Educational Philosophy and Theory, Vol. 37, No. 5, 2005Explaining Learning: From analysis toJohnExplainingO5372005 UKSepte...
668 John Clarkteachers have a deeper appreciation of learning derived from the theoretical studiesof researchers.   To dat...
Explaining Learning 669third criterion, mastery, insists that ‘learning always has an object (x), mastery ofwhich is essen...
670 John ClarkBehavioural Theories of LearningIn a very broad sense, all humans are, at least on occasions, behaviourist: ...
Explaining Learning 671   the basis of tending to repeat behaviours which are followed by consequences which   they find de...
672 John Clarkhas ‘spread its wings from its learning theory origins’ (p. 16); he remarks that thefollowing dimensions of ...
Explaining Learning 673system. As such, schemata do not have physical counterparts and are not observ-able. They are infer...
674 John Clarkpatterns of meaning are shaped by the relations between the learner, their experi-ence of the world and the ...
Explaining Learning 675which framed the sort of linguistic analysis of learning offered by Hamm. There isno ‘first philosop...
676 John Clark   For the computational cognitive scientist, the computer and the brain functionin similar ways. The comput...
Explaining Learning 677as it does on following them. Computers take syntactical and semantic rules as agiven, supplied to ...
678 John Clarkit is well on the way to becoming the best theory of learning currently available.Within the connectionist r...
Explaining Learning 679physics (e.g. the sun orbits the earth) was replaced by scientific explanations (e.g.the earth orbit...
680 John Clarkpolarities. The neuron strength is just that sum total of inputs from the dendriticconnections. If the synap...
Explaining Learning 681of sensory activations occur. For example, if eating an apple activates a particularset of sensory ...
682 John Clarkin its life, synaptic connections may be limited, but as the information inputincreases the neurons make mor...
Explaining Learning 683     is the fact that there is as yet no principled description specifying what     general class o...
684 John Clarkable to recognise patterns not easily reduced to rules. Finally, connectionist modelshave made some rapid ad...
Explaining Learning 685well in algorithmic computations. This suggests that some other basis of learningis required; one p...
686 John Clarkto establishing a sound research programme in neurophilosophy as well, since ithas as much a chance of succe...
Explaining Learning 687Livingston, K. (1996) The Neurocomputational Mind Meets Normative Epistemology,       Philosophical...
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  1. 1. Educational Philosophy and Theory, Vol. 37, No. 5, 2005Explaining Learning: From analysis toJohnExplainingO5372005 UKSeptember© riginal Philosophy Education Society of Australasia0013-1857 Philosophy andEducational2005EPAT Clark Learningof Ltd. TheoryOxford, ArticleBlackwell Publishing,paralysis to hippocampusJ CMassey University, Palmerston North, NZAbstractThis paper seeks to explain learning by examining five theories of learning—conceptualanalysis, behavioural, constructivist, computational and connectionist. The first two arefound wanting and rejected. Piaget’s constructivist theory offers a general explanatoryframework (assimilation and accommodation) but fails to provide an adequate account ofthe empirical mechanisms of learning. Two theories from cognitive science offering rivalexplanations of learning are finally considered; it is argued that the brain is not like acomputer so the computational model is rejected in favour of a neurally-based connectionistmodel of learning. Keywords: learning, connectionism, cognitive science, neurophilosophyWe are born and we die; between, we learn. We learn to distinguish colours, redfrom blue; we learn a language, so speak, read and write of what we have learned;we learn to do things, like riding a bicycle; we learn that certain things are so—the names of countries and their capital cities; we learn about ourselves, of whatmakes us happy and sad; we learn that the world of which we are a part isconstituted in such ways that we shape it and it us. Some things are so general andcommonplace—most of us learn that putting a hand on something very hot israther painful; other things are quite specific—I learn a bit more about myself froma particular experience—the youthful misery of love lost. Most of us, with a fewsad exceptions, learn and continue to learn from first day to last, such is the humancondition. Many of us go on to learn about learning, for as teachers, learning isour business. A few of us seek deeper theoretical accounts of learning—such is thetask of researchers, philosophical and empirical alike. It may be asked, why do we need such deep explanatory theories of learning?After all, children learn, exceeding well, in the absence of such theorising. Havedone so as far back in time to when humans first started learning. Likewise,teachers have a sufficient practical grasp of learning to promote children’s learningeven though they may not know the finer details of learning: successfully so sinceadults first started teaching children to learn this and that. Quite so, but in anincreasingly complex age of learning, and with systematic advances in our under-standing of the processes of learning, children’s learning may well be enhanced if© 2005 Philosophy of Education Society of AustralasiaPublished by Blackwell Publishing, 9600 Garsington Road, Oxford, OX4 2DQ, UK and350 Main Street, Malden, MA 02148, USA
  2. 2. 668 John Clarkteachers have a deeper appreciation of learning derived from the theoretical studiesof researchers. To date, the public record of explanatory accounts of learning to be consideredhere—conceptual analysis, behaviourism, constructivism—has not been good, butwith the emergence of cognitive science the future for learning and learning theorylooks promising. Why so? Well, the one thing which binds these three approachestogether and separates them from the latter is their reluctance to utilise the fullepistemic resources of science to explain learning. Conceptual analysis is, as itsname suggests, no more than the linguistic analysis of a concept having no explanat-ory power; behaviourism and constructivism fail to explain learning processes—theformer eschews it and the latter offers no more than a metaphor—so leaving thefield in a state of explanatory paralysis. If we are ever to get to the bottom ofexplaining what learning is, it will be by rigorous and systematic empirical studiesof brains, in particular that part of the brain which is the hippocampus. This paper will take us on a journey, albeit briefly, through four very differenttheories of learning, three of which will be found wanting. What separates the threefrom the one is the former’s autonomy from science which the latter rejects. Deeperstill is an underlying disagreement about the relationship between philosophy andscience; cleaved or continuous.Conceptual Analysis of LearningLearning, along with a raft of related concepts such as education, teaching, indoc-trination and the like, came in for a good deal of close scrutiny by philosophers ofeducation who, employing conceptual analysis, thought that the meaning of aconcept such as learning could be revealed by identifying the necessary and suffi-cient conditions for its use. The attempts to do so were numerous (e.g. Dearden,1967; Hamlyn, 1967; Komisar, 1965) but a detailed examination of them here isnot necessary. Rather, consideration will be given to one account, that of Hamm,which synthesised the various analyses of the concept; in so doing, he distinguishedthree criteria central to the concept of learning. Hamm (1989) defined learning as ‘intentionally coming to know (or believe, orperform, etc) as a result of experience’ (p. 91). He then identified three necessaryand sufficient conditions for the concept of learning, such that a social practice isone of learning if it meets all three criteria, namely, intentionality, experience andmastery. The first criterion, intentionality, requires that ‘learning is an activity that oneengages in with purpose and intention to come up to a certain standard’ (ibid.p. 91). Hamm admits that only some learning meets this criterion, for he concedesthat non-intentional learning is possible, including sleep learning, learning byhypnosis, and learning through conditioning, all of which are instances of learningresulting from unconscious experience. The second criterion, experience, acknow-ledges that experience can be either conscious or unconscious, although Hammfinds the notion of unconscious experience troubling. He asks: can something thathappens to one of which one is not aware be called ‘experience’? (ibid. p. 92). The © 2005 Philosophy of Education Society of Australasia
