In the absence of effective therapies for dementia and its precursors, enhancing neuroplasticity by means of non-invasive brain stimulation such as anodal transcranial direct current stimulation (atDCS) might be a promising approach to counteract or delay the onset of cognitive decline, but effect sizes have been moderate so far. Previous reports indicate that increasing serotonin levels may enhance atDCS-induced neuroplasticity. However, evidence for serotonergic modulation of atDCS effects on memory is still lacking. Here, we conducted a doubleblind, randomized, sham-/placebo-controlled trial to investigate the impact of a selective serotonin reuptake inhibitor (SSRI; single dose of 20 mg citalopram) and atDCS over the right temporoparietal cortex (1 mA, 20 min) on memory formation. Twenty young and 20 older subjects completed an object-location learning task in each of the four conditions: sham+placebo, sham+SSRI, atDCS+placebo, and atDCS+SSRI. Outcome measures were performance in immediate (primary outcome) and delayed cued recall. While we found an SSRI effect, but no statistically significant effect of atDCS on immediate recall scores, young and older adults benefited most from the combined application (comparisons: atDCS+SSRI4atDCS+placebo and atDCS+SSRI4sham+placebo). Thus, our data provide evidence that atDCS improves memory formation if serotonergic neurotransmission is enhanced simultaneously. Further studies are needed to assess whether these findings extend to clinical populations with memory impairment and translate into clinically relevant improvements after long-term serotonergic enhancement and repeated stimulation.

To realize a successful and collaborative interaction between human and robots remains a big challenge. Emotional reactions of the user provide crucial information for a successful interaction. These reactions carry key factors to prevent errors and fatal bidirectional misunderstanding. In cases where human–machine interaction does not proceed as expected, negative emotions, like frustration, can arise. Therefore, it is important to identify frustration in a human–machine interaction and to investigate its impact on other influencing factors such as dominance, sense of control and task performance. This paper presents a study that investigates a close cooperative work situation between human and robot, and explore the influence frustration has on the interaction. The task for the participants was to hand over colored balls to two different robot systems (an anthropomorphic robot and a robotic arm). The robot systems had to throw the balls into appropriate baskets. The coordination between human and robot was controlled by various gestures and words by means of trial and error. Participants were divided into two groups, a frustration- (FRUST) and a no frustration- (NOFRUST) group. Frustration was induced by the behavior of the robotic systems which made errors during the ball handover. Subjective and objective methods were used. The sample size of participants was N = 30 and the study was conducted in a between-subject design. Results show clear differences in perceived frustration in the two condition groups and different behavioral interactions were shown by the participants. Furthermore, frustration has a negative influence on interaction factors such as dominance and sense of control. The study provides important information concerning the influence of frustration on human–robot interaction (HRI) for the requirements of a successful, natural, and social HRI. The results (qualitative and quantitative) are discussed in favor of how a successful und effortless interaction between human and robot can be realized and what relevant factors, like appearance of the robot and influence of frustration on sense of control, have to be regarded.

Abstract:The reduction of cognitive tasks brought about by new developments in service-robots' collaboration with humans in working environments has given rise to new challenges as to how to address safety issues. This paper presents insights from biology, cognitive/neural sciences and sociology that can conquer these new challenges. The main focus lies in sociological variables that ensure safe human-robot interaction in working environments rather than addressing biological ones (avoiding bodily harm) or purely cognitive ones (avoiding any signals that are outside the human's sensory comfort zones). We will present an approach on how to integrate behavioral patterns into the robotic system in order to prevent the problem of reduced cognition in relation to essential features, which are necessary for carrying out this pattern in the context of a human-robot interaction with non-humanoid robots (which is the most typical design of robots used in work environments).

Robotic developments are seen as a next level in technology with intelligent machines, which automate tedious tasks and serve our needs without complaints. But nevertheless, they have to be fair and smart enough to be intuitively of use and safe to handle. But how to implement this kind of intelligence, does it need feelings and emotions, should robots perceive the world as we do as a human role model, how far should the implementation of synthetic consciousness lead and actually, what is needed for consciousness in that context? Additionally in Human-Robot-Interaction research, science mainly makes use of the tool phenomenography, which is exclusively subjective, so how to make it qualify for Artificial Intelligence? These are the heading aspects of this chapter for conducting research in the field of social robotics and suggesting a conscious and cognitive model for smart and intuitive interacting robots, guided by biomimetics.

No abstract