The comparison of navigational behaviour across species that inhabit different sensory ecologies is a cornerstone of comparative neuroethology. Here Cheng et al review their recent productive years of research on a range of desert ant species, for what it can tell us about the ways navigation can be tuned to one’s environment. Abstract: ”In a synthetic approach to studying navigational abilities in desert ants, we review recent work comparing ants living in different visual ecologies. Those living in a visually rich habitat strewn with tussocks, bushes, and trees are compared to those living in visually barren salt pans, as exemplified by the Central Australian Melophorus bagoti and the North African Cataglyphis fortis, respectively. In bare habitats the navigator must rely primarily on path integration, keeping track of the distance and direction in which it has travelled, while in visually rich habitats the navigator can rely more on guidance by the visual panorama. Consistent with these expectations, C. fortis performs better than M. bagoti on various measures of precision at path integration. In contrast, M. bagoti learned a visually based associative task better than C. fortis, the latter generally failing at the task. Both these ants, however, exhibit a similar pattern of systematic search as a ‗back up‘ strategy when other navigational strategies fail. A newly investigated salt-pan species of Melophorus (as yet unnamed) resembles C. fortis more, and its congener M. bagoti less, in its path integration. The synthetic approach would benefit from comparing more species chosen to address evolutionary questions.”
Cheng, K., Schultheiss, P., Schwarz, S., Wystrach, A., & Wehner, R. (2013). Beginnings of a synthetic approach to desert ant navigation. Behavioural Processes.
It is well known that insects are capable of complex learning tasks. One example being those behavioural experiments where the performance of insects (most often bees) can suggest that they have learnt an abstract rule to drive performance. It is this type of experiment which is reviewed here. The authors are putting the case for bees being able to learnt ‘concepts’, claiming that: “concepts such as ‘same’, ‘different’,‘above/below of’ or ‘left/right are well mastered by bees.” There is no doubt that these experiments demonstrate some wonderful behaviour. However, we are still quite far from understanding if this kind of behaviour in bees is based on homologous neural computations to the concept learning of vertebrates. And, it is still unclear how the “concept” learning of bees is built-on or driven-by ecological and foraging constraints. Of course, not knowing these things is what makes this topic so intriguing.
The latest edition of Current Biology has a special collection of papers on memory. Insects are represented via two particularly fruitful model systems: The olfactory learning of fruit flies and the spatial learning evident across insects but most notably in hymenopteran foragers. The paper most relevant here (Collett (M) et al.) covers familiar ground in terms of navigational mechanisms, i.e. view-based homing and Path Integration, as well as the ways that individuals might acquire and organise them. However, it is useful to have this in one place in an up-to-date review. But this is an extensive paper and it comes into its own with some more general content. The first page is a lovely guide to spatial behaviour in a broader range of insects than we usually see; And we end with some particularly interesting speculation around recognition and guidance and whether there might be 2 kinds of memory process underpinning insect navigation.
Matthew Collett,Lars Chittka,Thomas S. Collett (2013) Spatial memory in insect navigation. Current Biology, Volume 23, Issue 17, R789-R800
We all know that within our field there are many high-profile papers which report on “surprisingly sophisticated” learning behaviours. of course these examples are fascinating but focussing on them doesn’t give us a holistic picture of the learning of insects and how this is implemented in the insect brain. In this review, Perry et al propose, and begin, the process of creating a unified view of different types of learning and their potential substrates.
Abstract: “Diverse invertebrate species have been used for studies of learning and comparative cognition. Although we have gained invaluable information from this, in this study we argue that our approach to comparative learning research is rather deficient. Generally invertebrate learning research has focused mainly on arthropods, and most of that within the Hymenoptera and Diptera. Any true comparative analysis of the distribution of comparative cognitive abilities across phyla is hampered by this bias, and more fundamentally by a reporting bias toward positive results. To understand the limits of learning and cognition for a species, knowing what animals cannot do is at least as important as reporting what they can. Finally, much more effort needs to be focused on the neurobiological analysis of different types of learning to truly understand the differences and similarities of learning types. In this review, we first give a brief overview of the various forms of learning in invertebrates. We also suggest areas where further study is needed for a more comparative understanding of learning. Finally, using what is known of learning in honeybees and the well‐studied honeybee brain, we present a model of how various complex forms of learning may be accounted for with the same neural circuitry required for so‐called simple learning types. At the neurobiological level, different learning phenomena are unlikely to be independent, and without considering this it is very difficult to correctly interpret the phylogenetic distribution of learning and cognitive abilities.“
Perry Clint J, Barron Andrew B, Cheng Ken (2013) Invertebrate learning and cognition: relating phenomena to neural substrate. WIREs Cogn Sci 2013, 4: 561-582. doi: 10.1002/wcs.1248
In this TiNs paper, Martin Giurfa presents a review of recent experimental literature related to cognition in insects. Of course, the key issue with experiments in this area is to tread the delicate line between underestimating or anthropomorphising the insects we are studying. Martin suggests that “Focusing on the neural bases of insect higher-order learning is a way to avoid this, because the characterization of neural architectures should be a dispassionate endeavor”. Such a bottom-up philosophy is admirable but evidently not always possible and there are still ongoing issues whenever we have to describe insect behaviour in an experimental setting.
For me, the most interesting part of the paper is Martin’s list of future research directions. It is pleasing to imagine what the next 10 years will bring in terms of our understanding of insect cognition.
Martin Giurfa (2013) Cognition with few neurons: higher-order learning in insects, Trends in Neurosciences.
What better way to start the new year than 2 review articles from the Annual Review of Entomology. Both relate to issues that are integral to the understanding of insect navigation. Perry and Barron review what we know about reward mechanisms in insects. Paulk et al look at our understanding of the visual system of drosophila, something which is going to be key for insect navigation studies as we gradually learn more about what drosphila are capable of with regard to navigation.
Angelique Paulk, S. Sean Millard, and Bruno van Swinderen (2013) Vision in Drosophila: Seeing the World Through a Models Eyes. Ann Rev Entomol, 58.
Clint J. Perry and Andrew B. Barron (2013) Neural Mechanisms of Reward in Insects. Ann Rev Entomol, 58.
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
On this forum we are all aware of the value of the honeybee as a model system for research into learning and memory. Occasionally, a review article comes along that reminds us of this and updates the case with recent research. This paper from Randolf Menzel is one such paper but is particularly useful because it highlights the specific methods and technologies that will ensure the honeybee will maintain its position as a effective model animal.
Randolf Menzel (2012) The honeybee as a model for understanding the basis of cognition. Nature Reviews Neuroscience 13, 758-768 (November 2012) | doi:10.1038/nrn3357