Neural and Computational Principles of Action and Social Processing

Researchers:

Ardestani, Mohammad Hovaidi; Fedorov, Leonid; Taubert, Nick

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 at the HIH and the CIN. 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 extraction of intent information from visual stimuli.

  

Current Projects

Computer graphics approaches for the study of the neural basis of face processing

Computer graphics approaches for the study of the neural basis of face processing

Even though most facial expressions have dynamic component; emotion research is mostly focused on static faces. The problem is, especially for monkey faces, the exact stimulus control. Also the use of synthetic stimuli results often in not very realistic animations with reduced expressiveness. But modern computer animation methods in relation with physically correct materials enable to create quite realistic stimuli. These stimuli are used in experiments as basic for the development of neural models.
Neural mechanisms underlying the visual analysis of intent

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.
RELEVANCE: How body relevance drives brain organization

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 ERC-SYNERGY 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 behaviour.
Neurodynamic model for multi-stability in action perception

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

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.
  

Finished Projects

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

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.
Production and perception of interactive emotional body expressions

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.
Influence of action execution on biological motion perception

Influence of action execution on biological motion perception

The perception and execution of motor actions are tightly interlinked, and numerous experiments suggest the existence of common sensory-motor representations.Using a virtual-reality setup we aim to investigate the influence of self-generated body motion on the perception of online generated biological motion in combined motor behaviour and psychophysical studies.
Neural representations of sensory predictions for perception and action

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.
Processing of emotional body expressions in health and disease

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.
  

Publications

Pomper, J. K., Spadacenta, S., Bunjes, F., Arnstein, D., Giese, M. A. & Thier, P. (2020). Representation of the observer's predicted outcome value in mirror and nonmirror neurons of macaque F5 ventral premotor cortex. J Neurophysiol, 124(3), 941-961. [More] 
Taubert, N., St-Amand, J., Kumar, P., Gizzi, L. & Giese, M. A. (2020). Reactive Hand Movements from Arm Kinematics and EMG Signals Based on Hierarchical Gaussian Process Dynamical Models. Artificial Neural Networks and Machine Learning – ICANN 2020 29th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 15–18, 2020, Proceedings, Part I. Springer, Berlin(127-140). [More] 
Stettler, M., Taubert, N., Azizpour, T., Siebert, R., Spadacenta, S., Dicke, P. et al. (2020). Physiologically-inspired Neural Circuits for the Recognition of Dynamic Faces. Artificial Neural Networks and Machine Learning – ICANN 2020 29th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 15–18, 2020, Proceedings, Part I. Springer, Berlin(168-179). [More] 
Ardestani, M. H., Mukovskiy, A., Stettler, M., Saini, N. & Giese, M. A (2020). Physiologically-inspired neural model for the visual recognition of social interactions from abstract and natural stimuli. VSS 2020, 19-24 Jun . [More] 
Siebert, R., Taubert, N., Spadacenta, S., Dicke, P. W., Giese, M. A. & Thier, P. (2020). A naturalistic dynamic monkey head avatar elicits species-typical reactions and overcomes the uncanny valley. ENEURO.0524-19.2020. [More]