PSYK3500 Evolution and Behavior Course description

The course provides an introduction to how theories on evolution and behaviour analysis can explain the behaviour of animals and humans. The course covers key topics in modern evolutionary biology, behavioural ecology and selection in relation to consequences. This includes an introduction to topics from biology, evolutionary psychology, anthropology and behaviour analysis views on selection and culture.

Work and teaching methods used in the course are lectures, self-study, presentation of texts and group work. Seminars will also be held where the students present academic texts. Students will present texts from the syllabus, encourage discussion and receive guidance on further reading.

During the course, the students must submit three assignments

After completing the course, the student is expected to have achieved the following learning outcomes defined in terms of knowledge, skills and competence:

Knowledge

The student

• has knowledge of similarities and differences between selection in evolutionary biology, behaviour analysis and cultures
• is familiar with interactions between selection principles at the different levels
• has broad knowledge of basic principles and theories in modern evolution biology
• has broad knowledge of key topics in behavioural ecology
• has knowledge of key topics in modern genetics and understanding of heredity
• is familiar with the significance of natural selection to the nervous system’s structure and function
• has knowledge of basic research areas in evolutionary psychology

Skills

The student is capable of

• using principles of cultural selection in relation to changes in organisations and groups
• finding examples of how specific behaviour can be explained from an evolutionary perspective
• reflecting on topics and theories in evolution and behaviour

Competence

The student

• has insight into the biological basis of behaviour in animals, including humans
• is capable of describing selection as an explanatory model both orally and in writing
• is familiar with new ideas and innovation processes in behaviour analysis as a holistic discipline based on selection sciences

On successful completion of the course, students should have the following learning outcomes defined in terms of knowledge, skills, and general competence.

Knowledge

The student:

• is knowledgeable about supervised, unsupervised, reinforcement learning
• has a good understanding of the principles of state-of-the-art deep neural networks such as convolutional neural networks, sequential models (RNN, LSTM), Transformers, GenerativeAI (Autoencoder, GAN, Diffusion models), and reinforcement learning.
• has a good understanding of both theoretical and practical know-how required to use machine learning and deep learning methods effectively.

Skills

The student can:

• build, train, test, and deploy machine learning and deep learning models
• analyze machine learning methods in regard to their performance and effectiveness
• use existing deep learning networks, improve and/or customize them to apply to new problems

General competence

The student:

• has both theoretical and practical understanding of machine learning and deep learning methods
• can discuss relevance, strength, and limitations of machine learning and deep learning in solving real-world problems
• can work on effectively relevant research projects

The course consists of lectures, group consultations, presentation seminars, and project work. In the seminars, students will read papers, present, and also actively participate in other presentations. This will facilitate research-oriented education in the field. Research projects will be aimed at cultivating the students towards good future researchers.

None

Exam in two parts:

• A group project: implementation and report (about 7000 words). A group of 2-3 students will be formed during the course. Each group member receives an individual grade based on their contribution to the project.
• Individual oral exam (about 30 minutes).

Each of them carries 50% weight in the final grade. The oral examination cannot be appealed.

Both exams must be passed in order to pass the course.

New/postponed exam

In case of failed exam or legal absence, the student may apply for a new or postponed exam. New or postponed exams are offered within a reasonable time span following the regular exam. The student is responsible for registering for a new/postponed exam within the time limits set by OsloMet. The Regulations for new or postponed examinations are available in Regulations relating to studies and examinations at OsloMet.

Grade scale A-F.

Two internal examiners. External examiner is used periodically.