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Appendix to Chapter 2 Part D - Higher kinds of Learning in Insects

Back to Chapter 2 Back to Chapter 2 part D SUMMARY of Conclusions reached References

This appendix is divided into the following sections:

1. Evidence for Higher-Order Learning in Insects

1.1 Reversal Learning (an update on Gary Varner's claims regarding desire in animals)
1.1.1 Rapid reversal learning in honeybees: evidence for the acquisition of beliefs?
1.1.2 Is progressive adjustment in multiple reversal learning trials evidence for the acquisition of beliefs?
1.2 Concept Formation in Insects
1.2.1 Can honeybees form categorical concepts?
1.2.2 Can honeybees form the concepts of "same" and "different"?

2. Evidence for Higher-Order Learning in Octopuses


2.D.1 Evidence for higher-order learning in insects

2.D.1.1 Meta-learning in insects: reversal learning

2.D.1.1.1 Is rapid reversal learning evidence for the acquisition of beliefs?

Although learning in the broadest sense (i.e. the acquisition of new skills) may not necessarily require mental states, there is no doubt that meta-learning ("learning to learn") is a critical capacity that presupposes an ability to evaluate and correct one's actions. A creature with this ability would qualify as what Dennett (1997, p. 112ff.) calls a "Popperian creature". Such a creature is one level above a Skinnerian creature, which is capable of learning from its trial-and-error mistakes and successes, and can associate information about one kind of event with information about another kind. A Skinnerian creature may stumble upon a "smart move", but it cannot predict what works and what does not. Its first move may be a fatal one, if it is unlucky.

A "Popperian creature" can avoid such an outcome, because it can foresee the consequences of its actions in the "inner environment" of its imagination, which lets the creature manipulate information in its memory, about its external environment. In this inner environment, try-outs or simulations can be executed without harming the animal, allowing it to select the best course of action and make a smart first move in its external ("real") environment. The advantage of foresight is that it "permits our hypotheses to die in our stead", as Popper put it (Dennett, 1997, p. 116). The creature can make a smart first move, because it can think about smart moves.

Varner (1998), following Bitterman (1965), has suggested an experimental way to identify meta-learning in animals and resolve the question of whether they have mental states. He has argued that if they are genuinely learning, (and not merely mechanically associating), they should be forming hypotheses about the changes in their environment. He has proposed that reversal tests offer a good way to test animals' abilities. Multiple reversal tests involve repeatedly reversing the reward pattern in simple learning experiments. For instance, a rat is first presented with two levers and rewarded for pressing the left lever instead of the right. When the rat has learned to press the left lever all the time, the reward pattern is reversed. Once the rat has learned the new reward pattern, it is reversed again, and so on. Varner suggests that if an animal shows no improvements in the time it takes to adjust to subsequent reversals, that suggests an inflexible, non-cognitive mechanism is governing its behaviour. By contrast, Bitterman predicted that an animal that can form hypotheses should take longer to learn the new pattern the first time it is reversed, but should adjust more and more rapidly to subsequent reversals, as it learns to quickly revise its expectations.

Varner's proposal invites two questions. First, is rapid reversal learning a sign of intelligence? Second, does progressive improvement in multiple reversal tests indicate the presence of mental states?

The ability to adapt rapidly to changes sounds like a mind-like feature. However, the consensus from animal behaviourists is that it need not be so. According to Ben-Shahar (personal email communication, 19 August 2003), the rapid reversal learning of honey bees surpasses even that of pigeons and rats. However, Ben-Shahar cautions against the use of reversal learning per se as a measure of intelligence in animals, as the rapid reversal learning appears to be an adaptive trait for some animals, and adaptive behaviour is not necessarily intelligent:

I'm not convinced that reversal learning is necessarily directly related to intelligence. It is possible that for some species, reversal is highly adaptive, and hence the good performance. In bees one could speculate that reversal is very important to an animal that forages on unstable resources. In bees and other social species this is even more critical since they use communal foraging strategies. Bees will follow other bees to resources previously identified. If these have dried out the new forager has to look for new resources fast or she will come back empty - a big waste of time (personal email communication, 19 August 2003).

