What makes you think you are conscious? An agnosticist manifesto

Abstract

The qualitative character of consciousness, its "what-it-is-likeness", is a contested issue, both in philosophy and psychology. I argue that, rather than by conceptual analyses, the status of "what-it-is-likeness" has to be decided by empirical investigation. Pending the outcome, we should maintain an agnostic stance, in order to remove the bias in favor of fictionalism from our study of consciousness,. I illustrate this with the notion of "ownership unity". People adhere to the belief of a single, unified self as the owner of their experiences, in spite of abundant dis-unities in the informational content of their experience. On one reading, this supports the notion that the unity of experience is no more than a convenient fiction, based on an illusory experience of unity. Cognitive neuroscience is slanted in favor of such understanding, insofar it emphasizes functional specialization and localization. To restore the balance, I present a complementary perspective: the view that the experience of unity is afforded by the intrinsic, multiscale brain dynamics. This approach offers a biological substrate for unity of experience as a regular scenario within certain boundary conditions, as well mechanisms that may let it go astray.

Keywords: brain diseases; cognitive neuroscience; hard problem; personal identity; phenomenal experience; philosophy of mind; selfhood; theories of consciousness.

Figure 1

Ambiguity in the Necker cube is not constrained by local bias. Circles indicated by arrows represent alternative areas to which a prior instruction draws attention. No arrows or circles were present in the actual display. Area 1 contains an X-junction indicating an ambiguous cube; Area 2 contains a T-junction which yields a strong bias to the orientation of the cube. When attention is drawn to Area 1, the cube reverses despite the presence of a local disambiguation in Area 2. Adapted from van Leeuwen and Smit (2012) after Peterson and Hochberg (1983).

Figure 2

A random network prior to (left) and after (right) several iterations of adaptive rewiring (From van Leeuwen, 2008).