Bands Incorporated — OUseful.Info, the blog…

A few weeks ago, as I was doodling with some Companies House director network mapping code and simple Companies House chatbot ideas, I tweeted an example of Iron Maiden’s company structure based on co-director relationships. Depending on the original search is seeded, the maps may also includes elements of band members’ own personal holdings/interests. The […]

via Bands Incorporated — OUseful.Info, the blog…

What is Attention?

James (1890, pp. 403-404):

Everyone knows what attention is. It is the taking possession of the mind, in clear and vivid form, of one out of what seem several simultaneously possible objects or trains of thought. Focalization, concentration of consciousness are of its essence. It implies withdrawal from some things in order to deal effectively with others …”

A little bit more recently Shiffrin 1988, p. 739):

 “Attention has been used to refer to all those aspects of human cognition that the subject can control … and to all aspects of cognition having to do with limited resources or capacity, and methods of dealing with such constraints”

And even more recently Cowan (1995) writes about selective attention’ in a sense that is close to James’ definition of attention. The selective attention is not necessarily voluntary. Also selective attention is a limited capacity process.



Great list for you if you would like to learn how to create experiments using OpenSesame.  You will find tutorials in the form of text, videos, and links to blog posts and other useful resources.

OpenSesame is a presentation and reaction-time measurement software with Python scripting option Download, tutorial, information about Python scripting etc. Forum: Review: Citation and publications…

Källa: OpenSesame

Introduction Video to Statsmodels

I found this introduction to Statsmodels. For you that don’t know Statsmodels is a great Python library for conducting statistical analysis. Many common methods are covered by the package. If you want to learn more Python and Data Analysis you will most likely enjoy this Youtube video:

I surely learned more Python data analysis by watching it anyway. It makes some tasks a lot easier and makes Python more similar to R.

Quality Python sources

The Python community is exceptional at sharing specified resources and supporting beginners discover ways to code with the language. There are so many of sources obtainable although that it can be tough  to locate them all of them.

This post page aggregates the exceptional Python assets with descriptions of what they offer to readers.

Python for specific occupations

Python is powerful for many professions. in case you‘re in search of to use Python in a particular subject, this type of pages may be appropriate for you.

  • Python for Social Scientists here you can find a textbook, a course and slides for a university course that teaches social scientists to use Python. Pretty awesome for Psychologists (or other Social scientists, of course!)
  • Practical Business Python is a blog that covers topics such as automation of the creation of large Excel spreadsheets. Also covers how to perform analysis when your data is locked in Microsoft Office files.
  • Python for the Humanities is a basic Python textbook and course. It covers text processing. It will pretty quick become hard so after you have read the first chapter you may want to consult another introductory Python course or book at the same time.

Some iPython/Jupyter Notebooks:

I have found some useful iPython/Jupyter notebooks for learning Python. They will be categorized into fields of research.

Psychology/Cognitive Neuroscience

First we start with some specific on Psychology and neuroscience:

  • Python for Vision Research. Here you will find a three-day course for vision researchers. Focus is on building experiments with PsychoPy and psychopy_ext, learning the fMRI multi-voxel pattern analysis with PyMVPA, and understanding image processing in Python.
  • Modeling psychophysical data with non-linear functions by Ariel Rokem. General knowledge in Python is needed. You will learn a definition of modeling and get to know why models are useful, different fitting strategies, how to fit a simple model, and model selection, and more.

Statistics-related notebooks

Bayesian Data Analysis Using PyMC3

Introduction to Linear Regression – Linear regression is a commonly used statistical method in social sciences (e.g., Psychology)

Great packages/libraries to use for Data Analysis

I think that I also need to mention som great Python packages that makes data analysis way more easier in Python.

First, there is IPython. IPython is a command shell for interactive computing in multiple programming languages. It was, however, first developed for the Python programming language. For doing Scientific computing IPython is really a must (replacing the interpreter that comes with installation of Python). It offers enhanced introspection, rich media, additional shell syntax, tab completion, and rich history. You can also, as been seen above, create IPython notebooks that are, basically, html. Great! iPython is known as juPyter nowadays: Jupyter.

Here is a user manual that I got from asking a question at a blogJupyter Manual. Looks very promising.

pandas is an open source, BSD-licensed library presenting highperformance, easy-to-use statistics structures and data analysis equipment for the Python. Pandas is enabling you to carry out your data analysis workflow in Python while not having to have an extra domain specific language like R. Pandas let you do summary statistics using Python very easy. If you are familiar with R you will like the methods head, describe, and so on.

matplotlib is a 2D plotting library which enables you to create figures in publication quality. You can also create interactive environments across platforms. atplotlib can be used in python scripts, the python and iPython shell (similar to MATLAB or Mathematica), web application servers, and more. matplotlib aims to make easy things easy and hard things possible. Generation of plots, histograms, power spectra, bar charts, errorcharts, scatterplots, etc, can be carried out with just a few lines of code. For simple plotting the pyplot interface is very similar MATLAB.  Especially, when used within IPython, Spyder, or Rodeo (the last two are great Python IDEs).


There are, of course, a plethora of Python blogs. I will mention a few of them that are related to cognitive science.

There are so many more resources. Hopefully, I will update this post or make a new one later. Hope it helps you in the Python jungle, though!


Programming in Psychology

Psychology graduates often need to be, at least, computer literate on the basic level. Computers are, as in most field, used in many ways. The selection and learning of relevant packages for tasks we do are needed. However, many few psychology graduates do know computer coding. Many will have great knowledge in word-processing and statistical analysis. As you will see knowing programming will get you and advantage. You will be able to carry out tasks that not many other Psychological researchers can.

Increasingly, Psychological researchers find themselves facing exponentially larger data sets available on the internet (e.g., data mining) and elsewhere without the proper tools to handle them. A huge amount of Psychologists use spreadsheet software (e.g., Excel) for processing data. Often,  this can mean spending hours clicking around in the interface or copying and pasting. With new datasets the procedure will be repeated. Apart from being a huge waste of time, but the reproducibility the work that have previously been carried out suffer hard. It may often be completely impossible to do the exact same procedure again and the work carried out may be useless.

What kind of language should a Psychologist learn? Some programming languages, such as MATLAB and R, are commonly used by psychologists (and other cognitive scientists). These languages focus on mathematical and statistical operations. In MATLAB you are able to do psychological experiments (e.g., using Psych Toolbox), create mathematical and statistical models of cognitive functions or phenomena. Of course you can also code your statistical analysis with MATLAB. R is more focused on statistical computing, as far as I know anyway. I have seen some modelling and neural network packages for R also. However, I am yet to see a package for conducting experiments. Python is another language that has received increasing use lately. It is a general-purpose language, similar as tC and C++. However, it is a interpretative language. That is, it is a scripting language and one of the easiest languages to use. There are some packages for creating psychological experiments, such as PsychoPy,  and a bunch of libraries that can be used for scraping the web of data. Also you can conduct statistical analysis using packages such as NumPy, Pandas, and Statsmodels. In fact, the SciPy stack is very useful.

There are also JavaScript libraries to use to create experiments to run online. Running experiments online seems to be a new phenomena but give you the possibility to collect a huge amount of data in short periods of time. For instance, using Amazon Mechanical Turk seems to be popular in the US.

Programming is fun, try it!