We can formulate the following negative conclusion:

L.18 The capacity for rapid reversal learning in an animal does not, by itself, warrant the ascription of mental states to it.

2.D.1.1.2 Is progressive adjustment in multiple reversal learning trials evidence for the acquisition of beliefs?

The second and more interesting question is whether the existence of progressive adjustment in multiple reversal learning trials indicates intelligence.

The ability to improve in multiple reversal learning trials is readily explained by the hypothesis that the animal is forming a hypothesis about changes in its environment. I have not been able to find a non-cognitive explanation as to why such improvement might occur. Certainly, the fact that the cognitive explanation makes a highly specific prediction (that the animal should take longer to learn the new pattern the first time it is reversed), which has been experimentally confirmed, tends to bear out a mentalistic interpretation. It should be borne in mind, however, that even if the behaviour cannot be accounted for in terms of associative learning, that does not necessarily make it cognitive.

Even if progressive adjustment shows that an animal has mental states, that does not necessarily make it a Popperian creature. The ability to formulate primitive hypotheses need not imply the ability to foresee the consequences of one's actions in the "inner environment" of one's imagination.

L.19 Progressive adjustments in serial reversal tests constitute good prima facie evidence that an animal is trying to adjust to sudden changes in its environment, by rapidly revising its expectations.

Basing his arguments on research by Morton Bitterman (1965), Varner has claimed (1998, p. 32) that progressive adjustment in multiple reversal learning trials is found only in reptiles, birds and mammals. Since then, it has become apparent that fish (Wakelin, 2003) and honeybees (Komischke, Giurfa, Lachnit and Malun, 2002), are also capable of this kind of learning. Komischke, Giurfa, Lachnit and Malun (2002) compared the responses of bees that had experienced reversals with those of bees that had not experienced such reversals when both were confronted with a new reversal situation. They found that bees that had experienced three previous reversals were better in solving the final reversal task than bees with no previous reversal experience. They also showed that one reversal learning trial was enough for bees to perform better in the final reversal task.

The evidence to date from serial reversal learning suggests that honeybees, at least, are capable of learning to learn. This ability may turn out to be widespread among insects, but very little research has been done with most groups of insects. Brembs claims that serial reversal learning in insects is not confined to honeybees:

Drosophila can reversal learn and if the pattern-heat contingency is reversed, learning is faster (personal email communication, 11 August 2003).

However, neither Brembs nor Drosophila researcher Josh Dubnau was able to supply a reference to serial reversal learning by Drosophila melanogaster in the published literature. Dubnau admitted that "not many people have looked carefully at reversal learning in flies" (personal email communication, 14 August 2003).

The evidence from serial reversal learning is thus of limited value. At most, it suggests that honeybees are capable of meta-learning, while saying nothing about other insects.


2.D.1.2 Meta-learning in insects - concept formation

2.D.1.2.1 Are honeybees capable of forming categorical concepts?

Concept formation is another area in which meta-learning can be shown to take place in insects. Tests by Gould and Gould (1988) showed that bees could learn to recognise and distinguish human letters, regardless of size, colour, position or font. Giurfa, Eichmann and Menzel (1996) trained foragers to associate symmetrical shapes with food. Asymmetrical shapes were not rewarded. (In another test, asymmetrical shapes were rewarded while symmetrical ones were not.) By the seventh visit, the bees could choose a correct novel stimulus over an incorrect one. Gould argues that this kind of learning differs from associative tasks:

The learning curve is different from that of more standard tests in which bees are taught that a particular odor, color, or shape is always rewarded. During concept learning there is no evident improvement over chance performance until about the fifth or sixth test, whereas in normal learning there is incremental improvement beginning with the first test. This delay is characteristic of what has been called 'learning how to learn', which is interpreted as a kind of 'ah-ha' point at which the animal figures out the task.

The main difference is that honey bees are much quicker at deciphering what the experimenter wants than are pigeons and other standard laboratory animals (Gould, 2002, pp. 43-44, italics mine).

