Orientation Decoding in Human Visual Cortex: New Insights from an Unbiased Perspective

The development of multivariate pattern analysis or brain “decoding” methods has substantially altered the field of fMRI research. Although these methods are highly sensitive to whether or not decodable information exists, the information they discover and make use of for decoding is often concealed within complex patterns of activation. This opacity of interpretation is embodied in influential studies showing that the orientation of visual gratings can be decoded from brain activity in human visual cortex with fMRI. Although these studies provided a compelling demonstration of the power of these methods, their findings were somewhat mysterious as the scanning resolution was insufficient to resolve orientation columns, i.e., orientation information should not have been accessible. Two theories have been put forth to account for this result, the hyperacuity account and the biased map account, both of which assume that small biases in fMRI voxels are the source of decodable information. In the present study, we use Hubel and Wiesel's (1972) classic ice-cube model of visual cortex to show that the orientation of gratings can be decoded from an unbiased representation. In our analysis, we identify patterns of activity elicited by the edges of the stimulus as the source of the decodable information. Furthermore, these activation patterns masquerade as a radial bias, a key element of the biased map account. This classic model thus sheds new light on the mystery behind orientation decoding by unveiling a new source of decodable information.

[1]  Damien J. Mannion,et al.  Orientation anisotropies in human visual cortex. , 2010, Journal of neurophysiology.

[2]  Wim Vanduffel,et al.  The Radial Bias: A Different Slant on Visual Orientation Sensitivity in Human and Nonhuman Primates , 2006, Neuron.

[3]  Colin W. G. Clifford,et al.  Discrimination of the local orientation structure of spiral Glass patterns early in human visual cortex , 2009, NeuroImage.

[4]  G. Rees,et al.  Predicting the orientation of invisible stimuli from activity in human primary visual cortex , 2005, Nature Neuroscience.

[5]  Jascha D. Swisher,et al.  Multiscale Pattern Analysis of Orientation-Selective Activity in the Primary Visual Cortex , 2010, The Journal of Neuroscience.

[6]  Geoffrey M Boynton,et al.  Imaging orientation selectivity: decoding conscious perception in V1 , 2005, Nature Neuroscience.

[7]  Hans P. Op de Beeck,et al.  Against hyperacuity in brain reading: Spatial smoothing does not hurt multivariate fMRI analyses? , 2010, NeuroImage.

[8]  Yasuhito Sawahata,et al.  Spatial smoothing hurts localization but not information: Pitfalls for brain mappers , 2010, NeuroImage.

[9]  Jeremy Freeman,et al.  Orientation Decoding Depends on Maps, Not Columns , 2011, The Journal of Neuroscience.

[10]  F. Tong,et al.  Decoding the visual and subjective contents of the human brain , 2005, Nature Neuroscience.

[11]  Jack L. Gallant,et al.  Encoding and decoding in fMRI , 2011, NeuroImage.

[12]  J D Schall,et al.  Retinal constraints on orientation specificity in cat visual cortex , 1986, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[13]  Brian A. Wandell,et al.  Population receptive field estimates in human visual cortex , 2008, NeuroImage.

[14]  T. Poggio,et al.  Hierarchical models of object recognition in cortex , 1999, Nature Neuroscience.

[15]  Sean M. Polyn,et al.  Beyond mind-reading: multi-voxel pattern analysis of fMRI data , 2006, Trends in Cognitive Sciences.

[16]  Nikolaus Kriegeskorte,et al.  fMRI orientation decoding in V1 does not require global maps or globally coherent orientation stimuli , 2012, Front. Psychol..

[17]  Nikolaus Kriegeskorte,et al.  How does an fMRI voxel sample the neuronal activity pattern: Compact-kernel or complex spatiotemporal filter? , 2010, NeuroImage.

[18]  Thomas Serre,et al.  Robust Object Recognition with Cortex-Like Mechanisms , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[19]  Gabriel Kreiman,et al.  Visual population codes : toward a common multivariate framework for cell recording and functional imaging , 2012 .

[20]  A. Leventhal,et al.  Relationship between preferred orientation and receptive field position of neurons in cat striate cortex , 1983, The Journal of comparative neurology.

[21]  Jeremy Freeman,et al.  Coarse-Scale Biases for Spirals and Orientation in Human Visual Cortex , 2013, The Journal of Neuroscience.

[22]  W. Levick,et al.  Analysis of orientation bias in cat retina , 1982, The Journal of physiology.

[23]  S. Sutherland Eye, brain and vision , 1993, Nature.

[24]  D. Hubel,et al.  Laminar and columnar distribution of geniculo‐cortical fibers in the macaque monkey , 1972, The Journal of comparative neurology.

[25]  Jean-Baptiste Poline,et al.  Inverse retinotopy: Inferring the visual content of images from brain activation patterns , 2006, NeuroImage.

[26]  J. Gallant,et al.  Identifying natural images from human brain activity , 2008, Nature.

[27]  B. Spehar,et al.  The Foveal Confluence in Human Visual Cortex , 2009, The Journal of Neuroscience.

[28]  D. Hubel,et al.  Receptive fields of single neurones in the cat's striate cortex , 1959, The Journal of physiology.