Christoffer Lundbak Olesen, Peter Thestrup Waade, Larissa Albantakis, Christoph Mathys
PLoS computational biology 2023 OctThe Free Energy Principle (FEP) and Integrated Information Theory (IIT) are two ambitious theoretical approaches. The first aims to make a formal framework for describing self-organizing and life-like systems in general, and the second attempts a mathematical theory of conscious experience based on the intrinsic properties of a system. They are each concerned with complementary aspects of the properties of systems, one with life and behavior, the other with meaning and experience, so combining them has potential for scientific value. In this paper, we take a first step towards such a synthesis by expanding on the results of an earlier published evolutionary simulation study, which show a relationship between IIT-measures and fitness in differing complexities of tasks. We relate a basic information theoretic measure from the FEP, surprisal, to this result, finding that the surprisal of simulated agents' observations is inversely related to the general increase in fitness and integration over evolutionary time. Moreover, surprisal fluctuates together with IIT-based consciousness measures in within-trial time. This suggests that the consciousness measures used in IIT indirectly depend on the relation between the agent and the external world, and that it should therefore be possible to relate them to the theoretical concepts used in the FEP. Lastly, we suggest a future approach for investigating this relationship empirically. Copyright: © 2023 Olesen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Christoffer Lundbak Olesen, Peter Thestrup Waade, Larissa Albantakis, Christoph Mathys. Phi fluctuates with surprisal: An empirical pre-study for the synthesis of the free energy principle and integrated information theory. PLoS computational biology. 2023 Oct;19(10):e1011346
PMID: 37862364
View Full Text