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Copy pathanalyze_vsaGeneralize.m
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analyze_vsaGeneralize.m
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function [h, data2plot,distances_euclidean,distances_defined] = analyze_vsaGeneralize(dPs,window,bPlot)
if nargin < 1 || isempty(dPs)
dPs = get_dataPaths_vsaGeneralize;
end
if nargin < 2 || isempty(window)
window = [25 75];
end
if ~isequal(size([25 50]),[1 2]) || any(window>100) || any (window<1)
warning('Analysis window must be a 1 x 2 vector of percentages from 1-100. Using default window (25-50%).')
window = [];
end
if nargin < 3 || isempty(bPlot)
bPlot = 0;
end
exptName = 'vsaGeneralize';
[data2plot] = preprocess(dPs, window);
[distances_euclidean,distances_defined,nearestTrainVowel,distances_all] ...
= measure_nearest_vowels(data2plot.posLists);
saveDir = get_acoustLoadPath('vsaGeneralize');
saveFile = fullfile(saveDir,sprintf('processedData_%.0f-%.0f.mat',window(1),window(2)));
bSave = savecheck(saveFile);
if bSave
save(saveFile,'data2plot','distances_euclidean','distances_defined','nearestTrainVowel','distances_all')
end
if bPlot
h = [];
plotting(h, data2plot,length(dPs),distances_euclidean,distances_defined,nearestTrainVowel);
end
end
%%
function [distances_euclidean,distances_defined,nearestTrainVowel,distances_all] = measure_nearest_vowels(posLists)
numParticipants = length(posLists);
numGenVowels = 5;
% init distances to nearest vowels
distances_euclidean = zeros(numParticipants, numGenVowels);
nearestTrainVowel = zeros(numParticipants, numGenVowels);
% init distances to self-defined ones
distances_defined.ih2iy = zeros(numParticipants);
distances_defined.ey2front = zeros(numParticipants);
distances_defined.eh2ae = zeros(numParticipants);
distances_defined.ou2back = zeros(numParticipants);
distances_defined.ah2aa = zeros(numParticipants);
for p = 1:numParticipants
for i = 1:numGenVowels
% calculate distances to nearest vowels
dists = euclidean(posLists{p}.baselineGen(i,:), posLists{p}.baselineTrain);
vowInd = find(dists == min(dists));
nearestTrainVowel(p, i) = vowInd;
distances_euclidean(p, i) = dists(vowInd);
vow = num2vowstr(i+4);
distances_all{p}.(vow) = dists;
end
% calculate distances to self-defined vowels
distances_defined.ih2iy(p) = euclidean(posLists{p}.baselineGen(1, :), posLists{p}.baselineTrain(1, :));
distances_defined.ey2front(p) = euclidean(posLists{p}.baselineGen(2, :), mean(posLists{p}.baselineTrain([1 2], :)));
distances_defined.eh2ae(p) = euclidean(posLists{p}.baselineGen(3, :), posLists{p}.baselineTrain(2, :));
distances_defined.ou2back(p) = euclidean(posLists{p}.baselineGen(4, :), mean(posLists{p}.baselineTrain([3 4], :)));
distances_defined.ah2aa(p) = euclidean(posLists{p}.baselineGen(5, :), posLists{p}.baselineTrain(4, :));
end
end
function h = plotting(h, data2plot,num,distances_euclidean,distances_defined,nearestTrainVowel)
% h = plot_vsa(h, data2plot.posLists);
h = plot_pdist(h, data2plot);
h = plot_sp_normalized(h,data2plot,nearestTrainVowel,num);
nwords = 9;
[h,mag_c] = plot_compensation(h,data2plot,num,nwords);
[h,mag_sp] = plot_scalar_proj(h,data2plot,num,nwords);
[h,efficient_sp] = plot_efficient_comp(h,mag_c,mag_sp);
[h,mag_vp] = plot_vec_proj(h,data2plot,num,nwords);
[h,efficient_vp] = plot_efficient_comp(h,mag_c,mag_vp);
% h = plot_dist_vs_vp_by_participant(h, data2plot.vector_projs, data2plot.posLists);
h = plot_dist_vs_sp_by_participant(h, data2plot.scalar_projs,data2plot.posLists,nearestTrainVowel,num);
% h = plot_vsa_train_vs_gen(h,data2plot.vsaTraining,data2plot.vsaGeneralize);
h = plot_pdist_train_vs_gen(h,data2plot.tPdistAbsNormalized,data2plot.gPdistAbsNormalized,...
data2plot.tPdistPercentNormalized,data2plot.gPdistPercentNormalized);
end
function h = plot_vsa(h, posLists)
nwords = 9;
posBase = zeros(nwords, 2); posNonBase = zeros(nwords, 2);
for p = 1:length(posLists)
posBase(1:2,:) = posLists{p}.baselineTrain(1:2,:);
posBase(3,:) = posLists{p}.baselineTrain(4,:);
posBase(4,:) = posLists{p}.baselineTrain(3,:);
posBase(5:7,:) = posLists{p}.baselineGen(1:3,:);
posBase(8,:) = posLists{p}.baselineGen(5,:);
posBase(9,:) = posLists{p}.baselineGen(4,:);
posNonBase(1:2,:) = posLists{p}.train(1:2,:);
posNonBase(3,:) = posLists{p}.train(4,:);
posNonBase(4,:) = posLists{p}.train(3,:);
posNonBase(5:7,:) = posLists{p}.gen(1:3,:);
posNonBase(8,:) = posLists{p}.gen(5,:);
posNonBase(9,:) = posLists{p}.gen(4,:);
h(end+1) = figure;
scatter(posBase(1:4,1), posBase(1:4,2), 'ko');
hold all
scatter(posBase(5:9,1), posBase(5:9,2), 'kx');
scatter(posNonBase(1:4,1), posNonBase(1:4,2), 'ro');
scatter(posNonBase(5:9,1), posNonBase(5:9,2), 'rx');
plot([posBase(1:4,1); posBase(1,1)], [posBase(1:4,2); posBase(1,2)], 'k-');
plot([posBase(5:9,1); posBase(5,1)], [posBase(5:9,2); posBase(5,2)], 'k--');
plot([posNonBase(1:4,1); posNonBase(1,1)], [posNonBase(1:4,2); posNonBase(1,2)], 'r-');
plot([posNonBase(5:9,1); posNonBase(5,1)], [posNonBase(5:9,2); posNonBase(5,2)], 'r--');
legend({'train base' 'generalize base' 'train' 'generalize'});
xlabel('formant1')
ylabel('formant2')
title('vowel space')
hold off
end
end
function h = plot_pdist(h,data2plot)
h(end+1) = figure;
scatter(ones(1,length(data2plot.tPdistAbsNormalized)), data2plot.tPdistAbsNormalized, 'ko');
hold on
scatter(2.*ones(1,length(data2plot.gPdistAbsNormalized)), data2plot.gPdistAbsNormalized, 'ro');
for i = 1:length(data2plot.tPdistAbsNormalized)
plot([1 2], [data2plot.tPdistAbsNormalized(i) data2plot.gPdistAbsNormalized(i)], 'b-');
end
xx = 0:0.05:3;
plot(xx, zeros(1,length(xx)), 'k.');
xlim([0 3])
xticks([1, 2])
xticklabels({'train', 'generalize'})
xlabel('condition');
ylabel('mean pairwise distances');
title('normalized by subtraction')
hold off
h(end+1) = figure;
scatter(ones(1,length(data2plot.tPdistPercentNormalized)), data2plot.tPdistPercentNormalized, 'ko');
hold on
scatter(2.*ones(1,length(data2plot.gPdistPercentNormalized)), data2plot.gPdistPercentNormalized, 'ro');
for i = 1:length(data2plot.tPdistPercentNormalized)
plot([1 2], [data2plot.tPdistPercentNormalized(i) data2plot.gPdistPercentNormalized(i)], 'b-');
end
