The Embodied Crossmodal Self Forms Language and Interaction: A Computational Cognitive Review

Frontiers in Psychology Volume 12, pages 3374, doi: 10.3389/fpsyg.2021.716671 - Aug 2021 Open Access
Associated documents :  
Human language is inherently embodied and grounded in sensorimotor representations of the self and the world around it. This suggests that the body schema and ideomotor action-effect associations play an important role in language understanding, language generation, and verbal/physical interaction with others. There are computational models that focus purely on non-verbal interaction between humans and robots, and there are computational models for dialog systems that focus only on verbal interaction. However, there is a lack of research that integrates these approaches. We hypothesize that the development of computational models of the self is very appropriate for considering joint verbal and physical interaction. Therefore, they provide the substantial potential to foster the psychological and cognitive understanding of language grounding, and they have significant potential to improve human-robot interaction methods and applications. This review is a first step toward developing models of the self that integrate verbal and non-verbal communication. To this end, we first analyze the relevant findings and mechanisms for language grounding in the psychological and cognitive literature on ideomotor theory. Second, we identify the existing computational methods that implement physical decision-making and verbal interaction. As a result, we outline how the current computational methods can be used to create advanced computational interaction models that integrate language grounding with body schemas and self-representations.


 	 author =  {Röder, Frank and Özdemir, Ozan and Nguyen, D.H. Phuong and Wermter, Stefan and Eppe, Manfred},  
 	 title = {The Embodied Crossmodal Self Forms Language and Interaction: A Computational Cognitive Review}, 
 	 journal = {Frontiers in Psychology},
 	 number = {},
 	 volume = {12},
 	 pages = {3374},
 	 year = {2021},
 	 month = {Aug},
 	 publisher = {},
 	 doi = {10.3389/fpsyg.2021.716671},