Natural and Artificial Cognition
On the Proper Place of Reason

Willem A. Labuschagne
Department of Computer Science
University of Otago
PO Box 56
Dunedin New Zealand
E-mail: willem@cs.otago.ac.nz

Johannes Heidema
Department of Mathematical Sciences
University of South Africa
PO Box 392
0003 UNISA
South Africa
E-mail: heidej@unisa.ac.za

Natural and Artificial Cognition: On the Proper Place of Reason

Abstract
We explore the psychological foundations of Logic and Artificial Intelligence, touching on representation, categorisation, heuristics, consciousness, and emotion. Specifically, we challenge Dennett’s view of the brain as a syntactic engine that is limited to processing symbols according to their structural properties. We show that cognitive psychology and neurobiology support a dual-process model in which one form of cognition is essentially semantical and differs in important ways from the operation of a syntactic engine. The dual-process model illuminates two important events in Logic and Artificial Intelligence, namely the emergence of non-monotonicity and of embodiment, events that changed the traditional paradigms of ‘Logic = the study of deductive inference’ and ‘Symbolic AI’.

1.	Introduction
Daniel Dennett articulates a widely-shared view when he asserts:
	…brains are just very complicated physical organs; whatever they react to must be some physical change or difference in the stimuli they encounter. In short, as physical mechanisms they can only be syntatic [sic] engines, responding only to structural or formal properties. According to the traditional distinction in linguistics, a sentence’s form or syntax is one thing and its meaning or semantics is another. Now how does the brain manage to get semantics from syntax? It couldn’t. (Dennett 1984 p28)
	Few philosophers of mind have been as influential in recent times as Dennett. Nevertheless, our purpose is to demolish his claim. We shall accomplish this in two steps. First, we show that a key inference in his argument does not follow. Second, we provide an alternative view, based on the convergence of evidence from several subdisciplines of cognitive science.
	Dennett’s argument has a weak link. We may accept his premiss, namely that brains, being physical organs, react only to physical changes in stimuli. But this does not oblige us to accept the conclusion, namely that brains can respond only to the form of a sentence. The error lies in Dennett’s rejection of non-symbolic mental representations. If it be granted that the brain processes representations other than symbols (as well as symbols, of course), then it can no longer be maintained that a response to a structural property of such a representation is a response to the form of a sentence. The remainder of this paper is devoted to the development of a model of cognition in which non-symbolic representations play a vital role. As a result, a new perspective will be gained on recent trends in Artificial Intelligence.

2.   Artificial intelligence and deduction
John McCarthy invented the term ‘Artificial Intelligence’ (AI) at the Dartmouth conference in 1956. The research field denoted by the term was understood in a manner informed by a vision of human cognition very similar to Dennett’s. The Strong AI Hypothesis is the conjecture that artificial agents can be designed so as to function in a manner that is equivalent, in some yet to be adequately defined sense, to human cognition, and the hypothesis has been hotly debated. There has been less debate about the supposed features of human cognition, which was assumed  to proceed by deductive inference.
	Admittedly, humans also make use of a more informal process, a kind of holistic pattern-matching often called analogy. However, the use of analogy has proved to be difficult to explicate, and difficult to simulate by computer programs. Creditable attempts at explication have been made (Hofstadter et al. 1995; Holyoak and Thagard 1995), but it is fair to say that very little in the way of general agreement has been reached in the AI community (Hofstadter 1995; Forbus et al. 1998).
Deduction is a more attractive target for explication than analogy is. Deductive reasoning is a step-by-step process of moving from sentence to sentence as sanctioned by inference rules. For example, a human agent whose database of beliefs contains both the sentence “John is snoring” and the sentence “If John is snoring then John is asleep” would be justified in adding to the database the belief “John is asleep”, because the rule of inference modus ponens legitimises the detachment of a consequent  provided the conditional sentence If  then  and the antecedent  are both available as premisses. Logic was seen for most of the 19th and 20th centuries as the study of rules such as modus ponens, and researchers who focused on deduction could draw on this logical heritage. In one of the high points of the 1956 Dartmouth conference, Simon and Newell took the first step towards lasting fame when they presented a program called Logic Theorist, which “was able to prove theorems in Whitehead and Russell’s Principia Mathematica, a feat of intelligence by anybody’s standards … They alone had managed to do what everyone at Dartmouth had faith was possible but had been unable to accomplish: they had made a machine that could think.” (McCorduck 1979 p104)
	The favourable judgment accorded the Logic Theorist reflects a belief that while analogy is all very well as a source of ideas, creativity, and imagination, nevertheless rational decision-making in humans relies on deductive inference. Since deductive inference involves the syntactic manipulation of symbolic representations according to rules that operate on the basis of the shapes of symbols rather than what the symbols may stand for, programs which simulate human reasoning by rule-based symbol-manipulation seem obviously on the right track. Let us refer to the assumption that rational decision-making does or should rest primarily on deductive inference by the phrase cognition as conscious deduction. This assumption has a venerable history. During the seventeenth century Leibniz had advocated the development of an all-encompassing deductive theory consisting of a characteristica universalis or universal scientific language, a calculus ratiocinator or comprehensive set of rules whereby the symbolic expressions of the language could be transformed, and an ars combinatoria, a comprehensive set of definition-templates for the formation of new concepts. Disputes would then be settled by men of good will taking up their pens and saying “Calculemus — Let us calculate” (Heidema 1979). The best of human cognition was far removed, in this vision, from the blood and guts of emotional human beings, as one might expect in a philosophical landscape that had been shaped largely by Descartes’ separation of mind (res cogitans) from body (res extensa).
