Dr. Leonid Perlovsky is Principal Research Physicist and Technical Advisor at the AF Research Lab and Visiting Scholar at Harvard University. He leads research projects on mathematical models of the mind, cognitive algorithms, language, and cultural evolution. As Chief Scientist at Nichols Research, a $500mm high-tech organization, he led the corporate research in intelligent systems. He served as professor at Novosibirsk Engineering Institute and New York University; as a principal in commercial startups developing tools for biotechnology and financial predictions. His company predicted the market crash following 9/11 a week before the event, detecting ripples from Al Qaeda trades and later helped SEC looking for perpetrators. He is invited as a keynote speaker worldwide, including most prestigious venues such as the Nobel Forum; published more than 465 publications, 4 books in Oxford and Springer, received 2 patents. Dr. Perlovsky participates in organizing conferences on Computational Intelligence, serves as Chair for the IEEE Boston Computational Intelligence Chapter; Chair for the IEEE Task Force on The Mind and Brain, on the International Neural Network Society (INNS) Board of Governors as Chair of The Award Committee. He serves on the Editorial Board of ten professional journals, has founded and serves as Editor-in-Chief for"Physics of Life Reviews," (IF=7.2, ranked #4 in the world among 82 biophysics journals by Thomson Reuters). He received National and International awards including the Gabor Award; and the John McLucas Award, the highest US Air Force Award for basic research.
Is physics of the mind possible? How would it differ from biophysics or neural networks?
Physics looks for the first principles describing a wide area of reality. Physics develops testable predictive theories. Experimental physics tests predictions and measures events revealing fundamentals of the nature. At the workshop we discuss steps towards a physical theory of the mind. First we discuss known first principles of the mind. These include mechanisms of concepts, emotions, the knowledge instinct, the mind hierarchy, and dynamic logic. This dynamic process-logic replaces classical logic operating with static statements. Dynamic logic serves as a basis for a mathematical theory of learning, combining the listed first principles into a hierarchical system of mental processes. Each process in the mind-brain proceeds "from vague to crisp," from vague representation-concepts to crisp ones. We conduct a 1-min experiment demonstrating that our minds operate with dynamic logic. Then we discuss brain imaging experiments (Bar et al 2006; Kveraga et al 2007) confirming this in greater details as an adequate model of the brain perception and cognition.
Dynamic logic, the mathematical basis of the physical theory of the mind, overcame the difficulty of computational complexity plaguing modeling of the mind, artificial intelligence, and machine learning since the 1960s.
We discuss how this difficulty relates to Gödelian problems in logic: computational complexity is a manifestation of Gödelian incompleteness in finite systems, such as computers or brains. The mind is "not logical." The Aristotle's theory of mind is closer to dynamic logic than to classical logic. Engineering applications demonstrate orders of magnitude improvement in classical problems of pattern recognition, data mining, information integration, financial predictions.
We discuss the dual hierarchy model of interactions between language and cognition. A number of "mysteries" involved in this interaction (what is the difference between language and cognition; what is the role of language in cognition, why children can talk before they really understand, how much adults are different from children in this respect, etc.). These are explained in the model, and explanations are confirmed in brain imaging experiments (Binder et al 2005; Price 2012).
The knowledge instinct drives acquisition of cognitive ability and is a foundation of all our higher cognitive abilities. Its satisfaction is experienced as aesthetic emotions (experimentally confirmed in Cabanac et al 2010). The hierarchy of aesthetic emotions is discussed from understanding of everyday objects to understanding of representation-concepts near the top of the mental hierarchy. I discuss contents of these "highest" concepts and relate the corresponding aesthetic emotions to the beautiful. Experimental tests of this conjecture are for the near future.
Contradictions among knowledge are experienced as negative aesthetic emotions, cognitive dissonances. This mechanism counteracts the knowledge instinct and would prevent accumulation of knowledge and the entire human evolution, if not a special ability evolved for overcoming these emotions. It follows from the dual hierarchy model that this ability is music. This theoretical prediction has been experimentally confirmed (Masataka et al 2012, 2013). We explain the origin and evolution of music, what Darwin called the greatest mystery.
