Bridging the Explanatory Gap between Neuronal Activity and Cognition by Multilevel Causal Modeling
Explaining how neuronal activity gives rise to cognition arguably remains the most significant challenge in cognitive neuroscience. In the first part of this talk, I introduce neuro-cognitive multilevel causal modeling (NC-MCM), a mathematical framework that bridges the explanatory gap between neuronal activity and cognition by construing cognitive states as causally consistent abstractions of neuronal states. NC-MCM enables us to consistently reason about the neuronal- and cognitive causes of behavior while maintaining a physicalist (in contrast to a
dualist) position. In the second part of this talk, I introduce an algorithm for learning cognitive level causal models from neuronal activation patterns and demonstrate its ability to learn cognitive states of the nematode C.~elegans from calcium imaging data.