Motion perception is the process of inferring the speed and direction of elements in a scene based on visual, vestibular and proprioceptive inputs. Although this process appears straightforward to most observers, it has proven to be a difficult problem from a computational perspective, and difficult to explain in terms of neural processing.
Motion perception is studied by many disciplines, including psychology (i.e. visual perception), neurology, neurophysiology, engineering, and computer science.
Neuropsychology The inability to perceive motion is called akinetopsia and it may be caused by a lesion to cortical area V5 in the extrastriate cortex. Neuropsychological studies of a patient who could not see motion, seeing the world in a series of static "frames" instead, suggested that visual area V5 in humans is homologous to motion processing area V5/MT in primates.
First-order motion perception
When two or more stimuli are alternatively switched on and off, they can produce two distinct motion perceptions. The first, known as beta movement, is demonstrated in the yellow-ball figure and forms the basis for electronic news ticker displays. However, at faster alternation rates, and when the distance between the stimuli is optimal, an illusory "object"—matching the background color—appears to move between the stimuli, alternately occluding them. This phenomenon is called the phi phenomenon and is often described as an example of "pure" motion detection, uncontaminated by form cues, unlike beta movement. Nevertheless, this description is somewhat paradoxical since creating such motion without figural percepts is impossible.
The phi phenomenon has been referred to as "first-order" motion perception. Werner E. Reichardt and Bernard Hassenstein have modelled it in terms of relatively simple "motion sensors" in the visual system, that have evolved to detect a change in luminance at one point on the retina and correlate it with a change in luminance at a neighbouring point on the retina after a short delay. Sensors that are proposed to work this way have been referred to as either Hassenstein-Reichardt detectors after the scientists Bernhard Hassenstein and Werner Reichardt, who first modelled them, motion-energy sensors, or Elaborated Reichardt Detectors. These sensors are described as detecting motion by spatio-temporal correlation and are considered by some to be plausible models for how the visual system may detect motion. (Although, again, the notion of a "pure motion" detector suffers from the problem that there is no "pure motion" stimulus, i.e. a stimulus lacking perceived figure/ground properties). There is still considerable debate regarding the accuracy of the model and exact nature of this proposed process. It is not clear how the model distinguishes between movements of the eyes and movements of objects in the visual field, both of which produce changes in luminance on points on the retina.
Second-order motion perception Second-order motion is when the moving contour is defined by contrast, texture, flicker or some other quality that does not result in an increase in luminance or motion energy in the Fourier spectrum of the stimulus. There is much evidence to suggest that early processing of first- and second-order motion is carried out by separate pathways. Second-order mechanisms have poorer temporal resolution and are low-pass in terms of the range of spatial frequencies to which they respond. (The notion that neural responses are attuned to frequency components of stimulation suffers from the lack of a functional rationale and has been generally criticized by G. Westheimer (2001) in an article called "The Fourier Theory of Vision.") Second-order motion produces a weaker motion aftereffect unless tested with dynamically flickering stimuli.
The aperture problem
The motion direction of a contour is ambiguous, because the motion component parallel to the line cannot be inferred based on the visual input. This means that a variety of contours of different orientations moving at different speeds can cause identical responses in a motion sensitive neuron in the visual system.
Motion integration Some have speculated that, having extracted the hypothesized motion signals (first- or second-order) from the retinal image, the visual system must integrate those individual local motion signals at various parts of the visual field into a 2-dimensional or global representation of moving objects and surfaces. (It is not clear how this 2D representation is then converted into the perceived 3D percept) Further processing is required to detect coherent motion or "global motion" present in a scene.
The ability of a subject to detect coherent motion is commonly tested using motion coherence discrimination tasks. For these tasks, dynamic random-dot patterns (also called random dot kinematograms) are used that consist in 'signal' dots moving in one direction and 'noise' dots moving in random directions. The sensitivity to motion coherence is assessed by measuring the ratio of 'signal' to 'noise' dots required to determine the coherent motion direction. The required ratio is called the motion coherence threshold.
Motion in depth
As in other aspects of vision, the observer's visual input is generally insufficient to determine the true nature of stimulus sources, in this case their velocity in the real world. In monocular vision for example, the visual input will be a 2D projection of a 3D scene. The motion cues present in the 2D projection will by default be insufficient to reconstruct the motion present in the 3D scene. Put differently, many 3D scenes will be compatible with a single 2D projection. The problem of motion estimation generalizes to binocular vision when we consider occlusion or motion perception at relatively large distances, where binocular disparity is a poor cue to depth. This fundamental difficulty is referred to as the inverse problem.
