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Quantitatively Assessing Aging Effects In Rapid Motor Behaviours

Neuroscience, Science

A still image from the Object-Hit task. Two green paddles, controlled by the participant, are shown as three red targets created by the task are falling "down" the virtual workspace.

BLUF: Rapid motor decisions deteriorate with age. There is evidence that this occurs due to less capable processing of the task environment rather than physical weakness. Aging affects everyone and it affects all aspects of our lives. On an individual level, our motor skills – the ability to control how…

Bayesian Data Analysis for Science: A Reading List

Data Analysis, Science, Statistical Significance

Bayesian data analysis allows us to quantify the certainty surrounding our conclusions. I’ve previously talked about how traditional null hypothesis significance testing (NHST) and its related p-values is tied to concerns about reproducible science. One of the ways that Bayesian data analysis improves upon NHST is by asking the right…

Statistical Significance and Reproducible Science: A Reading List

Science, Statistical Significance

In the big picture, the goal of science is to build on our knowledge of how the world works. This can take the form of being clear about what we know and what we don’t know, and it can also be seen when scientists try to quantify their degree of…

Why I am a Bayesian (and why you should be too)

Data Analysis, Science, Statistical Significance

Going through graduate school in the sciences, you learn a lot outside of your core research area. You learn–or hopefully you learn–supporting skills like experimental design, producing effective visualizations, and writing for academic audiences. And one skill that most of us learn along the way is how to go about…

Cribbage as a Reinforcement Learning Problem

Reinforcement Learning

One of the hardest parts of solving a reinforcement learning problem is to decide what problem you want to solve. But we can take heart from Herbert Simon who said Solving a problem simply means representing it so as to make the solution transparent. Although we want to use reinforcement…

Distances in Machine Learning (Pt. 3 – Model Distances)

Big Data, Data Mining, Machine Learning

We’ve seen how distances can be used in machine learning and data mining for different types of data, but we don’t only look at data in these areas. Remember in part one we looked at traditional, numerical distances that match our intuitions about distances and in part two we look…

Distances in Machine Learning (Pt. 2 – String Distances)

Big Data, Data Mining, Machine Learning

Distances are used throughout machine learning to help us (and our algorithms) figure out many things such as how far apart two instances are or how far our prediction is from the ground truth. The first part of this series looked at traditional distances that line up with our every…

Distances in Machine Learning (Pt.1 – Traditional Distances)

Big Data, Data Mining, Machine Learning

While you were reading about machine learning or developing your own algorithms, you may have come across the term distance. This is natural, since we often want to know how far apart two things are: instances that are very close together in our representational space are probably in the same…

Thinking about Intelligence

Artificial Intelligence

When we think about artificial intelligence, we often think that the artificial part is what will be challenging. After all, in engineering we want to build solutions that in some sense contain the problem they are trying to solve. But the real difficulty is figuring what we mean by intelligence. And that’s important, because as a society we are building systems that are supposed to be intelligent.

Clustering in the Presence of Concept Drift

Big Data, Data Analysis

Not all types of unsupervised learning algorithms have the same level of performance in dynamic environments. Algorithms with certain characteristics are more robust to changes in the data being produced.

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