For instance, Yuille and Bülthoff [44] describe the Bayesian approach to perception in terms of faithful depiction: “We define vision as perceptual inference, the estimation of scene properties from an image or sequence of images . . . ther…44. Yuille, A., and Bülthoff, H. (1996). Bayesian decision theory and psychophysics, in Perception as Bayesian inference, ed. D. Knill and W. Richards (Cambridge University Press, Cambridge).
45. Zollman, K. (2005). Talking to neighbors: T…
Heinrich Bülthoff
scientist · 2 mentions across 1 reading
In this course
Heinrich Bülthoff is a computational neuroscientist known for work on vision and perception as Bayesian inference, a framework that models how the brain estimates scene properties from sensory data under uncertainty. In the course readings, he appears primarily through his collaboration with Yuille on formalizing perception as probabilistic inference, a foundational concept for understanding how AI systems and biological vision systems both navigate incomplete information. This positions Bayesian approaches as central to both cognitive science and machine learning—bridging the natural and artificial intelligence that the seminar examines.
Mentioned in 1 reading
Appears alongside
People mentioned in the same passages — sorted by co-occurrence weight.