Abstract
Understanding the neural bases of subjective experience remains one of the great challenges of the natural sciences. Higher-order theories of consciousness are typically defended by assessments of neural activity in higher cortical regions during perception, often with disregard to the nature of the neural computations that these regions execute. We have sought to refocus the problem toward identification of those neural computations that are necessary for subjective experience with the goal of defining the sorts of neural architectures that can perform these operations. This approach removes reliance on behaviour and brain homologies for appraising whether non-human animals have the potential to subjectively experience sensory stimuli. Using two basic principles—first, subjective experience is dependent on complex processing executing specific neural functions and second, the structure-determines-function principle—we have reasoned that subjective experience requires a neural architecture consisting of stacked forward models that predict the output of neural processing from inputs. Given that forward models are dependent on appropriately connected processing modules that generate prediction, error detection and feedback control, we define a minimal neural architecture that is necessary (but not sufficient) for subjective experience. We refer to this framework as the hierarchical forward models algorithm. Accordingly, we postulate that any animal lacking this neural architecture will be incapable of subjective experience.
Highlights
The subjective experience of sensory stimuli is variously referred to as conscious awareness, subjective awareness, inner awareness, phenomenal consciousness, qualia, and feelings
There are many different theories of subjective consciousness, we are interested here in theories that derive from the broad field of neuroscience. Those theories that fall outside of this category include physical theories such as the field theory, the quantum theory, the resonance theory, the electromagnetic field theory (Pockett, 2002) as well as philosophical theories such as phenomenal externalism and dualism
Our strategy applies two basic principles to define a minimal neural architecture necessary for subjective experience
Summary
Reviewed by: Yoonsuck Choe, Texas A&M University, United States Larissa Albantakis, University of Wisconsin-Madison, United States. We have sought to refocus the problem toward identification of those neural computations that are necessary for subjective experience with the goal of defining the sorts of neural architectures that can perform these operations. Given that forward models are dependent on appropriately connected processing modules that generate prediction, error detection and feedback control, we define a minimal neural architecture that is necessary (but not sufficient) for subjective experience. We refer to this framework as the hierarchical forward models algorithm. We postulate that any animal lacking this neural architecture will be incapable of subjective experience
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