Research - ERC

ERC grant (2017-2022): Characterizing neural mechanisms underlying the efficiency of naturalistic vision

The efficient detection of goal-relevant objects in our environment is of critical importance in daily life. For example, the majority of road accidents are caused by insufficient attention to relevant objects (e.g., pedestrians) in scenes. Our daily-life visual environments, such as city streets and living rooms, contain a multitude of objects. Out of this overwhelming amount of sensory information, our brains must efficiently select and recognize those objects that are relevant for current goals. Visual and attention systems have developed and evolved to optimally perform real-world tasks like these, as reflected in the remarkable efficiency of naturalistic object detection. It is increasingly appreciated that the brain makes use of a wide range of available information to facilitate object detection in real scenes. Our research aims to characterize the neural mechanisms that contribute to the efficiency of goal-directed naturalistic human vision.

The brain systems underlying the detection of task-relevant information in cluttered displays have primarily been studied using artificial and highly simplified displays. While these studies have been fundamentally important for revealing basic neural mechanisms involved in perception and attention, they ultimately fall short in fully explaining how the brain so rapidly detects familiar objects in complex but meaningful real-world scenes (see figure below). A large body of behavioural work has shown that the detection of goal-relevant objects in real-world scenes is dramatically more efficient than the detection of targets in apparently much simpler artificial displays: while several attention shifts are required to find the red horizontal line in the figure below, most observers will need only a brief glance at a visually more complex real-world scene to decide whether objects like people, cars, or trees are present.

Behavioral studies have identified several factors that contribute to the efficiency of naturalistic object detection, including: 1. The use of effective attentional templates; 2. Attentional guidance by scene context and episodic memory; 3. Context-based predictions facilitating object recognition; and 4. Inter-object grouping based on real-world regularities. While these factors are increasingly appreciated and studied in behavioral and theoretical work, very little is currently known about their neural basis. Our goal is to fill this gap, using psychophysics, fMRI, MEG, and TMS to improve our understanding of the neural mechanisms underlying the efficiency of object detection in natural scenes.

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