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COGSCI138

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COGSCI 138 - Optical Illusions: Brain, Machine, World

Interdisciplinary Social Science Programs Undergraduate CLS - College of Letters and Science

Subject

COGSCI

Course Number

138

Department

Interdisciplinary Social Science Programs

Course Level

Undergraduate

Course Title

Optical Illusions: Brain, Machine, World

Course Description

Optical illusions are entertaining, but more than just visual tricks: They reveal deep insights into perception. This course critically explores what constitutes an illusion, and how an expansive definition can bridge human and machine perception. Illusions appear in nature, art, and our increasingly digital world. We’ll learn how to measure illusion perception in both humans and machines, and examine state-of-the-art machine perception through the lens of illusion—exploring machine hallucination, image generation, and adversarial imagery. This three-week intensive blends lectures, discussions, readings, hands-on labs, and a research project.

Minimum Units

2

Maximum Units

3

Grading Basis

Default Letter Grade; P/NP Option

American Cultures Requirement

No

Reading and Composition Requirement

None of the Reading and Composition Requirement

Prerequisites

Introductory understanding of human vision, such as Cognitive Science C126 or similar
Introductory understanding of machine learning and vision, such as Data Science C8, Computer Science 180, Computer Science 182, Computer Science 189, or similar
Familiarity with Python and Jupyter notebooks

Repeat Rules

Course is not repeatable for credit.

Credit Restriction Courses. Students will receive no credit for this course after completing the course(s) below.

-

Credit Replacement Courses

-

Student Learning Outcomes

1. Learn ways in which optical illusions have been defined, as well as challenges in doing so 2. Discover the broad range of ways illusions occur in the world from naturally-occuring illusions to those intentionally designed for art, public and digital spaces 3. Understand research methods for measuring illusion perception and ways that has informed either the underlying neural mechanisms or understanding of the strengths and limitations of machine perception 4. Compare and contrast human and machine perception, identifying their respective reactions to illusions 5. Discover illusions that may emerge from machine learning models

Formats

Lecture

Term

Summer

Weeks

6 weeks

Weeks

6

Lecture Hours Min

4.5

Lecture Hours Max

6

Lecture Mode of Instruction

In Person

Outside Work Hours Min

12

Outside Work Hours Max

18

Term

Summer

Weeks

Other

Weeks

3

Lecture Hours Min

7

Lecture Hours Max

15

Lecture Mode of Instruction

In Person

Outside Work Hours Min

23

Outside Work Hours Max

30