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:

  • Deen B, Schwiedrzik C, Sliwa J, Freiwald WA (2023) Specialized Networks for Social Cognition in the Primate Brain, Annual Review of Neuroscience, 46:381–401

  • Manenti G, Dizaji A, Schwiedrzik C (2023) Variability in training unlocks generalization in visual perceptual learning through invariant representations. Current Biology, 33(5), 817–826.e3

  • Schwiedrzik CM & Sudmann SS (2020) Pupil Diameter Tracks Statistical Structure in the Environment to Increase Visual Sensitivity. Journal of Neuroscience 40(23): 4565-4575.

  • 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, Zarco W, Everling S, Freiwald WA (2015) Face patch resting state networks link face processing to social cognition. PLoS Biology, 13(9): e1002245.

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)

The project "Feedback as the way forward: sensory predictions in the primate face processing hierarchy" has received funding from the German Research Foundation

Emmy Noether

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.

EU Marie Curie