Modelfree estimation of a psychometric function 


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Unpublished data from S. Carcagno, Lancaster University, July 2008.
MatLab R The subject had to identify the interval containing a tone whose fundamental frequency was different from that in the other two intervals. The symbols in the figure below show the proportion of correct responses as the difference between the tones varied. There were 3–49 trials at each stimulus level.
Parametric and local linear fitting
Three different parametric models and the local linear fitting are used and fits are plotted against the measured psychometric data. Three different parametric models are fitted to these data: Gaussian (probit), Weibull, and reverse Weibull. Local linear fitting is also performed with the bandwidth
bwd
chosen by the minimising crossvalidated deviance.Load the data and plot the measured psychometric data (black dots):
clear, load examples/example_03;
figure; plot( x, r ./ m, 'k.'); axis([1.85 4.45 0.2 1.02]); axis square;1. For the Gaussian cumulative distribution function (black curve):
degpol = 1; % Degree of the polynomial
guessing = 1/3; % guessing rate
lapsing = 0; % lapsing rate
b = binomfit_lims( r, m, x, degpol, 'probit', guessing, lapsing );
numxfit = 199; % Number of new points to be generated minus 1
xfit = [min(x):(max(x)min(x))/numxfit:max( x ) ]';
% Plot the fitted curve
pfit = binomval_lims( b, xfit, 'probit', guessing, lapsing );
hold on, plot( xfit, pfit, 'k' );2. For the Weibull function (red curve):
initK = 2; % Initial power parameter in Weibull/reverse Weibull model
[ b, K ] = binom_weib( r, m, x, degpol, initK, guessing, lapsing );
% Plot the fitted curve
pfit = binomval_lims( b, xfit, 'weibull', guessing, lapsing, K );
hold on, plot( xfit, pfit, 'r' );3. For the reverse Weibull function (green curve):
[ b, K ] = binom_revweib( r, m, x, degpol, initK, guessing, lapsing );
% Plot the fitted curve
pfit = binomval_lims( b, xfit, 'revweibull', guessing, lapsing, K );
hold on, plot( xfit, pfit, 'g' );4. For the local linear fit (blue curve):
bwd_min = min( diff( x ) );
bwd_max = max( x )  min( x );
bwd = bandwidth_cross_validation( r, m, x, [ bwd_min, bwd_max ] );
% Plot the fitted curve
bwd = bwd(3); % choose the third estimate, which is based on crossvalidated deviance
pfit = locglmfit( xfit, r, m, x, bwd );
hold on, plot( xfit, pfit, 'b' );