THE AUDITORY MODELING TOOLBOX
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exp_eurich2022
Reproduces figures from Eurich et al. (2022)
Program code:
function exp_eurich2022(varargin)
%exp_eurich2022 Reproduces figures from Eurich et al. (2022)
% Usage: data = exp_eurich2022(flag)
%
% EXP_EURICH2022(flag) reproduces figures of the study from
% Eurich et al. (2022)
%
% The following flags can be specified:
%
% 'fig3' Reproduces Figure 3 from Eurich et al. (2022).
% Experimental data from van der Heijden and
% Trahiotis (1999) (symbols). The continuous lines show the predictions of
% the presented model including the across-channel incoherence interference.
% The dashed lines show predictions for ODN excluding interference (single-
% channel version), equivalent to Encke and Dietz (2022). Upper panel:
% Detection thresholds with S_0 targets. Lower panel: S_pi targets.
%
% 'fig4' Reproduces Figure 4.
% Experimental data from Marquardt and McAlpine
% (2009) (symbols) and model predictions (lines). Detection thresholds are
% given as function of the inner-band bandwidth. The inner band contains
% delayed noise (triangles) or opposingly delayed noises (diamonds and bul-
% lets) with a fixed ITD of 1 ms while the flanking bands have a constant IPD
% of +pi/2 (upward triangle and diamond) and -pi/2, or vice versa (down-
% ward triangle and bullet). Continuous and dashed lines again show predic-
% tions with and without across-frequency incoherence interference,
% respectively.
%
% 'fig5' Reproduces Figure 5.
% Symbols denote data from binaural detection experiments with
% the configuration N_{pi 0pi} Spi as a function of the inner-band (N_0)
% bandwidth; continuous and dotted line: model prediction with and without
% across-incoherence incoherence interference, respectively.
%
%
% Further, cache flags (see AMT_CACHE) and plot flags can be specified:
%
% 'plot' Plot the output of the experiment. Default.
%
% 'no_plot' Don't plot, only return data.
%
% 'accurate' Accurate results, slow calculations, but as originally
% done to reproduce figures from Eurich et al. (2022).
% Default.
%
% 'fast' Faster calculations by using a subset of conditions, less repetitions, and shorter
% signals. These results won't be cached.
%
%
% Examples:
% ---------
%
% To display Figure 3 use :
%
% exp_eurich2022('fig3');
%
% To display Figure 4 use :
%
% exp_eurich2022('fig4');
%
% To display Figure 5 use :
%
% exp_eurich2022('fig5');
%
%
% See also: eurich2022, eurich2022_processing, eurich2022_decision,
% sig_vanderheijden1999, sig_marquardt2009, sig_kolarik2010
%
% References:
% J. Encke and M. Dietz. A hemispheric two-channel code accounts for
% binaural unmasking in humans. Communications Biology, 5:1122, Oct.
% 2022.
%
% T. Marquardt and D. McAlpine. Masking with interaurally
% “double-delayed” stimuli: The range of internal delays in the human
% brain. The Journal of the Acoustical Society of America,
% 126(6):EL177--EL182, 11 2009.
%
% M. van der Heijden and C. Trahiotis. Masking with interaurally delayed
% stimuli: The use of “internal” delays in binaural detection. The
% Journal of the Acoustical Society of America, 105(1):388--399, 01 1999.
%
% B. Eurich, J. Encke, S. D. Ewert, and M. Dietz. Lower interaural
% coherence in off-signal bands impairs binaural detection. The Journal
% of the Acoustical Society of America, 151(6):3927--3936, 06 2022.
%
%
% Url: http://amtoolbox.org/amt-1.6.0/doc/experiments/exp_eurich2022.php
% #Author: Bernhard Eurich (2023): Original code
% #Author: Piotr Majdak (2023): Integration in the AMT 1.4
% #Author: Piotr Majdak (2024): 'fast' and 'accurate' options added, some rework for AMT 1.6.
% This file is licensed unter the GNU General Public License (GPL) either
% version 3 of the license, or any later version as published by the Free Software
% Foundation. Details of the GPLv3 can be found in the AMT directory "licences" and
% at <https://www.gnu.org/licenses/gpl-3.0.html>.
% You can redistribute this file and/or modify it under the terms of the GPLv3.
% This file is distributed without any warranty; without even the implied warranty
% of merchantability or fitness for a particular purpose.
