| Course Type | Elective Course |
|---|
| Course Level | Master's Degree With Thesis |
|---|
| Year of Study | - |
|---|
| Semester | - |
|---|
| ECTS | 7 |
|---|
| Name of Lecturer(s) |
- |
| Learning Outcomes |
At the end of this course, the students;
- Become acquainted with the aim of cognitive modelling
- Understand and use recent computational neuroscience tools
- Become acquainted with the mathematical basis of cognitive modelling
- Understand and use psychological models
|
| Mode of Delivery | Face to face |
|---|
| Prerequisites | No |
|---|
| Recommended Optional Programme Components | None |
|---|
| Course Description | This course examines different modeling techniques that are used in psychological research. Topics covered in the course include: the concept of scientific model in general and the place of modeling in scientific research, the concept of model in psychology and the models of human behavior and cognitive processes. The course also focuses on different modeling approaches in psychology such as computational modeling, computer simulations of complex cognitive processes, artificial neural networks, connectionism and parallel distributed processing, mathematical modeling and the models of brain-body-environment systems. Using sample models of perception, attention, memory, decision-making, development and learning, the course aims to help students to appreciate the importance and the use of modeling in psychological research. |
|---|
| Course Contents |
| WEEK |
TOPICS |
|
1st Week |
Introduction to Cognitive Modelling |
|
2nd Week |
Evaluations of Models |
|
3rd Week |
Modelling Neurons: Differential Equations |
|
4th Week |
Computational Models for Action Potentials |
|
5th Week |
Modelling Neurons: Hodgkin and Huxley Model |
|
6th Week |
Neural Network Mathematics : Vectors and Matrices |
|
7th Week |
Introduction to Neural Networks |
|
8th Week |
Mid-term Exam |
|
9th Week |
Hopfield Networks |
|
10th Week |
Probability and Psychological Models |
|
11th Week |
Decision |
|
12th Week |
Cognitive Modelling as Logic and Rules |
|
13th Week |
Production Rules and Cognition |
|
14th Week |
Cognitive Architecture |
|
15th Week |
Agent-Based Modelling |
|
| Recommended or Required Reading | Anderson, B. (2014). Computational Neuroscience and Cognitive Modelling. London: SAGE |
|---|
| Planned Learning Activities and Teaching Methods |
- Lecture
- Discussion
- Presentation
|
|---|
| Assessment Methods and Criteria |
|
Quantity |
Percentage (%) |
| Mid-term |
1 |
30 |
| Assignment |
1 |
30 |
| Total(%) |
|
60 |
| Contribution of In-term Studies to Overall Grade(%) |
|
60 |
| Contribution of Final Examination to Overall Grade(%) |
|
40 |
| Total(%) |
|
100 |
|
| ECTS Allocated Based on Student Workload |
| Activities |
Quantity |
Duration (Hour) |
Total Workload (Hour) |
| Midterm exam |
1 |
3 |
3 |
| Preparation for Quiz |
|
|
|
| Individual or group work |
14 |
8 |
112 |
| Preparation for Final exam |
1 |
20 |
20 |
| Course hours |
15 |
3 |
45 |
| Preparation for Midterm exam |
|
|
|
| Laboratory (including preparation) |
|
|
|
| Final exam |
1 |
3 |
3 |
| Homework |
1 |
30 |
30 |
| Total Work Load | 213 |
| Total Work Load / 30 (h) | 7.1 |
| ECTS Credit of the Course | 7 |
|
| Language of Instruction | Turkish |
|---|
| Work Placement(s) | No |
|---|
