Extraction of optical velocity by use of multi-input Reichardt detectors

The Rotating Snakes Illusion Is a Straightforward Consequence of Nonlinearity in Arrays of Standard Motion Detectors

The Rotating Snakes illusion is a motion illusion based on repeating, asymmetric luminance patterns. Recently, we found certain gray-value conditions where a weak illusory motion occurs in the opposite direction. Of the four models for explaining the illusion, one also explains the unexpected perceived opposite direction.We here present a simple new model, without free parameters, based on an array of standard correlation-type motion detectors with a subsequent nonlinearity (e.g., saturation) before summing the detector outputs. The model predicts (a) the pattern-appearance motion illusion for steady fixation, (b) an illusion under the real-world situation of saccades across or near the pattern (pattern shift), (c) a relative maximum of illusory motion for the same gray values where it is found psychophysically, and (d) the opposite illusion for certain luminance values. We submit that the new model's sparseness of assumptions justifies adding a fifth model to explain this illusion.

Towards Computational Models and Applications of Insect Visual Systems for Motion Perception: A Review

Motion perception is a critical capability determining a variety of aspects of insects' life, including avoiding predators, foraging, and so forth. A good number of motion detectors have been identified in the insects' visual pathways. Computational modeling of these motion detectors has not only been providing effective solutions to artificial intelligence, but also benefiting the understanding of complicated biological visual systems. These biological mechanisms through millions of years of evolutionary development will have formed solid modules for constructing dynamic vision systems for future intelligent machines. This article reviews the computational motion perception models originating from biological research on insects' visual systems in the literature. These motion perception models or neural networks consist of the looming-sensitive neuronal models of lobula giant movement detectors (LGMDs) in locusts, the translation-sensitive neural systems of direction-selective neurons (DSNs) in fruit flies, bees, and locusts, and the small-target motion detectors (STMDs) in dragonflies and hoverflies. We also review the applications of these models to robots and vehicles. Through these modeling studies, we summarize the methodologies that generate different direction and size selectivity in motion perception. Finally, we discuss multiple systems integration and hardware realization of these bio-inspired motion perception models.

A test bed for insect-inspired robotic control

Flying insects are remarkable examples of sophisticated sensory-motor control systems. Insects have solved the fundamental challenge facing the field of mobile robots: robust sensory-motor mapping. Control models based on insects can contribute much to the design of robotic control systems. We present our work on a preliminary robotic control system inspired by current behavioural and physiological models of the fruit fly, Drosophila melanogaster. We designed a five-degrees-of-freedom robotic system that serves as a novel simulation/mobile robot hybrid. This design has allowed us to implement a fly-inspired control system that uses visual and mechanosensory feedback. Our results suggest that a simple control scheme can yield surprisingly robust fly-like robotic behaviour.

Motion detection in insect orientation and navigation

The visual systems of insects are exquisitely sensitive to motion. Over the past 40 years or so, motion processing in insects has been studied and characterised primarily through the optomotor response. This response, which is a turning response evoked by the apparent movement of the visual environment, serves to stabilise the insect's orientation with respect to the environment. Research over the past decade, however, is beginning to reveal the existence of a variety of other behavioural responses in insects, that use motion information in different ways. Here we review some of the recently characterised behaviours, describe the inferred properties of the underlying movement-detecting processes, and propose modified or new models to account for them.

Attentional modulation in perception of visual motion events

Identical visual targets moving across each other with equal and constant speed can be perceived either to bounce off or to stream through each other. This bistable motion perception has been studied mostly in the context of motion integration. Since the perception of most ambiguous motion is affected by attention, there is the possibility of attentional modulation occurring in this case as well. We investigated whether distraction of attention from the moving targets would alter the relative frequency of each percept. During the observation of the streaming/bouncing motion event in the peripheral visual field, visual attention was disrupted by an abrupt presentation of a visual distractor at various timings and locations (experiment 1; exogenous distraction of attention) or by the demand of an additional discrimination task (experiments 2 and 3; endogenous distraction of attention). Both types of distractions of attention increased the frequency of the bouncing percept and decreased that of the streaming percept. These results suggest that attention may facilitate the perception of object motion as continuing in the same direction as in the past.

Recruitment mechanisms in speed and fine-direction discrimination tasks

The minimum information necessary to specify motion requires a change in position across time. Previous studies have shown that human motion measurements improve with more than two frames of motion. This study clarifies how motion information is integrated to produce the best speed and direction discrimination. Using random-dot kinematograms, fine-direction discrimination thresholds and speed discrimination thresholds are assessed as a function of dot lifetime. Specifically, we ask if performance on both tasks depends on dot lifetime in the same manner. If both speed and direction discrimination performance improve the same way with increasing dot lifetime, this would indicate that both tasks have the same integration limit and both tasks may depend on the same underlying mechanisms. Experiment 1 shows that for both tasks a four-frame dot lifetime is necessary for observers to reach asymptotic threshold levels. The absolute level of performance improves with increasing stimulus duration or signal-to-noise ratio, but the integration limit itself does not vary. Experiment 2 examines whether this integration limit is constrained by the number of frames or by the temporal duration of the dot lifetime. The data in Experiment 2 suggest that both a minimum number of samples and a minimal temporal integration period determine the integration limit for recruitment mechanisms. The results suggest that speed and fine-direction discrimination depend upon the same underlying motion mechanisms. These results are discussed in relation to possible underlying physiological substrates and computational models of motion measurement.

Motion interference: perturbing perceived direction

The minimum stimulus necessary to define motion is a change in position from one location to another in time, but past studies have provided evidence that the human motion system integrates motion over more than two positions. In this study we demonstrate strong sequential interactions affecting perceived direction in apparent-motion sequences; a perturbing dot can bias the perceived direction of motion between two test dots to which it is relatively close in space (up to 100 min arc) and time (up to 300 msec). These sequential interactions suggest a motion mechanism sensitive to the spatial characteristics of motion trajectories; the interactions are greatest for evenly spaced targets positioned along a single axis. The implications for motion-detection models and models based on attention as a mechanism to create apparent motion are discussed.

Families of stationary patterns producing illusory movement: insights into the visual system

A computational explanation of the illusory movement experienced upon extended viewing of Enigma, a static figure painted by Leviant, is presented. The explanation relies on a model for the interpretation of three-dimensional motion information contained in retinal motion measurements. This model shows that the Enigma figure is a special case of a larger class of figures exhibiting the same illusory movement and these figures are introduced here. Our explanation suggests that eye movements and/or accommodation changes cause weak retinal motion signals, which are interpreted by higher-level processes in a way that gives rise to these illusions, and proposes a number of new experiments to unravel the functional structure of the motion pathway.