Universität Wien
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200233 SE Theory and Empirical Research (Mind and Brain) 1 (2021W)

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

TEWA 1: Scientific Computing in Python for Cognitive Psychology

22 November - 12 Dezember Online format!

This course will take place in a hybrid format, however, in person presence is preferred, and remote attendance is only a back-up option, that will require extra work.

  • Monday 04.10. 15:00 - 16:30 Hörsaal B Psychologie, NIG 6.Stock A0610
  • Wednesday 06.10. 11:30 - 13:00 Hörsaal B Psychologie, NIG 6.Stock A0610
  • Monday 11.10. 15:00 - 16:30 Hörsaal B Psychologie, NIG 6.Stock A0610
  • Wednesday 13.10. 11:30 - 13:00 Hörsaal B Psychologie, NIG 6.Stock A0610
  • Monday 18.10. 15:00 - 16:30 Hörsaal B Psychologie, NIG 6.Stock A0610
  • Wednesday 20.10. 11:30 - 13:00 Hörsaal B Psychologie, NIG 6.Stock A0610
  • Monday 25.10. 15:00 - 16:30 Hörsaal B Psychologie, NIG 6.Stock A0610
  • Wednesday 27.10. 11:30 - 13:00 Hörsaal B Psychologie, NIG 6.Stock A0610
  • Wednesday 03.11. 11:30 - 13:00 Hörsaal B Psychologie, NIG 6.Stock A0610
  • Monday 08.11. 15:00 - 16:30 Hörsaal B Psychologie, NIG 6.Stock A0610
  • Wednesday 10.11. 11:30 - 13:00 Hörsaal B Psychologie, NIG 6.Stock A0610
  • Monday 15.11. 15:00 - 16:30 Hörsaal B Psychologie, NIG 6.Stock A0610
  • Wednesday 17.11. 11:30 - 13:00 Hörsaal B Psychologie, NIG 6.Stock A0610
  • Monday 22.11. 15:00 - 16:30 Hörsaal B Psychologie, NIG 6.Stock A0610
  • Wednesday 24.11. 11:30 - 13:00 Hörsaal B Psychologie, NIG 6.Stock A0610
  • Monday 29.11. 15:00 - 16:30 Hörsaal B Psychologie, NIG 6.Stock A0610
  • Wednesday 01.12. 11:30 - 13:00 Hörsaal B Psychologie, NIG 6.Stock A0610
  • Monday 06.12. 15:00 - 16:30 Hörsaal B Psychologie, NIG 6.Stock A0610
  • Monday 13.12. 15:00 - 16:30 Hörsaal B Psychologie, NIG 6.Stock A0610
  • Wednesday 15.12. 11:30 - 13:00 Hörsaal B Psychologie, NIG 6.Stock A0610
  • Monday 10.01. 15:00 - 16:30 Hörsaal B Psychologie, NIG 6.Stock A0610
  • Wednesday 12.01. 11:30 - 13:00 Hörsaal B Psychologie, NIG 6.Stock A0610
  • Monday 17.01. 15:00 - 16:30 Hörsaal B Psychologie, NIG 6.Stock A0610
  • Wednesday 19.01. 11:30 - 13:00 Hörsaal B Psychologie, NIG 6.Stock A0610
  • Monday 24.01. 15:00 - 16:30 Hörsaal B Psychologie, NIG 6.Stock A0610
  • Wednesday 26.01. 11:30 - 13:00 Hörsaal B Psychologie, NIG 6.Stock A0610
  • Monday 31.01. 15:00 - 16:30 Hörsaal B Psychologie, NIG 6.Stock A0610

Information

Aims, contents and method of the course

The goal of this course is to introduce students to the use of the Python programming language for solving data analysis problems commonly encountered in psychology research.
The first part of the course will be a general introduction to Python and the most important libraries for data analysis: numpy, scipy, matplotlib, pandas.
The second part of the course will focus on general data science methods (statistical inference with resampling methods, regression models, machine learning).
The final part of the course will apply the previously learned programming on the analysis of behavioral data, with a focus on eye-movements and perceptual decision-making.
While the focus of the course will be on the practical and programming aspects, we will also discuss the theoretical relevance of these topics for cognitive science.
Monday classes will focus on theory, with programming tutorials on Wednesdays.

Assessment and permitted materials

Discussion participation and small theory homeworks: 25%
Tutorial participation and coding homeworks: 50%
Final Project: 25%

Minimum requirements and assessment criteria

Active participation in class and programming tutorials.

[Assessment criteria]
1: >87%
2: 76 - 87%
3: 64 - 75%
4: 51 - 63%
5: <=50%

Examination topics

Able to use Python for basic data analysis and visualization tasks

Understands resampling methods for statistical analysis and can implement it in code

Understands the use of random simulations for data analysis

Understands basic linear regression, and how it is related to more advanced regression models

Understands the main concepts of Signal Detection theory

Familiar with the main tools of machine learning

Reading list

Books:

Introduction to Modern Statistics (2021): https://openintro-ims.netlify.app/index.html
Think Bayes 2: http://allendowney.github.io/ThinkBayes2/index.html

Gelman, Hill, Vethari (2021): Regression and Other Stories

Statistical Thinking for the 21st Century:
https://statsthinking21.github.io/statsthinking21-core-site/

Papers:

Stanislaw, H., & Todorov, N. (1999). Calculation of signal detection theory measures. Behavior research methods, instruments, & computers, 31(1), 137-149.

Le Meur, O., & Baccino, T. (2013). Methods for comparing scanpaths and saliency maps: strengths and weaknesses. Behavior research methods, 45(1), 251-266.

Daw, N. D. (2011). Trial-by-trial data analysis using computational models. Decision making, affect, and learning: Attention and performance XXIII, 23(1).

Körding, K. P., & Wolpert, D. M. (2004). Bayesian integration in sensorimotor learning. Nature, 427(6971), 244-247.

Association in the course directory

Last modified: Sa 20.11.2021 09:08