  3. 3. Explaining Learning 669third criterion, mastery, insists that ‘learning always has an object (x), mastery ofwhich is essential for learning to occur’ (ibid. p. 92), with x being either a skill oran item of propositional knowledge or belief. Concluding his analysis of the con-cept of learning, Hamm states: It is very difficult to answer the question, ‘How does learning take place?’ for the answer depends on what is learnt. There are as many forms of learning as there are kinds of learning objectives and the latter would appear to be almost endless. It is not therefore surprising that generalisable learning patterns and principles are few and far between and not easily established on a scientific basis. (ibid. p. 93)A number of objections can be raised against this sort of account of learning. Twomust be considered here, one substantive, the other methodological. The substantivepoint is that any appeal to an ordinary language analysis of a concept will generatea meaning for the concept but will never allow us to explain learning itself.Hamm’s efforts to provide a conceptual understanding of learning is hampered byhis acceptance of a fundamental contradiction, namely, learning is intentional yetunintentional learning is possible. On the question he poses, ‘How does learningtake place?’, Hamm is mistaken in thinking that the answer lies in what is learned;this leads to his assertion that because there are endless learning objectives noscientific generalisations about learning are possible. But how learning takes placeis not a matter of what is learned but of explaining where learning takes place and itsmodus operandi. Now, empirical generalisation is not only possible but very likely. The second objection relates to and underpins the first. Methodologically, con-ceptual analysis relies on a sharp separation of science and philosophy; science isa first-order activity which through the use of concepts and theories seeks todescribe and explain empirical phenomena, to account for observables by positingunobservables (e.g. gravity, magnetism) while philosophy brings linguistic analysisto bear on clarifying the necessary and sufficient conditions for the use of first-orderconcepts (Hirst & Peters, 1970, pp. 2–8). In rebuttal, (1) the practice of philosophyis as much a first order activity as any other activity, for anyone can engage inphilosophical reflection on their daily activities; (2) conceptual clarification is notthe province of philosophy alone, for science itself creates and refines concepts ina rigorous and systematic way guided by such epistemic principles as precision,empirical relevance and theoretical coherence; (3) conceiving philosophy as a second-order conceptual activity cuts it off from utilising all of the powerful scientificresources available to it; (4) given the commonality of language and experience,science and philosophy are continuous, not disconnected; and (5) conceptual analysisis but one method in philosophy, and, being largely discredited, it is not all thateffective on its own. True, proponents of conceptual analysis could reasonably claim that their ana-lyses of learning were designed to explicate the concept, and not to advance anexplanatory theory of learning. On the first count, their explications are not all thatinsightful; on the second count, when they do stray into the explanatory, theirefforts are less than adequate.© 2005 Philosophy of Education Society of Australasia
  4. 4. 670 John ClarkBehavioural Theories of LearningIn a very broad sense, all humans are, at least on occasions, behaviourist: whichparent, which teacher has not at some time or other sought to bring about changesin children’s behaviour by rewards (kind words of praise, a gift) and punishments(strong words of discouragement, withdrawal of a privilege) without thinking toomuch about what was going on inside the child’s head. A change of behaviour issought and through rewards and punishments a change of behaviour is achieved. But in another sense of the term, few of us are, within a strictly psychologicaldefinition of the term, behaviourists. Yet in schools behaviourism has had a power-ful influence in shaping teachers’ practice directed at student’s learning, and itslegacy is still with us. Numerous behavioural accounts of learning abound, but theposition advanced in Behaviourism and Learning Theory in Education (Fontana, 1984)is representative and serves the purpose well. According to Blackman (1984),behaviourism focuses on ‘the overt and therefore publicly observable phenomenaof behaviour rather than on the covert and essentially private world of mental life’(p. 4). In so doing, it: … emphasises the functional relationships between environmental events and behavioural events. Such relationships are to be found in reinforcement and punishment, where environmental events which follow a particular pattern of behaviour increase or decrease the future probability of that behaviour. … It is therefore an empirical matter to identify the circumstances in which social consequences for behaviour such as praise or blame exert reinforcing or punishing effects on the behaviour of the individual. (Blackman, 1984, p. 5)Further to this, ‘behaviour is said to occur because of its relationship to antecedentevents (discriminative stimulus) and consequences (reinforces/punishers)’ (ibid. p. 6).Finally, ‘it is not the existence or reality of mental or psychological processes whichis at issue, but rather the level of explanation which empirical psychologists shouldseek for the behaviour they observe’ (ibid. p. 6). Building upon this general account of behaviourism, Wheldall and Merrett(1984) offer a summarised behavioural account of learning as follows:1. The concern of psychology (and hence teaching) is with the observable. This means that teachers who adopt the behavioural approach … concern themselves with what a child actually does, i.e. his behaviour, rather than speculating about unconscious motives or the processes underlying his behaviour.2. For the most part, and certainly for most practical purposes, behaviour is learned. In other words behaviour, what people do, is assumed to have been learned as a result of the individual interacting with his environment …3. Learning means change in behaviour … The only way we know (that we can know) that learning has taken place is by observing a change in a child’s behaviour.4. Changes in behaviour (i.e. learning) are governed primarily by the ‘law of effect’. This means that children (and adults, and other animals for that matter) learn on © 2005 Philosophy of Education Society of Australasia
  5. 5. Explaining Learning 671 the basis of tending to repeat behaviours which are followed by consequences which they find desirable or rewarding; similarly, they tend not to repeat behaviours, the consequences of which they find aversive or punishing.5. Behaviours are also governed by the contexts in which they occur. In any situation some behaviours are more appropriate than others and we learn which situations are appropriate for which behaviour. If a child’s behaviour is appropriate for the circum- stances in which it occurs it is likely to be rewarded; if it occurs in inappropriate circumstances reward is less likely and the behaviour may even lead to punishing consequences. As a result of this we rapidly learn not only to perform a certain behaviour, but when and where to perform it. (Wheldall and Merrett, 1984, pp. 16–17)Whatever benefits behaviourism might have in terms of accounting for the condi-tions relevant to changes in behaviour, it has little explanatory power as a theoryof learning. First, learning is not reducible to change in behaviour. Much that welearn results in no change in behaviour: watching the TV news, I learn aboutsomething that happened in a far-off country but this learning leads to no changein my behaviour, now or in the future; I read an historical treatise on an event longago—interesting, absorbing even, but learning something about the ancient Greeksneed produce no change in my behaviour. Countless other examples we could givewould make exactly the same point. On the other hand, there can be changes inbehaviour which are not the result of learning, or to put it more accurately, on thebasis of past learning but not any new learning. Assume I behave in a certain way.One day I reflect upon why I behave as I do, re-evaluating the weightings I placeon the various causes of my behaviour. I learn nothing new but my behaviourchanges as a result of revising