Generate sine wave/tone Matlab

Download
genTone.m


How-to
Generating tones or sine waves of specific frequencies in Matlab is conceptually simple, but generating the correct frequency over the correct time period at the correct sampling rate can be a bit confusing at first. This article deals mainly to generating sine wave tones, and genTone.m is designed to simplify this process, but the timing aspects also apply to any signal output from a digital system.

Fitting a better psychometric curve (Wichmann and Hill 2001)

Code
GitHub: Matlab code and Python package

Introduction
Psychometric curves quantify the relationship between stimulus parameters and an observer's subjective experience. For an introduction to the concept of psychometric curves in signal analysis theory and a function and instructions to fit a basic curve in Matlab, see Introduction to psychometric curves and fitting a simple psychometric function. This article will focus on fitting a more complex curve with additional parameters that allow for the subjects fallibility, which improves estimation of the key parameter, discrimination sensitivity.

The psychometric function used here is from the paper The psychometric function:I. Fitting, sampling, and goodness of fit, F. Wichmann and N. Hill 2001. This paper, and its companion paper, provide detailed theory and methods for fitting a psychometric function, assessing its goodness of fit, and finding confidence intervals for the ranges of the parameters estimated. This article will demonstrate how to fit Wichmann and Hill's psychometric function in Matlab using the same data from a fictional 2AFC experiment in this post. This will allow an interested reader to make a direct comparison between the Wichmann and Hill curve and the approach using Matlab's glmfit function with a built in binomial distribution and a logit link function.

Introduction to psychometric curves and fitting a simple psychometric function

Code
GitHub: Matlab code and Python package

Introduction
An important aim of psychophysics is to quantify the relationship between stimulus parameters and an observer's subjective appreciation of what's going on. Signal detection theory approaches this problem by attributing perceptual sensitivity to thresholds that act at multiple levels in a system. In this context, a threshold could, for example, be how loud a sound needs to be to determine its location to a certain degree of accuracy, or how long a light needs to be on to determine its direction of movement. Above threshold, and the task is possible to a certain degree of accuracy. Below threshold and it's not possible to discriminate as accurately.

Experimentally, thresholds are often measured in two-alternative forced choice (2AFC) tasks or in go-no go paradigms (GNG). In 2AFC tasks a subject is asked to indicate one of two choices in response to a stimulus. For example, indicate white if a colour appears white, or black if it appears black in response to a shade of grey. Alternatively, GNG tasks require a subject to not respond ("no go") until a stimuli meets a certain criteria, then respond ("go").

In both of these types of task, a subjects performance can be measured as a function of the stimulus parameters - either as % correct, or similar, in 2AFC or d' in GNG, where d' is a function of hits, misses, and false alarms. Regardless of the exact metric used, the subjects performance can be described by two main parameters; bias (the "point of subject equality", PSE) and discrimination sensitivity (as in ability to discriminate between two conditions, such as white and black).

For example, imagine a subject performing a 2AFC task where they're presented with a shade of grey and they have to press one button to categorise the colour as white, or another button to categorise the colour as black. Intuitively, they're more likely to classify a grey stimulus as black the closer it is to black, and less likely the colour it is to white. So plotting their responses as a proportion of black responses (y) against the stimuli presented (x). We could use this sort of task to see how other factors, like the colour of a background, could affect perception of the shade of grey.

Race model inequality script; R. Ulrich, J. Miller, H. Schröter, 2007

http://link.springer.com/article/10.3758/BF03193160#

Behavior Research Methods

May 2007, Volume 39, Issue 2, pp 291-302

Testing the race model inequality: An algorithm and computer programs

Rolf Ulrich, Jeff Miller, Hannes Schröter

In psychophysics the race model is used to compare reaction time data from two conditions (X,Y) to a third condition (Z). For example, response times to an auditory task and a visual task, compared to an audiovisual task. It tests if reaction times are faster in the third condition, and if so, they violate the race model and (possibly, depending on the task) represent multisensory integration. The race model inequality, simply, is defined as  P(RTz) < P(RTx) + P(RTy). For a better, and far more detailed explanation and run through - see the above paper!

In this paper, the authors present an algorithm to test the race model inequality and include a set of functions for Matlab that implements the algorithm, although no supplementary .m file appears to be included. The code is in the .pdf, but copying from a .pdf is, as usual, an unnecessarily painful experience.

Here is a slightly modified version of the code included in the paper, it's copied from the .pdf and corrected for various illegal characters, line breaks, etc. etc. Apart from a couple of minor edits, nothing else is changed from the authors original work.

Also included as an example of how to use RaceModel.m is a script (RaceModelTester.m) that generates some reaction time data in the correct form to use with RaceModel.m. The RT data supplied to RaceModel.m should be in three rows, rounded and in milliseconds. There should also be a vector of probabilities, and a true or false to indicate whether to create a plot or not.