Blog of Sara Jakša

Presenting UExperience on MyData Meetup

Today, I am having a presentation on MyData meetup. It is a weird feeling, because for the first time I am actually presenting something, that I am getting paid to do.

What I am presenting today is UExperience, an app to allow people to study their experience. What do I mean by that? Well, let me ask you, how do you feel right now? Are the feelings strong? Are you experiencing your body? What are hearing anything? Do you feel your personal state?

This is what I mean by experience. And I made an app, that allows you to study the parts of experience one is interested in.

There are some interesting studies done with experience. In the decision making, there is an interesting article. It is something that I recommend as reading to anybody interested in decision making.

My presentation can be found on https://sarajaksa.eu/content/presentations/2019/mydata-july-2019-uexperience/.

Some more information about the app can be found on GitHub or my very disorganized and incomplete documentation or the original site.

Added 2019-07-29: A friend of mine was kind enough to record me. You can download the video here (100 MB).

My MeiCogSci Presentation on the Topic Modeling of Cognitive Science Abstracts

I am a bit behind on my blog posts. I mean, right now writing about my presentation, that I had about a month ago tells me, that I had not written anything for at least a month. I guess it was a busy month. :) Well, last month I had a presentation on the topic modeling of the abstracts, that were published in this very conference in the last decades and some. So that can give us a pretty good indication of what people in our study program find interesting. That was a fun project, that probably took about two months of my life.

I have spend a lot of time playing around with different models. On the end, I used the model with 21 topics. Looking back now, I think I would get a better results with less topics. That is my intuition, because I also played a lot with different number of topic models and I think the ones in the 10-15 topics were a bit clearer and more straight forward. But in the end, I ended up using the best model based on the numerical indications. Also, for most of my time, I was really annoyed, that the three topics were together: constructivism, sense-making and empirical phenomenology. To me, these were separate topics. Or at least more separate topics than reinforcement learning and neural networks, that got divided.

Well, the model was right and I was wrong. The feedback that I got was that these is how it should be. I got the info that the constructivism-phenomenology group should be together. Apparently Varela, who is one of the most prominent empirical phenomenologist, went through both constructivism and sense making. At the presentation, I found out that people have a strong opinion of why neural network and reinforcement learning were supposed to be separate. The first explanation was, that reinforcement learning is just one method of neural networks, but otherwise they are separate. The second has to do with explainability of the model? I don't think I completely understood that explanation.

Well, all this feedback came to late, so I ended up with a model with too many topics. Maybe what I can learn from that is, that if there is something that appear no matter the preprocessing and number of topic selection, it will probably be right, so don't mess up the model to try and correct it.

The visualization of the final model can be found on my page. I also included the simplified model as a web app, so everybody can check the topics of any texts (but if used on text other that cognitive science ones, I can not guarantee any sensible results). Feel free to play with it.

Now, for some interesting results, that I have gotten. Probably a lot more interesting for the people connected to the study program than anybody else. The analysis could be found on my github.

The most popular topics are constructivism, society, learning, decision making, neuroscience, language, perception, modeling, movement, neural networks and reinforcement learning. I put them so many, because of the next difference.

There are difference in which topics are popular in which place. In the study program, there are currently four universities: Ljubljana, Vienna, Bratislava and Budapest. So I also analyzed one of the years (2015) in order to see which topics are popular in which places. There were not enough abstracts from Budapest in that year, so I only used the other three.

For Ljubljana, the most topics of interest were constructivism, learning and neuroscience. In Vienna, the topics were society, decision making, constructivism and perception. And in Bratislava it was reinforcement learning, learning, modeling and language. In the general perception it is, that you go in Ljubljana in you are interesting in first-person research and neuroscience (which is shown), Bratislava if you are interested in computational modeling and maybe language (which is also shown) and in Vienna if you want something else or have no idea what you want to do, since they were supposed to have the most variety.

For the people in Ljubljana, we have to hear a lot about the connection between the first-person and third-person. So it was interesting to see that neuroscience and constructivism have the least amount of collaboration. But what it can also be seen is, that the amount of interdisciplinarity is increasing through the years.

I also checked which topic humanizes the participants and which do not (according to how one of the people in the audience described it). I only checked if the used subject or participant. The topics with most human participants were study of perception, attention, non-typicality, categorization, neuroscience and decision making. The humanizing ones (using participants) were studying of language, decision making and attention. The others (using subjects) were neuroscience, neural networks and the studying of pitch.