that which I already had. So, as a definition oflearning, behaviourism is deficient. As for explaining learning, of elucidating theinner processes of learning, behaviourism is silent for it can offer nothing but theexternal conditions of learning. It does not, indeed cannot, tell us how children’sthinking changes their behaviour, and it is this rather than the rewards and pun-ishments which give us an insight into how learning takes place. Behaviourism, as a self-identified science, certainly is at odds with philosophy.The former, with its obsession with observable behaviour and external causes ofbehaviour, pays no attention to the details of mental life. Philosophy, on the otherhand, is particularly interested in describing and explaining the inner life of humanbeings. Behaviourism is avowedly scientific and has deliberately distanced itselffrom conceptual philosophy. Not that it has cut its links with philosophy altogether,for behaviourism does unashamedly draw inspiration from a once influentialphilosophy of science—logical positivism. It’s just unfortunate that both have beendiscredited. For all its early promise, behaviourism failed to deliver on learning,and in doing so paralysed research into learning.Constructivist Theories of LearningUnlike behaviourism, constructivist theories of learning do attend to what goes onin children’s heads. Unfortunately, as Matthews (2000) observes, constructivism© 2005 Philosophy of Education Society of Australasia
  6. 6. 672 John Clarkhas ‘spread its wings from its learning theory origins’ (p. 16); he remarks that thefollowing dimensions of constructivism need to be separated—constructivism as atheory of cognition, learning, teaching, education, personal knowledge, scientificknowledge, educational ethics and politics, and as a world view. Matthews continues: … cutting across these divisions is the fundamental distinction between constructivism as a theory of meaning (a semantic theory) and constructivism as a theory of knowledge (an epistemological theory). These categories are frequently, and erroneously, merged. To give an account of how meaning is generated, or how ideas are formed, is not to give an account of the correctness of the ideas or propositions. (Matthews, 2000, p. 164)Here, we will be concerned with a theory of learning, one which is a theory ofmeaning only. In the same volume, Constructivism in Education (Phillips, 2000),Gunstone (2000) provides, in summary form, a representative constructivist viewof learning which emphasizes:1. Learning outcomes depend not only on the learning environment but also on the knowledge of the learner.2. Learning involves the construction of meanings. Meanings constructed by students from what they see or hear may or may not be those intended. Construction of meaning is influenced to a large extent by our existing knowledge.3. The construction of meaning is a continuous and active process.4. Meanings, once constructed are evaluated and can be accepted or rejected.5. Learners have the final responsibility for their learning.6. There are patterns in the types of meanings students construct due to shared experiences with the physical world and through natural languages. (Gunstone, 2000, p. 263)The constructivist approach to learning has generated a number of quite specifictheories of learning; Piaget and Vygotsky are two of the more influential figures.Here, one, Piaget, will be examined as an example of how a constructivist theoryof learning attempts to explain learning but does not quite succeed in doing so.Piaget is one of the founding fathers of modern constructivist theories of learning.For example, Solomon (1998), in a discussion on educational philosophy, Piagetand constructivism, remarked: ‘The notion that knowledge is self-constructed ismost frequently connected to the work of Jean Piaget’ (p. 40). Piaget proposed atheory of learning which, unlike behaviourism, sought to explain what goes oninside the learner’s head: schemata, assimilation, accommodation and equilibrationare now familiar terms to all those many teachers who have been introduced toPiaget’s ideas. Wadsworth (1996) lays out Piaget’s theory of learning as the foun-dation of constructivism. Here, following Wadsworth, I shall do no more thanbriefly outline Piaget’s thesis. Schemata: ‘are the cognitive or mental structures by which individuals intellectuallyadapt to and organise the environment. … The structures are inferred to exist. …Schemata are not physical objects; they are viewed as processes within the nervous © 2005 Philosophy of Education Society of Australasia
  7. 7. Explaining Learning 673system. As such, schemata do not have physical counterparts and are not observ-able. They are inferred to exist and are properly called hypothetical constructs’(Wadsworth, 1996, p. 14). Put another way, schemata can be thought of as ‘conceptsor categories … used to process and identify or classify incoming stimuli. In thisway, the organism is able to differentiate between stimulus events and to generalise’(ibid. p. 14). Assimilation: ‘is the cognitive process by which a person integrates new perceptual,motor, or conceptual matter into existing schemata or patterns of behaviour. …Assimilation theoretically does not result in a change of schemata, but it does affectthe growth of schemata and is thus a part of development’ (Wadsworth, 1996,p. 17). Accommodation: is concerned with changes in schemata. Since adult schemata aredifferent from those of children, an explanation is required for this change: When confronted with a new stimulus, a child tries to assimilate it into existing schemata. Sometimes this is not possible. Sometimes a stimulus cannot be assimilated because there are no schemata into which it readily fits. The characteristics of the stimulus do not approximate those required in any of the child’s available schemata. What does the child do? Essentially, one can do one of two things: one can create a new schema in which to place the stimulus … or one can modify an existing schema so that the stimulus fits into it. Both are forms of accommodation and result in the configuration of one or more schemata. Thus, accommodation is the creation of new schemata or the modification of old schemata. Both actions result in a change in, or a development of, cognitive structures (schemata). Once accommodation has taken place, a child can try to assimilate the stimulus. Because the structure has changed, the stimulus is readily assimilated. Assimilation is always the end product. (Wadsworth, 1996, pp. 17–18)Equilibration ‘Equilibrium is a state of balance between assimilation and accommoda-tion. Disequilibration is a state of imbalance between assimilation and accommodation.Equilibration is the process of moving from disequilibration to equilibration. This isa self-regulatory process whose tools are assimilation and accommodation’ (ibid.p. 19). There is something appealing about Piaget’s theory of learning; on the surface,it appears to offer an explanation of how learning takes place. Children haveschemata and through assimilation and accommodation growth and developmentoccur within equilibration. Piaget’s theory of learning is consistent with Gunstone’sconstructivist principles of learning outlined above. For Piaget, learning doesdepend on both the learning environment and the knowledge of the learner andtheir interaction; learning does involve constructing meaning which is assimilatedto or accommodated by our existing knowledge; learning, as the construction ofmeaning, is an active process by the learner; such meanings as are actively con-structed are either accepted or rejected as part of the process of equilibrium;© 2005 Philosophy of Education Society of Australasia
