Applied Hearing Science Lab
UML: A Matlab toolbox
Updated Maximum-likelihood Procedure for the Estimation of the Psychometric functions
The estimation of the psychometric function is fundamental to psychophysics. The updated maximum-likelihood (UML) procedure enables efficient data collection for the estimation of the psychometric function by the use of an optimized strategy for stimulus sampling. This UML procedure is is an extension of the traditional maximum-likelihood-based adaptive procedures (Green, 1990) in that multiple parameters of the psychometric functions are estimated simultaneously (Shen and Richards, 2012). This Matlab toolbox enables rapid implementations of the UML procedure. Object-oriented programming in Matlab was used in constructing the tool box, allowing an intuitive organization of the relevant variables and operations
This toolbox includes the following features:
- Easy to use in designing new experiments
- Allows Logistic or Weibull formulations of the psychometric function
- Intuitive organizations of the data
- Flexible parameter-space configurations
- Straight forward implementations of simulations
- Extensions to procedures with interleaved tracks
Download UML
Click here to download the latest version of UML toolbox.
References
- Green, D. M. (1990). “Stimulus selection in adaptive psychophysical procedures,” J. Acoust. Soc. Am. 87, 2662-2674.
- Shen, Y. and Richards, V. M. (2012). “A maximum-likelihood procedure for estimating psychometric functions: Thresholds, slopes, and lapses of attention,” J. Acoust. Soc. Am. 132, 957-967.
- Shen, Y., Dai, W., and Richards, V.M.(2014). “A MATLAB toolbox for the efficient estimation of the psychometric function using the updated maximum-likelihood adaptive procedure,” Behav. Res. Methods., 1-14.