Abstract
We live in an age when our phones incorporate real-time updates on traffic and subway delays to help us navigate complex urban environments, vacuum cleaning robots map the layout of our apartments to optimize their cleaning strategies, and cars are beginning to drive themselves. But, amazing though today’s artificial cognition systems may seem, the genuine mystery is the flexibility and adaptability with which their precursors and creators, brains, acquire and use knowledge. For at least two centuries, psychologists and cognitive scientists have studied human and animal behavior in an effort to better understand the faculties that support natural cognition: working memory, attention, multisensory integration, value-based decision-making, and analogical reasoning. They have helped organize our thinking about the likely algorithms the brain must use, and suggested central brain regions that are likely involved in executing them. Thus, it may seem like the stage is set for neurophysiologists to work out neural mechanisms underlying cognitive processes. However, despite tremendous progress over the past half century in understanding peripheral sensory and motor systems, the sheer complexity of central brain circuits has meant that cognition has retained most of its veils. For this issue, we asked contributors to define the experimental and theoretical challenges that cognition poses to neuroscience researchers, and to offer suggestions for how the field might productively tackle the complexity that comes with working on this frontier.