Predictive processing simplified: The infotropic machine

TL;DR: It is argued that the gap between theory and its biological and computational bases contributes to the arrested development of PP as a unificatory theory and is urged to focus on its critical problems instead of offering mere re‐descriptions of known phenomena.

TL;DR: A Predictive Processing formalization based on causal Bayesian networks is provided and it is argued that the level of detail of generative models and predictions modulates prediction error and establishes a key open empirical question to address: How does the brain optimize the trade‐off between high precision and information gain when making its predictions.

TL;DR: In this article, Tenenbaum and Yuille presented a probabilistic model of cognition based on conceptual foundations, which they called Probabilistic Models of Cognition (POC).

TL;DR: For instance, the authors discusses the multiplicity of the consciousness of self in the form of the stream of thought and the perception of space in the human brain, which is the basis for our work.

TL;DR: The author examines the role of entropy, inequality, and randomness in the design of codes and the construction of codes in the rapidly changing environment.

TL;DR: The Mathematical Theory of Communication (MTOC) as discussed by the authors was originally published as a paper on communication theory more than fifty years ago and has since gone through four hardcover and sixteen paperback printings.

TL;DR: The relationship between Stimulation and Stimulus Information for visual perception is discussed in detail in this article, where the authors also present experimental evidence for direct perception of motion in the world and movement of the self.