Looming Objects Research Articles

An unescapable looming threat paradigm for assessing anxiety-like responses in rats.

Rapidly approaching visual stimuli (i.e. looming objects) are known to evoke unconditioned defense responses across species. In rodents, this threat reactivity repertoire includes freezing and fleeing behavior. Although components of the circuitry underlying unconditioned response to a looming threat have been elucidated, both a temporal characterization and drug effects on the freezing response have not yet been reported. Here, we describe a modified version of a looming threat task in which no escape route is available. In this task, we observed unconditioned freezing prior to, during, and after exposure to a looming threat stimulus. In Long Evans (LE) and Sprague-Dawley (SD) rats, we report looming stimulus-specific freezing response. We further explored the specificity and pharmacosensitivity of this response in male and female LE rats. Administration of a GABA-A receptor negative allosteric modulator (FG-7142) did not re-establish freezing in habituated animals; however, administration of a GABA-A receptor positive allosteric modulator (diazepam) in naïve LEs significantly reduced freezing during the post-looming period in a sex-dependent manner. Presentation of an unescapable looming stimulus results in freezing that extends beyond the acute threat exposure. Because freezing responses outlast the initial threat, and display only modest sensitivity to conventional anxiolytic therapy, this may represent a platform for screening agents in treatment-refractory anxiety.

Doppler detection triggers instantaneous escape behavior in scanning bats

Animals must instantaneously escape from predators for survival, which requires quick detection of approaching threats. Although the neural mechanisms underlying the perception of looming objects have been extensively studied in the visual system, little is known about their auditory counterparts. Echolocating bats use their auditory senses to perceive not only the soundscape, but also the physical environment through active sensing. Although object movement induces both echo delay changes and Doppler shifts, the actual information required to perceive movement has been unclear. Herein, we addressed this question by playing back phantom echoes mimicking an approaching target to horseshoe bats and found that they relied only on Doppler shifts. This suggests that the bats do not perceive object motion in the spatiotemporal dimension (i.e., positional variation), as in vision, but rather take advantage of acoustic sensing by directly detecting velocity, thereby enabling them to respond instantaneously to approaching threats.

Mosquitoes escape looming threats by actively flying with the bow wave induced by the attacker

To detect and escape looming threats, night-flying insects must rely on other senses than vision alone. Nocturnal mosquitoes can evade looming objects in the dark, but how they achieve this is still unknown. Here, we show how night-active female malaria mosquitoes escape from rapidly looming objects that simulate defensive actions of blood-hosts. First, we quantified the escape performance of flying mosquitoes from an event-triggered mechanical swatter, showing that mosquitoes use swatter-induced airflow to increase their escape success. Secondly, we used high-speed videography and deep-learning-based tracking to analyze escape flights in detail, showing that mosquitoes use banked turns to evade the threat. By combining escape kinematics data with numerical simulations of attacker-induced airflow and a mechanistic movement model, we unraveled how mosquitoes control these banked evasive maneuvers: they actively steer away from the danger, and then passively travel with the bow wave produced by the attacker. Our results demonstrate that night-flying mosquitoes can detect looming objects when visual cues are minimal, suggesting that they use attacker-induced airflow both to detect the danger and as a fluid medium to move with away from the threat. This shows that escape strategies of flying insects are more complex than previous visually induced escape flight studies suggest. As most insects are of similar or smaller sizes than mosquitoes, comparable escape strategies are expected among millions of flying insect species. The here-observed escape maneuvers are distinct from those of mosquitoes escaping from odor-baited traps, thus providing new insights for the development of novel trapping techniques for integrative vector management.

An Angular Acceleration Based Looming Detector for Moving UAVs.

