Louise Barrett

Abstract Recent approaches to analysing social networks suggest that modeling the edges of the network and using multilevel models will produce more informative estimates. These recent methods have been proposed as a way to better handle the dependency structures of social networks, account for biases in data collection, and retain uncertainty when making inferences about social network structures.

1 INTRODUCTION Quantitative descriptions of the vocal repertoires of several primate species have revealed sex differences in the structure and production of vocal signals. These differences manifest as males and females producing specific vocalizations at different rates or in different contexts, or as differences in the acoustic structure of specific call types (e.g., Arnedo et al., 2010; Briseño-Jaramillo et al., 2017; Dubreuil et al., 2015; Harris et al., 2006; Hohmann, 1991).

Over the past quarter century and more, disciplines from anthropology to robotics have argued that cognition is concretely grounded in bodily sensation and movement. Starting in the 1990s, the concept of ‘embodied cognition’ was hailed by many as a major paradigm shift for cognitive science, promising new insight into everything from visual perception to child development, social cognition and language [1–3]. However, it is not clear to all that this promise has been realized.