%% ------ Check input options --------------------------------------------
definput.import={'amt_cache'};
definput.flags.type = {'missingflag','fig3','fig4','fig5'};
definput.flags.plot = {'plot','no_plot'};
definput.flags.speed = {'accurate','fast'};
definput.keyvals.FontSize = 9;
definput.keyvals.MarkerSize = 3;
definput.import={'amt_cache'};
flags = ltfatarghelper({'FontSize','MarkerSize'},definput,varargin);
%the error message:
if flags.do_missingflag
flagnames=[sprintf('%s, ',definput.flags.type{2:end-2}),...
sprintf('%s or %s',definput.flags.type{end-1},definput.flags.type{end})];
error('%s: You must specify one of the following flags: %s.',upper(mfilename),flagnames);
end
%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%
%% ------ FIGURE 3 -----------------------------------------------------------
if flags.do_fig3
%any calculations associated to Figure 3 in the publication
if flags.do_plot
fs = 48e3;
dur = 300e-3; % s
if flags.do_fast, dur = 100e-3; end
cos_rise_time = 0; % because actually not used 20e-3;
bw = 900;
fc = 500;
spl = 45.5; % noise spectrum level
% variable stimulus parameters
itd = (0:0.125:4)*1e-3; % this is the tracking variable
if flags.do_fast, itd = ([0:0.5:2 4])*1e-3; end
sphase = [-1 1];
tone_level = 30:2:65; % target level / dB SPL
noise_mode = [1 2]; % 1: single delayed noise; 2: opposingly delayed noises
repititions = 1:50;
if flags.do_fast, repititions=1:25; end
tic
% model parameters (default -- adjusted parameters for S0 condition are set in local_evaluation_fig3)
definput = arg_eurich2022; mpar=definput.keyvals;
mpar.interference_sigma = [0 mpar.interference_sigma];
filename = 'processed_average_fig3';%1
processed_average_fig3 = amt_cache('get',filename,flags.cachemode); %2
if isempty(processed_average_fig3)%3
parfor i_itd = 1:length(itd)
processed_average_fig3(i_itd,:,:,:,:,:,:) = local_evaluation_fig3(itd(i_itd),noise_mode,sphase,tone_level,repititions,fs,dur,cos_rise_time,bw,fc,spl,mpar);
end
if flags.do_accurate, amt_cache('set',filename,processed_average_fig3); end % do not save when fast
end
% decision
filename2 = 'dprime_fig3';%1
dprime_fig3 = amt_cache('get',filename2,flags.cachemode);
if isempty(dprime_fig3)
for i_interference_sigma = 1:length(mpar.interference_sigma)
for i_noise_mode = 1:length(noise_mode)
for i_itd = 1:length(itd)
for i_sphase = 1:length(sphase)
for i_tone_level = 1:length(tone_level)
for i_repititions = 1:length(repititions)
dprime_fig3(i_interference_sigma,i_noise_mode,i_itd, i_sphase,i_tone_level) = eurich2022_decision(squeeze(processed_average_fig3(i_itd,i_interference_sigma,i_noise_mode, i_sphase,i_tone_level,:,:)), mpar);
end
end
end
end
end
end
if flags.do_accurate, amt_cache('set',filename2,dprime_fig3); end % do not save when fast
end
% determine threshold
% dprime scheint jetzt zu stimmen -- ab hier noch updaten bzgl. interference
dprime_threshold = 0.78;
% range of assumed linear psychometric function
dprime0 = 0.5;
dprime1 = 10^0.4;
for i_interference_sigma = 1:length(mpar.interference_sigma)
for i_noise_mode = 1:length(noise_mode)
for i_itd = 1:length(itd)
for i_sphase = 1:length(sphase)
dprime_temp = squeeze(dprime_fig3(i_interference_sigma,i_noise_mode,i_itd,i_sphase,:));
idx0 = find(dprime_temp > dprime0 & dprime_temp < dprime1);
d_eval = squeeze(log10(dprime_temp(idx0)));
p0 = 20e-6;
level = tone_level(idx0);
% level(:,c,b,a) = (p0 * 10.^(spar.dbspl_tone(idx0) / 20)) ./ val; %
% fit regression line
d_eval(~isfinite(d_eval)) = NaN;
level1 = [ones(size(level')) level'];
slope = level1 \ d_eval;
d_fit = level1 * slope;
level_thresh(i_interference_sigma,i_noise_mode,i_itd, i_sphase) = (log10(dprime_threshold) - slope(1)) / slope(2);
end
end
end
end
% Plotting
x.paperdata_pi=amt_load('eurich2022', 'vdHTr3b_1.mat');
x.paperdata_0=amt_load('eurich2022', 'vdHTr3a_2.mat');
data_raw_pi = x.paperdata_pi.paperdata_pi(:,2:3);
data_raw_0 = x.paperdata_0.paperdata_0(:,2:3);
data_pi(:,1) = data_raw_pi(~isnan(data_raw_pi(:,1)),1);
data_pi(:,2) = data_raw_pi(~isnan(data_raw_pi(:,2)),2);
data_0(:,1) = data_raw_0(~isnan(data_raw_0(:,1)),1);
data_0(:,2) = data_raw_0(~isnan(data_raw_0(:,2)),2);
itd_data = 0:0.125:4.125;
col = [0 0.3647 0.6235; 0.5000 0.5000 0.5000; ...