	Cognition as conscious deduction gives priority to a linguistic process by which sentences are transformed according to inference rules. The application of the rules is consciously trackable, controllable by acts of will, and effortful — it is a characteristic of deduction that it demands attentional resources lest it deviate from the logical ideal, leading to the commission of formal or informal fallacies, or the violation of the laws of probability (Kahneman, Slovic, and Tversky 1982).
	In partial harmony with the view of human cognition as conscious deduction is the approach called Symbolic AI and exemplified by Logic Theorist. A respected paradigm, Symbolic AI is founded on the assumption that intelligent behaviour can be achieved by agents who are provided (only) with declarative information (that is, sentences of some knowledge representation language) as well as an inference engine (in other words, an algorithm for applying rules of inference to the sentences). An alternative name for Symbolic AI is the physical symbol system hypothesis, which refers to the classic exposition of this paradigm by Newell and Simon (1976). A modern exposition is given in the textbook by Genesereth and Nilsson (1987). Symbolic AI has influenced the way in which researchers conceive of the generalisation from single agents to multi-agent systems (Konolige 1986). Symbolic AI simplifies cognition as conscious deduction by dropping the requirement of consciousness, and advocates of Symbolic AI have tried to explain consciousness away (Dennett 1991).
	Symbolic AI has not been free of criticism. Experimentally, it has become clear that deduction without consciousness suffers from the lack of the notion of relevance that accompanies ‘attention paid to’, so that automated reasoning systems exhibit a resultant, and often crippling, inefficiency. The more successful automated reasoners, such as OTTER, cope with the generation of irrelevant inferences by inviting human agents to act as partners and to guide the inferential process by such means as imposing different weights on different symbols, leading to different priorities in the selection of sentences to be transformed (Wos and Pieper 1999). This restores consciousness, via the human agent, to the deductive process, inviting the question whether a useful notion of relevance can be gained without such a full return to consciousness and its associated mechanism of attention.
In a more theoretical vein, the Chinese Room thought experiment (Searle 1990) examines a scenario in which a man who knows no Chinese is enclosed in a room having rules written on the walls — in English, the man’s supposed native language — for transforming Chinese symbols. If the man were, with the aid of these rules, to respond to messages in Chinese by sending out Chinese symbols that made perfect sense to a Chinese-speaking recipient, would we consider the processing of the symbols to be intelligent behaviour? For Searle and many others it seems clear that the man’s uncomprehending execution of the transformations legitimised by the rules would not constitute intelligent behaviour but, at best, an artful simulation thereof. Another theoretical criticism (Penrose 1989, 1995, 1997) applies Gödel’s incompleteness theorem to demonstrate that human understanding cannot fully be captured by deduction. Gödel’s theorem may be understood as exhibiting a number-theoretic sentence which is demonstrably true of the natural numbers but cannot be deduced by the standard rules of inference from the standard sort of axioms. Both Searle’s Chinese Room and Penrose’s application of Gödel’s incompleteness theorem are aimed at demolishing Symbolic AI, and while they may not have accomplished this aim conclusively both have generated a great deal of discussion (Searle and commentators, 1980, 1992; Preston and Bishop 2002; LaForte et al. 1998).
	The experience gained from automated reasoners like OTTER, thought experiments like the Chinese Room scenario, and Gödel’s theorem can all be seen as non-psychological evidence suggesting that there is more to cognition than can be captured by mere inference rules. In the sections to follow we examine research in psychology that bears upon the assumption that cognition is conscious deduction. First we review evidence that the decision-making which governs our lives cannot all be consciously guided.  Then we discuss, from a neuropsychological perspective, the role of non-symbolic representations. Finally a description is given of the mechanism by which both relevance and analogy enter into cognition, the action of which mechanism we call intuition. Having completed the psychological exploration, we muse on some of the implications for Logic and AI.

3. The limits of consciousness
Recent research in cognitive psychology bears out Whitehead’s dictum (Whitehead 1911):
It is a profoundly erroneous truism, repeated by all copy-books and by eminent people making speeches, that we should cultivate the habit of thinking what we are doing. The precise opposite is the case. Civilisation advances by extending the number of operations which we can perform without thinking about them. Operations of thought are like cavalry charges in battle — they are strictly limited in number, they require fresh horses, and must only be made at decisive moments.
The experimental study of ‘ego depletion’ has demonstrated that humans have strictly limited conscious self-regulatory capacities (Muraven, Tice, and Baumeister 1998; Baumeister et al. 1998). An act of will in one domain, such as not eating any of the chocolate chip cookies sitting in front of the subject, reduces the subject’s ability to engage in self-control in a subsequent unrelated task, such as persistence in a verbal task. These experiments falsify the hypothesis that the conscious self guides most of the decision-making in our daily lives. The alternative to conscious control is automaticity: non-conscious mental processes operating effortlessly and without demanding attentional resources. The most familiar forms of automaticity are those associated with perception and with learning.