Nonetheless, some humans do perceive motion in depth. There are indications that the brain uses various cues, in particular temporal changes in disparity as well as monocular velocity ratios, for producing a sensation of motion in depth. Two different binocular cues of the perception motion in depth are hypothesized: Inter-ocular velocity difference (IOVD) and changing disparity (CD) over time. Motion in depth based on inter-ocular velocity differences can be tested using dedicated binocularly uncorrelated random-dot kinematograms. Study results indicate that the processing of these two binocular cues – IOVD and CD – may use fundamentally different low-level stimulus features, which may be processed jointly that later stages. Additionally, as monocular cue, also the changing size of retinal images contributes to motion in depth detection.
Perceptual learning of motion Detection and discrimination of motion can be improved by training with long-term results. Participants trained to detect the movements of dots on a screen in only one direction become particularly good at detecting small movements in the directions around that in which they have been trained. This improvement was still present 10 weeks later. However perceptual learning is highly task and stimulus specific. For motion-direction specific tasks, participants that trained on tasks on one motion direction showed improved performance of the same task, within certain range of the trained direction. This range narrows for harder task and widens for harder tasks. However, improvement on one specific motion task does not always improve performances on other motion taks, or for other sorts of stimuli.
Cognitive map A cognitive map is a type of mental representation which serves an individual to acquire, code, store, recall, and decode information about the relative locations and attributes of phenomena in their spatial environment. Place cells work with other types of neurons in the hippocampus and surrounding regions of the brain to perform this kind of spatial processing, but the ways in which they function within the hippocampus are still being researched..
Many species of mammals can keep track of spatial location even in the absence of visual, auditory, olfactory, or tactile cues, by integrating their movements—the ability to do this is referred to in the literature as path integration. A number of theoretical models have explored mechanisms by which path integration could be performed by neural networks. In most models, such as those of Samsonovich and McNaughton (1997) or Burak and Fiete (2009), the principal ingredients are (1) an internal representation of position, (2) internal representations of the speed and direction of movement, and (3) a mechanism for shifting the encoded position by the right amount when the animal moves. Because cells in the Medial Entorhinal Cortex (MEC) encode information about position (grid cells) and movement (head direction cells and conjunctive position-by-direction cells), this area is currently viewed as the most promising candidate for the place in the brain where path integration occurs.
Neurophysiology Motion sensing using vision is crucial for detecting a potential mate, prey, or predator, and thus it is found both in vertebrates and invertebrates vision throughout a wide variety of species, although it is not universally found in all species. In vertebrates, the process takes place in retina and more specifically in retinal ganglion cells, which are neurons that receive input from bipolar cells and amacrine cells on visual information and process output to higher regions of the brain including, thalamus, hypothalamus, and mesencephalon.
The study of directionally selective units began with a discovery of such cells in the cerebral cortex of cats by David Hubel and Torsten Wiesel in 1959. Following the initial report, an attempt to understand the mechanism of directionally selective cells was pursued by Horace B. Barlow and William R. Levick in 1965. Their in-depth experiments in rabbit's retina expanded the anatomical and physiological understanding of the vertebrate visual system and ignited the interest in the field. Numerous studies that followed thereafter have unveiled the mechanism of motion sensing in vision for the most part. Alexander Borst and Thomas Euler's 2011 review paper, "Seeing Things in Motion: Models, Circuits and Mechanisms". discusses certain important findings from the early discoveries to the recent work on the subject, coming to the conclusion of the current status of the knowledge.
Direction selective (DS) cells Direction selective (DS) cells in the retina are defined as neurons that respond differentially to the direction of a visual stimulus. According to Barlow and Levick (1965), the term is used to describe a group of neurons that "gives a vigorous discharge of impulses when a stimulus object is moved through its receptive field in one direction." This direction in which a set of neurons respond most strongly to is their "preferred direction". In contrast, they do not respond at all to the opposite direction, "null direction". The preferred direction is not dependent on the stimulus—that is, regardless of the stimulus' size, shape, or color, the neurons respond when it is moving in their preferred direction, and do not respond if it is moving in the null direction. There are three known types of DS cells in the vertebrate retina of the mouse, ON/OFF DS ganglion cells, ON DS ganglion cells, and OFF DS ganglion cells. Each has a distinctive physiology and anatomy. Analogous directionally selective cells are not thought to exist in the primate retina.
ON/OFF DS ganglion cells