John clark

Educational Philosophy and Theory, Vol. 37, No. 5, 2005

Explaining Learning: From analysis to
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Massey University, Palmerston North, NZ

Abstract
This paper seeks to explain learning by examining five theories of learning—conceptual
analysis, behavioural, constructivist, computational and connectionist. The first two are
found wanting and rejected. Piaget’s constructivist theory offers a general explanatory
framework (assimilation and accommodation) but fails to provide an adequate account of
the empirical mechanisms of learning. Two theories from cognitive science offering rival
explanations of learning are finally considered; it is argued that the brain is not like a
computer so the computational model is rejected in favour of a neurally-based connectionist
model of learning.

Keywords: learning, connectionism, cognitive science, neurophilosophy

We are born and we die; between, we learn. We learn to distinguish colours, red
from 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 what
makes us happy and sad; we learn that the world of which we are a part is
constituted in such ways that we shape it and it us. Some things are so general and
commonplace—most of us learn that putting a hand on something very hot is
rather painful; other things are quite specific—I learn a bit more about myself from
a particular experience—the youthful misery of love lost. Most of us, with a few
sad exceptions, learn and continue to learn from first day to last, such is the human
condition. Many of us go on to learn about learning, for as teachers, learning is
our business. A few of us seek deeper theoretical accounts of learning—such is the
task 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. Have
done 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 learning
even though they may not know the finer details of learning: successfully so since
adults first started teaching children to learn this and that. Quite so, but in an
increasingly 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 Australasia
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668 John Clark

teachers have a deeper appreciation of learning derived from the theoretical studies
of researchers.
To date, the public record of explanatory accounts of learning to be considered
here—conceptual analysis, behaviourism, constructivism—has not been good, but
with the emergence of cognitive science the future for learning and learning theory
looks promising. Why so? Well, the one thing which binds these three approaches
together and separates them from the latter is their reluctance to utilise the full
epistemic resources of science to explain learning. Conceptual analysis is, as its
name suggests, no more than the linguistic analysis of a concept having no explanat-
ory power; behaviourism and constructivism fail to explain learning processes—the
former eschews it and the latter offers no more than a metaphor—so leaving the
field in a state of explanatory paralysis. If we are ever to get to the bottom of
explaining what learning is, it will be by rigorous and systematic empirical studies
of 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 different
theories of learning, three of which will be found wanting. What separates the three
from the one is the former’s autonomy from science which the latter rejects. Deeper
still is an underlying disagreement about the relationship between philosophy and
science; cleaved or continuous.

Conceptual Analysis of Learning
Learning, 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 of
education who, employing conceptual analysis, thought that the meaning of a
concept 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 is
not necessary. Rather, consideration will be given to one account, that of Hamm,
which synthesised the various analyses of the concept; in so doing, he distinguished
three criteria central to the concept of learning.
Hamm (1989) defined learning as ‘intentionally coming to know (or believe, or
perform, etc) as a result of experience’ (p. 91). He then identified three necessary
and sufficient conditions for the concept of learning, such that a social practice is
one of learning if it meets all three criteria, namely, intentionality, experience and
mastery.
The first criterion, intentionality, requires that ‘learning is an activity that one
engages 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 concedes
that non-intentional learning is possible, including sleep learning, learning by
hypnosis, and learning through conditioning, all of which are instances of learning
resulting from unconscious experience. The second criterion, experience, acknow-
ledges that experience can be either conscious or unconscious, although Hamm
finds the notion of unconscious experience troubling. He asks: can something that
happens to one of which one is not aware be called ‘experience’? (ibid. p. 92). The


Explaining Learning 669

third criterion, mastery, insists that ‘learning always has an object (x), mastery of
which is essential for learning to occur’ (ibid. p. 92), with x being either a skill or
an 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. Two
must be considered here, one substantive, the other methodological. The substantive
point is that any appeal to an ordinary language analysis of a concept will generate
a 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 by
his acceptance of a fundamental contradiction, namely, learning is intentional yet
unintentional learning is possible. On the question he poses, ‘How does learning
take 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 no
scientific generalisations about learning are possible. But how learning takes place
is not a matter of what is learned but of explaining where learning takes place and its
modus 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 is
a first-order activity which through the use of concepts and theories seeks to
describe and explain empirical phenomena, to account for observables by positing
unobservables (e.g. gravity, magnetism) while philosophy brings linguistic analysis
to bear on clarifying the necessary and sufficient conditions for the use of first-order
concepts (Hirst & Peters, 1970, pp. 2–8). In rebuttal, (1) the practice of philosophy
is as much a first order activity as any other activity, for anyone can engage in
philosophical reflection on their daily activities; (2) conceptual clarification is not
the province of philosophy alone, for science itself creates and refines concepts in
a 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 scientific
resources available to it; (4) given the commonality of language and experience,
science and philosophy are continuous, not disconnected; and (5) conceptual analysis
is but one method in philosophy, and, being largely discredited, it is not all that
effective 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 an
explanatory theory of learning. On the first count, their explications are not all that
insightful; on the second count, when they do stray into the explanatory, their
efforts are less than adequate.


