61,591 research outputs found

    Cross-cultural comparison of Spanish and British “service-with-a-smile” outcomes

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    PurposeEmployees working in the leisure service industry are required to show positive emotions when dealing with customers. However, empirical evidence confirms that faking emotions can lead to burnout. In contrast, employees that try to experience the emotions required by the role (i.e. deep acting (DA)) can lead to healthier outcomes. However, little is known about the process that underpins the link between DA and positive outcomes. Building on Côte’s social interaction model of emotion regulation and evidence linking customer satisfaction and DA, it was hypothesized that DA would be associated with employees’ self-actualization through customer interactions. This, in turn, was expected to explain the influence that DA has on relevant job attitudes (i.e. commitment, efficacy, turnover intentions). The model was tested in two countries with different emotional culture: Spain (i.e. impulsive) and the UK (i.e. institutional). Although UK was expected to report higher levels of effortful DA, the hypothesized process was expected to be the same. The paper aims to discuss these issues.Design/methodology/approachA cross-national design with theme park employees from Spain (n = 208) and UK (n = 204) was used. Hypotheses were tested with multigroup confirmatory factor analysis. FindingsThe relationship between job commitment and DA was mediated by self-actualization, and commitment partially explained the association between DA and professional efficacy in both countries. The impulsive-oriented country showed lower levels of DA and more positive job attitudes.Originality/valueIt is concluded that training employees to re-interpret costume

    EMPATH: A Neural Network that Categorizes Facial Expressions

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    There are two competing theories of facial expression recognition. Some researchers have suggested that it is an example of "categorical perception." In this view, expression categories are considered to be discrete entities with sharp boundaries, and discrimination of nearby pairs of expressive faces is enhanced near those boundaries. Other researchers, however, suggest that facial expression perception is more graded and that facial expressions are best thought of as points in a continuous, low-dimensional space, where, for instance, "surprise" expressions lie between "happiness" and "fear" expressions due to their perceptual similarity. In this article, we show that a simple yet biologically plausible neural network model, trained to classify facial expressions into six basic emotions, predicts data used to support both of these theories. Without any parameter tuning, the model matches a variety of psychological data on categorization, similarity, reaction times, discrimination, and recognition difficulty, both qualitatively and quantitatively. We thus explain many of the seemingly complex psychological phenomena related to facial expression perception as natural consequences of the tasks' implementations in the brain

    Emotional Brain-Computer Interfaces

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    Research in Brain-computer interface (BCI) has significantly increased during the last few years. In addition to their initial role as assisting devices for the physically challenged, BCIs are now proposed for a wider range of applications. As in any HCI application, BCIs can also benefit from adapting their operation to the emotional state of the user. BCIs have the advantage of having access to brain activity which can provide signicant insight into the user's emotional state. This information can be utilized in two manners. 1) Knowledge of the inuence of the emotional state on brain activity patterns can allow the BCI to adapt its recognition algorithms, so that the intention of the user is still correctly interpreted in spite of signal deviations induced by the subject's emotional state. 2) The ability to recognize emotions can be used in BCIs to provide the user with more natural ways of controlling the BCI through affective modulation. Thus, controlling a BCI by recollecting a pleasant memory can be possible and can potentially lead to higher information transfer rates.\ud These two approaches of emotion utilization in BCI are elaborated in detail in this paper in the framework of noninvasive EEG based BCIs
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