Evolution of Plasticity and Learning

Course manual 2025/2026

Course content

Nature is astonishing and there are many puzzles to solve. One intriguing puzzle is the ability of organisms to flexibly adjust to their environments based on their experiences. Such plasticity and learning requires evolutionary explanation. This course thus addresses questions such as: In which environmental conditions does natural selection result in plasticity and learning? Why do organisms exhibit plasticity in some traits rather than others? Which factors explain variation in plasticity and learning between species, individuals, and traits? Why do certain traits exhibit sensitive periods, in which the impact of experience on their development is larger than other periods? In addition to discussing general theory, we also zoom in on empirical cases: Why have meadow voles evolved the capacity to use hormonal signals from their mother (prenatally) to determine the thickness of their fur coats (postnatally)? Why do zebra finches learn new songs only early in life, but great tits across their entire lifetimes? Why do children learn faster about danger than other types of information? In this course, students learn to evaluate applications of evolutionary theory to the study of plasticity and learning across levels of analysis, including the brain, cognition, and behavior. Students also learn to apply mathematical models exploring how organisms adaptively balance the acquisition and use of information relative to other tasks relevant for survival and reproduction. This course is taught in a highly accessible manner and for a broad audience, enabling students to build knowledge and skills needed to bridge the biological and social sciences.

Study materials

Literature

• Scientific articles related to the content of the lectures and seminars will be provided via Canvas.

Objectives

• Operationalize key concepts—such as evolution, development, plasticity, and learning—to answer research questions about the evolution of plasticity and learning in humans and other animals.
• Evaluate key frameworks, theories, and hypotheses in the study of the evolution of plasticity and learning, such as natural selection, life history theory, parent-offspring conflict, developmental programming, and reinforcement learning.
• Evaluate applications of evolutionary theory to the study of plasticity and learning at multiple levels of analysis, including the brain, cognition, and behavior.
• Interpret and explain mathematical modeling approaches used to study how organisms acquire information and use it to produce adaptive behavior (e.g., the expected values of behavioral options in a particular environment), including statistical decision theory, Bayesian updating, agent-based simulation, dynamic programming, and game theory.
• Analyze the relations between disciplinary perspectives on adaptive processes, such as biologists, psychologists, economists, and anthropologists using different concepts, theories, and methods (e.g., psychologists tend to use controlled experiments to study mental processes involved in learning, anthropologists tend to observe participants in their natural environment to understand how behavior is acquired).
• Select and integrate theory and evidence from different disciplines to answer research questions about the evolution of plasticity and learning (e.g., comparative psychology, primatology, anthropology, demography, bioarcheology, evolutionary biology).
• Explain how adaptive processes on different timescales—such as evolution, development, and learning—are related and interact with each other (e.g., natural selection shapes mechanisms that construct brains which produce behavior).
• Generate research questions about the evolution of plasticity and learning, and plan and execute a strategy for answering them, by collaborating in an interdisciplinary team through active listening, promoting dialogue, and managing conflicts; and sharing your findings through a short video or podcast with scientists and the general public.

Teaching methods

• Lecture
• Presentation/symposium
• Self-study
• Office hour
• Seminar

Learning activities

Attendance

Additional requirements for this course:

You are allowed to miss 2 (out of the 8) seminars in this course. There are no opportunities to retake seminars or to receive substitute assignments.

If you are absent from the seminars more than twice without a valid reason, a "conditions not met" (NAV) will be recorded for the course, and therefore no final grade can be awarded.

The materials and activities during the seminars will not be made available in other ways. These materials and activities are directly related to achieving several course objectives (e.g., operationalized key concepts, interpret mathematical modelling). These materials and activities will also enhance your graded writing assignment and group project.

If you are unable to attend a seminar (e.g., due to illness or family circumstances), please contact the course coordinator, Willem Frankenhuis, by email (w.e.frankenhuis@uva.nl), preferably before the seminar starts. If you are unable to attend the lectures or seminars for a longer period (longer than 1 week), please also contact your study advisor.

Attendance at the lectures is strongly recommended, but not mandatory. The lectures will not be recorded. Questions for the teachers can be asked during the lectures and seminars.

Assessment

• Individual writing assignment: 50%
• Group product (40%) and presentation (10%): jointly 50%
• Seminar participation (pass/fail); required to pass the course

There will one opportunity for a retake of the individual writing assignment and one opportunity for a retake of the group product and presentation. The maximum grade for a retake is 6.

Assignments

The descriptions of the assignments and grading criteria (Rubric) will be provided in the module General Information via Canvas.

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

Exit qualifications

Zie de Zichtbare Leerlijnen Creator voor de koppeling van vakleerdoelen, leerlijndoelen en eindtermen.

https://datanose.nl/#program[BSc%20PB]/trajectories

https://datanose.nl/#program[BSc%20PB]/outcomes

Additional information

This is a 4-week fulltime course in period 2b (December). 

The course is taught in English.

Attendance of the seminars is mandatory. Students need to be available fulltime during the course.

More information will be available on Canvas and in the study manual.

Processed student feedback

This is a new course! I look forward to receiving feedback for next year.

Contact information

Coordinator

• dr. Willem Frankenhuis

Staff

• dr. Thomas Blankers
• dr. E.R. Burdfield-Steel
• prof. dr. A.T. Groot
• dr. Lucas Molleman
• dr. P.C.S. Vermeent
• dr. N. Walasek
• dr. I. Godoy