Evidence for Distinct Facial Signals of Reward, Affiliation, and Dominance from Both Perception and Production Tasks.

Smiles are nonverbal signals that convey social information and influence the social behavior of recipients, but the precise form and social function of a smile can be variable. In previous work, we have proposed that there are at least three physically distinct types of smiles associated with specific social functions: reward smiles signal positive affect and reinforce desired behavior; affiliation smiles signal non-threat and promote peaceful social interactions; dominance smiles signal feelings of superiority and are used to negotiate status hierarchies. The present work advances the science of the smile by addressing a number of questions that directly arise from this smile typology. What do perceivers think when they see each type of smile (Study 1)? How do perceivers behave in response to each type of smile (Study 2)? Do people produce three physically distinct smiles in response to contexts related to each of the three social functions of smiles (Study 3)? We then use an online machine learning platform to uncover the labels that lay people use to conceptualize the smile of affiliation, which is a smile that serves its social function but lacks a corresponding lay concept. Taken together, the present findings support the conclusion that reward, affiliation, and dominance smiles are distinct signals with specific social functions. These findings challenge the traditional assumption that smiles merely convey whether and to what extent a smiler is happy and demonstrate the utility of a social-functional approach to the study of facial expression.

Correction to: Evidence for Distinct Facial Signals of Reward, Affiliation, and Dominance from Both Perception and Production Tasks.

[This corrects the article DOI: 10.1007/s42761-020-00024-8.].

The khmer software package: enabling efficient nucleotide sequence analysis.

The khmer package is a freely available software library for working efficiently with fixed length DNA words, or k-mers. khmer provides implementations of a probabilistic k-mer counting data structure, a compressible De Bruijn graph representation, De Bruijn graph partitioning, and digital normalization. khmer is implemented in C++ and Python, and is freely available under the BSD license at  https://github.com/dib-lab/khmer/.