Contributions of low- and high-level properties to neural representation of real-world scenes in the human brain

Mar 21, 2017
Location: BME Conference Room (301 Engineering Terrace)
Speaker: Iris Groen, Ph.D., Laboratory of Brain and Cognition, NIMH


Everyday visual perception requires the processing of a multitude of information from complex real-world scenes. The perception of entire scenes in humans has been characterized by the activation of regions in extrastriate cortex that respond more strongly to scenes than to isolated objects. While these regions have often been interpreted as representing high-level properties of scenes (e.g. category), they also exhibit substantial sensitivity to low-level (e.g. spatial frequency) and mid-level (e.g. spatial layout) properties, and it is unclear how these disparate findings can be united in a single framework. In this talk, I will suggest that this problem can be resolved by questioning the utility of the classical low- to high-level framework of visual perception for scene processing, and discuss why low- and mid-level properties may be particularly diagnostic for the behavioural goals specific to scene perception as compared to object recognition. I will illustrate the contributions of low- and high-level scene properties to scene representation by comparing their relative ability to predict both the temporal dynamics of visual processing (with EEG) as well as spatial patterns of brain activity in scene-selective cortex (with fMRI). Finally, I will present convergent EEG and fMRI evidence demonstrating how the broader scene context influences rapid recognition of objects in scenes by affecting the accumulation of evidence required to reach a decision about the presence of a target object. Together, these results suggest a more expansive framework for visual scene analysis that includes a more dynamic model of visual processing and a larger role for low-level information.

Hosted by Paul Sajda.

500 W. 120th St., Mudd Room 524, New York, NY 10027    212-854-5660               
©2014-2017 Columbia University