Pattern activation/recognition theory of mind.
Bottom Line: I have implemented the model as a probabilistic programming language specialized in activation/recognition grammatical and neural operations.I use this prototype to compute and present diagrams for each stochastic grammar and corresponding neural circuit.I then discuss the theory as it relates to artificial network developments, common coding, neural reuse, and unity of mind, concluding by proposing potential paths to validation.
Affiliation: Schlumberger Research Houston, TX, USA.
In his 2012 book How to Create a Mind, Ray Kurzweil defines a "Pattern Recognition Theory of Mind" that states that the brain uses millions of pattern recognizers, plus modules to check, organize, and augment them. In this article, I further the theory to go beyond pattern recognition and include also pattern activation, thus encompassing both sensory and motor functions. In addition, I treat checking, organizing, and augmentation as patterns of patterns instead of separate modules, therefore handling them the same as patterns in general. Henceforth I put forward a unified theory I call "Pattern Activation/Recognition Theory of Mind." While the original theory was based on hierarchical hidden Markov models, this evolution is based on their precursor: stochastic grammars. I demonstrate that a class of self-describing stochastic grammars allows for unifying pattern activation, recognition, organization, consistency checking, metaphor, and learning, into a single theory that expresses patterns throughout. I have implemented the model as a probabilistic programming language specialized in activation/recognition grammatical and neural operations. I use this prototype to compute and present diagrams for each stochastic grammar and corresponding neural circuit. I then discuss the theory as it relates to artificial network developments, common coding, neural reuse, and unity of mind, concluding by proposing potential paths to validation.
No MeSH data available.
Mentions: Turing's infinite sequence is actually an enumeration of the natural number set, perhaps not a biological object. However, recognition of numbers is certainly a common and early human activity (Libertus et al., 2011). Grammar “A = B / C. B = 0/1. C = B A.” expresses any digit sequence; the first rule differentiates finishing a sequence and pursuing one, the second rule produces digits, and the third rule adds to sequences. Following the same pattern, but with different terminals, grammar “A = B / C. B = DrawSquare/DrawCircle. C = B A.” produces a number of squares and circles in sequences mapping the production of digits, grammar “A = B / C. B = SpotSquare/SpotCircle. C = B A.” recognizes such a sequence, and grammar “A = B / C. B = SpotSquare/DrawCircle. C = B A.” mixes identifying squares and producing circles. The non-terminal grammar part common to digits and geometrical figures expresses a metaphor from one domain (digits) to another (figures), a subject I will come back to further on (Figure 7).
No MeSH data available.