Can Big Data Help Us Understand Human Vision?
Michael J. Tarr, Elissa Aminoff · Psychology Press eBooks · 2016
Big Data seems to have an ever-increasing impact on our daily lives. Its application to human vision has been no less impactful. In particular, Big Data methods have been applied to both content and data analysis, enabling a new, more fine-grained understanding of how the brain encodes information about the visual environment. With respect to content, the most significant advance has been the use of large-scale, hierarchical models-typically “convolutional neural networks” or “deep networks”—to explicate how high-level visual tasks such as object categorization can be achieved based on learning across millions of images. With respect to data analysis, complex patterns underlying visual behavior can be identified in neural data using modern machine-learning methods or “multi-variate pattern analysis.” In this chapter, we discuss the pros and cons of these applications of Big Data, including limitations in how we can interpret results. In the end, we conclude that Big Data methods hold great promise for pursuing the challenges faced by both vision scientists and, more generally, cognitive neuroscientists.