The Perception and Plasticity Group of Caspar Schwiedrzik at the DPZ is looking for an outstanding postdoc interested in studying the neural basis of high-dimensional category learning in vision. The project investigates neural mechanisms of category learning at the level of circuits and single cells, utilizing electrophysiology, functional magnetic resonance imaging, behavioral testing in humans and non-human primates, and computational modeling. It is funded by an ERC Consolidator Grant (Acronym DimLearn; “Flexible Dimensionality of Representational Spaces in Category Learning”). The postdoc’s project will focus on investigating the neural basis of visual category learning in macaque monkeys combining chronic multi-electrode electrophysiological recordings and electrical microstimulation. In addition, the postdoc will have the opportunity to cooperate with other lab members on parallel computational investigations using artificial neural networks as well as comparative research exploring the same questions in humans. The postdoc will play a key role in our research efforts in this area. The lab is located at Ruhr-University Bochum and the German Primate Center in Göttingen. At both locations, the lab is embedded into interdisciplinary research centers with international faculty and students pursuing cutting-edge research in cognitive and computational neuroscience. The main site for this part of the project will be Göttingen. The postdoc will have access to state-of-the-art electrophysiology, an imaging center with a dedicated 3T research scanner, and behavioral setups. The project will be conducted in close collaboration with the labs of Fabian Sinz, Alexander Gail, and Igor Kagan.

There is a fundamental tension between storing discrete traces of individual experiences, which allows recall of particular moments in our past without interference, and extracting regularities across these experiences, which supports generalization and prediction in similar situations in the future. One influential proposal for how the brain resolves this tension is that it separates the processes anatomically into Complementary Learning Systems, with the hippocampus rapidly encoding individual episodes and the neocortex slowly extracting regularities over days, months, and years. But this does not explain our ability to learn and generalize from new regularities in our environment quickly, often within minutes. We have put forward a neural network model of the hippocampus that suggests that the hippocampus itself may contain complementary learning systems, with one pathway specializing in the rapid learning of regularities and a separate pathway handling the region’s classic episodic memory functions. This proposal has broad implications for how we learn and represent novel information of specific and generalized types, which we test across statistical learning, inference, and category learning paradigms. We also explore how this system interacts with slower-learning neocortical memory systems, with empirical and modeling investigations into how the hippocampus shapes neocortical representations during sleep. Together, the work helps us understand how structured information in our environment is initially encoded and how it then transforms over time.

A key component of humans' striking creativity in solving problems is our ability to construct novel descriptions to help us characterize novel categories. Bongard problems, which challenge the problem solver to come up with a rule for distinguishing visual scenes that fall into two categories, provide an elegant test of this ability. Bongard problems are challenging for both human and machine category learners because only a handful of example scenes are presented for each category, and they often require the open-ended creation of new descriptions. A new sub-type of Bongard problem called Physical Bongard Problems (PBPs) is introduced, which require solvers to perceive and predict the physical spatial dynamics implicit in the depicted scenes. The PATHS (Perceiving And Testing Hypotheses on Structures) computational model which can solve many PBPs is presented, and compared to human performance on the same problems. PATHS and humans are similarly affected by the ordering of scenes within a PBP, with spatially and temporally juxtaposed scenes promoting category learning when they are similar and belong to different categories, or dissimilar and belong to the same category. The core theoretical commitments of PATHS which we believe to also exemplify human open-ended category learning are a) the continual perception of new scene descriptions over the course of category learning; b) the context-dependent nature of that perceptual process, in which the scenes establish the context for one another; c) hypothesis construction by combining descriptions into logical expressions; and d) bi-directional interactions between perceiving new aspects of scenes and constructing hypotheses for the rule that distinguishes categories.