Visual perception equips unmanned aerial vehicles (UAVs) with increasingly comprehensive and instant environmental perception, rendering it a crucial technology in intelligent UAV obstacle avoidance. However, the rapid movements of UAVs cause significant changes in the field of view, affecting the algorithms' ability to extract the visual features of collisions accurately. As a result, algorithms suffer from a high rate of false alarms and a delay in warning time. During the study of visual field angle curves of different orders, it was found that the peak times of the curves of higher-order information on the angular size of looming objects are linearly related to the time to collision (TTC) and occur before collisions. This discovery implies that encoding higher-order information on the angular size could resolve the issue of response lag. Furthermore, the fact that the image of a looming object adjusts to meet several looming visual cues compared to the background interference implies that integrating various field-of-view characteristics will likely enhance the model's resistance to motion interference. Therefore, this paper presents a concise A-LGMD model for detecting looming objects. The model is based on image angular acceleration and addresses problems related to imprecise feature extraction and insufficient time series modeling to enhance the model's ability to rapidly and precisely detect looming objects during the rapid self-motion of UAVs. The model draws inspiration from the lobula giant movement detector (LGMD), which shows high sensitivity to acceleration information. In the proposed model, higher-order information on the angular size is abstracted by the network and fused with multiple visual field angle characteristics to promote the selective response to looming objects. Experiments carried out on synthetic and real-world datasets reveal that the model can efficiently detect the angular acceleration of an image, filter out insignificant background motion, and provide early warnings. These findings indicate that the model could have significant potential in embedded collision detection systems of micro or small UAVs.

A Robust Visual System for Looming Cue Detection Against Translating Motion.

Collision detection is critical for autonomous vehicles or robots to serve human society safely. Detecting looming objects robustly and timely plays an important role in collision avoidance systems. The locust lobula giant movement detector (LGMD1) is specifically selective to looming objects which are on a direct collision course. However, the existing LGMD1 models cannot distinguish a looming object from a near and fast translatory moving object, because the latter can evoke a large amount of excitation that can lead to false LGMD1 spikes. This article presents a new visual neural system model (LGMD1) that applies a neural competition mechanism within a framework of separated ON and OFF pathways to shut off the translating response. The competition-based approach responds vigorously to monotonous ON/OFF responses resulting from a looming object. However, it does not respond to paired ON-OFF responses that result from a translating object, thereby enhancing collision selectivity. Moreover, a complementary denoising mechanism ensures reliable collision detection. To verify the effectiveness of the model, we have conducted systematic comparative experiments on synthetic and real datasets. The results show that our method exhibits more accurate discrimination between looming and translational events-the looming motion can be correctly detected. It also demonstrates that the proposed model is more robust than comparative models.

A fractional-order visual neural network for collision sensing in noisy and dynamic scenes

Autonomously sensing collision threats in intelligent mobile machines is a significant challenge. Inspired by the perfect collision sensing system of locusts, various neural networks based on lobular giant motion detectors (LGMD) have been proposed. These networks use integer-order differential operators to simulate the simple and efficient visual neural system of locusts. However, some biological studies have shown that the neurodynamics of neuronal response should be fractional-order, and fractional-order differential operators can accurately describe the biological logic brain neural response process. In view of this, firstly, this paper uses a fractional-order differential operator to simulate the membrane potential response of neurons, and a new fractional-order LGMD collision sensing visual neural network is proposed. This network is designed to accurately detect looming objects and generate reliable response spikes. Then, through experimental analysis, a range [0.36, 0.4] of order in the fractional-order system is given, which is considered to be a reasonable interval for modeling the behavior of biological nervous systems. This maybe contribute to the study of the locust’s fractional-order biological nervous system. Finally, the network was systematically tested in noisy and complex dynamic environments. The experimental results demonstrated that the fractional-order LGMD visual neural network generated responses with high adaptability and reliability, showcasing significantly improved collision detection performance.