0.5792 0.5930 0; 0.0235 0.5922 0.8392; ...
0.7000 0 0.2000; 0 0.1621 0.2771; ...
0.3000 0.3000 0.3; 0 0.3242 0.5542; ...
0 0.2837 0.4849; 0.7000 0.7000 0.7000; ...
0 0.2431 0.4157];
figure('Name','fig3', 'Position',[500 80 400 20/16*400]);
subplot 211
hold on;
plot(itd_data, data_0(:,1), 's','Color',col(1,:));
plot(itd_data, data_0(:,2), 'd','Color',col(2,:));
% i_interference_sigma,i_noise_mode,i_itd, i_sphase
plot(itd*1e3,squeeze(level_thresh(1,2,:,2)),'LineStyle',':','Color',col(1,:));
plot(itd*1e3,squeeze(level_thresh(1,1,:,2)),'LineStyle',':','Color',col(2,:));
plot(itd*1e3,squeeze(level_thresh(2,2,:,2)),'LineStyle','-','Color',col(1,:));
plot(itd*1e3,squeeze(level_thresh(2,1,:,2)),'LineStyle','-','Color',col(2,:));
ylim([45 61])
ylabel('Threshold (dB)');
xlim([-0.1 4.2])
xticks([0 1 2 3 4])
yticks([46:2:60])
grid on;
set(gca,'GridLineStyle',':','LineWidth',1)
subplot 212
hold on;
plot(itd_data, data_pi(:,1), 's','Color',col(1,:));
plot(itd_data, data_pi(:,2), 'd','Color',col(2,:));
plot(itd*1e3,squeeze(level_thresh(1,2,:,1)),'LineStyle',':','Color',col(1,:));
plot(itd*1e3,squeeze(level_thresh(1,1,:,1)),'LineStyle',':','Color',col(2,:));
plot(itd*1e3,squeeze(level_thresh(2,2,:,1)),'LineStyle','-','Color',col(1,:));
plot(itd*1e3,squeeze(level_thresh(2,1,:,1)),'LineStyle','-','Color',col(2,:));
xlim([-0.1 4.2])
xticks([0 1 2 3 4])
yticks(46:2:60)
ylim([45 61])
grid on;
set(gca,'GridLineStyle',':','LineWidth',1)
ylabel('Threshold (dB)');
xlabel('ITD (ms)')
annotation('textbox',[.85 .57 .1 .1],'String','$S_0$','interpreter','Latex','EdgeColor',[1 1 1])
annotation('textbox',[.85 .1,.1 .1],'String','$S_{\pi}$','interpreter','Latex','EdgeColor',[1 1 1])
legend( 'SDN', 'ODN','Location','South','NumColumns',2);
legend boxoff
end
end
%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%
% ------ FIGURE 4 -----------------------------------------------------------
if flags.do_fig4
%any calculations associated to Figure 4 in the publication
if flags.do_plot
fs = 48e3;
dur = 300e-3; % s
if flags.do_fast, dur = 100e-3; end
cos_rise_time = 0; % actually not used 20e-3;
bw = 900;
fc = 500;
spl = 45.5; % noise spectrum level
% variable stimulus parameters
tone_level = 40:2:70; % target level / dB SPL
noise_mode = [1 2]; % 1: single delayed noise; 2: opposingly delayed noises
flanking_phase = [pi/2 -pi/2]; % the other flank gets the other sign
innerbw = [900 600 400 200 100 50 0];
if flags.do_fast, innerbw = [900 400 200 100 0]; end
repititions = 1:50;
if flags.do_fast, repititions = 1:30; end
tic
% model parameters
definput = arg_eurich2022; mpar=definput.keyvals;
mpar.interference_sigma = [0 mpar.interference_sigma];
% adjust model parameters as optimized for predicting the data by
% Marquardt & McAlpine
mpar.binaural_internal_noise_sigma = 0.24;
mpar.monaural_internal_noise_sigma = 0.4;
mpar.rho_max = 0.89;
mpar.interference_sigma = [0 0.65];
filename = 'processed_average_fig4';%1
processed_average_fig4 = amt_cache('get',filename,flags.cachemode); %2
if isempty(processed_average_fig4)%3
parfor i_innerbw = 1:length(innerbw)
processed_average_fig4(i_innerbw,:,:,:,:,:,:) = ...
