View-based organization and interplay of spatial working and long-term memories

Perceived spatial presence and body orientation affect the recall of out-of-sight places in an immersive sketching experiment

The orientation of sketch maps of remote but familiar city squares produced from memory has been shown to depend on the distance and airline direction from the production site to the remembered square (position-dependent recall, Röhrich et al. in PLoS One 9(11): e112793, 2014). Here, we present a virtual reality version of the original experiment and additionally study the role of body orientation. Three main points can be made: First, "immersive sketching" is a novel and useful paradigm in which subjects sketch maps live on paper while being immersed in virtual reality. Second, the original effect of position-dependent recall was confirmed, indicating that the sense of being present at a particular location, even if generated in a virtual environment, suffices to bias the imagery of distant places. Finally, the orientation of the produced sketch maps depended also on the body orientation of the subjects. At each production site, body orientation was controlled by varying the position of the live feed in the virtual environment, such that subjects had to turn towards the prescribed direction. Position-dependent recall is strongest if subjects are aligned with the airline direction to the target and virtually goes away if they turn in the opposite direction. We conclude that the representation of out-of-sight target places depends on both the current airline direction to the target and the body orientation.

Facial Fat Augmentation Finesse

Volume restoration with autologous fat augmentation has become a key technique in facial rejuvenation. It is highly versatile and useful as an independent operation or as an adjunct to rhytidectomy and blepharoplasty. The goal of this article and the accompanying videos is to provide a detailed step-by-step guide to achieving predictable and optimized surgical results.

No single, stable 3D representation can explain pointing biases in a spatial updating task

People are able to keep track of objects as they navigate through space, even when objects are out of sight. This requires some kind of representation of the scene and of the observer’s location but the form this might take is debated. We tested the accuracy and reliability of observers’ estimates of the visual direction of previously-viewed targets. Participants viewed four objects from one location, with binocular vision and small head movements then, without any further sight of the targets, they walked to another location and pointed towards them. All conditions were tested in an immersive virtual environment and some were also carried out in a real scene. Participants made large, consistent pointing errors that are poorly explained by any stable 3D representation. Any explanation based on a 3D representation would have to posit a different layout of the remembered scene depending on the orientation of the obscuring wall at the moment the participant points. Our data show that the mechanisms for updating visual direction of unseen targets are not based on a stable 3D model of the scene, even a distorted one.

Place recognition from distant landmarks: human performance and maximum likelihood model

We present a simple behavioral experiment on human place recognition from a configuration of four visual landmarks. Participants were asked to navigate several paths, all involving a turn at one specific point, and while doing so incidentally learned the position of that turning point. In the test phase, they were asked to return to the turning point in a reduced environment leaving only the four landmarks visible. Results are compared to two versions of a maximum likelihood model of place recognition using either view-based or depth-based cues for place recognition. Only the depth-based model is in good qualitative agreement with the data. In particular, it reproduces landmark configuration-dependent effects of systematic bias and statistical error distribution as well as effects of approach direction. The model is based on a place code (depth and bearing of the landmarks at target location) and an egocentric working memory of surrounding space including current landmark position in a local, map-like representation. We argue that these elements are crucial for human place recognition.