{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-deep-neural-model-of-emotion-appraisal","title":"A Deep Neural Model Of Emotion Appraisal","arxiv_id":"1808.00252","date":"2018-08-01","proceeding":null,"authors":["Pablo Barros","Emilia Barakova","Stefan Wermter"],"abstract":"Emotional concepts play a huge role in our daily life since they take part\ninto many cognitive processes: from the perception of the environment around us\nto different learning processes and natural communication. Social robots need\nto communicate with humans, which increased also the popularity of affective\nembodied models that adopt different emotional concepts in many everyday tasks.\nHowever, there is still a gap between the development of these solutions and\nthe integration and development of a complex emotion appraisal system, which is\nmuch necessary for true social robots. In this paper, we propose a deep neural\nmodel which is designed in the light of different aspects of developmental\nlearning of emotional concepts to provide an integrated solution for internal\nand external emotion appraisal. We evaluate the performance of the proposed\nmodel with different challenging corpora and compare it with state-of-the-art\nmodels for external emotion appraisal. To extend the evaluation of the proposed\nmodel, we designed and collected a novel dataset based on a Human-Robot\nInteraction (HRI) scenario. We deployed the model in an iCub robot and\nevaluated the capability of the robot to learn and describe the affective\nbehavior of different persons based on observation. The performed experiments\ndemonstrate that the proposed model is competitive with the state of the art in\ndescribing emotion behavior in general. In addition, it is able to generate\ninternal emotional concepts that evolve through time: it continuously forms and\nupdates the formed emotional concepts, which is a step towards creating an\nemotional appraisal model grounded in the robot experiences.","url_abs":"http://arxiv.org/abs/1808.00252v1","url_pdf":"http://arxiv.org/pdf/1808.00252v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-deep-neural-model-of-emotion-appraisal","repo_url":"https://github.com/knowledgetechnologyuhh/EmotionRecognitionBarros","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"a-deep-neural-model-of-emotion-appraisal","repo_url":"https://github.com/pablovin/AffectiveMemoryFramework","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"developmental-learning","task_name":"Developmental Learning"},{"task_slug":"model","task_name":"model"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}