Vision is central to our interactions with the world. Aside from recognizing faces and communicating with people, our daily activities are also organized around two fundamental tasks: recognizing our environment and navigating through it. The research program of Dr. Aude Oliva constitutes a new integration of behavioral, computational and cognitive neuroscience research on scene perception. A growing body of evidence from behavioral, imaging and computational investigations has shown that the perception of complex real-world scenes engages distinct cognitive and neural mechanisms from those engaged in object recognition. To date, however, this evidence has not resulted in a comprehensive framework for understanding scene processing. Here, the PI proposes to test the novel hypothesis that real-world scene analysis is performed in a network of distinctive brain regions, with each region specialized in representing a different level of scene information. Since scenes are inherently three-dimensional spaces, she will show that the brain capitalizes on information uniquely derived from the space encompassed by a scene, rather than an exclusively object-based description. In other words, before knowing the """"""""gist of a scene,"""""""" we analyze the """"""""gist of the space."""""""" Understanding the nature of the brain's representations of visual scenes is an enterprise that will push the development of fast and reliable rehabilitation strategies for individuals with visual and spatial impairments, and push forward the development of aid-based systems that rely on an understanding of the visual space. Real-world scene recognition is an unsolved mystery that will have implications for neuroscience, computational vision, artificial intelligence, robotics and psychology.
The research program constitutes a new integration of computational and cognitive neuroscience research on scene and space perception, with the aim of unraveling how the understanding of visual environments (where we are, where to navigate) arises in the human brain.
|Martin Cichy, Radoslaw; Khosla, Aditya; Pantazis, Dimitrios et al. (2017) Dynamics of scene representations in the human brain revealed by magnetoencephalography and deep neural networks. Neuroimage 153:346-358|
|Cichy, Radoslaw Martin; Khosla, Aditya; Pantazis, Dimitrios et al. (2016) Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence. Sci Rep 6:27755|
|Bainbridge, Wilma A; Oliva, Aude (2015) A toolbox and sample object perception data for equalization of natural images. Data Brief 5:846-51|
|Park, Soojin; Konkle, Talia; Oliva, Aude (2015) Parametric Coding of the Size and Clutter of Natural Scenes in the Human Brain. Cereb Cortex 25:1792-805|
|Bainbridge, Wilma Alice; Oliva, Aude (2015) Interaction envelope: Local spatial representations of objects at all scales in scene-selective regions. Neuroimage 122:408-16|
|Cichy, Radoslaw Martin; Pantazis, Dimitrios; Oliva, Aude (2014) Resolving human object recognition in space and time. Nat Neurosci 17:455-62|
|Konkle, Talia; Oliva, Aude (2012) A real-world size organization of object responses in occipitotemporal cortex. Neuron 74:1114-24|