A key feature of facial behavior is its dynamic quality. However, most previous research has been limited to the use of static images of prototypical expressive patterns. This article explores the role of facial dynamics in the perception of emotions, reviewing relevant empirical evidence demonstrating that dynamic information improves coherence in the identification of affect (particularly for degraded and subtle stimuli), leads to higher emotion judgments (i.e., intensity and arousal), and helps to differentiate between genuine and fake expressions. The findings (...) underline that using static expressions not only poses problems of ecological validity, but also limits our understanding of what facial activity does. Implications for future research on facial activity, particularly for social neuroscience and affective computing, are discussed. (shrink)

Emotions are foremost self-regulating processes that permit rapid responses and adaptations to situations of personal concern. They have biological bases and are shaped ontogenetically via learning and experience. Many situations and events of personal concern are social in nature. Thus, social exchanges play an important role in learning about rules and norms that shape regulation processes. I argue that (a) emotions often are actively auto-regulating—the behavior implied by the emotional reaction bias to the eliciting event or situation modifies or terminates (...) the situation; (b) certain emotion components are likely to habituate dynamically, modifying the emotional states; (c) emotions are typically intra- and interpersonal processes at the same time, and modulating forces at these different levels interact; (d) emotions are not just regulated—they regulate. Important conclusions of my arguments are that the scientific analysis of emotion should not exclude regulatory processes, and that effortful emotion regulation should be seen relative to a backdrop of auto-regulation and habituation, and not the ideal notion of a neutral baseline. For all practical purposes unregulated emotion is not a realistic concept. (shrink)

Affective computing adopts a computational approach to study affect. We highlight the AC approach towards automated affect measures that jointly model machine-readable physiological/behavioral signals with affect estimates as reported by humans or experimentally elicited. We describe the conceptual and computational foundations of the approach followed by two case studies: one on discrimination between genuine and faked expressions of pain in the lab, and the second on measuring nonbasic affect in the wild. We discuss applications of the measures, analyze measurement accuracy (...) and generalizability, and highlight advances afforded by computational tipping points, such as big data, wearable sensing, crowdsourcing, and deep learning. We conclude by advocating for increasing synergies between AC and affective science and offer suggestions toward that direction. (shrink)

This paper investigates the effects of relative position and proxemics in the engagement process involved in Human-Robot collaboration. We evaluate the differences between two experimental placement conditions for an autonomous robot in a collaborative task with a user across two different types of robot behaviours. The study evaluated placement and behaviour types around a touch table with 80 participants by measuring gaze, smiling behaviour, distance from the task, and finally electrodermal activity. Results suggest an overall user preference and higher engagement (...) rates with the helpful robot in the frontal position. We discuss how behaviours and position of the robot relative to a user may affect user engagement and collaboration, in particular when the robot aims to provide help via socio-emotional bonding. (shrink)

This paper investigates the effects of relative position and proxemics in the engagement process involved in Human-Robot collaboration. We evaluate the differences between two experimental placement conditions for an autonomous robot in a collaborative task with a user across two different types of robot behaviours. The study evaluated placement and behaviour types around a touch table with 80 participants by measuring gaze, smiling behaviour, distance from the task, and finally electrodermal activity. Results suggest an overall user preference and higher engagement (...) rates with the helpful robot in the frontal position. We discuss how behaviours and position of the robot relative to a user may affect user engagement and collaboration, in particular when the robot aims to provide help via socio-emotional bonding. (shrink)

Appraisal theory assumes that the individual variability of emotional reactions to the same situation is due to individual differences in appraisal. However, the question of how interindividual differences in appraisal come about has been rarely formally addressed. We focus on one of the central dimensions of appraisal—problem-focused coping potential—and attempt to explain individual differences in appraisals along this dimension using self-efficacy theory. We integrate outcome expectancies, self-efficacy expectations, and problem-focused coping potential into a single framework and outline their personality antecedents. (...) Using generalized self-efficacy beliefs as an example, we then discuss the mechanisms underlying the effects of personality dispositions on appraisal and the conditions that moderate their influence. We conclude with the implications for theory, research, and practice. (shrink)

Understanding the impact of robot errors in child-robot-interactions (CRI) is critical, as current technological systems are still limited and may randomly present a variety of mistakes during interactions with children. In this study we manipulate a task-based error of a NAO robot during a semi-autonomous computational thinking task implemented with the Cozmo robot. Data from 72 children aged 7–10 were analysed regarding their attitudes towards NAO (social trust, competency trust, liking, and perceived agency), their behaviour towards the robot (self-disclosure, following (...) recommendations), as well as their task performance. We did not find quantitative effects of the robot’s error on children’s self-reported attitudes, behaviour, or task performance. Age was also not significantly related to either social attitudes or behaviours towards NAO, although there were some age-related differences in task performance. Potential reasons behind the lack of statistical effects and limitations of the study with regards to the manipulation of robot errors are discussed and insights into the design of future CRI studies provided. (shrink)