Sensorimotor Learning & Decision-Making
One of the most striking features that sets human motor control apart from its robotic counterparts is the remarkable adaptability that allows us to cope with a vast range of complex and variable environments. The research goal of our group is to investigate the computational and biological principles underlying this unrivalled adaptability both experimentally and theoretically.
In contrast to playing chess, which has served as a classical paradigm of intelligence, kicking a penalty is an expression of embodied intelligence, a primordial form of intelligence shared amongst all living beings that move. It involves situated autonomous agents who pursue their own goals in dynamic environments that are highly uncertain, but still well-structured. These agents interact with each other and their environment in a sensorimotor fashion, i.e. they are coupled structurally in continuous feedback loops of actions and observations.
Movements play a key role within the context of embodied intelligence, as they are the only way to communicate with the world. In fact, each movement can be regarded as a primitive decision that is selected from a vast set of alternatives. In this paradigm, intelligence is conceptualized as the ability to make the right "moves", or decisions, in a possibly large range of different environments. This also requires agents to be able to learn and to co-adapt within this range of environments. Thus, adaptive control becomes a central metaphor of intelligent behavior at the heart of which lies sensorimotor learning and decision-making.
In our group we are interested in the mathematical principles that underlie embodied intelligent behaviour. In behavioural experiments, we test mathematical hypotheses experimentally in human sensorimotor control. Virtual reality technology allows us to expose human subjects to diverse, novel and possibly complex environments. Our investigations can roughly be categorized in three sub-projects:
- We study how the human motor system exploits the structure and the causal dependencies in its environment to enhance adaptation and to integrate information for action.
- We study neuro-economical principles that can explain human motor control and learning.
- Our experimental studies are backed up by theoretical investigations of principles for adaption and control that take the bounded resources of agents into account.
We have moved to University of Ulm.