Is It Me or the Robot? A Critical Evaluation of Human Affective State Recognition in a Cognitive Task.

A key goal in human-robot interaction (HRI) is to design scenarios between humanoid robots and humans such that the interaction is perceived as collaborative and natural, yet safe and comfortable for the human. Human skills like verbal and non-verbal communication are essential elements as humans tend to attribute social behaviors to robots. However, aspects like the uncanny valley and different technical affinity levels can impede the success of HRI scenarios, which has consequences on the establishment of long-term interaction qualities like trust and rapport. In the present study, we investigate the impact of a humanoid robot on human emotional responses during the performance of a cognitively demanding task. We set up three different conditions for the robot with increasing levels of social cue expressions in a between-group study design. For the analysis of emotions, we consider the eye gaze behavior, arousal-valence for affective states, and the detection of action units. Our analysis reveals that the participants display a high tendency toward positive emotions in presence of a robot with clear social skills compared to other conditions, where we show how emotions occur only at task onset. Our study also shows how different expression levels influence the analysis of the robots' role in HRI. Finally, we critically discuss the current trend of automatized emotion or affective state recognition in HRI and demonstrate issues that have direct consequences on the interpretation and, therefore, claims about human emotions in HRI studies.

Ligand-activated epidermal growth factor receptor (EGFR) signaling governs endocytic trafficking of unliganded receptor monomers by non-canonical phosphorylation.

The canonical description of transmembrane receptor function is initial binding of ligand, followed by initiation of intracellular signaling and then internalization en route to degradation or recycling to the cell surface. It is known that low concentrations of extracellular ligand lead to a higher proportion of receptor that is recycled and that non-canonical mechanisms of receptor activation, including phosphorylation by the kinase p38, can induce internalization and recycling. However, no connections have been made between these pathways; i.e. it has yet to be established what happens to unbound receptors following stimulation with ligand. Here we demonstrate that a minimal level of activation of epidermal growth factor receptor (EGFR) tyrosine kinase by low levels of ligand is sufficient to fully activate downstream mitogen-activated protein kinase (MAPK) pathways, with most of the remaining unbound EGFR molecules being efficiently phosphorylated at intracellular serine/threonine residues by activated mitogen-activated protein kinase. This non-canonical, p38-mediated phosphorylation of the C-tail of EGFR, near Ser-1015, induces the clathrin-mediated endocytosis of the unliganded EGFR monomers, which occurs slightly later than the canonical endocytosis of ligand-bound EGFR dimers via tyrosine autophosphorylation. EGFR endocytosed via the non-canonical pathway is largely recycled back to the plasma membrane as functional receptors, whereas p38-independent populations are mainly sorted for lysosomal degradation. Moreover, ligand concentrations balance these endocytic trafficking pathways. These results demonstrate that ligand-activated EGFR signaling controls unliganded receptors through feedback phosphorylation, identifying a dual-mode regulation of the endocytic trafficking dynamics of EGFR.

Physiological responses to workload change. A test/retest examination.

The purpose of this study is to examine the test/retest consistency of physiological responses induced by mental tasks. Fifteen healthy male university students were recruited as participants. They were instructed to perform a 5-min Multi-Attribute Task Battery (MATB) trial three times successively. The task difficulty level of the tracking task of the second trial was set as medium (M). The first one was set as more difficult (H) and the last trial was easiest (L). The difficulty levels of the other two tasks (resource management and system monitoring) of the MATB were identical for all three trials. The participants repeated this procedure on three different days separated by at least a 1-day interval. The order of the tasks was the same for all repeated trials, i.e., H-M-L. Tissue blood volume from the tip of the nose using a laser Doppler blood flow meter, skin potential level (SPL), ECG from three leads on the chest, systolic time intervals (pre-ejection period, left ventricular ejection time), and hemodynamic parameters (stroke volume, cardiac output) were recorded during the task trials and before and after 5-min resting periods. The participants reported their subjective workload via NASA-TLX after each task trial. Autonomic nervous system parameters derived from the above-mentioned signals, subjective workload scores, and performance indices of MATB were analyzed, and test/retest reliability was investigated. The results showed that a significant test/retest correlation was obtained for SPL for more participants than in the other parameters, although there were large individual differences.

Lane-change detection using a computational driver model.

OBJECTIVE: This paper introduces a robust, real-time system for detecting driver lane changes. BACKGROUND: As intelligent transportation systems evolve to assist drivers in their intended behaviors, the systems have demonstrated a need for methods of inferring driver intentions and detecting intended maneuvers. METHOD: Using a "model tracing" methodology, our system simulates a set of possible driver intentions and their resulting behaviors using a simplification of a previously validated computational model of driver behavior. The system compares the model's simulated behavior with a driver's actual observed behavior and thus continually infers the driver's unobservable intentions from her or his observable actions. RESULTS: For data collected in a driving simulator, the system detects 82% of lane changes within 0.5 s of maneuver onset (assuming a 5% false alarm rate), 93% within 1 s, and 95% before the vehicle moves one fourth of the lane width laterally. For data collected from an instrumented vehicle, the system detects 61% within 0.5 s, 77% within 1 s, and 84% before the vehicle moves one-fourth of the lane width laterally. CONCLUSION: The model-tracing system is the first system to demonstrate high sample-by-sample accuracy at low false alarm rates as well as high accuracy over the course of a lane change with respect to time and lateral movement. APPLICATION: By providing robust real-time detection of driver lane changes, the system shows good promise for incorporation into the next generation of intelligent transportation systems.