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Modeling cognitive processes from multimodal signals

 
: Putze, F.; Hild, J.; Sano, A.; Kasneci, E.; Solovey, E.; Schultz, T.

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Association for Computing Machinery -ACM-; Association for Computing Machinery -ACM-, Special Interest Group on Computer and Human Interaction -SIGCHI-:
ICMI 2018, 20th ACM International Conference on Multimodal Interaction. Proceedings : MCPMD 2018, Workshop on Modeling Cognitive Processes from Multimodal Data, Boulder, Colorado, October 16th-20th, 2018
New York: ACM, 2018
ISBN: 978-1-4503-6072-2
ISBN: 978-1-4503-5692-3
S.663
International Conference on Multimodal Interaction (ICMI) <20, 2018, Boulder/Colo.>
Workshop on Modeling Cognitive Processes from Multimodal Data (MCPMD) <2018, Boulder/Colo.>
Englisch
Konferenzbeitrag
Fraunhofer IOSB ()

Abstract
Multimodal signals allow us to gain insights into internal cognitive processes of a person, for example: speech and gesture analysis yields cues about hesitations, knowledgeability, or alertness, eye tracking yields information about a person's focus of attention, task, or cognitive state, EEG yields information about a person's cognitive load or information appraisal. Capturing cognitive processes is an important research tool to understand human behavior as well as a crucial part of a user model to an adaptive interactive system such as a robot or a tutoring system. As cognitive processes are often multifaceted, a comprehensive model requires the combination of multiple complementary signals. In this workshop at the ACM International Conference on Multimodal Interfaces (ICMI) conference in Boulder, Colorado, USA, we discussed the state-of-the-art in monitoring and modeling cognitive processes from multi-modal signals.

: http://publica.fraunhofer.de/dokumente/N-581604.html