local_evaluation_fig4(innerbw(i_innerbw),noise_mode,flanking_phase,tone_level,repititions,fs,dur,cos_rise_time,bw,fc,spl,mpar);
end
if flags.do_accurate, amt_cache('set',filename,processed_average_fig4); end
end
%% decision
filename2 = 'dprime_fig4';%1
dprime_fig4 = amt_cache('get',filename2,flags.cachemode);
if isempty(dprime_fig4)
for i_interference_sigma = 1:length(mpar.interference_sigma)
for i_noise_mode = 1:length(noise_mode)
for i_innerbw = 1:length(innerbw)
for i_flanking_phase = 1:length(flanking_phase)
for i_tone_level = 1:length(tone_level)
dprime_fig4(i_interference_sigma,i_noise_mode,i_innerbw, i_flanking_phase,i_tone_level) = eurich2022_decision(squeeze(processed_average_fig4(i_innerbw,i_interference_sigma,i_noise_mode,i_flanking_phase,i_tone_level,:,:)), mpar);
end
end
end
end
end
if flags.do_accurate, amt_cache('set',filename2,dprime_fig4); end
end
%% determine threshold
dprime_threshold = 1.14;
% range of assumed linear psychometric function
dprime0 = 0.5;
dprime1 = 10^0.4;
for i_interference_sigma = 1:length(mpar.interference_sigma)
for i_noise_mode = 1:length(noise_mode)
for i_innerbw = 1:length(innerbw)
for i_flanking_phase = 1:length(flanking_phase)
dprime_temp = squeeze(dprime_fig4(i_interference_sigma,i_noise_mode,i_innerbw,i_flanking_phase,:));
idx0 = find(dprime_temp > dprime0 & dprime_temp < dprime1);
d_eval = squeeze(log10(dprime_temp(idx0)));
p0 = 20e-6;
level = tone_level(idx0);
% level(:,c,b,a) = (p0 * 10.^(spar.dbspl_tone(idx0) / 20)) ./ val; %
% fit regression line
d_eval(~isfinite(d_eval)) = NaN;
level1 = [ones(size(level')) level'];
slope = level1 \ d_eval;
d_fit = level1 * slope;
level_thresh(i_interference_sigma,i_noise_mode,i_innerbw, i_flanking_phase) = (log10(dprime_threshold) - slope(1)) / slope(2);
end
end
end
end
% Plotting
x.paperdata = amt_load('eurich2022', 'MqMc_2c.mat');
data_raw = x.paperdata.paperdata(:,2:5);
legstr = {'$+\pi/2$, SDN, $-\pi/2$'; '$+\pi/2$, ODN, $-\pi/2$';'$-\pi/2$, SDN, $+\pi/2$'; '$-\pi/2$, ODN, $+\pi/2$';'diotic'};
col = [0 0.3647 0.6235; 0.5000 0.5000 0.5000; ...
0.5792 0.5930 0; 0.0235 0.5922 0.8392; ...
0.7000 0 0.2000; 0 0.1621 0.2771; ...
0.3000 0.3000 0.3; 0 0.3242 0.5542; ...
0 0.2837 0.4849; 0.7000 0.7000 0.7000; ...
