scholarly journals Computational Models of Emotion Inference in Theory of Mind: A Review and Roadmap

2018 ◽  
Vol 11 (2) ◽  
pp. 338-357 ◽  
Author(s):  
Desmond C. Ong ◽  
Jamil Zaki ◽  
Noah D. Goodman
Keyword(s):  
Theory Of Mind ◽  
Computational Models ◽  
Computational Models Of Emotion

scholarly journals Chapter 9 - Theory of Mind and Joint Attention - The Handbook on Socially Interactive Agents

2021 ◽  
Author(s):  
Jairo Pérez-Osorio ◽  
Eva Wiese ◽  
Agnieszka Wykowska
Keyword(s):  
Theory Of Mind ◽  
Joint Attention ◽  
Cognitive Neuroscience ◽  
Computational Models ◽  
Social Cognitive ◽  
Virtual Agents ◽  
Artificial Agents ◽  
Future Directions ◽  
Social Cognitive Neuroscience ◽  
Intelligent Virtual Agents

The present chapter provides an overview from the perspective of social cognitive neuroscience (SCN) regarding theory of mind (ToM) and joint attention (JA) as crucial mechanisms of social cognition and discusses how these mechanisms have been investigated in social interaction with artificial agents. In the final sections, the chapter reviews computational models of ToM and JA in social robots (SRs) and intelligent virtual agents (IVAs) and discusses the current challenges and future directions.

scholarly journals Classification of Theory of Mind tasks and their computational models

2019 ◽  
Author(s):  
Tessa Rusch ◽  
Jan Gläscher
Keyword(s):  
Theory Of Mind ◽  
Computational Modeling ◽  
Computational Models ◽  
Mental States ◽  
Social Contexts ◽  
Decision Processes ◽  
Human Cognition ◽  
Intentional States ◽  
Social Decisions

The ability to from a Theory of Mind (ToM) that is to theorize about others mental states and explain and predict behavior in relation to attributed intentional states, constitutes a hallmark of human cognition. These abilities are multi-faceted and include a variety of different cognitive sub-functions. Here, we focus on decision processes in social contexts and review a number of experimental and computational modeling approaches in this field. We classify experimental accounts and formal computational models with respect to two categories: interactivity and uncertainty. Thereby, we aim at capturing the most relevant variations in ToM-related cognitive sub-functions and highlight the nuances of what broadly is referred to as social decisions processes. Finally, we discuss the most typical neuroimaging findings in light of the categorization results.

Modeling the Experience of Emotion

2010 ◽  
Vol 1 (1) ◽  
pp. 1-17 ◽  
Author(s):  
Joost Broekens
Keyword(s):  
Computational Modeling ◽  
Affective Computing ◽  
Computational Models ◽  
Appraisal Theory ◽  
Content Change ◽  
Primary Focus ◽  
Computational Models Of Emotion ◽  
Research Initiatives ◽  
And Behavior ◽  
Do So

Affective computing has proven to be a viable field of research comprised of a large number of multidisciplinary researchers, resulting in work that is widely published. The majority of this work consists of emotion recognition technology, computational modeling of causal factors of emotion and emotion expression in virtual characters and robots. A smaller part is concerned with modeling the effects of emotion on cognition and behavior, formal modeling of cognitive appraisal theory and models of emergent emotions. Part of the motivation for affective computing as a field is to better understand emotion through computational modeling. In psychology, a critical and neglected aspect of having emotions is the experience of emotion: what does the content of an emotional episode look like, how does this content change over time, and when do we call the episode emotional. Few modeling efforts in affective computing have these topics as a primary focus. The launch of a journal on synthetic emotions should motivate research initiatives in this direction, and this research should have a measurable impact on emotion research in psychology. In this article, I show that a good way to do so is to investigate the psychological core of what an emotion is: an experience. I present ideas on how computational modeling of emotion can help to better understand the experience of motion, and provide evidence that several computational models of emotion already address the issue.

scholarly journals Modeling the Experience of Emotion

2012 ◽  
pp. 1-18 ◽  
Author(s):  
Joost Broekens
Keyword(s):  
Computational Modeling ◽  
Affective Computing ◽  
Computational Models ◽  
Appraisal Theory ◽  
Content Change ◽  
Primary Focus ◽  
Computational Models Of Emotion ◽  
Research Initiatives ◽  
And Behavior ◽  
Over Time

Affective computing has proven to be a viable field of research comprised of a large number of multidisciplinary researchers, resulting in work that is widely published. The majority of this work consists of emotion recognition technology, computational modeling of causal factors of emotion and emotion expression in virtual characters and robots. A smaller part is concerned with modeling the effects of emotion on cognition and behavior, formal modeling of cognitive appraisal theory and models of emergent emotions. Part of the motivation for affective computing as a field is to better understand emotion through computational modeling. In psychology, a critical and neglected aspect of having emotions is the experience of emotion: what does the content of an emotional episode look like, how does this content change over time and when do we call the episode emotional. Few modeling efforts in affective computing have these topics as a primary focus. The launch of a journal on synthetic emotions should motivate research initiatives in this direction, and this research should have a measurable impact on emotion research in psychology. In this paper, I show that a good way to do so is to investigate the psychological core of what an emotion is: an experience. I present ideas on how computational modeling of emotion can help to better understand the experience of emotion, and provide evidence that several computational models of emotion already address the issue.

scholarly journals Theory of mind and decision science: Towards a typology of tasks and computational models

2020 ◽  
Vol 146 ◽  
pp. 107488 ◽  
Author(s):  
Tessa Rusch ◽  
Saurabh Steixner-Kumar ◽  
Prashant Doshi ◽  
Michael Spezio ◽  
Jan Gläscher
Keyword(s):  
Theory Of Mind ◽  
Computational Models ◽  
Decision Science
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