xx = 0:0.05:3;
plot(xx, ones(1,length(xx)), 'k.');
xlim([0 3])
xticks([1, 2])
xticklabels({'train', 'generalize'})
xlabel('condition');
ylabel('mean pairwise distance');
title('normalized by division')
hold off
[h_t,p_t,ci_t,stats_t] = ttest(data2plot.tPdistPercentNormalized, 1)
[h_g,p_g,ci_g,stats_g] = ttest(data2plot.gPdistPercentNormalized, 1)
end
function h = plot_sp_normalized(h,data2plot,nearestTrainVowel,num)
numTrainVowels = 4;
numGenVowels = 5;
% ih, eh, ey, ow, ah
cals = zeros(num, numGenVowels);
defs = zeros(num, numGenVowels);
for p = 1:num
for i = 1:numGenVowels
genVow = num2vowstr(numTrainVowels+i);
trainVow = num2vowstr(nearestTrainVowel(p,i));
% using nanmean() bc scalar_projs have badTrails
cals(p,i) = nanmean(data2plot.scalar_projs{p}.(genVow)) / nanmean(data2plot.scalar_projs{p}.(trainVow));
end
defs(p,1) = nanmean(data2plot.scalar_projs{p}.ih) / nanmean(data2plot.scalar_projs{p}.iy);
defs(p,2) = nanmean(data2plot.scalar_projs{p}.eh) / nanmean([data2plot.scalar_projs{p}.iy data2plot.scalar_projs{p}.ae]);
defs(p,3) = nanmean(data2plot.scalar_projs{p}.ey) / nanmean(data2plot.scalar_projs{p}.ae);
defs(p,4) = nanmean(data2plot.scalar_projs{p}.ow) / nanmean([data2plot.scalar_projs{p}.aa data2plot.scalar_projs{p}.uw]);
defs(p,5) = nanmean(data2plot.scalar_projs{p}.ah) / nanmean(data2plot.scalar_projs{p}.aa);
end
h(end+1) = figure;
xx = 0:0.05:6;
plot(xx, ones(1,length(xx)), 'k.');
hold on
for i = 1:numGenVowels
scatter(ones(1,num)*i, cals(:,i), 'ko');
hold on
scatter(i, nanmean(cals(:,i)), 700, 'r.');
end
xlim([0 6])
xticks([1, 2, 3, 4, 5])
xticklabels({'ih', 'eh', 'ey', 'ow', 'ah'})
xlabel('vowel');
ylabel('normalized scalar projections');
title('calculated nearest training vowels');
h(end+1) = figure;
xx = 0:0.05:6;
plot(xx, ones(1,length(xx)), 'k.');
hold on
for i = 1:numGenVowels
scatter(ones(1,num)*i, defs(:,i), 'ko');
hold on
scatter(i, nanmean(defs(:,i)), 700, 'r.');
end
xlim([0 6])
xticks([1, 2, 3, 4, 5])
xticklabels({'ih', 'eh', 'ey', 'ow', 'ah'})
xlabel('vowel');
ylabel('normalized scalar projections');
title('self-defined nearest trainig vowel');
[h_cal,p_cal,ci_cal,stats_cal] = ttest(cals(:), 1)
[h_def,p_def,ci_def,stats_def] = ttest(defs(:), 1)
[h_cal_mean,p_cal_mean,ci_cal_mean,stats_cal_mean] = ttest(nanmean(cals, 1), 1)
[h_def_mean,p_def_mean,ci_def_mean,stats_def_mean] = ttest(nanmean(defs, 1), 1)
end
function [h,magnitude] = plot_compensation(h,data2plot,num,nwords)
magnitude = zeros(1, nwords);
compensations.iy = []; compensations.ae = []; compensations.aa = []; compensations.uw = [];
compensations.ih = []; compensations.eh = []; compensations.ey = []; compensations.ow = []; compensations.ah = [];
for i = 1:num
magnitude = magnitude + ...
[nanmean(vector_norm(data2plot.compensations{i}.iy)), nanmean(vector_norm(data2plot.compensations{i}.ae)), ...
nanmean(vector_norm(data2plot.compensations{i}.uw)), nanmean(vector_norm(data2plot.compensations{i}.aa)), ...
nanmean(vector_norm(data2plot.compensations{i}.ih)), nanmean(vector_norm(data2plot.compensations{i}.eh)), ...
nanmean(vector_norm(data2plot.compensations{i}.ey)), nanmean(vector_norm(data2plot.compensations{i}.ow)), ...
nanmean(vector_norm(data2plot.compensations{i}.ah))];
vn = vector_norm(data2plot.compensations{i}.iy); compensations.iy(:, length(compensations.iy)+1:length(compensations.iy)+length(vn)) = vn;
vn = vector_norm(data2plot.compensations{i}.ae); compensations.ae(:, length(compensations.ae)+1:length(compensations.ae)+length(vn)) = vn;
vn = vector_norm(data2plot.compensations{i}.aa); compensations.aa(:, length(compensations.aa)+1:length(compensations.aa)+length(vn)) = vn;
vn = vector_norm(data2plot.compensations{i}.uw); compensations.uw(:, length(compensations.uw)+1:length(compensations.uw)+length(vn)) = vn;
vn = vector_norm(data2plot.compensations{i}.ih); compensations.ih(:, length(compensations.ih)+1:length(compensations.ih)+length(vn)) = vn;
vn = vector_norm(data2plot.compensations{i}.eh); compensations.eh(:, length(compensations.eh)+1:length(compensations.eh)+length(vn)) = vn;
vn = vector_norm(data2plot.compensations{i}.ey); compensations.ey(:, length(compensations.ey)+1:length(compensations.ey)+length(vn)) = vn;
vn = vector_norm(data2plot.compensations{i}.ow); compensations.ow(:, length(compensations.ow)+1:length(compensations.ow)+length(vn)) = vn;
vn = vector_norm(data2plot.compensations{i}.ah); compensations.ah(:, length(compensations.ah)+1:length(compensations.ah)+length(vn)) = vn;
end
h(end+1) = figure;
magnitude = magnitude ./ num;
sz = 100;
scatter(1*ones(1, length(compensations.iy)), compensations.iy, 'ko');
hold all
scatter(1, magnitude(1), sz, 'bd', 'filled');
scatter(2*ones(1, length(compensations.ae)), compensations.ae, 'ko'); scatter(2, magnitude(2), sz, 'bd', 'filled')
scatter(3*ones(1, length(compensations.uw)), compensations.uw, 'ko'); scatter(3, magnitude(3), sz, 'bd', 'filled')
scatter(4*ones(1, length(compensations.aa)), compensations.aa, 'ko'); scatter(4, magnitude(4), sz, 'bd', 'filled')
scatter(5*ones(1, length(compensations.ih)), compensations.ih, 'ro'); scatter(5, magnitude(5), sz, 'md', 'filled')
scatter(6*ones(1, length(compensations.eh)), compensations.eh, 'ro'); scatter(6, magnitude(6), sz, 'md', 'filled')
scatter(7*ones(1, length(compensations.ey)), compensations.ey, 'ro'); scatter(7, magnitude(7), sz, 'md', 'filled')
scatter(8*ones(1, length(compensations.ow)), compensations.ow, 'ro'); scatter(8, magnitude(8), sz, 'md', 'filled')
scatter(9*ones(1, length(compensations.ah)), compensations.ah, 'ro'); scatter(9, magnitude(9), sz, 'md', 'filled')
xlim([0 10])
xticks([1, 2, 3, 4, 5, 6, 7, 8, 9])
xticklabels({'iy', 'ae', 'uw', 'aa', 'ih', 'eh', 'ey', 'ow', 'ah'})
xlabel('vowel');
ylabel('compensation (Mels)');
title('magnitudes of compensations')
hold off
end
function [h,mag_scalar_proj] = plot_scalar_proj(h,data2plot,num,nwords)
mag_scalar_proj = zeros(1, nwords);
scalar_projs.iy = []; scalar_projs.ae = []; scalar_projs.aa = []; scalar_projs.uw = [];
scalar_projs.ih = []; scalar_projs.eh = []; scalar_projs.ey = []; scalar_projs.ow = []; scalar_projs.ah = [];
for i = 1:num
mag_scalar_proj = mag_scalar_proj + ...