0 0.2431 0.4157];
datadiotic = 61.95;
preddiotic = 62.1;
f = figure('Position',[60 100 309.9213 193.7008]);
hold on;
% Predictions
% with interference
p1 = [fliplr(squeeze(level_thresh(2,2,:,1)))];
p2 = [fliplr(squeeze(level_thresh(2,1,:,1)))];
p3 = [fliplr(squeeze(level_thresh(2,2,:,2)))];
p4 = [fliplr(squeeze(level_thresh(2,1,:,2)))];
%without interference
p1_no = [fliplr(squeeze(level_thresh(1,2,:,1)))];
p2_no = [fliplr(squeeze(level_thresh(1,1,:,1)))];
p3_no = [fliplr(squeeze(level_thresh(1,2,:,2)))];
p4_no = [fliplr(squeeze(level_thresh(1,1,:,2)))];
h1(1) = plot(p1,'-','Color',col(1,:),'DisplayName','none','LineWidth',2);
hold on;
h1(2) = plot(p2,'-','Color',col(2,:),'DisplayName','none','LineWidth',2);
h1(3) = plot(p3,'-','Color',col(3,:),'DisplayName','none','LineWidth',2);
h1(4) = plot(p4,'-','Color',col(4,:),'DisplayName','none','LineWidth',2);
h1(1) = plot(p1_no,'-','Color',col(1,:),'DisplayName','none','LineStyle',':','LineWidth',2);
h1(2) = plot(p2_no,'-','Color',col(2,:),'DisplayName','none','LineStyle',':','LineWidth',2);
h1(3) = plot(p3_no,'-','Color',col(3,:),'DisplayName','none','LineStyle',':','LineWidth',2);
h1(4) = plot(p4_no,'-','Color',col(4,:),'DisplayName','none','LineStyle',':','LineWidth',2);
plot(7,preddiotic,':','Color',col(1,:))
plot(7,preddiotic,':','Color',col(2,:))
plot(7,preddiotic,':','Color',col(3,:))
plot(7,preddiotic,':','Color',col(4,:))
if flags.do_accurate
xticks(1:length(innerbw));
xticklabels({'full','600','400','200','100','50','0'});
xlabel('Inner-band Bandwidth (Hz)');
end
% Data
markerstyles = {'^','v','d','s'};
paperdata2 = data_raw;%(:,2:end);
for n = 1:size(paperdata2,2)
data(:,n) = paperdata2(~isnan(paperdata2(:,n)),n);
k(n) = plot(flipud(data(:,n)),'LineStyle','none','Marker',markerstyles{n},'Color',col(n,:),'MarkerFaceColor',col(n,:),'LineWidth',2);
end
diotic = plot(7,datadiotic,'o','Color',[0.3 0.3 0.3],'LineWidth',2);
ylim([50 63])
xlim([0.5 7.3])
ylabel('Threshold (dB)');
grid on;
set(gca,'GridLineStyle',':','LineWidth',0.7)
lg = legend([k(1),k(3),diotic,k(2),k(4)], legstr([1 2 5 3 4]),'Location','Northwest', 'NumColumns',2,'FontSize',8,'interpreter','latex');
legend boxoff
lg.Position(1) = 0.06;
lg.Position(2) = 0.7;
lg.ItemTokenSize(1) = 10;
end
end
%% ------ FIGURE 5 -----------------------------------------------------------
if flags.do_fig5
%any calculations associated to Figure 5 in the publication
if flags.do_plot
fs = 48e3;
dur = 300e-3; % s
if flags.do_fast, dur = 150e-3; end
cos_rise_time = 0; % because actually unused 20e-3;
bw = 1000;
fc = 500;
spl = 45.5; % noise spectrum level
noise_mode = 1; % 1: single delayed noise; 2: opposingly delayed noises (not used here)
flanking_phase = pi; % IPD of flanking bands
% variable stimulus parameters
tone_level = [40:2:70]; % target level / dB SPL
innerbw = [0 50 100 200 300 400 600 800 1000];
if flags.do_fast, innerbw = [0 200 600 1000]; end
repititions = 1:100;
if flags.do_fast, repititions = 1:50; end
% model parameters
definput = arg_eurich2022; mpar=definput.keyvals;
mpar.interference_sigma = [0 mpar.interference_sigma];
mpar.rho_max = 0.91;
filename = 'processed_average_fig5';%1
processed_average_fig5 = amt_cache('get',filename,flags.cachemode); %2
if isempty(processed_average_fig5)%3
parfor i_innerbw = 1:length(innerbw)
processed_average_fig5(i_innerbw,:,:,:,:,:,:) = ...
