Path integration (dead reckoning)
The best studied mechanism is path integration, which allows insects to navigate on bare terrain, in the absence of visual landmarks. Collett and Collett (2002, p. 546) describe it as follows:
The insect's working memory of its "global" position - its distance and direction from the nest - is continually updated as it moves. An insect's global vector allows it to return home in a straight line from any point in its path. Tests have shown that if a desert ant returning to its nest is moved to unfamiliar terrain, it continues on the same course for a distance roughly equal to the distance it was from the nest. Finding no nest, it then starts a spiral search for the nest. If obstacles are placed in its path, the ant goes around them and adjusts its course appropriately (Collett and Collett, 2002, p. 546; Gallistel, 1998, p. 24; Corry, 2003). When ants have to navigate around obstacles, they memorise the sequence of motor movements corresponding to movement around the obstacle, thereby cutting their information processing costs (Schatz, Chameron, Beugnon and Collett, 1999).
Landmark navigation
The other way by which insects navigate is the use of landmarks which they observe en route and at their home base. Naive foragers initially rely on path integration to find their way home, but when they repeat their journey, they learn the appearance of new landmarks and associate local vectors with them, which indicate the distance to the next landmark. With experience, these local vectors take precedence over the global vectors used in dead reckoning.
A landmark navigation system can also work hand-in-hand with path integration: for instance, the Mediterranean ant uses the latter as a filter to screen out unhelpful landmarks, learning only those that can guide subsequent return trips to the nest - particularly those that are close to the nest (Schatz, Chameron, Beugnon and Collett, 1999). Visual landmarks help keep path vector navigation calibrated, as short local vectors are more precise than a route-long global vector, while the learning of visual landmarks is guided by path integration (Collett and Collett, 2002).
It is still not known how insects encode landmarks, and what features of the image are stored in its memory. However, it is agreed that ants, bees and wasps not only memorise landmarks, but guide their final approach to their goal by matching their visual image of the landmark with their stored image (snapshot?) of how it looks from the goal. It appears that when comparing their current view to a stored image, they use the retinal positions of the edges, spots of light, the image's centre of gravity and colour. They also learn the appearance of an object from more than one distance, as their path home is divided into separate segments, each guided by a separate view of the object. Additionally, consistency of view is guaranteed because the insect, following the sun or some other cue, always faces the object in the same direction. Finally, the insect's view of the distant panorama from a landmark can help to identify it (Collett and Collett, 2002).
Can we describe the navigation systems of insects as representational? If so, is it a map-like representation, like the one I proposed for operant conditioning, and does it qualify an insect to be a belief-holder?
Is path integration (dead reckoning) representational?
Gallistel (1998) regards the nervous system of an insect as a computational system and argues that the ability of insects to perform dead reckoning is strong evidence that their brains are symbol processors, which can represent their movements. The ability of insects' nervous systems to perform computations using the azimuth position of the sun and the pattern of polarised light in the sky, although impressive, does not entail that they have mental states (Conclusion C.3) - as we have seen, even viruses can compute.
More importantly, Gallistel contends that because there is a functional isomorphism between the symbols and rules in an insect's nervous system and events in the outside world, we can call it a representation. The nervous system contains vector symbols: neural signals which represent the insect's net displacement from the nest, and are continually updated as the insect moves about. The current sum of the displacements made by the insect while moving represents its present position and at the same time tell it how to get home.
As Gallistel puts it:
Insects' ability to hold a course using a sun-compass and to determine distance from parallax are other examples of insect representation cited by Gallistel.
It was argued earlier that a bona fide representation should contain within itself the possibility of mis-representation (Conclusion R.1). The case of the displaced ant, which sets a path that would normally return it to its nest, seems to meet this criterion. Likewise, the honeybee has a built-in visual odometer that estimates distance travelled by integrating image motion over time. The odometer misreads distances if the bee collects food in a short, narrow tunnel (Esch, Zhang, Srinivasan and Tautz, 2001).
Corry (2003) points out that isomorphism is not a sufficient condition for a representation: the ability of system A to track system B does not mean that A represents B. However, he proposes that if an individual (e.g. an ant) uses a system (e.g. its internal home vector system) which is functionally isomorphic with the real world, in order to interact with the real world, then the system can be said to represent the real world. This condition is satisfied in the case of dead reckoning by an ant. Can we call such a representation a belief?
Ramsey proposed that a "belief of the primary sort is a map of neighbouring space by which we steer" (1990, p. 146). However, if we look at the path integration system alone, the representation fails to meet even the minimal conditions which I suggested above for a map-like representation, as neither the goal nor the current position is represented: only the directional displacement from the goal is encoded. Without an internal representation of one's goal, we cannot speak of agency, or of control. A naive foraging insect, relying solely on path integration, is merely following its internal compass, which is continually updated by events beyond its control.
The same remarks apply to the ability of monarch butterflies to navigate from as far north as Massachusetts to central Mexico, in autumn. Scientists have recently discovered that the monarch uses an in-built sun-compass and a biological clock to find its way (Mouritsen and Frost, 2002). Since there is no reason to believe that the monarchs know where they are going, or why, we cannot describe them as agents when they migrate.
