Exploring Representations in Computational Cognitive Science and Neural Networks

The chapter discusses the historical evolution of studying representations in psychology, highlighting the importance of empirical evidence, behavioral experiments, and neural data. It explores the differences between representations in deep neural networks and human cognition, touching on issues of understanding, generalization challenges, and the debate between more structure versus more data in AI systems. The conversation also delves into the debates on empiricism versus nativism theories in neural network modeling and learning processes as compared to human cognition.