On Information and the Drivers of Cognition

In the last decade Shannon information experienced a revival in the interest of cognitive and Artificial Life modellers. With new conceptual and technical tools available, it has become clear that it offers new opportunities to analyse, predict and construct cognitive models in a principled way, while reducing assumptions about the particular cognitive architecture of interest.

Several of these have already received wide interest, such as rate/distortion-type arguments ("relevant information") for decision-making regularization, or predictive information and impowerment as intrinsic motivation mechanisms.

In my talk, I would like to argue that information theory offers much more than just being a specialized tool to formulate specific models for cognitive dynamics: in fact, its use means adopting a whole philosophy about modelling cognition and its development in nature. The talk will present a collection of examples (some more, some less known) for that point, including a sensor evolution scenario that sheds new light on a non-obvious observation from biological evolution.