The goal of our lab is to understand how the human brain represents visual images and makes perceptual decisions about these images. We use a combined experimental and computational approach that seeks to develop models that characterize the stimulus transformations perfomed by the brain. Our primary measurement technique is functional magnetic resonance imaging (fMRI), which is ideally suited to identify these transformations, given its excellent spatial resolution and ability to monitor activity across the numerous areas of visual cortex. Recent increases in magnetic field strength (10.5T) are expected to provide substantial gains in spatial resolution and signal-to-noise ratio, enabling the acquisition of large, high-quality datasets that can be used to resolve functional differences across cortical layers and columns. In the spirit of reproducible research, we make freely available tools and resources (e.g. experiments, data, code) developed in the course of our research.
(For a comprehensive list of recent publications, refer to PubMed, a service provided by the National Library of Medicine.)
- Zhou J, Benson NC, Kay K, Winawer J. Compressive temporal summation in human visual cortex. J Neurosci. 2017 Nov 30. pii: 1724-1717.
- Kim D, Kay K, Shulman GL, Corbetta M. A new modular brain organization of the BOLD signal during natural vision. Cereb Cortex. 2017 Jul 13:1-17.
- Kay KN. Principles for models of neural information processing. Neuroimage. 2017; pii: S1053-8119(17)30663-8.
- Grill-Spector K, Weiner KS, Kay K, Gomez J. The functional neuroanatomy of human face perception. Annu Rev Vis Sci. 2017 Sep 15;3:167-196.
- Weiner KS, Barnett MA, Witthoft N, Golarai G, Stigliani A, Kay KN, Gomez J, Natu VS, Amunts K, Zilles K, Grill-Spector K. Defining the most probable location of the parahippocampal place area using cortex-based alignment and cross-validation. Neuroimage. 2017 Apr 18. pii: S1053-8119(17)30333-6.
- Kay KN, Yeatman JD. Bottom-up and top-down computations in word- and face-selective cortex. Elife. 2017 Feb 22;6. pii: e22341.
- Strappini F, Gilboa E, Pitzalis S, Kay K, McAvoy M, Nehorai A, Snyder AZ. Adaptive smoothing based on Gaussian processes regression increases the sensitivity and specificity of fMRI data. Hum Brain Mapp. 2017 Mar;38(3):1438-1459.
- Vu AT, Phillips JS, Kay K, Phillips ME, Johnson MR, Shinkareva SV, Tubridy S, Millin R, Grossman M, Gureckis T, Bhattacharyya R, Yacoub E. Using precise word timing information improves decoding accuracy in a multiband-accelerated multimodal reading experiment. Cogn Neuropsychol. 2016 May-Jun;33(3-4):265-75.
- Naselaris T, Kay KN. Resolving ambiguities of MVPA using explicit models of representation. Trends Cogn Sci. 2015 Oct;19(10):551-4.
- Kay KN, Weiner KS, Grill-Spector K. Attention reduces spatial uncertainty in human ventral temporal cortex. Curr Biol. 2015 Mar 2;25(5):595-600.
- Kay KN, Rokem A, Winawer J, Dougherty RF, Wandell BA. GLMdenoise: a fast, automated technique for denoising task-based fMRI data. Front Neurosci. 2013 Dec 17;7:247.
- Kay KN, Winawer J, Rokem A, Mezer A, Wandel BA. A Two-Stage Cascade Model of BOLD Responses in Human Visual Cortex. PLoS Comp Biol. 2013;9:1–16.
- Kay KN, Winawer J, Mezer A, Wandell BA. Compressive spatial summation in human visual cortex. J Neurophysiol. 2013;110:481–493.
- Naselaris T, Kay KN, Nishimoto S, Gallant JL. Encoding and decoding in fMRI. NeuroImage 2011;56:400–410.