Milestones, Promises and Pitfalls

2 EC

Semester 1, period 1

5244MIPP2Y

Owner Master Brain and Cognitive Sciences
Coordinator dr. R. Rouw
Part of Master Brain and Cognitive Sciences,

Course manual 2025/2026

Course content

Welcome to Milestones, Promises and Pitfalls! During this very first course of the research master Brain and Cognitive Sciences, you will get to know the different aspects of this interdisciplinary Master.

The course is meant to make you acquainted with:

  • the broader research field of Brain and Cognitive Sciences, and showcase some of the top-research going on in Amsterdam
  • Interdisciplinary Group work: presenting an interdisciplinary research proposal,
  • Python programming: Learn the basics (or improve your skills) in Python programming for Brain and Cognitive Sciences,
  • each other! Get to know each other's background, ideas, ambitions... through introductions, discussions, group work and socials.

The setup of this week-long course is simple: from Monday to Thursday, there will be  lectures and a workgroup activities. During the lectures, you will hear from active researchers in brain and cognitive sciences, with a special emphasis on historical milestones, promise of the future and pitfalls in the praxis of interdisciplinary research. During the workgroups, you will work on group assignments or on individual Python assignments.

Objectives

  • Explain ethical considerations facing today's researchers in brain and cognitive sciences
  • Propose interdisciplinary research in brain and cognitive sciences
  • Read, interpret and design basic/intermediate computer programs in Python for research purposes

Teaching methods

  • Lecture
  • Self-study
  • Presentation/symposium
  • Seminar
  • Laptop seminar
  • The lectures are meant to engage with active research and inspire you to think about the future of the field
  • The seminars are there to put that inspiration into a tangible form, by working on a Future Scenario together with other students
  • The laptop seminars are there to provide you with basic Python proficiency and to explore topics from the course in a hands-on way
  • The presentations are moments to share your work with the larger group and engage in a Q&A to explore your ideas, and those of others
  • Self-study exists to elaborate on your in-class work (or catch up if you've been delayed a bit)

Learning activities

Activity

Hours

  

Lectures

9

  

Laptop seminars

12

  

Seminars

4

  

Presentations

6

  

Self study

25

  

Total

56

(2 EC x 28 hours)

Attendance

  • Some course components require compulsory attendance. If compulsory attendance applies, this will be indicated in the Course Catalogue which can be consulted via the UvA-website. The rationale for and implementation of this compulsory attendance may vary per course and, if applicable, is included in the Course Manual.

    • Assessment

    Item and weight Details

    Final grade

    Assessment for the course is on a pass/fail basis. It consists of:

    • Presence during the activities (lectures, practicals, presentations)
    • Assessment of the Future Scenario Assignment
    • Assessment of a Python crash course exam

    Inspection of assessed work

    The manner of inspection will be communicated via the digitial learning environment.

    Assignments

    Future Scenario

    • The student proposes (interdisciplinary) research that would lead to new insights relevant to brain and cognitive sciences

    Python Programming

    • A closing exercise measures whether the student has obtained basic proficiency in Python for scientific programming

    • Each component is measured as pass/fail
    • Feedback is provided ad hoc during formative activities.
    • Summative feedback is provided in written form

    Fraud and plagiarism

    The 'Regulations governing fraud and plagiarism for UvA students' applies to this course. This will be monitored carefully. Upon suspicion of fraud or plagiarism the Examinations Board of the programme will be informed. For the 'Regulations governing fraud and plagiarism for UvA students' see: www.student.uva.nl

    Course structure

    Day Speaker(s) Topic Literature Seminar activities
    1

    Huib Mansvelder

    The neural basis of intelligence

    Galakhova, A. A., Hunt, S., Wilbers, R., Heyer, D. B., de Kock, C. P., Mansvelder, H. D., & Goriounova, N. A. (2022). Evolution of cortical neurons supporting human cognition. Trends in cognitive sciences, 26(11), 909-922.
    1. Introduction to Python programming
    2

    Tessa Blanken

    Complex Systems in Psychology

    Blanken, T. F., Bathelt, J., Deserno, M. K., Voge, L., Borsboom, D., & Douw, L. (2021). Connecting brain and behavior in clinical neuroscience: A network approach. Neuroscience & Biobehavioral Reviews, 130, 81-90.
    1. Work on Future Scenario assignment
    2. Deliverable: draft presentation
    3

    Jelle Zuidema

    		 Language representations Sinclair, A., Jumelet, J., Zuidema, W., & Fernández, R. (2021). Syntactic persistence in language models: Priming as a window into abstract language representations.
    1. Python programming
    4      
    1. Future Scenario Presentations
    2. Nomination per seminar group
    5      
    1. Python Final Exercise
    2. MPP Award Presentations
    3. Socials with second-years

     

    Additional information

    • First-year Brain and Cognitive Sciences master students are automatically registered to participate in this course.
    • This course is part of a pilot study into the use of large language models (LLMs) in higher education, and one group of students will be allowed to use a UvA-hosted LLM for support during the Python programming seminars. In case of any unclarities about permissible use, please contact the course coordinator.
    • If you do not wish to use LLMs in education, you can choose to opt out and work on the programming assignments without LLM assistance.
    • Participation in the study requires your informed consent. Please see Canvas for more information.

    Use of GenAI in MBCS

    Within the Research Master Brain and Cognitive Sciences, you are generally allowed to use Generative AI (GenAI) to support your learning process. For example, you can use large language models (LLMs) to help your self-study by generating flashcards, or generating explanations of concepts. You do so at your own risk:  an LLM may generate inaccurate or incomplete information for your studies. You are never allowed to use GenAI to generate work that you will hand in as an assignment, unless the assignment description explicitly allows you to do so.

    Note: GenAI should be a support tool to help you reach the course's learning objectives, not a system to which you delegate activities that are meant to promote your learning. The course examiner has final say on which use cases are permissible or not within their course.

    Never share personal information, research data, or course materials with a GenAI system, except for UvA AI Chat (https://aichat.uva.nl/). This UvA-hosted system was built with GDPR compliance and data security in mind. If you are in doubt about sharing information, don't share. You can always check with your course coordinator whether any intended use case is responsible.  

    Teachers are never allowed to use GenAI to grade your work. They may, however, use it to formulate their feedback.

    Course-specific rules on GenAI use

    Within this specific course, permissible GenAI use is limited to:

    • Asking assistance during the Python practicals (conditional on you being placed the LLM-assisted seminar group

    Last year's student feedback

    In order to provide students some insight how we use the feedback of student evaluations to enhance the quality of education, we decided to include the table below in all course guides.

    Course Name (#EC) N  
    Strengths
    • Active participation was encouraged
    • Good way to feel welcome and get to know fellow students
    • Good, clear lecturers and interesting topics
    • Strong social programme
    		 Notes for improvement
    • The course could be more challenging
    • Strong dependence on group work, which is not always enjoyable
    • Discrepancies between online descriptions and oral instructions for the assignments
    Response lecturer:
    • The course is intended as a meeting point: students seeing typical topics in brain and cognitive sciences, but also getting to know each other. So group work, while it can be frustrating, is hard to avoid. 
    • We've restructured the course so that it includes a Python primer, which should also be helpful in other block 1 courses. This is likely to increase the challenge for many.

    Contact information

    Coordinator

    • dr. R. Rouw