PSY309 Cognitive Neuroscience
Syllabus | International University of Sarajevo - Last Update on Sep 09, 2025
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Psychology
Pinar Unal Aydin
Course Lecturer
Course Objectives
This course offers an introduction to cognitive neuroscience, focusing on how human cognition is studied using neuroimaging techniques in healthy individuals and neuropsychological testing in patients with brain damages. Students will explore the functions and interactions of various cortical and subcortical brain regions, uncovering how these areas collaborate to reconstruct reality for the interaction with the external world, using memory, attention, and emotion for goal-oriented behaviors. Additionally, the course provides hands-on experience in analyzing EEG data, enabling students to extract and interpret meaningful brain activity from neurophysiological signals recorded at the scalp.
Learning Outcomes
After successful completion of the course, the student will be able to:
Course Materials
Required Textbook
Michael S. Gazzaniga, Richard B. Ivry, and George R. Mangun (2014) Cognitive Neuroscience The Biology of the Mind, 4th Edition. W. W. Norton & Company, Inc., 500 Fifth Avenue, New York, NY
Additional Literature
Scietific papers related to the material discussed in the lectureTeaching Methods
Course delivery will be via lecture
Open discussions
And practical work.
Weekly Topics
| Week | Topic | Readings / References |
|---|---|---|
| 1 | Introduction to cognitive neuroscience | Chapter 1 |
| 2 | Structure and Function of the Nervous System | Chapter 2 |
| 3 | Neuroimaging methods | Chapter 3 |
| 4 | Neuroimaging methods | Chapter 3 |
| 5 | Perception and Action | Chapters 5-6 |
| 6 | QUIZ - Practical work – getting familiar with EEG | |
| 7 | Practical work – recording EEG data | |
| 8 | MIDTERM EXAM | |
| 9 | Perception and Action | Chapter 8 |
| 10 | Memory | Chapter 7 |
| 11 | Attention | Chapter 9 |
| 12 | Emotion | Chapter 10 |
| 13 | QUIZ - Practical work – data processing | |
| 14 | Practical work – data processing and analysis | |
| 15 | Practical work - interpretation and discussion of the EEG results |
Course Schedule (All Sections)
| Section | Type | Day 1 | Venue 1 | Day 2 | Venue 2 |
|---|---|---|---|---|---|
| PSY309.1 | Course | Wednesday 15:00 - 16:50 | B F2.16 | Wednesday 17:00 - 17:50 | A F1.3 - Computer Lab |
Office Hours & Room
Assessment Methods and Criteria
Assessment Components
Final Exam
AI: Not AllowedAlignment with Learning Outcomes :
Midterm Exam
AI: Not AllowedAlignment with Learning Outcomes :
Quiz
AI: Not AllowedAlignment with Learning Outcomes :
IUS Grading System
| Grading Scale | IUS Grading System | IUS Coeff. | Letter (B&H) | Numerical (B&H) |
|---|---|---|---|---|
| 0 - 44 | F | 0 | F | 5 |
| 45 - 54 | E | 1 | ||
| 55 - 64 | C | 2 | E | 6 |
| 65 - 69 | C+ | 2.3 | D | 7 |
| 70 -74 | B- | 2.7 | ||
| 75 - 79 | B | 3 | C | 8 |
| 80 - 84 | B+ | 3.3 | ||
| 85 - 94 | A- | 3.7 | B | 9 |
| 95 - 100 | A | 4 | A | 10 |
Late Work Policy
Information about late submission policies will be shared during class and posted in this section. Please check back for official guidelines.
ECTS Credit Calculation
📚 Student Workload
This 6 ECTS credit course corresponds to 150 hours of total student workload, distributed as follows:
Lecture Hours
45 hours ⏳ (3 week × 15 h)
Quiz 1
15 hours ⏳ (3 week × 5 h)
Quiz 2
20 hours ⏳ (4 week × 5 h)
In-term Exam study
35 hours ⏳ (7 week × 5 h)
Final Exam Study
35 hours ⏳ (7 week × 5 h)
150 Total Workload Hours
6 ECTS Credits
Course Policies
Academic Integrity
All work submitted must be your own. Plagiarism, cheating, or any form of academic dishonesty will result in disciplinary action according to university policies. When in doubt about citation practices, consult the instructor.
Attendance Policy
Students are expected to adhere to the attendance requirements as outlined in the International University of Sarajevo Study Rules and Regulations. Excessive absences, whether excused or unexcused, may impact academic performance and eligibility for assessment. Mandatory sessions (e.g., labs, workshops) require attendance unless formally exempted. For detailed policies on absences, documentation, and penalties, please refer to the official university regulations.
Technology & AI Policy
Laptops/tablets may be used for note-taking only during lectures. Phones should be silenced and put away during all class sessions. Audio/video recording requires prior permission from the instructor.
Artificial Intelligence (AI) Usage: The use of AI tools (e.g., ChatGPT, Copilot, Gemini) varies by assessment component. Please refer to the AI usage indicator next to each assessment item in the Assessment Methods and Criteria section above. Submitting AI-generated content as your own work, where AI is not explicitly allowed, constitutes an academic integrity violation.
