Palamedes Toolbox Forum

I added some edits to my original reply to correct an omission. I also realized that one of my suggestions needs to be implemented differently when analysis includes multiple participants. Edits in original reply are indicated with bold font.

Not sure I understand all your questions fully, but let’s see how this goes and if questions remain, let me know.

Indeed, if all parameters are unconstrained, there will be no sharing of information between conditions and you might as well fit all conditions separately. If multiple subjects are ...

Ah, yes, I see. Thanks for pointing that out. The palamedestoolbox.org/weibullandfriends.html page was created before the PFHB routines even existed. The page demonstrates things using the maximum-likelihood fitting scheme (i.e., using the PAL_PFML routines). And these do use a different syntax ...

You can specify the form of the psychometric function by using the 'PF' argument to the PAL_PFHB_fitModel function. Type 'help PAL_PFHB_fitModel' to see how to use the optional arguments. We very strongly suggest to log-transform stimulus values and using the Gumbel instead of using the Weibull on ...

The assumption to test here is not normality of the data. The difference between the parametric bootstrap and the non-parametric bootstrap is that the parametric bootstrap assumes that the fitted psychometric function (PF) is accurate. The non-parametric bootstrap does not make that assumption ...

Glad to hear I was helpful. Correct, Palamedes currently does not support including between-subject factors in a factorial (or other) design. Effects across multiple factors and their interactions can be defined but only across within-subject conditions.

Hmmm... There's a lof information here. Where is the unexpected estimate coming from? From the [P]AL_PFBA_Fit line in the code? Or the value of PM.threshold after running the PSI method? Or somewhere else yet? What would really help is some code that demonstrates the issue and that needs only Matlab ...

Hi Gerard,
Glad to hear you find Palamedes useful and gives you good fits.
Currently, the PFHB routines do not allow comparing groups of observers. You can compare individual observers against other individual observers but that’s it. You can of course run two separate analyses, one for each group ...

Yes, I did misunderstand. I thought you were trying to keep the data but make the fits acceptable, one way or another. There really is no ‘clean’ way to exclude bad data from an analysis. There is no rule that says that if your goodness-of-fit p is less than 0.05 (or any other number) you can simply ...

Ask twelve people for their opinion on this and you’re likely to get 13 different answers. Maybe others will join in to this discussion but here’s my two cents. Your model makes assumptions, e.g., ‘the true relation between stimulus intensity and the probability that the participant will make ...