Among its many roles in body and brain, oxytocin influences social behavior. Understanding the precise nature of this influence is crucial, both within the broader theoretical context of neurobiology, social neuroscience and brain evolution, but also within a clinical context of disorders such as anxiety, schizophrenia, and autism. Research exploring oxytocin’s role in human social behavior is difficult owing to its release in both body and brain and its interactive effects with other hormones and neuromodulators. Additional difficulties are due to the intricacies of the blood-brain barrier and oxytocin’s instability, which creates measurement issues. Questions concerning how to interpret behavioral results of human experiments manipulating oxytocin are thus made all the more pressing. The current paper discusses several such questions. We highlight unresolved fundamental issues about what exactly happens when oxytocin is administered intranasally, whether such oxytocin does in fact reach appropriate receptors in brain, and whether central or peripheral influences account for the observed behavioral effects. We also highlight the deeper conceptual issue of whether the human data should be narrowly interpreted as implicating a specific role for oxytocin in complex social cognition, such a generosity, trust, or mentalizing, or more broadly interpreted as implicating a lower-level general effect on general states and dispositions, such as anxiety and social motivation. Using several influential studies, we show how seemingly specific, higher-level social-cognitive effects can emerge via a process by which oxytocin’s broad influence is channeled into a specific social behavior in a context of an appropriate social and research setting.

Research on the neurobiological and behavioral effects of oxytocin (OT), as well as on its possible therapeutic applications, has intensified in the past decade. Accurate determination of peripheral OT levels is essential to reach meaningful conclusions and to motivate, support and inform clinical interventions. Different, but concordant, methods for measuring plasma OT have been developed over the past four decades, but since 2004 several commercially available methods have been favored in research with humans. Evaluation of these methods reveals that they lack reliability when used on unextracted samples of human fluids, and that they tag molecules in addition to OT, yielding estimates that are wildly discrepant with an extensive body of earlier findings that were obtained using methods that are well validated, but more laborious. An accurate, specific, and readily available method for measuring OT that can be adopted as the standard in the field is urgently needed for advances in our understanding of OT's roles in cognition and behavior.

Big data has transformed fields such as physics and genomics. Neuroscience is set to collect its own big data sets, but to exploit its full potential, there need to be ways to standardize, integrate and synthesize diverse types of data from different levels of analysis and across species. This will require a cultural shift in sharing data across labs, as well as to a central role for theorists in neuroscience research.Big data, the buzz phrase of our time, has arrived on the neuroscientific scene, as it has already in physics, astronomy and genomics. It offers enlightenment and new depths of understanding, but it can also be a bane if it obscures, obstructs and overwhelms. The arrival of big data also marks a cultural transition in neuroscience, from many isolated 'vertical' efforts applying single techniques to single problems in single species to more 'horizontal' efforts that integrate data collected using a wide range of techniques, problems and species. We face five main issues in making big data work for us.First, data in neuroscience exist at an astonishing range of scales of both space and time. Neuroscientific data are obtained from a wide range of techniques, from patch clamping to optogenetics to fMRI (Fig. 1). Most of these techniques are used one at a time. One lab will record spikes from an array of neurons, but not be able to determine which types of neurons they are or how they are connected to other neurons. Another lab will reconstruct the wiring diagram of the same circuit, but without recording data to identify the properties of the reconstructed neurons. In some heroic cases, functional data have been laboriously combined with anatomical reconstructions 1 , but rarely if ever in a broad behavioral context.

How is it that we can perceive, learn and be aware of the world? The development of new techniques for studying large-scale brain activity, together with insights from computational modeling and a better understanding of cognitive processes, have opened the door for collaborative research that could lead to major advances in our understanding of ourselves.

Before this book was published in 1992, conceptual frameworks for brain function were based on the behavior of single neurons, applied globally. This book developed a different conceptual framework, based on large populations of neurons. This was done by showing that patterns of activities among the units in trained artificial neural network models had properties that resembled those recorded from populations of neurons recorded one at a time. It is one of the first books to bring together computational concepts and behavioral data within a neurobiological framework. Aimed at a broad audience of neuroscientists, computer scientists, cognitive scientists, and philosophers, the book is written for both expert and novice. This anniversary edition offers a new preface by the authors that puts the book in the context of current research. This approach influenced a generation of researchers in the field of neuroscience. Even today, when neuroscientists can routinely record from hundreds of neurons using optics rather than electricity, and the 2013 White House BRAIN initiative heralded a new era in innovative neurotechnologies, the main message of this book is still relevant.