[nanmean(data2plot.scalar_projs{i}.iy), nanmean(data2plot.scalar_projs{i}.ae), ...
nanmean(data2plot.scalar_projs{i}.uw), nanmean(data2plot.scalar_projs{i}.aa), ...
nanmean(data2plot.scalar_projs{i}.ih), nanmean(data2plot.scalar_projs{i}.eh), ...
nanmean(data2plot.scalar_projs{i}.ey), nanmean(data2plot.scalar_projs{i}.ow), nanmean(data2plot.scalar_projs{i}.ah)];
scalar_projs.iy(:, length(scalar_projs.iy)+1:length(scalar_projs.iy)+length(data2plot.scalar_projs{i}.iy)) = data2plot.scalar_projs{i}.iy;
scalar_projs.ae(:, length(scalar_projs.ae)+1:length(scalar_projs.ae)+length(data2plot.scalar_projs{i}.ae)) = data2plot.scalar_projs{i}.ae;
scalar_projs.aa(:, length(scalar_projs.aa)+1:length(scalar_projs.aa)+length(data2plot.scalar_projs{i}.aa)) = data2plot.scalar_projs{i}.aa;
scalar_projs.uw(:, length(scalar_projs.uw)+1:length(scalar_projs.uw)+length(data2plot.scalar_projs{i}.uw)) = data2plot.scalar_projs{i}.uw;
scalar_projs.ih(:, length(scalar_projs.ih)+1:length(scalar_projs.ih)+length(data2plot.scalar_projs{i}.ih)) = data2plot.scalar_projs{i}.ih;
scalar_projs.eh(:, length(scalar_projs.eh)+1:length(scalar_projs.eh)+length(data2plot.scalar_projs{i}.eh)) = data2plot.scalar_projs{i}.eh;
scalar_projs.ey(:, length(scalar_projs.ey)+1:length(scalar_projs.ey)+length(data2plot.scalar_projs{i}.ey)) = data2plot.scalar_projs{i}.ey;
scalar_projs.ow(:, length(scalar_projs.ow)+1:length(scalar_projs.ow)+length(data2plot.scalar_projs{i}.ow)) = data2plot.scalar_projs{i}.ow;
scalar_projs.ah(:, length(scalar_projs.ah)+1:length(scalar_projs.ah)+length(data2plot.scalar_projs{i}.ah)) = data2plot.scalar_projs{i}.ah;
end
h(end+1) = figure;
mag_scalar_proj = mag_scalar_proj ./ num;
sz = 100;
scatter(1*ones(1, length(scalar_projs.iy)), scalar_projs.iy, 'ko');
hold all
scatter(1, mag_scalar_proj(1), sz, 'bd', 'filled');
scatter(2*ones(1, length(scalar_projs.ae)), scalar_projs.ae, 'ko'); scatter(2, mag_scalar_proj(2), sz, 'bd', 'filled')
scatter(3*ones(1, length(scalar_projs.uw)), scalar_projs.uw, 'ko'); scatter(3, mag_scalar_proj(3), sz, 'bd', 'filled')
scatter(4*ones(1, length(scalar_projs.aa)), scalar_projs.aa, 'ko'); scatter(4, mag_scalar_proj(4), sz, 'bd', 'filled')
scatter(5*ones(1, length(scalar_projs.ih)), scalar_projs.ih, 'ro'); scatter(5, mag_scalar_proj(5), sz, 'md', 'filled')
scatter(6*ones(1, length(scalar_projs.ey)), scalar_projs.ey, 'ro'); scatter(6, mag_scalar_proj(6), sz, 'md', 'filled')
scatter(7*ones(1, length(scalar_projs.eh)), scalar_projs.eh, 'ro'); scatter(7, mag_scalar_proj(7), sz, 'md', 'filled')
scatter(8*ones(1, length(scalar_projs.ow)), scalar_projs.ow, 'ro'); scatter(8, mag_scalar_proj(8), sz, 'md', 'filled')
scatter(9*ones(1, length(scalar_projs.ah)), scalar_projs.ah, 'ro'); scatter(9, mag_scalar_proj(9), sz, 'md', 'filled')
xlim([0 10])
xticks([1, 2, 3, 4, 5, 6, 7, 8, 9])
xticklabels({'iy', 'ae', 'uw', 'aa', 'ih', 'ey', 'eh', 'ow', 'ah'})
xlabel('vowel');
ylabel('scalar projections (Mels)');
title('magnitudes of dot projections to the direction of perturbations (dot)')
hold off
end
function [h,effcient_comp] = plot_efficient_comp(h,mag_c,mag_p)
effcient_comp = mag_p ./ mag_c;
h(end+1) = figure;
c = categorical({'iy', 'ae', 'uw', 'aa', 'ih', 'eh', 'ey', 'ow', 'ah'});
c = reordercats(c, {'iy', 'ae', 'uw', 'aa', 'ih', 'eh', 'ey', 'ow', 'ah'});
b = bar(c, effcient_comp);
b.FaceColor = 'flat';
b.CData(1:4,:) = [.5 0 .5; .5 0 .5; .5 0 .5; .5 0 .5;];
xlabel('words')
ylabel('efficient compensation magnitude (Mels)')
title('magnitudes of efficient compensations')
end
function [h,mag_vec_proj] = plot_vec_proj(h,data2plot,num,nwords)
mag_vec_proj = zeros(1, nwords);
vec_projs.iy = []; vec_projs.ae = []; vec_projs.aa = []; vec_projs.uw = [];
vec_projs.ih = []; vec_projs.eh = []; vec_projs.ey = []; vec_projs.ow = []; vec_projs.ah = [];
for i = 1:num
mag_vec_proj = mag_vec_proj + ...
[nanmean(data2plot.vector_projs{i}.iy, 2), nanmean(data2plot.vector_projs{i}.ae, 2), ...
nanmean(data2plot.vector_projs{i}.uw, 2), nanmean(data2plot.vector_projs{i}.aa, 2), ...
nanmean(data2plot.vector_projs{i}.ih, 2), nanmean(data2plot.vector_projs{i}.eh, 2), ...
