I’ve already talked about how to potentially obtain the dimensions of a conceptual space with artificial neural networks in a previous blog post. That approach is based on machine learning techniques, but there’s also a more traditional way of extracting a conceptual space: Conducting a psychological experiment and using a type of algorithm called “multidimensional scaling”. Today, I would like to give a quick overview of this approach.
Over the past few weeks, I have been pretty busy fulfilling my teaching duties. As I haven’t done much researching, I won’t talk about research today, but about “Constructive Alignment”, which is an approach for planning lectures, seminars and other courses.
The constructive alignment process consists of three steps:
- Defining the learning targets
- Planning the examination
- Planning the course
But wait a second, why does planning the course appear as the last step in this process?
About half a year ago, I mentioned “Logic Tensor Networks” in my short summary of the Dagstuhl seminar on neural-symbolic computation. I think that this is a highly interesting approach, and as I intend to work with it in the future, I will shortly introduce this framework today.
In one of my previous posts, I’ve shown a little overview diagram of my PhD research. One component of this diagram was called “language games” and so far I have not explained what that means. Well, today I’m going to give a short introduction into this topic.
Language games  focus on the question of “how can language come into existence?”, i.e., “What are possible mechanisms that allow different individuals to come up with a shared vocabulary that they can use to communicate about things in the world?”. I admit that this sounds a bit abstract, so let me illustrate the problem with an example:
In my PhD project, I do research in the area of “concept formation”. Before starting to talk about my PhD research in more detail, I would like to use this post to give a quick introduction into the area of concept formation.