Open Access

We describe and illustrate a new method of graphically diagramming disputants’ points of view called cognitive-affective mapping. The products of this method—cognitive-affective maps (CAMs)—represent an individual’s concepts and beliefs about a particular subject, such as another individual or group or an issue in dispute. Each of these concepts and beliefs has its own emotional value. The result is a detailed image of a disputant’s complex belief system that can assist in-depth analysis of the ideational sources of the dispute and thereby aid its resolution. We illustrate the method with representations of the beliefs of typical individuals involved in four contemporary disputes of markedly different type: a clash over German housing policy, disagreements between Israelis over the meaning of the Western Wall, contention surrounding exploitation of Canada’s bitumen resources, and the deep dispute between people advocating action on climate change and those skeptical about the reality of the problem.

We are pleased that all the commentators seem to agree that a theory-driven integration across disciplines is a worthwhile endeavor to better understand the social constitution of emotion. In our reply, we first take up the idea of relating affect control theory (ACT) to cultural priming and suggest links to an ACT-inspired constraint satisfaction explanation of priming. Second, we address reservations concerning ACT’s capability to account for emotions with nonconceptual content and to explain stability and change in affective meanings. Third, we clarify the relation of affect control theory to psychological constructionism, in particular with regard to conceptualizations of culture and society. Finally, we suggest that computational models are an adequate tool to address multilevel issues in the study of emotion.

Priming influences holistic representations of social situations and subsequent actions through interactive competition among relevant concepts such as the prime, the self, a partner, or other features of the environment. The constraints among these representations stem from culturally shared affective meanings of concepts acquired in socialization. Our theory is implemented in a localist connectionist model, which in simulations reproduced major experimental results on priming. The neural plausibility of our proposal comes from semantic pointers, a neural mechanism that integrates symbolic concepts with underlying emotional and sensorimotor processes. The compositional nature of semantic pointers also explains the interaction of priming with more deliberate and intentional forms of social cognition.

Coaching lässt sich als ein Prozess der Bedeutungsschaffung durch sprachliche Interaktion verstehen. Bedeutungen sind kohärente Begriffsnetzwerke, die zum einen sozial geteilt, zum anderen im emotionalen Erleben von Personen fundiert sind. Wir stellen den aktuellen Forschungsstand in der Kognitionswissenschaft und Sozialpsychologie dar und erläutern am Beispiel eines am Markt etablierten Coaching-Modells („Contextuelles Coaching“) die Bedeutung dieser Forschung für die Coaching-Praxis.

This paper describes a novel method for building affectively intelligent human-interactive agents. The method is based on a key sociological insight that has been developed and extensively verified over the last twenty years, but has yet to make an impact in artificial intelligence. The insight is that resource bounded humans will, by default, act to maintain affective consistency. Humans have culturally shared fundamental affective sentiments about identities, behaviours, and objects, and they act so that the transient affective sentiments created during interactions confirm the fundamental sentiments. Humans seek and create situations that confirm or are consistent with, and avoid and suppress situations that disconfirm or are inconsistent with, their culturally shared affective sentiments. This “affect control principle” has been shown to be a powerful predictor of human behaviour. In this paper, we present a probabilistic and decision-theoretic generalisation of this principle, and we demonstrate how it can be leveraged to build affectively intelligent artificial agents. The new model, called BayesAct, can maintain multiple hypotheses about sentiments simultaneously as a probability distribution, and can make use of an explicit utility function to make value-directed action choices. This allows the model to generate affectively intelligent interactions with people by learning about their identity, predicting their behaviours using the affect control principle, and taking actions that are simultaneously goal-directed and affect-sensitive. We demonstrate this generalisation with a set of simulations. We then show how our model can be used as an emotional “plug-in” for artificially intelligent systems that interact with humans in two different settings: an exam practice assistant (tutor) and an assistive device for persons with a cognitive disability.