Active wing-pitching mechanism in hummingbird escape maneuvers

Previous studies suggested that wing pitching, i.e. the wing rotation around its long axis, of insects and hummingbirds is primarily driven by an inertial effect associated with stroke deceleration and acceleration of the wings and is thus passive. Here we considered the rapid escape maneuver of hummingbirds who were initially hovering but then startled by the frontal approach of a looming object. During the maneuver, the hummingbirds substantially changed their wingbeat frequency, wing trajectory, and other kinematic parameters. Using wing kinematics reconstructed from high-speed videos and computational fluid dynamics modeling, we found that although the same inertial effect drove the wing flipping at stroke reversal as in hovering, significant power input was required to pitch up the wings during downstroke to enhance aerodynamic force production; furthermore, the net power input could be positive for wing pitching in a complete wingbeat cycle. Therefore, our study suggests that an active mechanism was present during the maneuver to drive wing pitching. In addition to the powered pitching, wing deviation during upstroke required twice as much power as hovering to move the wings caudally when the birds redirected the aerodynamic force vector for escaping. These findings were consistent with our hypothesis that enhanced muscle recruitment is essential for hummingbirds’ escape maneuvers.

Dual Receptive Fields Underlying Target and Wide-Field Motion Sensitivity in Looming-Sensitive Descending Neurons.

Responding rapidly to visual stimuli is fundamental for many animals. For example, predatory birds and insects alike have amazing target detection abilities, with incredibly short neural and behavioral delays, enabling efficient prey capture. Similarly, looming objects need to be rapidly avoided to ensure immediate survival, as these could represent approaching predators. Male Eristalis tenax hoverflies are nonpredatory, highly territorial insects that perform high-speed pursuits of conspecifics and other territorial intruders. During the initial stages of the pursuit, the retinal projection of the target is very small, but this grows to a larger object before physical interaction. Supporting such behaviors, E. tenax and other insects have both target-tuned and loom-sensitive neurons in the optic lobes and the descending pathways. We here show that these visual stimuli are not necessarily encoded in parallel. Indeed, we describe a class of descending neurons that respond to small targets, to looming and to wide-field stimuli. We show that these descending neurons have two distinct receptive fields where the dorsal receptive field is sensitive to the motion of small targets and the ventral receptive field responds to larger objects or wide-field stimuli. Our data suggest that the two receptive fields have different presynaptic input, where the inputs are not linearly summed. This novel and unique arrangement could support different behaviors, including obstacle avoidance, flower landing, and target pursuit or capture.

Real-Time Tracking of Multiple Moving Mosquitoes.

Tracking mosquitoes in real time, as opposed to recording video files and performing the tracking step later, is useful for two reasons. The first is efficiency. Real-time tracking requires less storage because video images do not need to be saved and followed by a tracking step. The second is that tracking data can be used to interact with the animal in some way, such as triggering the approach of a looming object. In this protocol, we discuss the use of Braid, free software for performing real-time, multicamera, multianimal tracking. We describe a setup with four cameras capable of tracking the three-dimensional (3D) position of mosquitoes at 100 frames per second in a volume of 30 cm × 30 cm × 60 cm with millimeter accuracy. The specific hardware configuration is flexible and can be substituted using different or additional components to adjust the tracking parameters as needed.

Bioinspired approach-sensitive neural network for collision detection in cluttered and dynamic backgrounds

Rapid, accurate and robust detection of looming objects in cluttered moving backgrounds is a significant and challenging problem encountered when designing robotic visual systems to perform collision detection and avoidance tasks. Inspired by the neural circuit involved in the elementary motion vision of the mammalian retina, this study proposes a bioinspired approach-sensitive neural network (ASNN). The three main contributions of this work are as follows. First, a direction-selective visual processing module is built based on the spatiotemporal energy framework, which can estimate motion direction accurately via only two mutually perpendicular spatiotemporal filtering channels. Second, a novel approach-sensitive neural network is modeled as a push–pull structure formed by ON and OFF pathways, which responds strongly to approaching motion, but is insensitive to lateral motion. Finally, a method of direction-selective inhibition is introduced, which is able to effectively suppress translational backgrounds. Extensive synthetic and real robotic experiments indicate that the proposed model is able to not only detect collisions accurately and robustly in cluttered and dynamic backgrounds but also extract additional information on the approaching object, such as position and direction, which is critical for guiding rapid decision making.

