# Causal Event Networks: Cognition, Complexity and Physical Laws

arXiv: https://arxiv.org/abs/1402.7038 · HTML: https://mlsanity.com/paper/1402.7038/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Feb 2014.

## Built on by (most cited first, 1 of 1)

- [TrackMPNN: A Message Passing Graph Neural Architecture for Multi-Object Tracking](https://mlsanity.com/paper/2101.04206/) (2021)

## Abstract (arXiv)

Information flow framed in a computational and complexity context is relevant to the understanding of cognitive processes and awareness. In this paper, we begin with analyzing an information theory framework developed in recent years under Information and Integration Theory (IIT) based on interactions among partitions of cognitive information sets. We discuss the scope and limitations of these ideas, introducing a related measure for partitioning information sets. We introduce a set of postulates describing cognition as a partially ordered set of events in space and time. We consider the relevant sequential and concurrent computational concepts in an idealized minimal cognitive device. The concept of fundamental cognitive chain formalizes temporal limits of cognition.