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DE MELO, Celso, PETTERS, Dean, PARTHEMORE, Joel, MOFFATT, David and BECKER-ASANO, Christian (2021). Introduction to the special issue on computational modelling of emotion. IEEE Transactions on Affective Computing, 12 (2), 277-278.
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Abstract
The papers in this special issue focus on computational modeling of emotion recognition. Emotions play a pervasive role in personal, social, and professional life. As artificially intelligent systems become pervasive in our lives, it is important that these systems are able to understand emotion in humans and simulate the function of emotion to be effective in their interactions with people. Computational models of emotion contribute towards this goal by, on the one hand, serving as a means to test emotion theories and help understand the function of emotion and, on the other, as the end in itself by simulating appropriate emotion and its downstream consequences – such as expressions of emotion – in computational agents. This special issue presents a critical overview of this cross-disciplinary field, with contributions from some of the leading scholars in cognitive psychology and affective computing, focusing both on theory and practice.
| Item Type: | Article |
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| Additional Information: | © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
| Uncontrolled Keywords: | 0801 Artificial Intelligence and Image Processing; 0806 Information Systems; 1702 Cognitive Sciences |
| Identification Number: | https://doi.org/10.1109/taffc.2021.3073214 |
| Page Range: | 277-278 |
| SWORD Depositor: | Symplectic Elements |
| Depositing User: | Symplectic Elements |
| Date Deposited: | 27 Apr 2022 08:58 |
| Last Modified: | 27 Apr 2022 12:02 |
| URI: | https://shura.shu.ac.uk/id/eprint/30142 |
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