Predicting the orientation of invisible stimuli from activity in human primary visual cortex

By John-Dylan Haynes, Geraint E. Rees. Nature Neuroscience, 2005

930 citations recorded by the source corpus.

Humans can experience aftereffects from oriented stimuli that are not consciously perceived, suggesting that such stimuli receive cortical processing. Determining the physiological substrate of such effects has proven elusive owing to the low spatial resolution of conventional human neuroimaging techniques compared to the size of orientation columns in visual cortex. Here we show that even at conventional resolutions it is possible to use fMRI to obtain a direct measure of orientation-selective processing in V1. We found that many parts of V1 show subtle but reproducible biases to oriented stimuli, and that we could accumulate this information across the whole of V1 using multivariate pattern recognition. Using this information, we could then successfully predict which one of two oriented stimuli a participant was viewing, even when masking rendered that stimulus invisible. Our findings show that conventional fMRI can be used to reveal feature-selective processing in human cortex, even for invisible stimuli.

Subfields: Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/9ed5d4718b0dbabb339fad395a97c4950c39a08a

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In Cognitive Neuroscience

Citations in the corpus

A taxonomy of external and internal attention. (2011, positive): “Furthermore , pattern classification methods allow researchers to decode what someone is attending to or anticipating (Haynes & Rees 2005, Kamitani & Tong 2005, Stokes et al. 2009).”
PyMVPA: a Python Toolbox for Multivariate Pattern Analysis of fMRI Data (2009, background): “For example, two such classier-based studies by Haynes and Rees (2005) and Kamitani and Tong (2005) were able to predict the orientation of visual stimuli from fMRI data recorded in human primary visual cortex.”
Neural bases of the non-conscious perception of emotional signals (2010, background): “, whereas non-conscious perception induces activity in subcortical or early sensory area”
High-field fMRI unveils orientation columns in humans (2008, background): “Recent studies (28, 29) have demonstrated the presence of voxels with orientation preferences in humans by using fMRI; however, these low-resolution studies lack any information regarding the intrinsic spatial organization of these preferences.”
Visual image reconstruction from human brain activity using a combination of multiscale local image decoders. (2008, background): “Of particular interest is to examine if such subjective percepts share the same representation as stimulus-evoked percepts (Kamitani and Tong, 2005, 2006; Haynes and Rees, 2005).”
Reading the mind's eye: decoding category information during mental imagery (2009, background): “…of a viewed stimulus (e.g., the orientation of a grating) (Kamitani and Tong, 2005), the attentional state of the observer (Kamitani and Tong, 2005), properties of a stimulus that was not consciously perceived (Haynes and Rees, 2005) and the identity of viewed natural images (Kay et al., 2008).”
Reading hidden intentions in the human brain. (2007, background): “For example, in visual cortex, information encoded in similar fine-grained patterns of visual cortex can be read out by pattern recognition [12, 13].”
The neural basis of visual body perception (2007, methodology): “Methods that have been developed for extracting orientation information from primary visual cortex activit”
A Primer on Pattern-Based Approaches to fMRI: Principles, Pitfalls, and Perspectives. (2015, background): “The orientation bias T reflects the degree to which a voxel responds more strongly to either a left-tilted or a right-tilted orientation stimulus (Haynes and Rees, 2005).”
Combining multivariate voxel selection and support vector machines for mapping and classification of fMRI spatial patterns (2008, neutral): “Amore restrictive formof univariate feature selection is based on the selection of voxels that show a significant difference between the two conditions (Mitchell et al., 2004, Haynes and Rees, 2005, MouraoMiranda et al., 2006).”
Less Is More: Expectation Sharpens Representations in the Primary Visual Cortex (2012, positive): “We adjudicated between these hypotheses by noninvasively measuring neural activity and representational content in the early visual cortex of human volunteers, using functional magnetic resonance imaging (fMRI) and multivariate pattern analysis (MVPA) techniques (Haxby et al., 2001; Haynes and Rees,”
Multivariate pattern analysis of fMRI: The early beginnings (2012, background): “Enthusiasm for MVPA ticked up significantly with two reports that appeared back-to-back in Nature Neuroscience in 2005 (Haynes and Rees, 2005; Kamitani and Tong, 2005), both of which showed that MVPA could be applied to a visual feature with a well-understood neural basis, namely edge orientation.”
PRoNTo: Pattern Recognition for Neuroimaging Toolbox (2013, background): “…applied to fMRI have made it possible to decode the category of an object (Spiridon and Kanwisher 2002; Cox and Savoy 2003; Shinkareva et al. 2008) and orientation of a striped pattern (Haynes and Rees 2005; Kamitani and Tong 2005) visually presented to the subject, solely from the image patterns.”