Psychological science in the public interest : a journal of the American Psychological Society最新文献

It is commonly assumed that a person's emotional state can be readily inferred from his or her facial movements, typically called emotional expressions or facial expressions. This assumption influences legal judgments, policy decisions, national security protocols, and educational practices; guides the diagnosis and treatment of psychiatric illness, as well as the development of commercial applications; and pervades everyday social interactions as well as research in other scientific fields such as artificial intelligence, neuroscience, and computer vision. In this article, we survey examples of this widespread assumption, which we refer to as the common view, and we then examine the scientific evidence that tests this view, focusing on the six most popular emotion categories used by consumers of emotion research: anger, disgust, fear, happiness, sadness, and surprise. The available scientific evidence suggests that people do sometimes smile when happy, frown when sad, scowl when angry, and so on, as proposed by the common view, more than what would be expected by chance. Yet how people communicate anger, disgust, fear, happiness, sadness, and surprise varies substantially across cultures, situations, and even across people within a single situation. Furthermore, similar configurations of facial movements variably express instances of more than one emotion category. In fact, a given configuration of facial movements, such as a scowl, often communicates something other than an emotional state. Scientists agree that facial movements convey a range of information and are important for social communication, emotional or otherwise. But our review suggests an urgent need for research that examines how people actually move their faces to express emotions and other social information in the variety of contexts that make up everyday life, as well as careful study of the mechanisms by which people perceive instances of emotion in one another. We make specific research recommendations that will yield a more valid picture of how people move their faces to express emotions and how they infer emotional meaning from facial movements in situations of everyday life. This research is crucial to provide consumers of emotion research with the translational information they require.

What would a comprehensive atlas of human emotions include? For 50 years, scientists have sought to map emotion-related experience, expression, physiology, and recognition in terms of the "basic six"-anger, disgust, fear, happiness, sadness, and surprise. Claims about the relationships between these six emotions and prototypical facial configurations have provided the basis for a long-standing debate over the diagnostic value of expression (for review and latest installment in this debate, see Barrett et al., p. 1). Building on recent empirical findings and methodologies, we offer an alternative conceptual and methodological approach that reveals a richer taxonomy of emotion. Dozens of distinct varieties of emotion are reliably distinguished by language, evoked in distinct circumstances, and perceived in distinct expressions of the face, body, and voice. Traditional models-both the basic six and affective-circumplex model (valence and arousal)-capture a fraction of the systematic variability in emotional response. In contrast, emotion-related responses (e.g., the smile of embarrassment, triumphant postures, sympathetic vocalizations, blends of distinct expressions) can be explained by richer models of emotion. Given these developments, we discuss why tests of a basic-six model of emotion are not tests of the diagnostic value of facial expression more generally. Determining the full extent of what facial expressions can tell us, marginally and in conjunction with other behavioral and contextual cues, will require mapping the high-dimensional, continuous space of facial, bodily, and vocal signals onto richly multifaceted experiences using large-scale statistical modeling and machine-learning methods.

Almost everyone struggles to act in their individual and collective best interests, particularly when doing so requires forgoing a more immediately enjoyable alternative. Other than exhorting decision makers to "do the right thing," what can policymakers do to reduce overeating, undersaving, procrastination, and other self-defeating behaviors that feel good now but generate larger delayed costs? In this review, we synthesize contemporary research on approaches to reducing failures of self-control. We distinguish between self-deployed and other-deployed strategies and, in addition, between situational and cognitive intervention targets. Collectively, the evidence from both psychological science and economics recommends psychologically informed policies for reducing failures of self-control.

Collaborative problem solving (CPS) has been receiving increasing international attention because much of the complex work in the modern world is performed by teams. However, systematic education and training on CPS is lacking for those entering and participating in the workforce. In 2015, the Programme for International Student Assessment (PISA), a global test of educational progress, documented the low levels of proficiency in CPS. This result not only underscores a significant societal need but also presents an important opportunity for psychological scientists to develop, adopt, and implement theory and empirical research on CPS and to work with educators and policy experts to improve training in CPS. This article offers some directions for psychological science to participate in the growing attention to CPS throughout the world. First, it identifies the existing theoretical frameworks and empirical research that focus on CPS. Second, it provides examples of how recent technologies can automate analyses of CPS processes and assessments so that substantially larger data sets can be analyzed and so students can receive immediate feedback on their CPS performance. Third, it identifies some challenges, debates, and uncertainties in creating an infrastructure for research, education, and training in CPS. CPS education and assessment are expected to improve when supported by larger data sets and theoretical frameworks that are informed by psychological science. This will require interdisciplinary efforts that include expertise in psychological science, education, assessment, intelligent digital technologies, and policy.