In 2007, the CIN started with 25 principal investigators as cluster applicants, as stipulated in the DFG call for bids. When the CIN cluster was approved further scientists from a range of institutions were incorporated, to make up the 48 'founding members' of the CIN. Since the beginning of 2014 the CIN has consisted of over 80 scientists in total. The membership process involves an application to the steering committee in which the candidate outlines his or her scientific profile and submits a list of publications. The committee's decision is based purely on the scientific excellence of each candidate.
Field of Research
My team is a highly interdisciplinary research team that focuses on how the brain works in general. In pursuing this highly challenging endeavour, we currently focus our research on three key aspects: first, on cognitive bodyspaces, that is, on how the brain represents the body in the surrounding space, on anticipations, that is, on how the brain represents the world for anticipatory behavioural control and cognition, and finally, on how objects are represented in bodyspaces and in anticipation of potential object interactions.
In particular, my team is investigating cognitive bodyspaces, that is, interactive spatial representations of the body within its environment. In particular, we explore how the brain represents the body in space and the space around the body. Doing so we conduct experimental psychological studies and we develop cognitive models of the observed results. In particular, we explore phenomenal and behavioural effects of these representations and their (multimodal, modular and hierarchical) interactions. We model the observations on several levels including normative, Bayesian, and neural levels of cognitive modeling. Moreover, we apply and develop machine learning approaches to reveal the necessary computational mechanisms for learning such representations.
We furthermore pursue the idea that representations in the brain, such as bodyspace representations, are primarily not for rerepresenting the world (homunculus problem) but rather they are for anticipating the potential consequences when actively interacting with the world. We are convinced that representations in the brain primarily develop for identifying ecologically relevant aspects of the world, for guiding action decision making, and for successfully controlling behavior. Based on this presupposition we also investigate how more abstract, conceptual representations can be developed out of these spatial representations – ultimately leading to the capability of understanding and producing language. Moreover, we also model aspects of short-term memory and attention from this perspective.
These investigations on anticipatory representations of bodyspaces also lead to research on how objects are represented within these bodyspaces and on how object interactions are selectively activated and pursued. Objects are ubiquitous in our world and the number of objects present is still increasing in our culture. Objects occur dominantly in particular contexts, have particular behaviour-relevant properties, afford particular interactions, and also are of particular ecological and motivational relevance for us humans. Not surprisingly, object concepts are thus highly distributed in our brain and go way beyond mere visual object recognition. Recently, we are investigating how these object aspects are activated, interact, and unfold over time, especially when actively planning and controlling object interactions.
Functionality of Mind and Brain
Flexible, sensorimotor control and learning.
Development of higher cognitive functions (such as concepts and compositionality) based on sensorimotor behavioural competencies.
Butz, M. V. (2008). How and why the brain lays the foundations for a conscious self. Constructivist Foundations, 4, 1-42.
Butz, M. V. (2013). Separating goals from behavioral control: Implications from learning predictive modularizations. New Ideas in Psychology. 10.1016/j.newideapsych.2013.04.001.
Butz, M. V., Herbort, O., & Hoffmann, J. (2007). Exploiting redundancy for flexible behavior: Unsupervised learning in a modular sensorimotor control architecture. Psychological Review, 114, 1015-1046.
Butz, M. V., Thomaschke, R., Linhardt, M. J., & Herbort, O. (2010). Remapping motion across modalities: Tactile rotations influence visual motion judgments. Experimental Brain Research, 207, 1-11.
Ehrenfeld, S., & Butz, M. V. (2013). The modular modality frame model: continuous body state estimation and plausibility-weighted information fusion. Biological Cybernetics, 107, 61-82.