local_evaluation_fig5(innerbw(i_innerbw),noise_mode,flanking_phase,tone_level,repititions,fs,dur,cos_rise_time,bw,fc,spl,mpar);
end
if flags.do_accurate, amt_cache('set',filename,processed_average_fig5); end
end
% decision
filename2 = 'dprime_fig5';%1
dprime_fig5 = amt_cache('get',filename2,flags.cachemode);
if isempty(dprime_fig5)
for i_interference_sigma = 1:length(mpar.interference_sigma)
for i_noise_mode = 1:length(noise_mode)
for i_innerbw = 1:length(innerbw)
for i_flanking_phase = 1:length(flanking_phase)
for i_tone_level = 1:length(tone_level)
dprime_fig5(i_interference_sigma,i_noise_mode,i_innerbw, i_flanking_phase,i_tone_level) = eurich2022_decision(squeeze(processed_average_fig5(i_innerbw,i_interference_sigma,i_noise_mode,i_flanking_phase,i_tone_level,:,:)), mpar);
end
end
end
end
end
if flags.do_accurate, amt_cache('set',filename2,dprime_fig5); end
end
% determine threshold
dprime_threshold = 0.78;
% range of assumed linear psychometric function
dprime0 = 0.5;
dprime1 = 10^0.4;
for i_interference_sigma = 1:length(mpar.interference_sigma)
for i_noise_mode = 1:length(noise_mode)
for i_innerbw = 1:length(innerbw)
for i_flanking_phase = 1:length(flanking_phase)
dprime_temp = squeeze(dprime_fig5(i_interference_sigma,i_noise_mode,i_innerbw,i_flanking_phase,:));
idx0 = find(dprime_temp > dprime0 & dprime_temp < dprime1);
d_eval = squeeze(log10(dprime_temp(idx0)));
p0 = 20e-6;
level = tone_level(idx0);
% level(:,c,b,a) = (p0 * 10.^(spar.dbspl_tone(idx0) / 20)) ./ val; %
% fit regression line
d_eval(~isfinite(d_eval)) = NaN;
level1 = [ones(size(level')) level'];
slope = level1 \ d_eval;
d_fit = level1 * slope;
level_thresh(i_interference_sigma,i_noise_mode,i_innerbw, i_flanking_phase) = (log10(dprime_threshold) - slope(1)) / slope(2);
end
end
end
end
% Plotting
% represent simulations as relative to NoSpi
thresh_level_med_rel = level_thresh - level_thresh(:,:,1);
% read literature data
x.paperdata_H98_2 = amt_load('eurich2022', 'Holube98_2_ll.mat');
x.paperdata_H98_3 = amt_load('eurich2022', 'Holube98_3_ll.mat');
x.paperdata_SG66 = amt_load('eurich2022', 'Sondhi66_6.mat');
x.paperdata_KC10 = amt_load('eurich2022', 'KolCul10_4_500.mat');
% common x axis: inner band bandwidth
paperdata_y = [0 0 0 100 100 100 141 141 141 200 200 200 283 283 283 400 400 400]';
% prepare data Kolarik & Culling 2010
data_KC10 = x.paperdata_KC10.paperdata_KC10;
KolCul10_1 = data_KC10(~isnan(data_KC10(:,3)),3);
data_KC10(:,1) = paperdata_y;
% prepare data by Sondhi & Guttman 1966 and Holube et al. 1998
innerbw_KC10 = [0 100 141 200 283 400]';
innerbw_SoGu = [0 10 40 100 200 300 bw-20];
innerbw_Hol = [0 20 50 100 150 200 400 600 800 bw-20];
data_SG66 = NaN(size(x.paperdata_SG66.paperdata_SG66));
data_Hol2 = NaN(size(x.paperdata_H98_2.paperdata_H98_2));
data_Hol3 = NaN(size(x.paperdata_H98_3.paperdata_H98_3));
for s = 2:size(data_SG66,2)
rawSo = x.paperdata_SG66.paperdata_SG66(~isnan(x.paperdata_SG66.paperdata_SG66(:,s)),s);
data_SG66(1:length(rawSo),s) = rawSo;
end
data_SG66 = data_SG66(1:7,2:end);
for s = 2:size(data_Hol3,2)
rawH98_2 = x.paperdata_H98_2.paperdata_H98_2(~isnan(x.paperdata_H98_2.paperdata_H98_2(:,s)),s);
rawH98_3 = x.paperdata_H98_3.paperdata_H98_3(~isnan(x.paperdata_H98_3.paperdata_H98_3(:,s)),s);
data_Hol2(1:length(rawH98_2),s) = rawH98_2;
data_Hol3(1:length(rawH98_3),s) = rawH98_3;
end
data_Hol2 = data_Hol2(1:10,2:end);
data_Hol3 = data_Hol3(1:10,2:end);
% Plot
f = figure('Position',[60 100 309.9213 193.7008]);
col = [0 0.3647 0.6235; 0.5000 0.5000 0.5000; ...
0.5792 0.5930 0; 0.0235 0.5922 0.8392; ...
0.7000 0 0.2000; 0 0.1621 0.2771; ...
0.3000 0.3000 0.3; 0 0.3242 0.5542; ...
0 0.2837 0.4849; 0.7000 0.7000 0.7000; ...
