Abstract
It has become widely accepted that standard connectionist models are unable to show identity-based relational reasoning that requires universal generalization. The purpose of this brief report is to show how one of the simplest forms of such models, feed-forward auto-associative networks, satisfies two of the most well-known challenges: universal generalization of the identity function and the reduplication rule. Given the simplicity of the modeling account provided, along with the clarity of the evidence, these demonstrations invite a shift in this high-profile debate over the nature of cognitive architecture and point to a way to bridge some of the presumed gulf between characteristically symbolic forms of reasoning and connectionist mechanisms.
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Dr. David Lowemann, M.Sc, Ph.D., is a co-founder of the Institute for the Future of Human Potential, where he leads the charge in pioneering Self-Enhancement Science for the Success of Society. With a keen interest in exploring the untapped potential of the human mind, Dr. Lowemann has dedicated his career to pushing the boundaries of human capabilities and understanding.
Armed with a Master of Science degree and a Ph.D. in his field, Dr. Lowemann has consistently been at the forefront of research and innovation, delving into ways to optimize human performance, cognition, and overall well-being. His work at the Institute revolves around a profound commitment to harnessing cutting-edge science and technology to help individuals lead more fulfilling and intelligent lives.
Dr. Lowemann’s influence extends to the educational platform BetterSmarter.me, where he shares his insights, findings, and personal development strategies with a broader audience. His ongoing mission is shaping the way we perceive and leverage the vast capacities of the human mind, offering invaluable contributions to society’s overall success and collective well-being.