Attention Improves Object Representation in Visual Cortical Field Potentials

Selective attention improves perception and modulates neuronal responses, but how attention-dependent changes of cortical activity improve the processing of attended objects is an open question. Changes in total signal strength or enhancements in signal-to-noise ratio have been proposed as putative mechanisms. However, it is still not clear whether, and to what extent, these processes contribute to the large perceptual improvements. We studied the ability to discriminate states of activity in visual cortex evoked by differently shaped objects depending on selective attention in monkeys. We found that gamma-band activity from V4 and V1 contains a high amount of information about stimulus shape, which increases for V4 recordings considerably with attention in successful trials, but not in case of behavioral errors. This effect resulted from enhanced differences between the stimulus-specific distributions of power spectral amplitudes. It could be explained neither by enhancements of signal-to-noise ratios, nor by changes in total signal power. Instead our results indicate that attention causes underlying cortical network states to become more distinct for different stimuli, providing a new neurophysiological explanation for improvements of behavioral performance by attention. The absence of the enhancement in discriminability in trials with behavioral errors demonstrates the relevance of this novel neural mechanism for perception.

[1]  W. Singer,et al.  Stimulus‐Dependent Neuronal Oscillations in Cat Visual Cortex: Inter‐Columnar Interaction as Determined by Cross‐Correlation Analysis , 1990, The European journal of neuroscience.

[2]  R. Elul The genesis of the EEG. , 1971, International review of neurobiology.

[3]  Idan Segev,et al.  Excitable dendrites and spines: earlier theoretical insights elucidate recent direct observations , 1998, Trends in Neurosciences.

[4]  W. J. Nowack Neocortical Dynamics and Human EEG Rhythms , 1995, Neurology.

[5]  L. Bour,et al.  The Double Magnetic Induction Method for Measuring Eye Movement - Results in Monkey and Man , 1984, IEEE Transactions on Biomedical Engineering.

[6]  Dawn M. Taylor,et al.  Direct Cortical Control of 3D Neuroprosthetic Devices , 2002, Science.

[7]  Bruce L McNaughton,et al.  Cannabinoids reveal importance of spike timing coordination in hippocampal function , 2006, Nature Neuroscience.

[8]  D. T. Kaplan,et al.  Nonlinearity and nonstationarity: the use of surrogate data in interpreting fluctuations , 1997 .

[9]  J. Wolfe,et al.  Preattentive Object Files: Shapeless Bundles of Basic Features , 1997, Vision Research.

[10]  C. Gross,et al.  Visual topography of V2 in the macaque , 1981, The Journal of comparative neurology.

[11]  R. Desimone,et al.  Modulation of Oscillatory Neuronal Synchronization by Selective Visual Attention , 2001, Science.

[12]  W. Singer,et al.  Modulation of Neuronal Interactions Through Neuronal Synchronization , 2007, Science.

[13]  W. Singer,et al.  Stimulus‐Dependent Neuronal Oscillations in Cat Visual Cortex: Receptive Field Properties and Feature Dependence , 1990, The European journal of neuroscience.

[14]  S. Treue,et al.  Feature-Based Attention Increases the Selectivity of Population Responses in Primate Visual Cortex , 2004, Current Biology.

[15]  R. Desimone,et al.  Selective attention gates visual processing in the extrastriate cortex. , 1985, Science.

[16]  D. Tucker,et al.  EEG coherency. I: Statistics, reference electrode, volume conduction, Laplacians, cortical imaging, and interpretation at multiple scales. , 1997, Electroencephalography and clinical neurophysiology.

[17]  D. V. van Essen,et al.  Spatial Attention Effects in Macaque Area V4 , 1997, The Journal of Neuroscience.

[18]  R. Desimone,et al.  Attention Increases Sensitivity of V4 Neurons , 2000, Neuron.

[19]  R. Desimone,et al.  Gamma-band synchronization in visual cortex predicts speed of change detection , 2006, Nature.

[20]  Robert Desimone,et al.  Parallel and Serial Neural Mechanisms for Visual Search in Macaque Area V4 , 2005, Science.

[21]  Bernhard Schölkopf,et al.  Estimating the Support of a High-Dimensional Distribution , 2001, Neural Computation.

[22]  Carrie J. McAdams,et al.  Effects of Attention on Orientation-Tuning Functions of Single Neurons in Macaque Cortical Area V4 , 1999, The Journal of Neuroscience.

[23]  W. Newsome,et al.  Local Field Potential in Cortical Area MT: Stimulus Tuning and Behavioral Correlations , 2006, The Journal of Neuroscience.

[24]  A. Toga,et al.  The Rhesus Monkey Brain in Stereotaxic Coordinates , 1999 .

[25]  M K Habib,et al.  Dynamics of neuronal firing correlation: modulation of "effective connectivity". , 1989, Journal of neurophysiology.

