| Study Project: Topics in Embodied Cognition |
| DozentIn:Prof. Dr. med. Peter König, M. Sc. John Jairo Madrid Carvajal |
| Veranstaltungstyp:Studienprojekt (Offizielle Lehrveranstaltungen) |
| Ort:50/E07 |
| Semester:WiSe 2025/26 |
Zeiten:Mo. 08:00 - 10:00 (wöchentlich)
Erster Termin:Montag, 13.10.2025 08:00 - 10:00, Ort: 50/E07 |
Beschreibung:Dear Students, read carefully:
We are implementing an new approach for Study Project distribution. Registering for individual study projects is not possible. Please enroll in the course "Cognitive Science Study Project Distribution" (https://go.uos.de/H58P0) and apply for up to three study projects. You will find course descriptions and self-assessments in the Vips. The deadline is Sunday, 5.10.25, 23.59 pm.
Project Description
Embodied Cognition in Real-World Construction Tasks: Analysis of Eye Tracking and EEG Data in LEGO Assembly
Overview: This study project investigates embodied cognition in a complex real-world sensorimotor task, building on the work of Melnik et al. (2018). Participants will assemble LEGO models following visual templates while their eye movements and brain activity (EEG) are recorded. The project seeks to understand how perception, cognition, and action interact during goal-directed behavior, and how people use their environment as an "external memory" to manage cognitive load.
Project members will:
Design and implement an experimental protocol using LEGO assembly, eye tracking, and EEG.
Gather, preprocess, and analyze real multi-modal datasets.
Apply advanced quantitative and computational methods to extract meaningful patterns from noisy behavioral and physiological data.
Interpret results within embodied cognition frameworks and contribute to ongoing research in real-world cognition.
Collaborate as a team to produce high-quality reports and presentations.
Learning Outcomes:
Advanced hands-on experience in experimental cognitive science.
Proficiency with real-world, multi-modal data analysis workflows.
Deeper understanding of statistical, computational, and signal processing techniques in cognitive neuroscience.
Teamwork and scientific communication skills.
Reference: Melnik, A., Schüler, F., Rothkopf, C. A., & König, P. (2018). The world as an external memory: The price of saccades in a sensorimotor task. Frontiers in Behavioral Neuroscience, 12, 253. Link
Required Skills and Prerequisites
Essential
Strong Working Knowledge of Python: Confident use of Python for data analysis (NumPy, Pandas, matplotlib/seaborn, etc.).
Practical Data Analysis Experience: Hands-on experience with scientific or behavioral data sets (project work, courses, or previous research).
Statistics and Data Literacy: Comfortable with statistical inference, hypothesis testing, and interpretation of results.
Experience with Scientific Computing Libraries: Familiarity with toolkits for neuro/cognitive science data (e.g., MNE for EEG data, PyGaze or Tobii SDK for eye tracking, ...).
Cognitive-Theoretical Background: Understanding of embodied cognition, sensorimotor integration, or related theories.
Bonus Skills (Advantageous)
Spectral Analysis: Experience with spectral methods in EEG or time-series data (e.g., Fourier/wavelet analysis).
Linear Mixed Models: Familiarity with (generalized) linear mixed-effects modeling for analyzing complex/within-participant data.
Application and Admission Procedure
Admission requirement: All applicants must complete a programming and data analysis exercise (details will be provided to shortlisted candidates shortly before the course starts). Performance on this exercise, not just application materials, will be used for selection.
Application materials:
A competency statement (max 1 page) that documents your relevant technical and methodological skills, hands-on experience, prior exposure to experimental or data analysis work, and any other information that puts your background in a positive light for this specific project.
If available, provide evidence of technical skills (e.g., sample code, screenshots of analysis results, research paper, preprints, or reports).
A transcript or list of relevant coursework may be included as an appendix.
Note: Since selection will be based primarily on demonstrated skills and experience, applicants are encouraged to include any material—projects, coursework, independent work, or code documentation—that supports their ability to contribute effectively to the project. |
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