The time course of natural scene perception with reduced attention.

Attention is thought to impose an informational bottleneck on vision by selecting particular information from visual scenes for enhanced processing. Behavioral evidence suggests, however, that some scene information is extracted even when attention is directed elsewhere. Here, we investigated the neural correlates of this ability by examining how attention affects electrophysiological markers of scene perception. In two electro-encephalography (EEG) experiments, human subjects categorized real-world scenes as manmade or natural (full attention condition) or performed tasks on unrelated stimuli in the center or periphery of the scenes (reduced attention conditions). Scene processing was examined in two ways: traditional trial averaging was used to assess the presence of a categorical manmade/natural distinction in event-related potentials, whereas single-trial analyses assessed whether EEG activity was modulated by scene statistics that are diagnostic of naturalness of individual scenes. The results indicated that evoked activity up to 250 ms was unaffected by reduced attention, showing intact categorical differences between manmade and natural scenes and strong modulations of single-trial activity by scene statistics in all conditions. Thus initial processing of both categorical and individual scene information remained intact with reduced attention. Importantly, however, attention did have profound effects on later evoked activity; full attention on the scene resulted in prolonged manmade/natural differences, increased neural sensitivity to scene statistics, and enhanced scene memory. These results show that initial processing of real-world scene information is intact with diminished attention but that the depth of processing of this information does depend on attention.

[1]  Neil A. Macmillan,et al.  Detection Theory: A User's Guide , 1991 .

[2]  Arnold W. M. Smeulders,et al.  Brain responses strongly correlate with Weibull image statistics when processing natural images. , 2009, Journal of vision.

[3]  D J Field,et al.  Local Contrast in Natural Images: Normalisation and Coding Efficiency , 2000, Perception.

[4]  J. Marshall,et al.  Hemispheric asymmetries in global⧹local processing are modulated by perceptual salience , 1998, Neuropsychologia.

[5]  G. Woodman,et al.  Event-related potential studies of attention , 2000, Trends in Cognitive Sciences.

[6]  Kendrick N. Kay,et al.  Attention Reduces Spatial Uncertainty in Human Ventral Temporal Cortex , 2015, Current Biology.

[7]  Bruce C Hansen,et al.  Different spatial frequency bands selectively signal for natural image statistics in the early visual system. , 2012, Journal of neurophysiology.

[8]  S. Dakin,et al.  Context influences contour integration. , 2009, Journal of vision.

[9]  Steven W. Zucker,et al.  Local Scale Control for Edge Detection and Blur Estimation , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  R. Rosenholtz,et al.  A summary statistic representation in peripheral vision explains visual search. , 2009, Journal of vision.

[11]  Ana B. Chica,et al.  Attentional Routes to Conscious Perception , 2012, Front. Psychology.

[12]  Victor A. F. Lamme,et al.  The influence of inattention on the neural correlates of scene segmentation , 2006, Brain Research.

[13]  Eero P. Simoncelli,et al.  Metamers of the ventral stream , 2011, Nature Neuroscience.

[14]  Gidon Felsen,et al.  A natural approach to studying vision , 2005, Nature Neuroscience.

[15]  S. Thorpe,et al.  Rapid categorization of foveal and extrafoveal natural images: Associated ERPs and effects of lateralization , 2005, Brain and Cognition.

[16]  M. Boucart,et al.  Scene perception in age-related macular degeneration. , 2010, Investigative ophthalmology & visual science.

[17]  M. Potter Meaning in visual search. , 1975, Science.

[18]  Victor A. F. Lamme,et al.  Low-level contrast statistics are diagnostic of invariance of natural textures , 2012, Front. Comput. Neurosci..

[19]  Leslie G. Ungerleider Two cortical visual systems , 1982 .

[20]  J. Rieger,et al.  BOLD responses in human V1 to local structure in natural scenes: Implications for theories of visual coding. , 2013, Journal of vision.

[21]  G. Woodman,et al.  Neural fate of ignored stimuli: dissociable effects of perceptual and working memory load , 2004, Nature Neuroscience.

[22]  F. Perrin,et al.  Spherical splines for scalp potential and current density mapping. , 1989, Electroencephalography and clinical neurophysiology.

[23]  A. Treisman,et al.  Perception of objects in natural scenes: is it really attention free? , 2005, Journal of experimental psychology. Human perception and performance.

[24]  A. Woods,et al.  Context Modulates the Contribution of Time and Space in Causal Inference , 2012, Front. Psychology.

[25]  Krista A. Ehinger,et al.  Rethinking the Role of Top-Down Attention in Vision: Effects Attributable to a Lossy Representation in Peripheral Vision , 2011, Front. Psychology.

