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The lab investigates the neurocomputational mechanisms underlying human object recognition and learning as a gateway to understanding the neural bases of intelligent behavior. The ability to recognize objects is a fundamental cognitive skill in every sensory modality, e.g., for friend/foe discrimination, social communication, reading, and speech perception. Amazingly, the typical human brain can efficiently learn and perform these computationally complex tasks with about three pounds of hardware running on the power of a lightbulb. Our aim is to understand how the brain accomplishes this feat.
In our work, we combine computational models with human behavioral, functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) data. This comprehensive approach addresses one of the major challenges in neuroscience today, that is, the necessity to combine experimental data from a range of approaches in order to develop a rigorous and predictive model of human brain function that quantitatively and mechanistically links neurons to behavior. This is of interest not only for basic research, but also for the identification of neuroscience-based treatments for neuropsychological disorders. Building computational models of how the brain learns and makes sense of the world is also of significant relevance for Artificial Intelligence, as “natural intelligence” is still superior to even the best AI systems in critical respects, such as the ability to leverage prior learning to rapidly learn novel tasks, avoid hallucinations and perform reasoning. Finally, a mechanistic understanding of the neural processing networks that enable the brain to make sense of stimuli across different senses opens the door to supporting and extending human cognitive abilities in this area through, for instance, hybrid brain-machine systems (“augmented cognition”) and novel technologies, e.g., for sensory substitution.
Most of the work in the lab has traditionally focused on the domain of vision, reflecting its status as the most accessible sensory modality. However, given that similar problems have to be solved in other sensory modalities as well, it is likely that similar computational principles underlie processing in those domains, and we are interested in understanding commonalities and differences in processing between sensory modalities.