Although research on facial emotion recognition abounds, there has been little attention on the nature of the underlying mechanisms. In this article, using a "reverse engineering" approach, we suggest that emotion inference from facial expression mirrors the expression process. As a strong case can be made for an appraisal theory account of emotional expression, which holds that appraisal results directly determine the nature of facial muscle actions, we claim that observers first detect specific appraisals from different facial muscle actions and then use implicit inference rules to categorize and name specific emotions. We report three experiments in which, guided by theoretical predictions and past empirical evidence, we systematically manipulated specific facial action units individually and in different configurations via synthesized avatar expressions. Large, diverse groups of participants judged the resulting videos for the underlying appraisals and/or the ensuing emotions. The results confirm that participants can infer targeted appraisals and emotions from synthesized facial actions based on appraisal predictions. We also report evidence that the ability to correctly interpret the synthesized stimuli is highly correlated with emotion recognition ability as part of emotional competence. We conclude by highlighting the importance of adopting a theory-based experimental approach in future research, focusing on the dynamic unfolding of facial expressions of emotion. (PsycINFO Database Record

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a b s t r a c tWe present a novel way to implement hierarchical structure and test its learnability in an artificial language involving structure-dependent, long-distance agreement relations. In Experiment 1, the grammar was exclusively cued by phonological and prosodic markers similar to those found in natural languages. Experiment 2 contained additional semantic cues in the form of a reference world. At the group level, successful generalization of the phrase structure rules to new words was found in both experiments. Analyses of individual profiles show that a subset of participants also generalized their knowledge to novel phrase structure rules, instantiating a natural extension of the training grammar, based on recursion of coordination. Rule induction improves across-the-board in the presence of semantic cues. It is concluded that adults are able to develop, to some extent, abstract knowledge of hierarchical, structure-dependent representations despite impoverished input data and minimal training.

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