[26]  Bruce C Hansen,et al.  The role of higher order image statistics in masking scene gist recognition , 2010, Attention, perception & psychophysics.

[27]  J L Gallant,et al.  Sparse coding and decorrelation in primary visual cortex during natural vision. , 2000, Science.

[28]  Aude Oliva,et al.  Spatial ensemble statistics are efficient codes that can be represented with reduced attention , 2009, Proceedings of the National Academy of Sciences.

[29]  Michèle Fabre-Thorpe,et al.  Interaction of top-down and bottom-up processing in the fast visual analysis of natural scenes. , 2004, Brain research. Cognitive brain research.

[30]  G. Mangun,et al.  Neural Mechanisms of Global and Local Processing: A Combined PET and ERP Study , 1998, Journal of Cognitive Neuroscience.

[31]  A. Keil,et al.  The influence of response competition on cerebral asymmetries for processing hierarchical stimuli revealed by ERP recordings , 2002, Experimental Brain Research.

[32]  A. Oliva,et al.  Coarse Blobs or Fine Edges? Evidence That Information Diagnosticity Changes the Perception of Complex Visual Stimuli , 1997, Cognitive Psychology.

[33]  V. Lamme,et al.  The distinct modes of vision offered by feedforward and recurrent processing , 2000, Trends in Neurosciences.

[34]  Bruno A Olshausen,et al.  Timecourse of neural signatures of object recognition. , 2003, Journal of vision.

[35]  John H. R. Maunsell,et al.  Visual processing in monkey extrastriate cortex. , 1987, Annual review of neuroscience.

[36]  Michelle R. Greene,et al.  Scene categorization at large visual eccentricities , 2013, Vision Research.

[37]  L. Robertson,et al.  Neuropsychological contributions to theories of part/whole organization , 1991, Cognitive Psychology.

[38]  R. Marois,et al.  Capacity limits of information processing in the brain , 2005, Trends in Cognitive Sciences.

[39]  A. Mack,et al.  Gist perception requires attention , 2012 .

[40]  A. Smeulders,et al.  A Biologically Plausible Model for Rapid Natural Image Identi cation , 2009 .

[41]  Antonio Torralba,et al.  Statistics of natural image categories , 2003, Network.

[42]  Radoslaw Martin Cichy,et al.  Resolving human object recognition in space and time , 2014, Nature Neuroscience.

[43]  R. Rosenholtz,et al.  A summary-statistic representation in peripheral vision explains visual crowding. , 2009, Journal of vision.

[44]  G. Volberg,et al.  On the role of response conflicts and stimulus position for hemispheric differences in global/local processing: an ERP study , 2004, Neuropsychologia.

[45]  M. Koivisto,et al.  Recurrent Processing in V1/V2 Contributes to Categorization of Natural Scenes , 2011, The Journal of Neuroscience.

[46]  Lester C. Loschky,et al.  The contributions of central versus peripheral vision to scene gist recognition. , 2009, Journal of vision.

[47]  David J. Field,et al.  Emergence of simple-cell receptive field properties by learning a sparse code for natural images , 1996, Nature.

[48]  M. Seghier,et al.  The Neural Substrates and Timing of Top–Down Processes during Coarse-to-Fine Categorization of Visual Scenes: A Combined fMRI and ERP Study , 2010, Journal of Cognitive Neuroscience.

[49]  Abel G. Oliva,et al.  Gist of a scene , 2005 .

[50]  Michèle Fabre-Thorpe,et al.  Rapid processing of complex natural scenes: A role for the magnocellular visual pathways? , 1999, Neurocomputing.

[51]  E Donchin,et al.  A new method for off-line removal of ocular artifact. , 1983, Electroencephalography and clinical neurophysiology.

[52]  M. A. Bouman,et al.  Opponent color coding: A mechanistic model and a new metric for color space , 1972, Kybernetik.

[53]  P. Perona,et al.  Rapid natural scene categorization in the near absence of attention , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[54]  Zijiang J. He,et al.  Vertical and horizontal references determined by linear perspective and optic flow information , 2010 .

[55]  Lynn A. Olzak,et al.  Contributions of contrast gain and response gain in pattern masking , 2010 .

[56]  Antonio Torralba,et al.  Building the gist of a scene: the role of global image features in recognition. , 2006, Progress in brain research.

[57]  Marina Schmid,et al.  An Introduction To The Event Related Potential Technique , 2016 .

[58]  Michelle R. Greene,et al.  Visual search in scenes involves selective and nonselective pathways , 2011, Trends in Cognitive Sciences.

[59]  D. Navon Forest before trees: The precedence of global features in visual perception , 1977, Cognitive Psychology.

[60]  U. Neisser VISUAL SEARCH. , 1964, Scientific American.

