The Evolution of Means-End Reasoning
David Papineau

1.  Introduction

When I woke up a few days ago, the following thoughts ran through my mind.  'I need a haircut.  If I don't get it first thing this morning, I won't have another chance for two weeks.  But if I go to the barber down the road, he'll want to talk to me about philosophy.  So I'd better go to the one in Camden Town.  The tube will be very crowded, though.  Still, it's a nice day.  Why don't I just walk there?  It will only take twenty minutes.  So I'd better put on these shoes now, have breakfast straight away, and then set out for Camden.'

This is a paradigm case of what I shall call 'means-end reasoning'.  In such reasoning, we consider the consequences of various courses of action, and choose the course best suited to our overall purposes.  I take it to be uncontroversial that all human beings are capable of such means-end reasoning, and that this kind of reasoning guides many of our actions.  Indeed I take it that this power of means-end reasoning is one of the most important differencesÑif not the most important differenceÑbetween humans and other animals.

Yet for some reason this topic has become unfashionable.  Means-end reasoning seems to have disappeared from the theoretical agendas of many of those you would expect to be most interested, namely those who work on human cognition in a comparative or evolutionary context.  There are now large industries devoted to theory of mind, to language, and to other 'modules' putatively characteristic of human cognition.  But means-end reasoning itself gets brushed under the carpet, as somehow not quite the thing to talk about in modish theoretical society.

In this paper I want to make a plea for this somewhat old-fashioned topic.  While language, and theory of mind, and no doubt other modules, have clearly played a crucial role in human evolution, I think that as good a case can be made for the significance of means-end reasoning.  It is of course a tricky matter to chart the exact evolutionary dependencies between the different cognitive abilities peculiar to humans, and the remarks I make on this specific topic towards the end of this paper will be abstract and speculative at best.  But by that stage I hope at least to have persuaded you that means-end reasoning is a evolutionarily important topic in its own right.

My first task will be to be more specific about what I mean by 'means-end reasoning'.  Care on this point is obviously needed, if I am to persuade you that 'means-end reasoning' is important for human evolution.  For, if we set the standards too low, 'means-end reasoning' will be widespread throughout the animal kingdom, and not a peculiarly human adaptation.  After all, nearly all animals have some ways of selecting behaviours which are appropriate to current needs and circumstances.  Nor, in the other direction, will it do to set the standards too high, as requiring literacy or calculation, say.  For then there will no reason to suppose that 'means-end reasoning' has anything to do with human biology, however important it might be for the development of higher civilization.

Accordingly, in the next two sections I shall aim to specify an understanding of 'means-end reasoning' which is consonant with my claims about its importance for human evolution.  After that I shall attempt to defend these claims.

Before proceding, however, it will perhaps be worth commenting on one specific influence that has diverted current theoretical fashion away from means-end reasoning.  In many contemporary minds, I suspect, 'means-end reasoning' is thought of as antithetical to 'modularity'.  This is because means-end-reasoning tends to be associated with the kind of general-purpose learner-and-problem-solver that traditional psychology took to be the seat of all animal intelligence.  Enthusiastic advocates of modularity, however, reject this domain-general conception of animal intelligence, and argue that all real advances in cognitive power, and in particular the distinctive features of human psychology, consist of purpose-built 'modules' selected for specific intellectual tasks (cf. Cosmides and Tooby, 1992, pp. 39 et passim).  And so enthusiastic modularists tend to be impatient with talk of means-end reasoning, because they see it as a return to the bad old days of general-purpose learning and problem-solving. 

However, I do not think of 'means-end reasoning' as opposed to modularity in this way.  Insofar as there is a well-formed antithesis between general-purpose traditional mechanisms and modules, I would be inclined to place means-end reasoning on the side of the modules.  Means-end reasoning may be domain-general in its content, in the sense that there is no limit to the kinds of information that it can operate with.  But the same could be said of our linguistic abilities, yet these are widely taken to be the paradigm of 'modular' powers.

Moreover, means-end reasoning, as I think of it, is not to be thought of as providing a general interface between perceptual inputs and behavioural outputs, along the lines of the non-modular 'central system' that Jerry Fodor interposed between perception and action in his original The Modularity of Mind (1983).  Rather, I take means-end reasoning to be an add-on that arrived late in evolution, in service of specific needs, and which itself interfaces with whichever pre-existing mechanisms co-ordinate perception and action.

Sceptics often respond to the modularist metaphor of the mind as a 'Swiss Army knife' by asking what decides which blades to use in which circumstances.  This is a reasonable enough question, and some of my remarks later will indicate possible answers.  But means-end reasoning itself does not play this role.  Rather, means-end reasoning is a specialised mechanism, which gets activated when appropriate by whichever processes do co-ordinate the different aspects of cognition.  From this perspective, means-end reasoning is simply another fancy tool in the Swiss Army knife, not some meta-device that co-ordinates the whole show.

2.  Before Means-End Rationality

These last remarks are intended only as a pointer to my overall story.  Details of the plot will be filled in as we proceed.  The first step is to explain in more detail what I mean by 'means-end reasoning'.  In this section I shall attack this question from the bottom up, so to speak.  I shall consider how behaviour might be adapted to circumstances in animals who clearly lack means-end reasoning in any sense.  By this means I hope to identify a sense of means-end reasoning in which there are interesting questions about its evolutionary emergence.  The strategy, in effect, will be to isolate an important sense of means-end reasoning by considering what is lacking in those animals who manage without it.

I shall proceed abstractly, and in stages.  I shall only consider very general features of cognitive design.  And I shall start with the simplest possible such designs, and then proceed to more sophisticated ones.

Level 0Ñ'Monotomata'ÑDo R

At the very simplest level, level zero, so to speak, would be the kind of animal that always does the same thing, R.  For example, it might move around at random, blindly opening and closing its mouth parts, thereby ingesting anything that happens to get in its way.

Level 1Ñ'Opportunists'ÑIf C, do R

A step up from this would be animals who suit their behaviour to immediate conditions C, saving their energy for situations where their behaviour R will bear fruit.  For example, they move their mouth parts only when they detect the presence of food.  (In such cases we can expect also that the behaviour R will itself be 'shaped' by sensitivity to conditions.  The frog's fabled fly-catching behaviour fits this bill.  Not only do the frogs shoot their tongues out at specific times, namely when the environment offers some promise of food;  they also shoot their tongues out in specific directions, towards the point where the food is promised.)

Level 2Ñ'Needers'ÑIf C and D, do R

At the next level will be animals whose behaviour is sensitive, not just to current opportunities, but also to current needs.  For example, we can imagine insect-eaters who don't shoot their tongues out at passing targets unless they also register some nutritional lack.  Apparently frogs are not like this, and so are prone to overfeed.  Even after their nutritional needs are satisfied, they still shoot their tongues out at passing flies.  Still, even if frogs manage without a need-sensitive cognitive design, it can manifestly be advantageous to evolve one, and many animals have clearly done so.

Before proceding to the next level of complexity, it will be well to enter a word of caution.  It is natural, and indeed often very helpful, to characterise simple cognitive designs in representational terms, and I shall do so throughout this paper.  But there are dangers of putting more into the representational description than the specified design warrants, and mistaking overblown representational description for serious explanation.  In principle we should always demonstrate carefully that attributions of representational content are fully warranted.  It would make this paper far too long to do this properly at every stage, but I shall try as far as I can to ensure that my representational descriptions are grounded in explicit specifications of cognitive design.

By way of illustrating the danger, consider the distinction I have just introduced between Cs, which signify sensitivity to environmental 'conditions', and Ds, which register current needs (and so might thus be thought of as akin to 'desires' or, more cautiously, as 'drives').  It might seem entirely natural to distinguish informational Cs from motivational Ds in this way.  However, nothing I have yet said justifies any such contrast.  After all, C and D appear quite symmetrically in the schematic disposition which heads this sub-sectionÑIf C and D, do R.  So far we have been given no basis for treating these states as playing distinct roles in the regulation of behaviour.  