  8. 8. 674 John Clarkpatterns of meaning are shaped by the relations between the learner, their experi-ence of the world and the language they use to voice that experience; however,whether learners have the final responsibility for their learning is, for a Piagetian,perhaps a little more problematic since taking responsibility for learning, let aloneanything, requires a level of conceptual understanding and moral insight whichmany young children are simply not capable of and should not be held accountablefor. Attractive as it is, at least in comparison with its behavioural rival, Piaget’s theorysimply fails to explain learning. What, exactly, are schemata? It is not all thathelpful to be told they are ‘inferred to exist and are properly called hypotheticalconstructs’. How, exactly, do children ‘integrate new perceptual, motor or concept-ual matter into existing schemata’? How, exactly, does one ‘create new schema’ inwhich to place the stimulus? In what way is equilibration ‘a self-regulatory process’?These are questions Piaget and his followers simply fail to address. Like behaviour-ism, constructivist theories of learning have ended in paralysis. A caveat needs tobe entered here. The rejection of Piaget’s constructivist theory of learning is not,and should not be taken to be, a rejection of other constructivist theories ofcognition, teaching, education, science, knowledge and the like nor of constructiv-ism in its more general sense. Insofar as the objections are directed solely at aconstructivist theory of learning then they cannot be generalised as objections tothese other constructivist theses. Accordingly, constructivists who do not adhere toa Piagetian theory of learning are not open to the criticism levelled against it.Cognitive SciencePiaget’s theory of learning, involving schemata, assimilation, accommodation andequilibrium, is an attempt to provide an answer to a deeper philosophical problem:given our current conceptual scheme how do we determine when new additionscan just be joined to the old in an incremental way and when must the existingframework be revised, partially or profoundly, to incorporate new elements whichwere previously incompatible with the old. Although Piaget’s account provides auseful way of conceptualising learning, especially that of children, it does notprovide a satisfactory explication of the underlying mechanisms. If we acceptPiaget’s terminology, that in their learning children assimilate and accommodatenew experiences, this merely describes what they do; it does not explain how or whythey assimilate or accommodate. What is required is some plausible explanatorytheory about the underlying mechanism(s) which govern these cognitive processes. Cognitive science has picked up the challenge to provide a satisfactory explana-tion of how learning occurs. As a developing field of investigation, cognitive sciencehas embraced a range of inquiries which bring together research in neuroscience(structure and function of the brain and its extensions), cognitive psychology(study of thinking), artificial intelligence (making machines that can do the kindsof things humans can do) and philosophy (especially epistemology, philosophy ofscience and philosophy of mind). Here, the relation of philosophy to science isquite different from the second order/first order conception of analytic philosophy © 2005 Philosophy of Education Society of Australasia
  9. 9. Explaining Learning 675which framed the sort of linguistic analysis of learning offered by Hamm. There isno ‘first philosophy’ (Quine, 1969) against which science is to be judged; rather,philosophy and science are continuous such that the findings of science are relevantto philosophical arguments and philosophical understandings are built into thescientific enterprise. Philosophy and science therefore come together in our theo-rising about ourselves for we utilise both conceptual and empirical resources inorder to describe and explain ourselves. Because no clear line can be drawn between the two, scientists and philosophersshould work collaboratively in using all available epistemological resources to bringempirical findings to bear on conceptual issues and vice versa. But this endeavour,promoted by Churchland (1986) and others, is not without difficulty: The project to naturalise philosophy provokes considerable sympathy from most psychologists and cognitive scientists, especially when it is the philosophies of mind, language, and science that are to be naturalised. The effort seems an endorsement of the empirical approach to these topics, and this flatters the scientist’s decision to leave the armchair for the laboratory. But when the naturalistic philosopher begins to claim that the standard explanatory concepts in which scientists of mind trade are bankrupt and ought to be eliminated from our discourse and theory, the scientist’s enthusiasm for the philosopher’s endeavours is likely to wane. (Livingston, 1996, p. 33)Despite this, there are two emergent research programmes in cognitive science,computationalism and connectionism, which, drawing off different traditions ofphilosophy and science, compete to explain learning. The former, with its folkpsychology links into constructivism, is at odds with the latter which seeks toeliminate mental explanations and replace them with neural network theory.ComputationalismThe computational programme goes beyond commonsense to provide a deeperexplanation. Impressed with the computational power of computers to processalphabetic and numerical symbols, a parallel is drawn between cognition and com-puters: the brain is like computer hardware, the mind is akin to the software andpropositional attitudes match the symbolic information which is processed. This isnot to say that brains, minds and propositional attitudes have the same structuralproperties as computer hardware, software and data, but rather that the two func-tion in similar computational ways. In this sense, computationalism has no interestin the brain as such, so neuroscience plays little part. On the other hand, artificialintelligence is central, for if humans process information computationally then thestudy of computer processing might offer insights into human computation. Theanalogy runs something along these lines: the computer contains the hardware, towhich is added software which allows for the computational processing of informa-tion; the brain is the hardware, the mind is the software but the question is, howare propositional attitudes processed in what we call thinking and learning?© 2005 Philosophy of Education Society of Australasia
  10. 10. 676 John Clark For the computational cognitive scientist, the computer and the brain functionin similar ways. The computer works in binary (0.1) with electronic circuits eitheropen or closed. Block (1990), in discussing the computer model of the mind,points to the same logical constructions in our thinking: ‘and’ and ‘or’ serve asbinary gates for propositions (‘and’ sentences could be 0.0, ‘or’ sentences could be1.0). Just as the computer processes information according to the logic of com-putational gates so too, claims Block, does the mind. If computers ‘think’ in thesame way as humans think then it should be possible to build machines that thinkas we do which would help us to have a deeper understanding of how humans thinkand learn; indeed, if computers ‘think’ like we think then such machines could beused to help children learn more effectively! If the mind is a sort of computation device, what sort of information does itprocess, and how does it process it? Computers manipulate symbols, so too dominds. The manipulation of symbols is not random but according to syntacticalrules; in the case of language and propositions, syntactical rules include both therules required to make a sentence a sentence (e.g. ends with a full stop, questionmark, etc.) and the rules which relate sentences (the use of such connectives as‘and’ and ‘if ’ as well as deductive logic). Computation, whether by computer ormind, is algorithmic in that both follow a given set of operations resulting in anassured and a particular outcome. Follow the rule and the result will eventuate: inmathematics, children learn to follow the rule of addition and so arrive at the rightanswer regardless of the numbers in the equation. Further, one can follow thealgorithmic rule successfully yet not grasp what one was doing. Long division is acase in point for many young children. This suggests that there is more to the computation of symbols than syntaxalone. The symbols also represent, or stand for something beyond themselves.Here, semantic rules are also required to give an interpretation or meaning to thesymbols being computated; in short, propositional attitudes account for both whatpeople learn (propositional) and why they learn them (attitudes): ‘I believe it willrain today’ says something about a proposition (‘It will rain today’) and what itrepresents (It will rain today) as well as an explanation for learning it or holdingto it, namely that one believes (or hopes or wishes or wants) the proposition suchthat what it represents will come about, i.e. that it will rain today. A computational theory of learning has considerable attraction for two groups ofpeople, those drawn to the power of computers which in relevant ways functionsimilarly to brains computationally, and those who see merit in retaining mentalexplanations of human learning. By bringing these two interests together, addedstrength is given to both positions: the computerists are able to add semantics tosymbols while those who defend folk psychology can do so by grounding it in theempirical project of computer science. There are some unresolved problems with the computational theory of learning.First, unlike computers and their hardware/software which remain in a constantfixed state unless externally altered, brains have a facility to change over time whichalters their functioning. So, whereas the computer simply applies the algorithmicrules given it, human learning relies as much on revising and changing the rules © 2005 Philosophy of Education Society of Australasia