I would agree with Gould's claim that the only satisfactory explanation for the sudden improvement in the bees' performance is a cognitive one: the bees formed a generalised notion of a symmetrical (or asymmetrical) object. Prior associative learning of rewarded and unrewarded stimuli cannot explain their performance, as (a) the relevant property was not a low-level property such as "red" that they were hard-wired to recognise, and (b) the bees had to transfer their discriminatory abilities to novel stimuli. This requires "a capacity to detect and generalize symmetry or asymmetry" (Giurfa, Eichmann and Menzel, 1996, p. 458). It was also shown experimentally that bees have an innate preference for symmetrical shapes, but this in no way undermines a mentalistic explanation of their performance. The existence of a pattern preference in honeybees does not explain how the concept "asymmetrical" is acquired, or how bees suddenly "figure out" what to look for in novel stimuli, after several trials. These facts can only be accounted for by supposing that the bees were trying to learn what they had to do in order to obtain their reward consistently, and finally managed to form a general concept of what the rewards had in common. The fact that trained bees tend to pay more attention to symmetrical rather than asymmetrical stimuli simply shows that they can form some concepts (e.g. "symmetrical") more easily than others ("asymmetrical"). For that matter, as Gould (2002, p. 44) points out, human infants display the same innate preference.

Other research has shown that bees can discriminate vertical from horizontal stripes, and apply this distinction to novel stimuli showing the same patterns. These experiments satisfy the standard requirements for categorisation: (i) a variety of stimuli sharing some common feature are rewarded, rather than an individual stimulus; and (ii) the animals can transfer the concept to novel stimuli (Menzel and Giurfa, 2001, p. 66).

L.20 An animal's ability to form categorical concepts and apply them to novel stimuli indicates the presence of mental processes - in particular, meta-learning.

Is there a hidden bias in the search for conceptual learning? How do we form concepts for smells?

Incidentally, I would suggest that there is a hidden bias in the search for conceptual learning, which has so far impeded our search for this kind of learning in "simpler" animals such as worms. From a cognitive perspective, a concept, properly speaking, is more than a mere category such as "red", which an animal could be neurally hard-wired to process. A cognitive concept requires an ability on the part of an individual to unify stimuli previously perceived as disparate. The kinds of stimuli most amenable to this kind of conceptualisation are visual and auditory stimuli. It is easy to see how different shapes can be grouped under a family concept (e.g. "triangular" or "symmetrical"). One can also classify sounds by their acoustic properties (e.g. "C" or "single note").

However, many invertebrates have a very weak capacity to discriminate between colours and sounds, and may therefore be unable to form most or all audiovisual concepts. The predominant sensory modality for these animals is usually smell. But how does one go about categorising smells? Although there are words in our language for individual smells, there are very few words that signify a common feature of disparate smells. ("Fragrant", "pungent" and "rank" are three possible examples.) The whole notion of what a family of related smells needs to be thought out carefully, before we can investigate the concept-forming abilities of animals (such as worms) whose sensory modality is predominantly olfactory rather than audiovisual.

"Aha!" Insight learning in honeybees

The sudden improvement in performance noted by Gould in categorical tasks (2002, p. 44) suggests a useful experimental way of identifying the presence of insight in animals. Cognitive tasks for animals should be designed with the aim of eliciting this kind of "Aha!" result.


2.D.1.2.2 Are honeybees capable of forming the concepts of "same" and "different"?

Even more impressively, honeybees seem to be able to form highly abstract concepts such as "same" and "different", according to research by Giurfa, Zhang, Jenett, Menzel & Srinivasam (2001). The authors summarise their findings as follows:

...[R]ecent research has found that bees are capable of cognitive performances that were previously thought to occur only in some vertebrate species. For example, honeybees can interpolate visual information, exhibit associative recall, categorize visual information and learn contextual information. Here we show that honeybees can form "sameness" and "difference" concepts. They learn to solve delayed-matching-to-sample [DMS - V.T.] and delayed-non-matching to sample [DNMS - V.T.] discriminations and transfer the learned rules to novel stimuli of the same or a different sensory modality. Thus, bees can, not only learn specific objects and their physical parameters, but also master abstract interrelationships, such as "sameness" and "difference".