Now I have raised this point, let me pursue it for a moment.  To focus the issue, let me stipulate that both the Cs and the Ds are henceforth to be understood as internal states which trigger resulting behaviour R.  (There must be some such internal states, if distal conditions and needs are to affect behaviour.)  At first sight there may seem to be an obvious basis for distinguishing motivational Ds from informational Cs.  If some D is triggered by low blood sugar level, say, then won't it play a distinctively motivational role, by contrast with an informational C that, say, registers passing insects?  Isn't the D required to activate the animal, by contrast with the C, which just provides factual information, and so gives no motivational 'push'?  But this is an illusory contrast.  The C is equally required to activate the animalÑhowever low its blood sugar, the animal won't stick its tongue out until there is something to catch.  So far everything remains symmetrical, and both C and D should be counted as simultaneously motivational and informationalÑas 'pushmi-pullyu' states, in Ruth Millikan's terminology (Millikan, 1996).  They can both be thought of imperatively, as saying 'Do R (if the other state is also on)', and also indicatively, as saying 'Here is an occasion for doing R (if the other state is also on)'.

A substantial division between motivational and informational states only arises when there is some extra structure behind the Cs and Ds.  Without going into too much detail, let me give the rough idea.  A state C will become informational rather than motivational when it ceases to be tied to any particular behaviour, and instead provides information that is used by many different behavioural dispositions.  We can expect behaviourally complex animals to develop sensory states which respond reliably to external objects and properties and which are available to trigger an open-ended range of activities.  This will be especially advantageous when animal are capable of learning (see level 4 below).  When an internal state C ceases in this way to be devoted to any specific behavioural routines, it will cease to have any imperative content, and can be viewed as purely informational.  (Cf. Millikan, forthcoming.)

Motivational states can become specialised in a converse way.  Here again the states will cease to be tied to particular behaviours.  But in the motivational case this won't be because the states come to provide generally useful information, but rather because they acquire the distinctive role of signalling that certain results are needed.  The reason this detaches motivational states from specific behaviours is that different behaviours will be effective for those results in different circumstances.  Such motivational Ds can still perhaps be thought of as having some informational contentÑblood sugar is low, maybeÑbut they will be different from purely informational Cs, given that they will have the special responsibility of mobilising whichever behaviours will produce some requisite result, whereas informational Cs will have no such result to call their own. 

Level 3Ñ'Choosers'ÑIf Ci and Di, do Ri, WHEN Di is the dominant need

Once animals have states whose role is to register needs, then there is potential for another level of complexity.  It would be advantageous to have a mechanism to decide priorities when some Ci and Di prompt behaviour Ri, and another Cj and Dj prompt incompatible behaviour Rj.  The obvious system is somehow to compare Di with Dj, and select between Rs depending on which need is more important.  It is not hard to imagine mechanisms which would so rank needs in either a qualitative or quantitative way.

Level 4Ñ'Learners'ÑAFTER experience shows that Ci, Di and R lead to reward, then (as before):  If Ci and Di, do Ri, when Di is the dominant need

So far I have implicitly been supposing that the prompting Cs and Ds are 'hard-wired' to the responsive behavioural RsÑthat is, that the relevant C, DÑ>R links have been established by inter-generational genetic evolution and will develop in any animal which matures normally.  Learning adds a further level of animal sophistication.  By 'learning' here I simply mean that the C, DÑ>R links can be influenced by the animals' specific experience of which behaviours produce which results in which circumstances.  The obvious way to allow this is via mechanisms which will reinforce C, DÑ>R links just in case R in D and C leads to some specific result.

In the schema above I have characterised the relevant reinforcing result simply as 'reward'.  This raises a number of issues.  One is whether the removal, or reduction, of the need-registering D is always implicated in the 'reward' which reinforces the likelihood of R given C and D in future.  A mechanism along these lines is suggested by the fact that many drives, like hunger, or thirst, have functions (ingestion of food, or water) whose fulfilments are standardly necessary and sufficient for the reduction of the drives themselves.  So learning mechanisms in which these drive reductions led to reinforcement would thereby select behaviour which was suited to fulfilling the functions of the drives (that is, getting food, or water).

Still, it is not obvious that all drives are designed to produce results which will then, as a matter of course, assuage those drives.  For example, when there are signs of danger, an animal will need to identify anything unusual in its environment.  Suppose there is some drive DÑalertness, vigilanceÑwhich registers the need to identify anything unusual, and whose function is to get the animal to make such identifications.  It wouldn't seem a good idea for this drive to be assuaged whenever it succeeds in fulfilling its function.  If you find something unusual, you should become more vigilant, not less (and, conversely, your vigilance should be quieted if you fail to find anything unusual).  More generally, there seems no reason to rule out drives which will continue through their own fulfilments, or even be enhanced by them.  However, if there are such drives, then any associated learning mechanisms will not be able to use the drive's disappearance as a source of reinforcement, since this disappearance won't provide a good proxy for the drive's fulfilling its function.  Rather, the learning mechanism will have to work with some other sign of a behaviour's being an effective means to the drive's fulfilment (such as actually finding something unusual).

I am not sure whether nature has in fact bequeathed any such leaning mechanisms to the animal kingdom, or whether all actual animal learning mechanisms work with drive reductions as reinforcers.  A further issue, which arises whatever the answer to this question, is whether all animal learning derives from a single mechanism, which reduces all the drive-specific reinforcers (sating of hunger, quenching of thirst, perception of something unusual) to a common currency (the release of opiates, say), or whether there are a number of distinct learning mechanisms, one for each drive, each of which attaches behaviours to its own drive, depending on which behaviours have proved effective in past experience in fulfilling that drive's specific function.  The latter option would allow the more sophisticated control of behaviour, but again it is an empirical question how far nature has availed itself of this opportunity.

At this stage I propose to stop my initial classification of cognitive structures.  We have enough Levels to be getting on with.  They will provide a basis, in the next section, for an initial characterization of 'means-end reasoning'.

But before proceding it will be worth pausing briefly to remark on the way that even these initial levels have shown the category of drive to be surprisingly many-sided.  In a sense drive is a primitive version of desire, and I shall say a bit more in section 4(v) below about the extra elements in desire.  But even before we get to the more sophisticated level of desires, drives themselves turn out to have a confusing number of facets.  At the primitive Level 1, when need-registering drives are not really distinguishable from states which register environmental conditions, 'drives' simply signal the current appropriateness of some specific behaviour.  Once drives do become so distinguishable, at Level 2, as registers of need rather than conditions, they will function to prompt whichever behaviours will satisfy those needs in current circumstances.  A further role, which characterises level 3, is to compete with other drives, when those other drives prompt incompatible behaviours.  Note that the Level 2 role does not guarantee the Level 3 role:  we can imagine an animal, like Buridan's ass, who functions well when only one need is active, but freezes in the face of more;  more realistically, we can imagine animals who resolve competition between drives in some arbitrary non-functional way.  And at Level 4 we find drives which play a yet further role, by reinforcing behaviours which lead to the reduction of drive (though this will only work, as I observed, for drives whose fulfilment leads naturally to their extinction).  Again, this Level 4 role is not guaranteed by the roles at lower Levels, since the lower roles can be present in animals who do no learning at all.

At first, the notion of 'drive' can seem simple enough.  But it turns out to pack in a number of dissociable features, which won't always be found together in real animals._

3.  General Knowledge

Let me now draw attention to one striking feature of animals at Levels 0 to 4 (I shall call these 'simple animals' from now on).  They nowhere explicitly represent any general information of the form 'All As or Bs', or generic causal information to the effect that 'As cause Bs', or even conditional information about present circumstances like 'If A were to occur, then so will B'.  In line with this, let me now specify, as a first approximation, that by means-end reasoning we should understand the use of this kind of explicit general_ information to guide action.  This initial definition of means-end reasoning will soon need to be significantly refined and qualified, but it will do to get us started.  

Note that I have defined means-end reasoning in terms of using general information to guide actions, rather than in terms of using any information.  This is because even simple animals clearly use particular information about their circumstances to guide behaviour.  From Level 1 upwards they have states whose function is to represent particular features of their environment.  Even so, no simple organisms explicitly represent any general facts.  It one thing to be able to represent the whereabouts of a particular pond, or apple, or lion.  It is another to represent that the ponds contain water, or that apples are a source of food, or that lions are bad for your health. 

This last point means that we cannot equate my question of which animals have means-end reasoning with the perhaps more familiar question of which animals should be counted as having beliefs.  For the issue is not whether the animals have any powers of explicitly representing information, but whether they have the power of explicitly representing general information.  I have no objection to saying that simple animals, certainly from Level 2 upwards, have particular beliefs about particular circumstances.  But this won't qualify them as means-end reasoners, on my definition, given that they will still lack beliefs on general matters.

