How do we measure human experience in space? Can we predict a design’s impact on human comfort, performance, or preference before it’s built? As environments are increasingly read by machines, through sensors, models, and predictive algorithms, designers need new ways to observe, analyze, and evaluate human spatial experience. Understanding where human and machine perceptions converge and diverge is essential to rethinking spatial experience and design.

Drawing from concepts in architecture, cognitive science, and computer vision, this course explores methods for translating subjective human experience into quantifiable insights. Through lectures, critical readings, and conceptually driven projects, students will investigate human perceptions of scale, depth, attention, and memory, alongside computational techniques such as object detection, classification, and semantic segmentation. These core methods will be complimented with scientific approaches such as eye-tracking and immersive virtual environments, that offer different ways to capture and analyze experience. We will interrogate the limits of both human and computational perspectives and examine what it means to use perception as a design input, an evaluative tool, or even a dataset. Real-world case studies ranging from feedback loops in adaptive buildings to surveillance systems in cities, will ground our discussion of bias, ethics, and the risks of relying on computational technologies to understand and shape environments.

Hands-on experimentation is central to the course. Students will work with pre-trained models and computing tools, learning to collect perceptual survey data, generate visual scores, and apply image-based analysis to explore patterns in human spatial experience. Rather than emphasizing technical development from scratch, the course treats computational systems as design frameworks and materials. Short in-class exercises and two mini take-home assignments will build progressively towards a midterm and final project. The assignments and projects equip students to work critically and creatively at the intersection of human experience, spatial thinking, and emerging technologies.

By the end of the course, students will be able to design, critique, and deploy analytical tools that bridge subjective human experience and objective spatial data, offering new ways to quantify, evaluate, and design environments at the building and urban scales.

There are no prerequisites for the course, and non-GSD students are welcome to attend. Prior programming or image processing experience is welcomed, but not required.