2015/07/15 by Bertrand du Castel · 14 citations
Psychology · Neuroscience · #Child and Animal Learning Development #Action Observation and Synchronization #Embodied and Extended Cognition #Computer science #Artificial intelligence #Probabilistic logic #Rule-based machine translation #Theoretical computer science #Grammar #Natural language processing #Linguistics
paper · pdf · doi:10.3389/fncom.2015.00090
published in Frontiers in Computational Neuroscience 9, 90 (Frontiers Media)
openalex publication_date 2015/07/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.