Can landmarks serve as maps to steer by?
Path integration on its own works perfectly well even for an untrained insect: it does not require the insect to associate a motor pattern or sensory stimulus with attaining its goal. Navigation by landmarks, on the other hand, requires extensive learning. The location and features (colour, size, edge orientation and centre of gravity) of each landmark have to be memorised. Multiple views of each landmark have to be stored in the insect's brain. Additionally, some insects use panoramic cues to recognise local landmarks. Finally, a local vector has to be associated with each landmark (Collett and Collett, 1998, 2002). The fact that insects are capable of learning new goals and new patterns of means-end behaviour means that they satisfy a necessary condition for the ascription of mental states (Conclusion L.11) - though by itself not a sufficient one, as we have seen that associative learning can take place in the absence of mental states (Conclusion L.12).
While navigating by landmarks, insects such as ants and bees can learn to associate their final goal (the nest) with views of nearby landmarks, which guide them home, even when their final goal is out of sight. These insects often follow "a fixed route that they divide into segments, using prominent objects as sub-goals" (Collett and Collett, 2002, p. 547).
It is a matter of controversy whether insects possess a global (or allocentric) map of their terrain, which combines multiple views and movements in a common frame of reference (Giurfa and Menzel, 2003; Collett and Collett, 2002; Harrison and Schunn, 2003; Giurfa and Capaldi, 1999; Gould, 1986, 2002). A more complete discussion is contained within the Appendix. However, even if an insect has "only a piecemeal and fragmented spatial memory of its environment" (as suggested by Collett and Collett, 2002, p. 549), it clearly meets the requirements for a minimal map. Its current position is represented by the way its nervous system encodes its view of the external world (either as a visual snapshot or as a set of parameters), its short-term goal is the landmark it is heading for, and the path is its local vector, which the insect recalls when the landmark comes into view (Collett and Collett, 2002, pp. 546, 547, 549). The map-like representation employed here is a sensory map, which uses the visual modality.
Are navigating insects agents?
The statement by Collett and Collett that "the primary role of a landmark is to serve as a signpost that tells an insect what to do next" (2002, p. 547), recalls Ramsey's claim that a belief is a map by which we steer. Should we then attribute agency, beliefs and desires to an insect navigating by visual landmarks?
Corry (2003) thinks not, since the insect is not consciously manipulating the symbols that encode its way home. It does not calculate where to go; its nervous system does. Corry has a point: representations are not mentalistic per se. As we have seen, the autonomic nervous system represents, but we do not say it has a mind of its own. Nevertheless, Corry's "consciousness" requirement is unhelpful. He is proposing that a physical event E (an insect following a home vector) warrants being interpreted as a mental event F (the insect is trying to find its way home) only if E is accompanied by another mental event G (the insect must be consciously performing the calculations), but he fails to stipulate the conditions that have to be satisfied in order for G to be met.
I would suggest that Ramsey's steering metaphor can resolve the question of whether insects navigate mindfully. Steering suggests control of one's bodily movements. The autonomic nervous system, as its name suggests, works perfectly well without us having to control it. On the other hand, Drosophila at the torque meter needed to control its motor movements in order to stabilise the arena and escape the heat. Which side of the divide does insect navigation fall on?
Earlier, I proposed that an animal is controlling its movements if it can compare the efferent copy from its motor output with its incoming sensory inputs, and make suitable adjustments. The reason why this behaviour merits the description of "agency" is that fine-tuned adjustment is self-generated: it originates from within the animal's nervous system, instead of being triggered from without. This is an internal, neurophysiological measure of control, and its occurrence could easily be confirmed empirically for navigating insects.
In the meantime, one could use external criteria for the existence of control: self-correcting patterns of movement. The continual self-monitoring behaviour of navigating insects suggests that they are indeed in control of their bodily movements. For instance, wood ants subdivide their path towards a landmark into a sequence of segments, each guided by a different view of the same object (Collett and Collett, p. 543). Von Frisch observed that honeybees tend to head for isolated trees along their route, even if it takes them off course (Collett and Collett, p. 547). Insects also correct for changes in their environmental cues
Two facts may be urged against the idea that a navigating insect is exercising control over its movements, when it steers itself towards its goal. First, insects appear to follow fixed routines when selecting landmarks to serve as their sub-goals (Collett and Collett, 2002, p. 543). Second, cues in an insect's environment (e.g. the panorama it is viewing) may determine what it remembers when pursuing its goal (e.g. which "snapshot" it recalls - see Collett and Collett, 2002, p. 545). In fact, it turns out that no two insects' maps are the same. Each insect's map is the combined outcome of:
(i) its exploratory behaviour, as it forages for food;
(ii) its ability to learn about its environment;
(iii) the position and types of objects in its path;
(iv) the insect's innate response to these objects; and
(v) certain fundamental constraints on the kinds of objects that can serve as landmarks (Collett and Collett, 2002, pp. 543, 548-549). As each insect has its own learning history and foraging behaviour, we cannot say that an insect's environment determines its map.
