text:
7.000525},
	Volume = {10},
	Year = {1987},
	Bdsk-Url-1 = {http://dx.doi.org/10.1146/annurev.ne.10.030187.000525}}

@article{Kawano99,
	Author = {Kawano, K.},
	Citeulike-Article-Id = {11423857},
	Date-Added = {2013-12-25 18:24:41 +0000},
	Date-Modified = {2013-12-25 18:24:52 +0000},
	Journal = {Current {O}pinion in {N}eurobiology},
	Keywords = {area, control, eye, eye-movements, following, khoei13jpp, motion, movements, mst, mt, ocular, oculomotor, okn, processing, pursuit, review, smooth, vergence, visual; thesis},
	Priority = {2},
	Title = {Ocular tracking: behavior and neurophysiology},
	Volume = {9},
	Year = {1999}}

@article{Sotiropoulos11,
	Abstract = {Our perceptions are fundamentally altered by our knowledge of the world. When cloud-gazing, for example, we tend spontaneously to recognize known objects in the random configurations of evaporated moisture. How our brains acquire such knowledge and how it impacts our perceptions is a matter of heated discussion. A topic of recent debate has concerned the hypothesis that our visual system 'assumes' that objects are static or move slowly [1] rather than more quickly [1] , [2] and [3] . This hypothesis, or 'prior on slow speeds', was postulated because it could elegantly explain a number of perceptual biases observed in situations of uncertainty [2]. Interestingly, those biases affect not only the perception of speed, but also the direction of motion. For example, the direction of a line whose endpoints are hidden (as in the 'aperture problem') or poorly visible (for example, at low contrast or for short presentations) is more often perceived as being perpendicular to the line than it really is --- an illusion consistent with expecting that the line moves more slowly than it really does. How this 'prior on slow speeds' is shaped by experience and whether it remains malleable in adults is unclear. Here, we show that systematic exposure to high-speed stimuli can lead to a reversal of this direction illusion. This suggests that the shaping of the brain's prior expectations of even the most basic properties of the environment is a continuous process.},
	Author = {Sotiropoulos, Grigorios and Seitz, Aaron R. and Seri\`{e}s, Peggy},
	Citeulike-Article-Id = {10017701},
	Citeulike-Linkout-0 = {http://dx.doi.org/10.1016/j.cub.2011.09.013},
	Date-Added = {2013-12-25 18:24:41 +0000},
	Date-Modified = {2013-12-25 18:24:52 +0000},
	Doi = {10.1016/j.cub.2011.09.013},
	Issn = {09609822},
	Journal = {Current Biology},
	Keywords = {kaplan13, khoei13jpp, prior\_probability; thesis},
	Month = nov,
	Number = {21},
	Pages = {R883--R884},
	Priority = {2},
	Title = {Changing expectations about speed alters perceived motion direction},
	Url = {http://dx.doi.org/10.1016/j.cub.2011.09.013},
	Volume = {21},
	Year = {2011},
	Bdsk-Url-1 = {http://dx.doi.org/10.1016/j.cub.2011.09.013}}

@article{deXivry07,
	Abstract = {Saccades and smooth pursuit eye movements are two different modes of oculomotor control. Saccades are primarily directed toward stationary targets whereas smooth pursuit is elicited to track moving targets. In recent years, behavioural and neurophysiological data demonstrated that both types of eye movements work in synergy for visual tracking. This suggests that saccades and pursuit are two outcomes of a single sensorimotor process that aims at orienting the visual axis.},
	Author = {de~Xivry, Jean-Jacques O. and Lef\`{e}vre, Philippe},
	Citeulike-Article-Id = {5126636},
	Citeulike-Linkout-0 = {http://dx.doi.org/10.1113/jphysiol.2007.139881},
	Citeulike-Linkout-1 = {http://jp.physoc.org/content/584/1/11.abstract},
	Citeulike-Linkout-2 = {http://jp.physoc.org/content/584/1/11.full.pdf},
	Citeulike-Linkout-3 = {http://view.ncbi.nlm.nih.gov/pubmed/17690138},
	Citeulike-Linkout-4 = {http://www.hubmed.org/display.cgi?uids=17690138},
	Date-Added = {2013-12-25 18:24:41 +0000},
	Date-Modified = {2014-07-21 21:45:26 +0000},
	Day = {01},
	Doi = {10.1113/jphysiol.2007.139881},
	Issn = {1469-7793},
	Journal = {The Journal of Physiology},
	Keywords = {khoei13jpp, saccades, spem; thesis},
	Month = oct,
	Number = {1},
	Pages = {11--23},
	Pmid = {17690138},
	Priority = {2},
	Publisher = {The Physiological Society},
	Title = {Saccades and pursuit: two outcomes of a single sensorimotor process},
	Url = {http://dx.doi.org/10.1113/jphysiol.2007.139881},
	Volume = {584},
	Year = {2007},
	Bdsk-Url-1 = {http://dx.doi.org/10.1113/jphysiol.2007.139881}}

@inbook{DeLucia04,
	Author = {DeLucia, Patricia R.},
	Booktitle = {Time-to-Contact},
	Citeulike-Article-Id = {11379283},
	Citeulike-Linkout-0 = {http://dx.doi.org/10.1016/S0166-4115(04)80013-X},
	Citeulike-Linkout-1 = {http://dx.doi.org/10.1016/s0166-4115(04)80013-x},
	Date-Added = {2013-12-25 18:24:41 +0000},
	Date-Modified = {2013-12-25 18:24:52 +0000},
	Doi = {10.1016/S0166-4115(04)80013-X},
	Isbn = {9780444510457},
	Issn = {0166-4115},
	Journal = {Advances in Psychology},
	Keywords = {kaplan13, khoei13jpp, motion-extrapolation, motion\_extrapolation; thesis},
	Pages = {243--285},
	Priority = {2},
	Publisher = {Elsevier},
	Title = {Chapter 11 Multiple sources of information influence time-to-contact judgments: Do heuristics accommodate limits in sensory and cognitive processes?},
	Url = {http://dx.doi.org/10.1016/S0166-4115(04)80013-X},
	Volume = {135},
	Year = {2004},
	Bdsk-Url-1 = {http://dx.doi.org/10.1016/S0166-4115(04)80013-X}}

@article{Rosenbaum75,
	Abstract = {A moving target disappeared behind a screen and subjects predicted when the target passed behind a marker on the screen. When the target moved with constant velocity, predictions were extremely accurate, regardless of the spatial and temporal exposure and concealment of the target and regardless of its rate of velocity. When the target accelerated, accuracy of prediction decreased with increasing acceleration and with increasing target concealment. Analyses of the results suggest that the perception of velocity and acceleration is direct and accurate and that extrapolation of velocity and acceleration incorporates concrete and abstract characteristics of the motion that was seen. It is proposed that the motion perception system is tuned to accelerated rather than to constant velocity movement.},
	Author = {Rosenbaum, D. A.},
	Citeulike-Article-Id = {11379252},
	Citeulike-Linkout-0 = {http://dx.doi.org/10.1037//0096-1523.1.4.395},
	Citeulike-Linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/1185126},
	Citeulike-Linkout-2 = {http://www.hubmed.org/display.cgi?uids=1185126},
	Date-Added = {2013-12-25 18:24:41 +0000},
	Date-Modified = {2013-12-25 18:24:52 +0000},
	Doi = {10.1037//0096-1523.1.4.395},
	Issn = {0096-1523},
	Journal = {Journal of experimental psychology. Human perception and performance},
	Keywords = {kaplan13, khoei13jpp, motion-extrapolation, motion\_extrapolation; thesis},
	Month = nov,
	Number = {4},
	Pages = {395--403},
	Pmid = {1185126},
	Priority = {2},
	Title = {Perception and extrapolation of velocity and acceleration.},
	Url = {http://dx.doi.org/10.1037//0096-1523.1.4.395},
	Volume = {1},
	Year = {1975},
	Bdsk-Url-1 = {http://dx.doi.org/10.1037//0096-1523.1.4.395}}

@article{Jancke04-n,
	Abstract = {Exploring visual illusions reveals fundamental principles of cortical processing. Illusory motion perception of non-moving stimuli was described almost a century ago by Gestalt psychologists. However, the underlying neuronal mechanisms remain unknown. To explore cortical mechanisms underlying the 'line-motion' illusion, we used real-time optical imaging, which is highly sensitive to subthreshold activity. We examined, in the visual cortex of the anaesthetized cat, responses to five stimuli: a stationary small square and a long bar; a moving square; a drawn-out bar; and the well-known line-motion illusion, a stationary square briefly preceding a long stationary bar presentation. Whereas flashing the bar alone evoked the expected localized, short latency and high amplitude activity patterns, presenting a square 60-100 ms before a bar induced the dynamic activity patterns resembling that of fast movement. The preceding square, even though physically non-moving, created gradually propagating subthreshold cortical activity that must contribute to illusory motion, because it was indistinguishable from cortical representations of real motion in this area. These findings demonstrate the effect of spatio-temporal patterns of subthreshold synaptic potentials on cortical processing and the shaping of perception.},
	Address = {Department of Neurobiology and the Grodetsky Center for Studies of Higher Brain Function, The Weizmann Institute of Science, 76100 Rehovot, Israel. jancke@neurobiologie.ruhr-uni-bochum.de).},
	Author = {Jancke, D. and Chavane, F. and Naaman, S. and Grinvald, A.},
	Citeulike-Article-Id = {1392833},
	Citeulike-Linkout-0 = {http://dx.doi.org/10.1038/nature02396},
	Citeulike-Linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/15042090},
	Citeulike-Linkout-2 = {http://www.hubmed.org/display.cgi?uids=15042090},
	Date-Added = {2013-12-25 18:24:41 +0000},
	Date-Modified = {2014-05-28 11:29:35 +0200},
	Day = {25},
	Doi = {10.1038/nature02396},
	Issn = {1476-4687},
	Journal = {Nature},
	Keywords = {khoei13jpp, line-motion-illusion, optical-imaging; thesis},
	Month = mar,
	Number = {6981},
	Pages = {423--426},
	Pmid = {15042090},
	Priority = {0},
	Title = {Imaging cortical correlates of illusion in early visual cortex.},
	Url = {http://dx.doi.org/10.1038/nature02396},
	Volume = {428},
	Year = {2004},
	Bdsk-Url-1 = {http://dx.doi.org/10.1038/nature02396}}

@article{Eckmiller1978Pursuit,
	Abstract = {1. Single units in the 3. and 6. nerve nuclei were recorded, together with the stimulus and eye movements in trained macaques during pursuit eye movements. 2. The relationship between the impulse rate of an oculomotor motoneuron and the corresponding eye movements can be described by a first order differential equation only, if distinctions are made between the modes of the oculomotor system (e.g., fixation or pursuit) and between the agonist phase and the antagonist phase of the corresponding eye muscle. 3. The trained monkeys showed a frequency response during pursuit eye movements, which was comparable to that of humans and which clearly indicates the existence of a predictor mechanism. 4. After sudden stimulus disappearance in the pursuit mode, both the neural impulse rate and the eye movement performed smooth changes for more than 1s. These slow post-pursuit eye movements were related to the time course before stimulus disappearance. 5. Our findings lead to the hypothesis, that pursuit eye movements in primates, if elicited by small moving visual stimuli, are generated by means of a feedback system consisting of a predictor mechanism, the parameters of which are continuously corrected by an updating process in the afferent visual system.},
	Author = {Eckmiller, R. and Mackeben, M.},
	Citeulike-Article-Id = {11378821},
	Citeulike-Linkout-0 = {http://view.ncbi.nlm.nih.gov/pubmed/103071},
	Citeulike-Linkout-1 = {http://www.hubmed.org/display.cgi?uids=103071},
	Date-Added = {2013-12-25 18:24:41 +0000},
	Date-Modified = {2013-12-25 18:24:52 +0000},
	Day = {18},
	Issn = {0031-6768},
	Journal = {Pfl\"{u}gers Archiv : European journal of physiology},
	Keywords = {khoei13jpp, monkeys, motion-extrapolation; thesis},
	Month = oct,
	Number = {1},
	Pages = {15--23},
	Pmid = {103071},
	Priority = {2},
	Title = {Pursuit eye movements and their neural control in the monkey.},
	Url = {http://view.ncbi.nlm.nih.gov/pubmed/103071},
	Volume = {377},
	Year = {1978},
	Bdsk-Url-1 = {http://view.ncbi.nlm.nih.gov/pubmed/103071}}

