To some degree, all current models of visual motion-perception mechanisms depend on the power of the visual signal in various spatiotemporal-frequency bands. Here we show how to construct counterexamples: visual stimuli that are consistently perceived as obviously moving in a fixed direction yet for which Fourier-domain power analysis yields no systematic motion components in any given direction. We provide a general theoretical framework for investigating non-Fourier motion-perception mechanisms; central are the concepts of drift-balanced and microbalanced random stimuli. A random stimulus S is drift balanced if its expected power in the frequency domain is symmetric with respect to temporal frequency, that is, if the expected power in S of every drifting sinusoidal component is equal to the expected power of the sinusoid of the same spatial frequency, drifting at the same rate in the opposite direction. Additionally, S is microbalanced if the result WS of windowing S by any spacetime-separable function W is drift balanced. We prove that (i) any space-time-separable random (or nonrandom) stimulus is microbalanced; (ii) any linear combination of pairwise independent microbalanced (respectively, driftbalanced) random stimuli is microbalanced and drift balanced if the expectation of each component is uniformly zero; (iii) the convolution of independent microbalanced and drift-balanced random stimuli is microbalanced and drift balanced; (iv) the product of independent microbalanced random stimuli is microbalanced; and (v) the expected response of any Reichardt detector to any microbalanced random stimulus is zero at every instant in time. Examples are provided of classes of microbalanced random stimuli that display consistent and compelling motion in one direction. All the results and examples from the domain of motion perception are transposable to the spacedomain problem of detecting orientation in a texture pattern. 1.

Electrophysiological studies indicate that neurons in the Middle Temporal (MT) area of the primate brain are selective for the velocity of visual stimuli. This paper describes a computational model of MT physiology, in which local image velocities are represented via the distribution of MT neuronal responses. The computation is performed in two stages, corresponding to neurons in cortical areas V1 and MT. Each stage computes a weighted linear sum of inputs, followed by rectification and divisive normalization. V1 receptive field weights are designed for orientation and direction selectivity. MT receptive field weights are designed for velocity (both speed and direction) selectivity. The paper includes computational simulations accounting for a wide range of physiological data, and describes experiments that could be used to further test and refine the model.

Lu and Sperling [Vision Res. 35, 2697 (1995)] proposed that human visual motion perception is served by three separate motion systems: a first-order system that responds to moving luminance patterns, a second-order system that responds to moving modulations of feature types—stimuli in which the expected luminance is the same everywhere but an area of higher contrast or of flicker moves, and a third-order system that computes the motion of marked locations in a ‘‘salience map,’ ’ that is, a neural representation of visual space in which the locations of important visual features (‘‘figure’’) are marked and ‘‘ground’ ’ is unmarked. Subsequently, there have been some strongly confirmatory reports: different gain-control mechanisms for first- and second-order motion, selective impairment of first- versus second- and/or third-order motion by different brain injuries, and the classification of new third-order motions, e.g., isoluminant chromatic motion. Various procedures have successfully discriminated between second- and third-order motion (when first-order motion is excluded): dual tasks, second-order reversed phi, motion competition, and selective adaptation. Meanwhile, eight apparent contradictions to the three-systems theory have been proposed. A review and reanalysis here of the new evidence, pro and con, resolves the challenges and yields a more clearly defined and significantly strengthened theory. © 2001 Optical Society of America OCIS codes: 330.4150.

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this article.

A neural network model of visual motion perception and speed discrimination is presented. The model shows how a distributed population code of speed tuning, that realizes a size-speed correlation, can be derived from the simplest mechanisms whereby activations of multiple spatially short-range filters of different size are transformed into speed-tuned cell responses. These mechanisms use transient cell responses to moving stimuli, output thresholds that covary with filter size, and competition. These mechanisms are proposed to occur in the V1® MT cortical processing stream. The model reproduces empirically derived speed discrimination curves and simulates data showing how visual speed perception and discrimination can be affected by stimulus contrast, duration, dot density and spatial frequency. Model motion mechanisms are analogous to mechanisms that have been used to model 3-D form and figure-ground perception. The model forms the front end of a larger motion processing system that h...

A number of psychophysical nd physiological studies have suggested that first- and second-order motion signals are processed, at least initially, by independent pathways, and that the two pathways both consist of multiple motion-detecting channels that are each narrowly tuned to a different spatial scale (spatial frequency). However, the precise number and nature of the mechanisms that subserve first- and second-order motion perception in human vision remain both controversial and speculative. We sought o clarify this issue by conducting selective adaptation experiments, in which modulation-depth thresholds for identifying the direction of stimulus motion of first-order (luminance-defined) and second-order (contrast-defined) drifting gratings were measured both prior to and following adaptation to motion. The drift direction, spatial frequency and stimulus type (either first- or second-order) of the adaptation and test stimuli were systematically manipulated. When the adaptation and test stimuli were either both first-order gratings or both second-order gratings, robust elevations of direction-identification thresholds were found and, importantly, these aftereffects exhibited both direction-selectivity and spatial-frequency selectivity. Cross-over-adaptation effects between first- and second-order gratings were also sometimes observed, but were very weak and not spatial-frequency selective. These findings give direct support for the existence of multiple-scale processing for first- and second-order motion in the human visual system and provide additional evidence that the two varieties of motion are initially processed by independent pathways. © 1997 Elsevier Science Ltd Motion First-order Second-order Adaptation Direction Spatial frequency

A model that is capable of maintaining the identities of individuated elements as they move is described. It solves a particular problem of underdetermination, the motion correspondence problem, by simultaneously applying 3 constraints: the nearest neighbor principle, the relative velocity principle, and the element integrity principle. The model generates the same correspondence solutions as does the human visual system for a variety of displays, and many of its properties are consistent with what is known about the physiological mechanisms underlying human motion perception. The model can also be viewed as a proposal of how the identities of attentional tags are maintained by visual cognition, and thus it can be differentiated from a system that serves merely to detect movement.

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We present a new approach to computing from image sequences the two-dimensional velocities of moving objects that are occluded and transparent. The new motion model does not attempt to provide an accurate representation of the velocity flow field at fine resolutions but coarsely segments an image into regions of coherent motion, provides an estimate of velocity in each region, and actively selects the most reliable estimates. The model uses motion-energy filters in the first stage of processing and computes, in parallel, two different sets of retinotopically organized spatial arrays of unit responses: one set of units estimates the local velocity, and the second set selects from these local estimates those that support global velocities. Only the subset of local-velocity measurements that are the most reliable is included in estimation of the velocity of objects. The model is in agreement with many of the constraints imposed by the physiological response properties of cells in primate visual cortex, and its performance is similar to that of primates on motion transparency. 1.