Dragonfly the hunter

For vertebrates and invertebrates alike, hunting is a complex behavior.  Even if it seems to involve just a simple flick of the tongue, the hunter must first note the presence of its prey, and then successfully capture it, even when the prey makes unpredictable moves. Vertebrates hunt by predicting and planning, relying on what philosophers of mind call 'internal models' that allow them to anticipate the movement of their prey and respond accordingly, but whether invertebrates do the same has not been known.  The typical human-centered reflex is to dismiss insects as mere genetic robots, mechanically linking sensory input to automatic, hard-wired action.

But that may be far too egocentric, because a new paper in the January 15 issue of Nature ("Internal models direct dragonfly interception steering," Mischiati et al.) describes the hunting behavior of dragonflies, and suggests that dragonflies have internal models as well.

Prediction and planning, essential to the high-performance control of behaviour, require internal models. Decades of work in humans and non-human primates have provided evidence for three types of internal models that are fundamental to sensorimotor control: physical models to predict properties of the world; inverse models to generate the motor commands needed to attain desired sensory states; and forward models to predict the sensory consequences of self-movement

Dragonflies generally don't hunt indoors, so Mischiati et al. decked out a laboratory to look like familiar hunting grounds, brought some dragonfly fodder indoors, and videotaped and otherwise assessed the behavior of the dragonflies in pursuit of their next meals to determine what they were looking at, and to assess their body movements as they pursued their prey.  These measurements suggested to them that the heads of the dragonflies were moving in sync with their prey, meaning that they were anticipating rather than reacting to the flight of their prey.

Anisoptera (Dragonfly), Pachydiplax longipennis (Blue Dasher), female, photographed in the Town of Skaneateles, Onondaga County, New York. Creative Commons

And this in turn suggests that, like vertebrates, dragonflies have internal models that facilitate their hunting. Rather than dashing after insects after they've already moved, dragonflies are able to predict their movements, and successfully capture their prey 90-95% of the time.  Compared with, say, echolocating bats, this is a remarkable success rate -- e.g., estimates of the success rate of Eptesicus nilssonii, a Eurasian bat, range from 36% for moths to 100% for the slow-moving dung beetle (Rydell, 1992). And it's an even more remarkable success rate compared with Pennsylvania deer hunters -- for every 3 or 4 hunting licenses sold, 1 deer was killed in 2012-13, which means that if, like dragonflies or bats, people had to rely on venison for their survival, they'd be in deep trouble.



But, apparently humans, bats and dragonflies are using essentially the same kind of internal model to hunt, a model that allows them to anticipate the future and take action accordingly.  More specifically, the model is a 'forward model', and it has been thought to be the foundation for cognition in vertebrates, but is at least the basis of motor control (as described here and here).  You can dismissively call it just 'computing' or you can acknowledge it as 'intelligent', but it is clearly more than simple hard-wired reflex: it involves judgment.

This is interesting and relevant, because if all that's required is the ability to predict and plan accordingly, why is there so much variation in the success rate of the hunt, even within a given species?  Clearly other factors and abilities are required -- other aspects of the nervous system, for example, or speed relative to prey, and population density of predator and prey.  Indeed, insects would be expected to vary in their 'intelligence' the way people do, in a way that means that most are able to succeed.

It seems that the study of insect behavior is building a more and more complex model of how insects do what they do.  The view of the insect brain is broadening into one that allows for much more complexity than robotic hard-wired behavior, or motor responses to sensory input.  A few months ago, we blogged about bee intelligence, writing about a PNAS paper that described how bees find their way home, credibly by using a cognitive map.

The author of a recent paper in Trends in Neuroscience ("Cognition with few neurons: higher-order learning in insects," Martin Giurfa, 2013) speculated about unexpected insect cognitive abilities, welcoming an approach to understanding plastic insect behavior that allows for the possibility of complex, sophisticated learned rather than mere associative learning.  But Giurfa cautions that there are many reasons why we don't yet understand insect behavior, including our tendency to anthropomorphize, using words for insect behavior derived from what we know about human abilities that, when applied to insects, imply more complexity than warranted, or to interpret experimental results as though they represent all that insects can do, rather than all that they were asked to do in the study.

On the other hand, many of the genes insects use for their sensory and neural functions are evolutionarily related to the genes mammals, including humans, use. So we likely share many similar genetically based mechanisms.

From the outside of this field looking in, it seems as though it's early days in understanding invertebrate brains.  And it seems to me that this is largely because observational studies are difficult to do on insects, must be interpreted because insects can't talk, and our interpretations are necessarily built on our assumptions about insect behavior, which in turn seem to follow trends in what people are currently thinking about cognition.  Until recently, researchers have assumed that insects, with far fewer neurons than we have, are pretty dumb.  The dragonfly hunter's success rate alone should be humbling enough to challenge this assumption.

In this sense, it's wrong to think simply that size matters.  Maybe its organization that matters more.