Diurnal and nocturnal mosquitoes escape looming threats using distinct flight strategies

Flying insects have evolved the ability to evade looming objects, such as predators and swatting hands. This is particularly relevant for blood-feeding insects, such as mosquitoes that routinely need to evade the defensive actions of their blood hosts. To minimize the chance of being swatted, a mosquito can use two distinct strategies-continuously exhibiting an unpredictable flight path or maximizing its escape maneuverability. We studied how baseline flight unpredictability and escape maneuverability affect the escape performance of day-active and night-active mosquitoes (Aedes aegypti and Anopheles coluzzii, respectively). We used a multi-camera high-speed videography system to track how freely flying mosquitoes respond to an event-triggered rapidly approaching mechanical swatter, in four different light conditions ranging from pitch darkness to overcast daylight. Results show that both species exhibit enhanced escape performance in their natural blood-feeding light condition (daylight for Aedes and dark for Anopheles). To achieve this, they show strikingly different behaviors. The enhanced escape performance of Anopheles at night is explained by their increased baseline unpredictable erratic flight behavior, whereas the increased escape performance of Aedes in overcast daylight is due to their enhanced escape maneuvers. This shows that both day and night-active mosquitoes modify their flight behavior in response to light intensity such that their escape performance is maximum in their natural blood-feeding light conditions, when these defensive actions by their blood hosts occur most. Because Aedes and Anopheles mosquitoes are major vectors of several deadly human diseases, this knowledge can be used to optimize vector control methods for these specific species.

A Looming Spatial Localization Neural Network Inspired by MLG1 Neurons in the Crab Neohelice.

Similar to most visual animals, the crab Neohelice granulata relies predominantly on visual information to escape from predators, to track prey and for selecting mates. It, therefore, needs specialized neurons to process visual information and determine the spatial location of looming objects. In the crab Neohelice granulata, the Monostratified Lobula Giant type1 (MLG1) neurons have been found to manifest looming sensitivity with finely tuned capabilities of encoding spatial location information. MLG1s neuronal ensemble can not only perceive the location of a looming stimulus, but are also thought to be able to influence the direction of movement continuously, for example, escaping from a threatening, looming target in relation to its position. Such specific characteristics make the MLG1s unique compared to normal looming detection neurons in invertebrates which can not localize spatial looming. Modeling the MLG1s ensemble is not only critical for elucidating the mechanisms underlying the functionality of such neural circuits, but also important for developing new autonomous, efficient, directionally reactive collision avoidance systems for robots and vehicles. However, little computational modeling has been done for implementing looming spatial localization analogous to the specific functionality of MLG1s ensemble. To bridge this gap, we propose a model of MLG1s and their pre-synaptic visual neural network to detect the spatial location of looming objects. The model consists of 16 homogeneous sectors arranged in a circular field inspired by the natural arrangement of 16 MLG1s' receptive fields to encode and convey spatial information concerning looming objects with dynamic expanding edges in different locations of the visual field. Responses of the proposed model to systematic real-world visual stimuli match many of the biological characteristics of MLG1 neurons. The systematic experiments demonstrate that our proposed MLG1s model works effectively and robustly to perceive and localize looming information, which could be a promising candidate for intelligent machines interacting within dynamic environments free of collision. This study also sheds light upon a new type of neuromorphic visual sensor strategy that can extract looming objects with locational information in a quick and reliable manner.

Knowledge of collision modulates defensive multisensory responses to looming insects in arachnophobes.

We investigated the role of contextual knowledge in defensive responses to visual stimuli (spiders and butterflies) looming toward the hand. Human participants responded to tactile stimuli delivered to the same hand at 6 possible locations during an insect's approach. Tactile reaction times were faster when looming stimuli were closer to the hand, especially for spiders, and faster when insects loomed on a collision path than on a near-miss path. This latter finding suggests that human reactions to looming stimuli are not merely automatic reflexes but that contextual knowledge about the trajectory of looming objects is included in predicting their impact. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Static visual predator recognition in jumping spiders

