#Matlab 2009 grid alpha software#

The method of constant stimuli, which dates to Fechner ( Treutwein, 1995), is a common data collection method used to estimate the psychometric function. For the bottom panels, the values of О± and О» are fixed, and the different curves indicate differences in the value of ОІ For the top panels, the values of ОІ and О» are fixed, and the different curves reflect differences in О±.

For these examples, the chance proportion correct without a signal was. Logistic and Weibull formulations of the psychometric functions are presented in the left and right panels, respectively. The О» parameter is referred to as the вЂњlapse rate,вЂќ indicating the proportion of times that the listenerвЂ™s attention has lapsed. When О» = 0, the psychometric function approaches one as the signal intensity increases, whereas when О» > 0, the proportion correct fails to reach one even at very high signal intensities. Comparing the upper and lower panels, it is evident that the value of О» determines the distance between the upper asymptote of the function and one. 1, whereas for the lower panels, the value of О» is 0. For the upper panels, the value of О» is set to. As the slope parameter ОІ increases (lower panels), the psychometric functions become steeper. As the threshold parameter О± increases (upper panels), the psychometric functions shift to higher signal intensities. 1.ДЇigure 1 shows the effects of manipulating the parameters of the logistic and Weibull psychometric function on both a linear and a logarithmic abscissa. Where, on a logarithmic axis, О± is the threshold parameter and ОІ is the slope/spread parameter the parameters Оі and О» are as in Eq. The logistic psychometric function is given by Two commonly used formulations are the logistic and Weibull (cumulative density) functions. For parametrically described psychometric functions, a variety of parameterization have been used, including Gaussian, Weibull, and logistic cumulative distributions. The shape of the psychometric functions may be described parametrically or nonparametrically (e.g., Poppe, Benner, & Elze, 2012 Zchaluk & Foster, 2009), that is, the psychometric function can be modeled using an analytical function whose shape is determined by a set of model parameters or can be model-free and characterized by smoothing the dependent variables across discrete signal intensities. When considering alternative forms of the psychometric function, both the dependent variable (proportion correct, proportion of a particular response, etc.) and the shape of the function are key factors.

#Matlab 2009 grid alpha software#

The estimation of the psychometric function is fundamental to psychophysical experimentation, and therefore, software is available to fit the psychometric function to psychophysical data using a variety of methods. The function that summarizes the relationship between the signal intensity and proportion correction is the psychometric function (e.g., Urban, 1910 see Klein, 2001, for a review), which typically exhibits a sigmoidal shape.

As the physical property of the stimulus (the signal intensity) is manipulated, the task performance-for example, in terms of proportion correct, systematically changes. In a typical psychophysical experiment, observers perform detection or discrimination tasks. At the core of psychophysics is the quantification of the relationship between changes in physical stimuli and changes in perception.