The following report of the authors' research, suggesting that bees possess the concepts of sameness and difference, is taken from the San Francisco Chronicle (article by science writer K. Davidson, Thursday April 19, 2001):

Bees brighter than we knew, study finds. They pass cognitive tests usually given apes, people

Bees are famously busy -- but they're also pretty brainy.

Our pollen-hunting friends possess "higher cognitive functions," judging by cunning experiments in which the creatures learned to compare and distinguish different colors and patterns, according to today's issue of Nature.

In what an outside expert praises as "an exciting discovery," the French researcher Martin Giurfa and four colleagues showed that honeybees -- that's Apis mellifera to bee fanciers -- excel at cognitive tests normally performed by lab primates and human volunteers.

To demonstrate this, Giurfa and his team exposed bees to a simple Y-shaped maze. The entrance to the maze was marked with a particular symbol -- say, the color yellow.

As the Nature article shows, bees also can engage in abstract thought. The creatures can "master abstract inter-relationships," specifically the cognitive concepts of "sameness" and "difference," Giurfa and his team report. Hence, "higher cognitive functions are not a privilege of vertebrates," that is, creatures with backbones and much more complex nervous systems.

A bee flying through the entrance encountered a branching pathway. One branch was marked with the color yellow, another with the color blue. Bees that pursued the yellow-marked path discovered at its end a vial rich in sugar.

Bees that took the blue path got no sugar.

Normally, bees would have been just as likely to fly one way as another. But via Giurfa's experiment, the bees learned that sugar lay at the end of the route marked with the same symbol as that marking the outside entrance. In other words, "same" equals "sugar."

The bees demonstrated an ability to recognize "sameness" and "difference" - fundamental skills on any test of cognitive abilities.

In a second experiment, the bees showed they could apply the concepts of "sameness" and "difference" beyond what they had learned in the first experiment.

In subsequent experiments, the opening to the maze was marked by a different symbol -- such as vertical dark lines. In that case, on entering the maze the bees re-encountered the two pathways, which this time were marked not with colors but, rather, with lines -- vertical lines on one path, horizontal lines on the other.

Had the bees remembered the lesson of the first experiment, namely that "same" equals "sugar"? They had. In the second experiment, more than 70 percent of the bees promptly flew down the path marked by vertical dark lines, the same symbol as that above the entrance.

To perform the above task, it was necessary for the bees to acquire the rule that provides the goal of matching - "always choose the stimulus that is the same as the sample" - and it is also necessary to hold the information about the sample "in mind" to perform the test discrimination. Success in this task also presupposes sophisticated discrimination abilities in an insect and suggests the existence of an analogue of declarative memory (memory for facts). Honey bees have performed well in a range of DMS tasks, showing the ability to transfer their concept of "same" from one context (same colour) to another (same pattern of stripes) in successive trials, or vice versa.

One might attempt to account for the bees' DMS learning feats by positing that they are storing a "snapshot" of the sample in their memories, which elicits their response to a subsequent matching stimulus. On this account, the bees are not forming an abstract notion of "same", but simply matching pixels. However, the fact that bees can recognise and distinguish human letters, regardless of size, colour, position or font (Gould and Gould, 1988) refutes this "snapshot" hypothesis. Nor can such a hypothesis account for bees' abilities to identify general features of stimuli (e.g. asymmetry).

Even more impressively, honey bees are capable of solving a delayed non-matching to sample task, where they have to choose the stimulus that is different from the original sample (Giurfa, 2003).

The point that needs to be kept in mind here is that "sameness" and "difference" are not physical properties as such: they do not describe a measurable property of an object or group of objects. While one can describe what the bees are doing in a particular DMS task, using empirical terminology (e.g. the bees are looking for a stimulus whose colour matches the sample's), it is impossible to describe what the bees are doing in the ensemble of tasks, where they have to match colours or patterns, without employing abstract, non-empirical terminology (the bees are looking for a stimulus that is the same as the sample). The strategy for success in a DMS task has to be formulated at this level.