Abstract Visually detecting, recognizing and responding appropriately to predators increases survival. Failure to detect a predator or long decision time carries high and potentially fatal costs. Consequently, many animals show general anti‐predatory responses towards threatening stimuli, for example, looming objects. However, in the context of lurking or stalking predators, visual recognition is based on static visual cues, making this task computationally demanding. Jumping spiders (Salticidae) have superb vision and are excellent predators but they can equally fall prey to other jumping spiders. In a hierarchical decision‐making setup, we tested whether the common zebra jumping spider Salticus scenicus can visually recognize stationary predators. We measured the spiders’ behavioural responses towards predator (naturally co‐occurring, non‐co‐occurring and artificial) and non‐predator objects as well as towards objects with modified features. Our experiments show that salticids demonstrate a robust, fast and repeatable ‘freeze and retreat’ behaviour when presented with stationary predators, but not similarly sized non‐predator objects. Anti‐predator responses were triggered by co‐occurring and non‐co‐occurring salticid predators, as well as by 3D‐printed salticid models (based on micro‐CT scans), suggesting a generalized predator detection/classification. Using modified 3D‐printed models, we found evidence that eyes act as an important cue. However, eyes alone did not explain the responses, suggesting that underlying processes rely on multiple rather than single features. To address the role of learning and memory, we tested newly emerged spiderlings and found the same behavioural responses towards predator objects suggesting an innate response. The ability of jumping spiders to innately recognize a non‐moving threat is surprising in terms of underlying cognitive processes and the evolution thereof. Escaping from a predator before an attack has been launched likely carries sufficient selective benefits. From a cognitive perspective, the overlap of static visual characteristics between salticid predators, prey and conspecifics invites further questions considering the mechanisms of such nuanced visual discrimination and categorization in animals with complex vision but relatively small nervous systems. A free Plain Language Summary can be found within the Supporting Information of this article.

Visual Looming Influences Loudness Change: A Preliminary Investigation

ABSTRACT Looming objects generate unique multisensory information and can signal potential danger. While visual judgments of arrival time are relatively accurate, auditory judgments tend to be anticipatory. When presented with information from both modalities, observers tend to rely on visual information for successful interaction. Previous work has shown that auditory information can influence perception of visual looming and receding. Here, we examined how visual information affects the auditory perception of looming and receding sounds. Participants judged the loudness change of sounds accompanied by visual looming and receding stimuli. We found that looming and receding visual stimuli influenced estimates of loudness change. Sounds were perceived to change more in loudness when presented with a larger visual change. We also found that looming sounds were perceived to change more in loudness than equivalent receding sounds and that participants showed better discrimination of the change between receding sounds than looming sounds.

OFF-transient alpha RGCs mediate looming triggered innate defensive response

Animals respond to visual threats, such as a looming object, with innate defensive behaviors. Here, we report that a specific type of retinal ganglion cell (RGC), the OFF-transient alpha RGC, is critical for the detection of looming objects. We identified Kcnip2 as its molecular marker. The activity of the Kcnip2-expressing RGCs encodes the size of the looming object. Ablation or suppression of these RGCs abolished or severely impaired the escape and freezing behaviors of mice in response to a looming object, while activation of their somas in the retina, or their axon terminals in the superior colliculus, triggered immediate escape behavior. Our results link the activity of a single type of RGC to visually triggered innate defensive behaviors and underscore that ethologically significant visual information is encoded by a labeled line strategy as early as in the retina.

The disturbance leg-lift response (DLR): an undescribed behavior in bumble bees.

