Explaining Conscious Experience with Brain Resonance


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 Towards Solving the Hard Problem of Consciousness:

The Varieties of Brain Resonances and the Conscious Experiences that they Support

by

Stephen Grossberg

Director, Center for Adaptive Systems

Wang Professor of Cognitive and Neural Systems

Professor of Mathematics & Statistics, Psychological & Brain Sciences, and

Biomedical Engineering, Boston University

steve@bu.edu, http://cns.bu.edu/~steve

 

What happens in our brains when we consciously experience sights, sounds, feelings, and knowledge about them? The Hard Problem of Consciousness is the problem of explaining how this happens. To solve this problem, a theory of consciousness needs to link brain to mind by modeling how brain dynamics give rise to conscious experiences, and specifically how the emergent properties of brain dynamics generate properties of individual experiences and of the psychological and neurobiological data that they generate. This talk summarizes evidence that Adaptive Resonance Theory, or ART, is accomplishing this goal. ART is the currently most advanced cognitive and neural theory, with the broadest explanatory and predictive range, of how advanced brains autonomously learn to attend, recognize, and predict objects and events in a changing world.
 
ART has predicted that “all conscious states are resonant states” as part of its specification of mechanistic links between processes of consciousness, learning, expectation, attention, resonance, and synchrony. ART has now reached sufficient maturity to begin classifying the brain resonances that support conscious experiences of seeing, hearing, feeling, and knowing. ART hereby explains how conscious states help us to adapt to a changing world. For example, seeing helps to ensure effective reaching. Hearing helps to ensure effective speaking. And feeling helps to ensure effective goal-oriented action. The talk will review these resonances and their similarities and differences, including where they occur in our brains, and how they interact when we feel and know about what we see and hear. Many normal and clinical psychological and neurobiological data are explained and predicted, which have not been explained by alternative theories. The talk also explains why some resonances do not become conscious, and why not all brain dynamics are resonant. These insights build on the realization that brain specialization is achieved by using computationally complementary cortical processing streams.
 
Six brain resonances and their conscious functional roles are discussed in this talk:
Surface-shroud resonances support conscious seeing of visual qualia
Feature-category resonances support conscious recognition of visual objects and scenes
Stream-shroud resonances support conscious hearing of auditory qualia
Spectral-pitch-and-timbre resonances support conscious recognition of sources in auditory streams
Item-list resonances support conscious recognition of speech and language
Cognitive-emotional resonances support conscious feelings and recognition of them
 
These and many more results are explained in greater detail in the following Open Access article:
Grossberg, S. (2017). Towards solving the hard problem of consciousness: The varieties of brain resonances and the conscious experiences that they support. Neural Networks, 87, 38-95.
http://www.sciencedirect.com/science/article/pii/S0893608016301800
 

 Key Words for Grossberg IJCNN 2017 Plenary Lecture 

consciousness; hard problem of consciousness; adaptive resonance; seeing; hearing; feeling; knowing; neural networks; attention; complementary computing; hierarchical resolution of uncertainty; biological learning; machine learning; autonomous intelligence; vision; image processing; figure-ground perception; visual boundary; visual surface; spatial attention; parietal visual neglect; visual agnosia; crowding; cognition; pattern recognition; object attention; expectation; biased competition; prediction; invariant object recognition; attentional shroud; predictive remapping; gain field; visual search; working memory; chunking; prediction; motor control; circular reaction; motor-equivalent reaching; tool use; robotics; audition; auditory streaming; cocktail party problem; auditory continuity illusion; auditory neglect; speech perception; speech production; phonemic restoration; language learning; emotion; cognitive-emotional interactions; classical conditioning; trace conditioning; reinforcement learning; memory consolidation; adaptive timing; spectral timing; motivated attention; Weber law; amnesia; visual cortex; V1, V2, V3A, V4, inferotemporal cortex; ITa, ITp, LIP, parietal cortex; orbitofrontal cortex; superior colliculus; auditory cortex; amygdala; hippocampus; hypothalamus; surface-shroud resonance; feature-category resonance; stream-shroud resonance; spectral-pitch-and-timbre resonance; item-list resonance; cognitive-emotional resonance; What cortical stream; Where cortical stream; ART, VAM, DIRECT, DIVA, ARTSTREAM. cARTWORD, CogEM, START, nSTART