Accurate navigation often requires the maintenance of a robust internal estimate of heading relative to external surroundings. We present a model for angular velocity integration in a desert locust heading circuit, applying concepts from early theoretical work on heading circuits in mammals to a novel biological context in insects. In contrast to similar models proposed for the fruit fly, this circuit model uses a single 360° heading direction representation and is updated by neuromodulatory angular velocity inputs. Our computational model was implemented using steady-state firing rate neurons with dynamical synapses. The circuit connectivity was constrained by biological data, and remaining degrees of freedom were optimised with a machine learning approach to yield physiologically plausible neuron activities. We demonstrate that the integration of heading and angular velocity in this circuit is robust to noise. The heading signal can be effectively used as input to an existing insect goal-directed steering circuit, adapted for outbound locomotion in a steady direction that resembles locust migration. Our study supports the possibility that similar computations for orientation may be implemented differently in the neural hardware of the fruit fly and the locust.

A sense of control over the environment can stem from mere motor control to overarching belief systems of control. Sense of agency is defined as perceiving oneself as the cause of an action or its effects. It can be conceptualized as the low-level experience of online motor control over one’s actions. Self-efficacy is the high-level belief in one’s ability to achieve intended goals. Both constructs have been frequently studied on their own, but this is the first study that empirically investigates a possible link between the two. To this end, we conducted a virtual reality (VR) experiment in which participants had to trace shapes while experiencing both movement and feedback distortions. The experiment used a 2x2 design with the first factor being the translation of the participant’s movements into VR (accurate vs distorted) and the second factor being feedback upon task completion (real vs hyper-positive). We found that these two factors manipulated the sense of agency and, in turn, influenced self-efficacy, and see this as a first step in the investigation of a possible causal link between the two constructs. Thus, the constructs of agency and self-efficacy appear more closely linked than previous research suggests. Future research targeting the sense of agency as a bottom-up influence on self-efficacy beliefs holds promising implications for both clinical and positive psychological interventions as well as motor rehabilitation.

We investigate the role of prediction in biological movement perception by comparing different representations of human movement in a virtual reality (VR) and online experiment. Predicting movement enables quick and appropriate action by both humans and artificial agents in many situations, e.g. when the interception of objects is important. We use different predictive movement primitive (MP) models to probe the visual system for the employed prediction mechanism. We hypothesize that MP-models, originally devised to address the degrees-of-freedom (DOF) problem in motor production, might be used for perception as well.

We compared the perceptual validity of human avatar walking animations driven by six different representations of human movement using a graphics Turing test. All six representations are based on movement primitives (MPs), which are predictive models of full-body movement that differ in their complexity and prediction mechanism. Assuming that humans are experts at perceiving biological movement from noisy sensory signals, it follows that these percepts should be describable by a suitably constructed Bayesian ideal observer model. We build such models from MPs and investigate if the perceived naturalness of human animations are predictable from approximate Bayesian model scores of the MPs. We found that certain MP-based representations are capable of producing movements that are perceptually indistinguishable from natural movements. Furthermore, approximate Bayesian model scores of these representations can be used to predict perceived naturalness. In particular, we could show that movement dynamics are more important for perceived naturalness of human animations than single frame poses. This indicates that perception of human animations is highly sensitive to their temporal coherence. More generally, our results add evidence for a shared MP-representation of action and perception. Even though the motivation of our work is primarily drawn from neuroscience, we expect that our results will be applicable in virtual and augmented reality settings, when perceptually plausible human avatar movements are required.

Building on our previous work [Taubert et al. 2012], we present an approach for real-time interaction between a real human and an avatar. We generate reactive motions by a dynamical extension of a hierarchical Gaussian process latent variable model, including latent dimensions for emotional style variation and target positions. This allows the avatar to produce accurate reactive motions to the human. To validate our approach, we developed a real-time application where an avatar and a human actor engage in emotional 'high fives'. Furthermore, we show preliminary results indicating that humans do perceive emotions more accurately when engaging in interaction as opposed to passive observation.