But why should a mind be needed to identify non-empirical properties? What I am proposing here is not that "non-empirical" equates to "mental", but that the identification of non-empirical properties is inherently mentalistic. Whereas empirical properties can be identified by some process of association and recall, non-empirical properties, such as sameness, have to be identified by looking for the rule which generates instantiations of these properties. The activity of attempting to follow a rule is a mentalistic one, as it can only be characterised in intentional terms.

L.21 An animal's ability to identify non-empirical properties is a sufficient condition for its having mental states (intentional acts). Such an animal can apply non-empirical concepts, by following a rule.

Does the ability of honey bees to solve DMS tasks mean that they have the mental concepts of "same" and "different"? I suggest that bees have a concept of sameness, without knowing what they have concepts of, and without knowing that they have a concept of sameness. The last two abilities, but not the first, presuppose the possession of a language which is capable of expressing abstract concepts. There are no indications that bees possess such a language. (The phenomenon of bee "language" will be discussed in chapter 4.) The distinctions I have invoked here reflect Dretske's (1995) distinctions between being conscious of something (e.g. burning toast), being conscious of what you are conscious of, and being conscious that you are conscious of it.

Is a bee's concept of "sameness" the same as ours? I propose that this question can be resolved if we consider the following three questions: (i) do bees make the same responses as we do in DMS tests?, (ii) do they make responses for the same range of objects as we do?, and (iii) do they make appropriate responses for all objects that are empirically accessible to them? The answer to the first two questions is obviously negative: (i) bees cannot make the same responses, as their discriminatory abilities are different from ours (e.g. their vision is poorer than ours), and (ii) there are certain kinds of objects of which bees are unaware, as they can only be apprehended through abstract language. The third and more substantial question relates to whether they have a general concept of an "object", which they can apply to all kinds of stimuli that they can sense. Can they, for instance, apply the concept of "sameness" to smells, or only to visual stimuli? (If the latter is the case, then bees' concept of "sameness" is indeed a lower-level one than ours, as its scope is limited to one sensory mode.) And can they apply their concept across sensory modes - e.g. can they compare a visual stimulus (e.g. a disk marked with red paint) with a smell (e.g. a disk with no marks, impregnated with the smell of eucalyptus) and judge them to be different?

My proposals for testing the boundaries of bees' concepts of "sameness" and "difference"

The following suggestions relate to the question of whether bees can (i) generalise their concepts of sameness and difference across different senses and (ii) form a multi-modal concept of sameness. I propose the following two tests:

(i) After being trained to enter a Y-shaped maze whose entrance is marked by a visual cue, and then fly down the arm of the maze marked with the same visual cue, can they generalise across modalities, and apply the "sameness" rule to a maze whose entrance is marked by a neutral odour, and which has branching arms, one of which has the same odour as the entrance?

(ii) Can they form a multi-modal concept of sameness, such that if they enter a maze marked with a visual cue and an odour, and then encounter a fork with multiple branches, they fly down the one with the same visual cue and odour?


2.D.2 Evidence for Higher kinds of learning in octopuses

Conceptual Learning

Experiments on octopuses performed by J. Z. Young in the 1950s and 1960s showed that they can learn to distinguish between shapes, orientations, sizes and degrees of brightness:

In one experiment, Young trained octopuses to select between large and small squares, horizontal and vertical stripes, and black and white circles. He found that the animals could retain all three preferences at once (Hamilton, 1997, p. 34).

However, discrimination is not the same as conceptualisation. Evidence for the latter would be more convincing if it could be demonstrated that octopuses, like honey bees, were able to make distinctions at a more abstract level - e.g. between symmetrical and asymmetrical, or same and different. Research to date on whether octopuses get "the oddity concept" is inconclusive (Mather, personal email, 8 September 2003).