Research on perception and priming has shown that the early stages of perceptual analysis (encoding of environmental events) are preconscious (there is no awareness that the process is taking place) and without intention or effort. Stimulus information perceived without awareness leads to further automatic reactions. For example, when a visual stimulus is perceived without awareness it influences which stimuli are subsequently perceived with awareness and how subsequent visual stimuli are consciously experienced (Merikle, Smilek and Eastwood 2001). Subjects consider the line segment whose angled lines go away to be longer than the (equally long) line segment whose angled lines point inward (see figure 1), even if the angular lines are made so faint that they fall below the participants’ threshold for reporting awareness.

	Figure 1

	More familiar is the automaticity associated with learning (skill acquisition). Over time (and with practice) intentional, goal-directed processes (such as playing a topspin backhand in tennis or changing the gears of a car) become more efficient until they can operate without conscious guidance. While these processes become effortless and cease to place demands on attentional resources, they may require an act of will as trigger, in order to start operation. Childhood is a period dedicated to automating such skills as brushing teeth, crossing the road safely, reading, and interacting socially.
	Recently it has become evident that automaticity encompasses much more than low-level perception and skill acquisition (Bargh and Chartrand 1999). Information-processing goals, such as the goal to remember information and the goal to form an impression of someone, can be activated nonconsciously and then guide subsequent cognition. For example, subjects primed unobtrusively by a ‘language test’ containing synonyms of evaluation, and who were then presented with a series of descriptions of behaviours, later recalled more behaviours and showed greater organisation of the material in memory around trait categories relevant to the target’s behaviour (e.g. sociable, intelligent) than those subjects who were not primed in this manner. Behavioural goals can be activated similarly. Subjects primed by a ‘word search task’ in which synonyms of achievement (e.g. strive, succeed) were presented, subsequently outperformed control participants on verbal tasks, although extensive questioning of the participants revealed no awareness of a possible effect of the priming task on their later performance.
	If conscious deduction cannot assume responsibility for everyday decision-making, what can? The form of cognition called intuition by Freud and Jung has regained respectability since Zajonc demonstrated that preferences (affectively valenced evaluations) are formed ubiquitously and rapidly, before any conscious processing has taken place and without evidence of symbol-manipulation (Zajonc 1980). The sections to follow describe how information is gleaned from the environment by a human agent, the status of symbolic representations of information, and what intuition is. It will become apparent that since intuition exploits categorisation, it gets relevance and analogy for free.

4. Representations and symbol-grounding
Whatever knowledge we may have of the world is rooted in the changing states of our own bodies. As John Pollock puts it in his refutation of externalism: “Cognition can make use of any states to which it has direct access, but those are just the internal states” (Pollock and Cruz 1999 pp130-142). The beginnings of information about the environment are patterns of neural excitation in the special sensory probes — the retina at the back of the eye, the cochlea in the inner ear, the semicircular canals of the vestibule (also in the inner ear), the olfactory nerve endings in the nasal mucosae, the gustatory papillae in the back of the tongue, and the nerve terminals in the superficial layers of the skin. When an agent sees his Aunt Maud, the pattern of excitation in the retina bears a topological resemblance to the stimulus object (Aunt Maud). For example, the parts of the retinal image that correspond to Aunt Maud’s arms will be on either side of the part that corresponds to her torso, rather than being randomly distributed about the field of vision. Thus the earliest impression formed in a sensory probe is what C.S. Peirce called an iconic representation, a representation that resembles the stimulus object in much the way that a painting of a landscape resembles the landscape — the representation is reduced and simplified, detail has been omitted, but parts that are close together in the stimulus object are close together in the representation.
	The nerve terminals in the special sensory probes send these patterns to circumscribed entry points in the brain — the early sensory cortices — where the iconic representations form topographically organised patterns of activity (Damasio 1994 p91, Damasio 2003 p197). Iconic representations form the raw material for two further processes. On the one hand, iconic representations are transformed into categorical representations, with which symbolic representations are linked. On the other hand certain changes are made on the basis of the iconic representations to a set of body maps, with consequences described in the next section.
	There are two main explanations for the way in which iconic representations are transformed to categorical representations, and neither has yet been fully elucidated at the neural level. The conceptual spaces approach (Gärdenfors 2000) begins with quality dimensions such as temperature, weight, brightness, and pitch, corresponding to different ways in which an agent can judge stimuli to be similar or different. These dimensions form clusters in the sense that, for example, a stimulus object cannot have a hue without also having a brightness, nor can a sound have a pitch without at the same time having a loudness. Such clusters are called domains. A conceptual space is formed of one or more domains. A  property such as “is red” corresponds to a convex region in a conceptual space. The transformation of iconic representations into categorical representations results from the partitioning of the conceptual space into convex regions by means of a Voronoi tessellation, which has a discretising effect and is compatible with Rosch’s prototype theory of categorisation (Rosch 1978). Roughly speaking, Voronoi tessellations are produced as follows. Each prototypical point in space determines a convex region by annexing for its region all points that lie closer to it than to any other prototype. Finally, the discretised categorical representation is linked with a symbolic representation: “a symbol just summarizes the information contained in a region of a domain of a conceptual space by refering [sic] to the prototypical element of the region” (Gärdenfors 2000 p258).