  11. 11. Explaining Learning 677as it does on following them. Computers take syntactical and semantic rules as agiven, supplied to them by humans; human computation, on the other hand, is at itsbest when the syntactical rules are altered to allow for new linguistic permutationsand the semantic rules are revised to permit new meanings. So when, for example,Popper (1959) set out the logic of scientific discovery, codifying syntactical andsemantic rules, Kuhn (1970) and Feyerabend (1975) responded by pointing outhow the history of scientific progress rested not on a set of logical rules but on theflouting of them. So too with learning more generally. The computational theoryof learning supposes there to be a fixed set of rules to be followed and while thismight be suitable for understanding computations performed by computers, giventhe vagaries of human thought then computation is an inappropriate theory oflearning, for we learn from our mistakes in ways that computers cannot. Second, the ontological status of mental states, in particular the belief-desirepsychology that underpins the attitudes towards propositions, is problematic. Dowords such as ‘belief ’, ‘hope’, ‘wish’ and the like refer to actual beliefs, hopes andwishes which humans are said to possess which causally account for what we sayand do, or are they like the word ‘unicorn’ which refers to nothing for there areno unicorns? How do we know that we have beliefs, hopes, wishes—what evidenceis there for their existence? In short, is folk psychology a good theory or a false one? A third difficulty is that the computational theory of learning falls short ofproviding an explanation of how, in Piagetian terms, children learn to assimilateand accommodate information. It is hard to see what algorithmic rules could beformulated to guide learning except for that learning which lends itself to the rotelearning of fixed rules. Given that learning consists also of finding exceptions torules, of breaking the rules, of creatively applying the rules in novel ways, and soon, then computation may not be the best approach to take in cognitive science.Like behaviourism and constructivism, computationalism looks also to be paralyticin its ability to explain learning.Connectionism (Hippocampus)The connectionist approach to learning sets out to solve the difficulties inherent inthe computational approach: it begins with studies of the brain, its structure andfunctions, in order to explain how the stunning achievements of human thoughtand learning are possible. So, for connectionism neuroscience is to the fore ratherthan artificial intelligence; this turns the computational theory on its head—ratherthan model human learning on computer computation, computers are used tomodel theories of learning. Further, if folk psychology is incompatible with thefindings of neuroscience then it will need to be eliminated and replaced by anempirical theory of how brains learn. And lastly, a detailed study of the brain mayoffer an empirical explanation of the mechanisms which operate to facilitate thatwhich we call the assimilation and accommodation processes of learning. Cognitiveneuroscience attempts to explain cognitive processes without trying to separatethem from theories of brain mechanisms. Thus, if connectionism is able to over-come the difficulties of its rivals and explain what they are unable to explain, then© 2005 Philosophy of Education Society of Australasia
  12. 12. 678 John Clarkit is well on the way to becoming the best theory of learning currently available.Within the connectionist research programme there are several platforms beingdeveloped. One is by Stich (1983) who argues for the elimination of folk psychologyand its replacement with cognitive science. Another, related, position, neurophilosophy,(the application of neuroscientific concepts and findings to traditional philosophicalquestions) advanced by the Churchlands (1986, 1989), as well as gaining somerespectability within naturalistic philosophy, has also been influential in the thinkingof several naturalistically inclined philosophers of education (Evers & Lakomski,1991, 1996, 2000; Walker, 1991). But neurophilosophy is not just a scholarly theory limited to those within theconnectionist movement. It is also slowly making its presence felt in the publicarena. Business Week, for example, in a cover story on how drugs to stave off age-induced memory impairment may be on the horizon, briefly set out the neurophil-osophical approach in a way that the lay reader could readily understand:1. A phone number you’ve just heard is captured by the brain’s cells, or NEURONS, as a pattern of electrical signals that transport it to a processing centre deep inside the brain called the HIPPOCAMPUS, responsible for learning and memory.2. Once the phone number is lodged in the hippocampus, a cascade of brain chemicals called NEUROTRANSMITTERS is released. These messenger chemicals carry the information across tiny gaps, called SYNAPSES, connecting the neurons.3. The NEUROTRANSMITTERS deliver the phone number to the appropriate area of the brain for storage. The stronger the MEMORY, the more synapses are cre- ated, strengthening the connections between neurons. Eventually, a group of neu- rons band together to form a long-term storage space for the information. (Arnst, 2003, pp. 50–1)Two more small quotations will suffice to convey the public face of neurophiloso-phy: first; ‘The brain contains over a trillion neurons that constantly reconfigurethemselves to form new memories or purge old ones. When everything is goingright, they perform this task more efficiently than the world’s fastest supercomputers’(Arnst, 2003, p. 50); second; … study after study has shown that people with limited or no formal education before the age of 10 are at a higher risk of Alzheimer’s later in life. It may be that intensive learning when the brain is young and plastic greatly increases the number of synapses. The brain can call on these reserves as it ages, or in case of injury, such as a stroke. This is when those piano or French lessons your parents forced on you as a child might pay off. ‘The more synapses you form in your lifetime the more you tip the balance in your favour as you age’. (Arnst, 2003, p. 52)If lay people are beginning to establish links between brains, learning and otherfacets of life, then how much more important it is for educators to do so. There is an important point to be made about this small but growing public faceof neurophilosophy. It is in competition with folk psychology and just as folk © 2005 Philosophy of Education Society of Australasia
  13. 13. Explaining Learning 679physics (e.g. the sun orbits the earth) was replaced by scientific explanations (e.g.the earth orbits the sun) so too is it likely that folk psychology will be replaced bya connectionist cognitive psychology as the best empirical explanation of ourselvesand our learning. If this is to occur, then the connectionist theory will need to beworked out in far greater detail than is currently available in order to have explan-atory power. It goes without saying that neurophilosophy is in the early stages ofits development and is likely to evolve as new theoretical and empirical findingsemerge, but even at this time the general direction of the connectionist programmeis clearly evident, so it will be this rather than the finer details which will beconsidered here. To begin with, we need to have some understanding of the brain,its structure and how it functions. Our sensory mechanisms are disturbed in their various ways; ear drums vibrate,retinas irradiate, and so on. The effects of these disturbances are transmitted, viathe central nervous system, to the brain. The brain responds. We say or do thingsas a consequence. In the process, we learn. But from a neurophilosophical perspect-ive, the key questions remain unanswered: How does the brain respond? What islearning? Each requires an answer. The brain is an organ about which we have a reasonable understanding at thegross level of its structure—it consists of cells (neurons); from each neuron extendsa fibre (axon) which usually branches at the end to make synaptic connections withother neurons and their dendrites. According to Paul Churchland (1989): Each neuron thus receives inputs from a great many other neurons, which inputs tend to excite (or inhibit, according to the type of synaptic connection) its normal or default level of activation. The level of activation induced is a function of the number of connections, of their size or weight, of their polarity (stimulatory or inhibitory), and of the strength of the incoming signals. Furthermore, each neuron is constantly emitting an output signal along its own axon, a signal whose strength is a direct function of the overall level of activation in the originating cell body. (Churchland, 1989, p. 160)The synaptic connections vary in strength. At the input level, the neuron receivessignals from other neurons via the synaptic connections of the various weights andFigure 1: Neural Structure© 2005 Philosophy of Education Society of Australasia
