A computational neuroscience approach to consciousness.
Simultaneous recordings from populations of neurons in the inferior temporal visual cortex show that most of the information about which stimulus was shown is available in the number of spikes (or firing rate) of each neuron, and not from stimulus-dependent synchrony, so that it is unlikely that stimulus-dependent synchrony (or indeed oscillations) is an essential aspect of visual object perception. Neurophysiological investigations of backward masking show that the threshold for conscious visual perception may be set to be higher than the level at which small but significant information is present in neuronal firing and which allows humans to guess which stimulus was shown without conscious awareness. The adaptive value of this may be that the systems in the brain that implement the type of information processing involved in conscious thoughts are not interrupted by small signals that could be noise in sensory pathways. I then consider what computational processes are closely related to conscious processing, and describe a higher order syntactic thought (HOST) computational theory of consciousness. It is argued that the adaptive value of higher order thoughts is to solve the credit assignment problem that arises if a multistep syntactic plan needs to be corrected. It is then suggested that it feels like something to be an organism that can think about its own linguistic, and semantically-based thoughts. It is suggested that qualia, raw sensory and emotional feels, arise secondarily to having evolved such a higher order thought system, and that sensory and emotional processing feels like something because it would be unparsimonious for it to enter the planning, higher order thought, system and not feel like something.
Rolls
Edmund T
ET
University of Oxford, Department of Experimental Psychology, South Parks Road, Oxford OX1 3UD, England, United Kingdom. Edmund.Rolls@psy.ox.ac.uk
D000818:Animals; D003198:Computer Simulation; D003243:Consciousness / Q000502:physiology*; D006801:Humans; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology; D009488:Neurosciences; D014793:Visual Cortex / Q000166:cytology; D014796:Visual Perception / Q000502:physiology