@article{Becker1985Prediction,
	Abstract = {Eye movements were recorded in human subjects who tracked a target spot which moved horizontally at constant speeds. At random times during its trajectory, the target disappeared for variable periods of time and the subjects attempted to continue tracking the invisible target. The smooth pursuit component of their eye movements was isolated and averaged. About 190 ms after the target disappeared, the smooth pursuit velocity began to decelerate rapidly. The time course of this deceleration was similar to that in response to a visible target whose velocity decreased suddenly. After a deceleration lasting about 280 ms, the velocity stabilized at a new, reduced level which we call the residual velocity. The residual velocity remained more or less constant or declined only slowly even when the target remained invisible for 4 s. When the same target velocity was used in all trials of an experiment, the subjects' residual velocity amounted to 60\% of their normal pursuit velocity. When the velocity was varied randomly from trial to trial, the residual velocity was smaller; for target velocities of 5, 10, and 20 deg/s it reached 55, 47, and 39\% respectively. The subjects needed to see targets of unforeseeable velocity for no more than 300 ms in order to develop a residual velocity that was characteristic of the given target velocity. When a target of unknown velocity disappeared at the very moment the subject expected it to start, a smooth movement developed nonetheless and reached within 300 ms a peak velocity of 5 deg/s which was independent of the actual target velocity and reflected a "default" value for the pursuit system. Thereafter the eyes decelerated briefly and then continued with a constant or slightly decreasing velocity of 2-4 deg/s until the target reappeared. Even when the subjects saw no moving target during an experiment, they could produce a smooth movement in the dark and could grade its velocity as a function of that of an imagined target. We suggest that the residual velocity reflects a first order prediction of target movement which is attenuated by a variable gain element. When subjects are pursuing a visible target, the gain of this element is close to unity. When the target disappears but continued tracking is attempted, the gain is reduced to a value between 0.4 and 0.6.},
	Author = {Becker, W. and Fuchs, A. F.},
	Citeulike-Article-Id = {1344352},
	Citeulike-Linkout-0 = {http://dx.doi.org/10.1007/BF00237843},
	Citeulike-Linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/3979498},
	Citeulike-Linkout-2 = {http://www.hubmed.org/display.cgi?uids=3979498},
	Date-Added = {2013-12-25 18:24:41 +0000},
	Date-Modified = {2013-12-25 18:24:52 +0000},
	Doi = {10.1007/BF00237843},
	Issn = {0014-4819},
	Journal = {Experimental brain research},
	Keywords = {khoei13jpp, motion-extrapolation; thesis},
	Number = {3},
	Pages = {562--575},
	Pmid = {3979498},
	Priority = {2},
	Title = {Prediction in the oculomotor system: smooth pursuit during transient disappearance of a visual target.},
	Url = {http://dx.doi.org/10.1007/BF00237843},
	Volume = {57},
	Year = {1985},
	Bdsk-Url-1 = {http://dx.doi.org/10.1007/BF00237843}}

@article{Pavel92,
	Abstract = {We investigated observers' ability to extrapolate a linear trajectory of a moving point, in order to determine how effectively the visual system can combine orientation and position information for moving stimuli. Observers saw a probe dot moving along a straight line toward a stationary target dot. The probe dot extinguished before reaching the target, and the observers' task was to judge whether an extrapolation of the trajectory of the probe would pass to the left or right of the target. Performance was measured as a function of probe velocity, length of the visible trajectory, and location of the target. The empirical results indicated that over a range of conditions, performance on this task is qualitatively similar to, but somewhat less accurate than, that on an analogous task with static stimuli. A four-component model is presented to account for the results. The model specifies an accurate extraction of probe motion parameters, extrapolation of the motion by an ideal observer, and limitations on the input to these processes in the form of visual field spatial inhomogeneity and temporal decay of position information.},
	Author = {Pavel, M. and Cunningham, H. and Stone, V.},
	Citeulike-Article-Id = {11137165},
	Citeulike-Linkout-0 = {http://view.ncbi.nlm.nih.gov/pubmed/1304094},
	Citeulike-Linkout-1 = {http://www.hubmed.org/display.cgi?uids=1304094},
	Date-Added = {2013-12-25 18:24:41 +0000},
	Date-Modified = {2013-12-25 18:24:52 +0000},
	Issn = {0042-6989},
	Journal = {Vision research},
	Keywords = {khoei13jpp, motion-extrapolation; thesis},
	Month = nov,
	Number = {11},
	Pages = {2177--2186},
	Pmid = {1304094},
	Priority = {2},
	Title = {Extrapolation of linear motion.},
	Url = {http://view.ncbi.nlm.nih.gov/pubmed/1304094},
	Volume = {32},
	Year = {1992},
	Bdsk-Url-1 = {http://view.ncbi.nlm.nih.gov/pubmed/1304094}}

@article{Sokolov1997Motion,
	Abstract = {A study of the effect of the size of a moving target and the extent of its visible motion on motion extrapolation is reported. Targets (a horizontal pair of dots separated by either 0.2 or 0.8 deg) moved across a 10 deg rectilinear path and were then occluded. Observers pressed a key when they thought the leading dot of a hidden target had reached a randomly specified position (0-12 deg from the point of occlusion). In experiment 1, in agreement with velocity-transposition predictions, at moderate (5 deg s-1) and rapid (10 deg s-1) velocities extrapolation times were longer for large targets than for small ones. At slow velocity (2.5 deg s-1) this effect was reversed. In experiment 2 the effect of target size at moderate velocity was found for a short (2.5 deg) visible path. However, the extrapolation time increased with shorter (2.5 deg versus 10 deg) paths. A proposed account of these effects suggests that the visual system performs a spatiotemporal scaling, according to the velocity-transposition principle, not only of visible motion but also of extrapolated motion.},
	Author = {Sokolov, A. N. and Ehrenstein, W. H. and Pavlova, M. A. and Cavonius, C. R.},
	Citeulike-Article-Id = {10634370},
	Citeulike-Linkout-0 = {http://view.ncbi.nlm.nih.gov/pubmed/9509140},
	Citeulike-Linkout-1 = {http://www.hubmed.org/display.cgi?uids=9509140},
	Date-Added = {2013-12-25 18:24:41 +0000},
	Date-Modified = {2013-12-25 18:24:52 +0000},
	Issn = {0301-0066},
	Journal = {Perception},
	Keywords = {khoei13jpp, motion-extrapolation; thesis},
	Number = {7},
	Pages = {875--889},
	Pmid = {9509140},
	Priority = {5},
	Title = {Motion extrapolation and velocity transposition.},
	Url = {http://view.ncbi.nlm.nih.gov/pubmed/9509140},
	Volume = {26},
	Year = {1997},
	Bdsk-Url-1 = {http://view.ncbi.nlm.nih.gov/pubmed/9509140}}

@article{Barnes1991Mechanism,
	Abstract = {1. Experiments have been conducted on human subjects to determine the role of prediction in smooth eye movement control. Subjects were required to actively pursue a small target or stare passively at a larger display as it moved in the horizontal plane. 2. Target motion was basically periodic, but, after a random number of cycles an unexpected change was made in the amplitude, direction or frequency of target motion. Initially, the periodic stimulus took the form of a square waveform. In subsequent experiments, a triangular or sawtooth waveform was used, but in order to examine the timing of the response in relation to stimulus appearance, the target was tachistoscopically illuminated for 40-320 ms at the time that it passed through the mid-line position. 3. When subjects either actively pursued the target or stared passively at the larger display a characteristic pattern of steady-state eye movement was evoked composed of two phases, an initial build-up of eye velocity that reached a peak after 200 ms, followed by a decay phase with a time constant of 0.5-2 s. The build-up phase was initiated prior to target displacement for square-wave motion and before onset of target illumination for other waveforms. 4. The peak eye velocity evoked gradually increased over the first two to four cycles of repeated stimulation. Simultaneously, the response became more phase advanced, the reaction time between stimulus onset and the time at which peak velocity occurred decreasing from an average of 300 to 200 ms for triangular waveform stimuli. 5. When there was a sudden and unexpected change in amplitude and direction of the stimulus waveform, the eye movement induced had a peak velocity and direction that was inappropriate for the current visual stimulus, but which was highly correlated with the features of the preceding sequence in the stimulus. 6. When there was a sudden change in the frequency of the stimulus waveform the predictive eye movement was induced with a timing appropriate to the periodicity of the previous sequence but inappropriate to the new sequence. 7. The results indicate that prediction is carried out through the storage of information about both the magnitude and timing of eye velocity. The trajectory of the averaged eye velocity response was similar in form irrespective of the duration of target exposure or basic stimulus frequency, suggesting that the predictive estimate is released as a stereotyped volley of constant duration but varying magnitude under the control of a periodicity {estimator.(ABSTRACT} {TRUNCATED} {AT} 400 {WORDS})},
	Author = {Barnes, G. R. and Asselman, P. T.},
	Citeulike-Article-Id = {11339857},
	Citeulike-Linkout-0 = {http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1180117/},
	Citeulike-Linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/1895243},
	Citeulike-Linkout-2 = {http://www.hubmed.org/display.cgi?uids=1895243},
	Date-Added = {2013-12-25 18:24:41 +0000},
	Date-Modified = {2013-12-25 18:24:52 +0000},
	Issn = {0022-3751},
	Journal = {The Journal of physiology},
	Keywords = {freemove, khoei13jpp, motion-detection, prediction, spem; thesis},
	Month = aug,
	Pages = {439--461},
	Pmcid = {PMC1180117},
	Pmid = {1895243},
	Priority = {2},
	Title = {The mechanism of prediction in human smooth pursuit eye movements.},
	Url = {http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1180117/},
	Volume = {439},
	Year = {1991},
	Bdsk-Url-1 = {http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1180117/}}

@article{Reynaud12,
	Abstract = {To efficiently drive many behaviors, sensory systems have to integrate the activity of large neuronal populations within a limited time window. These populations need to rapidly achieve a robust representation of the input image, probably through canonical computations such as divisive normalization. However, little is known about the dynamics of the corticocortical interactions implementing these rapid and robust computations. Here, we measured the real-time activity of a large neuronal population in V1 using voltage-sensitive dye imaging in behaving monkeys. We found that contrast gain of the population increases over time with a time constant of ∼30 ms and propagates laterally over the cortical surface. This dynamic is well accounted for by a divisive normalization achieved through a recurrent network that transiently increases in size after response onset with a slow swelling speed of 0.007--0.014 m/s, suggesting a polysynaptic intracortical origin. In the presence of a surround, this normalization pool is gradually balanced by lateral inputs propagating from distant cortical locations. This results in a centripetal propagation of surround suppression at a speed of 0.1--0.3 m/s, congruent with horizontal intracortical axons speed. We propose that a simple generalized normalization scheme can account for both the dynamical contrast response function through recurrent polysynaptic intracortical loops and for the surround suppression through long-range monosynaptic horizontal spread. Our results demonstrate that V1 achieves a rapid and robust context-dependent input normalization through a timely push--pull between local and lateral networks. We suggest that divisive normalization, a fundamental canonical computation, should be considered as a dynamic process.},
	Author = {Reynaud, Alexandre and Masson, Guillaume S. and Chavane, Fr\'{e}d\'{e}ric},
	Citeulike-Article-Id = {11190936},
	Citeulike-Linkout-0 = {http://dx.doi.org/10.1523/jneurosci.1618-12.2012},
	Citeulike-Linkout-1 = {http://www.jneurosci.org/content/32/36/12558.abstract},
	Citeulike-Linkout-2 = {http://www.jneurosci.org/content/32/36/12558.full.pdf},
	Citeulike-Linkout-3 = {http://view.ncbi.nlm.nih.gov/pubmed/22956845},
	Citeulike-Linkout-4 = {http://www.hubmed.org/display.cgi?uids=22956845},
	Date-Added = {2013-12-25 18:24:41 +0000},
	Date-Modified = {2013-12-25 18:24:52 +0000},
	Day = {05},
	Doi = {10.1523/jneurosci.1618-12.2012},
	Issn = {1529-2401},
	Journal = {The Journal of Neuroscience},
	Keywords = {area-v1, center-surround-interactions, contrast\_response, divisive\_normalization, khoei13jpp, lateral\_connections, normalization; thesis},
	Month = sep,
	Number = {36},
	Pages = {12558--12569},
	Pmid = {22956845},
	Priority = {5},
	Publisher = {Society for Neuroscience},
	Title = {Dynamics of Local Input Normalization Result from Balanced Short- and {Long-Range} Intracortical Interactions in Area V1},
	Url = {http://dx.doi.org/10.1523/jneurosci.1618-12.2012},
	Volume = {32},
	Year = {2012},
	Bdsk-Url-1 = {http://dx.doi.org/10.1523/jneurosci.1618-12.2012}}