Communication Policy
All course-related communication should occur through official university channels (institutional email or SIS). Emails should include [PSY309] in the subject line.
Academic Quality Assurance Policy
Course Academic Quality Assurance is achieved through Semester Student Survey. At the end of each academic year, the institution of higher education is obliged to evaluate work of the academic staff, or the success of realization of the curricula.
Learning Tips
Be prepared to contribute thoughtfully during class discussions, labs, or collaborative work. Active participation deepens understanding and encourages critical thinking.
Complete assigned readings or prep materials before class. Take notes, highlight key ideas, and jot down questions. Aim to grasp core concepts and their applications—not just facts.
Use course frameworks or methodologies to analyze problems, case studies, or projects. Begin early to allow time for reflection and refinement. Seek feedback to improve your work.
Don’t hesitate to reach out when something is unclear. Use office hours, discussion boards, or peer networks to clarify concepts and stay on track.
Syllabus Last Updated on Sep 09, 2025 | International University of Sarajevo
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Referencing Curricula Print this page
| Course Code | Course Title | Weekly Hours* | ECTS | Weekly Class Schedule | ||||||
| T | P | |||||||||
| PSY309 | Cognitive Neuroscience | 2 | 1 | 6 | ||||||
| Prerequisite | None | It is a prerequisite to | - | |||||||
| Lecturer | Pinar Unal Aydin | Office Hours / Room / Phone | Monday: 9:30-11:15 Tuesday: 9:30-11:00 Thursday: 9:30-11:15 |
|||||||
| paydin@ius.edu.ba | ||||||||||
| Assistant | Assistant E-mail | |||||||||
| Course Objectives | This course offers an introduction to cognitive neuroscience, focusing on how human cognition is studied using neuroimaging techniques in healthy individuals and neuropsychological testing in patients with brain damages. Students will explore the functions and interactions of various cortical and subcortical brain regions, uncovering how these areas collaborate to reconstruct reality for the interaction with the external world, using memory, attention, and emotion for goal-oriented behaviors. Additionally, the course provides hands-on experience in analyzing EEG data, enabling students to extract and interpret meaningful brain activity from neurophysiological signals recorded at the scalp. | |||||||||
| Textbook | Michael S. Gazzaniga, Richard B. Ivry, and George R. Mangun (2014) Cognitive Neuroscience The Biology of the Mind, 4th Edition. W. W. Norton & Company, Inc., 500 Fifth Avenue, New York, NY | |||||||||
| Additional Literature |
|
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| Learning Outcomes | After successful completion of the course, the student will be able to: | |||||||||
|
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| Teaching Methods | Course delivery will be via lecture, open discussions, and practical work. | |||||||||
| Teaching Method Delivery | Face-to-face | Teaching Method Delivery Notes | ||||||||
| WEEK | TOPIC | REFERENCE | ||||||||
| Week 1 | Introduction to cognitive neuroscience | Chapter 1 | ||||||||
| Week 2 | Structure and Function of the Nervous System | Chapter 2 | ||||||||
| Week 3 | Neuroimaging methods | Chapter 3 | ||||||||
| Week 4 | Neuroimaging methods | Chapter 3 | ||||||||
| Week 5 | Perception and Action | Chapters 5-6 | ||||||||
| Week 6 | QUIZ - Practical work – getting familiar with EEG | |||||||||
| Week 7 | Practical work – recording EEG data | |||||||||
| Week 8 | MIDTERM EXAM | |||||||||
| Week 9 | Perception and Action | Chapter 8 | ||||||||
| Week 10 | Memory | Chapter 7 | ||||||||
| Week 11 | Attention | Chapter 9 | ||||||||
| Week 12 | Emotion | Chapter 10 | ||||||||
| Week 13 | QUIZ - Practical work – data processing | |||||||||
| Week 14 | Practical work – data processing and analysis | |||||||||
| Week 15 | Practical work - interpretation and discussion of the EEG results | |||||||||
| Assessment Methods and Criteria | Evaluation Tool | Quantity | Weight | Alignment with LOs | AI Usage |
| Final Exam | 1 | 40 | all | Not Allowed | |
| Semester Evaluation Components | |||||
| Midterm Exam | 1 | 30 | all | Not Allowed | |
| Quiz | 2 | 30 | all | Not Allowed | |
| *** ECTS Credit Calculation *** | |||||
| Activity | Hours | Weeks | Student Workload Hours | Activity | Hours | Weeks | Student Workload Hours | |||
| Lecture Hours | 15 | 3 | 45 | Quiz 1 | 5 | 3 | 15 | |||
| Quiz 2 | 5 | 4 | 20 | In-term Exam study | 5 | 7 | 35 | |||
| Final Exam Study | 5 | 7 | 35 | |||||||
| Total Workload Hours = | 150 | |||||||||
| *T= Teaching, P= Practice | ECTS Credit = | 6 | ||||||||
| Course Academic Quality Assurance: Semester Student Survey | Last Update Date: 22/09/2025 | |||||||||