[26]  P. Fries A mechanism for cognitive dynamics: neuronal communication through neuronal coherence , 2005, Trends in Cognitive Sciences.

[27]  I. Rock,et al.  Perception without attention: Results of a new method , 1992, Cognitive Psychology.

[28]  C. Gross,et al.  Visuotopic organization and extent of V3 and V4 of the macaque , 1988, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[29]  Jude F. Mitchell,et al.  Differential Attention-Dependent Response Modulation across Cell Classes in Macaque Visual Area V4 , 2007, Neuron.

[30]  R. Desimone,et al.  Competitive Mechanisms Subserve Attention in Macaque Areas V2 and V4 , 1999, The Journal of Neuroscience.

[31]  John Moody,et al.  Fast Learning in Networks of Locally-Tuned Processing Units , 1989, Neural Computation.

[32]  Carrie J. McAdams,et al.  Effects of Attention on the Reliability of Individual Neurons in Monkey Visual Cortex , 1999, Neuron.

[33]  W Singer,et al.  Visual feature integration and the temporal correlation hypothesis. , 1995, Annual review of neuroscience.

[34]  Wolf Singer,et al.  On the Role of Neural Synchrony in the Primate Visual Cortex , 1996 .

[35]  George L. Gerstein,et al.  From Neuron to Assembly: Neuronal Organization and Stimulus Representation , 1986 .

[36]  G. Karmos,et al.  Entrainment of Neuronal Oscillations as a Mechanism of Attentional Selection , 2008, Science.

[37]  T. Womelsdorf,et al.  Dynamic shifts of visual receptive fields in cortical area MT by spatial attention , 2006, Nature Neuroscience.

[38]  J. Bullier,et al.  Cortical mapping of gamma oscillations in areas V1 and V4 of the macaque monkey , 2001, Visual Neuroscience.

[39]  R. Eckhorn,et al.  High frequency (60-90 Hz) oscillations in primary visual cortex of awake monkey. , 1993, Neuroreport.

[40]  R. Reid,et al.  Synaptic Interactions between Thalamic Inputs to Simple Cells in Cat Visual Cortex , 2000, The Journal of Neuroscience.

[41]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[42]  J. Reynolds,et al.  Attentional modulation of visual processing. , 2004, Annual review of neuroscience.

[43]  Jerald D. Kralik,et al.  Real-time prediction of hand trajectory by ensembles of cortical neurons in primates , 2000, Nature.

[44]  John H. R. Maunsell,et al.  Attentional modulation of visual motion processing in cortical areas MT and MST , 1996, Nature.

[45]  James Theiler,et al.  Testing for nonlinearity in time series: the method of surrogate data , 1992 .

[46]  W. Freiwald,et al.  Coherent oscillatory activity in monkey area v4 predicts successful allocation of attention. , 2005, Cerebral cortex.

[47]  Schreiber,et al.  Improved Surrogate Data for Nonlinearity Tests. , 1996, Physical review letters.

[48]  E. Niebur,et al.  Growth patterns in the developing brain detected by using continuum mechanical tensor maps , 2022 .

[49]  R. Desimone,et al.  The Effects of Visual Stimulation and Selective Visual Attention on Rhythmic Neuronal Synchronization in Macaque Area V4 , 2008, The Journal of Neuroscience.

[50]  M. Carandini,et al.  Stimulus contrast modulates functional connectivity in visual cortex , 2009, Nature Neuroscience.

[51]  M. Carandini,et al.  Local Origin of Field Potentials in Visual Cortex , 2009, Neuron.

[52]  C. Gray,et al.  Adaptive Coincidence Detection and Dynamic Gain Control in Visual Cortical Neurons In Vivo , 2003, Neuron.

[53]  C. Malsburg Nervous Structures with Dynamical Links , 1985 .

[54]  B. C. Motter Focal attention produces spatially selective processing in visual cortical areas V1, V2, and V4 in the presence of competing stimuli. , 1993, Journal of neurophysiology.

[55]  Bernhard Schölkopf,et al.  New Support Vector Algorithms , 2000, Neural Computation.

[56]  Alan S. Gevins,et al.  Analysis of the Electromagnetic Signals of the Human Brain: Milestones, Obstacles, and Goals , 1984, IEEE Transactions on Biomedical Engineering.

[57]  Richard Kronland-Martinet,et al.  Analysis of Sound Patterns through Wavelet transforms , 1987, Int. J. Pattern Recognit. Artif. Intell..

[58]  I. Rock,et al.  The effect of inattention on form perception. , 1981, Journal of experimental psychology. Human perception and performance.

[59]  Olivier Bertrand,et al.  Scalp Current Density Mapping: Value and Estimation from Potential Data , 1987, IEEE Transactions on Biomedical Engineering.

[60]  B. Sakmann,et al.  Cortex Is Driven by Weak but Synchronously Active Thalamocortical Synapses , 2006, Science.