@article{Wuerger10,
	Abstract = {Many tasks involve the precise estimation of speed and position of moving objects, for instance to catch or avoid objects that cohabit in our environment. Many of these objects are characterised by signal representations in more than one modality, such as hearing and vision. The aim of this study was to investigate the extent to which the simultaneous presentation of auditory and visual signals enhances the estimation of motion speed and instantaneous position. Observers are asked to estimate the instant when a moving object arrives at a target spatial position by pressing a response button. This task requires observers to estimate the speed of the moving object and to calibrate the timing of their manual response such that it coincides with the true arrival time of the moving object. When both visual and auditory motion signals are available, the variability in estimating the arrival time of the moving object is significantly reduced compared to the variability in the unimodal conditions. This reduction in variability is consistent with optimal integration of the auditory and visual speed signals. The average bias in the estimated arrival times depends on the motion speed: for medium speeds (17 deg/s) observers' subjective arrival times are earlier than the true arrival times; for high speeds (47 deg/s) observers exhibit a (much smaller) bias in the other direction. This speed-dependency suggests that the bias is due to an error in estimating the motion speeds rather than an error in calibrating the timing of the motor response. Finally, in this temporal localization task, the bias and variability show similar patterns for motion defined by vision, audition or both.},
	Author = {Wuerger, Sophie and Meyer, Georg and Hofbauer, Markus and Zetzsche, Christoph and Schill, Kerstin},
	Citeulike-Article-Id = {11193049},
	Citeulike-Linkout-0 = {http://dx.doi.org/10.1016/j.inffus.2009.04.005},
	Date-Added = {2013-12-25 18:24:41 +0000},
	Date-Modified = {2013-12-25 18:24:52 +0000},
	Day = {22},
	Doi = {10.1016/j.inffus.2009.04.005},
	Issn = {1566-2535},
	Journal = {Information Fusion},
	Keywords = {kaplan13, khoei13jpp, motion-extrapolation, motion\_extrapolation; thesis},
	Month = jan,
	Number = {1},
	Pages = {45--50},
	Priority = {0},
	Title = {Motion extrapolation of auditory-visual targets},
	Url = {http://dx.doi.org/10.1016/j.inffus.2009.04.005},
	Volume = {11},
	Year = {2010},
	Bdsk-Url-1 = {http://dx.doi.org/10.1016/j.inffus.2009.04.005}}

@article{Graf2003,
	Abstract = {{PURPOSE}: The human visual system often encounters situations in which moving objects disappear behind an occluding surface. In such situations it might be beneficial to predict where the object will emerge on the other side of the occluder. Here we investigate the information in the scene, and in the motion of the object, that is important to observers when performing such a task. {METHODS}: Observers were asked to track a small dot on the screen as it moved towards a vertical occluder, which was clearly visible. The point initially moved purely horizontally. At subsequent time steps the speed was held constant while the direction of the velocity was sampled from a unimodal probability distribution which was symmetric about the current direction. As soon as the dot reached the occluder it disappeared. Observers were asked to estimate where the dot would emerge on the other side of the occluder, as well as indicate a vertical interval around that point that they were certain would capture the dot as it emerged. Between trials the variance of the direction distribution was changed, increasing or decreasing the 'randomness' of the path. Occluder width and dot speed were also manipulated between trials. {RESULTS}: We found that observers increased the size of the capture region as the variance of the direction distribution increased. The capture region also increased in size with increasing occluder width, however, we found no effect of speed. {CONCLUSIONS}: Human observers extrapolate motion by integrating what is known about the characteristics of the path with information about the scene. We discuss and model this data in terms of a probabilistic account of motion perception.},
	Author = {Graf, Erich and Warren, Paul A. and Maloney, Laurence T.},
	Citeulike-Article-Id = {11193048},
	Citeulike-Linkout-0 = {http://dx.doi.org/10.1167/3.9.791},
	Date-Added = {2013-12-25 18:24:41 +0000},
	Date-Modified = {2013-12-25 18:24:52 +0000},
	Day = {22},
	Doi = {10.1167/3.9.791},
	Journal = {Journal of Vision},
	Keywords = {khoei13jpp, motion-extrapolation; thesis},
	Month = oct,
	Number = {9},
	Pages = {791},
	Priority = {2},
	Publisher = {Association for Research in Vision and Ophthalmology},
	Title = {Extrapolation of motion paths behind an occluder},
	Url = {http://dx.doi.org/10.1167/3.9.791},
	Volume = {3},
	Year = {2003},
	Bdsk-Url-1 = {http://dx.doi.org/10.1167/3.9.791}}

@article{Meister2010,
	Abstract = {We rely on our visual system to cope with the vast barrage of incoming light patterns and to extract features from the scene that are relevant to our well-being. The necessary reduction of visual information already begins in the eye. In this review, we summarize recent progress in understanding the computations performed in the vertebrate retina and how they are implemented by the neural circuitry. A new picture emerges from these findings that helps resolve a vexing paradox between the retina's structure and function. Whereas the conventional wisdom treats the eye as a simple prefilter for visual images, it now appears that the retina solves a diverse set of specific tasks and provides the results explicitly to downstream brain areas.},
	Author = {Gollisch, Tim and Meister, Markus},
	Citeulike-Article-Id = {11193044},
	Citeulike-Linkout-0 = {http://dx.doi.org/10.1016/j.neuron.2009.12.009},
	Date-Added = {2013-12-25 18:24:41 +0000},
	Date-Modified = {2013-12-25 18:24:52 +0000},
	Day = {28},
	Doi = {10.1016/j.neuron.2009.12.009},
	Journal = {Neuron},
	Keywords = {kaplan13, khoei13jpp, motion-extrapolation, motion\_extrapolation; thesis},
	Month = jan,
	Number = {2},
	Priority = {0},
	Publisher = {Cell Press,},
	Title = {Eye Smarter than Scientists Believed: Neural Computations in Circuits of the Retina},
	Url = {http://dx.doi.org/10.1016/j.neuron.2009.12.009},
	Volume = {65},
	Year = {2010},
	Bdsk-Url-1 = {http://dx.doi.org/10.1016/j.neuron.2009.12.009}}

@article{Zhang2011,
	Abstract = {Binocular competition is thought to drive eye-specific segregation in the developing visual system, potentially through Hebbian synaptic learning rules that are sensitive to correlations in afferent activity. Altering retinal activity can disrupt eye-specific segregation, but little is known about the temporal features of binocular activity that modulate visual map development. We used optogenetic techniques to directly manipulate retinal activity in vivo and identified a critical period before eye opening in mice when specific binocular features of retinal activity drive visual map development. Synchronous activation of both eyes disrupted segregation, whereas asynchronous stimulation enhanced segregation. The optogenetic stimulus applied was spatially homogenous; accordingly, retinotopy of ipsilateral projections was markedly perturbed, but contralateral retinotopy was unaffected or even improved. These results provide direct evidence that the synchrony and precise temporal pattern of binocular retinal activity during a critical period in development regulates eye-specific segregation and retinotopy in the developing visual system.},
	Author = {Zhang, Jiayi and Ackman, James B. and Xu, Hong-Ping P. and Crair, Michael C.},
	Citeulike-Article-Id = {11193043},
	Citeulike-Linkout-0 = {http://dx.doi.org/10.1038/nn.3007},
	Date-Added = {2013-12-25 18:24:41 +0000},
	Date-Modified = {2013-12-25 18:24:52 +0000},
	Day = {18},
	Doi = {10.1038/nn.3007},
	Journal = {Nature neuroscience},
	Keywords = {development, khoei13jpp; thesis},
	Month = feb,
	Number = {2},
	Priority = {0},
	Publisher = {Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.},
	Title = {Visual map development depends on the temporal pattern of binocular activity in mice.},
	Url = {http://dx.doi.org/10.1038/nn.3007},
	Volume = {15},
	Year = {2012},
	Bdsk-Url-1 = {http://dx.doi.org/10.1038/nn.3007}}

@article{Yuille89,
	Author = {Yuille, Alan L. and Grzywacz, Norberto M.},
	Citeulike-Article-Id = {11193040},
	Citeulike-Linkout-0 = {http://dx.doi.org/10.1007/BF00126430},
	Citeulike-Linkout-1 = {http://www.usc.edu/programs/vpl/private/docs/publications/Yuille\_Grzywacz\_1989.pdf},
	Date-Added = {2013-12-25 18:24:41 +0000},
	Date-Modified = {2013-12-25 18:24:52 +0000},
	Doi = {10.1007/BF00126430},
	Issn = {1573-1405},
	Journal = {International Journal of Computer Vision},
	Keywords = {bayesian, kaplan13, khoei13jpp, motion-estimation, motion\_estimation; thesis},
	Month = jun,
	Number = {2},
	Pages = {155--175},
	Priority = {0},
	Title = {A mathematical analysis of the motion coherence theory},
	Url = {http://www.usc.edu/programs/vpl/private/docs/publications/Yuille\_Grzywacz\_1989.pdf},
	Volume = {3},
	Year = {1989},
	Bdsk-Url-1 = {http://www.usc.edu/programs/vpl/private/docs/publications/Yuille%5C_Grzywacz%5C_1989.pdf},
	Bdsk-Url-2 = {http://dx.doi.org/10.1007/BF00126430}}

@article{Norberto95,
	Abstract = {A recent challenge to the completeness of some influential models of local-motion detection has come from experiments in which subjects had to detect a single dot moving along a trajectory amidst noise dots undergoing Brownian motion. We propose and test a new theory of the detection and measurement of visual motion, which can account for these {signal-in-Brownian}-noise experiments. The theory postulates that the signals from local-motion detectors are made coherent in space and time by a special purpose network, and that this coherence boosts signals of features moving along non-random trajectories over time. Two experiments were performed to estimate parameters and test the theory. These experiments showed that detection is impaired with increasing eccentricity, an effect that varies inversely with step size. They also showed that detection improves over durations extending to at least 600 msec. An implementation of the theory accounts for these psychophysical detection measurements.},
	Author = {Grzywacz, N. M. and Watamaniuk, S. N. and McKee, S. P.},
	Citeulike-Article-Id = {11193038},
	Citeulike-Linkout-0 = {http://view.ncbi.nlm.nih.gov/pubmed/8533352},
	Date-Added = {2013-12-25 18:24:41 +0000},
	Date-Modified = {2013-12-25 18:24:52 +0000},
	Journal = {Vision research},
	Keywords = {khoei13jpp, motion-estimation; thesis},
	Month = nov,
	Number = {22},
	Priority = {0},
	Title = {Temporal coherence theory for the detection and measurement of visual motion.},
	Url = {http://view.ncbi.nlm.nih.gov/pubmed/8533352},
	Volume = {35},
	Year = {1995},
	Bdsk-Url-1 = {http://view.ncbi.nlm.nih.gov/pubmed/8533352}}

@article{Anstis83,
	Author = {Ramachandran, V. and Anstis, S.},
	Citeulike-Article-Id = {11193037},
	Citeulike-Linkout-0 = {http://dx.doi.org/10.1016/0042-6989(83)90044-5},
	Citeulike-Linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/6868383},
	Date-Added = {2013-12-25 18:24:41 +0000},
	Date-Modified = {2013-12-25 18:24:52 +0000},
	Doi = {10.1016/0042-6989(83)90044-5},
	Issn = {0042-6989},
	Journal = {Vision Research},
	Keywords = {khoei13jpp, motion-extrapolation; thesis},
	Month = jan,
	Number = {1},
	Pages = {83--85},
	Priority = {0},
	Title = {Extrapolation of motion path in human visual perception},
	Url = {http://view.ncbi.nlm.nih.gov/pubmed/6868383},
	Volume = {23},
	Year = {1983},
	Bdsk-Url-1 = {http://view.ncbi.nlm.nih.gov/pubmed/6868383},
	Bdsk-Url-2 = {http://dx.doi.org/10.1016/0042-6989(83)90044-5}}

@article{Anstis87,
	Abstract = {Four dots in an imaginary diamond were flashed in succession to give ambiguous apparent motion ({AM}). The top and bottom dots were flashed at time t1 and replaced by the left and right dots at time t2. If two priming dots were flashed beforehand at time t0 in line with two parallel sides of the diamond, {AM} was seen along those sides. We call this '' visual inertia''. The amount of visual inertia (measured by a null method) fell off with increasing angle between the priming dot and the side of the diamond. Visual inertia was still seen when the priming dots were presented to one eye and the test dots to the other, so the effect must be partly central. The timing and length of the priming path made little difference to visual inertia. However, static priming dots were ineffective. We conclude that the visual system was examining at least three successive time frames in deciding which items in one frame correspond with which items in succeeding frames.},
	Author = {Anstis, Stuart and Ramachandran, V. S.},
	Citeulike-Article-Id = {11193036},
	Citeulike-Linkout-0 = {http://dx.doi.org/10.1016/0042-6989(87)90073-3},
	Date-Added = {2013-12-25 18:24:41 +0000},
	Date-Modified = {2013-12-25 18:24:52 +0000},
	Doi = {10.1016/0042-6989(87)90073-3},
	Journal = {Vision Research},
	Keywords = {khoei13jpp, motion-inertia; thesis},
	Month = jan,
	Number = {5},
	Priority = {5},
	Title = {Visual inertia in apparent motion},
	Url = {http://dx.doi.org/10.1016/0042-6989(87)90073-3},
	Volume = {27},
	Year = {1987},
	Bdsk-Url-1 = {http://dx.doi.org/10.1016/0042-6989(87)90073-3}}