The online synthesis of stylized interactive movements with high levels of realism is a difficult problem in computer graphics. We present a new approach for the learning of structured dynamical models for the synthesis of interactive body movements that is based on hierarchical Gaussian process latent variable models. The latent spaces of this model encode postural manifolds and the dependency between the postures of the interacting characters. In addition, our model includes dimensions representing emotional style variations (for neutral, happy, angry, sad) and individually-specific motion style. The dynamics of the state in the latent space is modeled by a Gaussian Process Dynamical Model, a probabilistic dynamical model that can learn to generate arbitrary smooth trajectories in real-time. The proposed framework offers a large degree of flexibility, in terms of the definition of the model structure as well as the complexity of the learned motion trajectories. In order to assess the suitability of the proposed framework for the generation of highly realistic motion, we performed a 'Turing test': a psychophysical study where human observers classified the emotions and rated the naturalness of the generated and natural emotional handshakes. Classification results for both stimulus groups were not significantly different, and for all emotional styles, except for neutral, participants rated the synthesized handshakes equally natural as animations with the original trajectories. This shows that the proposed method generates highly-realistic interactive movements that are almost indistinguishable from natural ones. As a further extension, we demonstrate the capability of the method to interpolate between different emotional styles.

Natural body movements arise in the form of temporal sequences of individual actions. During visual action analysis, the human visual system must accomplish a temporal segmentation of the action stream into individual actions. Such temporal segmentation is also essential to build hierarchical models for action synthesis in computer animation. Ideally, such segmentations should be computed automatically in an unsupervised manner. We present an unsupervised segmentation algorithm that is based on Bayesian Binning (BB) and compare it to human segmentations derived from psychophysical data. BB has the advantage that the observation model can be easily exchanged. Moreover, being an exact Bayesian method, BB allows for the automatic determination of the number and positions of segmentation points. We applied this method to motion capture sequences from martial arts and compared the results to segmentations provided by humans from movies that showed characters that were animated with the motion capture data. Human segmentation was then assessed by an interactive adjustment paradigm, where participants had to indicate segmentation points by selection of the relevant frames. Results show a good agreement between automatically generated segmentations and human performance when the trajectory segments between the transition points were modeled by polynomials of at least third order. This result is consistent with theories about differential invariants of human movements.

We propose a novel application of Formal Concept Analysis (FCA) to neural decoding: instead of just trying to figure out which stimulus was presented, we demonstrate how to explore the semantic relationships between the neural representation of large sets of stimuli. FCA provides a way of displaying and interpreting such relationships via concept lattices. We explore the effects of neural code sparsity on the lattice. We then analyze neurophysiological data from high-level visual cortical area STSa, using an exact Bayesian approach to construct the formal context needed by FCA. Prominent features of the resulting concept lattices are discussed, including indications for a product-of-experts code in real neurons.

The peristimulus time historgram (PSTH) and its more continuous cousin, the spike density function (SDF) are staples in the analytic toolkit of neurophysiologists. The former is usually obtained by binning spiketrains, whereas the standard method for the latter is smoothing with a Gaussian kernel. Selection of a bin with or a kernel size is often done in an relatively arbitrary fashion, even though there have been recent attempts to remedy this situation \cite{ShimazakiBinningNIPS2006,ShimazakiBinningNECO2007}. We develop an exact Bayesian, generative model approach to estimating PSHTs and demonstate its superiority to competing methods. Further advantages of our scheme include automatic complexity control and error bars on its predictions.

We present an exact Bayesian treatment of a simple, yet sufficiently general probability distribution model. We consider piecewise-constant distributions P(X) with uniform (second-order) prior over location of discontinuity points and assigned chances. The predictive distribution and the model complexity can be determined completely from the data in a computational time that is linear in the number of degrees of freedom and quadratic in the number of possible values of X. Furthermore, exact values of the expectations of entropies and their variances can be computed with polynomial effort. The expectation of the mutual information becomes thus available, too, and a strict upper bound on its variance. The resulting algorithm is particularly useful in experimental research areas where the number of available samples is severely limited (e.g., neurophysiology). Estimates on a simulated data set provide more accurate results than using a previously proposed method.

We introduce a metric for probability distributions, which is bounded, information-theoretically motivated, and has a natural Bayesian interpretation. The square root of the well-known /spl chi//sup 2/ distance is an asymptotic approximation to it. Moreover, it is a close relative of the capacitory discrimination and Jensen-Shannon divergence.