670 John Clark

Behavioural Theories of Learning
In a very broad sense, all humans are, at least on occasions, behaviourist: which
parent, which teacher has not at some time or other sought to bring about changes
in children’s behaviour by rewards (kind words of praise, a gift) and punishments
(strong words of discouragement, withdrawal of a privilege) without thinking too
much about what was going on inside the child’s head. A change of behaviour is
sought 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 psychological
deﬁnition of the term, behaviourists. Yet in schools behaviourism has had a power-
ful inﬂuence in shaping teachers’ practice directed at student’s learning, and its
legacy is still with us. Numerous behavioural accounts of learning abound, but the
position 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 phenomena
of 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 antecedent
events (discriminative stimulus) and consequences (reinforces/punishers)’ (ibid. p. 6).
Finally, ‘it is not the existence or reality of mental or psychological processes which
is at issue, but rather the level of explanation which empirical psychologists should
seek 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



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 theory
of learning. First, learning is not reducible to change in behaviour. Much that we
learn results in no change in behaviour: watching the TV news, I learn about
something that happened in a far-off country but this learning leads to no change
in my behaviour, now or in the future; I read an historical treatise on an event long
ago—interesting, absorbing even, but learning something about the ancient Greeks
need produce no change in my behaviour. Countless other examples we could give
would make exactly the same point. On the other hand, there can be changes in
behaviour which are not the result of learning, or to put it more accurately, on the
basis 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 place
on the various causes of my behaviour. I learn nothing new but my behaviour
changes as a result of revising that which I already had. So, as a definition of
learning, behaviourism is deficient. As for explaining learning, of elucidating the
inner processes of learning, behaviourism is silent for it can offer nothing but the
external conditions of learning. It does not, indeed cannot, tell us how children’s
thinking 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 of
behaviour, pays no attention to the details of mental life. Philosophy, on the other
hand, is particularly interested in describing and explaining the inner life of human
beings. Behaviourism is avowedly scientific and has deliberately distanced itself
from conceptual philosophy. Not that it has cut its links with philosophy altogether,
for behaviourism does unashamedly draw inspiration from a once influential
philosophy of science—logical positivism. It’s just unfortunate that both have been
discredited. For all its early promise, behaviourism failed to deliver on learning,
and in doing so paralysed research into learning.

Constructivist Theories of Learning
Unlike behaviourism, constructivist theories of learning do attend to what goes on
in children’s heads. Unfortunately, as Matthews (2000) observes, constructivism


672 John Clark

has ‘spread its wings from its learning theory origins’ (p. 16); he remarks that the
following dimensions of constructivism need to be separated—constructivism as a
theory of cognition, learning, teaching, education, personal knowledge, scientific
knowledge, 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 of
meaning only. In the same volume, Constructivism in Education (Phillips, 2000),
Gunstone (2000) provides, in summary form, a representative constructivist view
of 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 specific
theories 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 theory
of 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, Piaget
and constructivism, remarked: ‘The notion that knowledge is self-constructed is
most frequently connected to the work of Jean Piaget’ (p. 40). Piaget proposed a
theory of learning which, unlike behaviourism, sought to explain what goes on
inside the learner’s head: schemata, assimilation, accommodation and equilibration
are now familiar terms to all those many teachers who have been introduced to
Piaget’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 than
briefly outline Piaget’s thesis.
Schemata: ‘are the cognitive or mental structures by which individuals intellectually
adapt to and organise the environment. … The structures are inferred to exist. …
Schemata are not physical objects; they are viewed as processes within the nervous



system. 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 ‘concepts
or categories … used to process and identify or classify incoming stimuli. In this
way, 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 affect
the growth of schemata and is thus a part of development’ (Wadsworth, 1996,
p. 17).
Accommodation: is concerned with changes in schemata. Since adult schemata are
different 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 is
a 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 have
schemata and through assimilation and accommodation growth and development
occur within equilibration. Piaget’s theory of learning is consistent with Gunstone’s
constructivist principles of learning outlined above. For Piaget, learning does
depend on both the learning environment and the knowledge of the learner and
their interaction; learning does involve constructing meaning which is assimilated
to or accommodated by our existing knowledge; learning, as the construction of
meaning, 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;


