A pluralist framework for the philosophy of social neuroscience

Abstract

The philosophy of neuroscience has been a dynamic field of research in the philosophy of science since the turn of the century. As a result of this activity, a new mechanistic philosophy has emerged as the dominant approach to explanation and scientific integration in neuroscience. Rather surprisingly, the philosophy of social neuroscience has remained an almost uncharted territory. In this chapter, we advance a pluralistic framework for such a field. Our framework seeks to ground the proliferation of modeling approaches, explanatory styles, and integrative trends within social neuroscience. First, we highlight the plurality of modeling approaches pursued by social neuroscientists by reviewing the distinctive features of mechanistic models, dynamical models, computational models, and optimality models. Second, we reject unitary explanatory perspectives and emphasize the plurality of explanatory styles that can emerge from those modeling approaches, considering their contents and vehicles. As regards their content, we present two kinds of information a model may provide, namely, causal/compositional or non-causal/structural information. As regards their vehicles, we examine and illustrate different guiding representational ideals (e.g., precision, generality, and simplicity). Third, we turn to integrative trends in social neuroscience, assessing the prospects of inter-theoretical reduction, mechanistic mosaic unity, and multi-level integrative analysis. We contend that the pluralist framework we develop is an adequate approach to scientific modeling, explanation, and integration in social neuroscience. We additionally address how this pluralistic perspective may shed light on the intersection between the neural and the social realms, in a context of greater interdisciplinary collaboration between neuroscientists and social scientists.