resp ', 16);
%% the function
function C_fin = McAuley_freezer (stim_seq, pha_init, per_init, Wpha, Wper)
pha = NaN(length(stim_seq)+1,1)'; pha(1) = pha_init;
per = NaN(length(stim_seq)+1,1)'; per(1) = per_init;
chist = NaN(length(stim_seq)+1,1)';
% M = table();
% model stim seq loop -------------------
s = 1; % stim counter
m = 1; % model counter
for i = 1:length(stim_seq)
stim_vs_per = stim_seq(s)/per(m);
nrep = floor(stim_vs_per);
if nrep < 2 % normal model -- stim corresponds model
C = mod(mod(pha(m),1) + mod(stim_vs_per,1),1);
if C > .5
C = C - 1;
end
% normal model equations
pha(m+1) = (1 - Wpha)*C;
per(m+1) = (1 + Wper*C)*per(m);
chist(m) = C;
corr_ok = 1;
% M.stim_index(m) = s;
% M.model_index(m) = m;
% M.stim_IOI(m) = stim_seq(s);
% M.stim_IOI_parts(m) = NaN;
% M.model_phase(m) = pha(m);
% M.model_period(m) = per(m);
% M.C(m) = C;
% M.was_corrected(m+1) = corr_ok;
m = m+1; % next model index
else % if stim interval multiplies model interval (per(i))
stim_int_parts = [repmat(per(m),1,nrep), stim_seq(s)-per(m)*(nrep)];
if stim_int_parts(end) == 0
maxj = nrep;
else
maxj = nrep+1;
end
for j = 1:maxj % remainder from floor() func
% chop long stim interval to exact multiples (and remainder)
C = mod(mod(pha(m),1) + mod(stim_int_parts(j)/per(m),1),1);
if C > .5
C = C - 1;
end
if j < maxj
% model doesn't correct for phase & period here (Wper = 0, Wpha =
% 0)
pha(m+1) = (1 - 0)*C;
per(m+1) = (1 + 0 *C)*per(m);
chist(m) = C;
corr_ok = 0;
else
% normal model equations to predict final interval (bcs
% there will be stim next )
pha(m+1) = (1 - Wpha)*C;
per(m+1) = (1 + Wper*C)*per(m);
chist(m) = C;
corr_ok = 1;
end
% M.stim_index(m) = s;
% M.model_index(m) = m;
% if j == 1
% M.stim_IOI(m) = stim_seq(s);
% else
% M.stim_IOI(m) = NaN;
% end
% M.stim_IOI_parts(m) = stim_int_parts(j);
% M.model_phase(m) = pha(m);
% M.model_period(m) = per(m);
% M.C(m) = C;
% M.was_corrected(m+1) = corr_ok;
m = m+1;
end
end
s = s+1;
end
C_fin = C;
end
function C_fin = McAuley_main (stim_seq, pha_init, per_init, Wpha, Wper)
pha = NaN(length(stim_seq)+1,1)'; pha(1) = pha_init;
per = NaN(length(stim_seq)+1,1)'; per(1) = per_init;
chist = NaN(length(stim_seq)+1,1)';
for i = 1:length(stim_seq)
if nrep < 2 % normal model -- stim corresponds model
C = mod(mod(pha(m),1) + mod(stim_vs_per,1),1);
if C > .5
C = C - 1;
end
% normal model equations
pha(m+1) = (1 - Wpha)*C;
per(m+1) = (1 + Wper*C)*per(m);
chist(m) = C;
corr_ok = 1;
% M.stim_index(m) = s;
% M.model_index(m) = m;
% M.stim_IOI(m) = stim_seq(s);
% M.stim_IOI_parts(m) = NaN;
% M.model_phase(m) = pha(m);
% M.model_period(m) = per(m);
% M.C(m) = C;
% M.was_corrected(m+1) = corr_ok;
m = m+1; % next model index
else % if stim interval multiplies model interval (per(i))
stim_int_parts = [repmat(per(m),1,nrep), stim_seq(s)-per(m)*(nrep)];
if stim_int_parts(end) == 0
maxj = nrep;
else
maxj = nrep+1;
end
for j = 1:maxj % remainder from floor() func
% chop long stim interval to exact multiples (and remainder)
C = mod(mod(pha(m),1) + mod(stim_int_parts(j)/per(m),1),1);
if C > .5
C = C - 1;
end
if j < maxj
% model doesn't correct for phase & period here (Wper = 0, Wpha =
% 0)
pha(m+1) = (1 - 0)*C;
per(m+1) = (1 + 0 *C)*per(m);
chist(m) = C;
corr_ok = 0;
else
% normal model equations to predict final interval (bcs
% there will be stim next )
pha(m+1) = (1 - Wpha)*C;
per(m+1) = (1 + Wper*C)*per(m);
chist(m) = C;
corr_ok = 1;
end
% M.stim_index(m) = s;
% M.model_index(m) = m;
% if j == 1
% M.stim_IOI(m) = stim_seq(s);
% else
% M.stim_IOI(m) = NaN;
% end
% M.stim_IOI_parts(m) = stim_int_parts(j);
% M.model_phase(m) = pha(m);
% M.model_period(m) = per(m);
% M.C(m) = C;
% M.was_corrected(m+1) = corr_ok;
m = m+1;
end
end
s = s+1;
end
C_fin = C;
end