Sensorimotor Control and Learning

In the Sensorimotor Control and Learning Group we are interested in human multisensory perception and goal-oriented behaviour in virtual reality (VR) as well as the real world. When we interact with our direct surroundings, e.g. picking up a cup of coffee, we appear to do so seemingly effortlessly. Yet, even such simple every-day tasks involve sensing the 3D position and orientation of the cup (sensory processing), making an educated guess whether it is currently full or empty to estimate its weight (cognitive processing), and having an idea of our own limb position and where it needs to go next (body perception and movement planning).

The research in our group focuses on how our sensory system selects and combines relevant pieces of information, both for perception of our environment, as well as for performing such goal-oriented tasks. As part of this we also investigate how our senses combine to form a sense of avatar/tool ownership and agency in Virtual Reality.

To address these topics, we use straightforward perceptual tasks (psychophysics), game-like target-pointing or tracking tasks to investigate goal-oriented behaviour, as well as mathematical modelling approaches (e.g. Bayesian inference) to approach these topics also from the theoretical side. Below we provide an outline of some of the topics we are currently working on in more detail.

One current research interest of the group lies in the area of sensory processing and how we use our senses to continuously predict the future positions and velocities of moving objects in our environment. Predictability of visual information has been shown to have a huge impact on our ability to behaviourally adapt to certain circumstances and certain tasks, particularly when the effect of our actions occurs at a delay (Rohde, van Dam & Ernst, 2014; van Dam & Stephens, 2018). What are the mechanisms for perceptual prediction, what are its limits and when does the prediction process reset? Own research suggests that resets of sensory processing may be marked by a reset of behaviour (e.g., eye movement planning) as shown by its link to changes in perception (see e.g., van Dam & van Ee, 2005; 2006). We are using psychophysical methods to investigate the perception of moving objects, combined with behavioural measures (e.g., eye/hand tracking movements).


  • Investigate how the perceptual system updates the processing of motion information based on stimulus reliability.
  • Investigate target-tracking behaviour for various target conditions and find the limits at which predicting target motion breaks down.
  • Determine to role of prior knowledge and learned associations (e.g., van Dam & Ernst, 2010; 2015a; 2015b; van Dam, Hawellek, Ernst, 2013) in the prediction process.
  • Use illusions such as motion dazzle and the curve-ball illusion to further investigate the interaction between motion and position sensing.

In order to adequately interact with our environment, we need to know where our own limbs are and where they are going. We have previously shown that for the perception of posture at the end of an arm movement our sensorimotor system is well equipped to obtain such information even up to the level of knowing about our own motor noise (van Dam & Ernst, 2013). The perception of our posture is however also influenced by visual information, such as shown in the form of a proprioceptive drift in the Rubber Hand Illusion. In this illusion a dummy hand is perceived as one’s own, which leads to proprioceptive estimates of the own arm being pulled towards the location of the dummy hand. This means that there is a strong interaction between at least proprioception and vision. However, posture and movement information do not necessarily transfer (van Dam, Plaisier, Glowania, Ernst, 2016; Glowania, Plaisier, Ernst, van Dam, 2020) or interact (Plaisier, van Dam, Glowania, Ernst, 2014). Therefore, how visual and kinaesthetic information about our limbs is combined for both perception of our movements and perception of our body is currently less clear. This interaction is the focus for the following aims.


  • Investigate the integration of visual and kinaesthetic information about our body movements and how this affects the subjective experience of Virtual Reality environments.
  • Explore the influence of the visual appearance of our movements in virtual environments on motor behaviour, agency and body ownership.
  • Investigate the effect of sensorimotor correspondences on bodily perception and the perception of action space.

Our group is part of “The Adaptive Mind” (TAM) project ( which is focussed on the dynamics of the processes underlying perception and action. Questions that are at the core of this project are for instance: How does our perception change in changing environments and how do we adapt our behaviours to compensate for these changes? Additionally, the project seeks to obtain a better understanding of how predictive and adaptive processes are affected in, for instance, patients with schizophrenia (SZ). It is for instance known that not only people with a diagnosis of SZ, but also people with high scores in schizotypal personally traits, can have altered perceptions of causal relationships between sensory stimuli as well as altered agency and body structure perception (Fotia, Jason, van Dam, Ferri, Romei, 2021; Fotia, van Dam, Sykes, Ambrosini, Costantini, Ferri, 2022). As part of the TAM project our group is working together with, for instance, Prof Benjamin Straube of the University of Marburg to build and test models of how the sensations of body ownership and agency may come about, and how these processes are altered in SZ.


  • Explore the link between the sensations of agency over own actions and predictability of the environment.
  • Use the behavioural data as a basis for developing a mathematical model at different levels of encoding (e.g., a Bayesian inference model on the high-level algorithmic end and neural implementations at the other extreme).
  • Explore the links between sensorimotor integration, agency and SZ to investigate how the model could account for the distorted experiences in this patient group.


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