Deep Explanatory Polytomous Item-Response Model for Predicting Idiosyncratic Affective Ratings

Yan Zhou, Tsukasa Ishigaki, Shiro Kumano

研究成果: 書籍の章/レポート/Proceedings会議への寄与査読

2 被引用数 (Scopus)

抄録

Towards explainable affective computing (XAC), researchers have invested considerable effort into post hoc approaches and reverse engineering to seek explanations for deep learning models. However, alternative, intrinsic approaches that aim to build inherently interpretable models by restricting their complexity are yet to be widely explored. In this study, we integrate an explanatory polytomous item response model that provides a well-established psychological interpretation for ordinal scales with deep neural networks to realize high prediction performance and good result interpretability. We conducted an experiment on a growing task (i.e., predicting the idiosyncratic perception of emotional faces of an individual); as expected theoretically, the topmost parameters of our model demonstrated strong correlations with those of the corresponding ordinal item response model: r = 0.928 to 1.00. Our proposed intrinsic approach can used as a complementary framework for post-hoc methods in XAC to coach and support human social interactions.

本文言語英語
ホスト出版物のタイトル2021 9th International Conference on Affective Computing and Intelligent Interaction, ACII 2021
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9781665400190
DOI
出版ステータス出版済み - 2021
イベント9th International Conference on Affective Computing and Intelligent Interaction, ACII 2021 - Nara, 日本
継続期間: 2021 9月 282021 10月 1

出版物シリーズ

名前2021 9th International Conference on Affective Computing and Intelligent Interaction, ACII 2021

会議

会議9th International Conference on Affective Computing and Intelligent Interaction, ACII 2021
国/地域日本
CityNara
Period21/9/2821/10/1

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