The Laban Movement Analysis system (LMA) is a widely used system for the description of human movement. Here we present results of an empirical analysis of the reliability of the LMA system. Firstly, we developed a directed graph-based representation for the formalization of LMA. Secondly, we implemented a custom video annotation tool for stimulus presentation and annotation of the formalized LMA. Using these two elements, we conducted an experimental assessment of LMA reliability. In the experimental assessment of the reliability, experts–Certified Movement Analysts (CMA)–were tasked with identifying the differences between a “neutral” movement and the same movement executed with a specific variation in one of the dimensions of the LMA parameter space. The videos represented variations on the pantomimed movement of knocking at a door or giving directions. To be as close as possible to the annotation practice of CMAs, participants were given full control over the number of times and order in which they viewed the videos. The LMA annotation was captured by means of the video annotation tool that guided the participants through the LMA graph by asking them multiple-choice questions at each node. Participants were asked to first annotate the most salient difference (round 1), and then the second most salient one (round 2) between a neutral and gesture and the variation. To quantify the overall reliability of LMA, we computed Krippendorff’s α. The quantitative data shows that the reliability, depending on how the two rounds are integrated, ranges between a weak and an acceptable reliability of LMA. The analysis of viewing behavior showed that, despite relatively large differences at the inter-individual level, there is no simple relationship between viewing behavior and individual performance (quantified as the level of agreement of the individual with the dominant rating). This research advances the state of the art in formalizing and implementing a reliability measure for the Laban Movement Analysis system. The experimental study we conducted allows identifying some of the strengths and weaknesses of the widely used movement coding system. Additionally, we have gained useful insights into the assessment procedure itself.

In principle it appears advantageous for single neurons to perform non-linear operations. Indeed it has been reported that some neurons show signatures of such operations in their electrophysiological response. A particular case in point is the Lobula Giant Movement Detector (LGMD) neuron of the locust, which is reported to locally perform a functional multiplication. Given the wide ramifications of this suggestion with respect to our understanding of neuronal computations, it is essential that this interpretation of the LGMD as a local multiplication unit is thoroughly tested. Here we evaluate an alternative model that tests the hypothesis that the non-linear responses of the LGMD neuron emerge from the interactions of many neurons in the opto-motor processing structure of the locust. We show, by exposing our model to standard LGMD stimulation protocols, that the properties of the LGMD that were seen as a hallmark of local non-linear operations can be explained as emerging from the dynamics of the pre-synaptic network. Moreover, we demonstrate that these properties strongly depend on the details of the synaptic projections from the medulla to the LGMD. From these observations we deduce a number of testable predictions. To assess the real-time properties of our model we applied it to a high-speed robot. These robot results show that our model of the locust opto-motor system is able to reliably stabilize the movement trajectory of the robot and can robustly support collision avoidance. In addition, these behavioural experiments suggest that the emergent non-linear responses of the LGMD neuron enhance the system's collision detection acuity. We show how all reported properties of this neuron are consistently reproduced by this alternative model, and how they emerge from the overall opto-motor processing structure of the locust. Hence, our results propose an alternative view on neuronal computation that emphasizes the network properties as opposed to the local transformations that can be performed by single neurons. The tiny brains of insects of about 1mm³ smoothly control a flying platform while avoiding obstacles, regulating its distance to objects and search for objects of interest. This is largely achieved through a complex hierarchical processing of signals from the multitude of ommatidia in their eye to a set of highly specialized neurons that are optimized to respond to specific properties of the visual world. One of these neurons, the Lobula Giant Movement Detector (LGMD) of the locust, has been recently shown to perform a functional multiplication of its synaptic inputs. If true, that would make the LGMD neuron a unique and highly sophisticated neuron that raises questions about the non-linear operations other neurons in other neuronal systems would be able to perform. Hence it is crucial to understand its properties, its role in behaviour and to evaluate whether its responses can be explained in simpler terms. Our results emphasize the role of network architecture and distributed computation as opposed to local complex non-linear computation. We show that our model reliably reproduces the known properties of the LGMD and can be used to control a high-speed robot.

People who experience an immersive VR system usually report feeling as if they were really in the displayed virtual situation, and can often be observed behaving in accordance with that feeling, even though they know that they're not actually there. Researchers refer to this feeling as "presence" in virtual environments, yet the term has come to have many uses and meanings, all of which evolved from the notion of telepresence in teleoperator systems. In Presenccia, we take an operational approach to the presence concept. Our approach lets us assess the extent of presence using tools beyond traditional questionnaires, and therefore we avoid many of the problems involved with sole reliance on these. Instead, we consider the extent to which mixed reality (MR) and VR participants realistically respond to virtually generated sensory data. Specifically, we measure the similarity of their response with what we might observe or predict if the sensory data-the situation, place, or events-were real, rather than virtual. We consider this response on several levels.