BackgroundBumble bees, primarily Bombus impatiens and B. terrestris, are becoming increasingly popular organisms in behavioral ecology and comparative psychology research. Despite growing use in foraging and appetitive conditioning experiments, little attention has been given to innate antipredator responses and their ability to be altered by experience. In this paper, we discuss a primarily undescribed behavior, the disturbance leg-lift response (DLR). When exposed to a presumably threatening stimulus, bumble bees often react by lifting one or multiple legs. We investigated DLR across two experiments.MethodsIn our first experiment, we investigated the function of DLR as a prerequisite to later conditioning research. We recorded the occurrence and sequence of DLR, biting and stinging in response to an approaching object that was either presented inside a small, clear apparatus containing a bee, or presented directly outside of the subject’s apparatus. In our second experiment, we investigated if DLR could be altered by learning and experience in a similar manner to many other well-known bee behaviors. We specifically investigated habituation learning by repeatedly presenting a mild visual stimulus to samples of captive and wild bees.ResultsThe results of our first experiment show that DLR and other defensive behaviors occur as a looming object approaches, and that the response is greater when proximity to the object is lower. More importantly, we found that DLR usually occurs first, rarely precedes biting, and often precedes stinging. This suggests that DLR may function as a warning signal that a sting will occur. In our second experiment, we found that DLR can be altered as a function of habituation learning in both captive and wild bees, though the captive sample initially responded more. This suggests that DLR may be a suitable response for many other conditioning experiments.

Effects of cognitive load and type of object on the visual looming bias.

According to the behavioral urgency hypothesis, organisms have evolved various mechanisms that facilitate their survival by focusing attention and resources on approaching danger. One example of such mechanisms is the looming bias-the tendency for an individual to judge an approaching object's distance as being closer or time-to-collision as being sooner than receding or stationary objects. To date, most research on the looming bias has explored the ways in which human factors and object characteristics influence the strength and direction of the bias. The current study expanded on this field of research in two novels ways by exploring (a) whether cognitive vulnerabilities may influence the strength of the looming bias in the visual domain, and (b) whether the combination of human factors (i.e., cognitive load) and object characteristics (i.e., object threat) interact to create an additive effect on looming bias strength. Findings appear to only partially support the hypotheses that cognitive vulnerabilities can influence looming bias strength in the visual domain, and that factors related to both the individual and the looming object may interact to create a stronger looming bias. These findings help to highlight possible evolutionary advantages of the looming bias and its presence across modalities, as well as add some strength to the claims that the margin of safety theory can be generalized to include psychological factors.

Corticostriatal control of defense behavior in mice induced by auditory looming cues

Animals exhibit innate defense behaviors in response to approaching threats cued by the dynamics of sensory inputs of various modalities. The underlying neural circuits have been mostly studied in the visual system, but remain unclear for other modalities. Here, by utilizing sounds with increasing (vs. decreasing) loudness to mimic looming (vs. receding) objects, we find that looming sounds elicit stereotypical sequential defensive reactions: freezing followed by flight. Both behaviors require the activity of auditory cortex, in particular the sustained type of responses, but are differentially mediated by corticostriatal projections primarily innervating D2 neurons in the tail of the striatum and corticocollicular projections to the superior colliculus, respectively. The behavioral transition from freezing to flight can be attributed to the differential temporal dynamics of the striatal and collicular neurons in their responses to looming sound stimuli. Our results reveal an essential role of the striatum in the innate defense control.

A Robust Collision Perception Visual Neural Network With Specific Selectivity to Darker Objects.

Building an efficient and reliable collision perception visual system is a challenging problem for future robots and autonomous vehicles. The biological visual neural networks, which have evolved over millions of years in nature and are working perfectly in the real world, could be ideal models for designing artificial vision systems. In the locust's visual pathways, a lobula giant movement detector (LGMD), that is, the LGMD2, has been identified as a looming perception neuron that responds most strongly to darker approaching objects relative to their backgrounds; similar situations which many ground vehicles and robots are often faced with. However, little has been done on modeling the LGMD2 and investigating its potential in robotics and vehicles. In this article, we build an LGMD2 visual neural network which possesses the similar collision selectivity of an LGMD2 neuron in locust via the modeling of biased-ON and -OFF pathways splitting visual signals into parallel ON/OFF channels. With stronger inhibition (bias) in the ON pathway, this model responds selectively to darker looming objects. The proposed model has been tested systematically with a range of stimuli including real-world scenarios. It has also been implemented in a micro-mobile robot and tested with real-time experiments. The experimental results have verified the effectiveness and robustness of the proposed model for detecting darker looming objects against various dynamic and cluttered backgrounds.