nanmean(data2plot.vector_projs{i}.ey, 2), nanmean(data2plot.vector_projs{i}.ow, 2), nanmean(data2plot.vector_projs{i}.ah, 2)];
end
h(end+1) = figure;
mag_vec_proj = mag_vec_proj ./ num;
mag_vec_proj = vector_norm(mag_vec_proj);
c = categorical({'iy', 'ae', 'uw', 'aa', 'ih', 'eh', 'ey', 'ow', 'ah'});
c = reordercats(c, {'iy', 'ae', 'uw', 'aa', 'ih', 'eh', 'ey', 'ow', 'ah'});
b = bar(c, mag_vec_proj);
hold on
b.FaceColor = 'flat';
b.CData(1:4,:) = [.5 0 .5; .5 0 .5; .5 0 .5; .5 0 .5; ];
xlabel('words')
ylabel('compensation (Mels)')
title('magnitudes of compensations after projecting to the perturbation')
end
function h = plot_dist_vs_mag(h,dists_eu,dists_def,mag_sp,mag_vp,efficient_sp,efficient_vp)
h(end+1) = figure;
scatter([dists_def.ih2iy dists_def.ey2front dists_def.eh2ae dists_def.ou2back dists_def.ah2aa], mag_sp(5:9), 'ro');
hold on
dx = 4; dy = 4;
text(dists_def.ih2iy+dx, mag_sp(5)-dy, cellstr('ih'));
text(dists_def.ey2front+dx, mag_sp(6)-dy, cellstr('ey'));
text(dists_def.eh2ae+dx, mag_sp(7)-dy, cellstr('eh'));
text(dists_def.ou2back+dx, mag_sp(8)-dy, cellstr('ow'));
text(dists_def.ah2aa+dx, mag_sp(9)-dy, cellstr('ah'));
xlabel('distances to the nearest vowel (Mels)')
ylabel('magnitudes of compensations (Mels)')
title('distances vs magnitudes of scalar projection (self-defined)')
hold off
h(end+1) = figure;
scatter([dists_def.ih2iy dists_def.ey2front dists_def.eh2ae dists_def.ou2back dists_def.ah2aa], mag_vp(5:9), 'ro');
hold on
dx = 4; dy = 4;
text(dists_def.ih2iy+dx, mag_vp(5)-dy, cellstr('ih'));
text(dists_def.ey2front+dx, mag_vp(6)-dy, cellstr('ey'));
text(dists_def.eh2ae+dx, mag_vp(7)-dy, cellstr('eh'));
text(dists_def.ou2back+dx, mag_vp(8)-dy, cellstr('ow'));
text(dists_def.ah2aa+dx, mag_vp(9)-dy, cellstr('ah'));
xlabel('distances to the nearest vowel (Mels)')
ylabel('magnitudes of compensations (Mels)')
title('distances vs magnitudes of vector projection (self-defined)')
hold off
h(end+1) = figure;
scatter([dists_def.ih2iy dists_def.ey2front dists_def.eh2ae dists_def.ou2back dists_def.ah2aa], efficient_sp(5:9), 'ro');
hold on
dx = 4; dy = .02;
text(dists_def.ih2iy+dx, efficient_sp(5)-dy, cellstr('ih'));
text(dists_def.ey2front+dx, efficient_sp(6)-dy, cellstr('ey'));
text(dists_def.eh2ae+dx, efficient_sp(7)-dy, cellstr('eh'));
text(dists_def.ou2back+dx, efficient_sp(8)-dy, cellstr('ow'));
text(dists_def.ah2aa+dx, efficient_sp(9)-dy, cellstr('ah'));
xlabel('distances to the nearest vowel (Mels)')
ylabel('magnitudes of compensations (Mels)')
title('distances vs magnitudes of efficient scalar projection (self-defined)')
hold off
%
h(end+1) = figure;
scatter([dists_def.ih2iy dists_def.ey2front dists_def.eh2ae dists_def.ou2back dists_def.ah2aa], efficient_vp(5:9), 'ro');
hold on
dx = 4; dy = .02;
text(dists_def.ih2iy+dx, efficient_vp(5)-dy, cellstr('ih'));
text(dists_def.ey2front+dx, efficient_vp(6)-dy, cellstr('ey'));
text(dists_def.eh2ae+dx, efficient_vp(7)-dy, cellstr('eh'));
text(dists_def.ou2back+dx, efficient_vp(8)-dy, cellstr('ow'));
text(dists_def.ah2aa+dx, efficient_vp(9)-dy, cellstr('ah'));
xlabel('distances to the nearest vowel (Mels)')
ylabel('magnitudes of compensations (Mels)')
title('distances vs magnitudes- of efficent vector projection (self-defined)')
hold off
h(end+1) = figure;
scatter(dists_eu, mag_sp(5:9), 'ro');
hold on
dx = 4; dy = 4;
text(dists_eu(1)+dx, mag_sp(5)-dy, cellstr('ih'));
text(dists_eu(2)+dx, mag_sp(6)-dy, cellstr('ey'));
text(dists_eu(3)+dx, mag_sp(7)-dy, cellstr('eh'));
text(dists_eu(4)+dy, mag_sp(8)-dy, cellstr('ow'));
text(dists_eu(5)+dy, mag_sp(9)-dy, cellstr('ah'));
xlabel('distances to the nearest vowel (Mels)')
ylabel('magnitudes of compensations (Mels)')
title('distances vs magnitudes of scalar projection (nearest in VSA)')
hold off
%
h(end+1) = figure;
scatter(dists_eu, mag_vp(5:9), 'ro');
hold on
dx = 4; dy = 4;
text(dists_eu(1)+dx, mag_vp(5)-dy, cellstr('ih'));
text(dists_eu(2)+dx, mag_vp(6)-dy, cellstr('ey'));
text(dists_eu(3)+dx, mag_vp(7)-dy, cellstr('eh'));
text(dists_eu(4)+dy, mag_vp(8)-dy, cellstr('ow'));
text(dists_eu(5)+dy, mag_vp(9)-dy, cellstr('ah'));
xlabel('distances to the nearest vowel (Mels)')
ylabel('magnitudes of compensations (Mels)')
title('distances vs magnitudes of vector projection (nearest in VSA)')
hold off
h(end+1) = figure;
scatter(dists_eu, efficient_sp(5:9), 'ro');
hold on
dx = 4; dy = 0.02;
text(dists_eu(1)+dx, efficient_sp(5)-dy, cellstr('ih'));
text(dists_eu(2)+dx, efficient_sp(6)-dy, cellstr('ey'));
text(dists_eu(3)+dx, efficient_sp(7)-dy, cellstr('eh'));
text(dists_eu(4)+dy, efficient_sp(8)-dy, cellstr('ow'));
text(dists_eu(5)+dy, efficient_sp(9)-dy, cellstr('ah'));
xlabel('distances to the nearest vowel (Mels)')
ylabel('magnitudes of compensations (Mels)')
title('distances vs magnitudes of efficient scalar projection (nearest in VSA)')
hold off
h(end+1) = figure;
scatter(dists_eu, efficient_vp(5:9), 'ro');
hold on
dx = 4; dy = 0.02;
text(dists_eu(1)+dx, efficient_vp(5)-dy, cellstr('ih'));
text(dists_eu(2)+dx, efficient_vp(6)-dy, cellstr('ey'));
text(dists_eu(3)+dx, efficient_vp(7)-dy, cellstr('eh'));
text(dists_eu(4)+dy, efficient_vp(8)-dy, cellstr('ow'));
text(dists_eu(5)+dy, efficient_vp(9)-dy, cellstr('ah'));
xlabel('distances to the nearest vowel (Mels)')
ylabel('magnitudes of compensations (Mels)')
title('distances vs magnitudes of efficient vector projection (nearest in VSA)')
hold off
end
function h = plot_dist_vs_vp_by_participant(h,vps,posLists)
NUM = length(vps);
