Donald D. Hoffman

In this course

Donald Hoffman is a cognitive scientist known for his Interface Theory of Perception, which challenges the assumption that natural selection favors accurate perception of objective reality, arguing instead that evolution selects for fitness-enhancing representations that may diverge radically from true world properties. In the context of this seminar, his work appears foundational to debates about how AI systems and artificial perception might fundamentally misalign with human intuitions about representation and reality, particularly in discussions concerning whether machine learning models approximate truth or merely functional utility. The readings invoke Hoffman to trouble the notion that either evolved or engineered perception systems need track objective properties, a claim crucial to understanding how algorithmic aesthetics and cybernetic systems might operate according to principles orthogonal to human notions of veridical experience.

Mentioned in 1 reading