There is also a rather more general reason for not wanting to pose my issue of means-end reasoning in terms of the possession of beliefs.  This is because many philosophers, most importantly Daniel Dennett (1978, 1987), take an 'interpretational' approach to the ascription of beliefs.  Dennett holds that the attribution of beliefs answers to the 'intentional stance', as opposed to the 'design stance', and thus that such attributions are justified as soon as they help to make sense of behaviour, even if there is nothing corresponding to the attributed belief in the organisms' causal workings.  On this view, even very simple animals might be held to have general beliefs (ponds contain water, say) on the grounds that their behaviour can usefully be rationalised by this belief, even if there is nothing in them which explicitly represents this information.

Fortunately, we can by-pass this largely terminological issue about the meaning of 'belief'.  Suppose I concede to Dennett, for the sake of the argument, that even simple animals can have general 'beliefs' in his sense.  It doesn't at all follow that they do means-end reasoning in my sense.  For I did not define means-end reasoning in terms of general 'beliefs', but in design-level terms, as a matter of using general representations to guide behaviour.  Whatever you think about the meaning of 'belief', there remains a substantial issue, namely, about which animals actually do use general representations in this way.  In what follows I shall try to keep the waters clear by continuing to avoid the terminology of 'belief'.

Some readers may be wondering at this point why I am so sure, by my own lights, that simple animals don't use general representations to guide their behaviour.  Isn't there a sense in which just such general representations are embodied in these animals' dispositions to behaviour?  Take an animal who is disposed to drink from ponds when it is thirsty, precisely because in its individual or ancestral past this has proved an effective route to water.  In this kind of case, it seems eminently natural to say that this disposition, which certainly guides the animal's behaviour, represents the fact that drinking from ponds will yield water.  After all, it is precisely because this behaviour has produced this result in the past that the animal now has the disposition, and correspondingly it is precisely insofar as drinking will yield water in this case that the disposition will fulfil its biological function.  Shouldn't we thus think of the disposition itself as the present embodiment of the general information that drinking from ponds will yield water?

I do not especially want to contest such attributions of content.  I am happy to allow that this kind of disposition embodies information about the general response-to-outcome connection (C&D, RÑ>O) which was responsible for instilling the disposition in the first place.

So, given this concession, I need to tighten up the definition of means-end reasoning, if this is to define a kind of thinking unavailable to simple animals.  In my initial definition of means-end reasoning, I referred to 'explicit' representations of general information.  Maybe I can make something of this phrase, and argue that general information is only implicitly represented by dispositions to action, not explicitly.  If means-end reasoning specifically requires explicit representations of general information, and mere dispositions to behaviour fail on this requirement, then there it will no longer follow that simple animals automatically qualify as means-end reasoners.

One way of developing this thought would be to hold that genuine explicit representation requires some kind of sentence-like vehicle, some articulated physical state to which we can ascribe a content.  Simple organisms' representations of particular facts, involving definite modifications of sensory processors, would be so explicit.  But their putative representation by mere behavioural dispositions would fail on this score, so this thought would go, because  such dispositions do not have the kind of physical tangibility required for fully explicit representation.

However, I don't think that there is much substance to this line of thought.  After all, dispositions to behaviour must have some kind of physical embodiment.  An animal who is disposed to drink from ponds must differ in some substantial causal way from an animal who hasn't yet learned to do this.  So why not let this real difference, whatever it is, serve as the vehicle of the content 'pond drinking yields water'?  Moreover this vehicle, whatever it is, will interact appropriately with representations of needs and conditions in generating behaviour.  The animal will only drink from the pond when this vehicle (which we are taking to represent 'pond drinking yields water') engages appropriately with the state which represents the need for water and the state which represents the presence of the pond, along the lines of the classical practical syllogism.  So, on all these counts, there seems no reason not to count the disposition as a perfectly explicit representer:  it must have some physical embodiment;  and moreover that embodiment will interact with other uncontroversial representers in a way appropriate to its putative content.

Still, perhaps there is another way in which such putative dispositional representers of general information fail to be sufficiently explicit.  In more familiar cases, general representations can be combined, one with another, to deliver new general representations.  We can take 'valleys contain ponds' and 'ponds contain water', to generate 'valleys contain water'.  But there is nothing in simple animals to allow anything like this.  Their behaviour dispositions may embody general information, but they have no system which processes these items of general information to yield new such general information.  At most, as outlined in the last paragraph, these dispositional representers will interact with items of particular information ('here's a pond') and drives ('water needed') to generate particular behaviours, as in the standard practical syllogism.

To make the point graphic, imagine a simple animal who has information along the lines of shaking those trees will yield fruit implicit in one behavioural disposition, and information like throwing missiles will repel bears implicit in another.  If it is a simple animal, it will have no way of putting these together so as to figure out that it would be a good idea to shake a tree when a bear is prowling nearby and no missiles are yet to hand.  Of course, this information may itself come to be embodied implicitly in some disposition, if natural selection or learning instils a specific disposition to shake trees and throw the resulting fruit when bears are nearby.  But the general point will still apply.  While the organism will have various bits of general information implicit in its various behavioural dispositions, it will have no system for combining them and using them to infer the worth of behaviour that is not already directed by its cognitive architecture.

This represents an extremely significant limitation.  It means that simple animals will never be led to perform behaviours except those which they, or their ancestors, have performed successfully in the past.  This is because the only items of information that can enter into their practical syllogisms, so to speak, are of the form 'In C&D, R leads to O', where R is some behaviour previously performed when C and D, and O the outcome whose past achievement has led to their present disposition to do R given C and D.  There is no possibility of their inferring that some R will lead to some O in some C and D from other items of general information, and then acting accordingly.  They are limited to acting on C&D, RÑ>O connections that they or their ancestors have directly experienced.

So let me now specify that means-end reasoning requires 'explicit' representation of general information in the specific sense that such information can be processed to deliver new items of general information.  Whether or not behavioural dispositions in simple animals carry 'explicit' general content in other senses, they do not satisfy  this requirement, since simple animals cannot combine their behavioural dispositions to generate new dispositions.  Because of this, simple animals cannot perform novel actions.  In effect, then, I have now defined means-end reasoning as the ability to perform novel actions.  (The notion of 'novelty' required here merits further discussion.  I shall return to this in section 4(i) below.)

Given this definition, we are now finally in a position to consider the importance of means-end reasoning for the evolution of higher cognition.  To focus this issue, let me propose the strong hypothesis that means-end reasoning in the sense now specified is a biological adaptation peculiar to human beings.  This paper certainly won't provide a knockdown defence of this hypothesis, and indeed as we proceed various modifications will prove to be necessary.  Still, it will provide a useful peg on which to hand the discussion.

In the rest of this paper, I shall consider two lines of objection to the claim means-end reasoning is a biological adaptation peculiar to humans.

First, there are those who think that means-end reasoning is too easy, and therefore widespread throughout the animal kingdom.  Against this I shall argue, in the next section, that non-human animals are sophisticated in many different ways, but that there is no compelling reason to think them capable of means-end reasoning in particular.

Second, there are those who think that means-end reasoning is too hard, and therefore no essential part of our evolutionary heritage.  On this view, means-end reasoning is simply a non-biological 'spandrel' which has spun off from other distinctive abilities that evolution has bequeathed to humans.  Against this I shall argue, in the final section, that standard explanations of this form do not work, and that in any case there are general reasons why no spandrel could fill the role played by human means-end reasoning.

4.  Non-Human Sophistication

I suspect that many readers who have lived through the cognitive revolution in psychology will have started to feel increasingly impatient during the analysis of the last two sections.  Don't we now know that most animals are far too sophisticated to be understood in terms of simple links between stimulating Cs, driving Ds and responsive Rs?  That was the way the original behaviourists and their various neo-behaviourist successors tried to understand animals.  But surely, so the thought would go, we now realise that animals are much more elaborate than that.  Animals are blessed with any number of refined cognitive devices which enable them to deal with their environments by figuring out sophisticated responses to their circumstances in real time.  As a result, animals have any number of behaviours in their repertoire which are manifestly inexplicable on behaviourist grounds, and clearly display levels of behavioural control unavailable to the simple creatures discussed in the last section.

The first point to make here is that there is nothing in my analysis so far to commit me to behaviourism about simple animals.  I may have schematised matters in terms of Cs, Ds and Rs, but this in itself scarcely convicts me of thinking in behaviourist terms.  Indeed a moment's thought will make it clear that there are a number of ways in which my simple animals transcend behaviourist limitations.