@article{Nijhawan94,
	Author = {Nijhawan, Romi},
	Citeulike-Article-Id = {11193035},
	Citeulike-Linkout-0 = {http://dx.doi.org/10.1038/370256b0},
	Date-Added = {2013-12-25 18:24:41 +0000},
	Date-Modified = {2013-12-25 18:24:52 +0000},
	Day = {28},
	Doi = {10.1038/370256b0},
	Journal = {Nature},
	Keywords = {khoei13jpp, motion-extrapolation; thesis},
	Month = jul,
	Number = {6487},
	Priority = {0},
	Publisher = {Nature Publishing Group},
	Title = {Motion extrapolation in catching},
	Url = {http://dx.doi.org/10.1038/370256b0},
	Volume = {370},
	Year = {1994},
	Bdsk-Url-1 = {http://dx.doi.org/10.1038/370256b0}}

@article{Hoge2008,
	Abstract = {An object moving in discrete steps can appear to move continuously even along sections of the path in which no stimulus is presented. We investigated whether the internal representation of such an object is constructed by extrapolation, along the expected trajectory of the object, or by interpolation, after the subsequent reappearance of the object. Observers viewed two discs moving in an unambiguous apparent motion display, which either occasionally reversed direction or continued moving along the predicted path. Observers carried out a speeded {2AFC} task on probes presented between the possible disc locations. In the continuous condition, observers' reaction times to detect and identify a probe were longer when it occurred ahead of the disc than when it occurred elsewhere on the motion path. Conversely, when the disc reversed direction, significantly less interference was observed ahead of the disc (along the predicted motion path), and significantly more interference was observed behind the disc (along the updated motion path). We conclude that the representation of a moving object in an apparent motion display is constructed by interpolation as well as extrapolation. We demonstrate that this representation is maintained and updated even outside the locus of focused attention, and that it is possible to dissociate the contributions of interpolation and extrapolation mechanisms to an object's representation.},
	Author = {Hogendoorn, Hinze and Carlson, Thomas A. and Verstraten, Frans A.},
	Citeulike-Article-Id = {11193034},
	Citeulike-Linkout-0 = {http://dx.doi.org/10.1016/j.visres.2007.12.019},
	Date-Added = {2013-12-25 18:24:41 +0000},
	Date-Modified = {2013-12-25 18:24:52 +0000},
	Doi = {10.1016/j.visres.2007.12.019},
	Journal = {Vision research},
	Keywords = {khoei13jpp, motion-extrapolation; thesis},
	Month = mar,
	Number = {7},
	Priority = {0},
	Title = {Interpolation and extrapolation on the path of apparent motion.},
	Url = {http://dx.doi.org/10.1016/j.visres.2007.12.019},
	Volume = {48},
	Year = {2008},
	Bdsk-Url-1 = {http://dx.doi.org/10.1016/j.visres.2007.12.019}}

@article{Makin09,
	Abstract = {Previous research suggests that there is overlap between the systems used for tracking visible and occluded moving targets. Both visually derived and stored velocity information provide input to the tracking system when the target is visible, but after occlusion the system receives its input solely from stored velocity representations. We used {EEG} to investigate the transition to purely memory guided tracking after the initial period of occlusion. Participants fixated while they covertly tracked targets moving horizontally rightward at velocities of 12 or 20 degrees. The targets were either continuously visible or disappeared for a short interval mid trajectory. Similar positive event related components were recorded over the right occipitoparietal region in both tasks, providing further evidence that the same neural systems are involved in tracking both visible and occluded targets. Furthermore, when the target was visible, the development of this positivity was linked to the spatial location of the target, peaking earlier in the 20 degrees condition. However, in the occluded condition the positive deflection began around 200 ms following the onset of occlusion and was not modulated by target velocity or location. This indicates a neural response after occlusion is cortically registered, consistent with the hypothesis that there is increased reliance on memory guided tracking at this point.},
	Author = {Makin, Alexis D. and Poliakoff, Ellen and El-Deredy, Wael},
	Citeulike-Article-Id = {11193033},
	Citeulike-Linkout-0 = {http://dx.doi.org/10.1016/j.neuropsychologia.2009.01.010},
	Citeulike-Linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/19350707},
	Date-Added = {2013-12-25 18:24:41 +0000},
	Date-Modified = {2013-12-25 18:24:52 +0000},
	Doi = {10.1016/j.neuropsychologia.2009.01.010},
	Issn = {0028-3932},
	Journal = {Neuropsychologia},
	Keywords = {khoei13jpp, occlusion; thesis},
	Month = mar,
	Number = {4},
	Pages = {1128--1137},
	Priority = {0},
	Title = {Tracking visible and occluded targets: Changes in event related potentials during motion extrapolation},
	Url = {http://view.ncbi.nlm.nih.gov/pubmed/19350707},
	Volume = {47},
	Year = {2009},
	Bdsk-Url-1 = {http://view.ncbi.nlm.nih.gov/pubmed/19350707},
	Bdsk-Url-2 = {http://dx.doi.org/10.1016/j.neuropsychologia.2009.01.010}}

@proceedings{Shi2010,
	Abstract = {Transmission delay of neural signals would introduce a spatial lag when an object moves across the visual field if it was not compensated. A visual predictive mechanism has been proposed to overcome such neural delay by extrapolating the position of the moving object. However, a forward shift is often absent at motion termination. A recent ??correction-for-extrapolation?? hypothesis suggests that the absence of forward shifts arises from biased competition process due to signals representing failed predictions. To test this hypothesis, we examined the perceived shifts of the final and the initial position of a dim moving object in the rod-free central fovea. The forward shifts were observed in both conditions. In the second experiment, we compared the perceived position shifts of a blue moving object and a green moving object. Given that the foveola is covered in a yellow macular pigment and absorbs blue light, the position signal of a blue object is weak or even absent. The forward shift at motion offset was only found with the blue moving object. The results provide new evidence for the ??correction-for-extrapolation?? hypothesis.},
	Author = {Shi, Z. and Nijhawan, R.},
	Citeulike-Article-Id = {11193031},
	Citeulike-Linkout-0 = {http://www.perceptionweb.com/abstract.cgi?id=v100339},
	Date-Added = {2013-12-25 18:24:41 +0000},
	Date-Modified = {2013-12-25 18:24:52 +0000},
	Journal = {Perception ECVP Abstract Supplement},
	Keywords = {khoei13jpp, motion-extrapolation; thesis},
	Number = {0},
	Pages = {138},
	Priority = {0},
	Publisher = {Pion Ltd},
	Title = {Motion extrapolation in the central fovea},
	Url = {http://www.perceptionweb.com/abstract.cgi?id=v100339},
	Volume = {39},
	Year = {2010},
	Bdsk-Url-1 = {http://www.perceptionweb.com/abstract.cgi?id=v100339}}

@article{Spering2005,
	Abstract = {It is well known that moving stimuli can appear to move more slowly when contrast is reduced (P. Thompson, 1982). Here we address the question whether changes in stimulus contrast also affect smooth pursuit eye movements. Subjects were asked to smoothly track a moving Gabor patch. Targets varied in velocity (1, 8, and 15 deg/s), spatial frequency (0.1, 1, 4, and 8 c/deg), and contrast, ranging from just below individual thresholds to maximum contrast. Results show that smooth pursuit eye velocity gain rose significantly with increasing contrast. Below a contrast level of two to three times threshold, pursuit gain, acceleration, latency, and positional accuracy were severely impaired. Therefore, the smooth pursuit motor response shows the same kind of slowing at low contrast that was demonstrated in previous studies on perception.},
	Author = {Spering, Miriam and Kerzel, Dirk and Braun, Doris I. and Hawken, Michael J. and Gegenfurtner, Karl R.},
	Citeulike-Article-Id = {11193030},
	Citeulike-Linkout-0 = {http://w.journalofvision.org/content/5/5/6.full.pdf},
	Date-Added = {2013-12-25 18:24:41 +0000},
	Date-Modified = {2013-12-25 18:24:52 +0000},
	Journal = {Journal of Vision},
	Keywords = {khoei13jpp, pursuit\_eye\_movements; thesis},
	Number = {5},
	Priority = {0},
	Title = {Effects of contrast on smooth pursuit eye movements},
	Url = {http://w.journalofvision.org/content/5/5/6.full.pdf},
	Year = {2005},
	Bdsk-Url-1 = {http://w.journalofvision.org/content/5/5/6.full.pdf}}

@techreport{Bishop95,
	Abstract = {In 1960, {R.E}. Kalman published his famous paper describing a recursive solution to the discrete-data linear filtering problem. Since that time, due in large part to advances in digital computing, the Kalman filter has been the subject of extensive research and application, particularly in the area of autonomous or assisted navigation. The Kalman filter is a set of mathematical equations that provides an efficient computational (recursive) means to estimate the state of a process, in a way that minimizes the mean of the squared error. The filter is very powerful in several aspects: it supports estimations of past, present, and even future states, and it can do so even when the precise nature of the modeled system is unknown. The purpose of this paper is to provide a practical introduction to the discrete Kalman filter. This introduction includes a description and some discussion of the basic discrete Kalman filter, a derivation, description and some discussion of the extended Kalman filter, and a relatively simple (tangible) example with real numbers \& results.},
	Address = {Chapel Hill, NC, USA},
	Author = {Welch, Greg and Bishop, Gary},
	Citeulike-Article-Id = {11193028},
	Citeulike-Linkout-0 = {http://portal.acm.org/citation.cfm?id=897831},
	Date-Added = {2013-12-25 18:24:41 +0000},
	Date-Modified = {2013-12-25 18:24:52 +0000},
	Keywords = {kalman, kaplan13, khoei13jpp; thesis},
	Priority = {0},
	Publisher = {University of North Carolina at Chapel Hill},
	Title = {An Introduction to the Kalman Filter},
	Url = {http://portal.acm.org/citation.cfm?id=897831},
	Year = {1995},
	Bdsk-Url-1 = {http://portal.acm.org/citation.cfm?id=897831}}

@article{Lisberger1987Visual,
	Author = {Lisberger, S. G. and Morris, E. J. and Tychsen, L.},
	Citeulike-Article-Id = {11005052},
	Citeulike-Linkout-0 = {http://www.annualreviews.org/doi/abs/10.1146/annurev.ne.10.030187.000525},
	Citeulike-Linkout-1 = {http://dx.doi.org/10.1146/annurev.ne.10.030187.000525},
	Date-Added = {2013-12-25 18:24:41 +0000},
	Date-Modified = {2013-12-25 18:24:52 +0000},
	Doi = {10.1146/annurev.ne.10.030187.000525},
	Journal = {Annual Review of Neuroscience},
	Keywords = {khoei13jpp, spem; thesis},
	Number = {1},
	Pages = {97--129},
	Priority = {2},
	Title = {Visual Motion Processing and {Sensory-Motor} Integration for Smooth Pursuit Eye Movements},
	Url = {http://dx.doi.org/10.1146/annurev.ne.10.030187.000525},
	Volume = {10},
	Year = {1987},
	Bdsk-Url-1 = {http://dx.doi.org/10.1146/annurev.ne.10.030187.000525}}

@article{Stocker06,
	Abstract = {Human visual speed perception is qualitatively consistent with a Bayesian observer that optimally combines noisy measurements with a prior preference for lower speeds. Quantitative validation of this model, however, is difficult because the precise noise characteristics and prior expectations are unknown. Here, we present an augmented observer model that accounts for the variability of subjective responses in a speed discrimination task. This allowed us to infer the shape of the prior probability as well as the internal noise characteristics directly from psychophysical data. For all subjects, we found that the fitted model provides an accurate description of the data across a wide range of stimulus parameters. The inferred prior distribution shows significantly heavier tails than a Gaussian, and the amplitude of the internal noise is approximately proportional to stimulus speed and depends inversely on stimulus contrast. The framework is general and should prove applicable to other experiments and perceptual modalities.},
	Author = {Stocker, Alan A. and Simoncelli, Eero P.},
	Citeulike-Article-Id = {10432490},
	Citeulike-Linkout-0 = {http://dx.doi.org/10.1038/nn1669},
	Citeulike-Linkout-1 = {http://www.nature.com/neuro/journal/v9/n4/abs/nn1669.html},
	Date-Added = {2013-12-25 18:24:41 +0000},
	Date-Modified = {2013-12-25 18:24:52 +0000},
	Day = {19},
	Doi = {10.1038/nn1669},
	Issn = {1097-6256},
	Journal = {Nature {N}euroscience},
	Keywords = {freemove, kaplan13, khoei13jpp, motion, motion-clouds, motion\_clouds, neural, noise, prior, psychophysics, sanz12jnp; thesis},
	Month = mar,
	Number = {4},
	Pages = {578--585},
	Priority = {5},
	Title = {Noise characteristics and prior expectations in human visual speed perception},
	Url = {http://www.nature.com/neuro/journal/v9/n4/abs/nn1669.html},
	Volume = {9},
	Year = {2006},
	Bdsk-Url-1 = {http://www.nature.com/neuro/journal/v9/n4/abs/nn1669.html},
	Bdsk-Url-2 = {http://dx.doi.org/10.1038/nn1669}}