I also checked the differences in personality. More agreeable are people studying health, non-typicality and decision making. Less agreeable were people studying reinforcement learning, neural networks and systems. More neurotic were people studying reinforcement learning, systems and tasks. Less neurotic were people studying non-typicality, health and pitch. The most extroverted researchers were the one studying decision making, society and attention. The least extroverted researchers studied neuroscience, TMS and health.

So these are some results, that I ended up finding about this data set. There are still many interesting questions to ask, but I think I will take a bit of a break and maybe return to this topic models later.

Why Our Parliament is not the Only Weird One

Sometimes it might seems like, our parliament is a really weird one. You don't believe me?

I remember once watching a parliamentary session, where the person had a couple minutes talk about, how there is the smell of glue. And that maybe there is a lot og glue in the air. So they all might have ended up high. Which, according to that speaker, would explain why people are giving the suggestions they do.

Or a more recent one. There was a meeting. A person admitted on the camera, that they stole a sandwich. Called it a social experiment... well you can imagine what happened in the end.

So, in order to counteract these, I am going to list some of the thing that are weird in the British parliament:

  • They sit 2 sword lengths apart, so that they are unable to cut opponent head off
  • They can't have a debate without a golder mace listening to them
  • They can not address each other directly (I guess they invented passive-aggressiveness?)
  • They are not allowed to say, that something is not true
  • They vote by walking to the room

Topic Modeling of Python Conversations on Tumblr

I had presented a lightning talk on May Python Meetup.

What I did was take all the Python tagged posts from Tumblr. Then I topic analysed them and tried to figure out some interesting things.

Here are the interesting things:

  1. Python is connected to three big topics: nature (the dark night with a dog barking one), startups and coding.

  2. When ignoring the nature one, the trends show that from 2013 to 2016 there was a lot of interest in learning python. But then the interest shifted to startups. (Not sure why. Would be interested in finding out...)

  1. There are people posting python code with comments in Japanese. So, a Japanese python community? (not sure, why it is on Tumblr...)

  2. People really like to create chat apps. (again Why???)

My slides and code are also available.

Sort of MBTI of Problem Solving

This is the idea, that I am toying around with, and I am trying it out from time to time. Not sure, how successful it is practically, but it might help with some people. What it basically does, is to try and see on the problem from different perspectives, in this case, perspectives of different functions.

MBTI was designed based on the Jung's function theory. So Jung's said that we have four functions: Thinking, Feeling, Intuiting and Sensing, and each can be oriented inside or outside. I have to admit, I understand a lot more about inside and outside orientation, since I took first person research. Before, they would sometimes get mixed. For example, I could not understand introverted intuition, but if I think about it like an extroverted intuition oriented in the inside, it makes sense. But it did not make sense, until I started to observe myself.

And each function wants something. The thinking wants order, the sensing wants information, the intuition wants creativity and the feeling wants humanity. And then each one is turned in one side, the thinking wants order, but introverted thinking wants order in the thinking, and the extroverted thinking wants order in the external world. The same with sensing, the extroverted sensing gets the information from the world, and the introverted sensing gets the information from the inside us. The extroverted feeling wants to express humanity toward other people, so that we are more human to each other, the introverted feeling wants to express inner humanity, which is why it is connected to the values a lot of times. And so on and so on.

Penelope Trunk actually summarized what makes each type (not function) makes one happy and refuel them. I copied the information in the table below:

Type Activity
INTJ Needs to create order and structure from theoretical abstraction.
ENTJ Needs to visualize where an organization is headed.
INTP Needs to generate new theories or to prove or disprove existing theories.
ENTP Needs to understand the world they live in.
ISTP Needs to understand the way things work.
ESTP Needs to take action and get the job done.
ISFP Needs to feel immersed in the world of senses.
ESFP Needs to feel excitement and drama.
ISTJ Needs to fulfill their duty.
ESTJ Needs to enforce rules and/or traditions.
ISFJ Needs to create harmony and cooperation.
ESFJ Needs to make people feel good about themselves.
INFJ Needs to see the world of hidden meanings and possibilities.
ENFJ Needs to bring out the best in others.
INFP Needs to make the world a better place.
ENFP Needs to inspire and motivate others.

(As the unrelated note, this can also be used to help type people :) )

So, going from that tangent (but this approach can also work with the needs above), how to use thins to solve problems? Well, when we have a problem, we can think about, how each function would approach this. How can be bring order and use this to solve problems? When can be get more information? Is there something else we did not consider? How do we bring fourth our humanity.