Pigeons use distinct stop phases to control pecking

Gaze tracking of large-billed crows (Corvus macrorhynchos) in a motion capture system

Previous studies often inferred the focus of a bird's attention from its head movements because it provides important clues about their perception and cognition. However, it remains challenging to do so accurately, as the details of how they orient their visual field toward the visual targets remain largely unclear. We thus examined visual field configurations and the visual field use of large-billed crows (Corvus macrorhynchos Wagler 1827). We used an established ophthalmoscopic reflex technique to identify the visual field configuration, including the binocular width and optical axes, as well as the degree of eye movement. A newly established motion capture system was then used to track the head movements of freely moving crows to examine how they oriented their reconstructed visual fields toward attention-getting objects. When visual targets were moving, the crows frequently used their binocular visual fields, particularly around the projection of the beak-tip. When the visual targets stopped moving, crows frequently used non-binocular visual fields, particularly around the regions where their optical axes were found. On such occasions, the crows slightly preferred the right eye. Overall, the visual field use of crows is clearly predictable. Thus, while the untracked eye movements could introduce some level of uncertainty (typically within 15 deg), we demonstrated the feasibility of inferring a crow's attentional focus by 3D tracking of their heads. Our system represents a promising initial step towards establishing gaze tracking methods for studying corvid behavior and cognition.

Head-tracking of freely-behaving pigeons in a motion-capture system reveals the selective use of visual field regions

Using a motion-capture system and custom head-calibration methods, we reconstructed the head-centric view of freely behaving pigeons and examined how they orient their head when presented with various types of attention-getting objects at various relative locations. Pigeons predominantly employed their retinal specializations to view a visual target, namely their foveas projecting laterally (at an azimuth of ± 75°) into the horizon, and their visually-sensitive "red areas" projecting broadly into the lower-frontal visual field. Pigeons used their foveas to view any distant object while they used their red areas to view a nearby object on the ground (< 50 cm). Pigeons "fixated" a visual target with their foveas; the intervals between head-saccades were longer when the visual target was viewed by birds' foveas compared to when it was viewed by any other region. Furthermore, pigeons showed a weak preference to use their right eye to examine small objects distinctive in detailed features and their left eye to view threat-related or social stimuli. Despite the known difficulty in identifying where a bird is attending, we show that it is possible to estimate the visual attention of freely-behaving birds by tracking the projections of their retinal specializations in their visual field with cutting-edge methods.

Motion parallax via head movements modulates visuo-motor control in pigeons

Although it has been proposed that birds acquire visual depth cues through dynamic head movements, behavioral evidence on how birds use motion parallax depth cues caused by self-motion is lacking. This study investigated whether self-generated motion parallax modulates pecking motor control and visual size perception in pigeons (Columba livia). We trained pigeons to peck a target on a touch monitor and to classify it as small or large. To manipulate motion parallax of the target, we changed the target position on the monitor according to the bird's head position in real time using a custom-built head tracker with two cameras. Pecking motor control was affected by the manipulation of motion parallax: when the motion parallax signified the target position farther than the monitor surface, the head position just before pecking to target was near the monitor surface, and vice versa. By contrast, motion parallax did not affect how the pigeons classified target sizes, implying that motion parallax might not contribute to size constancy in pigeons. These results indicate that motion parallax via head movements modulates pecking motor control in pigeons, suggesting that head movements of pigeons have the visual function of accessing motion parallax depth cues.

SnakeStrike: A Low-Cost Open-Source High-Speed Multi-Camera Motion Capture System

Current neuroethological experiments require sophisticated technologies to precisely quantify the behavior of animals. In many studies, solutions for video recording and subsequent tracking of animal behavior form a major bottleneck. Three-dimensional (3D) tracking systems have been available for a few years but are usually very expensive and rarely include very high-speed cameras; access to these systems for research is limited. Additionally, establishing custom-built software is often time consuming – especially for researchers without high-performance programming and computer vision expertise. Here, we present an open-source software framework that allows researchers to utilize low-cost high-speed cameras in their research for a fraction of the cost of commercial systems. This software handles the recording of synchronized high-speed video from multiple cameras, the offline 3D reconstruction of that video, and a viewer for the triangulated data, all functions previously also available as separate applications. It supports researchers with a performance-optimized suite of functions that encompass the entirety of data collection and decreases processing time for high-speed 3D position tracking on a variety of animals, including snakes. Motion capture in snakes can be particularly demanding since a strike can be as short as 50 ms, literally twice as fast as the blink of an eye. This is too fast for faithful recording by most commercial tracking systems and therefore represents a challenging test to our software for quantification of animal behavior. Therefore, we conducted a case study investigating snake strike speed to showcase the use and integration of the software in an existing experimental setup.