We argued above that control requires explanation in terms of an agent-centred intentional stance, as a goal-centred intentional stance is incapable of encoding the two-way interplay between the agent adjusting its motor output and the new sensory information it is receiving from its environment, which allows it to correct itself. In a goal-centred stance, the animal's goal-seeking behaviour is triggered by a one-way process: the animal receives information from its environment.
Finally, we cannot speak of agency unless there is trying on the part of the insect. The existence of exploratory behaviour, coupled with self-correcting patterns of movement, allows us to speak of the insect as trying to find food. Studies have also shown that when the landmarks that mark an insect's feeding site are moved, insects try to find the place where their view of the landmarks matches the view they see from their goal.
A model of agency in navigating insects
We can now set out the sufficient conditions for the existence of agency in navigating insects:
(i) a goal or end-state, which is internally encoded as a stored memory of a visual stimulus that the animal associates with attaining its goal;
(ii) sub-goals, which are internally encoded as stored memories of visual stimuli that help the animal attain its goal;
(iii) a pathway for reaching its goal, which is internally encoded as a local vector or a stored memory of a sequence of movements which allows the animal to steer itself towards its goal;
(iv) exploratory behaviour, as the insect tries to locate food sites;
(v) visual sensory inputs that inform the animal about its current position, in relation to its goal, and enable it to correct its movements if the need arises;
(vi) direct or indirect associations (a) between visual landmarks and local vectors; (b) between the animal's short term goals (landmarks) and long term goals (food sites or the nest). These associations are stored in the animal's memory and updated when the animal's environment changes;
(vii) the ability to store and compare internal representations of its current motor output (i.e. its efferent copy, which represents its current "position" on its internal map) and its afferent sensory inputs. Motor output and sensory inputs are linked by a two-way interaction;
(viii) fine-tuning behaviour: efferent motor commands which are capable of steering the animal towards a short or long-term goal. It has to be able to detect both matches (correlations between its view and the stored image of its goal) and mismatches or deviations - first, in order to approach its goal, and second, in order to keep track of it;
(ix) self-correction: abandonment of behaviour that increases, and continuation of behaviour that reduces, the animal's deviation from its desired state.
For a navigating insect, its objects of desire are its long-term goals (food and the safety of the nest) which trigger innate responses. Short term goals (e.g. landmarks) are desired insofar as they are associated with a long term goal. Even long term goals change over time, as new food sources supplant old ones. Additionally, insects have to integrate multiple goals, relating to the different needs of their community (Seeley, 1995; Hawes, 1995). In other words, they require an action selection mechanism. For example, a bee hive requires a reliable supply of pollen, nectar, and water. Worker field bees can assess which commodity seems to be in short supply within the hive, and search for it.
It has been argued that an animal's map-like means-end representation formed by a process under the control of the agent deserves to be called a belief, as it encodes the animal's current status, goal and means of attaining it. A navigating insect believes that it will attain its (short or long term) goal by adjusting its motor output and/or steering towards the sensory stimulus it associates with the goal.
Summing up, Carruthers (2004) offers a pragmatic argument in favour of ascribing beliefs and desires to animals with mental maps:
While I would endorse much of what Carruthers has to say, I should point out that his belief-desire architecture is somewhat different from mine. He proposes that perceptual states inform belief states, which interact with desire states, to select from an array of action schemata which determine the form of the motor behaviour. I propose that the initial selection from the array of action schemata is not mediated by beliefs, which only emerge when the animal fine-tunes its selected action schema in an effort to obtain what it wants. Moreover, I propose a two-way interaction between motor output and sensory input. Finally, the mental maps discussed by Carruthers are spatial ones, whereas I also allow for motor or sensorimotor maps.
Which intentional stance?
An insect's navigational behaviour, like its operant behaviour, cannot be described purely from the outside, by a third-person intentional stance. The continual two-way interplay between the animal's motor output (efference copy), which is controlled from within its body, and its sensory feedback from its external environment, enables it to make an "inside-outside" comparison by paying attention to its sensory inputs. The notions of "control" and "attention" make sense only if we retain the "inside-outside" distinction. A first-person intentional stance is therefore required.
When a honeybee or desert ant leaves its nest, it continually monitors its path, using a sun and polarized light compass to assess its direction of travel, and a measure of retinal image motion (bees) or motor output (ants) to estimate the distance that it covers. This information is used to perform path integration, updating an accumulator that keep a record of the insect's net distance and direction from the nest.
[T]he basic structure of each route segment is a landmark and an associated local vector or some other stereotyped movement (for example, turning left)... [T]he primary role of a landmark is to serve as a signpost that tells the insect what to do next, rather than as a positional marker... (Collett and Collett, 2002, p. 547).
The challenge posed by these findings to those who deny symbol processing capacity in [insect] brains is to come up with a process that looks like dead reckoning but really is not (1998, p. 24).
DF.2 We are justified in ascribing agency to a navigating animal if the following features can be identified:
If the animal can put together a variety of goals with the representations on a mental map, say, and act accordingly, then why shouldn't we say that the animal believes as it does because it wants something and believes that the desired thing can be found at a certain represented location on the map?
*** SUMMARY of conclusions reached
References
