Familiarity Detection with the Component Process Model

Joseph Garnier 1 Jean-Charles Marty 1 Karim Sehaba 1
1 SICAL - Situated Interaction, Collaboration, Adaptation and Learning
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : We propose a computational model for the Component Process Model (CPM) of Scherer, the most recent and the most complete model of emotion in psychology. This one proposes to appraise a stimulus through a sequence of sixteen appraisal variables dealing with a large number of its characteristics. As CPM is very abstract and high level, it is not really used in affective computing and no formal models exist for its appraisal variables. Based on the CPM, in this paper we propose a mathematical function for one appraisal variable detecting the familiarity of a perceived event according to the state of the cognitive component of an agent (goals, needs, semantic memory, and episodic memory).
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Joseph Garnier, Jean-Charles Marty, Karim Sehaba. Familiarity Detection with the Component Process Model. 16th International Conference on Intelligent Virtual Agents (IVA 2016), Sep 2016, Los Angeles, CA, United States. pp.373-377, ⟨10.1007/978-3-319-47665-0_36⟩. ⟨hal-01531558⟩

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