[61]  A. Kappers,et al.  Tactile perception of thermal diffusivity , 2009, Attention, perception & psychophysics.

[62]  Song-Chun Zhu,et al.  Prior Learning and Gibbs Reaction-Diffusion , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[63]  M. Carandini,et al.  The Suppressive Field of Neurons in Lateral Geniculate Nucleus , 2005, The Journal of Neuroscience.

[64]  P. Perona,et al.  Why does natural scene categorization require little attention? Exploring attentional requirements for natural and synthetic stimuli , 2005 .

[65]  Shingo Yamagata,et al.  Cerebral Asymmetry of the “Top-Down” Allocation of Attention to Global and Local Features , 2000, The Journal of Neuroscience.

[66]  A. Oliva,et al.  From Blobs to Boundary Edges: Evidence for Time- and Spatial-Scale-Dependent Scene Recognition , 1994 .

[67]  Tandra Ghose,et al.  Generalization between canonical and non-canonical views in object recognition. , 2013, Journal of vision.

[68]  S. Thorpe,et al.  How parallel is visual processing in the ventral pathway? , 2004, Trends in Cognitive Sciences.

[69]  A. Treisman How the deployment of attention determines what we see , 2006, Visual cognition.

[70]  Philippe G. Schyns,et al.  Beyond Gist , 2014, Psychological science.

[71]  Victor A. F. Lamme,et al.  Spatially Pooled Contrast Responses Predict Neural and Perceptual Similarity of Naturalistic Image Categories , 2012, PLoS Comput. Biol..

[72]  Dwight J. Kravitz,et al.  The ventral visual pathway: an expanded neural framework for the processing of object quality , 2013, Trends in Cognitive Sciences.

[73]  Shihui Han,et al.  Neural mechanisms of global/local processing of bilateral visual inputs: an ERP study , 2005, Clinical Neurophysiology.

[74]  Guillaume A. Rousselet,et al.  Parallel processing in high-level categorization of natural images , 2002, Nature Neuroscience.

[75]  M. Jenkin,et al.  Change Blindness: Implications for the Nature of Visual Attention , 2001 .

[76]  Li Fei-Fei,et al.  Binding is a local problem for natural objects and scenes , 2005, Vision Research.

[77]  Sabine Kastner,et al.  Visual attention as a multilevel selection process , 2004, Cognitive, affective & behavioral neuroscience.

[78]  George A. Alvarez,et al.  Natural-Scene Perception Requires Attention , 2011, Psychological science.

[79]  Michelle R. Greene,et al.  The Briefest of Glances: The Time Course of Natural Scene Understanding , 2009 .

[80]  Ronald A. Rensink,et al.  Change blindness: past, present, and future , 2005, Trends in Cognitive Sciences.

[81]  J. Wolfe,et al.  Is visual attention required for robust picture memory? , 2007, Vision Research.

[82]  Ronald Hübner,et al.  Functional hemispheric differences for the categorization of global and local information in naturalistic stimuli , 2009, Brain and Cognition.

[83]  Sennay Ghebreab,et al.  From Image Statistics to Scene Gist: Evoked Neural Activity Reveals Transition from Low-Level Natural Image Structure to Scene Category , 2013, The Journal of Neuroscience.

[84]  G. Mangun,et al.  Perceptual Load and Visuocortical Processing: Event-Related Potentials Reveal Sensory-Level Selection , 2001, Psychological science.

[85]  A. Torralba,et al.  The role of context in object recognition , 2007, Trends in Cognitive Sciences.

[86]  K. Dobkins,et al.  Attentional effects on contrast discrimination in humans: evidence for both contrast gain and response gain , 2004, Vision Research.

[87]  Guillaume A. Rousselet,et al.  Early ERPs to faces: aging, luminance, and individual differences , 2013, Front. Psychol..

[88]  S. Kastner,et al.  Attention in the real world: toward understanding its neural basis , 2014, Trends in Cognitive Sciences.

[89]  S. Thorpe,et al.  The Time Course of Visual Processing: From Early Perception to Decision-Making , 2001, Journal of Cognitive Neuroscience.

[90]  Christoph M. Michel,et al.  Hemispheric specialization of human inferior temporal cortex during coarse-to-fine and fine-to-coarse analysis of natural visual scenes , 2005, NeuroImage.

[91]  P. Mamassian Depth, but not surface orientation, from binocular disparities , 2010 .

[92]  N. Lavie Perceptual load as a necessary condition for selective attention. , 1995, Journal of experimental psychology. Human perception and performance.

[93]  E. Yund,et al.  An ERP study of the global precedence effect: the role of spatial frequency , 2003, Clinical Neurophysiology.

[94]  Jochen Braun,et al.  Natural scenes upset the visual applecart , 2003, Trends in Cognitive Sciences.