Brain signals for spatial attention predict performance in a motion discrimination task.

The reliability of visual perception is thought to reflect the quality of the sensory information. However, we show that subjects' performance can be predicted, trial-by-trial, by neural activity that precedes the onset of a sensory stimulus. Using functional MRI (fMRI), we studied how neural mechanisms that mediate spatial attention affect the accuracy of a motion discrimination judgment. The amplitude of blood oxygen level-dependent (BOLD) signals after a cue directing spatial attention predicted subjects' accuracy on 60-75% of the trials. Widespread predictive signals, which included dorsal parietal, visual extra-striate, prefrontal and sensory-motor cortex, depended on whether the cue correctly specified the stimulus location. Therefore, these signals indicate the degree of utilization of the cued information and play a role in the control of spatial attention. We conclude that variability in perceptual performance can be partly explained by the variability in endogenous, preparatory processes and that BOLD signals can be used to forecast human behavior.

[1]  D. Gitelman,et al.  Monetary incentives enhance processing in brain regions mediating top-down control of attention. , 2005, Cerebral cortex.

[2]  Luiz Pessoa,et al.  Quantitative prediction of perceptual decisions during near-threshold fear detection. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[3]  D. Knill,et al.  The Bayesian brain: the role of uncertainty in neural coding and computation , 2004, Trends in Neurosciences.

[4]  W. Newsome,et al.  Matching Behavior and the Representation of Value in the Parietal Cortex , 2004, Science.

[5]  W. Newsome,et al.  Representation of an abstract perceptual decision in macaque superior colliculus. , 2004, Journal of neurophysiology.

[6]  E. Rolls,et al.  The functional neuroanatomy of the human orbitofrontal cortex: evidence from neuroimaging and neuropsychology , 2004, Progress in Neurobiology.

[7]  A. Yuille,et al.  Object perception as Bayesian inference. , 2004, Annual review of psychology.

[8]  A. Grinvald,et al.  Spontaneously emerging cortical representations of visual attributes , 2003, Nature.

[9]  Martin Lauritzen,et al.  Brain Function and Neurophysiological Correlates of Signals Used in Functional Neuroimaging , 2003, The Journal of Neuroscience.

[10]  M. Goldberg,et al.  Neuronal Activity in the Lateral Intraparietal Area and Spatial Attention , 2003, Science.

[11]  John H. R. Maunsell,et al.  Dynamics of neuronal responses in macaque MT and VIP during motion detection , 2002, Nature Neuroscience.

[12]  Leslie G. Ungerleider,et al.  Neural Correlates of Visual Working Memory fMRI Amplitude Predicts Task Performance , 2002, Neuron.

[13]  D. Heeger,et al.  Retinotopy and Functional Subdivision of Human Areas MT and MST , 2002, The Journal of Neuroscience.

[14]  J. Maunsell,et al.  Attentional Modulation of Behavioral Performance and Neuronal Responses in Middle Temporal and Ventral Intraparietal Areas of Macaque Monkey , 2002, The Journal of Neuroscience.

[15]  M. Corbetta,et al.  Control of goal-directed and stimulus-driven attention in the brain , 2002, Nature Reviews Neuroscience.

[16]  Bijan Pesaran,et al.  Temporal structure in neuronal activity during working memory in macaque parietal cortex , 2000, Nature Neuroscience.

[17]  Leslie G. Ungerleider,et al.  The neural basis of biased competition in human visual cortex , 2001, Neuropsychologia.

[18]  W. Newsome,et al.  Neural basis of a perceptual decision in the parietal cortex (area LIP) of the rhesus monkey. , 2001, Journal of neurophysiology.

[19]  N. Logothetis,et al.  Neurophysiological investigation of the basis of the fMRI signal , 2001, Nature.

[20]  A. Parker,et al.  Perceptually Bistable Three-Dimensional Figures Evoke High Choice Probabilities in Cortical Area MT , 2001, The Journal of Neuroscience.

[21]  E. Rolls,et al.  Abstract reward and punishment representations in the human orbitofrontal cortex , 2001, Nature Neuroscience.

[22]  J. C. Johnston,et al.  Attention and performance. , 2001, Annual review of psychology.

[23]  Mingsha Zhang,et al.  Neuronal switching of sensorimotor transformations for antisaccades , 2000, Nature.

[24]  N. Kanwisher,et al.  Visual attention: Insights from brain imaging , 2000, Nature Reviews Neuroscience.