	The alternative account of the iconic-to-categorical process relies on accomplishing an analog-to-discrete transformation in a manner that produces the phenomenon called categorical perception (Harnad 1987). Again the starting point is an iconic representation, described as “being an analog of the sensory input (more specifically, of the proximal projection of the distal stimulus object on the device’s transducer surfaces)”, and these analogs “faithfully preserve the iconic character of the input for such purposes as same-different judgments, stimulus-matching, and copying” (Harnad 1987 pp551/2). Some form of filtering that picks out invariant features now produces a categorical representation. The filtering mechanism may be any of several candidates that have been proposed, and a constraint is that the filter will have to produce categorical perception (at least in categories of colours and phonemes). Categorical perception is the name given to the phenomenon that “for certain perceptual categories, within-category differences look much smaller than between-category differences even when they are the same size physically. For example, on color perception, differences between reds and between yellows look much smaller than equal-sized differences that cross the red/yellow boundary; the same is true of the phoneme categories /ba/ and /da/. Indeed, the effect of the category boundary is not merely quantitative, but qualitative” (Harnad 1987 p535). Categorical perception too is compatible with Rosch’s prototype theory of categorisation. Finally, categorical representations are linked to symbolic representations: labels associated with categorical representations “provide the elementary terms for a third representational system, the symbolic descriptions of natural language” (Harnad 1987 p22).
	Neurobiologically speaking, categorical representations are situated not in the early sensory cortices that house the topographically organised (iconic) representations but in the prefrontal sectors (Damasio 1994 p183). While the entire prefrontal region seems involved in categorising contingencies that involve personal relevance (the life experience of the organism), the bioregulatory and social domains appear to be aligned with the ventromedial sector, while knowledge of an ‘objective’ nature (mathematics, language, knowledge of the actions of objects in space-time) is aligned with the dorsolateral region. Categorical representations are special cases of what Damasio calls dispositional representations, “because what they do, quite literally, is order other neural patterns about, make neural activity happen elsewhere”. Dispositional representations generally occupy convergence zones located throughout the higher-order association cortices in occipital, temporal, parietal, and frontal regions, and in basal ganglia and limbic structures.
	Among the processes that dispositional representations control is the activation of those iconic representations (topographically organised and situated in the early sensory cortices) that become images in the mind. Dispositional representations can activate more than one representation at a time, holding them in working memory so that they may be attended, side by side or in rapid alternation (Damasio 1994 pp240/3). This is how symbols are linked to categorical representations — symbolic representations are just special iconic representations, which may be activated as images (visual or auditory) together with an appropriate categorical representation, by acquired dispositional representations. Thus the symbol string ‘cat’ is a visual image (iconic representation) coupled in our minds with a representation of the category of cats, and the coupling is effected by a third kind of representation, a dispositional representation. Clearly, our excursion into neurobiology draws into serious question Dennett’s assumption that the brain processes only symbols.
A human brain does process symbols, of course, and this may indeed occur in the context of deduction. But an agent performing a deductive inference would normally (unless the situation were deliberately artificial) be dealing with symbolic representations that are grounded, i.e. associated with categorical representations that themselves rest upon multiple iconic representations (Harnad 1994 p386). The grounding provides the symbols with meaning, with semantics. This is in stark contrast to the way in which automated reasoning programs manipulate meaningless (to them) strings of symbols purely in accordance with syntactic rules, and to the behaviour of the man in the Chinese Room, who understands the rules written in English on the walls of the room but not the Chinese symbols that the rules mention. When humans reason deductively, they have at their disposal not only the rules of inference but also the meanings —metaphorically, human deduction involves not just the poor fellow in the Chinese Room but also the Chinese speaker outside.
It is important to realise that symbol manipulations are pointless unless meaning can be attached in some way, and that being able to interpret symbol manipulations, not just locally but globally, is not a trivial criterion — “It is easy to pick a bunch of arbitrary symbols and to formulate arbitrary yet systematic rules for manipulating them, but this does not guarantee that there will be any way to interpret it all so as to make sense” (Harnad 1994). All rule-based symbol manipulation acquires its significance from semantics. Gödel’s incompleteness theorem too contrasts deductive inference with the semantic process of moving from object-level to meta-level, i.e. moving from symbols to a universe of sets within which the meanings of symbols reside as particular sets, and where it can be verified essentially by inspection that the Gödel sentence (asserting, say, that Peano arithmetic is consistent) is true since the set of natural numbers satisfies the Peano axioms (Enderton 2001 pp266-270).
We claim, therefore, that human deduction itself is intrinsically more than the syntactic manipulation Symbolic AI would seek to simulate it by, and that human cognition involves much more than mere deduction. The grounding of a symbol, specifically its association with a categorical representation, provides in principle for a notion of relevance — after all, what is a category but a grouping together of items that share a family resemblance? From this perspective it is the lack of symbol-grounding, i.e. the lack of semantic involvement in the inferential process, that bedevils automated reasoning systems and that provides the Chinese Room thought experiment with its force. The Strong AI Hypothesis may turn out to be true, but this will not be achieved by Symbolic AI. A growing awareness of this fact has led to the emergence of an alternative to Symbolic AI, namely Embodied AI (Clancey 1997; Artificial Intelligence 149(1) 2003). As we shall see, embodiment has become important in psychology also. Let us explore the role it plays in human cognition.

