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  • 2014

  • Bekele, Esubalew; Sarkar, Nilanjan (2014) : Psychophysiological feedback for adaptive human-robot interaction (HRI) In: Fairclough, Stephen H.; Gilleade, Kiel (Hg.): Advances in physiological computing: New York, NY: Springer-Verlag Publishing (Human-computer interaction series; ISSN: 1571-5035 (Print)), S. 141-167

    DOI: https://doi.org/10.1007/978-1-4471-6392-3_7 

    Abstract: Recent advances in robotics and sensing have given rise to a diverse set of robots and their applications. In recent years robots have increasingly applied in the service industry, search and rescue operations and therapeutic applications. The introduction of robots to interact with humans resulted in a dedicated field called human-robot interaction (HRI). Social HRI is of particular importance as it is the main focus of this chapter. This chapter presents an affect-inspired approach for social HRI. Physiological processing together with machine learning was employed to model affective states for an adaptive social HRI and its application in social interaction in the context of autism therapy was investigated. (PsycINFO Database Record (c) 2019 APA, all rights reserved)

    Keywords: Bedienung & Handhabung, Feedback, Human Machine Systems, Human robot interaction, human-robot interaction, machine learning, psychophysiological feedback, Psychophysiology, Robotics, social interaction, therapeutic applications, Therapeutic Processes

  • 1993

  • Izard, C. E. (1993): Four systems for emotion activation: cognitive and noncognitive processes. In: Psychological Review 100 (1), S. 68-90. DOI: 10.1037/0033-295x.100.1.68

    DOI: https://doi.org/10.1037/0033-295x.100.1.68 

    Abstract: The significant role of emotions in evolution and adaptation suggests that there must be more than 1 mechanism for generating them. Nevertheless, much of current emotion theory focuses on cognitive processes (appraisal, attribution, and construal) as the sole, or primary, means of eliciting emotions. As an alternative to this position, the present model describes 4 types of emotion-activating systems, 3 of which involve noncognitive information processing. From an evolutionary-developmental perspective, the systems maybe viewed as a loosely organized hierarchical arrangement, with neural systems, the simplest and most rapid, at the base and cognitive systems, the most complex and versatile, at the top. The emotion-activating systems operate under a number of constraints, including genetically influenced individual differences. The hierarchical organization of the systems for generating emotions provides an adaptive advantage.

    Keywords: Arousal/physiology, Cognition/physiology, Emotions/physiology, Humans, Mental Processes/physiology, Motivation, Psychophysiology, Social Environment
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