Behaviourism holds that all dispositions to actions (all C, DÑ>R links) are instilled in animals by general learning mechanisms operating on spaces of sensory inputs and behavioural outputs.  Nothing in my analysis of simple animals commits me to any such blanket view about the sources of input-output links.  True, I have allowed, at Level 4, for the possibility of learning by instrumental conditioning.  But this does not mean that simple animals cannot also have structured 'hard-wired' links between inputs and outputs which do not derive from learning.  After all, input-output links must be so 'hard-wired' in animals below Level 4;  and even at Level 4 there can be 'hard-wired' links alongside the learned ones.  Moreover, nothing I have said about simple animals implies that, when there is instrumental learning, at Level 4, it must derive from 'content-free' mechanisms which are equally ready to link any perceptual inputs with any behavioural outputs.  Instrumental learning may be highly constrained, with only a limited range of input-output paths being available for shaping by individual experience.

Similar points apply to the inputs and the outputs themselves.  Behaviourism assumes minimally structured spaces of sensory qualities as inputs and behavioural atoms as outputs.  But nothing commits me to this strongly empiricist picture.  I have, it is true, assumed some distinction between perceptual inputs and behavioural outputs in section 2 above (and I shall comment on this distinction further in section 4(i) below).  But beyond this I have said nothing to imply that inputs and outputs must be simple or unstructured.  So I have left it open that simple animals may have highly structured hard-wired input devices (perceptual 'modules', if you like), and highly structured hard-wired output devices (behavioural 'modules')._

So, to repeat, nothing in my analysis commits me to behaviourism about simple animals.  This clarification, however, is now likely to prompt a contrary reaction from committed cognitivists:  'All right, I see that when you say non-human animals are "simple", you are not saying that they are behaviourist dummies.  You allow that their input and output modules, and the links between them, may be highly structured.  But, if you allow this, why stop there?  Why not credit animals with means-end reasoning as well?  If you allow they have the mental wherewithal for sophisticated analysis of sensory stimuli, and for sophisticated control of behaviour, then why doubt that they can also figure out the best means to their ends?  After all, it is clear that many specialised perceptual and behavioural systems in animals, like vision and navigation, involve complex computational processing of representations.  Since this is clearly just the kind of processing required for means-end reasoning, wouldn't it be very surprising, to say the least, if evolution had never pressed such processing into the service of animal means-end reasoning?   Means-end reasoning is manifestly a very powerful adaptive tool, once you have it.  Given that the materials for such a means-end reasoner were readily to hand, so to speak, we should surely expect that evolution would have made use of them for just this purpose.'

Maybe recent connectionist ideas will make this line of thought seem less persuasive than it would have been a decade ago.  The introduction of connectionist thinking into cognitive science has cast doubt on the presupposition that all perceptual or behavioural sophistication must rest on computational processing of explicit representations.  From the connectionist perspective, the raw materials with which evolution builds brains are neural nets, not compositional computers.  So perhaps means-end reasoners are not so easy to make, evolutionarily speaking, after all.  For neural nets do not seem as immediately well-suited to the building of means-end reasoners as classical computations._

Still, I shall not press this point.  Questions of how means-end reasoners might or might not be built will be touched on again at the end of this paper.  Let me at this stage mount a rather different challenge to the claim that non-human animals can perform means-and reasoning, simply by querying whether there are any in fact any facets of non-human behaviour that cannot be explained by abilities at Levels 1-4.

Note in this connection that simple animals, I am thinking of them, will certainly be able to display a wide range of sophisticated behaviours, despite their lack of means-end reasoning.  Nothing stops such creatures from being sensitive to the most intricate features of their environment and performing extremely complex routines under the guidance of this information.  Their cognitive mechanisms can be highly structured, both within their input and output modules, and in the links between them.  All they lack is a system whose purpose is to put together items of general information in order to draw out further general conclusions.

It is perhaps worth emphasizing that input and output systems, as I am thinking of them, can be very complex indeed.  One point I have not yet made explicit is that learning can take place within peripheral systems, as well as between them.  Thus there could be perceptual systems which have acquired the ability, during individual development, to recognize different kind of animals or plants, or cars or aeroplanes, or individual faces or gaits, or musical compositions, or lack of pawn cohesion in the centre.  Again, there could be behavioural systems which have acquired the ability to execute a forehand, or multiply by ten, or make a sandwich, or drive to work, or be apologetic.

Other complexities are possible.  Thus, on the input side, some perceptual systems could receive information from others;  while others could lay down their findings in memory stores (allowing information about particular circumstances to guide actions over a temporal distance, as it were).  And, on the output side, the execution of behavioural routines could be guided by real-time informational resources deriving from special informational channels;  moreover, there could also be nesting of behavioural routines, with more complicated modules being built up from simpler ones._

At this stage a new suspicion about my line of argument is likely to arise.  If I am packing all this into 'simple animals', isn't there a danger that simple animals are already performing means-end reasoning in the sense I have defined it?  They may not have a definite 'theorem-prover' in their heads, a specific device that takes in sentence-like premises and derives conclusions via some mechanical realization of the predicate calculus.  But, given the amount of cognitive structure I am allowing them, won't they satisfy my requirements for means-end reasoning in any case, even without such a device?

In the rest of this section I shall consider five different ways in which this challenge might be developed.  I should admit at this stage that some of these will lead to significant modifications to my strong hypothesis of non-human simplicity.  However, even if the position I am left with is not exactly my strong hypothesis as originally advertised, it will still amount to a substantial claim about the distinctive abilities that humans use to select actions.

(i)  Modular Combinations

The first thought I want to consider is that the combined operation of different elements in simple cognitive systems can itself amount to means-end reasoning as I have defined it.  In particular, this arguably happens when input systems (or output systems) combine with input-output links to generate behaviour.

Recall how in section 3 above I allowed that dispositions to behaviour can properly be viewed as embodying general information.  If an animal is disposed to drink from ponds when thirsty, because in its individual or ancestral past so drinking led to water, then I agreed that this disposition can properly be viewed as representing the general claim that drinking from ponds will yield water. 

However, note now how a similar point could be made about perceptual dispositions.  For example, if an animal is disposed to judge that a pond is present when it receives retinal stimulation X, because in its individual or ancestral past it formed this judgement when so stimulated, then shouldn't I equally allow that this perceptual disposition represents the general claim that stimulation X indicates ponds?

I am indeed happy to allow this.  However, it now seems to follow that an animal will be performing means-end reasoning, in my sense, whenever two such dispositions combine to constitute a third, derived, dispositionÑto drink when thirsty on receipt of stimulation X.  For at the representational level this derivation amounts to combining the general claims that 'stimulation X indicates ponds' and 'drinking from ponds will yield water' to generate the further general claim that 'drinking after stimulation X will yield water'. 

Note in particular that an animal could well have this derived disposition even if stimulation X plus drinking had never been followed by water in its individual or ancestral past.  Provided stimulation X had previously been followed by pond-judgements, and (different) pond-judgements plus drinking had previously been followed by water, past experience would give the animal the premises, so to speakÑwhich it could then put together to derive the experientially novel conclusion, that stimulation X plus drinking will be followed by water.

A similar point arises in connection with behavioural dispositions.  For example, such a disposition could be held to embody the information 'in circumstances Y specific movements Z will constitute drinking from a pond', which together with 'drinking from ponds will yield water' will generate the possibly experientially novel conclusion  'in circumstances Y specific movements Z will yield water'.

My response to this line of thought is that while these derived dispositions are experientially 'novel' in one sense, there remains another important kind of 'novelty' which is possible for humans, but not simple animals.  For note that the examples just given do not involve behaviour which is novel from the perspective of the animal's perceptual and behavioural classifications.  After all, we are assuming an animal whose perceptual system classifies different stimuli together as 'ponds' when sending information to other cognitive systems.  So at this level, the level of the animal's perceptual classifications, there is nothing new in the fact that another pond yields water, given previous experience of ponds yielding water.  Similarly, on the output side, an action needn't count as 'novel' just because it involves some uniquely discriminable sequence of movements, if these are simply one instance of a behavioural type which has previously been triggered as such by behavioural control mechanisms.

So let me now specify that the kind of novelty required for genuine means-end reasoning is novelty relative to the structure of the animal's perceptual and behavioural systems.  Genuine novelty requires some new pairing within the animal's own perceptual and behavioural typology.