  14. 14. 680 John Clarkpolarities. The neuron strength is just that sum total of inputs from the dendriticconnections. If the synaptic weight were to change then the neuron would be eitherexcited or inhibited beyond normal in response to the same level of input signalsfrom the other neurons. It is the weight of the synapses which determine changesin signal strength, not a variation in the output strength from the other neuronswhich remain constant. At this input level, the sum of the inputs is transmittedalong the axon to become a synapse input to the next neuron. How the variousinputs from across the dendritic tree interact spatially and temporally to direct theneuronal output is unclear, but computer modelling, such as the GENESIS soft-ware (Bower & Beeman, 1995), is being used for programming neurally realisticnetworks to undertake the training up of models to perform particular tasks. This is just the starting point of theorising about the brain. Neurons, the billionsor trillions of them, are layered and form a network. For simplicity’s sake, theneural network can be diagramatically represented in a reduced way as a layer ofinput units (sensory receptor cells), a layer of output units (e.g. muscle activatingcells) and between these two a layer of hidden units which may, in neurologicalfact, represent a great many layers of neuronal connections. The input units of the network could be called the ‘sensory’ units because it isthey which receive the initial sensory stimulations. The initial input signals, whichmay vary from one input unit to the next, are ‘propagated’ upward, via input unit’saxons, to the next level, the multi-layered cells of the hidden units. Because thesynaptic weights vary, the units of the hidden level will have varying activationlevels, layer upon layer as well as within layers. At the output level, the output unitswill deliver output signals which have been transformed in myriad ways from theiroriginal input level. What does the transformation is the weightings of the synapseconnections which not only vary from one synapse to the next but also eachsynapse connection itself can change its weighting, possibly as the result of sensoryinput, and it is here that learning assumes significance. Experience begins with sensory neurons being activated. These sensory cells areconnected to cells in the second layer that become activated only if certain sortsFigure 2: Neural Network (adapted from Churchland, 1989, p. 162) © 2005 Philosophy of Education Society of Australasia
  15. 15. Explaining Learning 681of sensory activations occur. For example, if eating an apple activates a particularset of sensory cells then these in turn will activate the cell in the second layer whichhas become ‘hardwired’ through learning to detect the taste of apple. The cells ofthe second layer are not genetically ordained to detect apple or lemon but do soas a result of experience. It is the apple detecting cell or the lemon detecting cell,acquiring its appleness or lemonness through learning, that possibly provides forthe learned acquisition of the concepts of apple taste or lemon taste. If this is so,how is it possible? An answer can be found in the synapse weightings which canvary with experience. We have four taste receptors—sweet, sour, salty, bitter. Alltaste is governed by these four senses. Each sense is not like the computationalmodel, on/off or 0.1. Rather, think of a sophisticated stereo system with bass, treble,volume and speed. Each has a lever which can be moved up and down a ten pointscale. This provides for a 10 × 10 × 10 × 10 or 10,000 possible music combinations.Now suppose our four taste receptors work in a similar way (assuming 0–10). Thetaste of an apple might have one combination (sweet 6, sour 1, salty 0, bitter 1)while that of a lemon has another (sweet 1, sour 9, salty 0, bitter 3). While thelocalisation of cognitive functions to specific neural regions is becoming increas-ingly possible with the use of new evidence from neuro-imaging techniques (e.g.MRI), single neurons (or even clusters of them) do not necessarily represent eachbit of information the brain needs to store. Rather than there being a motherneuron which fires when we think of our mother, it is more likely that thoughtabout our mother involves quite complex patterns of parallel distributed processingacross many neural components. While the forward movement of information from sensory receptor cells to thesecond layer higher order brain cells may account for initial learning, it does notaccount for memory. Eating a lemon will activate the lemon taste memory, but inthe absence of a lemon what can activate the memory of lemon taste? The brainmust somehow be able to reverse the flow of information along descending path-ways in order to recall what a lemon tastes like (Martindale, 1991, p. 87). Usinga metaphor, Churchland (1995, pp. 99–100) likens this to a pipeline where theolder the information the further along the pipeline it is. As is sometimes the casein neurophilosophy, this philosophical metaphor has been cashed out as a neuro-logical explanation. Tortora & Grabowski (1996) suggest that, initially, newlylearned information is stored in the short-term memory which enables us to recallrecently acquired data. Short term memory appears to rely on particular electro-chemical events rather than on the development of more permanent structuralchanges associated with the formation of new synaptic connections. However, overa period of time, information may be transferred to a more permanent long-termmemory which is the result of structural changes in the brain. Why is this neurological point important? The brain has a life-long ability toreorganise neural pathways based on new experiences. As we learn, through eitherinstruction or experience, we acquire new insights. To be able to learn, or memo-rise, there must be constant functional changes to the brain that represent thislearning. A young baby is confronted by a plethora of sensory experiences with thesensory information being transmitted to the brain where it is processed. Early on© 2005 Philosophy of Education Society of Australasia
  16. 16. 682 John Clarkin its life, synaptic connections may be limited, but as the information inputincreases the neurons make more connections in order to transmit impulses to thebrain for processing. At an early age, the young infant’s genes somehow direct the‘neural pathway’ to the correct part of the brain from a specific sensory mechanism(e.g. apple taste is directed from the sensory cell to the appropriate second layerapple taste cell). Over the beginning years of life the brain grows as neuronsincrease in number. As a neuron develops it extends out an axon with branchesand evolves numerous dendrites thereby increasing the number of synaptic connec-tions and establishing particular connections with other neurons. So, at birth eachneuron in the cerebral cortex has approximately 2,500 synapses but by the age ofthree a child’s neurons have around 15,000 synaptic connections, which is abouttwice that of the average adult brain (Gopnik et al., 1999). No wonder Churchlandcould say, ‘The synaptic adjustments undergone by any normal infant make a seriesof conceptual revolutions that is never equalled in adult life’ (Churchland, 1995,p. 6). If this neurophilosophical account of learning is correct then it provides a verystrong base for those who argue for the importance of early childhood experience.This is further reinforced by the fact that as we grow older, synaptic connectionsare broken. This synaptic ‘pruning’ eliminates weaker neural contacts while strongconnections are not only retained but also strengthened. Our experiences deter-mine which synaptic connections will be strengthened and retained and which willbe pruned. Those