5. Somatic markers and intuition
An automated reasoner that thrashes about, energetically producing irrelevant inferences without getting any nearer its goal, is reminiscent of the behaviour of a human agent with a particular form of brain damage. Patients who have suffered damage, perhaps as a result of tumour, to the ventromedial part of the frontal cortex, just above the nose and behind the eyes, are capable of reasoning just as well as before, but their decision-making is poor. This apparent paradox should be illustrated by an example. Damasio reports discussing with a ventromedial patient the scheduling of his next appointment: “I suggested two alternative dates, both in the coming month and just a few days apart from each other. The patient pulled out his appointment book and began consulting the calendar […] For the better part of a half-hour, the patient enumerated reasons for and against each of the two dates: previous engagements, proximity to other engagements, possible meteorological conditions, virtually anything that one could reasonably think about concerning a simple date” (Damasio 1994 p193).
	The ventromedial patients can perform deduction perfectly, and even score as well as before the damage occurred on standard IQ tests. Nevertheless they are unable to hold down jobs, stay happily married, or even make simple decisions about when to see the doctor. To explain what has gone wrong in such cases, Damasio formulated the somatic-marker hypothesis. As background to the hypothesis, it should be noted that ventromedial patients have three features in common: damage to the ventromedial prefrontal cortex, a flattening of the emotions (particularly the social emotions), and an apparent inability to learn from experience. Let us look in turn at the role played by the ventromedial prefrontal cortex in emotions, at the evidence that ventromedial patients fail to learn from experience, and finally at the mechanism responsible simultaneously for relevance, analogy, and learning from experience.
	Emotional responses are of various sorts. Primary emotions are those which are wired in at birth. For example: “A baby chick in a nest does not know what eagles are, but promptly responds with alarm and by hiding its head when wide-winged objects fly overhead at a certain speed” (Damasio 1994 p132). The amygdala plays a key role in primary emotions. Secondary emotions involve not only the amygdala but also the prefrontal cortices, and are learned (or at least refined by experience). When iconic representations in the early sensory cortices are processed, signals arise that activate dispositional representations in the prefrontal cortex which embody knowledge of how certain types of situations have been paired with certain emotional responses. The damage suffered by ventromedial patients prevents them from activating the dispositional representations that pair certain categories of situation with emotions. Specifically, disturbing images that produce feelings of disgust or horror in normal individuals leave ventromedial patients unmoved, although they can remember that previously they would have experienced a strong reaction.
Nor can new dispositional representations of this kind be acquired. When ventromedial patients and normal participants were asked to play a card game that involved learning from rewards and penalties, the outcomes were very different. Each player starts with some money and gets four decks of cards. Two of the decks are good, in the sense that they confer more rewards than penalties, while the other two are bad and contain penalties that outweigh the rewards. Players do not know this but must learn by sampling. The sampling is rigged (unbeknownst to the participants) so that the initial cards from the bad decks offer large rewards. Both normal participants and ventromedial patients therefore gain an initial preference for the bad decks. Subsequently, cards from the bad decks impose even larger losses, while cards from the good decks maintain a pattern of smallish gains and even smaller losses. Normal participants learn to avoid the bad decks; ventromedial patients do not. (Damasio 1994 pp212-217) While the ventromedial deficit may not involve a complete inability to learn from experience, it amounts at least to an inability to revise beliefs in the face of new evidence.
	The somatic-marker hypothesis is that emotions are connected, by learning, to the predicted future outcomes of certain scenarios and that this serves to increase the accuracy and efficiency of decision-making. In this view, decision-making is not disembodied; instead biological information steers the decision-making process towards outcomes that are advantageous, based on past experiences with similar situations.
	If a dispositional representation is to connect an emotion to the iconic representation of some event, then emotions must be representations too. But what representations are these? The answer lies in the notion of a body map.
	The survival of the organism requires, for homeostasis, the co-ordination of the myriad body functions on which life depends. Energy sources must be found, energy incorporated, an interior chemical balance maintained, wear and tear repaired, and external agents of disease or injury fended off. In order for the brain to co-ordinate these activities, it needs to have information about what is going on in different body sectors so that various functions can be slowed down, or halted, or called into action. The brain needs, in short, to have maps in which the state of each body sector is represented moment by moment. The brain’s somatosensory complex represents the body by reference to a body schema having midline parts (trunk, head), appendicular parts (limbs), and a body boundary (skin). The somatosensory complex is distributed over several areas (the insular cortices, S1, S2) and concentrated in the right hemisphere. The body maps include a ‘primordial representation’ against which the current state of the body can be compared. Such comparisons allow a register of changes to the body to be kept up to date. This includes a representation of the outside world in terms of the modifications it causes in the body (Damasio 2003 pp96-118, Damasio 1994 p230/1).