vp.iy = NaN(2,NUM); vp.ae = NaN(2,NUM); vp.uw = NaN(2,NUM); vp.aa = NaN(2,NUM);
vp.ih = NaN(2,NUM); vp.eh = NaN(2,NUM); vp.ey = NaN(2,NUM); vp.ow = NaN(2,NUM); vp.ah = NaN(2,NUM);
pos.iy = NaN(NUM,2); pos.ae = NaN(NUM,2); pos.uw = NaN(NUM,2); pos.aa = NaN(NUM,2);
pos.ih = NaN(NUM,2); pos.eh = NaN(NUM,2); pos.ey = NaN(NUM,2); pos.ow = NaN(NUM,2); pos.ah = NaN(NUM,2);
for p = 1:NUM
vp.iy(:,p) = nanmean(vps{p}.iy,2); vp.ae(:,p) = nanmean(vps{p}.ae,2);
vp.uw(:,p) = nanmean(vps{p}.uw,2); vp.aa(:,p) = nanmean(vps{p}.aa,2);
vp.ih(:,p) = nanmean(vps{p}.ih,2); vp.eh(:,p) = nanmean(vps{p}.ey,2);
vp.ey(:,p) = nanmean(vps{p}.ey,2); vp.ow(:,p) = nanmean(vps{p}.ow,2);
vp.ah(:,p) = nanmean(vps{p}.ah,2);
pos.iy(p,:) = posLists{p}.baselineTrain(1,:); pos.ae(p,:) = posLists{p}.baselineTrain(2,:);
pos.uw(p,:) = posLists{p}.baselineTrain(3,:); pos.aa(p,:) = posLists{p}.baselineTrain(4,:);
pos.ih(p,:) = posLists{p}.baselineGen(1,:); pos.ey(p,:) = posLists{p}.baselineGen(2,:);
pos.eh(p,:) = posLists{p}.baselineGen(3,:); pos.ow(p,:) = posLists{p}.baselineGen(4,:);
pos.ah(p,:) = posLists{p}.baselineGen(5,:);
end
vp_norm.iy = vector_norm(vp.iy); vp_norm.ae = vector_norm(vp.ae);
vp_norm.uw = vector_norm(vp.uw); vp_norm.aa = vector_norm(vp.aa);
vp_norm.ih = vector_norm(vp.ih); vp_norm.eh = vector_norm(vp.eh);
vp_norm.ey = vector_norm(vp.ey); vp_norm.ow = vector_norm(vp.ow);
vp_norm.ah = vector_norm(vp.ah);
ih2iy = NaN(1, NUM); eh2ae = NaN(1, NUM); ey2front = NaN(1, NUM);
ou2back = NaN(1, NUM); ah2aa = NaN(1, NUM);
for p = 1:NUM
ih2iy(1,p) = euclidean(pos.ih(p,:), pos.iy(p,:));
eh2ae(1,p) = euclidean(pos.eh(p,:), pos.ae(p,:));
% ey2front(1,p) = nanmean([euclidean(pos.ey(p,:),pos.iy(p,:)) euclidean(pos.ey(p,:),pos.ae(p,:))]);
% ou2back(1,p) = nanmean([euclidean(pos.ow(p,:),pos.uw(p,:)) euclidean(pos.ow(p,:),pos.aa(p,:))]);
ey2front(1,p) = euclidean(pos.ey(p,:), mean([pos.iy(p,:); pos.ae(p,:)]));
ou2back(1,p) = euclidean(pos.ey(p,:), mean([pos.uw(p,:); pos.aa(p,:)]));
ah2aa(1,p) = euclidean(pos.ah(p,:), pos.aa(p,:));
end
sz = 35;
h(end+1) = figure;
p1 = scatter(nanmean(ih2iy), nanmean(vp_norm.ih), sz, 'MarkerEdgeColor', [0, 0.4470, 0.7410], 'MarkerFaceColor', [0, 0.4470, 0.7410]);
hold all
p2 = scatter(nanmean(ey2front), nanmean(vp_norm.ey), sz, 'MarkerEdgeColor', [0.8500, 0.3250, 0.0980], 'MarkerFaceColor', [0.8500, 0.3250, 0.0980]);
p3 = scatter(nanmean(eh2ae), nanmean(vp_norm.eh), sz, 'MarkerEdgeColor', [0.9290, 0.6940, 0.1250], 'MarkerFaceColor', [0.9290, 0.6940, 0.1250]);
p4 = scatter(nanmean(ou2back), nanmean(vp_norm.ow), sz, 'MarkerEdgeColor', [0.4940, 0.1840, 0.5560], 'MarkerFaceColor', [0.4940, 0.1840, 0.5560]);
p5 = scatter(nanmean(ah2aa), nanmean(vp_norm.ah), sz, 'MarkerEdgeColor', [0.4660, 0.6740, 0.1880], 'MarkerFaceColor', [0.4660, 0.6740, 0.1880]);
p6 = scatter(ih2iy, vp_norm.ih, sz, 'MarkerEdgeColor', [0, 0.4470, 0.7410]);
p7 = scatter(ey2front, vp_norm.ey, sz, 'MarkerEdgeColor', [0.8500, 0.3250, 0.0980]);
p8 = scatter(eh2ae, vp_norm.eh, sz, 'MarkerEdgeColor', [0.9290, 0.6940, 0.1250]);
p9 = scatter(ou2back, vp_norm.ow, sz, 'MarkerEdgeColor', [0.4940, 0.1840, 0.5560]);
p10 = scatter(ah2aa, vp_norm.ah, sz, 'MarkerEdgeColor', [0.4660, 0.6740, 0.1880]);
legend([p1,p2,p3,p4,p5], {'ih','ey','eh','ow','ah'})
xlabel('distances to the nearest vowel (Mels)')
ylabel('magnitudes of projectoins (Mels)')
title('distances vs vector projection by participant (self-defined)')
end
function h = plot_dist_vs_sp_by_participant(h,sps,posLists,nearestTrainVowel,num)
numGenVowels = 5;
x_cals = zeros(num, numGenVowels);
x_defs = zeros(num, numGenVowels);
for p = 1:num
for i = 1:numGenVowels
trainVow = nearestTrainVowel(p,i);
x_cals(p,i) = euclidean(posLists{p}.baselineGen(i,:), posLists{p}.baselineTrain(trainVow, :));
end
x_defs(p,1) = euclidean(posLists{p}.baselineGen(1,:), posLists{p}.baselineTrain(1, :) );
x_defs(p,2) = euclidean(posLists{p}.baselineGen(2,:), mean([posLists{p}.baselineTrain(1,:); posLists{p}.baselineTrain(2,:)], 1) );
x_defs(p,3) = euclidean(posLists{p}.baselineGen(3,:), posLists{p}.baselineTrain(2, :) );
x_defs(p,4) = euclidean(posLists{p}.baselineGen(4,:), mean([posLists{p}.baselineTrain(3,:); posLists{p}.baselineTrain(4,:)], 1) );
x_defs(p,5) = euclidean(posLists{p}.baselineGen(5,:), posLists{p}.baselineTrain(4, :) );
end
y = zeros(num, numGenVowels);
for p = 1:num
for i = 1:numGenVowels
genVow = num2vowstr(i);
y(p,i) = nanmean(sps{p}.(genVow));
end
end
sz = 35;
colors = [0, 0.4470, 0.7410; 0.8500, 0.3250, 0.0980; 0.9290, 0.6940, 0.1250; 0.4940, 0.1840, 0.5560; 0.4660, 0.6740, 0.1880];
h(end+1) = figure;
for i = 1:numGenVowels
scatter(x_cals(:,i), y(:,i), sz, 'MarkerEdgeColor', colors(i,:));
hold on
end
legend('ih', 'eh', 'ey', 'ow', 'ah');
for i = 1:numGenVowels
scatter(mean(x_cals(:,i)), nanmean(y(:,i)), sz, 'MarkerEdgeColor', colors(i,:), 'MarkerFaceColor', colors(i,:));
hold on
end
xlabel('distances to the nearest vowel (Mels)')
ylabel('magnitudes of projectoins (Mels)')
title('distances vs scalar projection by participant (calculated)')
h(end+1) = figure;
for i = 1:numGenVowels
scatter(x_defs(:,i), y(:,i), sz, 'MarkerEdgeColor', colors(i,:));
hold on
end
legend('ih', 'eh', 'ey', 'ow', 'ah');
for i = 1:numGenVowels
scatter(mean(x_defs(:,i)), nanmean(y(:,i)), sz, 'MarkerEdgeColor', colors(i,:), 'MarkerFaceColor', colors(i,:));
hold on
end
xlabel('distances to the nearest vowel (Mels)')
ylabel('magnitudes of projectoins (Mels)')
title('distances vs scalar projection by participant (self-defined)')
end
function h = plot_vsa_train_vs_gen(h,vsaTraining, vsaGeneralize)
