Social context cognition crowd-sourcing and semi-automatic parametrization

• Main Authors: KOCHANOWICZ, Jaroslaw, TAN, Ah-hwee, THALMANN, Daniel
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2016
• Subjects: automatic parametrization; cognitive modeling; crowd-sourcing; social cognition; social context; Computer and Systems Architecture; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/5253
• Institution: Singapore Management University

This paper presents a semi‐automatic method of parameterizing an existing social context cognition model. It discusses benefits of the social context cognition models for example in personality modeling and their key issue that is parametrization. It briefly introduces social context cognition model and describes a new method of its crowd‐sourcing‐based parametrization. Later, validation is provided, and ability to recreate social context cognition in the provided samples is presented with good generalization for the unknown cases. Finally, model's stability for the continuous stream of dynamic social context input data is shown. Presented system contributes to the believable agent modeling and social simulations by making much needed applications of social context cognition models easier by addressing the so far unsolved troublesome parametrization issues.