@article{Madelain03,
	Abstract = {Previous research has demonstrated learning in the pursuit system, but it is unclear whether these effects are the result of changes in visual or motor processing. The ability to maintain smooth pursuit during the transient disappearance of a visual target provides a way to assess pursuit properties in the absence of visual inputs. To study the long-term effects of learning on nonvisual signals for pursuit, we used an operant conditioning procedure. By providing a reinforcing auditory stimulus during periods of accurate tracking, we increased the pursuit velocity gain during target blanking from 0.59 in the baseline session to 0.89 after 8 to 10 daily sessions of training. Learning also reduced the occurrence of saccades. The learned effects generalized to untrained target velocities and persisted in the presence of a textured visual background. In a yoked-control group, the reinforcer was independent of the subjects' responses, and the velocity gain remained unchanged (from 0.6 to 0.63, respectively, before and after training). In a control group that received no reinforcer, gain increased slightly after repetition of the task (from 0.63 to 0.71, respectively, before and after training). Using a model of pursuit, we show that these effects of learning can be simulated by modifying the gain of an extra-retinal signal. Our results demonstrate that learned contingencies can increase eye velocity in the absence of visual signals and support the view that pursuit is regulated by extra-retinal signals that can undergo long-term plasticity.},
	Author = {Madelain, Laurent and Krauzlis, Richard J},
	Citeulike-Article-Id = {10887483},
	Citeulike-Linkout-0 = {http://dx.doi.org/10.1152/jn.00869.2002},
	Citeulike-Linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/12904499},
	Citeulike-Linkout-2 = {http://www.hubmed.org/display.cgi?uids=12904499},
	Date-Added = {2013-12-25 18:24:41 +0000},
	Date-Modified = {2014-07-22 14:08:15 +0200},
	Doi = {10.1152/jn.00869.2002},
	Issn = {0022-3077},
	Journal = {Journal of neurophysiology},
	Keywords = {freemove, khoei13jpp, learning, spem; thesis},
	Month = aug,
	Number = {2},
	Pages = {972--982},
	Pmid = {12904499},
	Priority = {0},
	Title = {Effects of learning on smooth pursuit during transient disappearance of a visual target.},
	Url = {http://dx.doi.org/10.1152/jn.00869.2002},
	Volume = {90},
	Year = {2003},
	Bdsk-Url-1 = {http://dx.doi.org/10.1152/jn.00869.2002}}

@article{Perrinet12pred,
	Abstract = {In low-level sensory systems, it is still unclear how the noisy information collected locally by neurons may give rise to a coherent global percept. This is well demonstrated for the detection of motion in the aperture problem: as luminance of an elongated line is symmetrical along its axis, tangential velocity is ambiguous when measured locally. Here, we develop the hypothesis that motion-based predictive coding is sufficient to infer global motion. Our implementation is based on a context-dependent diffusion of a probabilistic representation of motion. We observe in simulations a progressive solution to the aperture problem similar to physio-logy and behavior. We demonstrate that this solution is the result of two underlying mechanisms. First, we demonstrate the formation of a tracking behavior favoring temporally coherent features independent of their texture. Second, we observe that incoherent features are explained away, while coherent information diffuses progressively to the global scale. Most previous models included ad hoc mechanisms such as end-stopped cells or a selection layer to track specific luminance-based features as necessary conditions to solve the aperture problem. Here, we have proved that motion-based predictive coding, as it is implemented in this functional model, is sufficient to solve the aperture problem. This solution may give insights into the role of prediction underlying a large class of sensory computations.},
	Archiveprefix = {arXiv},
	Author = {Perrinet, Laurent U. and Masson, Guillaume S.},
	Citeulike-Article-Id = {10834812},
	Citeulike-Linkout-0 = {http://dx.doi.org/10.1162/NECO\_a\_00332},
	Citeulike-Linkout-1 = {http://arxiv.org/abs/1208.6471},
	Citeulike-Linkout-2 = {http://arxiv.org/pdf/1208.6471},
	Citeulike-Linkout-3 = {http://dx.doi.org/10.1162/neco\_a\_00332},
	Citeulike-Linkout-4 = {http://www.mitpressjournals.org/doi/abs/10.1162/NECO\_a\_00332},
	Citeulike-Linkout-5 = {http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3472550/},
	Citeulike-Linkout-6 = {http://view.ncbi.nlm.nih.gov/pubmed/22734489},
	Citeulike-Linkout-7 = {http://www.hubmed.org/display.cgi?uids=22734489},
	Date-Added = {2013-12-25 18:24:41 +0000},
	Date-Modified = {2013-12-25 18:24:52 +0000},
	Day = {31},
	Doi = {10.1162/NECO\_a\_00332},
	Eprint = {1208.6471},
	Issn = {1530-888X},
	Journal = {Neural Computation},
	Keywords = {aperture-problem, khoei13jpp, perrinet12pred, predictive-coding; thesis},
	Month = oct,
	Number = {10},
	Pages = {2726--2750},
	Pmcid = {PMC3472550},
	Pmid = {22734489},
	Priority = {0},
	Publisher = {MIT Press},
	Title = {{Motion-Based} Prediction Is Sufficient to Solve the Aperture Problem},
	Url = {http://dx.doi.org/10.1162/NECO\_a\_00332},
	Volume = {24},
	Year = {2012},
	Bdsk-Url-1 = {http://dx.doi.org/10.1162/NECO%5C_a%5C_00332}}

@article{Schwartz08,
	Abstract = {
                Pattern recognition is one of the most important tasks of the visual system, and uncovering the neural mechanisms underlying recognition phenomena has been a focus of researchers for decades. Surprisingly, at the earliest stages of vision, the retina is capable of highly sophisticated temporal pattern recognition. We stimulated the retina of tiger salamander (Ambystoma tigrinum) with periodic dark flash sequences and found that retinal ganglion cells had a wide variety of different responses to a periodic flash sequence with many firing when a flash was omitted. The timing of the omitted stimulus response ({OSR}) depended on the period, with individual cells tracking the stimulus period down to increments of 5 ms. When flashes occurred earlier than expected, cells updated their expectation of the next flash time by as much as 50 ms. When flashes occurred later than expected, cells fired an {OSR} and reset their temporal expectation to the average time interval between flashes. Using pharmacology to investigate the retinal circuitry involved, we found that inhibitory transmission from amacrine cells was not required, but on bipolar cells were required. The results suggest a mechanism in which the intrinsic resonance of on bipolars leads to the {OSR} in ganglion cells. We discuss the implications of retinal pattern recognition on the neural code of the retina and visual processing in general.
            },
	Author = {Schwartz, G. and Berry, M. J},
	Citeulike-Article-Id = {4028625},
	Citeulike-Linkout-0 = {http://dx.doi.org/10.1152/jn.01025.2007},
	Citeulike-Linkout-1 = {http://jn.physiology.org/content/99/4/1787.abstract},
	Citeulike-Linkout-2 = {http://jn.physiology.org/content/99/4/1787.full.pdf},
	Citeulike-Linkout-3 = {http://jn.physiology.org/cgi/content/abstract/99/4/1787},
	Citeulike-Linkout-4 = {http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2856926/},
	Citeulike-Linkout-5 = {http://view.ncbi.nlm.nih.gov/pubmed/18272878},
	Citeulike-Linkout-6 = {http://www.hubmed.org/display.cgi?uids=18272878},
	Date-Added = {2013-12-25 18:24:41 +0000},
	Date-Modified = {2014-07-21 21:42:23 +0000},
	Day = {23},
	Doi = {10.1152/jn.01025.2007},
	Issn = {0022-3077},
	Journal = {Journal of neurophysiology},
	Keywords = {khoei13jpp, motion-extrapolation; thesis},
	Month = jan,
	Number = {4},
	Pages = {1787--1798},
	Pmcid = {PMC2856926},
	Pmid = {18272878},
	Priority = {2},
	Publisher = {American Physiological Society},
	Title = {Sophisticated temporal pattern recognition in retinal ganglion cells.},
	Url = {http://dx.doi.org/10.1152/jn.01025.2007},
	Volume = {99},
	Year = {2008},
	Bdsk-Url-1 = {http://dx.doi.org/10.1152/jn.01025.2007}}

@article{Arnold07,
	Abstract = {Motion contained within a static object can cause illusory position shifts toward the direction of internal motion. Here we present data suggesting this illusion is driven by modulations of apparent contrast. We observe position shifts at blurred stimulus regions without corresponding changes to internal structure, and find that low-contrast targets are more difficult to detect at the trailing, as opposed to leading, edges of movement. Motion induced position shifts are also shown to occur without conscious appreciation of motion direction. Our data suggests that motion can influence spatial coding via interactions that modulate apparent contrast, thereby changing the regions of the stimulus that are visible.},
	Address = {School of Psychology, The University of Queensland, Australia. darnold@psy.uq.edu.au},
	Author = {Arnold, Derek H. and Thompson, Michael and Johnston, Alan},
	Citeulike-Article-Id = {1903377},
	Citeulike-Linkout-0 = {http://dx.doi.org/10.1016/j.visres.2007.04.025},
	Citeulike-Linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/17643464},
	Citeulike-Linkout-2 = {http://www.hubmed.org/display.cgi?uids=17643464},
	Date-Added = {2013-12-25 18:24:41 +0000},
	Date-Modified = {2013-12-25 18:24:52 +0000},
	Doi = {10.1016/j.visres.2007.04.025},
	Issn = {0042-6989},
	Journal = {Vision research},
	Keywords = {contrast, freemove, khoei13jpp, position; thesis},
	Month = aug,
	Number = {18},
	Pages = {2403--2410},
	Pmid = {17643464},
	Priority = {0},
	Title = {Motion and position coding.},
	Url = {http://dx.doi.org/10.1016/j.visres.2007.04.025},
	Volume = {47},
	Year = {2007},
	Bdsk-Url-1 = {http://dx.doi.org/10.1016/j.visres.2007.04.025}}

@article{Assad95,
	Abstract = {For many types of behaviours, it is necessary to monitor the position or movement of objects that are temporarily occluded. The primate posterior parietal cortex contains neurons that are active during visual guidance tasks: in some cases, even if the visual target disappears transiently. It has been proposed that activity of this sort could be related to current or planned eye movements, but it might also provide a more generalized abstract representation of the spatial disposition of targets, even when they are not visible. We have recorded from monkey posterior parietal cortex while the animal viewed a visual stimulus that disappeared, and then, depending on experimental context, could be inferred to be either moving or stationary. During this temporary absence of the stimulus, about half of the neurons were found to be significantly more active on those trials in which the stimulus could be presumed to be moving rather than stationary. The activity was thus present in the absence of either sensory input or motor output, suggesting that it may indeed constitute a generalized representation of target motion.},
	Address = {Division of Neuroscience S-603, Baylor College of Medicine, Houston, Texas 77030.},
	Author = {Assad, John A. and Maunsell, John H. R.},
	Citeulike-Article-Id = {90445},
	Citeulike-Linkout-0 = {http://wexler.free.fr/library/files/assad\%20(1995)\%20neuronal\%20correlates\%20of\%20inferred\%20motion\%20in\%20primate\%20posterior\%20parietal\%20cortex.pdf},
	Citeulike-Linkout-1 = {http://dx.doi.org/10.1038/373518a0},
	Citeulike-Linkout-2 = {http://dx.doi.org/10.1038/373518a0},
	Citeulike-Linkout-3 = {http://view.ncbi.nlm.nih.gov/pubmed/7845463},
	Citeulike-Linkout-4 = {http://www.hubmed.org/display.cgi?uids=7845463},
	Date-Added = {2013-12-25 18:24:41 +0000},
	Date-Modified = {2013-12-25 18:24:52 +0000},
	Day = {09},
	Doi = {10.1038/373518a0},
	Issn = {0028-0836},
	Journal = {Nature},
	Keywords = {kaplan13, khoei13jpp, motion-extrapolation, motion\_extrapolation; thesis},
	Month = feb,
	Number = {6514},
	Pages = {518--521},
	Pmid = {7845463},
	Priority = {0},
	Publisher = {Nature Publishing Group},
	Title = {Neuronal correlates of inferred motion in primate posterior parietal cortex},
	Url = {http://wexler.free.fr/library/files/assad\%20(1995)\%20neuronal\%20correlates\%20of\%20inferred\%20motion\%20in\%20primate\%20posterior\%20parietal\%20cortex.pdf},
	Volume = {373},
	Year = {1995},
	Bdsk-Url-1 = {http://wexler.free.fr/library/files/assad%5C%20(1995)%5C%20neuronal%5C%20correlates%5C%20of%5C%20inferred%5C%20motion%5C%20in%5C%20primate%5C%20posterior%5C%20parietal%5C%20cortex.pdf},
	Bdsk-Url-2 = {http://dx.doi.org/10.1038/373518a0}}

