Psychometric Curves in Python - teaser

This is a teaser video where I quickly (well for me at least!) go through some simple psychometric function curve plotting. In this 10 minute video (do I make it in time?!?).... I introduce how to create and plot Gaussian probability density functions, and their associated cumulative distribution functions.

The CDF is important as we use these when we try to model our experimental psychometric data. We are usually interested in how the threshold of our subjects change under different test conditions. And so in order to accurately model our data, we first need to know how to create and plot appropriate curves.

In this particular video we keep it to using a Gaussian curve (which is symmetrical about the average or 'location'). In our experiments however, we are not limited to Gaussian but rather may need a Gumbel, or Weibull, or Logistic curve to explain our data.

So, sit back and watch me try and introduce to you a simple approach to working with Gaussian PDF and CDF.

I hope you enjoyed this video - I am planning on uploading a more comprehensive set of videos where we really pull apart psychometric curves and how to plot and model our experimental data.

This video is not intended to be a complete tutorial on Python programming or statistical curve fitting. I assume that you have some prior Python programming abilities. If you do not, I have some introductory videos that may help bring you up to speed. There are also thousands of other videos available to help novice programmers.

All the best !!!