674 John Clark

patterns 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 alone
anything, requires a level of conceptual understanding and moral insight which
many young children are simply not capable of and should not be held accountable
for.
Attractive as it is, at least in comparison with its behavioural rival, Piaget’s theory
simply fails to explain learning. What, exactly, are schemata? It is not all that
helpful to be told they are ‘inferred to exist and are properly called hypothetical
constructs’. How, exactly, do children ‘integrate new perceptual, motor or concept-
ual matter into existing schemata’? How, exactly, does one ‘create new schema’ in
which 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 to
be 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 of
cognition, teaching, education, science, knowledge and the like nor of constructiv-
ism in its more general sense. Insofar as the objections are directed solely at a
constructivist theory of learning then they cannot be generalised as objections to
these other constructivist theses. Accordingly, constructivists who do not adhere to
a Piagetian theory of learning are not open to the criticism levelled against it.

Cognitive Science
Piaget’s theory of learning, involving schemata, assimilation, accommodation and
equilibrium, is an attempt to provide an answer to a deeper philosophical problem:
given our current conceptual scheme how do we determine when new additions
can just be joined to the old in an incremental way and when must the existing
framework be revised, partially or profoundly, to incorporate new elements which
were previously incompatible with the old. Although Piaget’s account provides a
useful way of conceptualising learning, especially that of children, it does not
provide a satisfactory explication of the underlying mechanisms. If we accept
Piaget’s terminology, that in their learning children assimilate and accommodate
new experiences, this merely describes what they do; it does not explain how or why
they assimilate or accommodate. What is required is some plausible explanatory
theory 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 science
has 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 kinds
of things humans can do) and philosophy (especially epistemology, philosophy of
science and philosophy of mind). Here, the relation of philosophy to science is
quite different from the second order/first order conception of analytic philosophy



which framed the sort of linguistic analysis of learning offered by Hamm. There is
no ‘first philosophy’ (Quine, 1969) against which science is to be judged; rather,
philosophy and science are continuous such that the findings of science are relevant
to philosophical arguments and philosophical understandings are built into the
scientific enterprise. Philosophy and science therefore come together in our theo-
rising about ourselves for we utilise both conceptual and empirical resources in
order to describe and explain ourselves.
Because no clear line can be drawn between the two, scientists and philosophers
should work collaboratively in using all available epistemological resources to bring
empirical 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 of
philosophy and science, compete to explain learning. The former, with its folk
psychology links into constructivism, is at odds with the latter which seeks to
eliminate mental explanations and replace them with neural network theory.

Computationalism
The computational programme goes beyond commonsense to provide a deeper
explanation. Impressed with the computational power of computers to process
alphabetic 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 and
propositional attitudes match the symbolic information which is processed. This is
not to say that brains, minds and propositional attitudes have the same structural
properties as computer hardware, software and data, but rather that the two func-
tion in similar computational ways. In this sense, computationalism has no interest
in the brain as such, so neuroscience plays little part. On the other hand, artificial
intelligence is central, for if humans process information computationally then the
study of computer processing might offer insights into human computation. The
analogy runs something along these lines: the computer contains the hardware, to
which 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, how
are propositional attitudes processed in what we call thinking and learning?


676 John Clark

For the computational cognitive scientist, the computer and the brain function
in similar ways. The computer works in binary (0.1) with electronic circuits either
open 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 as
binary gates for propositions (‘and’ sentences could be 0.0, ‘or’ sentences could be
1.0). 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 the
same way as humans think then it should be possible to build machines that think
as we do which would help us to have a deeper understanding of how humans think
and learn; indeed, if computers ‘think’ like we think then such machines could be
used to help children learn more effectively!
If the mind is a sort of computation device, what sort of information does it
process, and how does it process it? Computers manipulate symbols, so too do
minds. The manipulation of symbols is not random but according to syntactical
rules; in the case of language and propositions, syntactical rules include both the
rules required to make a sentence a sentence (e.g. ends with a full stop, question
mark, 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 or
mind, is algorithmic in that both follow a given set of operations resulting in an
assured and a particular outcome. Follow the rule and the result will eventuate: in
mathematics, children learn to follow the rule of addition and so arrive at the right
answer regardless of the numbers in the equation. Further, one can follow the
algorithmic rule successfully yet not grasp what one was doing. Long division is a
case in point for many young children.
This suggests that there is more to the computation of symbols than syntax
alone. The symbols also represent, or stand for something beyond themselves.
Here, semantic rules are also required to give an interpretation or meaning to the
symbols being computated; in short, propositional attitudes account for both what
people learn (propositional) and why they learn them (attitudes): ‘I believe it will
rain today’ says something about a proposition (‘It will rain today’) and what it
represents (It will rain today) as well as an explanation for learning it or holding
to it, namely that one believes (or hopes or wishes or wants) the proposition such
that what it represents will come about, i.e. that it will rain today.
A computational theory of learning has considerable attraction for two groups of
people, those drawn to the power of computers which in relevant ways function
similarly to brains computationally, and those who see merit in retaining mental
explanations of human learning. By bringing these two interests together, added
strength is given to both positions: the computerists are able to add semantics to
symbols while those who defend folk psychology can do so by grounding it in the
empirical 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 constant
ﬁxed state unless externally altered, brains have a facility to change over time which
alters their functioning. So, whereas the computer simply applies the algorithmic
rules given it, human learning relies as much on revising and changing the rules



