More What Duchenne Smiles Do, Less What They Express

The Emotion Authenticity Recognition (EAR) test: normative data of an innovative test using dynamic emotional stimuli to evaluate the ability to recognize the authenticity of emotions expressed by faces

Despite research has massively focused on how emotions conveyed by faces are perceived, the perception of emotions' authenticity is a topic that has been surprisingly overlooked. Here, we present the Emotion Authenticity Recognition (EAR) test, a test specifically developed using dynamic stimuli depicting authentic and posed emotions to evaluate the ability of individuals to correctly identify an emotion (emotion recognition index, ER Index) and classify its authenticity (authenticity recognition index (EA Index). The EAR test has been validated on 522 healthy participants and normative values are provided. Correlations with demographic characteristics, empathy and general cognitive status have been obtained revealing that both indices are negatively correlated with age, and positively with education, cognitive status and different facets of empathy. The EAR test offers a new ecological test to assess the ability to detect emotion authenticity that allow to explore the eventual social cognitive deficit even in patients otherwise cognitively intact.

The subjective value of genuine smiles guides real-world social behaviour

The mechanisms that underpin human social behaviour are poorly understood, in part because natural social behaviour is challenging to study. The task of linking the mechanisms thought to drive social behaviour to specific social behaviours in a manner that maintains ecological validity poses an even greater challenge. Here we report evidence that the subjective value people assign to genuine smiles, as measured in the laboratory, determines their responsiveness to genuine smiles encountered in a naturalistic social interaction. Specifically, participants (university undergraduates; age 17 to 36) who valued genuine smiles to a greater degree also showed stronger attention capture effects to neutral faces that were previously associated with genuine smiles and faster reciprocity of a social partner's smiles in a real social interaction. Additionally, the faster participants responded to the partner's genuine smiles the higher the partner's ratings of interaction quality were after the interaction. These data suggest that individual differences in subjective value of genuine smiles, measured in the lab, is one element that underpins responsiveness to natural genuine smiles and subsequent social outcomes.

Reading of ingroup politicians' smiles triggers smiling in the corner of one's eyes

Spontaneous smiles in response to politicians can serve as an implicit barometer for gauging electorate preferences. However, it is unclear whether a subtle Duchenne smile-an authentic expression involving the coactivation of the zygomaticus major (ZM) and orbicularis oculi (OO) muscles-would be elicited while reading about a favored politician smiling, indicating a more positive disposition and political endorsement. From an embodied simulation perspective, we investigated whether written descriptions of a politician's smile would trigger morphologically different smiles in readers depending on shared or opposing political orientation. In a controlled reading task in the laboratory, participants were presented with subject-verb phrases describing left and right-wing politicians smiling or frowning. Concurrently, their facial muscular reactions were measured via electromyography (EMG) recording at three facial muscles: the ZM and OO, coactive during Duchenne smiles, and the corrugator supercilii (CS) involved in frowning. We found that participants responded with a Duchenne smile detected at the ZM and OO facial muscles when exposed to portrayals of smiling politicians of same political orientation and reported more positive emotions towards these latter. In contrast, when reading about outgroup politicians smiling, there was a weaker activation of the ZM muscle and no activation of the OO muscle, suggesting a weak non-Duchenne smile, while emotions reported towards outgroup politicians were significantly more negative. Also, a more enhanced frown response in the CS was found for ingroup compared to outgroup politicians' frown expressions. Present findings suggest that a politician's smile may go a long way to influence electorates through both non-verbal and verbal pathways. They add another layer to our understanding of how language and social information shape embodied effects in a highly nuanced manner. Implications for verbal communication in the political context are discussed.

Understanding Naturalistic Facial Expressions with Deep Learning and Multimodal Large Language Models

This paper provides a comprehensive overview of affective computing systems for facial expression recognition (FER) research in naturalistic contexts. The first section presents an updated account of user-friendly FER toolboxes incorporating state-of-the-art deep learning models and elaborates on their neural architectures, datasets, and performances across domains. These sophisticated FER toolboxes can robustly address a variety of challenges encountered in the wild such as variations in illumination and head pose, which may otherwise impact recognition accuracy. The second section of this paper discusses multimodal large language models (MLLMs) and their potential applications in affective science. MLLMs exhibit human-level capabilities for FER and enable the quantification of various contextual variables to provide context-aware emotion inferences. These advancements have the potential to revolutionize current methodological approaches for studying the contextual influences on emotions, leading to the development of contextualized emotion models.