@article{Hedges2011Optimal,
	Abstract = {The local spatiotemporal pattern of light on the retina is often consistent with a single translational velocity which may also be interpreted as a superposition of spatial patterns translating with different velocities. Human perception reflects such interpretations, as can be demonstrated using stimuli constructed from a superposition of two drifting gratings. Depending on a variety of parameters, these stimuli may be perceived as a coherently moving plaid pattern or as two transparent gratings moving in different directions. Here, we propose a quantitative model that explains how and why such interpretations are selected. An observer's percept corresponds to the most probable interpretation of noisy measurements of local image motion, based on separate prior beliefs about the speed and singularity of visual motion. This model accounts for human perceptual interpretations across a broad range of angles and speeds. With optimized parameters, its components are consistent with previous results in motion perception.},
	Author = {Hedges, James H. and Stocker, Alan A. and Simoncelli, Eero P.},
	Citeulike-Article-Id = {10707010},
	Citeulike-Linkout-0 = {http://dx.doi.org/10.1167/11.6.14},
	Citeulike-Linkout-1 = {http://www.journalofvision.org/content/11/6/14.abstract},
	Citeulike-Linkout-2 = {http://www.journalofvision.org/content/11/6/14.full.pdf},
	Citeulike-Linkout-3 = {http://view.ncbi.nlm.nih.gov/pubmed/21602554},
	Citeulike-Linkout-4 = {http://www.hubmed.org/display.cgi?uids=21602554},
	Date-Added = {2013-12-25 18:24:41 +0000},
	Date-Modified = {2013-12-25 18:24:52 +0000},
	Day = {19},
	Doi = {10.1167/11.6.14},
	Issn = {1534-7362},
	Journal = {Journal of Vision},
	Keywords = {coherence, kaplan13, khoei13jpp, motion, spatio-temporal; thesis},
	Month = may,
	Number = {6},
	Pmid = {21602554},
	Priority = {0},
	Publisher = {Association for Research in Vision and Ophthalmology},
	Title = {Optimal inference explains the perceptual coherence of visual motion stimuli},
	Url = {http://dx.doi.org/10.1167/11.6.14},
	Volume = {11},
	Year = {2011},
	Bdsk-Url-1 = {http://dx.doi.org/10.1167/11.6.14}}

@article{Fu01,
	Author = {Fu, Yu-Xi and Shen, Yaosong and Dan, Yang},
	Citeulike-Article-Id = {10634357},
	Citeulike-Linkout-0 = {http://view.ncbi.nlm.nih.gov/pubmed/11588202},
	Citeulike-Linkout-1 = {http://www.hubmed.org/display.cgi?uids=11588202},
	Comment = {* "Processing delay in the neural pathway on the order of tens of milliseconds should cause a significant offset between the perceived and the actual positions of a moving object. An appealing hypothesis is that the visual system can compensate for the neural delay and reduce the perceptual misalignment by extrapolating the trajectory of the moving object (Nijhawan, 1994, 1997). Evidence for motion extrapolation originally came from the flash-lag illusion in which the position of a moving object is perceived to be ahead of a briefly flashed object when they are physically colocalized at the time of the flash (MacKay, 1958; Nijhawan, 1994, 1997)." * "A neuronal model for the perceptual extrapolation What is the mechanism underlying the blur-dependent perceptual extrapolation? In a visuotopically organized neuronal circuit, the spatial profile of the population neural response moves within the circuit when the target moves in the visual field, and the position of the response profile may determine the perceived position of the visual target. In the rabbit and salamander retina, the transient response property and the contrast-gain control mechanism have been shown to shift the response profile of the retinal ganglion cells in the direction of target motion, which has been proposed as a mechanism for motion extrapolation (Berry et al., 1999). To test whether these retinal response properties can account for the blur-dependent perceptual extrapolation observed in this study, we simulated the response of each neuron to the motion-stop stimuli using these response properties and predicted the spatial profile of the population response of an array of neurons (see Materials and Methods). Surprisingly, we found that a simple model incorporating only the biphasic temporal response property (Fig. 3A) is sufficient to account for the spatiotemporal characteristics of the observed perceptual extrapolation." * a simple second-order motion: "The contrast-defined stimulus was a stationary sinusoidal grating at a high spatial frequency, with its contrast modulated by a moving Gaussian envelope (Fig. 4A)."},
	Date-Added = {2013-12-25 18:24:41 +0000},
	Date-Modified = {2013-12-25 18:24:52 +0000},
	Day = {15},
	Issn = {1529-2401},
	Journal = {The Journal of Neuroscience},
	Keywords = {khoei13jpp, motion-extrapolation, motion\_2nd-order; thesis},
	Month = oct,
	Number = {20},
	Pages = {RC172},
	Pmid = {11588202},
	Priority = {0},
	Publisher = {Society for Neuroscience},
	Title = {{Motion-Induced} Perceptual Extrapolation of Blurred Visual Targets},
	Url = {http://view.ncbi.nlm.nih.gov/pubmed/11588202},
	Volume = {21},
	Year = {2001},
	Bdsk-Url-1 = {http://view.ncbi.nlm.nih.gov/pubmed/11588202}}

@article{Makin11,
	Abstract = {People are able to judge the current position of occluded moving objects. This operation is known as motion extrapolation. It has previously been suggested that motion extrapolation is independent of the oculomotor system. Here we revisited this question by measuring eye position while participants completed two types of motion extrapolation task. In one task, a moving visual target travelled rightwards, disappeared, then reappeared further along its trajectory. Participants discriminated correct reappearance times from incorrect (too early or too late) with a two-alternative forced-choice button press. In the second task, the target travelled rightwards behind a visible, rectangular occluder, and participants pressed a button at the time when they judged it should reappear. In both tasks, performance was significantly different under fixation as compared to free eye movement conditions. When eye movements were permitted, eye movements during occlusion were related to participants' judgements. Finally, even when participants were required to fixate, small changes in eye position around fixation (<2\$\\,^{\\circ}\$) were influenced by occluded target motion. These results all indicate that overlapping systems control eye movements and judgements on motion extrapolation tasks. This has implications for understanding the mechanism underlying motion extrapolation.},
	Author = {Makin, Alexis D. and Poliakoff, Ellen},
	Citeulike-Article-Id = {10634377},
	Citeulike-Linkout-0 = {http://www.tandfonline.com/doi/pdf/10.1080/17470218.2010.548562},
	Citeulike-Linkout-1 = {http://dx.doi.org/10.1080/17470218.2010.548562},
	Citeulike-Linkout-2 = {http://view.ncbi.nlm.nih.gov/pubmed/21480079},
	Citeulike-Linkout-3 = {http://www.hubmed.org/display.cgi?uids=21480079},
	Citeulike-Linkout-4 = {http://www.tandfonline.com/doi/abs/10.1080/17470218.2010.548562},
	Date-Added = {2013-12-25 18:24:41 +0000},
	Date-Modified = {2013-12-25 18:24:52 +0000},
	Day = {8},
	Doi = {10.1080/17470218.2010.548562},
	Issn = {1747-0226},
	Journal = {The Quarterly Journal of Experimental Psychology},
	Keywords = {khoei13jpp, motion-extrapolation; thesis},
	Month = jul,
	Number = {7},
	Pages = {1327--1343},
	Pmid = {21480079},
	Priority = {0},
	Publisher = {Psychology Press},
	Title = {Do common systems control eye movements and motion extrapolation?},
	Url = {http://www.tandfonline.com/doi/pdf/10.1080/17470218.2010.548562},
	Volume = {64},
	Year = {2011},
	Bdsk-Url-1 = {http://www.tandfonline.com/doi/pdf/10.1080/17470218.2010.548562},
	Bdsk-Url-2 = {http://dx.doi.org/10.1080/17470218.2010.548562}}

@article{Hunter07,
	Abstract = {Matplotlib is a {2D} graphics package for Python for application development, interactive scripting, and publication-quality image generation across user interfaces and operating systems.},
	Address = {Los Alamitos, CA, USA},
	Author = {Hunter, John D.},
	Booktitle = {Computing in Science \& Engineering},
	Citeulike-Article-Id = {2878517},
	Citeulike-Linkout-0 = {http://dx.doi.org/10.1109/MCSE.2007.55},
	Citeulike-Linkout-1 = {http://doi.ieeecomputersociety.org/10.1109/MCSE.2007.55},
	Citeulike-Linkout-2 = {http://dx.doi.org/10.1109/mcse.2007.55},
	Citeulike-Linkout-3 = {http://ieeexplore.ieee.org/xpls/abs\_all.jsp?arnumber=4160265},
	Date-Added = {2013-12-25 18:24:41 +0000},
	Date-Modified = {2013-12-25 18:24:52 +0000},
	Doi = {10.1109/MCSE.2007.55},
	Issn = {1521-9615},
	Journal = {Computing in Science and Engineering},
	Keywords = {assofield, kaplan13, khoei13jpp, perrinet12pred, python, reproducible-science; thesis},
	Month = may,
	Number = {3},
	Pages = {90--95},
	Priority = {0},
	Publisher = {IEEE Computer Society},
	Title = {Matplotlib: A {2D} Graphics Environment},
	Url = {http://dx.doi.org/10.1109/MCSE.2007.55},
	Volume = {9},
	Year = {2007},
	Bdsk-Url-1 = {http://dx.doi.org/10.1109/MCSE.2007.55}}

@article{Oliphant07,
	Abstract = {By itself, Python is an excellent "steering" language for scientific codes written in other languages. However, with additional basic tools, Python transforms into a high-level language suited for scientific and engineering code that's often fast enough to be immediately useful but also flexible enough to be sped up with additional extensions.},
	Address = {Los Alamitos, CA, USA},
	Author = {Oliphant, T. E.},
	Citeulike-Article-Id = {5662279},
	Citeulike-Linkout-0 = {http://dx.doi.org/10.1109/MCSE.2007.58},
	Citeulike-Linkout-1 = {http://doi.ieeecomputersociety.org/10.1109/MCSE.2007.58},
	Citeulike-Linkout-2 = {http://dx.doi.org/10.1109/mcse.2007.58},
	Citeulike-Linkout-3 = {http://ieeexplore.ieee.org/xpls/abs\_all.jsp?arnumber=4160250},
	Date-Added = {2013-12-25 18:24:41 +0000},
	Date-Modified = {2013-12-25 18:24:52 +0000},
	Doi = {10.1109/MCSE.2007.58},
	Issn = {1521-9615},
	Journal = {Computing in Science and Engineering},
	Keywords = {assofield, kaplan13, khoei13jpp, perrinet12pred, python, reproducible-science; thesis},
	Month = may,
	Number = {3},
	Pages = {10--20},
	Priority = {0},
	Publisher = {IEEE Computer Society},
	Title = {Python for Scientific Computing},
	Url = {http://dx.doi.org/10.1109/MCSE.2007.58},
	Volume = {9},
	Year = {2007},
	Bdsk-Url-1 = {http://dx.doi.org/10.1109/MCSE.2007.58}}

@article{Pack2001Temporal,
	Abstract = {A critical step in the interpretation of the visual world is the integration of the various local motion signals generated by moving objects. This process is complicated by the fact that local velocity measurements can differ depending on contour orientation and spatial position. Specifically, any local motion detector can measure only the component of motion perpendicular to a contour that extends beyond its field of view1, 2. This "aperture problem"3 is particularly relevant to direction-selective neurons early in the visual pathways, where small receptive fields permit only a limited view of a moving object. Here we show that neurons in the middle temporal visual area (known as {MT} or V5) of the macaque brain reveal a dynamic solution to the aperture problem. {MT} neurons initially respond primarily to the component of motion perpendicular to a contour's orientation, but over a period of approximately 60 ms the responses gradually shift to encode the true stimulus direction, regardless of orientation. We also report a behavioural correlate of these neural responses: the initial velocity of pursuit eye movements deviates in a direction perpendicular to local contour orientation, suggesting that the earliest neural responses influence the oculomotor response.},
	Author = {Pack, Christopher C. and Born, Richard T.},
	Citeulike-Article-Id = {3479542},
	Citeulike-Linkout-0 = {http://dx.doi.org/10.1038/35059085},
	Citeulike-Linkout-1 = {http://dx.doi.org/10.1038/4091040a0},
	Date-Added = {2013-12-25 18:24:41 +0000},
	Date-Modified = {2013-12-25 18:24:52 +0000},
	Day = {22},
	Doi = {10.1038/35059085},
	Issn = {0028-0836},
	Journal = {Nature},
	Keywords = {aperture-problem, area-v1, electrophysiological\_recordings, kaplan13, khoei13jpp, monkey, motion, mt, perrinet12pred, pursuit\_eye\_movements, saccade; thesis},
	Month = feb,
	Pages = {1040--1042},
	Priority = {0},
	Publisher = {Nature Publishing Group},
	Title = {Temporal dynamics of a neural solution to the aperture problem in visual area {MT} of macaque brain},
	Url = {http://dx.doi.org/10.1038/35059085},
	Volume = {409},
	Year = {2001},
	Bdsk-Url-1 = {http://dx.doi.org/10.1038/35059085}}

