Description

We investigate the mechanisms of the perception of body movements, and their relationship with motor execution and social signals. Our work combines psychophysical experiments and the development of physiologically-inspired neural models in close collaboration with electrophysiologists inside and outside of Tübingen. In addition, exploiting advanced methods from computer animation and Virtual Reality (VR), we investigate the perception of body movements (facial and body expressions) in social communication, and its deficits in psychiatric disorders, such as schizophrenia or autism spectrum disorders. A particular new focus is the study of intentional signals that are conveyed by bodily and facial expressions. For this purpose, we developed highly controlled stimulus sets, exploiting high-end methods from computer graphics. In addition, we develop physiologically-inspired neural models for neural circuits involved in the processing of bodies, actions, and the extraction of intent and social information from visual stimuli.

Current Projects

RELEVANCE: How body relevance drives brain organization

Social species, and especially primates, rely heavily on conspecifics for survival. Considerable time is spent watching each other’s behavior for preparing adaptive social responses. The project RELEVANCE aims to understand how the brain evolved special structures to process highly relevant social stimuli, such as bodies and to reveal how social vision sustains adaptive behavior.

Modelling and Investigation of Facial Expression Perception

Dynamic faces are essential for the communication of humans and non-human primates. However, the exact neural circuits of their processing remain unclear. Based on previous models for cortical neural processes involved for social recognition (of static faces and dynamic bodies), we propose a norm-based mechanism, relying on neurons that represent dierences between the actual facial shape and the neutral facial pose.

Neural mechanisms underlying the visual analysis of intent

Primates are very efficient in the recognition of intentions from various types of stimuli, involving faces and bodies, but also abstract moving stimuli, such as moving geometrical figures as illustrated in the seminal experiments by Heider and Simmel (1944). How such stimuli are exactly processed and what the underlying neural and computational mechanisms are remains largely unknown.

Neural model for shading pathway in biological motion stimuli

Biological motion perception is influenced by shading cues. We study the influence of such cues and develop neural models how the shading cues are integrated with other features in action perception.

Finished Projects

Neural field model for multi-stability in action perception

The perception of body movements integrates information over time. The underlying neural system is nonlinear and is charactrized by a dynamics that supports multi-stable perception. We have investigated multisstable body motion perception and have developed physiologically-inspired neural models that account for the observed psychophysical results.

Understanding the semantic structure of the neural code with Formal Concept Analysis

Mammalian brains consist of billions of neurons, each capable of independent electrical activity. From an information-theoretic perspective, the patterns of activation of these neurons can be understood as the codewords comprising the neural code. The neural code describes which pattern of activity corresponds to what information item. We are interested in the structure of the neural code.

Smart Eyes: Attending and Recognizing Instances of Salient Events – SEARISE

The core of this artificial cognitive visual system is a dynamic hierarchical architecture, inspired by computational models of visual processing in the brain. Information processing in Smart-Eyes will be highly efficient due to a multi-scale design: Controlled by the brain-inspired model, the active cameras will provide a multi-scale video record of salient events.

Production and perception of interactive emotional body expressions

A substantial amount of research has addressed the expression and perception of emotions with human faces. Body movements likely also contribute to our expression of emotions. However, this topic has received much less research interest so far. We use techniques from machine learning to synthesize highly-controlled emotional body movements and use them to study the perception and the neural mechanisms of the perception of emotion from bodily emotion expression.

Processing of emotional body expressions in health and disease

Body movements are an important source of information about the emotion of others. The perception of emotional body expressions is impaired in different psychiatric diseases. We have developed methods to generate emotional body motion srimuli with highly-controlled properties, and we exploitz them to study emotion perception in neurologiocal and psychiatric patients.

Neurodynamic model for multi-stability in action perception

Action perception is related to interesting dynamical phenomena, such as multi-stability and adaptation. The stimulus shown in this demo is bistable and can be seen as walking obliquely coming out or going into the image plane. Such multistability and associated spontaneous perceptual switches result form the dynamics of the neural representation of perceived actions. We investigate this dynamics pasychophysically and model it using neural network models.

Neuralphysiologically-inspired models of visual action perception and the perception of causality

The recognition of goal-directed actions is a challenging problem in vision research and requires the recognition not only of the movement of amd effector(e.g. the hand) but also the processing its relationship to goal objects, such as a grasped piece of food. In close collaborations with electrophysiologists, we develop models for the neural circuits in cortex that underly this visual function. These models also account for several properties of 'mirror neurons', and for the processing of stimuli (like the one shown in the icon) that suggest causal interactions between objects. In addition, we studied psychophysically the interaction between action observation and exertion using VR methods.

Neural representations of sensory predictions for perception and action

Attribution of percepts to consequences of own actions depends on the consistency between internally predicted and actual visual signals. However, is the attribution of agency rather a binary decision ('I did, or did not cause the visual consequences of the action'), or is this process based on a more gradual attribution of the degree of agency? Both alternatives result in different behaviors of causal inference models, which we try to distinguish by model comparison.

Dynamical Stability and Synchronization in Character Animation

An important domain of the application of dynamical systems in computer animation is the simulation of autonomous and collective behavior of many characters, e.g. in crowd animation.

Publications