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In the last decades, cognitive models of multisensory integration in human beings have been developed and applied to model human body experience. Recent research indicates that Bayesian and connectionist models might push developments in various branches of robotics: assistive robotic devices might adapt to their human users aiming at increased device embodiment, e.g., in prosthetics, and humanoid robots could be endowed with human-like capabilities regarding their surrounding space, e.g., by keeping safe or socially appropriate distances to other agents. In this perspective paper, we review cognitive models that aim to approximate the process of human sensorimotor behavior generation, discuss their challenges and potentials in robotics, and give an overview of existing approaches. While model accuracy is still subject to improvement, human-inspired cognitive models support the understanding of how the modulating factors of human body experience are blended. Implementing the resulting insights in adaptive and learning control algorithms could help to taylor assistive devices to their user's individual body experience. Humanoid robots who develop their own body schema could consider this body knowledge in control and learn to optimize their physical interaction with humans and their environment. Cognitive body experience models should be improved in accuracy and online capabilities to achieve these ambitious goals, which would foster human-centered directions in various fields of robotics.

Understanding the integration of user-proximal robots in the body schema of their human users has a distinct potential to improve human-robot interaction. Robotic devices can help to investigate the psychological fundamentals of body schema integration. While the Rubber Hand Illusion experiment indicates how artifacts can be perceived as a part of the own body, it relies on a passive limb that does not perform motions during the examinations. Novel setups aim at Robotic Hand/Leg Illusions induced by robotic devices which imitate human motions. Although such devices distinctly extend experimental possibilities, their design is rather proprietary and unstructured up to now. This paper analyzes the requirements of robotic hand and leg illusion setups based on systematic discussion of a multidisciplinary team of researchers from engineering and psychology. In a comparative study, requirements are collected and structured, their similarities and differences are determined, and the most important ones are extracted yielding design implications. The requirements with the highest priority are setup characteristics that concern the occurrence and quality of the illusion, i.e., hiding the real limb, anatomical plausibility, visual appearance, temporal delay, and softwarecontrolled experimental conditions. Based on the results, the design of future robotic devices for the exploration of human body schema integration might be guided and supported.

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Cognitive modeling of human behavior has advanced the understanding of underlying processes in several domains of psychology and cognitive science. In this article, we outline how we expect cognitive modeling to improve comprehension of individual cognitive processes in human-agent interaction and, particularly, human-robot interaction (HRI). We argue that cognitive models offer advantages compared to data-analytical models, specifically for research questions with expressed interest in theories of cognitive functions. However, the implementation of cognitive models is arguably more complex than common statistical procedures. Additionally, cognitive modeling paradigms typically have an explicit commitment to an underlying computational theory. We propose a conceptual framework for designing cognitive models that aims to identify whether the use of cognitive modeling is applicable to a given research question. The framework consists of five external and internal aspects related to the modeling process: research question, level of analysis, modeling paradigms, computational properties, and iterative model development. In addition to deriving our framework from a concise literature analysis, we discuss challenges and potentials of cognitive modeling. We expect cognitive models to leverage personalized human behavior prediction, agent behavior generation, and interaction pretraining as well as adaptation, which we outline with application examples from personalized HRI.

This paper reports on a three-part investigation into people’s perceptions of cybersecurity, based on their lived experiences. We sought thereby to reveal issues located within the Johari grid’s “Blind Spot” quadrant. We utilized research methodologies from both the Arts and Science in order firstly to identify blind spot issues, and secondly to explore their dimensions. Our investigation confirmed a number of aspects that we were indeed aware of, when it came to people’s lived cybersecurity experiences. We also identified one particular blind spot issue: widespread, but not universal, negativity towards cybersecurity. We then carried out an investigation using a recognized methodology from psychology, as a first attempt to assess the nature of this negativity and to get a sense of its roots. What our initial experiment revealed was that scoping cybersecurity-related emotions is nontrivial and will require the formulation of new measurement tools. We conclude by reporting on the challenges, to inform researchers who plan to extend the research reported in this paper.