connections which are activated most frequently are the onesmost likely to be secure, and the ones most likely to be preserved are those whichserve an ongoing function or purpose. Those which do not are at some point intime for pruning; the connection is lost and so that particular neural pathway isclosed off (Tortora & Grabowski, 1996). It is this plasticity of the brain thatpermits the opening of new and the closing of old synaptic connections—we learnnew things and remember them if they retain an ongoing function, old things arelong forgotten, so we recall some childhood experiences but have no recollectionof others. Over a person’s lifetime the number of cells, or neurons, change as oldones die and new ones are formed. While it would appear that new cells cannotemerge in the outer neocortex where complex functions such as planning, reason-ing and language take place, this is not so for the hippocampus, which is importantfor memory and learning, where neurogenesis (new cell growth) does occur (Gouldet al., 1999). Connectionism, composed of neural networks and parallel distributed process-ing, leading to the plasticity of the brain, does not lend itself to an easy explanationof learning, as Patricia Churchland points out: Consider, for example, patients who are so profoundly amnesic that they cannot remember the doctor they have seen day in and day out, or what they had for breakfast, or anything of a close relative’s visit earlier in the day. Yet these patients can learn some quite complex things, such as how to do mirror reading or how to solve the Tower of Hanoi puzzle … (though they do not remember that they have encountered the puzzle before or that they have learned to solve it). … Pertinent to the matter … © 2005 Philosophy of Education Society of Australasia
  17. 17. Explaining Learning 683 is the fact that there is as yet no principled description specifying what general class of thing these amnesic patients can still learn and what they cannot, and why they remember certain things and not others. So far, no theoretically grounded description has been winnowed out to specify the nature of the two capacities, if indeed such there be. (Churchland, 1986, p. 150)How are we to explain why some things are learned or remembered, and otherthings not? At a superficial level, we can point to the synaptic weights, for thesecontrol the strength of the neural signals from one neuron to the next. What wepresently are unable to do is explain how the current synaptic weights have beenarrived at and how new sensory inputs can lead to changes in synaptic weights andhence how the input signals (what a teacher says) are processed through neurallayers to be transformed into neural outputs (what a child says in response). If weknew this then we would have a far more powerful empirical handle on howchildren learn, the required conditions for learning, and the most effective andefficient strategies for promoting learning. Connectionism may provide us with a plausible solution to the problem of iden-tifying the mechanisms which permit the assimilation and accommodation of newexperience. Assimilation is the integration of new learning with that already learnedin a relatively straightforward way: neurologically, this is accounted for by theforward movement of data from the first layer sensory cells moving along existingascending pathways to the second layer neurons and by the backward movementof data along descending pathways from the second layer cells to bring priorexperience to bear on current experience in a manner which requires very littlemodification of synaptic connections and weights. This entails a minimum of con-ceptual redeployment of existing concepts constituted by current neural contactwith the second layer (e.g. lemon taste cell, apple taste cell) and the neural pathwayconnections. When our existing conceptual apparatus is unable to assimilate newexperience, accommodation is acquired either by revising that which has alreadybeen learned or by creating new conceptual structures: some second layer cellsacquire content (e.g. banana taste developed upon eating a banana for the firsttime) and/or new synaptic connections are formed either by revised weightings ornew dendritic linkages for the redeployment of concepts. Once these target cellshave relevant content and new neural pathways are formed to allow new experi-ences to be moved up and down the pathways then the learner is able to assimilatesimilar future experiences (e.g. banana eating). The connectionist approach has both strengths and some unresolved difficulties.On the positive side, it is evident that neural networks do match up with what weknow about neurology, at least in broad terms. Brains consist of neurons set in anetwork; so too is the neural network built on units and their connections. Neuralnetworks are well suited to solving problems which require complex parallelprocessing of information for their solutions. Neural networks also handle conceptswhich have blurred edges allowing for exceptions, whereas the computationalmodel, based on algorithmic rules, has difficulty doing so. Thus, connectionism is© 2005 Philosophy of Education Society of Australasia
  18. 18. 684 John Clarkable to recognise patterns not easily reduced to rules. Finally, connectionist modelshave made some rapid advances in demonstrating the power of neural networks tomaster cognitive tasks. Sejnowski and Rosenberg’s (1987) early work on a neuralnet (NETtalk) that could read English text coupled to phonics and a speechsynthesiser did a fairly good job of training up, over a period of time, the pro-nouncement of English text given to it. The most recent version of Dragon Dictateis now so sophisticated that it can be trained in minutes for voice recognition toturn spoken English into very accurate written English and vice versa. But the connectionist programme is not without its problems. There is a markedtendency to abstract out many aspects of the brain that may bear on learning (e.g.different kinds of neurons, effects of hormones). The model simplifies the activatedflow from inputs to hidden units and on to output units. More realistic models ofthe brain would trace the many layers of hidden units and connections back downwhich are required to explain short-term memory. Neural networks also seem torequire far too many repetitions for learning (training up) compared to brains.Given the limitations of computers, training a net to perform a task may take days/weeks. Some of the difficulty may be resolved when sufficient parallel connectedcomputer power is available to run neural networks. But whether computers canfully model human learning is problematic—humans can learn from a single expe-rience, but computers seem incapable of doing so. Lastly, connectionism does havedifficulty handling rule-based learning, which computationalism is more suited to. However, there are several reasons for thinking that brains are not like computersand so cannot be understood by the computational model. First, computers oper-ate in serial, one computation after the other. If one link in the chain fails, all fail(an example would be Christmas tree lights where if one bulb blows all the lightsgo out). But the human brain works in parallel with many operations performed intandem which allows for complex performances. Further, if one part of the systemshould close down or malfunction then in many cases new neural pathways can begenerated to maintain the performance. Second, brains are not like computerhardware which runs all sorts of software programmes. Rather, each neuron is likea small microcomputer wired up to perform a particular task. However the neuronitself is not the basic computational unit; rather, it is the parts of the neuron whichmatter as they change and interact with other neurons. Learning is not akin to therunning of new software, but occurs when there are changes to the hardwiring ( the synapse weightings). Learning can be characterised in terms of changes insynaptic weights in the neural network and the subsequent reduction of error innetwork output. Third, the computational account relies on syntax, hence sen-tences, or the manipulation of propositions, for learning to happen. But animalsand pre-linguistic children (new-born babies) learn but do not use sentences intheir learning, so pre-linguistic learning in young children cannot be sentential.Some other non-syntactical account of learning is therefore required. And even ifsentential processing is part of brain functioning, it does not explain some brainfunctioning (e.g. pattern recognition, physical skills) which are non-linguistic.Finally, if our learning is algorithmic then it is very evident that in areas wherealgorithms matter the most (mathematics, logic) children (and adults) fail to perform © 2005 Philosophy of Education Society of Australasia
