Detecting cooperative partners in situations that have financial stakes is crucial to successful social exchange. The authors tested whether humans are sensitive to subtle facial dynamics of counterparts when deciding whether to trust and cooperate. Participants played a 2-person trust game before which the facial dynamics of the other player were manipulated using brief (<6 s) but highly realistic facial animations. Results showed that facial dynamics significantly influenced participants' (a) choice of with whom to play the game and (b) decisions to cooperate. It was also found that inferences about the other player's trustworthiness mediated these effects of facial dynamics on cooperative behavior.

We present new deterministic methods that given two eigenspace models, each representing a set of n-dimensional observations will: (1) merge the models to yield a representation of the union of the sets; (2) split one model from another to represent the difference between the sets; as this is done, we accurately keep track of the mean. These methods are more efficient than computing new eigenspace models directly from the observations when the eigenmodels are dimensionally small compared to the total number of observations. Such methods are important because they provide a basis for novel techniques in machine learning, using a dynamic split-andmerge paradigm to optimally cluster observations. Here we present a theoretical derivation of the methods, empirical results relating to the efficiency and accuracy of the techniques, and three general applications, including the on-line construction of Gaussian mixture models.

Abstract. This paper addresses a problem arising in the reverse engineering of solid models from depth-maps. We wish to identify and fit surfaces of known type wherever these are a good fit. This paper presents a set of methods for the least-squares fitting of spheres, cylinders, cones and tori to three-dimensional point data. Least-squares fitting of surfaces other planes, even of simple geometric type, has been little studied. Our method has the particular advantage of being robust in the sense that as the principal curvatures of the surfaces being fitted decrease (or become more equal), the results which are returned naturally become closer and closer to those surfaces of 'simpler type', i.e. planes, cylinders, cones, or spheres which best describe the data, unlike other methods which may diverge as various parameters or their combination become infinite.1

We examined the effects of the temporal quality of smile displays on impressions and decisions made in a simulated job interview. We also investigated whether similar judgments were made in response to synthetic (Study 1) and human facial stimuli (Study 2). Participants viewed short video excerpts of female interviewees exhibiting dynamic authentic smiles, dynamic fake smiles, or neutral expressions, and rated them with respect to a number of attributes. In both studies, perceivers' judgments and employment decisions were significantly shaped by the temporal quality of smiles, with dynamic authentic smiles generally leading to more favorable job, person, and expression ratings than dynamic fake smiles or neutral expressions. Furthermore, authentically smiling interviewees were judged to be more suitable and were more likely to be short-listed and selected for the job. The findings show a high degree of correspondence in the effects created by synthetic and human facial stimuli, suggesting that temporal features of smiles similarly influence perceivers' judgments and decisions across the two types of stimulus.

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