The Radical Plasticity Thesis: How the Brain Learns to be Conscious

Abstract

In this paper, I explore the idea that consciousness is something that the brain learns to do rather than an intrinsic property of certain neural states and not others. Starting from the idea that neural activity is inherently unconscious, the question thus becomes: How does the brain learn to be conscious? I suggest that consciousness arises as a result of the brain's continuous attempts at predicting not only the consequences of its actions on the world and on other agents, but also the consequences of activity in one cerebral region on activity in other regions. By this account, the brain continuously and unconsciously learns to redescribe its own activity to itself, so developing systems of meta-representations that characterize and qualify the target first-order representations. Such learned redescriptions, enriched by the emotional value associated with them, form the basis of conscious experience. Learning and plasticity are thus central to consciousness, to the extent that experiences only occur in experiencers that have learned to know they possess certain first-order states and that have learned to care more about certain states than about others. This is what I call the "Radical Plasticity Thesis." In a sense thus, this is the enactive perspective, but turned both inwards and (further) outwards. Consciousness involves "signal detection on the mind"; the conscious mind is the brain's (non-conceptual, implicit) theory about itself. I illustrate these ideas through neural network models that simulate the relationships between performance and awareness in different tasks.

Keywords: consciousness; emotion; learning; neural networks; subjective experience.

Figure 1

General architecture of a metacognitive network. A first-order network, consisting for instance of a simple three-layers back-propagation network, has been trained to perform a simple classification task and thus contains knowledge that links inputs to outputs in such a way that the network can produce Type I responses. By design, this entire first-order network then constitutes the input to a second-order network, the task of which consists of redescribing the activity of the first-order network in some way. Here, the task that this second-order network is trained to perform is to issue Type II responses, that is, judgments about the extent to which the first-order network has performed its task correctly. One can think of the first-order network as instantiating cases where the brain learns about the world, and of the second-order network as instantiating cases where the brain learns about itself.

Figure 2

Architecture of a wagering network. A first-order network instantiates a simple pattern classifier trained to classify “visual” input patterns representing the shapes of digits 0–9 in 10 categories. A second-order network is assigned the task of wagering on the first-order network's performance based on the latter's internal representations of the stimulus. The second-order network thus performs judgments about the extent to which the first-order network is correct in its own decisions.

Figure 3

Performance of the first-order and second-order networks, as a function of training expressed as number of epochs.