Visual landmarks provide crucial information for human navigation. But what characteristics define a landmark? To be uniquely recognized, a landmark should be distinctive and salient, while providing precise and accurate positional information. It should also be permanent. For example, to find back to your car, a nearby church seems a better landmark compared with a distinct truck or bicycle, because you learned that there is a chance that these objects might move. To this end, we investigated human learning of landmark permanency for navigation while treating spatiotemporal permanency as a probabilistic property. We hypothesized that humans will be able to learn the probabilistic nature of landmark permanency and assign higher weight to more permanent landmarks. To test this hypothesis, we designed a homing task where participants had to return to a position that was surrounded by three landmarks. In the learning phase we manipulated the spatiotemporal permanency of one landmark by secretly repositioning it before participants returned home. In the test phase, we investigated the weight allocated to the nonpermanent landmark by analyzing its influence on the navigational performance during homing. We conducted four experiments: In the first two experiments we altered the statistics of permanency and accordingly found an influence on participants' behavior, nonpermanent objects were used less for finding home. In the last two experiments we investigated the role of short-term learning of novel statistics versus long-term knowledge about such statistics. No carry-over effects in Experiment 3 and very little influence of object identity with different long-term permanency characteristics in Experiment 4 revealed a dominance of short-term learning over the use of long-term a priori knowledge about object permanency. This indicates that long-term prior beliefs are quickly updated by the current permanency statistics. Taken together, consistent with a Bayesian account for navigation these results indicate that humans quickly learn and update the statistics of landmark permanency and use it in an effective way, assigning gradually more weight to the more permanent landmark and making it more important for navigation.

Modularity, modifiability, reusability, and API usability are important software qualities that determine the maintainability of software architectures. Virtual, Augmented, and Mixed Reality (VR, AR, MR) systems, modern computer games, as well as interactive human-robot systems often include various dedicated input-, output-, and processing subsystems. These subsystems collectively maintain a real-time simulation of a coherent application state. The resulting interdependencies between individual state representations, mutual state access, overall synchronization, and flow of control implies a conceptual close coupling whereas software quality asks for a decoupling to develop maintainable solutions. This article presents five semantics-based software techniques that address this contradiction: Semantic grounding, code from semantics, grounded actions, semantic queries, and decoupling by semantics. These techniques are applied to extend the well-established entity-component-system (ECS) pattern to overcome some of this pattern's deficits with respect to the implied state access. A walk-through of central implementation aspects of a multimodal (speech and gesture) VR-interface is used to highlight the techniques' benefits. This use-case is chosen as a prototypical example of complex architectures with multiple interacting subsystems found in many VR, AR and MR architectures. Finally, implementation hints are given, lessons learned regarding maintainability pointed-out, and performance implications discussed.

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