Consciousness: Drinking from the Firehose of Experience∗ (revised and expanded)

The problem of consciousness has captured the imagination of philosophers, neuroscientists, and the general public, but has received little attention within AI. However, concepts from robotics and computer vision hold great promise to account for the 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. This paper presents and evaluates such an account against eleven features of consciousness “that any philosophical-scientific theory should hope to explain”, according to the philosopher and prominent AI critic John Searle. The Problem of Consciousness Artificial Intelligence is the use of computational concepts to model the phenomena of mind. Consciousness is one of the most central and conspicuous aspects of mind. In spite of this, AI researchers have mostly avoided the problem of consciousness in favor of modeling cognitive, linguistic, perceptual, and motor control aspects of mind. However, in response to a recent discussion of consciousness by the wellknown philosopher and AI critic John Searle (Searle 2004), it seems to me that we are in a position to sketch out a plausible computational account of consciousness. Consciousness is a phenomenon with many aspects. Searle argues that the difficult aspects of consciousness are those that make up the subjective nature of first-person experience. There is clearly a qualitative difference between thinking about the color red with my eyes closed in a dark room, and my own immediate experiences of seeing a red rose or an apple or a sunset. Philosophers use the term qualia (singular quale) for these immediate sensory experiences. Furthermore, when I see a rose or an apple, I see it as an object in the world, not as a patch of color on my retina. Philosophers refer to this as intentionality. ∗This work has taken place in the Intelligent Robotics Lab at the Artificial Intelligence Laboratory, The University of Texas at Austin. Research of the Intelligent Robotics lab is supported in part by grants from the National Science Foundation (IIS-0413257), from the National Institutes of Health (EY016089), and by an IBM Faculty Research Award. Copyright c © 2005, American Association for Artificial Intelligence (www.aaai.org). All rights reserved. The position I argue here is that the subjective, firstperson nature of consciousness can be explained in terms of the ongoing stream of sensorimotor experience (the “firehose of experience” of the title) and the symbolic pointers into that stream (which we call “trackers”) that enable a computational process to cope with its volume. Consciousness also apparently constructs a plausible, coherent, sequential narrative for the activities of a large, unsynchronized collection of unconscious parallel processes in the mind. How this works, and how it is implemented in the brain, is a fascinating and difficult technical problem, but it does not seem to raise philosophical difficulties. Other Approaches to Consciousness There have been a number of recent books on the problem of consciousness, many of them from a neurobiological perspective. The more clinically oriented books (Sacks 1985; Damasio 1999) often appeal to pathological cases, where consciousness is incomplete or distorted in various ways, to illuminate the structure of the phenomenon of human consciousness through its natural breaking points. Another approach, taken by Crick and Koch (Crick & Koch 2003; Koch 2003), examines in detail the brain pathways that contribute to visual attention and visual consciousness in humans and in macaque monkeys. Minsky (1985), Baars (1988), and Dennett (1991) propose architectures whereby consciousness emerges from the interactions among large numbers of simple modules. John Searle is a distinguished critic of strong AI: the claim that a successful computational model of an intelligent mind would actually be an intelligent mind. His famous “Chinese room” example (Searle 1980) argues that even a behaviorally successful computational model would fail to have a mind. In some sense, it would just be “faking it.” In Searle’s recent book on the philosophy of mind (Searle 2004), he articulates a position he calls biological naturalism that describes the mind, and consciousness in particular, as “entirely caused by lower level neurobiological processes in the brain.” Although Searle rejects the idea that the mind’s relation to the brain is similar to a program’s relation to a computer, he explicitly endorses the notion that the body is a biological machine, and therefore that machines (at least biological ones) can have minds, and can even be conscious. In spite of being nothing beyond physical processes, Searle Revised and expanded version of a paper presented at National Conference on Artificial Intelligence (AAAI-05) http://www.cs.utexas.edu/ ̃qr/papers/Kuipers-aaai-05.html holds that consciousness is not reducible to those physical processes because consciousness “has a first-person ontology” while the description of physical processes occurring in the brain “has a third-person ontology.” He lays out eleven central features of consciousness “that any philosophicalscientific theory should hope to explain.” In the following three sections, I describe how a robotics researcher approaches sensorimotor interaction; propose a computational model of consciousness; and evaluate the prospects for using this model to explain Searle’s eleven features of consciousness. Sensorimotor Interaction in Robotics When a robot interacts continually with its environment through its sensors and effectors, it is often productive to model that interaction as a continuous dynamical system, moving through a continuous state space toward an attractor. In the situations we will consider, such a dynamical system can be approximated by a discrete but fine-grained computational model, so by taking this view of the robot we are not moving outside the domain of computational modeling. A Simple Robot in a Static World Consider a simple robot agent in a static environment. In a static world, the only possible changes are to the state of the robot’s body within the environment, which is represented by a time-varying state vector x(t). The derivative of x with respect to time is written ẋ. For a simple mobile robot moving on a planar surface, x would have the form (x, y, θ), representing the pose (position (x, y) plus orientation θ) of the robot within its environment. A robot with a more complex body would have a larger state vector x. We distinguish the environment and the robot’s body from the computational process (which we will call the “agent”), that receives the sense vector z(t) from the environment and determines a motor vector u(t) to send out to its body in the environment. Let m be the symbolic state of the agent’s internal computational process. Note that the agent has access to its sense vector z, and can set its own motor vector u, but it only has indirect access to its own state vector x. The coupled system consisting of the robot agent and its environment can be described as a dynamical system. (Here we superimpose two useful standard representations: the block diagram and the differential equation.) World: ẋ = F (x,u) (1) z = G(x) (2)