Outstanding research funded: How we perceive objects
Martin Hebart from the Max Planck Institute for Human Cognitive and Brain Sciences has been awarded one of the coveted 1.5 million euro grants from the European Research Council. Over the next five years the cognitive neuroscientist wants to determine which visual features our brains use to recognize objects.
We are able to perceive our environment with the help of the visual system in the brain. This allows us to recognize objects and relate them to our knowledge. All of this happens very quickly and almost automatically. However, it remains unclear exactly how our visual system this task: How do we manage to convert the photons hitting our retina to structure in the environment that we can recognize and assign meaning to?
For a long time, research has focused on the question of how perceived objects are recognized as belonging to certain categories. There has been a general agreement among scientists that visual brain regions operate in a hierarchy, first processing basic object properties such as edges, up versus down, or crooked versus straight. According to this view, these attributes are next assembled into whole objects in higher visual regions and eventually recognized. However, it is now known that higher visual regions also react to the basic visual features. More generally, our knowledge has remained incomplete about exactly which information is processed in which regions, and how it is passed from one region to the next.
The goal of Martin Hebart and his team is, on the one hand, to identify which tasks individual areas of the visual system perform. For example, do specific regions respond particularly strongly to certain patterns, and others to specific kinds of textures? On the other hand, they plan to identify how much of this processing can be attributed to the visual characteristics of the object and how much can be attributed to prior knowledge of the object. In other words, does the response of a brain region to an object reflect the category "dog", or rather, simply a dog’s shape, texture, and color?
The scientists will, among other things, investigate these correlations with the help of functional magnetic resonance imaging and a computer model of the visual system. This model is an artificial neural network that is built after the general structure of the visual system. Different parts of the neural network correspond to individual brain regions. With the model's help, the activity in all brain regions will be predicted simultaneously. When the study participants see different objects, the researchers will analyze which visual brain regions are activated and at which processing step. They will then determine which stimulus characteristics are most likely to activate each brain region. What is special about Hebart's approach is that their neural network suggests to them what these key stimuli might be. "This works like a kind of oracle that predicts which stimuli we should show to participants before we measure them, in order to learn the most about the visual system," says Hebart.
Background
With the "ERC Starting Grants", the European Research Council funds promising researchers who are at the beginning of an independent research career. The grants, which will total €677 million in 2022, help individuals build their own teams and conduct groundbreaking research in all disciplines. The individual research grants are part of the EU's research and innovation program, Horizon Europe. Martin Hebart is one of 11 Max Planck scientists to receive this funding this year. His project is supported with a total of 1.5 million euros for five years.
