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)

Advances in artificial intelligence and computer graphics digital technologies have contributed to a relative increase in realism in virtual characters. Preserving virtual characters’ communicative realism, in particular, joined the ranks of the improvements in natural language technology, and animation algorithms. This paper focuses on culturally relevant paralinguistic cues in nonverbal communication. We model the effects of an English-speaking digital character with different accents on human interactants (i.e., users). Our cultural influence model proposes that paralinguistic realism, in the form of accented (...) speech, is effective in promoting culturally congruent cognition only when it is self-relevant to users. For example, a Chinese or Middle Eastern English accent may be perceived as foreign to individuals who do not share the same ethnic cultural background with members of those cultures. However, for individuals who are familiar and affiliate with those cultures (i.e., in-group members who are bicultural), accent not only serves as a motif of shared social identity, it also primes them to adopt culturally appropriate interpretive frames that influence their decision making. (shrink)

This article offers a critical perspective on efforts to build computational models of human emotional processes. I argue that current computational scientists are missing an opportunity to bring simplicity and clarity to emotion research by adopting an overly literal interpretation of psychological theory. Rather, hearkening back to arguments from the early days of cognitive science, I suggest an approach of reinterpreting psychological phenomena through the lens of computation. I illustrate this approach through models of emotional dynamics and cultural differences.

Social judgment is a social inference process whereby an agent singles out individuals to blame or credit for multi-agent activities. Such inferences are a key aspect of social intelligence that underlie social planning, social learning, natural language pragmatics and computational models of emotion. With the advance of multi-agent interactive systems and the need of designing socially aware systems and interfaces to interact with people, it is increasingly important to model this human-centric form of social inference. Based on psychological attribution theory, (...) this paper presents a general computational framework to automate social inference based on an agent’s causal knowledge and observations of interaction. (shrink)