Some readers may fell that this is resting a lot on the animal's typology.  This raises large issues, but in the present context let me simply observe that we have been committed to the reality of such typologies from an early stage.  As soon as we reached Level 2 animals, with specialized informational systems, we were supposing a repertoire of perceptual judgements ('here's a pond', say) which maintain their identity across interactions with different drives and behaviours.  And by Level 4 learners, if not before, we were supposing a repertoire of responses ('approach', 'retreat', 'drink', 'eat', say) which maintain their identity across prompting by different drives and perceptions.  Given that these classifications were introduced earlier, and moreover that I then sketched their motivations, there is nothing particularly ad hoc in now using them in characterizing behavioural 'novelty'.

(ii)  Getting into Position

Ruth Millikan (forthcoming), following Randy Gallistel (1980), has pointed out how responses to conditions may be adaptive, not because they lead directly to advantageous outcomes, but because they are likely to put the animal in the way of some further condition which will then prompt a response which will lead to the advantageous outcome (or some further condition which will then prompt a response which is likely to put the animal in the way of some further condition . . .  which will then prompt a response which will lead to the advantageous outcome).  So a bird, once it feels hungry, will be prompted to fly, and then, if it sees a fruit tree, will be prompted to approach it, and then, if it sees a fruit, will be prompted to peck it.

At first sight this might look like means-end reasoning as I have characterized it.  When the bird takes flight, the virtue of this action lies in the fact that it is a means to seeing and approaching a fruit tree, which is a means to finding and eating a fruit, which is a means to food.

However, this is not a real instance of means-end reasoning in my sense.  This is because such general facts as that flight is a means to finding fruit trees need not be represented anywhere in this bird, even in the generous sense in which I have allowed that such information can be represented in behavioural dispositions.

To see this, note that, for all that has been said so far, the outcome which has instilled the disposition to fly when hungry may be nothing other than that so flying has led to food in the past_.  If this is so, then the disposition shouldn't be counted as representing anything except that flying when hungry leads to food.  In particular, it won't represent that flying leads to tree-finding which in turn leads to fruit-finding.  True, it is only in virtue of these further facts about these means that flying does lead to food.  But if the aim of flying, as fixed by phylogenetic or ontogenetic history, has been food, rather than these intermediary means, then there will be no rationale for taking the disposition to represent anything about these means.  (That would be like taking my belief that aspirin cures headaches to represent facts about brain chemistry about which I am quite ignorant.)

It might seem arbitrary to think of the flying as aiming directly at food, rather than at the intermediary means.  But note that animals get hungry when food is needed, and that the specific function of this drive is correspondingly to prompt behaviour which will lead to food.  Given this, the phylogenetic or ontogenetic selection of flying when hungry will hinge crucially on whether flying leads to food, and not on whether it leads to other outcomes.  (After all, if flying led to fruit trees, but not food, it wouldn't be selected;  but if it led to food, but not via fruit trees, it would be selected.)

There may seem to be a problem in supposing that a behaviour (like flying) can be selected by a pay-off (food) from which it is removed by a long series of intermediate means.  But note how such behaviours can evolve by accretion.  Once hungry birds are disposed to eat when they see fruit, this will create selection pressure for hungry birds to approach when they see fruit trees.  And once they are disposed to do this, then this will create selection pressure to fly when they are hungry.  Perhaps this kind of process will work better in intergenerational genetic selection than in ontogenetic learning.  For an ontogenetic learning mechanism will need somehow to connect the behaviour with the distal pay-off, and if the time-lag is too great this may prove difficult.  From the point of view of genetic selection, by contrast, this is not a problem:  the behaviour will be selected as long as its connection with the pay-off reliably influences survival and reproduction, however long the time-lag.

(iii)  Intra-Modular Means-End Reasoning

So far I have been assuming that means-end reasoning, if it is to be found anywhere, will somehow mediate between perceptual inputs and behavioural outputs (or perhaps, as in (i) above, arise from the interaction between peripheral modules and input-output links) .  But what about the possibility that means-end reasoning might be found within peripheral modules, and in particular within behaviour-generating systems?

For example, consider the special navigation abilities, common to many animals, which enable them to find their way back home, or back to previously perceived food, even when these targets are hidden and distant.  It is possible that some animals do this by combining given items of general (causal, conditional) information to derive new general conclusions.  For example, they might start with the information 'If I go West from here, I'll get to A' and 'If I go North from A, I'll get to the food', and use this to infer 'If I go West until I get to A, and then North, I'll reach the food'.

There are two possible responses I could make here.  One the one hand, I could appeal to the line I took in (i) above in order to exclude such intra-modular inferences from the category of means end-reasoning.  Recall that I there specified that means-end reasoning should yield input-output links which are novel relative to the animal's typology of behaviour.  Given this, then figuring out novel ways of 'going back home' will not count as means-end reasoning, whenever 'going back home' functions as a primitive behavioural unit relative to the structure of the animal's motivational and learning systems.  (Thus different drives might in different circumstances each prompt the 'going back home' routine;  again, 'going back home' may be a response that can be encouraged or extinguished as such by instrumental learning.)  In support of this exclusionary line, note that intra-modular inferential mechanisms are likely to be highly content-specific, by comparison with means-end reasoning in mature human beings, which can work with pretty much any kind of information.  A navigation system, for example, will deal only with spatial information, and will only select spatial routes as means.

On the other hand, all this seems a rather cheap way of denying means-end reasoning to animals.  If animals piece together different bits of general information within the systems which govern their behavioural routines, and thereby figure out what to do, this will certainly be similar to the kind of behavioural flexibility I have been concerned with in this paper.  After all, such animals will be able to extrapolate beyond specific behaviours which have proved advantageous in the past, and will do so by combining separate items of general information into inferences.  Why downgrade this, just because it can be regarded as taking place within 'modules', rather than between them?  And, again, why does it matter that these inferences will only operate on a specific subject matter?  It isn't as if we could seriously have expected non-human animals to perform inferences across the same wide range of subjects as humans.

I do not propose to pursue this essentially classificatory issue any further.  In the remainder on this paper I shall continue to focus on means-end reasoning which mediates between input and output systems in general, and which correspondingly has no inbuilt limitations on the kinds of content it deals with.  But in doing so I do not want to suggest that it is unimportant that similar inferential powers may be located within behavioural modules.  Even if my focus in this paper lies elsewhere, this is clearly a significant question about animal cognition in its own right.

Before leaving this issue, let me comment briefly on the substantial issue of whether animal spatial navigation does in fact involve content-specific intra-modular means-end reasoning.  This is not entirely straightforward.  It is uncontroversial that many animals, including birds and insects, form non-egocentric spatial maps of their environments and behavioural targets, and that they can place and orientate themselves with respect to such maps using landmarks.  However, this doesn't necessarily add up to means-end reasoning as I have been conceiving it.  It depends on how they use the maps to generate behaviour.

Perhaps their brains simply execute the analogue of drawing a straight line from their present positions to their targets (perhaps iterating this strategy as they approach their targets, and in particular after any detours to circumvent obstacles).  This wouldn't amount to anything like means-end reasoning as I am conceiving it.

It would be different, however, with animals who cognitively performed some analogue of tracing out a continuous obstacle-avoiding path from their starting positions to their targets, and then set themselves to follow these paths.  This would seem clearly to qualify as intra-modular means-end reasoning in my sense.  For it would be equivalent to combining separate items of causal information, in the way sketched aboveÑ'If I go West from here, I'll reach A, and then, if I go North from there, I'll reach the food'. 

(iv)  Classical Conditioning

So far the only kind of learning I have considered has been instrumental learning, in which the disposition to perform some response R given some C and D gets reinforced because so responding has led to some reinforcing outcome in the past.  However, there is also another kind of learning, classical or Pavlovian learning, where an animal learns to associate one stimulus B with another stimulus C after observing their past association, and shows this by now responding to B as it previously responded to C.  (Note that classical conditioning involves no feedback from previous 'rewards';  all it requires is that perceptual 'nodes' which were previously co-activated now come to activate each other.)

This now gives us another candidate for means-end reasoning.  Take an animal who initially responds to C (and D) with R, and then after classical conditioning of B with C comes to respond similarly to B.  I have previously agreed that the initial disposition here can properly be understood as representing the information 'R in C (and D) will yield O' (where O is the relevant advantageous result).  Given this, it is natural to regard the classical conditioning as yielding the extra information that 'All Bs are Cs', which is then combined with the prior information to generate 'R in B (and D) will yield O'.