h(end+1) = figure;
scatter(vsaTraining, vsaGeneralize, 'ro')
xlabel('vsaTraining (Hz)')
ylabel('vsaGeneralize (Hz)')
title('magnitudes of training vs generalization')
end
function h = plot_pdist_train_vs_gen(h,tPdistAbsNormalized,gPdistAbsNormalized,tPdistPercentNormalized,gPdistPercentNormalized)
h(end+1) = figure;
scatter( tPdistAbsNormalized, gPdistAbsNormalized, 'ro')
xlabel('training data pairwise distance differences')
ylabel('generalization data pairwise distance diffrences')
title('pairwise distance training vs generalization (differences)')
[rho_abs, p_abs] = corr(tPdistAbsNormalized', gPdistAbsNormalized', 'type', 'spearman')
h(end+1) = figure;
scatter( tPdistPercentNormalized, gPdistPercentNormalized, 'ro')
xlabel('training data pairwise distance percentage')
ylabel('generalization data pairwise distance percentage')
title('pairwise distance training vs generalization (percentage)')
[rho_percent, p_percent] = corr(tPdistPercentNormalized', gPdistPercentNormalized', 'type', 'spearman')
end
%% preprocessing
% vowels: expt.vowels
% data2plot.field(i): data to plot grouped by participant
%
function [data2plot] = preprocess(dPs, window)
for p = 1:length(dPs)
dataPath = dPs{p};
fprintf('Processing participant %d / %d\n',p,length(dPs))
dataPathPre = fullfile(dataPath,'pre');
fprintf('\t loading data\n')
load(fullfile(dataPath, 'data.mat'));
fprintf('\t loading expt\n')
load(fullfile(dataPath, 'expt.mat'));
fprintf('\t loading dataVals\n')
load(fullfile(dataPath, 'dataVals.mat'));
ntrials = length(expt.allWords);
vowels = expt.vowels;
%calculate pre-experiment vowel centeroid
preMeans = calc_vowelMeans(dataPathPre);
vowels = fieldnames(preMeans);
for v = 1:length(vowels) %convert to mels
vow = vowels{v};
preMeans.(vow) = hz2mels(preMeans.(vow));
end
fCenField = calc_vowelCentroid(preMeans);
% indexing
binSizeTrain = 40; binSizeGen = 50;
inds.bGenIndices = find(expt.allConds==1);
inds.bGenIndices = inds.bGenIndices(26:75); % only use last 50 trials
inds.bTrainIndices = find(expt.allConds==2);
inds.trainIndices = find(expt.allConds==3);
inds.genIndices = find(expt.allConds==4);
% last 40 (#train_vows * 10) words in the training phase
inds.lastTrain = inds.trainIndices(length(inds.trainIndices)-binSizeTrain+1:length(inds.trainIndices));
%calculate baseline vowel centeroid
baseMeans = calc_vowelMeans(dataPath,{'baselineTrain'});
vowels = fieldnames(baseMeans);
for v = 1:length(vowels) %convert to mels
vow = vowels{v};
baseMeans.(vow) = hz2mels(baseMeans.(vow));
end
[fCen] = calc_vowelCentroid(baseMeans);
[f1sIn,f2sIn,f1sOut,f2sOut,ints,f0Maxs,f0Ranges,durs] = setup_formants(dataVals, ntrials,window);
[vows,vsa,clearVars] = setup_vowels(f1sIn,f2sIn,ints,f0Maxs,f0Ranges,durs,binSizeTrain,binSizeGen,inds,expt.allVowels);
pertPhi = data(inds.trainIndices(1)).params.pertPhi;
fLims.f1Min = data(inds.trainIndices(1)).params.f1Min;
fLims.f1Max = data(inds.trainIndices(1)).params.f1Max;
fLims.f2Min = data(inds.trainIndices(1)).params.f2Min;
fLims.f2Max = data(inds.trainIndices(1)).params.f2Max;
[compensations,scalar_projs,vector_projs,dists,distsBase,distsEnd,clearVarsSorted,perts] = measure_changes(vows,clearVars,pertPhi,fLims,fCen);
vowelList_bt = expt.allVowels(inds.bTrainIndices);
vowelList_t = expt.allVowels(inds.lastTrain);
vowelList_bg = expt.allVowels(inds.bGenIndices);
vowelList_g = expt.allVowels(inds.genIndices);
[btPdist,bgPdist,tPdist,gPdist,baseTrainInds,posList] = measure_pdist(f1sIn,f2sIn,inds,vowelList_bt,vowelList_t,vowelList_bg,vowelList_g,expt.vowels);
data2plot.btPdists(p) = btPdist;
data2plot.bgPdists(p) = bgPdist;
data2plot.tPdists(p) = tPdist;
data2plot.gPdists(p) = gPdist;
data2plot.tPdistAbsNormalized(p) = tPdist - btPdist;
data2plot.gPdistAbsNormalized(p) = gPdist - bgPdist;
data2plot.tPdistPercentNormalized(p) = tPdist/btPdist;
data2plot.gPdistPercentNormalized(p) = gPdist/bgPdist;
data2plot.vsaTrainingAbsNormalized(p) = vsa.VSA_train - vsa.VSA_btrain;
data2plot.vsaGeneralizeAbsNormalized(p) = vsa.VSA_gen - vsa.VSA_bgen;
data2plot.vsaTrainingPercentNormalized(p) = vsa.VSA_train / vsa.VSA_btrain;
data2plot.vsaGeneralizePercentNormalized(p) = vsa.VSA_gen / vsa.VSA_bgen;
data2plot.compensations{p} = compensations;
data2plot.scalar_projs{p} = scalar_projs;
data2plot.vector_projs{p} = vector_projs;
data2plot.dists{p} = dists;
data2plot.distsEnd{p} = distsEnd;
data2plot.distsBase{p} = distsBase;
data2plot.intEnd{p} = clearVarsSorted.int.end;
data2plot.intBase{p} = clearVarsSorted.int.base;
data2plot.durEnd{p} = clearVarsSorted.dur.end;
data2plot.durBase{p} = clearVarsSorted.dur.base;
data2plot.f0MaxEnd{p} = clearVarsSorted.f0Max.end;
data2plot.f0MaxBase{p} = clearVarsSorted.f0Max.base;
data2plot.f0RangeEnd{p} = clearVarsSorted.f0Range.end;
data2plot.f0RangeBase{p} = clearVarsSorted.f0Range.base;
data2plot.posLists{p} = posList;
data2plot.vows{p} = vows;
data2plot.fCen{p} = fCen;
data2plot.fCenField{p} = fCenField;
data2plot.perts{p} = perts;
if p == 1
data2plot.VSA = NaN(length(dPs),length(vsa.VSA));
data2plot.AVS = NaN(length(dPs),length(vsa.VSA));
end
data2plot.VSA(p,:) = vsa.VSA;
data2plot.AVS(p,:) = vsa.AVS;
end
end
% mean formants between 0.25T to 0.75T
function [f1sIn,f2sIn,f1sOut,f2sOut,ints,f0Maxs,f0Ranges,durs] = setup_formants(dataVals, ntrials,window)
f1sIn = NaN(1,ntrials); f2sIn = NaN(1,ntrials);
f1sOut = NaN(1,ntrials); f2sOut = NaN(1,ntrials);
ints = NaN(1,ntrials);
f0Maxs = NaN(1,ntrials);
f0Ranges = NaN(1,ntrials);
durs = NaN(1,ntrials);
for j = 1:ntrials