as it does on following them. Computers take syntactical and semantic rules as a
given, supplied to them by humans; human computation, on the other hand, is at its
best when the syntactical rules are altered to allow for new linguistic permutations
and the semantic rules are revised to permit new meanings. So when, for example,
Popper (1959) set out the logic of scientific discovery, codifying syntactical and
semantic rules, Kuhn (1970) and Feyerabend (1975) responded by pointing out
how the history of scientific progress rested not on a set of logical rules but on the
flouting of them. So too with learning more generally. The computational theory
of learning supposes there to be a fixed set of rules to be followed and while this
might be suitable for understanding computations performed by computers, given
the vagaries of human thought then computation is an inappropriate theory of
learning, for we learn from our mistakes in ways that computers cannot.
Second, the ontological status of mental states, in particular the belief-desire
psychology that underpins the attitudes towards propositions, is problematic. Do
words such as ‘belief ’, ‘hope’, ‘wish’ and the like refer to actual beliefs, hopes and
wishes which humans are said to possess which causally account for what we say
and do, or are they like the word ‘unicorn’ which refers to nothing for there are
no unicorns? How do we know that we have beliefs, hopes, wishes—what evidence
is 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 of
providing an explanation of how, in Piagetian terms, children learn to assimilate
and accommodate information. It is hard to see what algorithmic rules could be
formulated to guide learning except for that learning which lends itself to the rote
learning of fixed rules. Given that learning consists also of finding exceptions to
rules, of breaking the rules, of creatively applying the rules in novel ways, and so
on, then computation may not be the best approach to take in cognitive science.
Like behaviourism and constructivism, computationalism looks also to be paralytic
in its ability to explain learning.

Connectionism (Hippocampus)
The connectionist approach to learning sets out to solve the difficulties inherent in
the computational approach: it begins with studies of the brain, its structure and
functions, in order to explain how the stunning achievements of human thought
and learning are possible. So, for connectionism neuroscience is to the fore rather
than artificial intelligence; this turns the computational theory on its head—rather
than model human learning on computer computation, computers are used to
model theories of learning. Further, if folk psychology is incompatible with the
findings of neuroscience then it will need to be eliminated and replaced by an
empirical theory of how brains learn. And lastly, a detailed study of the brain may
offer an empirical explanation of the mechanisms which operate to facilitate that
which we call the assimilation and accommodation processes of learning. Cognitive
neuroscience attempts to explain cognitive processes without trying to separate
them 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


678 John Clark

it is well on the way to becoming the best theory of learning currently available.
Within the connectionist research programme there are several platforms being
developed. One is by Stich (1983) who argues for the elimination of folk psychology
and its replacement with cognitive science. Another, related, position, neurophilosophy,
(the application of neuroscientific concepts and findings to traditional philosophical
questions) advanced by the Churchlands (1986, 1989), as well as gaining some
respectability within naturalistic philosophy, has also been influential in the thinking
of 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 the
connectionist movement. It is also slowly making its presence felt in the public
arena. 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 reconfigure
themselves to form new memories or purge old ones. When everything is going
right, 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 other
facets 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 face
of neurophilosophy. It is in competition with folk psychology and just as folk



physics (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 by
a connectionist cognitive psychology as the best empirical explanation of ourselves
and our learning. If this is to occur, then the connectionist theory will need to be
worked 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 of
its development and is likely to evolve as new theoretical and empirical findings
emerge, but even at this time the general direction of the connectionist programme
is clearly evident, so it will be this rather than the finer details which will be
considered 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, via
the central nervous system, to the brain. The brain responds. We say or do things
as a consequence. In the process, we learn. But from a neurophilosophical perspect-
ive, the key questions remain unanswered: How does the brain respond? What is
learning? Each requires an answer.
The brain is an organ about which we have a reasonable understanding at the
gross level of its structure—it consists of cells (neurons); from each neuron extends
a fibre (axon) which usually branches at the end to make synaptic connections with
other 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 receives
signals from other neurons via the synaptic connections of the various weights and

