Higher Cognitive Functions

5 EC

Semester 1, period 1

5244HICF5Y

Owner Master Brain and Cognitive Sciences
Coordinator dr. S. Pezzelle
Part of Master Brain and Cognitive Sciences, domain Cognitive Science,

Course manual 2025/2026

Course content

Within the cognitive sciences, there is often talk of higher and lower cognitive functions. As a heuristic, this categorization can serve a purpose: studies into reasoning, language use, creativity, musicality or deliberation are in many ways different from studies into perception or reaction time. Yet at the same time, it is not always clear how to demarcate 'high cognition' and 'low cognition', with different fields and methodologies bringing about different perspectives on what these terms mean to begin with.

The course Higher Cognitive Functions aims to showcase studies into some faculties that have classically been considered to be of a high level, such as language use, decision-making, and theory of mind. Students will learn how current insights on these topics have historically been constructed, what light they shed on the functioning of our minds, which debates are still ongoing and how theories of cognition are moving forward.

Study materials

Literature

  • selected papers

Objectives

  • Place current research in cognitive science in its historic context
  • Articulate important outstanding issues in cognitive science
  • Critically discuss developments in the field with experts
  • Integrate ideas from distinct subfields in cognitive science
  • Identify ethical aspects of and best practices for research in cognitive science

Teaching methods

  • Lecture
  • Seminar
  • Self-study
  • Presentation/symposium
  • Working independently on e.g. a project or thesis

Interactive lectures on current research will illustrate knowledge construction in the cognitive sciences.

Students will prepare interviews with experts to critically evaluate research and report on interviews/lectures.

Students will develop a research proposal to address key debates empirically, drawing on findings from multiple lines of research.

 

Learning activities

Activity

Number of hours

Lectures

20

Readings

80

Assignments

40

Total

140

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.

    • Additional requirements for this course:

    Students should attend at least all but one interview sessions and write a weekly report on each session (except when they are hosting it). The effect of missing a session on the grade is that no credits are given for that session. Interview attendance counts for in total 25%; this percentage can be obtained by attending  (and writing a report for) all sessions but one.

    The final essay  cannot be revised if the total grade is sufficient, but a new essay (on another topic) can be written.In case the total grade is not sufficient, it is also possible to revise the final essay based on the feedback. The new grade will be used to calculate a new overall grade, but this grade cannot exceed 7.0 (so, if the recalculated grade is below 7.0, it is the new grade; else the new grade is 7.0).

    Assessment

    Item and weight Details Remarks

    Final grade

    		For more details, see course Canvas page

    0.25 (25%)

    3 Weekly Assignments

    		NAP if missing

    0.3 (30%)

    1 Scientific Interview

    		NAP if missing

    0.45 (45%)

    Final Essay

    		NAP if missing

    Assignments

    Interview hosting

    • Students will be tasked to interview a visiting speaker, in groups

    Weekly assignment

    • Every week, students will individually respond to 4 questions about the module's topics.

    Final essay: literature comparison

    • Students will write an essay comparing and integrating the viewpoints of two disciplines (from two different lecturers) on one topic.

    Essay topic pitch

    • Students (individually) give a short presentation on the topic of their final essay. This assignment will not be graded.

    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

    Weeknummer Onderwerpen Studiestof
    1 Affordances and skilled action Lecture + paper(s)
    2 Episodic memory Lecture + paper(s)
    3 Language and Statistical Learning Lecture + paper(s)
    4 Music Perception Lecture + paper(s)
    5 Multimodal Communication Lecture + paper(s)
    6 Overall review Lecture + paper(s)
    7    
    8    

    Additional 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.

    For more on the MBCS policy, see the programme Canvas page.

    Course-specific rules on GenAI use

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

    • Supporting your self-study (e.g. by summarising articles, testing your understanding or generating explanations). Be mindful that information provided by any large language model, including UvA AI Chat, may be erroneous. Always question output and, if in doubt, bring your questions to class.
    • Get feedback on written assignments before submission. Please note that you remain accountable for any work that you submit, so make sure you understand and support any feedback that you use. Allowing GenAI use for feedback presupposes that you first went through steps like ideation, analysis or structuring arguments without technological assistance. By first doing the deep thinking yourself, your learning becomes better.

    Last year's student feedback

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

    Higher Cognitive Functions (6 EC)    
    Strengths
    • Positive feedback across the board
    • Lecturer is super nice
    • Level of the course is good
    		 Notes for improvement
    • Turnover of feedback could be higher
    • Students spent less hours on the course than intended
    • Goals of the course not clear to everyone
    • Writing assignment is challenging
    Response lecturer:
    • Focus in feedback turnover is on ongoing processes: feedback on one assignment should be in before next submission. Feedback on the interviews is less time-sensitive.
    • The course is kept accessible through summaries of readings, but this does mean some students do not do the readings themselves. Raising the bar on expectations should increase workload (and more importantly, learning).
    • The assignments are tied to the course learning objectives, and these necessitate a broad range of topics, which means thematic coherence can be low. However, the purpose of this is to get a good view of current cognitive science research.
    • Structuring text is difficult for students, but they do learn how to do it through this course. It is worth considering offering a template, e.g. in Overleaf.

    Contact information

    Coordinator

    • dr. S. Pezzelle