The reciprocal relationship between smiles and situational contexts

ABSTRACTSmiles provide information about a social partner's affect and intentions during social interaction. Although always encountered within a specific situation, the influence of contextual information on smile evaluation has not been widely investigated. Moreover, little is known about the reciprocal effect of smiles on evaluations of their accompanying situations. In this research, we assessed how different smile types and situational contexts affected participants' social evaluations. In Study 1, 85 participants rated reward, affiliation, and dominance smiles embedded within either enjoyable, polite, or negative (unpleasant) situations. Context had a strong effect on smile ratings, such that smiles in enjoyable situations were rated as more genuine and joyful, as well as indicating less superiority than those in negative situations. In Study 2, 200 participants evaluated the situations that these smiles were perceived within (rather than the smiles themselves). Although situations paired with reward (vs. affiliation) smiles tended to be rated more positively, this effect was absent for negative situations. Ultimately, the findings point toward a reciprocal relationship between smiles and contexts, whereby the face influences evaluations of the situation and vice versa.

Four Misconceptions About Nonverbal Communication

Research and theory in nonverbal communication have made great advances toward understanding the patterns and functions of nonverbal behavior in social settings. Progress has been hindered, we argue, by presumptions about nonverbal behavior that follow from both received wisdom and faulty evidence. In this article, we document four persistent misconceptions about nonverbal communication-namely, that people communicate using decodable body language; that they have a stable personal space by which they regulate contact with others; that they express emotion using universal, evolved, iconic, categorical facial expressions; and that they can deceive and detect deception, using dependable telltale clues. We show how these misconceptions permeate research as well as the practices of popular behavior experts, with consequences that extend from intimate relationships to the boardroom and courtroom and even to the arena of international security. Notwithstanding these misconceptions, existing frameworks of nonverbal communication are being challenged by more comprehensive systems approaches and by virtual technologies that ambiguate the roles and identities of interactants and the contexts of interaction.

Let's put a smile on that face-A positive facial expression improves aesthetics of portrait photographs

In today's age of social media and smartphones, portraits-such as selfies or pictures of friends and family-are very frequently produced, shared and viewed images. Despite their prevalence, the psychological factors that characterize a 'good' photo-one that people will generally like, keep, and think is especially aesthetically pleasing-are not well understood. Here, we studied how a subtle change in facial expression (smiling) in portraits determines their aesthetic image value (beyond a more positive appearance of the depicted person). We used AI-based image processing tools in a broad set of portrait photographs and generated neutral and slightly smiling versions of the same pictures. Consistent across two experiments, portraits with a subtle smile increased both spontaneous aesthetic preferences in a swiping task as well as improving more explicit aesthetic ratings after prolonged viewing. Participants distinguished between aspects associated with image beauty and the depicted person's attractiveness, resulting in specific interactions between variables related to participant traits, image content, and task. Our study confirms that a subtle-and in this case fully artificial-smile reliably increases the aesthetic quality of portraits, illustrating how current image processing methods can target psychologically important variables and thereby increase the aesthetic value of photographs.

These Aren't The Droids You Are Looking for: Promises and Challenges for the Intersection of Affective Science and Robotics/AI

AI research focused on interactions with humans, particularly in the form of robots or virtual agents, has expanded in the last two decades to include concepts related to affective processes. Affective computing is an emerging field that deals with issues such as how the diagnosis of affective states of users can be used to improve such interactions, also with a view to demonstrate affective behavior towards the user. This type of research often is based on two beliefs: (1) artificial emotional intelligence will improve human computer interaction (or more specifically human robot interaction), and (2) we understand the role of affective behavior in human interaction sufficiently to tell artificial systems what to do. However, within affective science the focus of research is often to test a particular assumption, such as "smiles affect liking." Such focus does not provide the information necessary to synthesize affective behavior in long dynamic and real-time interactions. In consequence, theories do not play a large role in the development of artificial affective systems by engineers, but self-learning systems develop their behavior out of large corpora of recorded interactions. The status quo is characterized by measurement issues, theoretical lacunae regarding prevalence and functions of affective behavior in interaction, and underpowered studies that cannot provide the solid empirical foundation for further theoretical developments. This contribution will highlight some of these challenges and point towards next steps to create a rapprochement between engineers and affective scientists with a view to improving theory and solid applications.

The nature and neurobiology of fear and anxiety: State of the science and opportunities for accelerating discovery

Fear and anxiety play a central role in mammalian life, and there is considerable interest in clarifying their nature, identifying their biological underpinnings, and determining their consequences for health and disease. Here we provide a roundtable discussion on the nature and biological bases of fear- and anxiety-related states, traits, and disorders. The discussants include scientists familiar with a wide variety of populations and a broad spectrum of techniques. The goal of the roundtable was to take stock of the state of the science and provide a roadmap to the next generation of fear and anxiety research. Much of the discussion centered on the key challenges facing the field, the most fruitful avenues for future research, and emerging opportunities for accelerating discovery, with implications for scientists, funders, and other stakeholders. Understanding fear and anxiety is a matter of practical importance. Anxiety disorders are a leading burden on public health and existing treatments are far from curative, underscoring the urgency of developing a deeper understanding of the factors governing threat-related emotions.