  19. 19. Explaining Learning 685well in algorithmic computations. This suggests that some other basis of learningis required; one possibility lies in pattern recognition which very young pre-linguisticchildren appear to do so well in. Thus, connectionism rejects the computationalaccount based on formal systems, sentences containing mental representation andsyntactical rules in favour of a neural, electro-chemical, pattern recognition theoryof learning. These considerations lead to questions about the relationship between the com-putational and connectionist approaches. One possibility is implementationconnectionism which seeks an accommodation between the two. The brain is con-ceptualised as a symbol processor, as a neural network but processing symbolicinformation at a higher level, so research is aimed at explaining how neural net-works support symbolic processing. On the other hand, radical connectionismrejects symbolic processing which is unable to explain many of the features ofhuman learning captured by neural networks. It thus advocates the elimination ofmental explanation, including folk psychology. How this tension will work itself outis far from clear, as the example of systematicity reveals. Fodor and Pylyshyn(1988) identify a feature of human intelligence called systematicity—the ability tounderstand some sentences is intrinsically connected to the ability to understandother sentences with a similar structure. They deny that connectionist models cando this; only computational models can. But as others (e.g. Aizawa, 1997) point out,computationism is no better at performing this task. Neither computationism norconnectionism alone seem able to do so. Some combination of the two may be required.ConclusionIf learning is to be explained then clearly it is not going to be explained byconceptual analysis, behaviourism or constructivism. None of these have theexplanatory power required to give an adequate empirical account of the mecha-nisms of learning, of how we either add new information to that which we alreadyhave or make the necessary adjustments required when the new is inconsistent withthe old. Cogitive science, which makes extensive use of the epistemic resources ofphilosophy and science, may offer a way forward, although for this to happen theconflict between the computational and connectionist approaches will need to beresolved, which may not be until well into the future. Within education, the computation position appears to have the inside running,especially with those psychologists and teachers working with ICT and computer-assisted learning which are being heavily promoted and progressively introducedinto schools and classrooms on a large scale. For many, the computational modelof learning sits comfortably with the growing emphasis on digital literacy andtechnological competence, thus giving it legitimacy. But the wholesale adoption ofcomputationalism is no guarantee that it really does explain learning. Given someof the successes of connectionism, it is very likely that it does not. Neurophilosophyis a viable alternative theory of learning. Rather than pouring all of the availableeducational resources into constructivist/computational approaches to learning, atthe expense of connectionism, it would not be unreasonable to also direct resources© 2005 Philosophy of Education Society of Australasia
  20. 20. 686 John Clarkto establishing a sound research programme in neurophilosophy as well, since ithas as much a chance of success in explaining, hence enhancing, children’s learn-ing as its well-supported rival. Not to do so would be ethically indefensible if thosecurrently supporting computationalism/constructivism eventually discover theywere backing the wrong horse. Against the odds, for my part, as a sound empiricalconjecture open to empirical refutation, I shall back neurophilosophy which, if itturns out to be the best explanatory theory of learning available, will carry with ita very large pay-back indeed, in terms of how teachers understand and promotechildren’s learning. And that, as they say, is what really matters!ReferencesAizawa, K. (1997) Explaining Systematicity, Mind & Language, 12, pp. 115–136.Arnst, C. (2003) I Can’t Remember, Business Week, 1 September, pp. 49–54.Blackman, D. E. (1984) The Current Status of Behaviourism and Learning Theory in Psychol- ogy, in: D. Fontana (ed.) Behaviourism and Learning Theory in Education (Edinburgh, Scottish Academic Press) pp. 3 –14.Block, N. (1990) The Computer Model of the Mind, in: D. N. Osherson & E. E. Smith (eds), Thinking: An invitation to cognitive science (Cambridge, MA, MIT Press).Bower, J. & Beeman, D. (1995) The Book of GENESIS (New York, Springer-Verlag).Churchland, P. M. (1989) A Neurocomputational Perspective (Cambridge, MA, MIT Press).Churchland, P. M. (1995) The Engine of Reason and the Sea of the Soul (Cambridge, MA, MIT Press).Churchland, P. S. (1986) Neurophilosophy (Cambridge, MA, MIT Press).Dearden, R. F. (1967) Instruction and Learning by Discovery, in: R. Peters (ed.) The Concept of Education (London, Routledge & Kegan Paul) pp. 135–155.Evers, C. & Lakomski, G. (1991) Knowing Educational Administration: Contemporary methodolog- ical controversies in educational administration (Oxford, Pergamon).Evers, C. & Lakomski, G. (1996) Exploring Educational Administration: Coherentist applications and critical debates (Oxford, Pergamon).Evers, C. & Lakomski, G. (2000) Doing Educational Administration: A theory of administrative practice (Oxford, Pergamon).Feyerabend, P. (1975) Against Method (London, Vergo).Fodor, J. & Pylyshyn, Z. (1988) Connectionism and Cognitive Architecture: A critical analysis, Cognition, 28, pp. 3 –71.Fontana, D. (1984) Behaviourism and Learning Theory in Education (Edinburgh, Scottish Academic Press).Gopnik, A., Meltzoff, A. & Kuhl, P. (1999) The Scientist in the Crib: What Early Learning Tells Us About The Mind (New York, Harper Collins).Gould, E., Beylin, A., Tanapat, P., Reeves, A. & Shors, T. (1999). Learning Enhances Adult Neurogenesis in the Hippocampus Formation, Nature and Neuroscience, 2, pp. 260 – 265.Gunstone, R. F. (2000) Constructivism and Learning Research in Science Education, in: D. Phillips, (ed.) Constructivism in Education (Chicago, National Society for the Study of Education) pp. 254 – 280.Hamlyn, D. (1967) The Logical and Psychological Aspects of Learning, in: R. Peters (ed.) The Concept of Education (London, Routledge & Kegan Paul) pp. 24–43.Hamm, C. (1989). Philosophical Issues in Education. New York: Falmer Press.Hirst, P. & Peters, R. S. (1970) The Logic of Education (London, Routledge and Kegan Paul).Komisar, P. (1965) More on the Concept of Learning, Educational Theory, 15, pp. 230–239.Kuhn, T. (1970) The Structure of Scientific Revolutions (Chicago, University of Chicago Press). © 2005 Philosophy of Education Society of Australasia
  21. 21. Explaining Learning 687Livingston, K. (1996) The Neurocomputational Mind Meets Normative Epistemology, Philosophical Psychology, 9:1, pp. 33 – 59.Martindale, C. (1991) Cognitive Psychology: A neural-network approach (Pacific Grove, CA, Brooks/Cole Publishing).Matthews, M. (2000) Appraising Constructivism in Science and Mathematics Education, in: D. Phillips (ed.) Constructivism in Education (Chicago, National Society for the Study of Education) pp. 161–192.Phillips, D. (ed.) (2000) Constructivism in Education (Chicago, National Society for the Study of Education).Popper, K. (1959) The Logic of Scientific Discovery (London, Hutchinson).Quine, W. (1969) Ontological Relativity and Other Essays (New York, Columbia University Press).Sejnowski, T. & Rosenberg, C. (1987) Parallel networks that learn to pronounce English text. Complex System, 1, pp. 145–168.Solomon, P. G. (1998) The Curriculum Bridge (Thousand Oaks, CA, Corwin Press).Stich, S. (1983) From Folk Psychology to Cognitive Science: The case against belief (Cambridge, MA, MIT Press).Tortora, G. & Grabowski, S. (1996) Principles of Anatomy and Physiology (8th edn.) (New York, Harper Collins).Wadsworth, B. J. (1996) Piaget’s Theory of Cognitive and Affective Development (5th edn.) (New York, Longman).Walker, J. (1991) Coherence and Reduction: Implications for educational inquiry, International Journal of Educational Research, 15:6, pp. 505–520.Wheldall, K. & Merrett, F. (1984) The Behavioural Approach to Classroom Management, in: D. Fontana (ed.) Behaviourism and Learning Theory in Education (Edinburgh, Scottish Academic Press) pp. 15 – 42.© 2005 Philosophy of Education Society of Australasia