Eyelid squinting during food pecking in pigeons

The visual control of pecking by pigeons (Columba livia) has latterly been thought to be restricted to the fixation stops interrupting their downward head movements because these stops prevent interference by motion blur. Pigeons were also assumed to close their eyes during the final head thrust of the peck. Here, we re-examined their pecking motions using high-speed video recordings and supplementary provisions that permitted a three-dimensional spatial analysis of the movement, including measurement of pupil diameter and eyelid slit width. The results confirm that pigeons do not close their eyes completely during the presumed optically ballistic phase of pecking. Instead, their eyelids are narrowed to a slit. The width of this slit is sensitive to both the ambient illumination level and the visual background against which seed targets have to be detected and grasped. There is also evidence of some interaction between pupil diameter and eyelid slit width. We surmise that besides being an eye-protecting reflex, the partial covering of the pupil with the eyelids may increase the depth of focus, enabling pigeons to obtain sharp retinal images of peck target items at very close range and during the beak-gape 'handling' of food items and occasional grit particles.

Systematic Analysis of Pigeons' Discrimination of Pixelated Stimuli: A Hierarchical Pattern Recognition System Is Not Identifiable

Pigeons learned to discriminate two different patterns displayed with miniature light-emitting diode arrays. They were then tested with 84 interspersed, non-reinforced degraded pattern pairs. Choices ranged between 100% and 50% for one or other of the patterns. Stimuli consisting of few pixels yielded low choice scores whereas those consisting of many pixels yielded a broad range of scores. Those patterns with a high number of pixels coinciding with those of the rewarded training stimulus were preferred and those with a high number of pixels coinciding with the non-rewarded training pattern were avoided; a discrimination index based on this correlated 0.74 with the pattern choices. Pixels common to both training patterns had a minimal influence. A pixel-by-pixel analysis revealed that eight pixels of one pattern and six pixels of the other pattern played a prominent role in the pigeons’ choices. These pixels were disposed in four and two clusters of neighbouring locations. A summary index calculated on this basis still only yielded a weak 0.73 correlation. The individual pigeons’ data furthermore showed that these clusters were a mere averaging mirage. The pigeons’ performance depends on deep learning in a midbrain-based multimillion synapse neuronal network. Pixelated visual patterns should be helpful when simulating perception of patterns with artificial networks.

Rapid adjustment of pecking trajectory to prism-induced visual shifts in crows as compared with pigeons

Pecking in birds is analogous to reaching and grasping movements in primates. Earlier studies on visuomotor control in birds, which were conducted mostly in pigeons, suggested that avian pecking is controlled feedforwardly, and is out of the control of visual guidance during movement. However, recent studies using crows suggested a role of vision in pecking control during movement. To unveil what visuomotor mechanisms underlie the flexibility of pecking in crows, we examined whether pigeons and crows adjust their pecking to the visual distortion induced by prisms. Because prisms induce visual shifts of object positions, birds were required to adjust their movements. Pecking kinematics were examined before and after attaching prisms in front of the birds' eyes. Analysis of lateral deviation caused by the prisms showed that crows rapidly adjusted their pecking trajectories, but pigeons did so slowly. Angular displacement also increased in pigeons after attachment of the prism, but decreased in crows. These responses to prisms were consistent among individuals in pigeons but varied in crows, though the adjustment of pecking commonly succeeded in crows. These results suggest that pecking in pigeons predominantly involves feedforward control and that the movement is determined depending on the visual information available before the initiation of pecking. In contrast, the results from crows suggest that their pecking trajectories are corrected during the movement, supporting on-line visual control. Our findings provide the first evidence to suggest the on-line visual control of pecking in birds.

Symmetry recognition by pigeons: Generalized or not?

This note looks into the reasons why earlier reports may have arrived at differing conclusions about pigeons’ capacity to categorize bilaterally symmetric and asymmetric visual patterns. Attention is drawn to pigeons’ comparatively superior visual flicker resolution and superior visual linear acuity by reporting results of two ad-hoc experiments. This circumstance turns out to constrain conclusions drawn by earlier symmetry–asymmetry studies that used computer-generated patterns displayed on cathode ray tube monitors as these suffered from pictorial distortions. Additionally one of the studies involved patterns of inconsistent symmetry at global and local levels. A smaller-scale experiment using slide-projected unequivocal symmetric and asymmetric patterns yielded results compatible with the supposition that pigeons are capable of a symmetry–asymmetry categorization. The possibility that an artfactual cue may have inadvertently accentuated this capability in an earlier own experiment is considered.