Neural and Computational Principles of Action and Social Processing

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.