Modeling the Representation of Object Boundary Contours in Human fMRI Data

13 Aug 2019
Dr. Mark Lescroart, Cognitive & Brain Sciences Group, Department of Psychology, University of Nevada, Reno
Page/Slide Count:
Time: 00:51:23
The human visual system consists of a hierarchy of areas, each of which represents different features of the visual world. Recent studies have revealed that most brain areas--and even many individual neurons--represent information about multiple visual features. Thus, a complete model of the brain must specify the relative importance of multiple visual features across the visual hierarchy. This talk will describe our work to estimate the importance of object boundary contours relative to other features.

Boundary contours define the edges of figural objects in scenes, and figure/ground segmentation has long been held to be a critical process in human vision. However, the relative importance of boundary contours compared to both lower- and higher-level features (e.g. motion energy and visual categories) remains unknown. To address this issue, we measured fMRI responses while human subjects viewed two sets of movies that varied in many feature dimensions: rendered movies of artificial scenes and cinematic movies. We modeled responses to both sets of movies independently using the same three models: models of motion energy, object boundary contours, and visual categories. We used the encoding models to predict withheld fMRI data, and used variance partitioning to determine whether the various models explained unique or shared variance in each dataset. We found that the pattern of unique variance explained by the three models was qualitatively consistent across both datasets, with unique variance explained by boundary contours in Lateral Occipital cortex and other areas. However, the three models also shared substantially more variance in the cinematic movies, likely due to correlations between model features. For example, much of the motion energy in the cinematic movies was a result of people moving. The shared variance between all three models in the cinematic movies in particular highlights the need for complex stimulus sets in which features in different models are de-correlated from each other.

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