A returning idea among some Bayesians in research on human visual perceptual organization is that the surprisal of something (i.e., the negative logarithm of its probability) expresses its complexity (i.e., the length of its shortest description). Bayes' rule is a powerful modeling tool and descriptive simplicity is a rich concept, but this idea is wishful thinking at best: If true, it would unify the simplicity and likelihood principles, which reflect two traditionally opposed schools of thought on perceptual organization. Some rapprochement between the two principles can certainly be discerned, but the aforementioned idea lacks formal underpinning and confounds otherwise perfectly good ideas. Here, this idea is revisited and its latest version is debunked step by step. In addition, I argue that its likely origin lies, inadvertently, in a standard Bayesian textbook: The author made (a) a pivotal mistake and (b) a compelling argument that was overinterpreted by others.
Keywords: Bayes’ rule and Occam’s razor; classical and modern information theory; human visual perceptual organization; likelihood and simplicity principles.