Estimation of the Timing of Human Visual Perception from Magnetoencephalography

To explore the timing and the underlying neural dynamics of visual perception, we analyzed the relationship between the manual reaction time (RT) to the onset of a visual stimulus and the time course of the evoked neural response simultaneously measured by magnetoencephalography (MEG). The visual stimuli were a transition from incoherent to coherent motion of random dots and an onset of a chromatic grating from a uniform field, which evoke neural responses in different cortical sites. For both stimuli, changes in median RT with changing stimulus strength (motion coherence or chromatic contrast) were accurately predicted, with a stimulus-independent postdetection delay, from the time that the temporally integrated MEG response crossed a threshold (integrator model). In comparison, the prediction of RT was less accurate from the peak MEG latency, or from the time that the nonintegrated MEG response crossed a threshold (level detector model). The integrator model could also account for, at least partially, intertrial changes in RT or in perception (hit/miss) to identical stimuli. Although we examined MEG–RT relationships mainly for data averaged over trials, the integrator model could show some correlations even for single-trial data. The model predictions deteriorated when only early visual responses presumably originating from the striate cortex were used as the input to the integrator model. Our results suggest that the perceptions for visual stimulus appearances are established in extrastriate areas [around MT (middle temporal visual area) for motion and around V4 (fourth visual area) for color] ∼150–200 ms before subjects manually react to the stimulus.

[1]  H G Vaughan,et al.  The functional relation of visual evoked response and reaction time to stimulus intensity. , 1966, Vision research.

[2]  Grice Gr,et al.  Stimulus intensity and response evocation. , 1968 .

[3]  N Osaka,et al.  VEP latency and RT as power functions of luminance in the peripheral visual field. , 1978, Electroencephalography and clinical neurophysiology.

[4]  S. Zeki Functional specialisation in the visual cortex of the rhesus monkey , 1978, Nature.

[5]  D. F. Fisher,et al.  Eye movements : cognition and visual perception , 1982 .

[6]  P. Lennie,et al.  Chromatic mechanisms in lateral geniculate nucleus of macaque. , 1984, The Journal of physiology.

[7]  D. Jeffreys,et al.  The influence of spatial frequency on the reaction times and evoked potentials recorded to grating pattern stimuli , 1985, Vision Research.

[8]  W. Newsome,et al.  A selective impairment of motion perception following lesions of the middle temporal visual area (MT) , 1988, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[9]  P. Jaśkowski,et al.  VEP latency and some properties of simple motor reaction-time distribution , 1990, Psychological research.

[10]  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.

[11]  C D Tesche,et al.  Signal-space projections of MEG data characterize both distributed and well-localized neuronal sources. , 1995, Electroencephalography and clinical neurophysiology.

[12]  J. Schall,et al.  Neural Control of Voluntary Movement Initiation , 1996, Science.

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

[14]  Stephen J. Anderson,et al.  Magnetoencephalographic Investigation of Human Cortical Area V1 Using Color Stimuli , 1997, NeuroImage.

[15]  Rolf Ulrich,et al.  Effects of stimulus intensity on the lateralized readiness potential , 1999 .

[16]  F. L. D. Silva,et al.  Event-related EEG/MEG synchronization and desynchronization: basic principles , 1999, Clinical Neurophysiology.

[17]  Milena Mihaylova,et al.  Peripheral and central delay in processing high spatial frequencies: reaction time and VEP latency studies , 1999, Vision Research.

[18]  S. Anderson,et al.  Assessment of cortical dysfunction in human strabismic amblyopia using magnetoencephalography (MEG) , 1999, Vision Research.

[19]  J M Zanker,et al.  Mechanisms of human motion perception: combining evidence from evoked potentials, behavioural performance and computational modelling , 2000, The European journal of neuroscience.

[20]  Karl J. Friston,et al.  A direct quantitative relationship between the functional properties of human and macaque V5 , 2000, Nature Neuroscience.

[21]  R Kakigi,et al.  Human visual motion areas determined individually by magnetoencephalography and 3D magnetic resonance imaging , 2000, Human brain mapping.

[22]  R. Kakigi,et al.  Magnetic response of human extrastriate cortex in the detection of coherent and incoherent motion , 2000, Neuroscience.

[23]  James L. McClelland,et al.  The time course of perceptual choice: the leaky, competing accumulator model. , 2001, Psychological review.

[24]  Norihiro Sadato,et al.  Visual detection of motion speed in humans: spatiotemporal analysis by fMRI and MEG , 2002, Human brain mapping.

[25]  Y. Ejima,et al.  Surround suppression in the human visual cortex: an analysis using magnetoencephalography , 2002, Vision Research.

[26]  Temporal summation of magnetic response to chromatic stimulus in the human visual cortex , 2002, Neuroreport.

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

[28]  A. Vassilev,et al.  On the delay in processing high spatial frequency visual information: reaction time and VEP latency study of the effect of local intensity of stimulation , 2002, Vision Research.

[29]  Ryusuke Kakigi,et al.  Human cortical responses to coherent and incoherent motion as measured by magnetoencephalography , 2002, Neuroscience Research.

[30]  Hiroshi Shibasaki,et al.  Human V5 demonstrated by magnetoencephalography using random dot kinematograms of different coherence levels , 2003, Neuroscience Research.

[31]  Jonathan R Wolpaw,et al.  Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[32]  Tsunehiro Takeda,et al.  MEG recording from the human ventro-occipital cortex in response to isoluminant color stimulation , 2005, Visual Neuroscience.

[33]  M. Shadlen,et al.  A representation of the hazard rate of elapsed time in macaque area LIP , 2005, Nature Neuroscience.