[25]  D. Heeger,et al.  Activity in primary visual cortex predicts performance in a visual detection task , 2000, Nature Neuroscience.

[26]  C L Colby,et al.  Visual, saccade-related, and cognitive activation of single neurons in monkey extrastriate area V3A. , 2000, Journal of neurophysiology.

[27]  Alan C. Evans,et al.  A new anatomical landmark for reliable identification of human area V5/MT: a quantitative analysis of sulcal patterning. , 2000, Cerebral cortex.

[28]  M. Corbetta,et al.  Voluntary orienting is dissociated from target detection in human posterior parietal cortex , 2000, Nature Neuroscience.

[29]  G. Mangun,et al.  The neural mechanisms of top-down attentional control , 2000, Nature Neuroscience.

[30]  M. Corbetta,et al.  Areas Involved in Encoding and Applying Directional Expectations to Moving Objects , 1999, The Journal of Neuroscience.

[31]  Michael L. Platt,et al.  Neural correlates of decision variables in parietal cortex , 1999, Nature.

[32]  Leslie G. Ungerleider,et al.  Increased Activity in Human Visual Cortex during Directed Attention in the Absence of Visual Stimulation , 1999, Neuron.

[33]  M. D’Esposito,et al.  The Variability of Human, BOLD Hemodynamic Responses , 1998, NeuroImage.

[34]  M. Corbetta,et al.  A Common Network of Functional Areas for Attention and Eye Movements , 1998, Neuron.

[35]  J. Desmond,et al.  Making memories: brain activity that predicts how well visual experience will be remembered. , 1998, Science.

[36]  A. Dale,et al.  Building memories: remembering and forgetting of verbal experiences as predicted by brain activity. , 1998, Science.

[37]  G A Orban,et al.  Human brain regions involved in direction discrimination. , 1998, Journal of neurophysiology.

[38]  H Barlow,et al.  Correspondence Noise and Signal Pooling in the Detection of Coherent Visual Motion , 1997, The Journal of Neuroscience.

[39]  A. Dale,et al.  Functional Analysis of V3A and Related Areas in Human Visual Cortex , 1997, The Journal of Neuroscience.

[40]  D G Pelli,et al.  The VideoToolbox software for visual psychophysics: transforming numbers into movies. , 1997, Spatial vision.

[41]  D H Brainard,et al.  The Psychophysics Toolbox. , 1997, Spatial vision.

[42]  J. Movshon,et al.  A computational analysis of the relationship between neuronal and behavioral responses to visual motion , 1996, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[43]  K. H. Britten,et al.  A relationship between behavioral choice and the visual responses of neurons in macaque MT , 1996, Visual Neuroscience.

[44]  J. Maunsell,et al.  Responses of neurons in the parietal and temporal visual pathways during a motion task , 1994, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[45]  William T. Freeman,et al.  The generic viewpoint assumption in a framework for visual perception , 1994, Nature.

[46]  John Harris,et al.  Vision: Coding and efficiency , 1994, Image Vis. Comput..

[47]  R. Desimone,et al.  Activity of neurons in anterior inferior temporal cortex during a short- term memory task , 1993, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[48]  Richard S. J. Frackowiak,et al.  Area V5 of the human brain: evidence from a combined study using positron emission tomography and magnetic resonance imaging. , 1993, Cerebral cortex.

[49]  J. Movshon,et al.  The analysis of visual motion: a comparison of neuronal and psychophysical performance , 1992, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[50]  Colin Blakemore,et al.  Vision: Coding and Efficiency , 1991 .

[51]  M Corbetta,et al.  Attentional modulation of neural processing of shape, color, and velocity in humans. , 1990, Science.

[52]  K. H. Britten,et al.  Neuronal correlates of a perceptual decision , 1989, Nature.

[53]  W. Geisler Sequential ideal-observer analysis of visual discriminations. , 1989, Psychological review.

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

[55]  A. Watson,et al.  Quest: A Bayesian adaptive psychometric method , 1983, Perception & psychophysics.

[56]  M. Posner,et al.  Attention and the detection of signals. , 1980, Journal of experimental psychology.

[57]  A. Holden Vertebrate Photoreception , 1979 .

[58]  D. Cox,et al.  The analysis of binary data , 1971 .

[59]  John A. Nelder,et al.  A Simplex Method for Function Minimization , 1965, Comput. J..

[60]  R J HERRNSTEIN,et al.  Relative and absolute strength of response as a function of frequency of reinforcement. , 1961, Journal of the experimental analysis of behavior.