	Let us term any change to the body map(s) an affect. Some changes set off alarm bells (metaphorically speaking); these are the negatively valenced affects. Some changes restore the body map to something more closely resembling the primordial representation; these are the positively valenced affects, those we may experience as well-being. Every significant change to the body map gives rise to signals that go to the body (including the brain) in order to orchestrate a response to the change in body map. Signals via peripheral nerves place the viscera (e.g. heart) in the state most commonly associated with the type of triggering situation; signals to the motor system prompt skeletal muscles to adjust body posture or facial expression; endocrine and peptide systems are activated and their chemical actions result in further changes to body and brain states; neurotransmitter nuclei are activated (Damasio 1994 p138). One consequence of the neurotransmitter responses is a change in the speed with which mental images are formed, discarded, attended, evoked, as well as a change in the style of thinking that operates on those images (Damasio 1994 p163). The effect is to associate with the triggering affect a momentary thought-action repertoire. To illustrate, consider affects that correspond to specific emotions: fear is linked with the urge to escape, anger with the urge to attack, disgust with the urge to expel. These urges are not invariably translated into action; rather, people’s ideas about possible courses of action narrow in on a specific set of behavioural options. The resultant thought-action repertoire aids decision-making by, as it were, shining the light on a subset of options and eliminating the remaining options by simply removing them from consideration. Reasoning has been equipped with a notion of relevance based on affect, which in turn is based on embodiment.
	The presence of somatic markers, deriving from changes to the body maps, supports a kind of cognition quite distinct from deduction. Instead of a slow, step-by-step process which is intentional, controllable, consciously accessible and viewable, sequential, and demanding of attentional resources, the system based on somatic markers typically operates swiftly and effortlessly, unintentionally and automatically, without demanding attentional resources. We may call the system based on somatic markers intuition, because only the results of processing enter awareness, not the intermediate steps. Intuition is basically a one-step process as far as conscious awareness is concerned. Not only is a notion of relevance built into intuition, this notion is more general than ‘relevance as attention-paid-to’, because the operation of somatic markers can be unconscious.
	Finally, the operation of intuition does not exclude the possibility of symbol manipulation, but clearly does not require it, and instead offers a natural form of analogical ‘reasoning’ by virtue of the links between a categorical representation and its subsidiary iconic representations. The human brain is a semantic engine, rather than the syntactic engine envisaged by Dennett.

6. The changing conceptions of Logic and AI
Until recently the consensus was that Logic was about deductive inference. Despite the formalisation of truth and the invention of model theory by Alfred Tarski, great importance was attached to ‘completeness proofs’. A completeness proof is a demonstration that the notion of semantic consequence (i.e. this will be true whenever that is true) can be simulated by some algorithm based on syntactic inference rules. Much fuss was made about different proof architectures, supporting different algorithms — Hilbert-style, Gentzen-style, natural deduction, semantic tableaux, resolution. And then something interesting began to happen in Artificial Intelligence. John McCarthy and others began to formalise common-sense reasoning.
	Common-sense reasoning is what we use when we infer that, since something is a bird, it can fly. The inference is defeasible, since there are circumstances in which the conclusion would be false, for instance when the bird is a penguin, or is dead, or has had its wings clipped, or has got its feet stuck in wet cement. What sanctions such a defeasible inference is not a traditional rule of inference, for traditional rules of inference are obliged always to produce conclusions which will be true if the premises are true. Defeasible inferences are supported by heuristics, also called default rules. Informally, a default rule contains information to the effect that something is normally thus and so: for example, if something is a bird, then it is normally able to fly. Thus a default rule differs from a universal generalisation (say, ‘All birds are able to fly’) by permitting the existence of exceptions.
	It should be noted that default rules need not be, and usually are not, probabilistic or statistical in nature. When a human driver enters an intersection because she has defeasibly inferred, from the fact that the traffic light for cross traffic had become red, that the cross traffic will stop, then that inference is not usually supported by tables of relative frequencies from which earnest calculations were made. In general humans do not use numerical calculations to support their common-sense inferences. When a human fielder moves to catch a cricket ball hit high into the air by an enterprising batsman, the trajectory of the ball is not being plotted by differential equations — instead the fielder is using a simple heuristic: keeping the angle between the eyes and the horizon constant (McLeod and Dienes 1996). The heuristic can be shown numerically to lead to the ball eventually being caught in front of the eyes, but of course this mathematical demonstration has nothing to do with the common-sense inference of the fielder.
	The formal representation of default rules in Logic, and the definition of the consequence relations which connect premises to the conclusions that may defeasibly be inferred according to the default rules, has been accomplished in various ways, of which the most successful and impressive are the nonmonotonic logics having a preferential semantics. Such logics originated in McCarthy’s idea of circumscription (McCarthy 1980, 1986). In circumscription, a predicate, ‘is abnormal,’ was allowed to have different extensions in different models, allowing those models in which the extension was minimal to be differentiated as the most normal (or least abnormal) models. In effect, models of a given premise (e.g. Tweety is a bird) are ordered in a way that reflects the relevant default rule, and a conclusion (e.g. Tweety can fly) is justified if it is true in all the minimal (i.e. most normal) models. An elegant generalisation of circumscription was achieved by Yoav Shoham and subsequently refined by Lehmann and Magidor (Shoham 1987; Kraus, Lehmann, and Magidor 1990, Lehmann and Magidor 1992). The general approach is called preferential semantics because default rules are represented by preference orderings of the states of the world, and the consequence relation of defeasible entailment is defined by requiring that  if and only if  is true in all the models of  that are most preferred according to the relevant ordering.
