Universität Wien
Warning! The directory is not yet complete and will be amended until the beginning of the term.

200222 SE Theory and Empirical Research (Mind and Brain) 1 (2025S)

8.00 ECTS (4.00 SWS), SPL 20 - Psychologie
Continuous assessment of course work

Dieses TEWA 1 kann für alle Schwerpunkte absolviert werden.
Moodle

Registration/Deregistration

Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).

Details

max. 20 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

  • Tuesday 04.03. 16:45 - 20:00 Hörsaal H Psychologie KG Liebiggasse 5
  • Tuesday 11.03. 16:45 - 20:00 Hörsaal H Psychologie KG Liebiggasse 5
  • Tuesday 18.03. 16:45 - 20:00 Hörsaal H Psychologie KG Liebiggasse 5
  • Tuesday 25.03. 16:45 - 20:00 Hörsaal H Psychologie KG Liebiggasse 5
  • Tuesday 01.04. 16:45 - 20:00 Hörsaal H Psychologie KG Liebiggasse 5
  • Tuesday 08.04. 16:45 - 20:00 Hörsaal H Psychologie KG Liebiggasse 5
  • Tuesday 29.04. 16:45 - 20:00 Hörsaal H Psychologie KG Liebiggasse 5
  • Tuesday 06.05. 16:45 - 20:00 Hörsaal H Psychologie KG Liebiggasse 5
  • Tuesday 13.05. 16:45 - 20:00 Hörsaal H Psychologie KG Liebiggasse 5
  • Tuesday 20.05. 16:45 - 20:00 Hörsaal H Psychologie KG Liebiggasse 5
  • Tuesday 27.05. 16:45 - 20:00 Hörsaal H Psychologie KG Liebiggasse 5
  • Tuesday 03.06. 16:45 - 20:00 Hörsaal H Psychologie KG Liebiggasse 5
  • Tuesday 10.06. 16:45 - 20:00 Hörsaal H Psychologie KG Liebiggasse 5
  • Tuesday 17.06. 16:45 - 20:00 Hörsaal H Psychologie KG Liebiggasse 5
  • Tuesday 24.06. 16:45 - 20:00 Hörsaal H Psychologie KG Liebiggasse 5

Information

Aims, contents and method of the course

TEWA 1: Computational Cognition

This course provides an introduction to the leading computational frameworks for understanding human behavior and cognition. Psychologists are increasingly confronted with large amounts of human behavioral data, and computational methods can help make sense of this data. The course focuses on practical and programming aspects, while also discussing the theoretical implications for psychology and cognitive science. To this end, the course will consist of conceptual lectures for the first 90 minutes, followed by programming labs for another 90 minutes.

The course will be divided into three parts. The first part will introduce the Python programming language and the most important libraries for data analysis, including NumPy, SciPy, Matplotlib, and pandas. The second part will focus on general data science methods, such as statistical inference with resampling methods, regression models, and machine learning. In the final section of the course, we will apply the programming skills we have acquired to model concepts that are particularly relevant to cognitive science, such as neural networks and reinforcement learning.

Weekly homework assignments will involve testing and implementing the techniques taught in the current and possibly previous weeks. The final project will require students to work in groups on a specific topic to analyze data, write a proposal, and present the project in class. By the end of the course, students will have a more comprehensive understanding of how computational methods advance psychology, how psychology can inform research in machine learning and AI, and how cognitive models are adapted and evaluated to understand behavioral data.

If you have no programming background, it is recommended that you spend some hours on introductory Python material (e.g. www.learnpython.org, www.datacamp.com) before the course starts. This will make the first few weeks of the course much easier!

Please bring a laptop.

Assessment and permitted materials

Homework assignments will be announced via Moodle and should be uploaded to Moodle by the due date.

The topics for the final project will be determined in May and students will work on the final project in the final weeks of the course. Full details, including deadlines, will be announced on Moodle.

Minimum requirements and assessment criteria

Weekly individual assignments (weighted with 50%, must have an average grade of at least 60% to pass the course)
Individual in-class evaluations (15%, maximum 2 class absences without an excuse)
Group final project (35%, all group members receive the same grade, must have a grade of at least 60% to pass the course)

The overall grade will be a weighted average of the above:

1: ≥ 90%
2: ≥ 80%
3: ≥ 70%
4: ≥ 60%
5: < 60%

Examination topics

There is no final exam, but there will be homework and a final project. All content covered in the course will be relevant. Supporting materials will be available on Moodle.

Reading list

Will be announced on Moodle.

Association in the course directory

Last modified: Tu 25.02.2025 13:27