Adobe Kuler

This is an introduction to a toy project that I worked in 2015 when I was applying for Insight Data Science Fellowship. This was my first data science project (still unfinished) using unsupervised learning for clustering popular color themes in Adobe Kuler. I will talk about important steps in the project in the following posts.

Measuring irrelevant memory

One of the projects I have been working on is to measure one's irrelevant memory. Wait, what? Yes, irrelevant memory. Let's say there is an object with color and orientation, like an ellipse with a color. I ask you to memorize orientation. Now, orientation is relevant and color is irrelevant. If I want to test whether color information has been automatically registered (or encoded) in your memory, what shall I do? There is one way to test this. Let's say we have 100 trials of an experiment. For the first 99 trials, I ask you to only memorize orientation from a display. At the end of each trial, I test your orientation memory. But at the 100th, last trial, I ask you to recall color. And yes, you did not see this coming. That is the most important part : you should not know about the last trial!