Motion perception
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[edit]
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[1] is homologous to motion processing area V5/MT in primates.[2][3][4]
Second-order motion perception[edit]
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.[9][10] There is much evidence to suggest that early processing of first- and second-order motion is carried out by separate pathways.[11] 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.[12]
Motion integration[edit]
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.[13]
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.
Perceptual learning of motion[edit]
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 specific. For example, the participants show no improvement when tested around other motion directions, or for other sorts of stimuli.[19]
Cognitive map[edit]
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.
[20][21] Place cells work with other types of neurons in the hippocampus and surrounding regions of the brain to perform this kind of spatial processing,[22] but the ways in which they function within the hippocampus are still being researched.[23]
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)[24] or Burak and Fiete (2009),[25] 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[26]) and movement (head direction cells and conjunctive position-by-direction cells[27]), this area is currently viewed as the most promising candidate for the place in the brain where path integration occurs.