Modelling Psychometric Curves in Python: Video 3 – arrays of arrays for days

In this series of videos we will use a modified method of constant stimuli (MOCS) to model a Gaussian cumulative distribution function in Python. The method of constant stimuli is an easy way of generating a psychometric function by presenting a subject a number stimuli at different intensities. For example, let’s say we want to test a subject’s ability to perceive a sound at intensities from 0 to 100. In MOCS we will split this up into 11 different intensities (0,10,20…100) and then present each intensity for a number of times (number trials). Once we have presented each intensity the same number of times, we simply calculate the proportion correct at each intensity and then plot these proportion correct against the stimulus intensity.

Now we turn our attention to preparing to do the MOCS simulation (that will be the next video). Chip’s responses for each trial will need to go somewhere, but where?

We will help her out by preparing a nice set of arrays where we will place her responses into as she completes the MOCS paradigm. Basically, we will be nesting empty arrays inside an array.

Confusing?!?!?

It’s ok, it will make sense soon.

Note: These videos are not intended as a complete tutorial or rigorous presentation of psychometric modelling. Rather, these are designed to show you some different ways of approaching psychometric modelling and using Python. Also, these videos assume you already have a working knowledge of Python programming (at a moderate level). If not, then perhaps have a quick look around for beginning tutorials and then join me back here!!!

I hope they are helpful.

Тэги:

#psychometric #psychophysics #Gaussian #cumulative_distribution_function #neuroscience #perception #python #programming_in_python #visual_studio_code

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