Exploring a Cognitive Architecture for Learning Arithmetic Equations
This research addresses the cognitive mechanisms of arithmetic learning for AI and neuroscience, but it appears incremental as it builds on existing connectionist models without claiming major breakthroughs.
The paper tackled the problem of understanding how arithmetic skills are learned by proposing a neurobiologically plausible cognitive architecture that simulates acquisition, using experiments to explore generalization, dyscalculia, and network effects.
The acquisition and performance of arithmetic skills and basic operations such as addition, subtraction, multiplication, and division are essential for daily functioning, and reflect complex cognitive processes. This paper explores the cognitive mechanisms powering arithmetic learning, presenting a neurobiologically plausible cognitive architecture that simulates the acquisition of these skills. I implement a number vectorization embedding network and an associative memory model to investigate how an intelligent system can learn and recall arithmetic equations in a manner analogous to the human brain. I perform experiments that provide insights into the generalization capabilities of connectionist models, neurological causes of dyscalculia, and the influence of network architecture on cognitive performance. Through this interdisciplinary investigation, I aim to contribute to ongoing research into the neural correlates of mathematical cognition in intelligent systems.