ABSTRACT
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.