@incollection{MassonMontagnini10,
	Abstract = {To accurately track a moving object of interest with appropriate smooth eye movements, the brain needs to reconstruct a single velocity vector describing the global motion of this object. Because of the aperture problem (see Chap. 1), the visual system must integrate piecewise local information from either elongated edges and contours or particular features such as corners and texture elements. Here, we show that investigating smooth eye movements unveil several dynamical properties of this visual motion integration stage. Signals are weighted according to their uncertainties. The integration is highly dynamical - eye movements being always launched first in the simplest, linear (vector sum) prediction. Tracking trajectories are then progressively adjusted to match the object trajectory after 200 ms of pursuit. Such strategy is immune to higher factors such as prediction about incoming {2D} target trajectory. On the contrary, mixing retinal and extra-retinal signals become important later during the pursuit to accommodate partial or total object motion occlusion for instance. We propose a framework computing and representing object motion through two recurrent loops ({V1-MT} and {MST}-{FEF}, respectively), with area {MST} playing the role of gear. Such architecture would accommodate two important constraints of motor behavior: quick reaction to a new visual event and utilization of extra-retinal information to smooth out transient changes in the image such as those occurring when an object moves in a crowded environment.},
	Address = {Boston, MA},
	Author = {Masson, Guillaume S. and Montagnini, Anna and Ilg, Uwe J.},
	Booktitle = {Dynamics of Visual Motion Processing},
	Chapter = {8},
	Citeulike-Article-Id = {10338802},
	Citeulike-Linkout-0 = {http://dx.doi.org/10.1007/978-1-4419-0781-3\_8},
	Citeulike-Linkout-1 = {http://www.springerlink.com/content/m8mw7x80rq031g83},
	Date-Added = {2013-12-25 18:24:41 +0000},
	Date-Modified = {2013-12-25 18:24:52 +0000},
	Doi = {10.1007/978-1-4419-0781-3\_8},
	Editor = {Ilg, Uwe J. and Masson, Guillaume S.},
	Isbn = {978-1-4419-0780-6},
	Keywords = {khoei13jpp, perrinet12pred, tracking-eye-movements; thesis},
	Pages = {161--188},
	Priority = {5},
	Publisher = {Springer US},
	Title = {When the Brain Meets the Eye: Tracking Object Motion},
	Url = {http://dx.doi.org/10.1007/978-1-4419-0781-3\_8},
	Year = {2010},
	Bdsk-Url-1 = {http://dx.doi.org/10.1007/978-1-4419-0781-3%5C_8}}

@article{Nowlan95,
	Author = {Nowlan, Steven J. and Sejnowski, Terrence J.},
	Citeulike-Article-Id = {10335527},
	Date-Added = {2013-12-25 18:24:41 +0000},
	Date-Modified = {2013-12-25 18:24:52 +0000},
	Journal = {Journal of {N}euroscience},
	Keywords = {kaplan13, khoei13jpp, perrinet12pred; thesis},
	Pages = {1195--1214},
	Priority = {0},
	Title = {A selection model for motion processing in area {MT} of primates},
	Volume = {15},
	Year = {1995}}

@article{Lorenceau92,
	Author = {Lorenceau, Jean and Shiffrar, M. and Wells, N. and Castet, Eric},
	Citeulike-Article-Id = {10132079},
	Citeulike-Linkout-0 = {http://www.ncbi.nlm.nih.gov/pubmed/8333170},
	Citeulike-Linkout-1 = {http://dx.doi.org/10.1016/0042-6989(93)90209-F},
	Date-Added = {2013-12-25 18:24:41 +0000},
	Date-Modified = {2013-12-25 18:24:52 +0000},
	Doi = {10.1016/0042-6989(93)90209-F},
	Issn = {0042-6989},
	Journal = {Vision {R}esearch},
	Keywords = {aperture-problem, khoei13jpp, perrinet12pred; thesis},
	Month = jun,
	Number = {9},
	Pages = {1207--1217},
	Priority = {2},
	Title = {Different motion sensitive units are involved in recovering the direction of moving lines},
	Url = {http://www.ncbi.nlm.nih.gov/pubmed/8333170},
	Volume = {33},
	Year = {1992},
	Bdsk-Url-1 = {http://www.ncbi.nlm.nih.gov/pubmed/8333170},
	Bdsk-Url-2 = {http://dx.doi.org/10.1016/0042-6989(93)90209-F}}

@article{Isard98,
	Abstract = {The problem of tracking curves in dense visual clutter is challenging. Kalman filtering is inadequate because it is based on Gaussian densities which, being unimodal, cannot represent simultaneous alternative hypotheses. The Condensation algorithm uses "factored sampling", previously applied to the interpretation of static images, in which the probability distribution of possible interpretations is represented by a randomly generated set. Condensation uses learned dynamical models, together...},
	Author = {Isard, Michael and Blake, Andrew},
	Citeulike-Article-Id = {10132078},
	Date-Added = {2013-12-25 18:24:41 +0000},
	Date-Modified = {2013-12-25 18:24:52 +0000},
	Journal = {International Journal of Computer Vision},
	Keywords = {freemove, khoei13jpp, particle-filter, perrinet12pred; thesis},
	Number = {1},
	Pages = {5--28},
	Priority = {0},
	Title = {Condensation -- conditional density propagation for visual tracking},
	Volume = {29},
	Year = {1998}}

@article{Castet93a,
	Author = {Castet, Eric and Lorenceau, Jean and Shiffrar, M. and Bonnet, C.},
	Citeulike-Article-Id = {10132077},
	Date-Added = {2013-12-25 18:24:41 +0000},
	Date-Modified = {2013-12-25 18:24:52 +0000},
	Journal = {Vision {R}esearch},
	Keywords = {aperture-problem, khoei13jpp, perrinet12pred; thesis},
	Number = {14},
	Pages = {1921--36},
	Priority = {2},
	Title = {Perceived speed of moving lines depends on orientation, length, speed and luminance.},
	Volume = {33},
	Year = {1993}}

@article{Rao01,
	Abstract = {When a flash is aligned with a moving object, subjects perceive the flash to lag behind the moving object. Two different models have been proposed to explain this ?flash-lag? effect. In the motion extrapolation model, the visual system extrapolates the location of the moving object to counteract neural propagation delays, whereas in the latency difference model, it is hypothesized that moving objects are processed and perceived more quickly than flashed objects. However, recent psychophysical experiments suggest that neither of these interpretations is feasible (Eagleman \& Sejnowski, 2000a, 2000b, 2000c), hypothesizing instead that the visual system uses data from the future of an event before committing to an interpretation. We formalize this idea in terms of the statistical framework of optimal smoothing and show that a model based on smoothing accounts for the shape of psychometric curves from a flash-lag experiment involving random reversals of motion direction. The smoothing model demonstrates how the visual system may enhance perceptual accuracy by relying not only on data from the past but also on data collected from the immediate future of an event. When a flash is aligned with a moving object, subjects perceive the flash to lag behind the moving object. Two different models have been proposed to explain this ?flash-lag? effect. In the motion extrapolation model, the visual system extrapolates the location of the moving object to counteract neural propagation delays, whereas in the latency difference model, it is hypothesized that moving objects are processed and perceived more quickly than flashed objects. However, recent psychophysical experiments suggest that neither of these interpretations is feasible (Eagleman \& Sejnowski, 2000a, 2000b, 2000c), hypothesizing instead that the visual system uses data from the future of an event before committing to an interpretation. We formalize this idea in terms of the statistical framework of optimal smoothing and show that a model based on smoothing accounts for the shape of psychometric curves from a flash-lag experiment involving random reversals of motion direction. The smoothing model demonstrates how the visual system may enhance perceptual accuracy by relying not only on data from the past but also on data collected from the immediate future of an event.},
	Author = {Rao, Rajesh P. N. and Eagleman, David M. and Sejnowski, Terrence J.},
	Citeulike-Article-Id = {10004713},
	Citeulike-Linkout-0 = {http://invibe.net/cgi-bin/biblio\_database\_access.cgi/Show?\_id=aeb1},
	Citeulike-Linkout-1 = {http://dx.doi.org/10.1162/08997660152002843},
	Citeulike-Linkout-2 = {http://www.mitpressjournals.org/doi/abs/10.1162/08997660152002843},
	Date-Added = {2013-12-25 18:24:41 +0000},
	Date-Modified = {2013-12-25 18:24:52 +0000},
	Day = {1},
	Doi = {10.1162/08997660152002843},
	Journal = {Neural Computation},
	Keywords = {khoei13jpp, motion, motion-extrapolation, optimization\_principles; thesis},
	Month = jun,
	Number = {6},
	Pages = {1243--1253},
	Priority = {5},
	Publisher = {MIT Press},
	Title = {Optimal Smoothing in Visual Motion Perception},
	Url = {http://invibe.net/cgi-bin/biblio\_database\_access.cgi/Show?\_id=aeb1},
	Volume = {13},
	Year = {2001},
	Bdsk-Url-1 = {http://invibe.net/cgi-bin/biblio%5C_database%5C_access.cgi/Show?%5C_id=aeb1},
	Bdsk-Url-2 = {http://dx.doi.org/10.1162/08997660152002843}}

@article{Burgi00,
	Abstract = {We develop a theory for the temporal integration of visual motion motivated by psychophysical experiments. The theory proposes that input data are temporally grouped and used to predict and estimate the motion flows in the image sequence. This temporal grouping can be considered a generalization of the data association techniques that engineers use to study motion sequences. Our temporal grouping theory is expressed in terms of the Bayesian generalization of standard Kalman filtering. To implement the theory, we derive a parallel network that shares some properties of cortical networks. Computer simulations of this network demonstrate that our theory qualitatively accounts for psychophysical experiments on motion occlusion and motion outliers. In deriving our theory, we assumed spatial factorizability of the probability distributions and made the approximation of updating the marginal distributions of velocity at each point. This allowed us to perform local computations and simplified our implementation. We argue that these approximations are suitable for the stimuli we are considering (for which spatial coherence effects are negligible).},
	Address = {Cambridge, MA, USA},
	Author = {Burgi, Pierre Y. and Yuille, Alan L. and Grzywacz, Norberto},
	Citeulike-Article-Id = {9566719},
	Citeulike-Linkout-0 = {http://portal.acm.org/citation.cfm?id=1121336},
	Citeulike-Linkout-1 = {http://portal.acm.org/citation.cfm?id=1121336},
	Comment = {follows older work from Grzywacz on work by McKee},
	Date-Added = {2013-12-25 18:24:41 +0000},
	Date-Modified = {2014-07-21 22:18:19 +0200},
	Issn = {0899-7667},
	Journal = {Neural Comput.},
	Keywords = {bayesian, khoei13jpp, modeling, motion-integration, perrinet12pred, predictive-coding; thesis},
	Month = aug,
	Number = {8},
	Pages = {1839--67},
	Priority = {0},
	Publisher = {MIT Press},
	Title = {Probabilistic Motion Estimation Based on Temporal Coherence},
	Url = {http://portal.acm.org/citation.cfm?id=1121336},
	Volume = {12},
	Year = {2000},
	Bdsk-Url-1 = {http://portal.acm.org/citation.cfm?id=1121336}}

