Emotion and motion: Toward emotion recognition based on standing and walking.

Emotion recognition is key to interpersonal communication and to human-machine interaction. Body expression may contribute to emotion recognition, but most past studies focused on a few motions, limiting accurate recognition. Moreover, emotions in most previous research were acted out, resulting in non-natural motion, which is unapplicable in reality. We present an approach for emotion recognition based on body motion in naturalistic settings, examining authentic emotions, natural movement, and a broad collection of motion parameters. A lab experiment using 24 participants manipulated participants' emotions using pretested movies into five conditions: happiness, relaxation, fear, sadness, and emotionally-neutral. Emotion was manipulated within subjects, with fillers in between and a counterbalanced order. A motion capture system measured posture and motion during standing and walking; a force plate measured center of pressure location. Traditional statistics revealed nonsignificant effects of emotions on most motion parameters; only 7 of 229 parameters demonstrate significant effects. Most significant effects are in parameters representing postural control during standing, which is consistent with past studies. Yet, the few significant effects suggest that it is impossible to recognize emotions based on a single motion parameter. We therefore developed machine learning models to classify emotions using a collection of parameters, and examined six models: k-nearest neighbors, decision tree, logistic regression, and the support vector machine with radial base function and linear and polynomial functions. The decision tree using 25 parameters provided the highest average accuracy (45.8%), more than twice the random guess for five conditions, which advances past studies demonstrating comparable accuracies, due to our naturalistic setting. This research suggests that machine learning models are valuable for emotion recognition in reality and lays the foundation for further progress in emotion recognition models, informing the development of recognition devices (e.g., depth camera), to be used in home-setting human-machine interactions.

Help others-be happy? The effect of altruistic behavior on happiness across cultures.

Research has established that altruistic behavior increases happiness. We examined this phenomenon across cultures, differentiating between individualistic and collectivist cultures. We propose that cultural variations in the notion of altruism lead to different effects of helping on the helper's happiness. For individualists, altruism is linked to self-interest ("impure" altruism), and helping others results in increased happiness for the helper. For collectivists, altruism is focused on the recipient ("pure" altruism), and helping others is less likely to enhance the helper's happiness. Four studies support our predictions. Study 1 measured the dispositions toward altruism among people with various cultural orientations. Consistent with our predictions, the findings showed that individualism (collectivism) was positively associated with tendencies reflecting more "impure" ("pure") altruism. Two experimental studies then examined the moderating role of cultural orientation on the effect of spending money on oneself versus others (Study 2) or of doing a kind action (making tea for oneself versus others; Study 3). Both experimental studies demonstrated that altruistic behavior had a positive effect on happiness for individualists but not for collectivists. Finally, Study 4, which utilized data from the World Values Survey to examine the altruism-happiness link in various countries, displayed a stronger link between altruistic behavior and happiness in individualistic (vs. collectivist) cultures. Altogether, this research sheds light on cultural differences in the display of altruism, revealing different motivations for and consequences of altruistic behaviors.

Preferences don't have to be personal: expanding attitude theorizing with a cross-cultural perspective.

Attitudes, theorized as behavioral guides, have long been a central focus of research in the social sciences. However, this theorizing reflects primarily Western philosophical views and empirical findings emphasizing the centrality of personal preferences. As a result, the prevalent psychological model of attitudes is a person-centric one. We suggest that incorporating research insights from non-Western sociocultural contexts can significantly enhance attitude theorizing. To this end, we propose an additional model-a normative-contextual model of attitudes. The currently dominant person-centric model emphasizes the centrality of personal preferences, their stability and internal consistency, and their possible interaction with externally imposed norms. In contrast, the normative-contextual model emphasizes that attitudes are always context-contingent and incorporate the views of others and the norms of the situation. In this model, adjustment to norms does not involve an effortful struggle between the authentic self and exogenous forces. Rather, it is the ongoing and reassuring integration of others' views into one's attitudes. According to the normative-contextual model, likely to be a good fit in contexts that foster interdependence and holistic thinking, attitudes need not be personal or necessarily stable and internally consistent and are only functional to the extent that they help one to adjust automatically to different contexts. The fundamental shift in focus offered by the normative-contextual model generates novel hypotheses and highlights new measurement criteria for studying attitudes in non-Western sociocultural contexts. We discuss these theoretical and measurement implications as well as practical implications for health and well-being, habits and behavior change, and global marketing. (PsycINFO Database Record (c) 2014 APA, all rights reserved).

Higher motivation - greater control? The effect of arousal on judgement.

This research examines control over the effect of arousal, a dimension of affect, on judgement. Past research shows that high processing motivation enhances control over the effects of affect on judgement. Isolating and studying arousal as opposed to valence, the other dimension of affect, and its effect on judgement, we identify boundary conditions for past findings. Drawing from the literature on processes by which arousal influences judgement, we demonstrate that the role of motivation is contingent upon the type of judgement task (i.e., memory- versus stimulus-based judgement). In stimulus-based judgement, individuals exert greater control over the effect of arousal on judgement under low compared to high motivation. In contrast, in memory-based judgement individuals exert greater control over the effect of arousal under high compared to low motivation. Theoretical implications and avenues for future research are discussed.

Impression Management in Survey Responding: Easier for Collectivists or Individualists?

Three experiments indicate that when individualists and collectivists engage in impression management on self-reports, they do so through different psychological mechanism s. Collectivists do so through a relatively automatic process. Thus, they can impression manage even when cognitively busy. Individualists impression manage through a more effortful process. Therefore, they can do so only when the situation permits effortful processing. These findings highlight distinct conditions under which social norms may influence consumer self-reports across cultures.