Anorexia nervosa (AN) is a severe psychiatric disorder characterized by a number of symptoms including food restriction and body perception distortions. In the present scoping review, we outline the current literature on sensory submodalities related to the somatosensory system in AN including affective touch, haptic perception, interoception, nociception, proprioception, and tactile perception as well as multisensory integration. The evidence suggests that individuals with AN exhibit abnormalities in multisensory integration, discrimination (but not detection) of complex haptic and tactile stimuli, and reduced sensitivity to nociceptive stimuli. This review provides an outline of the current literature, identifies gaps within the literature, and suggests novel directions for future research.

Virtual reality (VR) technology has become increasingly prevalent in our society and has been used for a myriad of applications ranging from psychotherapy to training members of the military. However, one issue that arises from the use of VR is motion sickness, thus making predictors and indicators of motion sickness desirable. To date, a number of indicators of motion sickness have been derived based on nonlinear characteristics of human motion recorded using motion capture systems. While it is known that nonlinear measures can be used to predict motion sickness, it is not known whether people are perceptually sensitive to these particular nonlinear parameters. The aims of this study included establishing whether individuals consistently sort phase plots of sick and well individuals’ postural motion without being explicitly told to do so; determining what nonlinear movement parameters could be used to represent these judgments; and assessing the stability of nonlinear measures found to be successful at predicting motion sickness by Smart et al. (2014) . Through two methods of analysis (perceptual and quantitative), this research demonstrated that participants can indeed sort the graphic depictions of sick and well participants’ postural motion and seem to be perceptually sensitive to nonlinear parameters (normalized path length, path length, elliptical area) that are known to be predictive of motion sickness.

The rubber hand illusion (RHI) is a perceptual illusion in which one experiences an object as part of their body when synchronous visuo-tactile stimulation is applied to one’s hand and the object. There are a variety of factors that can impact the occurrence of the RHI. In the present study, we demonstrate that experimentally induced peripheral ischemia can impact the RHI, namely it can result in larger alterations to the perceived position of one’s hand. This study suggests that alterations to the cardiovascular system may be a source of individual differences in the RHI literature. Future studies with samples of individuals with cardiovascular pathology are recommended.

The present study describes the development of a simplified complex figure for older adults. This new figure is based on theoretical organizational strategy models developed for the Rey Complex Figure Test and combines several new technologies for the enhancement of the data capture. The study shows that the accuracy and memory scores for the new measure correlate significantly with Trail Making Test, Five Point Test, and RBANS Complex Figure scores. The new figure's strategy scores correlate with predominantly nonverbal and to a lesser extent with verbal executive functions, supporting the construct validity of these scores and illustrating that computer-based data recording methodologies can reliably discern the multiple cognitive operations that impact performance on this and similar graphomotor measures.

Objective This article presents two studies (one simulation and one pilot) that assess a custom computer algorithm designed to predict motion sickness in real-time. Background Virtual reality has a wide range of applications; however, many users experience visually induced motion sickness. Previous research has demonstrated that changes in kinematic (behavioral) parameters are predictive of motion sickness. However, there has not been research demonstrating that these measures can be utilized in real-time applications. Method Two studies were performed to assess an algorithm designed to predict motion sickness in real-time. Study 1 was a simulation study that used data from Smart et al. (2014). Study 2 employed the algorithm on 28 new participants’ motion while exposed to virtual motion. Results Study 1 revealed that the algorithm was able to classify motion sick participants with 100% accuracy. Study 2 revealed that the algorithm could predict if a participant would become motion sick with 57% accuracy. Conclusion The results of the present study suggest that the motion sickness prediction algorithm can predict if an individual will experience motion sickness but needs further refinement to improve performance. Application The algorithm could be used for a wide array of VR devices to predict likelihood of motion sickness with enough time to intervene.