Vividness of Visual Imagery Depends on the Neural Overlap with Perception in Visual Areas

Research into the neural correlates of individual differences in imagery vividness point to an important role of the early visual cortex. However, there is also great fluctuation of vividness within individuals, such that only looking at differences between people necessarily obscures the picture. In this study, we show that variation in moment-to-moment experienced vividness of visual imagery, within human subjects, depends on the activity of a large network of brain areas, including frontal, parietal, and visual areas. Furthermore, using a novel multivariate analysis technique, we show that the neural overlap between imagery and perception in the entire visual system correlates with experienced imagery vividness. This shows that the neural basis of imagery vividness is much more complicated than studies of individual differences seemed to suggest. SIGNIFICANCE STATEMENT Visual imagery is the ability to visualize objects that are not in our direct line of sight: something that is important for memory, spatial reasoning, and many other tasks. It is known that the better people are at visual imagery, the better they can perform these tasks. However, the neural correlates of moment-to-moment variation in visual imagery remain unclear. In this study, we show that the more the neural response during imagery is similar to the neural response during perception, the more vivid or perception-like the imagery experience is.

[1]  I. Toni,et al.  Shared Representations for Working Memory and Mental Imagery in Early Visual Cortex , 2013, Current Biology.

[2]  Thomas Serre,et al.  Reading the mind's eye: Decoding category information during mental imagery , 2010, NeuroImage.

[3]  Kevin S. Brown,et al.  Cooperation between the default mode network and the frontal–parietal network in the production of an internal train of thought , 2012, Brain Research.

[4]  S. Kosslyn,et al.  Neural foundations of imagery , 2001, Nature Reviews Neuroscience.

[5]  S. Kosslyn,et al.  Brain areas underlying visual mental imagery and visual perception: an fMRI study. , 2004, Brain research. Cognitive brain research.

[6]  Frank Tong,et al.  Relationship between BOLD amplitude and pattern classification of orientation-selective activity in the human visual cortex , 2012, NeuroImage.

[7]  Rainer Goebel,et al.  Information-based functional brain mapping. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[8]  N. Logothetis,et al.  Disrupting Parietal Function Prolongs Dominance Durations in Binocular Rivalry , 2010, Current Biology.

[9]  R. Goebel,et al.  Matching two imagined clocks: the functional anatomy of spatial analysis in the absence of visual stimulation. , 2000, Cerebral cortex.

[10]  F. Tong,et al.  Decoding reveals the contents of visual working memory in early visual areas , 2009, Nature.

[11]  Karl J. Friston,et al.  Where bottom-up meets top-down: neuronal interactions during perception and imagery. , 2004, Cerebral cortex.

[12]  Joel Pearson,et al.  The sensory strength of voluntary visual imagery predicts visual working memory capacity. , 2014, Journal of vision.

[13]  Rainer Goebel,et al.  Integration of “what” and “where” in frontal cortex during visual imagery of scenes , 2012, NeuroImage.

[14]  Jean-Luc Velay,et al.  Visual presentation of single letters activates a premotor area involved in writing , 2003, NeuroImage.

[15]  Joel Pearson,et al.  Mental Imagery and Visual Working Memory , 2011, PloS one.

[16]  Frank Tong,et al.  The Functional Impact of Mental Imagery on Conscious Perception , 2008, Current Biology.

[17]  Leslie G. Ungerleider,et al.  Visual Imagery of Famous Faces: Effects of Memory and Attention Revealed by fMRI , 2002, NeuroImage.

[18]  Marcel A. J. van Gerven,et al.  Deep Neural Networks Reveal a Gradient in the Complexity of Neural Representations across the Ventral Stream , 2014, The Journal of Neuroscience.

[19]  Martin N. Hebart,et al.  Parietal and early visual cortices encode working memory content across mental transformations , 2015, NeuroImage.

[20]  Yi Chen,et al.  Statistical inference and multiple testing correction in classification-based multi-voxel pattern analysis (MVPA): Random permutations and cluster size control , 2011, NeuroImage.

[21]  Takashi Hanakawa,et al.  Differential effect of double-pulse TMS applied to dorsal premotor cortex and precuneus during internal operation of visuospatial information , 2010, NeuroImage.

[22]  D. F. Marks,et al.  Visual imagery differences in the recall of pictures. , 1973, British journal of psychology.

[23]  Á. Pascual-Leone,et al.  Spontaneous fluctuations in posterior alpha-band EEG activity reflect variability in excitability of human visual areas. , 2008, Cerebral cortex.

[24]  Steve Majerus,et al.  Fluctuations of Attentional Networks and Default Mode Network during the Resting State Reflect Variations in Cognitive States: Evidence from a Novel Resting-state Experience Sampling Method , 2017, Journal of Cognitive Neuroscience.

[25]  Arnaud Delorme,et al.  Frontal midline EEG dynamics during working memory , 2005, NeuroImage.

[26]  Wolf Singer,et al.  Smaller Primary Visual Cortex Is Associated with Stronger, but Less Precise Mental Imagery. , 2016, Cerebral cortex.

[27]  Christian F. Doeller,et al.  Reinstatement of Associative Memories in Early Visual Cortex Is Signaled by the Hippocampus , 2014, The Journal of Neuroscience.

[28]  Rainer Goebel,et al.  Dynamic Premotor-to-Parietal Interactions during Spatial Imagery , 2008, The Journal of Neuroscience.

[29]  John-Dylan Haynes,et al.  Searchlight-based multi-voxel pattern analysis of fMRI by cross-validated MANOVA , 2014, NeuroImage.

[30]  M. Fox,et al.  Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging , 2007, Nature Reviews Neuroscience.

[31]  P. Montague,et al.  Vividness of mental imagery: Individual variability can be measured objectively , 2007, Vision Research.

[32]  Chris I. Baker,et al.  Disentangling visual imagery and perception of real-world objects , 2012, NeuroImage.

[33]  Brice A. Kuhl,et al.  Reconstructing Perceived and Retrieved Faces from Activity Patterns in Lateral Parietal Cortex , 2016, The Journal of Neuroscience.

[34]  A. Ishai,et al.  Distributed neural systems for the generation of visual images , 2000, NeuroImage.

[35]  Janneke F. M. Jehee,et al.  Less Is More: Expectation Sharpens Representations in the Primary Visual Cortex , 2012, Neuron.