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.

Temporal dynamics have been increasingly recognized as an important component of facial expressions. With the need for appropriate stimuli in research and application, a range of databases of dynamic facial stimuli has been developed. The present article reviews the existing corpora and describes the key dimensions and properties of the available sets. This includes a discussion of conceptual features in terms of thematic issues in dataset construction as well as practical features which are of applied interest to stimulus usage. To identify the most influential sets, we further examine their citation rates and usage frequencies in existing studies.General limitations and implications for emotion research are noted and future directions for stimulus generation are outlined.KEYWORDS: facial expression, emotion, dynamic, dataset 3 Revised 9/6/2016 A Review of Dynamic Datasets for Facial Expression ResearchExisting research points towards the benefits of facial motion in emotion perception and recognition. By providing unique information about the direction, quality and speed of motion, dynamic stimuli enhance coherence in the identification of affect, lead to stronger emotion judgments, and facilitate the differentiation between posed and spontaneous expressions (for a review see Krumhuber, Kappas, & Manstead, 2013). In the last two decades, this advantage -paired with the stimuli's greater realism and ecological validity -has led to increased questioning and criticism regarding the use of static images (e.g., Tcherkassof, Bollon, Dubois, Pansu, & Adam, 2007; Wehrle, Kaiser, Schmidt, & Scherer, 2000), with a gradual shift in interest towards dynamic expressions.The trend is reflected in the literature with exponential increases of relevant entries over the past thirty-five years. For example, a Google Scholar search for the word "dynamic face" and related phrases i returned a mere 13 articles in 1980-1989 and 87 articles in 1990-1999. format of recordings, (e) visual or audio-visual modality of stimuli, (f) real human encoders, and (g) individual portrayals (as opposed to emotive interactions; note that some might contain both types).In an attempt to provide useful guidance for the readers of this paper, we classified databases in terms of three fundamental issues that are relevant to decisions about stimulus sets. These include a) conceptual features, which reflect thematic approaches in database construction and validation (Table 1), b) practical features, which concern applied aspects related to stimulus usage (Table 2), and c) citation and usage frequencies of dynamic datasets in the literature (Table 3 ii ), thereby elucidating their respective impact in the field. This latter issue can be categorized according to whether a dataset was used as stimulus material in research with human participants (social sciences) or for the training and testing of machine learning algorithms (computer sciences). With the tables designed to give specific information about each dataset, the accompanying text will focus...

We investigated the value of the Duchenne (D) smile as a spontaneous sign of felt enjoyment. Participants either smiled spontaneously in response to amusing material (spontaneous condition) or were instructed to pose a smile (deliberate condition). Similar amounts of D and non-Duchenne (ND) smiles were observed in these 2 conditions (Experiment 1). When subsets of these smiles were presented to other participants, they generally rated spontaneous and deliberate D and ND smiles differently. Moreover, they distinguished between D smiles of varying intensity within the spontaneous condition (Experiment 2). Such a differentiation was also made when seeing the upper or lower face only (Experiment 3), but was impaired for static compared with dynamic displays (Experiment 4). The predictive value of the D smile in these judgment studies was limited compared with other features such as asymmetry, apex duration, and nonpositive facial actions, and was only significant for ratings of the upper face and static displays. These findings raise doubts about the reliability and validity of the D smile and question the usefulness of facial descriptions in identifying true feelings of enjoyment.

The judgment that a smile is based on "true," usually positive, feelings affects social interaction. However, the processes underlying the interpretation of a smile as being more or less genuine are not well understood. The aim of the present research was to test predictions of the Simulation of Smiles Model (SIMS) proposed by Niedenthal, Mermillod, Maringer, and Hess (2010). In addition to the perceptual features that can guide the judgment of a smile as genuine, the model identifies the conditions that the judgments rely on: (a) the embodiment of the facial expression and its corresponding state, and (b) beliefs about the situations in which genuine smiles are most often expressed. Results of two studies are consistent with the model in that they confirm the hypotheses that facial mimicry provides feedback that is used to judge the meaning of a smile, and that beliefs about the situations in which a smile occurs guides such judgments when mimicry is inhibited.

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