@article{Rust06,
	Abstract = {Neurons in area {MT} (V5) are selective for the direction of visual motion. In addition, many are selective for the motion of complex patterns independent of the orientation of their components, a behavior not seen in earlier visual areas. We show that the responses of {MT} cells can be captured by a linear-nonlinear model that operates not on the visual stimulus, but on the afferent responses of a population of nonlinear V1 cells. We fit this cascade model to responses of individual {MT} neurons and show that it robustly predicts the separately measured responses to gratings and plaids. The model captures the full range of pattern motion selectivity found in {MT}. Cells that signal pattern motion are distinguished by having convergent excitatory input from V1 cells with a wide range of preferred directions, strong motion opponent suppression and a tuned normalization that may reflect suppressive input from the surround of V1 cells.},
	Address = {Howard Hughes Medical Institute, New York University, New York, New York 10003, USA. rust@mit.edu},
	Author = {Rust, Nicole C. and Mante, Valerio and Simoncelli, Eero P. and Movshon, J. Anthony},
	Citeulike-Article-Id = {9503667},
	Citeulike-Linkout-0 = {http://dx.doi.org/10.1038/nn1786},
	Comment = {* "To test this model, we developed a new technique that allowed us to fit the cascaded L-N model to data from individual MT cells. We show that the model captures the variety" * see Rust06supp ---=note-separator=--- * uses a whole set of data in MT neurons to fit different configurations of plaids. provides useful numbers for estimating input selectivity, integration width, divisive normalisation for different cell types (from component to pattern cells). * "To test this model, we developed a new technique that allowed us to fit the cascaded L-N model to data from individual MT cells. We show that the model captures the variety" * see Rust06supp ---=note-separator=--- * "To test this model, we developed a new technique that allowed us to fit the cascaded L-N model to data from individual {MT} cells. We show that the model captures the variety" * see Rust06supp},
	Date-Added = {2013-12-25 18:24:41 +0000},
	Date-Modified = {2013-12-25 18:24:52 +0000},
	Doi = {10.1038/nn1786},
	Journal = {Nature {N}euroscience},
	Keywords = {khoei13jpp, motion, motion-perception, perception, perrinet12pred, sanz12jnp; thesis},
	Number = {11},
	Pages = {1421--31},
	Priority = {2},
	Title = {How {MT} cells analyze the motion of visual patterns},
	Url = {http://dx.doi.org/10.1038/nn1786},
	Volume = {9},
	Year = {2006},
	Bdsk-Url-1 = {http://dx.doi.org/10.1038/nn1786}}

@incollection{Movshon85,
	Author = {Movshon, J. Anthony and Adelson, Edward H. and Gizzi, Martin S. and Newsome, William T.},
	Booktitle = {Pattern Recognition Mechanisms},
	Citeulike-Article-Id = {9447887},
	Date-Added = {2013-12-25 18:24:41 +0000},
	Date-Modified = {2013-12-25 18:24:52 +0000},
	Editor = {Chagas, C. and Gattass, R. and Gross, C.},
	Keywords = {khoei13jpp, motion, perrinet12pred, sanz12jnp; thesis},
	Pages = {117--151},
	Priority = {0},
	Publisher = {Rome: Vatican Press},
	Title = {The analysis of moving visual patterns},
	Volume = {54},
	Year = {1985}}

@article{Weiss02,
	Author = {Weiss, Yair and Simoncelli, Eero P. and Adelson, Edward H.},
	Citeulike-Article-Id = {9447885},
	Citeulike-Linkout-0 = {http://dx.doi.org/10.1038/nn858},
	Date-Added = {2013-12-25 18:24:41 +0000},
	Date-Modified = {2013-12-25 18:24:52 +0000},
	Day = {20},
	Doi = {10.1038/nn858},
	Issn = {1097-6256},
	Journal = {Nature {N}euroscience},
	Keywords = {bayesian, bayesian-models, kaplan13, khoei13jpp, model, motion, perrinet12pred, sanz12jnp; thesis},
	Month = jun,
	Number = {6},
	Pages = {598--604},
	Priority = {0},
	Title = {Motion illusions as optimal percepts},
	Url = {http://dx.doi.org/10.1038/nn858},
	Volume = {5},
	Year = {2002},
	Bdsk-Url-1 = {http://dx.doi.org/10.1038/nn858}}

@article{Montagnini07,
	Abstract = {The quality of the representation of an object's motion is limited by the noise in the sensory input as well as by an intrinsic ambiguity due to the spatial limi- tation of the visual motion analyzers (aperture prob- lem). Perceptual and oculomotor data demonstrate that motion processing of extended ob jects is initially dominated by the local {1D} motion cues orthogonal to the ob ject's edges, whereas {2D} information takes pro- gressively over and leads to the final correct represen- tation of global motion. A Bayesian framework ac- counting for the sensory noise and general expectancies for ob ject velocities has proven successful in explaining several experimental findings concerning early motion processing [1, 2, 3]. However, a complete functional model, encompassing the dynamical evolution of ob- ject motion perception is still lacking. Here we outline several experimental observations concerning human smooth pursuit of moving ob jects and more particu- larly the time course of its initiation phase. In addi- tion, we propose a recursive extension of the Bayesian model, motivated and constrained by our oculomotor data, to describe the dynamical integration of {1D} and {2D} motion information.},
	Author = {Montagnini, Anna and Mamassian, Pascal and Perrinet, Laurent and Castet, Eric and Masson, Guillaume S.},
	Booktitle = {Neuro-Computation: From Sensorimotor Integration to Computational Frameworks},
	Citeulike-Article-Id = {3015811},
	Citeulike-Linkout-0 = {http://dx.doi.org/10.1016/j.jphysparis.2007.10.013},
	Citeulike-Linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/18036790},
	Citeulike-Linkout-2 = {http://www.hubmed.org/display.cgi?uids=18036790},
	Citeulike-Linkout-3 = {http://www.sciencedirect.com/science/article/B6VMC-4R0CK36-1/1/c9cbab44aa802d6d6766bf52db72e037},
	Date-Added = {2013-12-25 18:24:41 +0000},
	Date-Modified = {2013-12-25 18:24:52 +0000},
	Doi = {10.1016/j.jphysparis.2007.10.013},
	Issn = {0928-4257},
	Journal = {Journal of Physiology-Paris},
	Keywords = {bayesian, kaplan13, khoei13jpp, perrinet12pred; thesis},
	Month = jan,
	Number = {1-3},
	Pages = {64--77},
	Pmid = {18036790},
	Priority = {0},
	Title = {Bayesian modeling of dynamic motion integration},
	Url = {http://dx.doi.org/10.1016/j.jphysparis.2007.10.013},
	Volume = {101},
	Year = {2007},
	Bdsk-Url-1 = {http://dx.doi.org/10.1016/j.jphysparis.2007.10.013}}

@article{Perrinet07,
	Abstract = {The machinery behind the visual perception of motion and the subsequent sensori-motor transformation, such as in ocular following response ({OFR}), is confronted to uncertainties which are efficiently resolved in the primate's visual system. We may understand this response as an ideal observer in a probabilistic framework by using Bayesian theory [Weiss, Y., Simoncelli, {E.P}., Adelson, {E.H}., 2002. Motion illusions as optimal percepts. Nature Neuroscience, 5(6), 598-604, doi:10.1038/nn858] which we previously proved to be successfully adapted to model the {OFR} for different levels of noise with full field gratings. More recent experiments of {OFR} have used disk gratings and bipartite stimuli which are optimized to study the dynamics of center-surround integration. We quantified two main characteristics of the spatial integration of motion: (i) a finite optimal stimulus size for driving {OFR}, surrounded by an antagonistic modulation and (ii) a direction selective suppressive effect of the surround on the contrast gain control of the central stimuli [Barth\'{e}lemy, {F.V}., Vanzetta, I., Masson, {G.S}., 2006. Behavioral receptive field for ocular following in humans: dynamics of spatial summation and center-surround interactions. Journal of Neurophysiology, (95), 3712-3726, doi:10.1152/jn.00112.2006]. Herein, we extended the ideal observer model to simulate the spatial integration of the different local motion cues within a probabilistic representation. We present analytical results which show that the hypothesis of independence of local measures can describe the spatial integration of the motion signal. Within this framework, we successfully accounted for the contrast gain control mechanisms observed in the behavioral data for center-surround stimuli. However, another inhibitory mechanism had to be added to account for suppressive effects of the surround.},
	Author = {Perrinet, Laurent U. and Masson, Guillaume S.},
	Citeulike-Article-Id = {8413461},
	Citeulike-Linkout-0 = {http://dx.doi.org/10.1016/j.jphysparis.2007.10.011},
	Citeulike-Linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/18042358},
	Citeulike-Linkout-2 = {http://www.hubmed.org/display.cgi?uids=18042358},
	Date-Added = {2013-12-25 18:24:41 +0000},
	Date-Modified = {2013-12-25 18:24:52 +0000},
	Doi = {10.1016/j.jphysparis.2007.10.011},
	Issn = {0928-4257},
	Journal = {Journal of Physiology-Paris},
	Keywords = {khoei13jpp, motion-integration; thesis},
	Month = jan,
	Number = {1-3},
	Pages = {46--55},
	Pmid = {18042358},
	Priority = {0},
	Title = {Modeling spatial integration in the ocular following response using a probabilistic framework},
	Url = {http://dx.doi.org/10.1016/j.jphysparis.2007.10.011},
	Volume = {101},
	Year = {2007},
	Bdsk-Url-1 = {http://dx.doi.org/10.1016/j.jphysparis.2007.10.011}}

@article{Barthelemy08a,
	Abstract = {Integrating information is essential to measure the physical {2D} motion of a surface from both ambiguous local {1D} motion of its elongated edges and non-ambiguous {2D} motion of its features such as corners or texture elements. The dynamics of this motion integration shows a complex time course as read from tracking eye movements: first, local {1D} motion signals are extracted and pooled to initiate ocular responses, then {2D} motion signals are integrated to adjust the tracking direction until it matches the surface motion direction. The nature of these {1D} and {2D} motion computations are still unclear. One hypothesis is that their different dynamics may be explained from different contrast sensitivities. To test this, we measured contrast-response functions of early, {1D}-driven and late, {2D}-driven components of ocular following responses to different motion stimuli: gratings, plaids and barberpoles. We found that contrast dynamics of {1D}-driven responses are nearly identical across the different stimuli. On the contrary, late {2D}-driven components with either plaids or barberpoles have similar latencies but different contrast dynamics. Temporal dynamics of both {1D}- and {2D}-driven responses demonstrates that the different contrast gains are set very early during the response time course. Running a Bayesian model of motion integration, we show that a large family of contrast-response functions can be predicted from the probability distributions of {1D} and {2D} motion signals for each stimulus and by the shape of the prior distribution. However, the pure delay (i.e. largely independent upon contrast) observed between {1D}- and {2D}-motion supports the fact that {1D} and {2D} probability distributions are computed independently. This two-pathway Bayesian model supports the idea that {1D} and {2D} mechanisms represent edges and features motion in parallel.},
	Author = {Barth\'{e}lemy, Fr\'{e}d\'{e}ric V. and Perrinet, Laurent U. and Castet, Eric and Masson, Guillaume S.},
	Citeulike-Article-Id = {8413330},
	Citeulike-Linkout-0 = {http://dx.doi.org/10.1016/j.visres.2007.10.020},
	Citeulike-Linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/18221979},
	Citeulike-Linkout-2 = {http://www.hubmed.org/display.cgi?uids=18221979},
	Date-Added = {2013-12-25 18:24:41 +0000},
	Date-Modified = {2013-12-25 18:24:52 +0000},
	Doi = {10.1016/j.visres.2007.10.020},
	Issn = {0042-6989},
	Journal = {Vision research},
	Keywords = {bayesian, khoei13jpp, model, motion-clouds, motion-integration, ocular\_following\_response, ofr, perrinet12pred, response\_latency, sanz12jnp; thesis},
	Month = feb,
	Number = {4},
	Pages = {501--522},
	Pmid = {18221979},
	Priority = {0},
	Title = {Dynamics of distributed {1D} and {2D} motion representations for short-latency ocular following.},
	Url = {http://dx.doi.org/10.1016/j.visres.2007.10.020},
	Volume = {48},
	Year = {2008},
	Bdsk-Url-1 = {http://dx.doi.org/10.1016/j.visres.2007.10.020}}

@article{Bogadhi11,
	Abstract = {Accuracy in estimating an object's global motion over time is not only affected by the noise in visual motion information but also by the spatial limitation of the local motion analyzers (aperture problem). Perceptual and oculomotor data demonstrate that during the initial stages of the motion information processing, {1D} motion cues related to the object's edges have a dominating influence over the estimate of the object's global motion. However, during the later stages, {2D} motion cues related to terminators (edge-endings) progressively take over, leading to a final correct estimate of the object's global motion. Here, we propose a recursive extension to the Bayesian framework for motion processing (Weiss, Simoncelli, \& Adelson, 2002) cascaded with a model oculomotor plant to describe the dynamic integration of {1D} and {2D} motion information in the context of smooth pursuit eye movements. In the recurrent Bayesian framework, the prior defined in the velocity space is combined with the two independent measurement likelihood functions, representing edge-related and terminator-related information, respectively to obtain the posterior. The prior is updated with the posterior at the end

license: MIT

license_type: permissive

branch_name: refs/heads/master

extension: bib