Here is another paper in that occasional series of theoretical considerations of Path Integration, I’ll confess that I haven’t read this particular paper – but if the abstract is anything to go by, then it will be of interest to lots of you.
Abstract: “We propose a mathematical model of the Path Integration (PI) process. Its core assumption is that orientations of a path are summarized by circular probability distributions. We compare our model with classical, deterministic models of PI and find that, although they are indistinguishable in terms of information encoded, the probabilistic model is more parsimonious when considering navigation strategies. We show how sensory events can enrich the probability distributions memorized, resulting in a continuum of navigation strategies, from PI to stimulus-triggered response. We analyze the combination of circular probability distributions (e.g., multi-cue fusion), and demonstrate that, contrary to the linear case, adding orientation cues does not always increase reliability of estimates. We discuss experimental predictions entailed by our model.”
Diard, Julien and Bessière, Pierre and Berthoz, Alain (2012) Spatial memory of paths using circular probability distributions: Theoretical properties, navigation strategies and orientation cue combination. Spatial Cognition & Computation, doi: 10.1080/13875868.2012.756490
To finish the year off we have a extensive review from the Bielefeld group, regarding the ways that insect visual systems are reliant on specific behavioural routines that shape the received input. The key quote from the review’s abstract is this: “The key idea of this review is that biological agents, such as flies or bees, acquire at least part of their strength as autonomous systems through active interactions with their environment and not by simply processing passively gained information about the world.”
Egelhaaf M, Boeddeker N, Kern R, Kurtz R and Lindemann JP (2012) Spatial vision in insects is facilitated by shaping the dynamics of visual input through behavioral action. Front. Neural Circuits 6:108. doi: 10.3389/fncir.2012.00108
It is well-known that even in trail laying ants, an individual forager will trust her own knowledge over that provided by the trail. Most of what we know about this interaction comes from T-maze experiments where the switch from public to private knowledge is extremely quick. Here Czaczkes et al. demonstrate that the need for pheromones is much more significant with more complex routes, for instance where ants are asked to learn alternating choices in a double T-maze.
Of course in nature complex routes are caused by physical objects which are absent in elevated T-mazes (as used here). A really interesting follow-up would be to replicate the experiment with physical objects ‘causing’ the bifurcations therefore creating a more natural visual ecology and a more realistic route learning task.
Czaczkes, T. J., Grüter, C., Ellis, L., Wood, E., & Ratnieks, F. L. (2012). Ant foraging on complex trails: route learning and the role of trail pheromones in Lasius niger. The Journal of Experimental Biology.
In the last 10 years of insect navigation research, one of the most interesting findings has been that not all species of ant will run off their entire Path Integrated home vector when displaced from a feeder to unfamiliar terrain. For instance, the Australian desert ant Melophorus has been shown to run off about 50% of their home vector before searching. Here, Cheung et al. take a modelling approach to ask whether there is a theoretically optimal point at which to stop following the path integrated vector and start searching. If one assumes that ants are familiar with a corridor of the world, due to their habitual idiosyncratic routes, then one can ask which search start-point is the most likely to lead to the discovery of a familiar part of the world when one has been displaced to unfamiliar terrain. Two different analytical methods lead to the same conclusion that one should start searching after 50% of your home vector has been run off. Of course this matches neatly with the result for Melophorus, though the interesting thing is to ask how these theoretical results will change for different species with different areas of familiar terrain.
Cheung A, Hiby L, Narendra A (2012) Ant Navigation: Fractional Use of the Home Vector. PLoS ONE 7(11): e50451. doi:10.1371/journal.pone.0050451
One of the most interesting set of view based homing methods are the flow methods where one uses the derived flow vectors between the current view and a stored view to infer the movement needed to move between the current location and the target location where the goal view was stored. This paper from Stewart et al uses such a method to move between waypoints along a route.
Abstract: “This paper investigates the problem of robot visual homing – the navigation to a goal location by a mobile robot using visual sensory input. The visual homing approach taken is to consider the ﬂow vectors between a robot’s current view and a desired milestone view. The ﬂow vectors can be used to determine an angular velocity command that attempts to align the two views under a constant forward speed. Experiments with a mobile robot have been conducted following the teach replay approach. By using a sequence of milestone images taken successively along a path, preliminary results show that a robot can successfully repeat the path and navigate to its goal autonomously. The method should be useful for route following and other applications involving visual navigation.”