	Because every default rule is represented by a different preference ordering on states of the world, there is no way, even in principle, to simulate the defeasible consequence relation by an algorithm based on syntactic inference rules. The shape of a symbol has simply no connection with the notion of normality or preference that may apply. The logics which formalise common-sense inferences have thus been liberated from, or deprived of (according to one’s taste), the assumption that Logic is about deductive inference and completeness proofs. No longer are premise and conclusion connected, even in principle, by a sequence of steps each representing the transformation wrought by a syntactic rule. No longer can the logician monitor the movement from premise to conclusion as it inches along the sequence of intermediate steps. Instead the defeasible consequence relation contains a number of one-step transitions from premise to conclusion, and each of these single-step transitions is supported by a preference, or disposition, realised as an ordering on states of the world. Thus nonmonotonic logics based on preferential semantics formalise intuition, not conscious deduction!
	The development of such nonmonotonic logics has occurred without much attempt to base them on a systematic psychological foundation. But at the same time, a psychological foundation has begun to emerge as a by-product of neurobiological and psychological research. It is possible that, as we learn more about the dispositional representations via which somatic markers exercise their influence, the kinds of ordering relations used in nonmonotonic logics may change. One may anticipate that the semantics of nonmonotonic logics will be elaborated so as to provide for explicit representations of the momentary thought-action repertoires of the agent as these are mediated by whatever preference (= dispositional) ordering has been activated. At present, nonmonotonic logics are provided with default rules a priori, and at some point one would like to see a mechanism for the formation of such rules on the basis of the agent’s interactions with its environment to be developed.
	Nonmonotonic logics with preferential semantics are closely related to AGM belief change theory (Alchourrón, Gärdenfors, and Makinson 1985), although the development was independent. It has been shown that every AGM belief revision operation can be carried out by simply taking the defeasible consequences according to an appropriate preference ordering (Meyer 1999 p75). Not only does this provide additional evidence that preferential semantics is the right way to formalise defeasible inference, it also provides a potentially fruitful perspective on a long-standing problem in belief change theory. AGM theory explains how an agent who is in a given ‘epistemic state’ should revise her beliefs upon receipt of new information. But what should the new epistemic state of the agent be? The new state is required if the agent is to be capable of iterated revision rather than being restricted to one-shot revision. Since epistemic states correspond to preference orderings, which represent default rules, it follows that the problem of iterated belief revision is just a different form of the problem of how default rules may be formed.
	Logic-based AI has become something very different from Symbolic AI, since the emphasis in Logic has moved away from deductive inference and towards semantics in general, preference orderings in particular. Thus logic-based AI is compatible with the role embodiment increasingly plays in AI. Once again, there is tremendous potential for a psychologically realistic foundation to play a role. Ever-increasing attention is paid in robotics to recognising, simulating, and even incorporating emotions (or emotion-analogs, if the reader prefers) (Picard 1998). But existing attempts lack the unification of emotions, drives, and motivations that can be achieved when a primordial body map is used as the basis for notions of affect, somatic markers, and momentary thought-action repertoires. A description of how artificial agents equipped with an architecture for somatic markers would differ from existing software and hardware realisations is given in (Heidema and Labuschagne, 2004). Such ‘emancipated’ agents would be able to work co-operatively in multi-agent societies rather than psychopathically seeking to maximise their own advantage, since the architecture required for somatic markers would allow social emotions such as sympathy and shame to modulate behaviour. And most remarkably, such emancipated agents would display intuition. Arguably, this constitutes one meaningful criterion for the achievement of Strong AI.

References
Alchourrón, C.E., Gärdenfors, P., and Makinson, D. 1985. On the logic of theory change: Partial meet 	functions for contraction and revision. Journal of Symbolic Logic 50:510-530.
Artificial Intelligence 149(1) 2003. Field Review on Embodied Cognition: Anderson, M.L. Embodied cognition: A field guide (pp 91-130); Chrisley, R. Embodied artificial intelligence (pp 131-150); Anderson, M.L. Response: Representation, symbols, and embodiment (pp 151-156).
Bargh, J.A. and Chartrand, T.L. 1999. The unbearable automaticity of being. American Psychologist 	54(7):462-479.
Baumeister, R.F., Bratslavsky, E., Muraven, M., and Tice, D.M. 1998. Ego depletion: Is the active self 	a limited resource? Journal of Personality and Social Psychology 74:1252-1265.
Clancey, W.J. 1997. Situated Cognition: On Human Knowledge and Computer Representations. 	Cambridge: Cambridge University Press.
Damasio, A.R. 1994. Descartes’ Error: Emotion, Reason, and the Human Brain. New York: 	HarperCollins.
Damasio, A.R. 2003. Looking for Spinoza: Joy, Sorrow, and the Feeling Brain. Orlando: Harcourt.
Dennett, D.C. 1984. Elbow Room: The Varieties of Free Will Worth Having. Oxford: Clarendon Press.
Dennett, D.C. 1991. Consciousness Explained. Boston: Little, Brown.
Enderton, H.B. 2001. A Mathematical Introduction to Logic (2nd edition). San Diego: 	Harcourt/Academic Press.
Forbus, K.D., Gentner, D., Markman, A.B., and Ferguson, R.W. 1998. Analogy just looks like high 	level perception: Why a domain-general approach to analogical mapping is right. Journal of 	Experimental and Theoretical Artificial Intelligence 10(2):231-257.
