Material qualities are an important aspect of the human visual experience, yet the neural processes by which surface material perception is mediated are still largely unstudied. This investigation focuses on one surface quality, whose underlying physical properties can be varied parametrically, namely apparent shininess. While previous studies have exclusively focused on stationary visual cues to shininess the present project departs drastically from those approaches by proposing a novel, ecologically valid direction of research that investigates spatio-temporal cues to surface shininess.
The proposed work will combine computational methods, psychophysical experimentation and functional Magnetic Resonance Imaging (fMRI) to achieve the following objectives: 1: Determine the spatio-temporal statistical regularities in the visual environment conveying that a surface is shiny. 2: Identify and study human cortical areas involved in the processing and integration of visual dynamic cues to shininess, with an emphasis on understanding how low-level processes are integrated to give rise to the final material percept.
This project is multidisciplinary in nature. Expected results of all, the computational analyses, behavioral and fMRI studies, promise to have a large impact on the fields of Cognitive Neuroscience, Computer Vision and Machine Intelligence.
The study of the neural processes underlying visual function constitutes a part of Cognitive Neuroscience in whose domain the here proposed project falls. Research in this area, with its direct applications to human health and well being at the community level, is of highest relevance to the European Research Area. Being novel in scope and execution, the proposed research will make a significant contribution to European scientific excellence.
Fields of science
- natural sciencesbiological sciencesneurobiologycognitive neuroscience
- natural sciencescomputer and information sciencesartificial intelligencecomputer vision
- natural sciencescomputer and information sciencescomputational science
- natural sciencescomputer and information sciencesartificial intelligencemachine learningdeep learning
- engineering and technologymedical engineeringdiagnostic imagingmagnetic resonance imaging
Call for proposal
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