With Psykinematix, collected data can be
fitted with psychometric functions immediately after
each session:
XY Datasets
Reaction Times
Click on the "Plotter" icon in the toolbar to display the Plotter Panel (or press shift-⌘R).
The curving fitting procedure provided by Psykinematix is based on the Levenberg-Marquardt least squares minimization technique.
Monotonic XY datasets, typically the subject's performance (% correct) as a function of some stimuli parameters, can be fitted with various psychometric functions either monotonically increasing or decreasing. All these functions are cumulative distribution function (CDF) types of the form:
 |
for a monotonic increase |
 |
for a monotonic decrease |
where 
is the performance as a function
of some stimulus parameter x,
is the chance level (eg:
50% in a 2AFC),
is the
miss rate,
is the cumulative
distribution function, with 
being the stimulus parameter,
and 
are the sensitivity parameters
that control the shape of the function.
| Weibull CDF |  |
| Logistic CDF |  |
|
Gaussian CDF |
 |
| Gumbel CDF |  |
Note that for the Weibull function,
and are analogous to the threshold
and slope respectively, while for all other functions
they are analogous to the offset and spread
respectively.
The threshold (t) and slope (s) for a specified probability level (p) (threshold criterion) of the psychometric functions are defined as:
All of these functions can be used as models for the Bayesian Method.
The graphical representation of the reaction times can be customized by selecting the "RT" tab: reaction times below a given level (anticipatory responses) and above a given level (late responses) can be filtered out, and the bin width can be set to any value between 10 and 100 ms. Post-stimulus RTs are included by default, but can be excluded from the histogram representation by unchecking the related check box.
The reaction time distribution can be fitted with a Weibull distribution using its probability density function:
where k > 0 is the shape parameter, λ
> 0 is the scale parameter of the distribution, and
is a translation parameter.
The mean and standard deviation of the Weibull distribution are given by:
Note that the accuracy of the fitting procedure depends on the bin width.