Neural Circuits and Cognition
Learning is a core building block of intelligent behavior. It endows complex systems with flexibility to adjust to changing environments and with the capacity to generalize to novel situations. We pursue the idea that inroads into understanding learning and generalization can be made in the visual system, where these complex problems can be broken down into tractable hypotheses. Visual processing hierarchies provide an ideal testing ground and offer unique opportunities to unravel the role of feedforward and feedback message passing along the hierarchy as a function of learning and generalization. To this end, we capitalize on combining noninvasive neuroimaging with electrophysiological recordings and causal manipulations of brain activity in non-human primates, and parallel experiments using fMRI in humans. We investigate learning at multiple time scales, from learning effects that build up within seconds to learning effects that take days and weeks to materialize, and across levels of complexity, from learning to discriminate simple visual features to high-level associative and statistical learning. Our overall goal is to determine the neural basis of the visual system’s capacity to learn and generalize through an explicitly comparative approach - a necessary step towards understanding the human mind and its complexity.
Selected recent publications:
Schwiedrzik CM, Sudmann SS, Thesen T, Wang X, Groppe DM, Mégevand P, Doyle W, Mehta AD, Devinsky O, Melloni L (2018). Medial prefrontal cortex supports perceptual memory. Current Biology, 28(18): R1094-R1095.
Schwiedrzik CM, Freiwald WA (2017). High-level prediction signals in a low-level area of the macaque face-processing hierarchy. Neuron, 96(1): 89-97.
Schwiedrzik CM, Bernstein B, Melloni L (2016). Motion along the mental number line reveals shared representations for numerosity and space. eLife, 5: e10806.
Schwiedrzik CM, Zarco W, Everling S, Freiwald WA (2015) Face patch resting state networks link face processing to social cognition. PLoS Biology, 13(9): e1002245.
Schwiedrzik CM, Ruff CC, Lazar A, Leitner FC, Singer W, Melloni L (2014). Untangling perceptual memory: hysteresis and adaptation map into separate cortical networks. Cerebral Cortex, 24(5): 1152-64.
Schwiedrzik CM, Singer W, Melloni L (2011). Subjective and objective learning effects dissociate in space and in time. Proceedings of the National Academy of Sciences USA, 108(11): 4506-4511.
The project VarPL (Specificity or generalization? Neural mechanisms for perceptual learning with variability) has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 802482)
2017-2018 we were supported by the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 706519.