Please use this identifier to cite or link to this item: https://www.um.edu.mt/library/oar/handle/123456789/121551
Full metadata record
DC FieldValueLanguage
dc.contributor.authorMakantasis, Konstantinos-
dc.contributor.authorPinitas, Kosmas-
dc.contributor.authorLiapis, Antonios-
dc.contributor.authorYannakakis, Georgios N.-
dc.date.accessioned2024-04-29T12:50:56Z-
dc.date.available2024-04-29T12:50:56Z-
dc.date.issued2022-
dc.identifier.citationMakantasis, 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.urihttps://www.um.edu.mt/library/oar/handle/123456789/121551-
dc.description.abstractAffective 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.isoenen_GB
dc.publisherInstitute of Electrical and Electronics Engineersen_GB
dc.rightsinfo:eu-repo/semantics/openAccessen_GB
dc.subjectGames -- Designen_GB
dc.subjectArtificial intelligenceen_GB
dc.subjectMachine learningen_GB
dc.titleThe invariant ground truth of affecten_GB
dc.typeconferenceObjecten_GB
dc.rights.holderThe 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.conferencename10th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW)en_GB
dc.bibliographicCitation.conferenceplaceNara, Japan. 18-21/10/2022en_GB
dc.description.reviewedpeer-revieweden_GB
Appears in Collections:Scholarly Works - InsDG

Files in This Item:
File Description SizeFormat 
the_invariant_ground_truth_of_affect.pdf1.94 MBAdobe PDFView/Open


Items in OAR@UM are protected by copyright, with all rights reserved, unless otherwise indicated.