Indeed there is nothing to stop such inferences being iterated.  An animal could learn that 'All Bs are Cs' and thenÑseparatelyÑthat 'All As are Bs', and consequently become disposed to behave on perceiving A in ways it originally behaved only on perceiving C.  It is in an empirical question how far such inferential chains might stretch in any actual animals.  But one would expect any mechanism underpinning classical conditioning to allow some such iteration.

These kinds of cases cannot be dismissed on the grounds that the extra general information lies within perceptual systems, as in (i) above.  For the new items of general information which get combined with the prior behavioural dispositions are now embodied in links between outputs of perceptual systems, not in structures within such systems.  (Note how this point will not be sensitive to any particular rationale for identifying 'perceptual modules'.  However we decide to discriminate 'modules', a general mechanism for associative learning will build links between them.)

I can see no reason not to allow that associative learning will in this way give rise to means-end reasoning as so far defined.  Note, however, that animals who can perform means-end reasoning only in this specific sense will still be cognitively extremely limited, by comparison with mature human reasoners.  This is because they can only draw inferences about the conditions which render actions appropriate, and not about the consequences actions may have.  They can use their general information to figure out that B provides just a good opportunity for R-ing in pursuit of O as C does.  But when it comes to figuring out what R-ing might be good for in the first place, they still lack any inferential powers.  They are stuck with information of the form 'R will yield O', where R is some item from their behavioural repertoire, and O the outcome which in the past has instilled the disposition to R.  In particular, they have no power to reason along such lines as:  'R will yield M' and 'M will yield O'Ñtherefore, 'R will yield O'.  

The point is that associative learning can allow animals to figure out that new conditions are suitable for old behavioural dispositions.  But it can't generate any new behavioural dispositions.  When it comes to information 'causally downstream' from behaviour, so to speak, the only means of representation available so far is embodiment in behavioural dispositions.  And the only mechanism for forming such dispositions is still phylogenetic or ontogenetic selection of some R which led to some O in the past.  As yet we have seen no way to acquire the information of the form 'R will lead to O', except via such direct selection.  

Both Gopnik, Glymour and Sobel (forthcoming) and Millikan (forthcoming) have compared this limitation to egocentricity in spatial mapping.  An egocentric spatial map locates objects solely in terms of their relation to the subject's own position and orientation.  Now, as I mentioned in the last section, many animals transcend this spatial egocentricity, and represent the spatial world in objective terms, in terms of maps in which they themselves are simply one item among many.  However, animals who are spatially objective in this way may still be causally egocentric.  In particular this will still be true of the Pavlovian animals we are currently considering.  Despite their classical associative powers, they will lack any conception of objective 'causal space', as containing many objectively interacting items, of which their actions are just one special case.  Instead all their causal information will necessarily be egocentric in form, with some piece of behaviour R at one end of the relation, and some reinforcing result O at the other.

So, at this stage, let me raise the stakes for full-fledged means-end reasoning once more, to include some degree of causal non-egocentricity.  Let me now require that it involve, not just any use of general information to infer new general conclusions, but specifically that it allow animals to infer new causal facts of the form 'R will lead to O' from other causal facts.  From now on, then, full-fledged means-end reasoning will use facts about objective causal relationships to figure out which behaviours will produce which requisite results.

(v)  Dickinson's Rats

Now that this issue is in focus, the obvious question is whether there is any direct evidence for non-egocentric awareness of causal relations in non-human animals.

As it happens, the few studies which have attacked this question head-on suggest that even apes and other primates are very limited in their ability to appreciate objective causal connections.  While apes can certainly learn to use tools in novel ways, they don't seem to represent causal connections in a way that can inform means-end reasoning.  There is no direct evidence that non-human primates ever take the knowledge that some intermediary cause M produces end result O, combine this with knowledge that behaviour R leads to M, and use the two items of information together 'to devise novel ways of producing the intermediary and thus the end result'.  (Tomasello and Call, 1997, p. 390.  See also the rest of their chs 3 and 12.)

At the same time, however, there is also some very detailed work in the animal learning tradition, especially from Anthony Dickinson and his associates, which at first sight suggests that rats can perform exactly the kind of inference I am now asking for (Heyes and Dickinson, 1990, Dickinson and Balleine, 1999).

Consider this experiment.  (This is just one of a range of related rat experiments, but it contains the essential points.)  Rats are trained while hungry but not thirsty, in an environment where they gain dry food pellets from pressing a lever, and a sucrose solution from pulling a chain.  Both the pellets and the sucrose solution satisfy hunger, but, as it happens, only the sucrose solution would satisfy thirst.  Now make some of these rats thirsty, and give them the choice of pressing the lever or pulling the chain.  With one important qualification to be entered shortly, the thirsty rats will straightaway display a preference for chain-pulling.

Since nothing during their training reinforced chain-pulling over lever-pressing, this experiment provides prima facie reason to suppose that the rats are explicitly storing the causal information that chain-pulling yields the sucrose solution, which they then combine with the fact that the solution quenches thirst, to derive the conclusion that chain-pulling will satisfy thirst.

It may seem as if there is a loophole in the argument.  Even if the rats weren't thirsty during training, wouldn't their thirst-drive, low as it was, have been yet further reduced by the liquid-from-chain-pulling, but not by the pellets-from-lever-pressing?  If the rats had a drive-specific learning mechanism (cf. the discussion of Level 4 above), then they may on this basis have acquired a preferential disposition to chain-pull when thirsty.  (The idea here would be that certain behavioursÑhere chain-pullingÑget attached to specific drivesÑthirstÑnot because they were generally rewarding in the past, but specifically because they quenched thirst in the past.)

However, this story is inconsistent with a further fact about the experiment.  This is the rider I mentioned a moment ago.  The trained rats won't chain-pull, even when thirsty, unless they are first given an opportunity to drink the sucrose solution when they are thirsty, and thereby discover that it quenches thirst.  On the story just suggested, this further experience ought not to be necessary, for on that story the result of the initial training should already be to chain-pull when thirsty.

So it seems undeniable that the rats are somehow deriving from their training the information that chain-pulling leads specifically to sucrose solution, even though the difference between sucrose solution and food pellets is as yet of no motivational significance to them.  Then, later on, the rats acquire the further information that sucrose solution quenches thirst.  And at that point the rats combine the two pieces of information, to infer that chain-pulling as opposed to lever-pressing is a means to satisfying thirst.  They thereby acquire a novel disposition to behaviour, a disposition that has never itself been reinforced for satisfying thirst.

So described, the rats would seem clearly to be capable of non-egocentric causal thinking, of the kind I am now requiring for full-fledged means-end reasoning.  But perhaps there is another way of viewing their cognitive achievements, a way that will distance them less from simple animals.  Suppose we regard their exposure to the sucrose solution when thirsty, not as giving them the factual information that sucrose solution quenches thirst, but rather as instilling in them a new 'acquired drive' for sucrose solution.

On this way of looking at things, the rats are not so different from simple animals after all.  We can view the preferential chain-pulling behaviour as the upshot of (a) a drive to get sucrose solution and (b) a behavioural disposition to chain-pull in pursuit of sucrose solution when this drive is active.  The rats will thus be prompted to act by just the same kind of practical syllogism as operates in other animals.  Provided the 'acquired sucrose solution drive' is activated whenever the rats are thirsty, this model predicts just the same behaviour as a means-end reasoning model.

Of course, we will still need to acknowledge that the rats differ in very important ways from any simple animals discussed so far.  Most obviously, we are now crediting them with an ability to acquire drives, where previously all drives were assumed to be innate.  This is in fact a huge extension of cognitive power.  Since there seems no reason why rats should not have the potential to acquire drives for any circumstance that they are capable of recognizing, we have here graduated from animals whose ends are limited to a few basic outcomes to animals that can embrace pretty much anything as an end.  If we want to draw a sharp line between 'drives' and 'desires', this seems as good a place as any.

In addition, and even more interestingly, we must also acknowledge that the rats are capable of acquiring a disposition to do R (chain-pull) in pursuit of O (sucrose solution), even though R's so leading to O has never previously satisfied any drives.  The line I am now pushing is that the information 'R leads to O' needn't be embodied anywhere except in a disposition to do R when a drive for O is activated.  But it is striking, to say the least, that such a disposition can be acquired even before any internal representation of O has yet acquired the status of a drive.