% skip bad trials
if isempty(dataVals(j).f1) || isempty(dataVals(j).f2)
f1sIn(j) = NaN; f1sOut = NaN;
f2sIn(j) = NaN; f2sOut = NaN;
ints(j) = NaN;
f0Maxs(j) = NaN;
f0Ranges(j) = NaN;
durs(j) = NaN;
continue;
end
ftrackInSamps1 = find(~isnan(dataVals(j).f1));
ftrackIn1 = dataVals(j).f1(ftrackInSamps1);
ftrackInSamps2 = find(~isnan(dataVals(j).f2));
ftrackIn2 = dataVals(j).f2(ftrackInSamps2);
ftrackOutSamps1 = find(~isnan(dataVals(j).f2));
ftrackOut1 = dataVals(j).f1(ftrackOutSamps1);
ftrackOutSamps2 = find(~isnan(dataVals(j).f2));
ftrackOut2 = dataVals(j).f2(ftrackOutSamps2);
ftrackLength = length(ftrackIn1); % TODO
if dataVals(j).vowel == 2 %only use specified analysis window for /ae/
p1 = round(ftrackLength.*window(1)./100);
p2 = round(ftrackLength.*window(2)./100);
else %use 25-75% window for all other vowels
p1 = round(ftrackLength.*25./100);
p2 = round(ftrackLength.*75./100);
end
f1sIn(j) = hz2mels(mean(ftrackIn1(p1:p2)));
f2sIn(j) = hz2mels(mean(ftrackIn2(p1:p2)));
f1sOut(j) = hz2mels(mean(ftrackOut1(p1:p2)));
f2sOut(j) = hz2mels(mean(ftrackOut2(p1:p2)));
ints(j) = max(dataVals(j).int);
f0Maxs(j) = max(dataVals(j).f0);
f0Ranges(j) = range(dataVals(j).f0);
durs(j) = dataVals(j).dur;
end
end
% vsa: stores all VSA of a participant
% vsa for training vowels - polyarea(i, a, u)
% vsa for generalization vowels - poluarea(ih, ey, eh, ah, ow) (could be
% NaN)
% vsa.VSA_bgen, vsa.VSA_btrain, vsa.VSA_train, vsa.VSA_gen
% vows: \in 2 x #total_trails; Formants In; For training vowels, it only
% contains the last 40 trials
function [vows,vsa,clearVars] = setup_vowels(f1sIn,f2sIn,ints,f0Maxs,f0Ranges,durs,binSizeTrain,binSizeGen,inds,allVowels)
vsa.VSA_bgen = NaN(1,1);
vsa.VSA_btrain = NaN(1,1);
vsa.VSA_train = NaN(1,1);
vsa.VSA_gen = NaN(1,1);
vsa.VSA = [vsa.VSA_bgen vsa.VSA_btrain vsa.VSA_train vsa.VSA_gen];
vsa.AVS = vsa.VSA;
iVSA = 0;
% baseline generalize
iVSA = iVSA+1;
currTrials = inds.bGenIndices;
vowelList = allVowels(currTrials);
ihLoc = currTrials(vowelList == 5); % bid
ehLoc = currTrials(vowelList == 7); % bed
eyLoc = currTrials(vowelList == 6); % bayed
owLoc = currTrials(vowelList == 8); % bode
ahLoc = currTrials(vowelList == 9); % bud
vows.ih_bgen(1) = nanmean(f1sIn(ihLoc)); vows.ih_bgen(2) = nanmean(f2sIn(ihLoc));
vows.eh_bgen(1) = nanmean(f1sIn(ehLoc)); vows.eh_bgen(2) = nanmean(f2sIn(ehLoc));
vows.ey_bgen(1) = nanmean(f1sIn(eyLoc)); vows.ey_bgen(2) = nanmean(f2sIn(eyLoc));
vows.ow_bgen(1) = nanmean(f1sIn(owLoc)); vows.ow_bgen(2) = nanmean(f2sIn(owLoc));
vows.ah_bgen(1) = nanmean(f1sIn(ahLoc)); vows.ah_bgen(2) = nanmean(f2sIn(ahLoc));
clearVars.int.ih_bgen = nanmean(ints(ihLoc));
clearVars.int.eh_bgen = nanmean(ints(ehLoc));
clearVars.int.ey_bgen = nanmean(ints(eyLoc));
clearVars.int.ow_bgen = nanmean(ints(owLoc));
clearVars.int.ah_bgen = nanmean(ints(ahLoc));
clearVars.f0Max.ih_bgen = nanmean(f0Maxs(ihLoc));
clearVars.f0Max.eh_bgen = nanmean(f0Maxs(ehLoc));
clearVars.f0Max.ey_bgen = nanmean(f0Maxs(eyLoc));
clearVars.f0Max.ow_bgen = nanmean(f0Maxs(owLoc));
clearVars.f0Max.ah_bgen = nanmean(f0Maxs(ahLoc));
clearVars.f0Range.ih_bgen = nanmean(f0Ranges(ihLoc));
clearVars.f0Range.eh_bgen = nanmean(f0Ranges(ehLoc));
clearVars.f0Range.ey_bgen = nanmean(f0Ranges(eyLoc));
clearVars.f0Range.ow_bgen = nanmean(f0Ranges(owLoc));
clearVars.f0Range.ah_bgen = nanmean(f0Ranges(ahLoc));
clearVars.dur.ih_bgen = nanmean(durs(ihLoc));
clearVars.dur.eh_bgen = nanmean(durs(ehLoc));
clearVars.dur.ey_bgen = nanmean(durs(eyLoc));
clearVars.dur.ow_bgen = nanmean(durs(owLoc));
clearVars.dur.ah_bgen = nanmean(durs(ahLoc));
vsa.VSA_bgen = polyarea([vows.ih_bgen(1) vows.ey_bgen(1)...
vows.eh_bgen(1) vows.ah_bgen(1) vows.ow_bgen(1)], ...
[vows.ih_bgen(2) vows.ey_bgen(2)...
vows.eh_bgen(2) vows.ah_bgen(2) vows.ow_bgen(2)]);
vsa.VSA(iVSA) = NaN;
%calculate AVS
posList = [vows.ih_bgen(1) vows.ih_bgen(2); ...
vows.ey_bgen(1) vows.ey_bgen(2); ...
vows.eh_bgen(1) vows.eh_bgen(2); ...
vows.ah_bgen(1) vows.ah_bgen(2); ...
vows.ow_bgen(1) vows.ow_bgen(2)];
vsa.AVS(iVSA) = mean(pdist(posList));
% baseline training
iVSA = iVSA+1;
currTrials = inds.bTrainIndices;
vowelList = allVowels(currTrials);
uLoc = currTrials(vowelList==3);
iLoc = currTrials(vowelList==1);
aLoc = currTrials(vowelList==4);
aeLoc = currTrials(vowelList==2);
vows.u_btrain(1) = nanmean(f1sIn(uLoc)); vows.u_btrain(2) = nanmean(f2sIn(uLoc));
vows.i_btrain(1) = nanmean(f1sIn(iLoc)); vows.i_btrain(2) = nanmean(f2sIn(iLoc));
vows.a_btrain(1) = nanmean(f1sIn(aLoc)); vows.a_btrain(2) = nanmean(f2sIn(aLoc));
vows.ae_btrain(1) = nanmean(f1sIn(aeLoc)); vows.ae_btrain(2) = nanmean(f2sIn(aeLoc));
clearVars.int.u_btrain = nanmean(ints(uLoc));
clearVars.int.i_btrain = nanmean(ints(iLoc));
clearVars.int.a_btrain = nanmean(ints(aLoc));
clearVars.int.ae_btrain = nanmean(ints(aeLoc));
clearVars.dur.u_btrain = nanmean(durs(uLoc));
clearVars.dur.i_btrain = nanmean(durs(iLoc));
clearVars.dur.a_btrain = nanmean(durs(aLoc));
clearVars.dur.ae_btrain = nanmean(durs(aeLoc));
clearVars.f0Max.u_btrain = nanmean(f0Maxs(uLoc));
clearVars.f0Max.i_btrain = nanmean(f0Maxs(iLoc));
clearVars.f0Max.a_btrain = nanmean(f0Maxs(aLoc));
clearVars.f0Max.ae_btrain = nanmean(f0Maxs(aeLoc));
clearVars.f0Range.u_btrain = nanmean(f0Ranges(uLoc));
clearVars.f0Range.i_btrain = nanmean(f0Ranges(iLoc));
clearVars.f0Range.a_btrain = nanmean(f0Ranges(aLoc));
clearVars.f0Range.ae_btrain = nanmean(f0Ranges(aeLoc));
vsa.VSA_btrain = polyarea([vows.i_btrain(1) vows.ae_btrain(1)...
vows.a_btrain(1) vows.u_btrain(1)], ...
