We are able to recognize objects, people, and places without effort and effectively interact with our visual world. This ability is remarkable, as it requires us to map ever-changing visual input to our knowledge of the regularities of the world and the repertoire of actions we can apply to it. But how is the light that hits our retina transformed into representations that enable us to recognize and interact with our environment? And how can we capture visual representations in a way that takes into account the complexities of the visual world and at the same time gives us an understanding of the regularities and laws that govern our visual system? In the Vision and Computational Cognition Group at the Max Planck Institute for Human Cognitive and Brain Sciences, we aim to answer these fundamental questions, using a combination of research tools from psychology, neuroscience, and computer science.
Our research aims at understanding vision from three perspectives. From a data science perspective, we collect and analyze large-scale representative datasets in both human behavior and neuroimaging (functional MRI, magnetoencephalography), allowing us to capture much of the complexity of our visual worlds and identify key characteristics that underlie our mental and neural representations. From a cognitive neuroscience perspective, we link these large-scale datasets to hypothesized representational properties and computational models, and we conduct targeted experiments testing the properties of visual recognition we have identified. From a computational perspective, we apply computational models of vision and semantics (deep neural networks, semantic embeddings), multivariate pattern analysis, and advanced machine learning methods to characterize representations in the human brain and behavior and identify interpretable representations in humans and artificial intelligence. To address longstanding questions about vision and computation in the brain, we develop novel analysis tools for a deeper, more powerful, and more fine-grained analysis of behavioral and neuroimaging data.
This Is Us
As an interdisciplinary team, we are spanning a broad bandwidth of science, from Psychology over Cognitive Neuroscience to Computer Science.
PhD candidate, interprets and compares representational properties of the visual stream to modern deep nets. Enjoys coding and is similarly fascinated by climbing, biking and chess.
Master student / research assistant, Student of Computer Science (M.Sc.) at University of Leipzig. Likes exploring deep AI models, improving online experiments, and spending 6 hours trying to automate something instead of doing it by hand in 5 minutes.
PhD candidate, working on improving fMRI methodology to enable faster data acquisition. Also interested in deep learning models of the visual system. Wants to own a giant orchard with a chicken coop and an outdoor bouldering wall one day.
PhD candidate, co-supervised with Radoslaw Cichy at Freie Universität Berlin. Did his Master's project in the lab on the representation of abstract object depictions (e.g. drawings) in the human brain and deep neural networks. Interested in multivariate analyses of electrophysiological data and the temporal dynamics of visual processing. Loves to make and get lost in music.
Postdoctoral reseacher, working with large-scale naturalistic datasets and analyzing them with modern neural networks and machine learning to gain better understanding of sensory representations. Sika deer whisperer.
Master student / research assistant, among others, expands the THINGS database by programming and conducting online studies. When speaking with her family, she can talk so fast that people think it's another language.
PhD candidate, co-supervised with Klaus-Robert Müller, working on methods for deciphering latent representations and using linear algebra for good. Things he loves: neural networks, making code run faster than the speed of light, and RückenFit.
Maggie Mae Mell
Postdoctoral researcher, working on acquiring massive functional and diffusion MRI data to learn more about the structure and function of visual cortex.
MD candidate, co-supervised with Christian Doeller and Johanna Bergmann, trying to deepen our understanding of how the brain constructs relationships between objects/concepts and whether the underlying principles comply with spatial neural codes. Avid meditator, runner, Crossfitter, and soon-to-by psychiatrist.
Data scientist and cognitive neuroscientist with a background in AI based in Montréal, Canada. Likes to study how representation patterns are transformed by experience. Compulsive collector of graduate degrees.
Principal investigator, loves addressing fundamental questions with a fun team of highly-talented researchers. Also loves spending time making his kids laugh, cycling in nature, deep conversations, and a glass of IPA.
PhD candidate, working on linking fMRI data to computational models of vision and behavior. Interested in brain representations underlying object recognition. Fueled by Carbonara, Colakracher, and feline attention.
PhD candidate, interested in improving representational similarity analysis and how the link between stages of cortical visual representations and behavior depends on the task. Likes all kinds of lemonade way too much, even while refereeing a basketball game.
Master Student / research assistant, student of Computer Science (M.Sc.) at University of Leipzig. Works on predicting brain activity with deep neural networks. Dreads the day his student software licenses run out.
Master student / research assistant, studies Social, Cognitive, and Affective Neuroscience (M.Sc.) at Freie Universität Berlin. Interested in human visual perception and cross modal interaction. Was a latin dancer before corona :')
Research assistant, studies Data Science (M.Sc.) at Freie Universität Berlin. Interested in deep learning and its applications to neuroscientific research. Likes brain decoding, juggling and lasagna.