Modelfree estimation of a psychometric function 


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Nascimento, S.M.C., Foster, D.H., & Amano, K. “Psychophysical estimates of the number of spectralreflectance basis functions needed to reproduce natural scenes”, Journal of the Optical Society of America AOptics Image Science and Vision, 22 (6), 10171022, 2005.
MatLab R The subject was shown an image of a natural scene and an approximation of this image based on principal component analysis. The task was to distinguish between the images. The symbols in the figure below show the proportion of correct responses as a function of number of components in the approximation. There were 200 trials at each level pooled over a range of natural scenes.
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), as follows. Notice that in the same example in Żychaluk and Foster(2009) Download PDF, the xaxis was reversed.
data("06_Nascimento")
x = example06$x
r = example06$r
m = example06$m
plot( x, r / m, xlim = c( 1.17, 7.8 ), ylim = c( 0.47, 1.03 ), type = "p", pch="*" ) # Limits set to match the MatLab1. For the Gaussian cumulative distribution function (black curve):
guess = 1/2 # guessing rate
laps = 0 # lapsing rate
val < binomfit_lims( r, m, x, link = "probit", guessing = guess, lapsing = laps )
numxfit < 199 # Number of new points to be generated minus 1
xfit < (max(x)min(x)) * (0:numxfit) / numxfit + min(x)
# Plot the fitted curve
pfit<predict( val$fit, data.frame( x = xfit ), type = "response" )
lines(xfit, pfit )2. For the Weibull function (red curve):
val < binom_weib( r, m, x, guessing = guess, lapsing = laps )
# Plot the fitted curve
pfit<predict( val$fit, data.frame( x = xfit ), type = "response" )
lines(xfit, pfit, col = "red" )3. For the reverse Weibull function (green curve):
val < binom_revweib( r, m, x, guessing = guess, lapsing = laps )
# Plot the fitted curve
pfit<predict( val$fit, data.frame( x = xfit ), type = "response" )
lines(xfit, pfit, col = "green" )4. For the local linear fit (blue curve):
bwd_min < min( diff( x ) ) # data is decreasing
bwd_max < max( x )  min( x )
bwd < bandwidth_cross_validation( r, m, x, c( bwd_min, bwd_max ) )
# Plot the fitted curve
bwd < bwd$deviance # choose the estimate based on crossvalidated deviance
pfit < locglmfit( xfit, r, m, x, bwd )$pfit
lines(xfit, pfit, col = "blue" )