Our results show reduced HF-HRV during cognitive reappraisal of negative stimuli in high neuroticism and indicate a specific link between loss of flexibility in the parasympathetic cardiovascular tone and emotion regulation, consistent with previous work. Such findings support the importance of exploring the combination of ANS adaptability and emotional dysregulation in neuroticism as different facets of a common psychosomatic vulnerability factor.

We present an approach for the verification and validation (V&V) of robot assistants in the context of human-robot interactions (HRI), to demonstrate their trustworthiness through corroborative evidence of their safety and functional correctness. Trust in robot assistants will allow them to transition from the laboratory into our everyday lives. Key challenges include the complex and unpredictable nature of the real world in which assistant and service robots operate, the limitations on available V&V techniques when used individually, and the consequent lack of confidence in the V&V results. Our approach, called corroborative V&V, addresses these challenges by combining several different V&V techniques; in this paper we use formal verification (model checking), simulation-based testing, and user validation in experiments with a real robot. We demonstrate our corroborative V&V approach through a handover task, the most critical part of a complex cooperative manufacturing scenario, for which we propose some safety and liveness requirements to verify and validate. We construct formal models, simulations and an experimental test rig for the HRI. To capture requirements we use temporal logic properties, assertion checkers and textual descriptions. This combination of approaches allows V&V of the HRI task at different levels of modelling detail and thoroughness of exploration, thus overcoming the individual limitations of each technique. Should the resulting V&V evidence present discrepancies, an iterative process between the different V&V techniques takes place until corroboration between the V&V techniques is gained from refining and improving the assets (i.e., system and requirement models) to represent the HRI task in a more truthful manner. Therefore, corroborative V&V affords a systematic approach to "'meta-V&V," in which different V&V techniques can be used to corroborate and check one another, increasing the level of certainty in the results of V&V.

Impairments in emotion regulation are thought to have a key role in the pathogenesis of anxiety disorders, but the neurobiological underpinnings contributing to vulnerability remain poorly understood. It has been a long-held view that exaggerated fear is linked to hyperresponsivity of limbic brain areas and impaired recruitment of prefrontal control. However, increasing evidence suggests that prefrontal–cortical networks are hyperactive during threat processing in anxiety disorders. This study directly explored limbic–prefrontal neural response, connectivity and heart-rate variability (HRV) in patients with a severe anxiety disorder during incidental versus intentional emotion regulation. During 3 Tesla functional magnetic resonance imaging, 18 participants with panic disorder and 18 healthy controls performed an emotion regulation task. They either viewed negative images naturally (Maintain), or they were instructed to intentionally downregulate negative affect using previously taught strategies of cognitive reappraisal (Reappraisal). Electrocardiograms were recorded throughout to provide a functional measure of regulation and emotional processing. Compared with controls, patients showed increased neural activation in limbic–prefrontal areas and reduced HRV during incidental emotion regulation (Maintain). During intentional regulation (Reappraisal), group differences were significantly attenuated. These findings emphasize patients' ability to regulate negative affect if provided with adaptive strategies. They also bring prefrontal hyperactivation forward as a potential mechanism of psychopathology in anxiety disorders. Although these results challenge models proposing impaired allocation of prefrontal resources as a key characteristic of anxiety disorders, they are in line with more recent neurobiological frameworks suggesting that prefrontal hyperactivation might reflect increased utilisation of maladaptive regulation strategies quintessential for anxiety disorders.

The software of robotic assistants needs to be verified, to ensure its safety and functional correctness. Testing in simulation allows a high degree of realism in the verification. However, generating tests that cover both interesting foreseen and unforeseen scenarios in human-robot interaction (HRI) tasks, while executing most of the code, remains a challenge. We propose the use of belief-desire-intention (BDI) agents in the test environment, to increase the level of realism and human-like stimulation of simulated robots. Artificial intelligence, such as agent theory, can be exploited for more intelligent test generation. An automated testbench was implemented for a simulation in Robot Operating System (ROS) and Gazebo, of a cooperative table assembly task between a humanoid robot and a person. Requirements were verified for this task, and some unexpected design issues were discovered, leading to possible code improvements. Our results highlight the practicality of BDI agents to automatically generate valid and human-like tests to get high code coverage, compared to hand-written directed tests, pseudorandom generation, and other variants of model-based test generation. Also, BDI agents allow the coverage of combined behaviours of the HRI system with more ease than writing temporal logic properties for model checking.