It may seem, especially given this last point, that I am trying to build a distinction out of thin air.  If I am admitting that somehow the O-productivity of R must be representable even when R isn't yet wired up to be triggered by a drive for O, then doesn't this already grant the rats the essential cognitive power at issue?  However, a crucial limitation may still lie in the fact that this causal knowledge needs to be embodied in a disposition-ready way, so to speak.  Before the rat has any drive directed at O, it is already able somehow to link behaviour R with an internal representation of O.  But this link may consist in nothing more than that the rat would do R were its representation of O to acquire 'drive-active' status.  If this is right, then the rats will still suffer from a form of causal egocentricity.  The only causal information available to them will still have an item of their own behaviour R at the front end.

If we do see the rats in this way, it will have the advantage of explaining why their powers of means-end reasoning are limited as they are._  In my terms, Dickinson's rats are certainly capable of novel actions.  By combining their stored knowledge that R will lead to O with some newly acquired drive for O, they can be led to actions they have not previously performed.  However, if their RÑ>O information must always be stored in a 'dormant disposition', in the way just suggested, then the generative power of their overall cognitive system will be limited by their ability to acquire new drives.  This is not the place to start considering details of the mechanisms by which animals acquire new drives (an issue which seems to me crucial, not just for understanding rats, but also for understanding humans).  But if, as seems plausible, these mechanisms must work by some kind of direct association with pre-existing drives, then the causally egocentric rats will be capable of far less behavioural novelty than reasoners who are also able to derive RÑ>O information from an unlimited range of non-egocentric causal links.

5.  Reasoning by Accident

Overall, the previous section was a response to the thought that means-end reasoning is too easy to be a peculiarly human adaptation, and so must be widespread through the animal kingdom.  I now want to address the converse thought, that means-end reasoning is too hard to be a biological adaptation, and so has only become available to humans as a side-effect of other biological developments.

On this view, means-end reasoning would be like arithmetic or music.  Proficiency at these practices may well have yielded a reproductive advantage in the period since they emerged, in the sense that adepts may have had more children on average.  But this doesn't make them evolutionary adaptations.  Other abilities, with independent evolutionary explanations, fully enable us to explain the emergence and preservation of arithmetic and music, once they get into our culture_.  And in any case there probably hasn't been enough time since these practices started for any genes favouring them to be selected.

On this model, then, means-end reasoning rests on other abilities with a biological purpose, but has no such purpose itself.  In the terminology made famous by Stephen Gay Gould and Richard Lewontin (1979), it would be a 'spandrel'.  The idea that means-end reasoning is a spandrel is perhaps found more often in conversation than in print.  But it is popular among a surprisingly wide range of theorists, from official 'evolutionary psychologists', through Dennettians, to neo-associationist experimentalists.

This general 'spandrel' suggestion can be combined with different hypotheses about the crucial prior ability.  One fashionable candidate is 'understanding of mind'.  Once our 'understanding of mind module' emerged, so this story goes, we would then have had the intellectual wherewithal for means-end reasoning, along with other cultural spin-offs like promising.

However, this particular suggestion seems to me to owe far more to intellectual fashion than serious analysis._  There is an obvious reason why means-end reasoning cannot have piggy-backed evolutionarily on understanding of mind.  This is that the standard accounts of understanding of mind only make sense if we suppose that 'mind-readers' are already capable of means-end thinking.  This applies to both the 'simulation-theory', which holds that understanding of mind rests largely on the ability to simulate the decision-making of others, and the 'theory-theory', which holds that understanding of mind derives from an articulated theory of mind.

The point is perhaps most obvious in connection with the 'simulation-theory'.  The central assumption of this theory is that 'mind-readers' proceed by simulating others' beliefs and desires and then mimicking their decision-making 'off-line'.  But such 'decision-making' is nothing but the means-end reasoning we are trying to understand.  Presumably an ability to perform such reasoning 'off-line' presupposes a prior ability to do it on-line.  So this version of the story simply assumes what we want to explain.

The 'theory-theory' version of understanding of mind fares little better.  On this version, 'mind-readers' anticipate others' reactions by drawing predictions from an articulated theory of mind.  That is, they attribute beliefs and desires to others, feed these in as initial conditions to a set of general assumptions about the mind, and on this basis figure out their best strategies for dealing with others' behaviour.  But this is then nothing but a special case of means-end reasoning as we have been considering it.  Once more, the story simply begs the question.

A rather more plausible version of the 'spandrel' approach would be that means-end reasoning piggy-backs, not on understanding of mind, but on language.  But here too there are difficulties.  To start with, this story too seems to be in danger of assuming what needs to be explained.  The worry here would be that the primary biological purpose of language is to increase each individual's stock of information.  But such extra information wouldn't be any use to creatures who can't yet do means-end reasoning, since they wouldn't be able to use it to draw any extra conclusions about appropriate behaviour.

Perhaps this is a bit quick.  Maybe language first evolved as a device for passing around pieces of particular information ('a tiger is coming', 'there are fruit in that tree', . . .).  Since even simple creatures are guided by particular information about their circumstances, the utility of this information doesn't yet call for any means-end reasoning.  So maybe means-end reasoning only emerged after our ancestors had first developed a relatively sophisticated language for reporting particular facts.  Building on this basis, perhaps language then biologically evolved to report and process general claims.

The problem now, however, is to explain this last bitÑwhat exactly was the extra biological pressure which led to language reporting and processing general information?  If the answer is that language evolved this feature to facilitate means-end reasoning, then this will mean that means-end reasoning is not a spandrel after all.  It may have been largely dependent on language, in the sense that its emergence had to wait on the biological selection of prior linguistic abilities.  But insofar as certain genes were then selected specifically because they helped us to do means-end reasoning, means-end reasoning will qualify as an adaptation in its own right, not a spandrel.

On the other hand, if the answer is that language evolved the ability to represent and process general information for some independent reason, then the puzzle will be to understand how this ability was then parlayed, spandrel-like, into means end-reasoning.  Suppose that our ancestors first became able to formulate general claims, and draw novel causal conclusions from them, for reasons quite independent of means-end reasoning.  How exactly would these novel theoretical conclusions then have come to make a difference to their practical activities?

The point here is that means-end reasoning must exert some control over behaviour.  However, pre-existing cognitive architectures, of the kind present in simple animals, have no place for anything to issue in behaviour except dispositions to behave in certain ways when triggered by perceptions and competing drives.  Somehow the ability to process general representations has to be able to be able to add to this set of dispositions (either temporarilyÑ'next time I see a post box I'll insert this letter', or permanentlyÑ'from now I'll eat fish instead of meat').  However, an ability to draw conclusions from general claims, even conclusions about means to ends, will not by itself ensure this.  In addition, the outputs of such reasoning will have to intrude on the control of behaviour.  Without being able to alter our behaviour-guiding programme in this way, means-end reasoning won't make any difference to what we do.

As it happens, this line of argument seems to me to present a difficulty, not just to the specific idea that means-end reasoning piggy-backs on language, but to any version of the view that it is a non-adaptational spandrel.  The problem is to understand how a new power to alter behaviour could arise without some basic biological alteration.  It scarcely makes sense to suppose that a pure spandrel could intervene in some unprecedented way in the biological systems that direct action.  Prior to means-end reasoning, behaviour is controlled by a set of dispositions that are laid down either by genes or by conditioning.  Somehow means-end reasoning, however it is realised, must involve the power to create new such dispositions.  It is hard to see how this could happen without some biological selection, some alteration of our biological design, which allowed the output of deliberative decisions to reset our dispositions to action.

Note that this is not yet an argument for supposing that this there is some quite separate mechanism in the human brain specifically devoted to means-reasoning.  There may well be such a mechanism, and I shall return to this possibility in my final remarks.  But the argument I have just given supports only the far weaker conclusion that there must have been some biological selection for means-end reasoning.  This could have been quite minimal, a matter of a few genetic alterations allowing some pre-existing cognitive activity to start exerting an influence on behaviour.  The most obviously possibility would be the one suggested above:  a linguistic ability to report and process general information evolved for some independent reason;  once that was in place, then a further evolutionary step allowed its outputs to influence behaviour.

