Color is an intuitive yet notoriously difficult concept to define. Is it a property of the object or the observer? A physical quantity or a subjective experience? The Phenomenon of Color is a five-lecture series exploring these questions from multiple perspectives. The first three lectures investigate the ontology of color. The fourth lecture reintegrates color as a holistic experience through the lens of art. The final lecture examines its application in concrete technologies. The course includes a hands-on laboratory session.
Lecture 1. Color as substance
The material and anatomical origins of color
We will explore a physics-based model of how light interacts with surfaces and retinal photopigments to produce color. Topics will include metameric surfaces, the dimensionality of color experience, and the concept of opponent-process signaling.
Lecture 2. Color as quale
The perceptual and cognitive basis of color
Moving beyond sensing, we will examine color illusions and illustrate how sensing, on its own, does not explain perception. We will discuss higher-order color experiences such as memory colors and color constancy. Finally, we will explore the interaction between language and color.
Lecture 3. Color as an emergent trait
The evolutionary and behavioral history of color
In this session we will address the question: why color ? We will discuss the existence of color quasi-invariants, and how they may relate to our evolutionary history. We will explore the diversity in color sensing across species, and discuss color as a key evolutionary trait that has shaped how species experience and interact with the world.
Lecture 4. Color and art
The artist as a deconstructor of the visual experience
This lecture aims to reintegrate color as a holistic experience. We will analyze how artists, acting as intuitive vision scientists, have exploited and subverted the principles of human color perception to convey the richness of visual experience.
Lecture 5. Color and technology
The role of colour in engineering and informatics
We will examine how the constraints of color perception drive human-optimized design in fields such as data storage and image processing. Case studies may include the Bayer filter, JPEG compression, and color-enhancing glasses for color blindness.
Color Lab. Color Science and Color Vision
Experiencing color theory
This hands-on session bridges theory and direct experience. Through a series of demonstrations like manipulating the components of digital color and monochromatic immersion, we will explore the gap between the physics of light and the experience of color.
Pre-requisites
None. Familiarity with basic concepts in optics, linear algebra, evolutionary theory, or signal processing may be helpful but is not required.
Instructor
Tushar Chauhan
Date
13, 16, 20, 23, 27 and 30 January, 2026 (Tuesdays and Fridays)
Time
12.00-13.45
Venue
Building 46, Room TBD
Enrollment
To enroll, please email the instructor at tchauhan AT mit DOT edu
Lectures
Self study
Course material
Attendance Policy
Office hours The lecturer will respond to queries by email.
Only mandatory if taking the course for credit.
Philosophy This course operates on the belief that true inquiry requires intellectual struggle – through reading, creation, argument, and sometimes, disagreement. The evaluations are designed to force this engagement. The AI policy exists to protect the integrity of your struggle.
Checkpoint evaluation After each session, the lecturer will send out a set of 3-5 statements. The students must agree or disagree with each, supporting their answer with a short paragraph. Figures (graphs, pictures, flowcharts) may also be included.
Final evaluation Mandatory for students taking the course for credit, optional for others. It will require participants to explore one or two closely related topics in greater depth. The final turn-in can be a 3-5 page essay, a working software/hardware demo, or an artwork. Participants will choose from amongst multiple problem-statements, which will be announced after lecture 3. The assignment will be due 4 weeks after the final session.
AI Policy Use of AI tools is NOT ALLOWED. They stifle experiential learning, are prone to factual errors, and operate with zero accountability for the quality of their output. Assignments judged to be completed with AI assistance will be evaluated using AI.
The objective is to interact with your unique voice.