Can the Integrated Information Theory Explain Consciousness from Consciousness Itself?
Review of Philosophy and Psychology August 3, 2022 DOI: 10.1007/s13164-022-00653-x (opens in new tab) via Semantic Scholar
Summary
AI-generated from the abstractIn consciousness science, theories differ in whether they start from experimental data or phenomenological data. The Integrated Information Theory (IIT), a prominent phenomenology-first approach, uses a self-evidencing explanation that begins with consciousness itself to explain consciousness. While this circular reasoning can be virtuous, IIT faces a data-fitting problem: there is insufficient information to determine whether its explanatory hypotheses are the best available. This issue, termed the self-evidencing problem, parallels the roadblock faced by experiment-driven approaches, where no current model is clearly superior. A possible solution for IIT is proposed.
Study at a glance
| Characteristics | Theoretical or philosophical paper Peer reviewed |
|---|---|
| Keywords | Philosophy |
| Key finding | Argues that Integrated Information Theory faces a self-evidencing problem: insufficient information is given to decide whether its explanatory hypotheses are best. |
Abstract
In consciousness science, theories often differ not only in the account of consciousness they arrive at, but also with respect to how they understand their starting point. Some approaches begin with experimentally gathered data, whereas others begin with phenomenologically gathered data. In this paper, I analyse how the most influential phenomenology-first approach, namely the Integrated Information Theory (IIT) of consciousness, fits its phenomenologically gathered data with explanatory hypotheses. First, I show that experimentally driven approaches hit an explanatory roadblock, since we cannot tell, at the present stage, which model of consciousness is best. Then, I show that IIT’s phenomenology-first approach implies a self-evidencing explanation according to which consciousness can be explained by starting from consciousness itself. I claim that IIT can take advantage of the virtuous circularity of this reasoning, but it also faces a data-fitting issue that is somehow similar to that faced by experiment-driven approaches: we are not given enough information to decide whether the explanatory hypotheses IIT employs to explain its phenomenological data are in fact best. I call this problem “the self-evidencing problem” for IIT, and after introducing it, I propose a possible way for IIT to solve it.