Welcome to the InfoSci Platform
Reference Hub4
Automated Recognition of Emotion Appraisals

Automated Recognition of Emotion Appraisals

Marcello Mortillaro, Ben Meuleman, Klaus R. Scherer
Copyright: © 2015 |Pages: 14
ISBN13: 9781466672789|ISBN10: 1466672781|EISBN13: 9781466672796
DOI: 10.4018/978-1-4666-7278-9.ch016
Cite Chapter Cite Chapter

MLA

Mortillaro, Marcello, et al. "Automated Recognition of Emotion Appraisals." Handbook of Research on Synthesizing Human Emotion in Intelligent Systems and Robotics, edited by Jordi Vallverdú, IGI Global, 2015, pp. 338-351. https://doi.org/10.4018/978-1-4666-7278-9.ch016

APA

Mortillaro, M., Meuleman, B., & Scherer, K. R. (2015). Automated Recognition of Emotion Appraisals. In J. Vallverdú (Ed.), Handbook of Research on Synthesizing Human Emotion in Intelligent Systems and Robotics (pp. 338-351). IGI Global. https://doi.org/10.4018/978-1-4666-7278-9.ch016

Chicago

Mortillaro, Marcello, Ben Meuleman, and Klaus R. Scherer. "Automated Recognition of Emotion Appraisals." In Handbook of Research on Synthesizing Human Emotion in Intelligent Systems and Robotics, edited by Jordi Vallverdú, 338-351. Hershey, PA: IGI Global, 2015. https://doi.org/10.4018/978-1-4666-7278-9.ch016

Export Reference

Mendeley
Favorite

Abstract

Most computer models for the automatic recognition of emotion from nonverbal signals (e.g., facial or vocal expression) have adopted a discrete emotion perspective, i.e., they output a categorical emotion from a limited pool of candidate labels. The discrete perspective suffers from practical and theoretical drawbacks that limit the generalizability of such systems. The authors of this chapter propose instead to adopt an appraisal perspective in modeling emotion recognition, i.e., to infer the subjective cognitive evaluations that underlie both the nonverbal cues and the overall emotion states. In a first step, expressive features would be used to infer appraisals; in a second step, the inferred appraisals would be used to predict an emotion label. The first step is practically unexplored in emotion literature. Such a system would allow to (a) link models of emotion recognition and production, (b) add contextual information to the inference algorithm, and (c) allow detection of subtle emotion states.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.