Introduction Psoriasis (PSO) is a disease that in the majority of patients is accompanied by itch, which imposes a great burden and positively relates to anxiety. Social anxiety, a facet of anxiety associated with social withdrawal, may be a predictor of itch intensity in this patient group. Moreover, anxiety is linked to the secretion of neuroendocrine and inflammatory parameters such as substance P (SP), interleukin (IL)-6 and IL-17, which are also related to itch. In this research project, we investigate first, whether there is a direct relationship between social anxiety and itch intensity in patients with PSO and second whether the secretion of SP, IL-6 and IL-17 in the skin mediates this relationship. Additionally, PSO-patients are compared to healthy skin controls regarding their level of social anxiety, itch intensity and the secretion of SP, IL-6 and IL-17. Methods and analyses For study 1, we aim to recruit 250 psoriasis patients and 250 healthy skin controls who complete questionnaires to assess social anxiety, itch intensity and control variables (e.g. sociodemographic variables and severity of PSO). A linear hierarchic regression will be used to determine whether social anxiety significantly contributes to itch intensity. In study 2, we plan to apply the suction blister method to 128 patients and healthy skin controls recruited from study 1 to determine SP, IL-6 and IL-17 in tissue fluid extracted from the skin. A mediation analysis will be conducted using the SPSS-macro PROCESS to test whether the relationship between social anxiety and itch is mediated by SP, IL-6 and IL-17. Trial registration numbers DRKS00023621 (study 1) and DRKS00023622 (study 2).

How humans visually select where to grasp objects is determined by the physical object properties (e.g., size, shape, weight), the degrees of freedom of the arm and hand, as well as the task to be performed. We recently demonstrated that human grasps are near-optimal with respect to a weighted combination of different cost functions that make grasps uncomfortable, unstable, or impossible, e.g., due to unnatural grasp apertures or large torques. Here, we ask whether humans can consciously access these rules. We test if humans can explicitly judge grasp quality derived from rules regarding grasp size, orientation, torque, and visibility. More specifically, we test if grasp quality can be inferred (i) by using visual cues and motor imagery alone, (ii) from watching grasps executed by others, and (iii) through performing grasps, i.e., receiving visual, proprioceptive and haptic feedback. Stimuli were novel objects made of 10 cubes of brass and wood (side length 2.5 cm) in various configurations. On each object, one near-optimal and one sub-optimal grasp were selected based on one cost function (e.g., torque), while the other constraints (grasp size, orientation, and visibility) were kept approximately constant or counterbalanced. Participants were visually cued to the location of the selected grasps on each object and verbally reported which of the two grasps was best. Across three experiments, participants were required to either (i) passively view the static objects and imagine executing the two competing grasps, (ii) passively view videos of other participants grasping the objects, or (iii) actively grasp the objects themselves. Our results show that, for a majority of tested objects, participants could already judge grasp optimality from simply viewing the objects and imagining to grasp them, but were significantly better in the video and grasping session. These findings suggest that humans can determine grasp quality even without performing the grasp—perhaps through motor imagery—and can further refine their understanding of how to correctly grasp an object through sensorimotor feedback but also by passively viewing others grasp objects.

Humans strongly rely on vision to guide grasping. Visual grasp selection is highly systematic and consistent across repetitions and participants, suggesting that humans employ a common set of constraints when visually selecting grasps. We formalized these constraints as a set of grasp-cost functions related to torque, grasp axis, grasp aperture, and object visibility, which we have shown predict grasping behavior with striking fidelity. Here, we test if humans can explicitly estimate grasp optimality derived from these grasp-cost functions. We additionally ask whether vision alone is sufficient to compute grasp optimality, or whether sensorimotor feedback is required to link vision to action selection. Stimuli were novel objects made of 10 cubes of brass and wood (side length 2.5 cm) in various configurations. On each object, an optimal and a sub-optimal grasp were selected based on one of the cost functions, while cost for the other constraints was kept approximately constant or counterbalanced. Participants were visually cued as to the location of the grasps on each object via colored markers. In a vision-only session, participants were required to judge which of the two grasps they believed to be better, without ever having grasped the object. In a vision-plus-grasp session, participants were required to attempt both grasps on each object, and again indicate which of the two grasps they judged to be better. Participants (N=11) were already able to judge grasp optimality above chance in the vision-only session (65+/−13% correct, p= 0.0035). Additionally, participants were significantly better at judging grasp optimality in the vision-plus-grasp session (77+/−7% correct, p=0.0081). Together, these findings show that humans can consciously access the visuomotor computations underlying grasp selection, and highlight the fundamental role of sensorimotor feedback in linking visual perception to motor control.