Mental Representation

Mental representation research investigates how information is encoded and processed in the mind, aiming to understand both human cognition and build more human-like artificial intelligence. Current research focuses on developing and analyzing computational models, including large language models and neural networks, to understand how these models learn and represent information, often drawing parallels to cognitive neuroscience and leveraging techniques like variational autoencoders and Markov Chain Monte Carlo methods. This work has implications for improving AI systems' reasoning and understanding capabilities, as well as providing insights into the fundamental mechanisms of human thought and perception.