Plot the distributions of two gaussians along with a criterion, in the style of decision theory.

plotDecision.m

function h = plotDecision(dprime,sdnoise,criterion)
% h = plotDecision(dprime, sdnoise, criterion)
%
% plotDecision makes a plot of two Gaussian functions, inspired by signal
% detection theory.  The inputs are given in terms of standard deviates of
% the signal distribution:
%
% dprime    - d', the distance between the means of the Gaussians
% sdnoise   - $\sigma$, the ratio of the standard deviation of the noise to
%             the standard deviation of the signal.
% criterion - The decision line between the two distributions.  In the same
%             units as d'
%
% The figure handle h is returned to the user for further manipulations.

% Change the settings here for how the gaussians are displayed.
xmin = -3; 
xmax = 3;
labelsize = 20;
numbersize = 16;
criterionlinewidth = 2;
doLegend = 1;

% Do the plotting!
ir = linspace(xmin,xmax,1e6);
h = figure();
plot(ir,normpdf(ir,0,1),'b','LineWidth',1);
hold on;
plot(ir,normpdf(ir,dprime,sdnoise),'r','LineWidth',1);

% Rescale the y axis so that you can clearly see the peaks
a = axis();
axis([a(1) a(2) a(3) a(4)*1.2]);
a = axis();

% Draw the line from top to bottom.
l = line([criterion; criterion],[a(3);a(4)]);
set(l,'Color','k','LineWidth',criterionlinewidth)
hold off

% Set the font sizes and other figure options.
ax = get(h,'CurrentAxes');
set(ax,'FontSize',numbersize)
xlabel('Standard Deviates','FontSize',labelsize)
set(ax,'YTickLabel',[])
if doLegend
    legend('Signal','Noise')
end
end