Stewart, R. L., Mills, M., & Zhang, H. (2012, September). Visual homing for a mobile robot using direction votes from flow vectors. In Multisensor Fusion and Integration for Intelligent Systems (MFI), 2012 IEEE Conference on (pp. 413-418). IEEE.
We are familiar with the idea that with increasing experience individual ants will develop longer foraging routes. This, of course, makes perfect sense in regard of the gradual learning of new route information. This lovely paper form Moron et al, show how this also makes perfect sense in regard to risk. By manipulating the life expectancy of foragers they show how a low life expectancy leads to higher risk taking as evidenced by foraging for longer at high temperatures, travelling further and visiting areas with a high density of predators.
Moroń, D., Lenda, M., Skórka, P., & Woyciechowski, M. (2012). Short-Lived Ants Take Greater Risks during Food Collection. The American Naturalist,180(6), 744-750.
I’m guessing that most of the people reading this would self declare as neuroethologists with one of the philosophies that goes with that being the aim of studying real behaviour in naturalistic environments. However, we have all to make compromises in the name of a controlled experiment. Here, Emily Baird and Marie Dacke look at flight control in bees and compare behaviour when they are presented with traditionally used artificial patterns versus more natural stimuli. Overall, these experiments show no systematic differences in behaviour, so perhaps we can keep our checkered walled tunnels for now.
Emily Baird and Marie Dacke (2012) Visual flight control in naturalistic and artificial environments. Journal of Comparative Physiology A 201210.1007/s00359-012-0757-7
This paper gives a lovely description of a spatial behaviour in the wandering desert spider which reinforces the idea that, despite being nocturnal, they in fact use visual information as part of their navigational reportoire. Naive spiders were recorded leaving their burrows prior to their nightly wander through the desert looking for females. Using LEDs Thomas Nørgaard was able to record, in detail, their paths. Early paths showed a particular sinuosity that became less pronounced with experience. Analysis of this sinuosity show that the paths enable the spiders to view the burrow direction with the Anterior Lateral Eyes suggesting a role for the ALEs in visual homing. As the burrow itself is inconspicuous, it is likely that the spiders are viewing the silhouette of the terrain contrasting against the night sky.
Nørgaard T, Gagnon YL, Warrant EJ (2012) Nocturnal Homing: Learning Walks in a Wandering Spider? PLoS ONE 7(11): e49263.doi:10.1371/journal.pone.0049263
The role of sleep in the consolidation of memories is well-established in many animals. Here, Beyaert et al investigate the role of sleep for navigating bees. Individual foragers were given a navigational task where they were displaced to a novel location from where they attempted to return to the hive. They were then probed for navigational learning, by being re-tested from the same release point on a subsequent day. Half of the bees were sleep deprived before testing. It was found that the sleep deprived bees were more likely to become lost on the second release than bees which had not been sleep deprived – thus suggesting a role for sleep in the consolidation of navigational memories in bees.
Beyaert, L., Greggers, U. and Menzel, R. (2012). Honeybees consolidate navigation memory during sleep. J. Exp. Biol. 215, 3981-3988.
For dung beetles, an important spatial task is to get their dung ball in a straight line away from the main pile. The role of celestial cues are clear in the beetle maintaining their straight line, this paper deals with whether terrestrial landmarks also make a contribution. One strong cue might come from the dung pile itself. As beetles push their ball with the back legs, they might keep the image of the pile on the frontal visual field in order to maintain a course away from it. Dacke et al show that manipulating the position of the main pile does not alter the beetle’s course. They also show that courses are not altered by shifting the beetle from a landmark rich arena to a plain arena, or vice versa. The authors suggest that this is the only visual navigator that ignores the information available from terrestrial landmarks – so much so that on overcast days they struggle to roll in a straight line, with or without landmarks.
Dacke, Marie, et al. “Dung beetles ignore landmarks for straight-line orientation.” Journal of Comparative Physiology A: Neuroethology, Sensory, Neural, and Behavioral Physiology (2012): 1-7.