The ability to change bodily appearance: camouflage, mimicry, signaling and deceit

Cephalopods have an ability to change their appearance which is unrivalled among other animals, thanks to the presence of thousands or even millions of rapidly migrating chromatophores (multi-celled organs containing pigment sacs of various colours) in their skin, which allow them to blend in with their background. It takes less than a second for cephalopods to adopt a new colour pattern, as the process is controlled by the brain through nervous impulses to the muscles. Additionally, cephalopods have soft, flexible bodies and muscles that allow them to change the texture of their skin (Hamilton, 1997; Langley, 2002; Milius, 2001).

Cephalopods use their ability to change their colour patterns and skin texture for various purposes. To avoid being eaten by a predator, they may either blend in with their background, or mimic animals that taste bad to the predator, or even mimic animals which feed on the predator (Hamilton, 1997; Langley, 2002; Milius, 2001).


Indonesian octopus (left column) mimics a banded
sole (top right) and a banded sea snake (bottom
right). Courtesy M. Norman and R. Steene.

An outstanding example is the newly discovered "mimic octopus" of Indonesia, described recently by Norman, Finn and Tregenza (2001). It is able to forage in broad daylight, thanks to its ability to impersonate toxic or predatory species as diverse as sea snakes and fish. The octopus also changes its postures and body movements to mimic its models:

Sometimes, the octopus fled with its arms aligned in a flattened, striped oval, looking much like a common poisonous flatfish. On four occasions when damselfish pestered an octopus, Norman saw it poke six of its legs down a burrow and spread the other two. They sported bands and waved gently, resembling the sea snakes that prey on damselfish.

When Norman saw a mimic octopus chugging along well above the seafloor, extended arms colored in stripes, he thought of the sunburst of striped, poisonous spines that lionfish flare (Milius, 2001).

Cephalopods also change their colour patterns and texture to camouflage themselves while hunting prey, to signal (or disguise) their intentions during courtship, and to deceive or ward off attacks by rival males (Hamilton, 1997).

The Caribbean reef squid affords a spectacular example of this behaviour:

[It] has at least 35 patterns in addition to its almost magical ability to blend in with its background. It can flash a different display on each side of its body when positioned between a potential mate, which sees a uniform light grey, and a rival male, which sees tiger striping called the "intense zebra display". If the positions change, so do the patterns (Hamilton, 1997, p. 33).

Similarly, male cuttlefish adopt female colouring, patterns, and shape, to gain access to females guarded by larger rivals (Scigliano, 2003).

The behaviour described here can easily be interpreted in intentional terms: mimicry, disguise, strategic planning and deception. A useful question to ask might be: do we need to adopt an agent-centred stance in order to account for the behaviour? To answer this question, we need to do three things. First, we need to discover what causes these colour and texture changes in cephalopods. Are they triggered by simple reflexes, or is there at least some scope for fine motor control, as we observed in Drosophila, which would allow us to speak of agency here? Unfortunately, the sheer rapidity of the changes makes it difficult to investigate their etiology.

Second, we have to find out whether cephalopods are physically capable of controlling (i.e. fine-tuning) their color and texture changes. In my discussion of agency in Drosophila, I suggested that fine motor control required an interaction between an animal's feedforward and feedback mechanisms, and that efferent copy played a vital role. Certainly, the body movements of the Indonesian mimic octopus appear fine-tuned to the circumstances.

Colour changes in cephalopods are more problematic. Although they are directed by the brain via the nervous system, no muscular movements appear to be involved. Can there be trying without muscular activity? It is hard to see how we can speak of a cephalopod as trying to turn black unless it can compare its current colour with that of its surroundings and adjust its bodily movements accordingly. On the other hand, processes which are involuntary in vertebrate nervous systems may not be so in cephalopods.

Third, we need to investigate whether the behaviour observed is a fixed action pattern or whether it is truly flexible, as defined in this thesis. For instance, is there any evidence of learning? Do young cephalopods display a "learning curve" when camouflaging their appearance, mimicking other animals or signaling to mates? And can they learn by watching their peers? (The answer to the last question is probably negative.)