Deep Learning: Implications for Human Learning and Memory

Recent years have seen an explosion of interest in deep learning and deep neural networks. Deep learning lies at the heart of unprecedented feats of machine intelligence as well as software people use every day. Systems built on deep learning have surpassed human capabilities in complex strategy games like go and chess, and we use them for speech recognition, image captioning, and a wide range of other applications. A consideration of deep learning is crucial for a Handbook of Human Memory, since human brains are deep neural networks, and an understanding of artificial deep learning systems may contribute to our understanding of how humans and animals learn and remember. Deep neural networks are complex, structured systems that process information in a parallel, distributed, and context sensitive fashion, and deep learning is the effort to use these systems to acquire capabilities we associate with intelligence through an experience dependent learning process. Within the field of Artificial Intelligence, work in deep learning is typically directed toward the goal of creating and understanding intelligence using all available tools and resources without consideration of their biological plausibility. Many of the ideas, however, at the heart of deep learning draw their inspiration from the brain and from characteristics of human intelligence we believe are best captured by these brain-inspired systems (Rumelhart, McClelland, and the PDP Research Group, 1986). Furthermore, ideas emerging from deep learning research can help inform us about memory and learning in humans and animals. Thus, deep learning research can be seen as fertile ground for engagement between researchers who work on related issues with implications for both biological and machine intelligence.We begin by introducing the basic constructs employed in deep learning and then consider several of the widely used learning paradigms and architectures used in these systems. We then turn to a consideration of how the constructs of deep learning relate to traditional constructs in the psychological literature on learning and memory. Next, we consider recent developments in the field of reinforcement learning that have broad implications for human learning and memory. We conclude with a consideration of areas where human capabilities still far exceed current deep learning approaches, and describe possible future directions toward understanding how these abilities might best be captured.