2019 Conference on Cognitive Computational Neuroscience

13-16 September 2019    Berlin, Germany

Technical Program

Paper Detail

Paper: PS-1A.9
Session: Poster Session 1A
Location: H Lichthof
Session Time: Saturday, September 14, 16:30 - 19:30
Presentation Time:Saturday, September 14, 16:30 - 19:30
Presentation: Poster
Publication: 2019 Conference on Cognitive Computational Neuroscience, 13-16 September 2019, Berlin, Germany
Paper Title: Using population receptive field models to elucidate spatial integration in high-level visual cortex
Manuscript:  Click here to view manuscript
License: Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
DOI: https://doi.org/10.32470/CCN.2019.1178-0
Authors: Sonia Poltoratski, Stanford University, United States; Kendrick Kay, University of Minnesota, United States; Kalanit Grill-Spector, Stanford University, United States
Abstract: While spatial information and biases have been consistently reported in high-level face regions, the functional contribution of this information toward face recognition behavior is unclear. Here, we propose that spatial integration of information plays a critical role in a hallmark phenomenon of face perception: holistic processing, or the tendency to process all features of a face concurrently rather than independently. We sought to gain insight into the neural basis of face recognition behavior by using a voxelwise encoding model of spatial selectivity to characterize the human face network using both typical face stimuli, and stimuli thought to disrupt normal face perception. We mapped population receptive fields (pRFs) using 3T fMRI in 6 participants using upright as well as inverted faces, which are thought to disrupt holistic processing. Compared to upright faces, inverted faces yielded substantial differences in measured pRF size, position, and amplitude. Further, these differences increased in magnitude along the face network hierarchy, from IOG- to pFus- and mFus-faces. These data suggest that pRFs in high-level regions reflect complex stimulus-dependent neural computations that underlie variations in recognition performance.