In the Introduction to this paper, I said that, if I had to choose, I would place means-end reasoning on the side of the 'modules', rather than with general-purpose cognitive mechanisms.  I am now rather better placed to explain the thrust of this remark.  My intention, in classifying means-end reasoning as modular, was not to claim that there is some dedicated processor in the brain which was built solely to perform means-end reasoning.  As I have just said, this may be so, but it is not something I am insisting on.  Rather, my concern was only to emphasise that means-end reasoning is a cognitive mechanism which is activated in specific circumstances to serve specific needs.  It is not some meta-system which controls all human activity, constantly selecting whichever behavioural outputs are best suited to current perceptual inputs.

An implicit assumption of much philosophical thinking is that all human behaviour, apart perhaps from crude reflexes, is selected by just this kind of meta-systemic means-end thinking.  However, this is not the picture that emerges from this paper.  I see no reason to doubt that behaviour in humans is determined in the first instance in just the same way as it is in simple animals.  We have a set of standing dispositions, which get triggered by current perceptual information and competing drives.  The only difference is that we humans have an extra way, beyond genetic inheritance and conditioning, of adjusting those dispositions.  Sometimes we take time out, to consider the pros and cons of various options, and we figure out that the best way to get O , under conditions C and D, is by R-ing.  And then we reset our standing dispositions, so that we become disposed to do R next time C and D arise.

Looking at things this way, we can see that it would probably not be a good idea for means-end reasoning to act as a constant mediator between perceptual inputs and behavioural outputs.  Means-end reasoning takes time, and action cannot always wait on deliberation.  If we always stopped to check whether we were doing the best thing, the time for action would normally be gone.  So for the most part we simply allow our standing dispositions to guide us.  But sometimes, when the issues are weighty, and time does not press, we delay acting, and instead activate our powers of means-end reasoning.  (Think of this itself as a standing disposition, triggered by the issues being weighty and by time not being pressing.)  Then, after means-end reasoning has done its job, we alter our current dispositions, and once more allow them to guide us. _

Let me finish this paper with some brief further reflections on the evolutionary emergence of means-end reasoning.  So far I have maintained only that means-end reasoning must have played at least some role in biological evolution._  As I argued a moment ago, even if means-end reasoning was biologically posterior to language, there must still have been some selection for means-end reasoning as such, to account for its power to influence behaviour.  Still, as I observed, this is consistent with means-end reasoning being a small biological add-on, as it were, to a language faculty that had evolved for quite independent reasons.

However, it is also possible that means-end reasoning may have played a more significant role in evolution.  Even if we stick with the idea that language is the sole medium of means-end reasoning_, there is the possibility that means-end reasoning is the primary function of language, and that communication is the spandrel, which spun off after language had initially evolved to facilitate means-end reasoning.  More plausibly, means-end reasoning and communication might both be biological functions of language.  This idea is most naturally filled out in a co-evolutionary model:  once the first small biological step along the linguistic path had been taken, to facilitate communication, say, then this would have made possible a further step, which facilitated means-end reasoning, which would have made possible a further step, which facilitated communication, and so on.

To complicate the picture still further, note that different aspects of language might call for different evolutionary models.  Earlier I distinguished the language of particular facts from the language of general facts.  Perhaps the language of particular facts evolved entirely for communicative purposes, as suggested earlier, while the language of general facts evolved primarily to serve means-end reasoning.  Or perhaps the language of general facts evolved under the co-evolutionary pressure of means-end reasoning and communication.  Or so on.  It is not hard to think of further possibilities.

All these last suggestions assume, in one way or another, that means-end reasoning arrived only with language.  And this is indeed an attractive assumption.  For one thing,  the combinatorial structure of language lends itself naturally to the kinds of inferences which are central to means-end reasoning.  Moreover, this assumption explains immediately why means-end reasoning should be restricted to humans.

Still, it is also interesting to speculate on whether some forms of means-end reasoning might not initially have evolved independently of language.  One obvious hypothesis would be that an initial stage of means-end reasoning made use of visual imagination.  Our ancestors played out various scenarios in their 'mind's eye', and used this to choose between alternative courses of action.

This use of visual imagination is so familiar that it is often taken for granted in theoretical contexts.  But this familiarity is deceptive.  There are any number of theoretical puzzles here.  Is means-end thinking the primary function of visual imagination?  How does this kind of 'visual anticipation' relate to visual memory?  Is the use of visual imagination for means-end reasoning a generalization of domain-specific uses like spatial manipulation of objects, or, indeed, of spatial navigation as discussed earlier?  And, to return to a central theme of this section, how did the upshot of visual imaginings acquire the power to influence pre-existing structures of action control?

In response to this last point, here is another hypothesis.  Perhaps a crucial evolutionary step came when our ancestors acquired the ability to copy complex sequences of actions from others.  This would require them visually to 'parse' what their teachers were doing, and then translate this visual information into action.  Once this was possible, it may then have been a small evolutionary step to translating an imaginative visual representation of your own behaviour into action. 

These are enough speculations to be getting on with.  They already provide the agenda for a number of other papers.  I hope that this paper has at least shown that means-end reasoning is a topic worth pursuing further. 

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_  This complexity generates a corresponding uncertainty about about the precise representational contents of drives.  Even if we can distinguish drives as a kind from purely informational states, along the lines indicated when discussing Level 2, this does not yet decide which specific objects drives aim at.  Perhaps we should deem them to be directed at any specific behaviours they may prompt (feeding behaviour, say);  or perhaps their content is the specific results they are designed to achieve (ingesting of food);  or perhaps, again, they represent the possibly different effects which will reinforce associated behaviours (such as raising blood sugar).  I myself would argue in favour of the second answer, the specific result the drive is designed to achieve, given that this is the most basic function of any state which properly qualifies as a drive.  But the rationale for assigning specific contents is a messy business, not only for motivational states like drives, but also for informational ones, and in what follows I shall not aim explicitly to justify any such specific attributions.  For those readers who feel queasy about loose representational talk, I can only say again that I shall always aim to ground my attributions of representation in explicit specifications of cognitive design.

_  There are philosophical contexts in which it is important to distinguish between generalizations, generic causal claims, and particular conditional claims.  For present purposes, however, nothing will be lost if we lump these together under the heading of 'general information'.

_  When I talk of 'modules' in what follows, I intend no specific theoretical commitments.  I shall use the term merely as a stylistic variant for 'mechanism' or 'system'.  In my view, the technical notion of a 'module' is a theoretical mess.  Fodor (1983) originally specified a number of necessary criteria for modularity, but omitted to tell us what to call the many interesting types of cognitive mechanism that satisfy some of these criteria but not others.  Moreover, some of these criteria themselves turn out to involve a number of distinct and dissociable requirements:  'hard-wired', 'domain-specific' and 'informationally encapsulated' have proved particularly troublesome in this respect.

_  Not that we should suppose it would be that easy to build a means-end reasoner, even if we could make use of classical computations.  The 'frame problem' presents a substantial barrier to any such construction.

_  Note how the possibility of all these sophistications in simple animals undercuts the thought that there must have been strong selection pressure for means-end reasoning from an early stage in evolutionary history.  If simple animals could already deal intelligently with every circumstance their environments threw at them, then why should they have undergone the selectional disadvantage of building an expensive theorem-prover?
 
_  It would be different if the bird were able to acquire desires for some of the relevant means, such as for finding fruit trees, since then a proximal behaviour like flying could be reinforced by achieving this desire, and not only by leading to food.  I shall not be able to deal with the acquisition of desires fully in this paper, however, though I shall make some remarks on the subject in section 4(v) below.

_  Moroever, it will also allow us to explain the above-mentioned limitations of apes and other primates.  Since primates will presumably share any cognitive sophistications present in rats, we will be hard pressed to explain the poor primate performance, if we allow full-fledged means-end reasoning to rats.

_  Which is not deny that these explanations themselves can be informed by biological facts.  Which practices are preserved by 'culture' depends crucially on the which dispositions have been bequeathed to us by natural selection.  Cf. Sperber, 1996.

_  However, see Sperber, 1997, for some interesting specific suggestions about the connection between understanding of mind and logic in general.

_  Note how this model, in which means-end reasoning 'resets' our dispositions to action, can easily accommodate plans, that is complicated sequences of actions needed to achieve some end.  This would only require that the means-end system be able to produce multiple action settings, settings which will trigger a sequence of behaviours as a sequence of cues are encountered (some of which might simply be the completion of previous behaviours).

_  At the same time, I take it to be uncontenious that, once means-end reasoning had emerged biologically, it then came to play a major role in the non-biological development of human civilization.

_  I should perhaps make it clear that by 'language' I mean to include mental processing of internalised sentences of public languages, as well the overt expression of those sentences.