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As applications for virtual reality (VR) and augmented reality (AR) technology increase, it will be important to understand how users perceive their action capabilities in virtual environments. Feedback about actions may help to calibrate perception for action opportunities (affordances) so that action judgments in VR and AR mirror actors’ real abilities. Previous work indicates that walking through a virtual doorway while wielding an object can calibrate the perception of one’s passability through feedback from collisions. In the current study, we aimed to replicate this calibration through feedback using a different paradigm in VR while also testing whether this calibration transfers to AR. Participants held a pole at 45°and made passability judgments in AR (pretest phase). Then, they made passability judgments in VR and received feedback on those judgments by walking through a virtual doorway while holding the pole (calibration phase). Participants then returned to AR to make posttest passability judgments. Results indicate that feedback calibrated participants’ judgments in VR. Moreover, this calibration transferred to the AR environment. In other words, after experiencing feedback in VR, passability judgments in VR and in AR became closer to an actor’s actual ability, which could make training applications in these technologies more effective. 

Teleporting, or jumping, is a common method of moving through virtual environments. It provides a simple user interface, but deprives users of self-motion cues that are important to acquiring spatial knowledge. This paper examines one parameter of the teleportation interface, the teleportation or jump distance, and how that may affect spatial knowledge acquisition. We report the results of an experiment that examined the effects of two different, but fixed teleportation distances on how users could acquire knowledge of landmarks and routes. The results suggest that the teleport distance does not matter, hence teleportation as an interface is robust. However, use of teleportation resulted in significantly increased simulator sickness, a surprising result. 

In computer-aided drug discovery, quantitative structure activity relation models are trained to predict biological activity from chemical structure. Despite the recent success of applying graph neural network to this task, important chemical information such as molecular chirality is ignored. To fill this crucial gap, we propose Molecular-Kernel Graph NeuralNetwork (MolKGNN) for molecular representation learning, which features SE(3)-/conformation invariance, chirality-awareness, and interpretability. For our MolKGNN, we first design a molecular graph convolution to capture the chemical pattern by comparing the atom's similarity with the learnable molecular kernels. Furthermore, we propagate the similarity score to capture the higher-order chemical pattern. To assess the method, we conduct a comprehensive evaluation with nine well-curated datasets spanning numerous important drug targets that feature realistic high class imbalance and it demonstrates the superiority of MolKGNN over other graph neural networks in computer-aided drug discovery. Meanwhile, the learned kernels identify patterns that agree with domain knowledge, confirming the pragmatic interpretability of this approach. Our code and supplementary material are publicly available at https://github.com/meilerlab/MolKGNN.