Drinking from the firehose of experience

Computational concepts from robotics and computer vision hold great promise to account for major aspects of the phenomenon of consciousness, including philosophically problematical aspects such as the vividness of qualia, the first-person character of conscious experience, and the property of intentionality.

We present a dynamical systems model describing human or robotic agents and their interaction with the environment. In order to cope with the enormous information content of the sensory stream, this model includes trackers for selected coherent spatio-temporal portions of the sensory input stream, and a self-constructed plausible coherent narrative describing the recent history of the agent's sensorimotor interaction with the world.

We describe how an agent can autonomously learn its own intentionality by constructing computational models of hypothetical entities in the external world. These models explain regularities in the sensorimotor interaction, and serve as referents for the agent's symbolic knowledge representation. The high information content of the sensory stream allows the agent to continually evaluate these hypothesized models, refuting those that make poor predictions. The high information content of the sensory input stream also accounts for the vividness and uniqueness of subjective experience. We then evaluate our account against 11 features of consciousness "that any philosophical-scientific theory should hope to explain", according to the philosopher and prominent AI critic John Searle.

The essential features of consciousness can, in principle, be implemented on a robot with sufficient computational power and a sufficiently rich sensorimotor system, embodied and embedded in its environment.