[vows.i_btrain(2) vows.ae_btrain(2)...
vows.a_btrain(2) vows.u_btrain(2)]);
vsa.VSA(iVSA) = vsa.VSA_btrain;
%calculate AVS
posList = [vows.i_btrain(1) vows.i_btrain(2); ...
vows.ae_btrain(1) vows.ae_btrain(2);...
vows.u_btrain(1) vows.u_btrain(2);...
vows.a_btrain(1) vows.a_btrain(2)];
vsa.AVS(iVSA) = mean(pdist(posList));
% training phase
for j = inds.trainIndices(1):binSizeTrain:inds.trainIndices(end)
iVSA = iVSA+1;
currTrials = j:j+binSizeTrain-1;
vowelList = allVowels(currTrials);
uLoc = currTrials(vowelList==3); iLoc = currTrials(vowelList==1);
aLoc = currTrials(vowelList==4); aeLoc = currTrials(vowelList==2);
vows.u_train(1) = nanmean(f1sIn(uLoc)); vows.u_train(2) = nanmean(f2sIn(uLoc));
vows.i_train(1) = nanmean(f1sIn(iLoc)); vows.i_train(2) = nanmean(f2sIn(iLoc));
vows.a_train(1) = nanmean(f1sIn(aLoc)); vows.a_train(2) = nanmean(f2sIn(aLoc));
vows.ae_train(1) = nanmean(f1sIn(aeLoc)); vows.ae_train(2) = nanmean(f2sIn(aeLoc));
clearVars.int.u_train = nanmean(ints(uLoc));
clearVars.int.i_train = nanmean(ints(iLoc));
clearVars.int.a_train = nanmean(ints(aLoc));
clearVars.int.ae_train = nanmean(ints(aeLoc));
clearVars.dur.u_train = nanmean(durs(uLoc));
clearVars.dur.i_train = nanmean(durs(iLoc));
clearVars.dur.a_train = nanmean(durs(aLoc));
clearVars.dur.ae_train = nanmean(durs(aeLoc));
clearVars.f0Max.u_train = nanmean(f0Maxs(uLoc));
clearVars.f0Max.i_train = nanmean(f0Maxs(iLoc));
clearVars.f0Max.a_train = nanmean(f0Maxs(aLoc));
clearVars.f0Max.ae_train = nanmean(f0Maxs(aeLoc));
clearVars.f0Range.u_train = nanmean(f0Ranges(uLoc));
clearVars.f0Range.i_train = nanmean(f0Ranges(iLoc));
clearVars.f0Range.a_train = nanmean(f0Ranges(aLoc));
clearVars.f0Range.ae_train = nanmean(f0Ranges(aeLoc));
vsa.VSA_train = polyarea([vows.i_train(1) vows.ae_train(1)...
vows.a_train(1) vows.u_train(1)], ...
[vows.i_train(2) vows.ae_train(2)...
vows.a_train(2) vows.u_train(2)]);
vsa.VSA(iVSA) = vsa.VSA_train;
%calculate AVS
posList = [vows.i_train(1) vows.i_train(2); ...
vows.ae_train(1) vows.ae_train(2);...
vows.u_train(1) vows.u_train(2);...
vows.a_train(1) vows.a_train(2)];
vsa.AVS(iVSA) = mean(pdist(posList));
end
% generalization phase
iVSA = iVSA+1;
currTrials = inds.genIndices;
vowelList = allVowels(currTrials);
ihLoc = currTrials(vowelList == 5); % bid
ehLoc = currTrials(vowelList == 7); % bed
eyLoc = currTrials(vowelList == 6); % bayed
owLoc = currTrials(vowelList == 8); % bode
ahLoc = currTrials(vowelList == 9); % bud
vows.ih_gen(1) = nanmean(f1sIn(ihLoc)); vows.ih_gen(2) = nanmean(f2sIn(ihLoc));
vows.eh_gen(1) = nanmean(f1sIn(ehLoc)); vows.eh_gen(2) = nanmean(f2sIn(ehLoc));
vows.ey_gen(1) = nanmean(f1sIn(eyLoc)); vows.ey_gen(2) = nanmean(f2sIn(eyLoc));
vows.ow_gen(1) = nanmean(f1sIn(owLoc)); vows.ow_gen(2) = nanmean(f2sIn(owLoc));
vows.ah_gen(1) = nanmean(f1sIn(ahLoc)); vows.ah_gen(2) = nanmean(f2sIn(ahLoc));
clearVars.int.ih_gen = nanmean(ints(ihLoc));
clearVars.int.eh_gen = nanmean(ints(ehLoc));
clearVars.int.ey_gen = nanmean(ints(eyLoc));
clearVars.int.ow_gen = nanmean(ints(owLoc));
clearVars.int.ah_gen = nanmean(ints(ahLoc));
clearVars.f0Max.ih_gen = nanmean(f0Maxs(ihLoc));
clearVars.f0Max.eh_gen = nanmean(f0Maxs(ehLoc));
clearVars.f0Max.ey_gen = nanmean(f0Maxs(eyLoc));
clearVars.f0Max.ow_gen = nanmean(f0Maxs(owLoc));
clearVars.f0Max.ah_gen = nanmean(f0Maxs(ahLoc));
clearVars.f0Range.ih_gen = nanmean(f0Ranges(ihLoc));
clearVars.f0Range.eh_gen = nanmean(f0Ranges(ehLoc));
clearVars.f0Range.ey_gen = nanmean(f0Ranges(eyLoc));
clearVars.f0Range.ow_gen = nanmean(f0Ranges(owLoc));
clearVars.f0Range.ah_gen = nanmean(f0Ranges(ahLoc));
clearVars.dur.ih_gen = nanmean(durs(ihLoc));
clearVars.dur.eh_gen = nanmean(durs(ehLoc));
clearVars.dur.ey_gen = nanmean(durs(eyLoc));
clearVars.dur.ow_gen = nanmean(durs(owLoc));
clearVars.dur.ah_gen = nanmean(durs(ahLoc));
vsa.VSA_gen = polyarea([vows.ih_gen(1) vows.ey_gen(1) vows.eh_gen(1)...
vows.ah_gen(1) vows.ow_gen(1)], ...
[vows.ih_gen(2) vows.ey_gen(2) vows.eh_gen(2)...
vows.ah_gen(2) vows.ow_gen(2)]);
vsa.VSA(iVSA) = NaN;
%calculate AVS
posList = [vows.ih_gen(1) vows.ih_gen(2); ...
vows.ey_gen(1) vows.ey_gen(2); ...
vows.eh_gen(1) vows.eh_gen(2); ...
vows.ah_gen(1) vows.ah_gen(2); ...
vows.ow_gen(1) vows.ow_gen(2)];
vsa.AVS(iVSA) = mean(pdist(posList));
end
% all in Mels; perturbations are calculated per vowel per individual
% compensations: train - baseline OR generalization - baseline;
% compensations.vow \in 2*10 (first two formants * binSize)
% scalar_projs: call helper function scalar_proj(compensation, getPertPhi)
% scalar_projs.vow \in 1*10
% vector_projs: call helper function vector_proj(compensation, getPertPhi)
% vector_projs.vow \in 2*10
function [compensations,scalar_projs,vector_projs,dists,distsBase,distsEnd,clearVarsSorted,perts] = measure_changes(vows,clearVars,pertPhi,fLims,fCen)
% setup variables
u_train = vows.u_train; u_btrain = vows.u_btrain;
i_train = vows.i_train; i_btrain = vows.i_btrain;
a_train = vows.a_train; a_btrain = vows.a_btrain;
ae_train = vows.ae_train; ae_btrain = vows.ae_btrain;
ih_gen = vows.ih_gen; ih_bgen = vows.ih_bgen;
eh_gen = vows.eh_gen; eh_bgen = vows.eh_bgen;
ey_gen = vows.ey_gen; ey_bgen = vows.ey_bgen;
ow_gen = vows.ow_gen; ow_bgen = vows.ow_bgen;
ah_gen = vows.ah_gen; ah_bgen = vows.ah_bgen;
compensations.('iy') = i_train - i_btrain;
compensations.('ae') = ae_train - ae_btrain;
compensations.('uw') = u_train - u_btrain;
compensations.('aa') = a_train - a_btrain;
compensations.('ih') = ih_gen - ih_bgen;
compensations.('eh') = eh_gen - eh_bgen;
compensations.('ey') = ey_gen - ey_bgen;
compensations.('ow') = ow_gen - ow_bgen;
compensations.('ah') = ah_gen - ah_bgen;