0 0.2431 0.4157];
hold on;
h0(1) = plot(innerbw_KC10, KolCul10_1(2:end), 'o','Color',col(1,:),'LineWidth',2);
ho(1).Marker = 'o';
innerbw_SoGu(end) = 1000;
h4 = plot(innerbw_SoGu,-data_SG66(:,2),'d','LineWidth',2);
innerbw_Hol(end) = 1000;
h1 = plot(innerbw_Hol,data_Hol2(:,1),'^','LineWidth',2);
h1.Color = col(3,:);
h1.Marker = '^';
h2 = plot(innerbw_Hol,data_Hol3(:,1),'^','LineWidth',2);
h2.Color = col(4,:);
h2.Marker = 'v';
% broken_innerbw = [innerbw(1:end-1) 900 innerbw(end)];
broken_koldata(1,:) = [squeeze(thresh_level_med_rel(1,:,1:end-1)); NaN; squeeze(thresh_level_med_rel(1,:,end))];
% h1(1) =
plot(innerbw, squeeze(thresh_level_med_rel(1,:,:)),'color',[0 0 0],'Linestyle',':','LineWidth',2);
% h1(2) =
plot(innerbw, squeeze(thresh_level_med_rel(2,:,:)),'color',[0 0 0],'LineWidth',2);
% plot break
% x_breakline = [870:0.1:885.5];
% y_breakline = [-2.18,-11.80];
% h1(3) = plot(x_breakline, linspace(y_breakline(1),y_breakline(2),length(x_breakline)),'DisplayName','None','Color','black','LineWidth',0.5);
% h1(4) = plot(x_breakline+24, linspace(y_breakline(1),y_breakline(2),length(x_breakline)),'DisplayName','None','Color','black','LineWidth',0.5);
if flags.do_accurate
xticks([innerbw([1 4 6 7 8]) 1000]);
xticklabels({'0','200','400','600','800','full'})
end
ylim([-16 3]);
grid on;
set(gca,'GridLineStyle',':','LineWidth',1)
xlabel('Inner-band Bandwidth (Hz)');
ylabel('Threshold Shift (dB)');
lg =legend('Kolarik \& Culling 2010','Sondhi \& Guttman 1966','Holube et al. 1998, subj. RN','Holube et al. 1998, subj. IH','Location','NorthEast','NumColumns',1,...
'FontSize',8,'Interpreter','Latex');
legend boxoff
lg.Position(1) = 0.4;
lg.Position(2) = 0.65;
lg.ItemTokenSize(1) = 10;
end
end
function processed_average = local_evaluation_fig3(itd,noise_mode,sphase,tone_level,repititions,fs,dur,cos_rise_time,bw,fc,spl,mpar)
all_interference_sigma = mpar.interference_sigma;
amt_disp(''); % start of volatile display
for i_interference_sigma = 1:length(all_interference_sigma) % length(mpar.interference_sigma)
for i_noise_mode = 1:length(noise_mode)
for i_sphase = 1:length(sphase)
for i_tone_level = 1:length(tone_level)
amt_disp(['Recalculation for ITD: ' num2str(itd) ...
', sigma: ' num2str(i_interference_sigma) '/' int2str(length(mpar.interference_sigma)) ...
', noise: ' num2str(i_noise_mode) '/' int2str(length(noise_mode)) ...
', sphase: ' num2str(i_sphase) '/' int2str(length(sphase)) ...
', level: ' num2str(i_tone_level) '/' int2str(length(tone_level))] ...
,'volatile');
for i_repititions = 1:length(repititions)
mpar.interference_sigma = all_interference_sigma(i_interference_sigma);
c_itd = itd;
c_sphase = sphase(i_sphase);
c_noise_mode = noise_mode(i_noise_mode);
c_repitition = repititions(i_repititions);
% stimulus noise only
c_tone_level = -inf;
mRef = sig_vanderheijden1999(c_itd,c_sphase,c_noise_mode,c_tone_level,fs,dur,cos_rise_time,bw,fc,spl);
% stimulus tone in noise
c_tone_level = tone_level(i_tone_level);
mTest = sig_vanderheijden1999(c_itd,c_sphase,c_noise_mode,c_tone_level,fs,dur,cos_rise_time,bw,fc,spl);
% as this model cannot distinguish between an N0Spi and
% an NpiS0 threshold, model parameters are adjusted in the S0 condition for
% precise predictions.
if c_sphase == 1
mpar.binaural_internal_noise_sigma = 0.17;
mpar.rho_max = 0.86;
if mpar.interference_sigma > 0
mpar.interference_sigma = 0.65;
end
end
% run model for this condition
[processed(:,:,:,i_repititions)] = eurich2022(mRef,mTest,mpar);
end
processed_average(i_interference_sigma,i_noise_mode, i_sphase,i_tone_level,:,:) = mean(processed,4);
end
end
end
end
amt_disp(); %end of volatile display
function processed_average = local_evaluation_fig4(innerbw,noise_mode,flanking_phase,tone_level,repititions,fs,dur,cos_rise_time,bw,fc,spl,mpar)
all_interference_sigma = mpar.interference_sigma;
amt_disp(''); % start of volatile display
for i_interference_sigma = 1:length(mpar.interference_sigma)
for i_noise_mode = 1:length(noise_mode)
for i_flanking_phase = 1:length(flanking_phase)
for i_tone_level = 1:length(tone_level)
amt_disp(['Recalculation for BW: ' num2str(innerbw) ...
