Shannon information and integrated information: message and meaning
Alireza Zaeemzadeh, Giulio Tononi
arXiv Preprint Archive December 14, 2024 preprint
Study at a glance
AI-extracted from the abstract| Characteristics | Theoretical or philosophical paper |
|---|---|
| Keywords | Q-bio.nc Cs.it |
| Key points | Contrasts Shannon information theory with integrated information theory, arguing that IIT defines meaning as a cause-effect structure intrinsic to a system in a state, and that communication of meaning requires similarity of such structures between sender and receiver. |
Abstract
Information theory, introduced by Shannon, has been extremely successful and influential as a mathematical theory of communication. Shannon's notion of information does not consider the meaning of the messages being communicated but only their probability. Even so, computational approaches regularly appeal to "information processing" to study how meaning is encoded and decoded in natural and artificial systems. Here, we contrast Shannon information theory with integrated information theory (IIT), which was developed to account for the presence and properties of consciousness. IIT considers meaning as integrated information and characterizes it as a structure, rather than as a message or code. In principle, IIT's axioms and postulates allow one to "unfold" a cause-effect structure from a substrate in a state, a structure that fully defines the intrinsic meaning of an experience and its contents. It follows that, for the communication of meaning, the cause-effect structures of sender and receiver must be similar.