More powerful yet are formal computational models. Depending on the nature and fit

of the model, the data together with the model can suggest more than correlation and argue for directional causal architectures. Ultimately, this is of course the kind of understanding learn more that we want to have, and often it is already implicit in the way we think about data, even when unjustified. Modern neuroimaging combined with computational models and vetted with truly causal methods such as optogenetics could thus be the methods armamentarium for the future of social neuroscience—also making explicit the need for studies that cut across species. As we noted, we expect that computational models will help to provide an economical inventory of processes and concepts, and moreover one that will likely cut across not only species but also levels of analysis. What exactly that vocabulary will look like is a major open question and brings us back to one overarching concern: is there anything special about social neuroscience? The investigation of social behavior defines the field; we should look for an inventory of parameters in our models that define what is unique about social interactions. As we alluded to above, some prior studies have done precisely that (Hampton et al., 2008). The challenge as we see it now is to build up our inventory of processes derived from model-based and data-mining

approaches, pit them against entrenched concepts already in use, and forge forward with a redefined notion of what social neuroscience is really all about. This work was supported in part by a Conte Center (R.A.) and K01 grant (K01MH099343 to D.A.S.) from NIMH. We thank SANS (in this website particular Mauricio Delgado) and S4SN (in particular Larry Young) for providing metrics on the societies and their members for providing the online data used in some of our figures. We also thank Naomi Eisenberger, Keise Izuma, Catherine Hartley, Cendri Hutcherson, and Bob Spunt for comments on the manuscript. We are particularly indebted to Markus Christen for help with bibliometric data shown in Figure 1A. “
“If motion is such an ultimate term, then to define it by means

of anything but synonyms is willfully to choose to dwell in a realm of darkness…” —Sachs (2005) From Aristotle onward, we mafosfamide have realized that movement defines the human condition. It is, ultimately, what shapes our relationship with the external world. Over the course of evolution, with little tolerance for sloppiness or error, motor strategies have been sculpted into the implements of will, tasked with translating decision and desire into action. The neural circuits that underlie these motor strategies face daunting demands: sensory signals in a variety of forms are channeled into the nervous system, processed, and converted into action. The job of the motor system is to interpret this signaling cacophony and elicit movements that are both cohesive and effective.