', sigma: ' num2str(i_interference_sigma) '/' int2str(length(mpar.interference_sigma)) ...
', noise: ' num2str(i_noise_mode) '/' int2str(length(noise_mode)) ...
', sphase: ' num2str(i_flanking_phase) '/' int2str(length(flanking_phase)) ...
', level: ' num2str(i_tone_level) '/' int2str(length(tone_level))] ...
,'volatile');
for i_repititions = 1:length(repititions)
mpar.interference_sigma = all_interference_sigma(i_interference_sigma);
c_innerbw = innerbw;
c_itd = 1e-3;
c_sphase = 1;
c_noise_mode = noise_mode(i_noise_mode);
c_flanking_phase = flanking_phase(i_flanking_phase);
c_repitition = repititions(i_repititions);
% stimulus noise only
c_tone_level = -inf;
mRef = sig_marquardt2009(c_innerbw,c_flanking_phase,c_noise_mode,c_tone_level,c_itd,c_sphase,fs,dur,cos_rise_time,bw,fc,spl);
% stimulus tone in noise
c_tone_level = tone_level(i_tone_level);
mTest = sig_marquardt2009(c_innerbw,c_flanking_phase,c_noise_mode,c_tone_level,c_itd,c_sphase,fs,dur,cos_rise_time,bw,fc,spl);
% run model for this condition
[processed(:,:,:,i_repititions)] = eurich2022(mRef,mTest,mpar);
end
processed_average(i_interference_sigma,i_noise_mode, i_flanking_phase,i_tone_level,:,:) = mean(processed,4);
end
end
end
end
amt_disp(); %end of volatile display
function processed_average = local_evaluation_fig5(innerbw,noise_mode,flanking_phase,tone_level,repititions,fs,dur,cos_rise_time,bw,fc,spl,mpar)
all_interference_sigma = mpar.interference_sigma;
amt_disp(''); % start of volatile display
for i_interference_sigma = 1:length(all_interference_sigma) %length(mpar.interference_sigma)
for i_noise_mode = 1:length(noise_mode)
for i_flanking_phase = 1:length(flanking_phase)
for i_tone_level = 1:length(tone_level)
amt_disp(['Recalculation for inner BW: ' num2str(innerbw) ...
': sigma: ' num2str(i_interference_sigma) '/' int2str(length(all_interference_sigma)) ...
', noise: ' num2str(i_noise_mode) '/' int2str(length(noise_mode)) ...
', sphase: ' num2str(i_flanking_phase) '/' int2str(length(flanking_phase)) ...
', level: ' num2str(i_tone_level) '/' int2str(length(tone_level))] ...
,'volatile');
for i_repititions = 1:length(repititions)
mpar.interference_sigma = all_interference_sigma(i_interference_sigma);
c_innerbw = innerbw;
c_itd = 0;
c_sphase = -1;
c_noise_mode = noise_mode(i_noise_mode);
c_flanking_phase = flanking_phase(i_flanking_phase);
% stimulus noise only
c_tone_level = -inf;
mRef = sig_kolarik2010(c_innerbw,c_flanking_phase, c_tone_level, c_sphase,fs,dur,cos_rise_time,bw,fc,spl);
% mRef = sig_kolarik2010(c_innerbw,c_flanking_phase,c_noise_mode,c_tone_level,c_itd,c_sphase,fs,dur,cos_rise_time,bw,fc,spl);
% stimulus tone in noise
c_tone_level = tone_level(i_tone_level);
mTest = sig_kolarik2010(c_innerbw,c_flanking_phase, c_tone_level, c_sphase,fs,dur,cos_rise_time,bw,fc,spl);
% mTest = sig_kolarik2010(c_innerbw,c_flanking_phase,c_noise_mode,c_tone_level,c_itd,c_sphase,fs,dur,cos_rise_time,bw,fc,spl);
% run model for this condition
[processed(:,:,:,i_repititions)] = eurich2022(mRef,mTest,mpar);
end
processed_average(i_interference_sigma,i_noise_mode, i_flanking_phase,i_tone_level,:,:) = mean(processed,4);
end
end
end
end
amt_disp(); %end of volatile display
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