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DC Field | Value | Language |
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dc.contributor.author | Makantasis, Konstantinos | - |
dc.contributor.author | Pinitas, Kosmas | - |
dc.contributor.author | Liapis, Antonios | - |
dc.contributor.author | Yannakakis, Georgios N. | - |
dc.date.accessioned | 2024-04-29T12:50:56Z | - |
dc.date.available | 2024-04-29T12:50:56Z | - |
dc.date.issued | 2022 | - |
dc.identifier.citation | Makantasis, K., Pinitas, K., Liapis, A., & Yannakakis, G. N. (2022, October). The invariant ground truth of affect. 10th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW). Nara, Japan. | en_GB |
dc.identifier.uri | https://www.um.edu.mt/library/oar/handle/123456789/121551 | - |
dc.description.abstract | Affective computing strives to unveil the unknown relationship between affect elicitation, manifestation of affect and affect annotations. The ground truth of affect, however, is predominately attributed to the affect labels which inadvertently include biases inherent to the subjective nature of emotion and its labeling. The response to such limitations is usually augmenting the dataset with more annotations per data point; however, this is not possible when we are interested in self-reports via first-person annotation. Moreover, outlier detection methods based on interannotator agreement only consider the annotations themselves and ignore the context and the corresponding affect manifestation. This paper reframes the ways one may obtain a reliable ground truth of affect by transferring aspects of causation theory to affective computing. In particular, we assume that the ground truth of affect can be found in the causal relationships between elicitation, manifestation and annotation that remain invariant across tasks and participants. To test our assumption we employ causation inspired methods for detecting outliers in affective corpora and building affect models that are robust across participants and tasks. We validate our methodology within the domain of digital games, with experimental results showing that it can successfully detect outliers and boost the accuracy of affect models. To the best of our knowledge, this study presents the first attempt to integrate causation tools in affective computing, making a crucial and decisive step towards general affect modeling. | en_GB |
dc.language.iso | en | en_GB |
dc.publisher | Institute of Electrical and Electronics Engineers | en_GB |
dc.rights | info:eu-repo/semantics/openAccess | en_GB |
dc.subject | Games -- Design | en_GB |
dc.subject | Artificial intelligence | en_GB |
dc.subject | Machine learning | en_GB |
dc.title | The invariant ground truth of affect | en_GB |
dc.type | conferenceObject | en_GB |
dc.rights.holder | The copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder. | en_GB |
dc.bibliographicCitation.conferencename | 10th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW) | en_GB |
dc.bibliographicCitation.conferenceplace | Nara, Japan. 18-21/10/2022 | en_GB |
dc.description.reviewed | peer-reviewed | en_GB |
Appears in Collections: | Scholarly Works - InsDG |
Files in This Item:
File | Description | Size | Format | |
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the_invariant_ground_truth_of_affect.pdf | 1.94 MB | Adobe PDF | View/Open |
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