Gärdenfors, P. 2000. Conceptual Spaces: The Geometry of Thought. Cambridge, MA: MIT Press.
Genesereth, M.R. and Nilsson, N.J. 1987. Logical Foundations of Artificial Intelligence. Los Altos: 	Morgan Kaufmann.
Harnad, S. 1987. Category induction and representation, in Harnad, S. (editor) Categorical Perception: 	The Groundwork of Cognition 535-565. Cambridge: Cambridge University Press.
Harnad, S. 1994. Computation is just interpretable symbol manipulation, cognition isn’t. Minds and	 Machines 4:379-390.
Heidema, J. 1979. Honderd jaar predikaatlogika. Notices of the South African Mathematical Society 	11(4):170-197.
Heidema, J. and Labuschagne, W.A. 2004. Emancipating agents: Need Schrödinger’s cat be let into the 	Chinese room? Junctures: The Journal for Thematic Dialogue 3:19-30.
Hofstadter, D. 1995. A review of Mental Leaps: Analogy in Creative Thought. AI Magazine Fall 1995 	75-80.
Hofstadter, D. and the Fluid Analogies Research Group 1995. Fluid Concepts and Creative Analogies. 	London: Penguin.
Holyoak, K.J. and Thagard, P. 1995. Mental Leaps: Analogy in Creative Thought. Cambridge, MA: 	MIT Press.
Kahneman, D., Slovic, P., and Tversky, A. 1982. Judgment under Uncertainty: Heuristics and Biases. 	New York: Cambridge University Press.
Konolige, K. 1986. A Deduction Model of Belief. Los Altos: Morgan Kaufmann.
Kraus, S., Lehmann, D., and Magidor, M. 1990. Nonmonotonic reasoning, preferential models and 	cumulative logics. Artificial Intelligence 44:167-207.
LaForte, G., Hayes, P.J., and Ford, K. M. 1998. Why Gödel’s theorem cannot refute computationalism. 	Artificial Intelligence 104:265-286.
Lehmann, D. and Magidor, M. 1992. What does a conditional knowledge base entail? Artificial 	Intelligence 55:1-60.
McCarthy, J. 1980. Circumscription — A form of nonmonotonic reasoning. Artificial Intelligence 	13:27-39.
McCarthy, J. 1986. Applications of circumscription to formalizing common-sense knowledge. 	Artificial Intelligence 28:89-116.
McCorduck, P. 1979. Machines Who Think. New York: WH Freeman.
McLeod, P. and Dienes, Z. 1996. Do fielders know where to go to catch the ball or only how to get there? Journal of Experimental Psychology: Human Perception and Performance 22:531-543.
Merikle, P.M., Smilek, D., and Eastwood, J.D. 2001. Perception without awareness: Perspectives from 	cognitive psychology. Cognition 79:115-134.
Meyer, T.A. 1999. Semantic Belief Change. PhD dissertation, University of South Africa. Available on 	the web: http://www.cse.unsw.edu.au/~tmeyer/pubs.html.
Muraven, M., Tice, D.M., and Baumeister, R.F. 1998. Self-control as limited resource: Regulatory 	depletion patterns. Journal of Personality and Social Psychology 74:774-789.
Newell, A. and Simon, H.A. 1976. Computer science as empirical enquiry: Symbols and search. Communications of the Association for Computing Machinery 19(3):113-126.
Penrose, R. 1989. The Emperor’s New Mind. Oxford: Oxford University Press.
Penrose, R. 1995. Shadows of the Mind. Oxford: Oxford University Press.
Penrose, R. (ed) 1997. The Large, the Small, and the Human Mind. Cambridge: Cambridge University 	Press.
Picard, R. 1998. Affective Computing. Cambridge, MA: MIT Press.
Pollock, J.L. and Cruz, J. 1999. Contemporary Theories of Knowledge (2nd edition). Lanham MD: 	Rowman & Littlefield.
Preston, J. and Bishop, M. (editors) 2002. Views into the Chinese Room: New Essays on Searle and Artificial Intelligence. Oxford: Oxford University Press.
Rosch, E. 1978. Principles of categorization, in Rosch, E. and Lloyd, B.B. (editors) Cognition and 	Categorization, Erlbaum, reprinted in Collins, A. and Smith, E.E. (editors) 1988. Readings in 	Cognitive Science 312-322. Los Altos: Morgan Kaufmann.
Searle, J.R. 1990. Is the brain’s mind a computer program? Scientific American 262(1):26-31.
Searle, J.R. and commentators, 1980. Minds, brains, and programs. The Behavioral and Brain Sciences 	3:417-457.
Searle, J.R. and commentators, 1992. Consciousness, explanatory inversion, and cognitive science. 	The Behavioral and Brain Sciences 13:585-642.
Shoham, Y. 1988. Reasoning about Change: Time and Causation from the Standpoint of Artificial 	Intelligence. Cambridge, MA: MIT Press.
Whitehead, A.N. 1911. An Introduction to Mathematics. New York: Holt.
Wos, L. and Pieper, G.W. 1999. A Fascinating Country in the World of Computing: Your Guide to 	Automated Reasoning. Singapore: World Scientific Publishing.
Zajonc, R.B. 1980. Feeling and thinking: Preferences need no inferences. American Psychologist 	35:151-175.