Over the past few years numerous proposals have appeared that attempt to
characterize consciousness in terms of what could be called its computational
correlates: Principles of information processing with which to
characterize the differences between conscious and unconscious processing.
Proposed computational correlates include architectural specialization (such as
the involvement of specific regions of the brain in conscious processing),
properties of representations (such as their stability in time or their strength),
and properties of specific processes (such as resonance, synchrony,
interactivity or competition).
In exactly the same way that one can engage in a search for the neural
correlates of consciousness, one can thus search for the computational
correlates of consciousness. The most direct way of doing so consists of
contrasting models of conscious vs. unconscious information processing.
In this talk I will review these developments and illustrate how computational
modeling of specific cognitive processes can be useful in exploring and in
formulating putative computational principles through which
to capture the differences between implicit, explicit, and automatic cognition.
What can be gained from such approaches to the problem of consciousness is an
understanding of the function it plays in information processing. Here, I
suggest that the central function of consciousness is to make it possible for
cognitive agents to exert flexible, adaptive control over behavior. Learning
processes therefore play a central role in shaping conscious experience.
From this perspective, consciousness is best characterized as involving a
continuum defined over quality of representation: Graded representational
systems that can be adaptively modified by ongoing experience are thus viewed as
a central feature of any successful model of the differences between conscious
and unconscious cognition.