Figure 1: Neural Structure


680 John Clark

polarities. The neuron strength is just that sum total of inputs from the dendritic
connections. If the synaptic weight were to change then the neuron would be either
excited or inhibited beyond normal in response to the same level of input signals
from the other neurons. It is the weight of the synapses which determine changes
in signal strength, not a variation in the output strength from the other neurons
which remain constant. At this input level, the sum of the inputs is transmitted
along the axon to become a synapse input to the next neuron. How the various
inputs from across the dendritic tree interact spatially and temporally to direct the
neuronal output is unclear, but computer modelling, such as the GENESIS soft-
ware (Bower & Beeman, 1995), is being used for programming neurally realistic
networks to undertake the training up of models to perform particular tasks.
This is just the starting point of theorising about the brain. Neurons, the billions
or trillions of them, are layered and form a network. For simplicity’s sake, the
neural network can be diagramatically represented in a reduced way as a layer of
input units (sensory receptor cells), a layer of output units (e.g. muscle activating
cells) and between these two a layer of hidden units which may, in neurological
fact, represent a great many layers of neuronal connections.
The input units of the network could be called the ‘sensory’ units because it is
they which receive the initial sensory stimulations. The initial input signals, which
may vary from one input unit to the next, are ‘propagated’ upward, via input unit’s
axons, to the next level, the multi-layered cells of the hidden units. Because the
synaptic weights vary, the units of the hidden level will have varying activation
levels, layer upon layer as well as within layers. At the output level, the output units
will deliver output signals which have been transformed in myriad ways from their
original input level. What does the transformation is the weightings of the synapse
connections which not only vary from one synapse to the next but also each
synapse connection itself can change its weighting, possibly as the result of sensory
input, and it is here that learning assumes signiﬁcance.
Experience begins with sensory neurons being activated. These sensory cells are
connected to cells in the second layer that become activated only if certain sorts

Figure 2: Neural Network (adapted from Churchland, 1989, p. 162)



of sensory activations occur. For example, if eating an apple activates a particular
set of sensory cells then these in turn will activate the cell in the second layer which
has become ‘hardwired’ through learning to detect the taste of apple. The cells of
the second layer are not genetically ordained to detect apple or lemon but do so
as 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 for
the 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 can
vary with experience. We have four taste receptors—sweet, sour, salty, bitter. All
taste is governed by these four senses. Each sense is not like the computational
model, 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 point
scale. 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). The
taste 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 the
localisation 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 each
bit of information the brain needs to store. Rather than there being a mother
neuron which fires when we think of our mother, it is more likely that thought
about our mother involves quite complex patterns of parallel distributed processing
across many neural components.
While the forward movement of information from sensory receptor cells to the
second layer higher order brain cells may account for initial learning, it does not
account for memory. Eating a lemon will activate the lemon taste memory, but in
the absence of a lemon what can activate the memory of lemon taste? The brain
must 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). Using
a metaphor, Churchland (1995, pp. 99–100) likens this to a pipeline where the
older the information the further along the pipeline it is. As is sometimes the case
in neurophilosophy, this philosophical metaphor has been cashed out as a neuro-
logical explanation. Tortora & Grabowski (1996) suggest that, initially, newly
learned information is stored in the short-term memory which enables us to recall
recently acquired data. Short term memory appears to rely on particular electro-
chemical events rather than on the development of more permanent structural
changes associated with the formation of new synaptic connections. However, over
a period of time, information may be transferred to a more permanent long-term
memory which is the result of structural changes in the brain.
Why is this neurological point important? The brain has a life-long ability to
reorganise neural pathways based on new experiences. As we learn, through either
instruction 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 this
learning. A young baby is confronted by a plethora of sensory experiences with the
sensory information being transmitted to the brain where it is processed. Early on


682 John Clark

in its life, synaptic connections may be limited, but as the information input
increases the neurons make more connections in order to transmit impulses to the
brain 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 layer
apple taste cell). Over the beginning years of life the brain grows as neurons
increase in number. As a neuron develops it extends out an axon with branches
and evolves numerous dendrites thereby increasing the number of synaptic connec-
tions and establishing particular connections with other neurons. So, at birth each
neuron in the cerebral cortex has approximately 2,500 synapses but by the age of
three a child’s neurons have around 15,000 synaptic connections, which is about
twice that of the average adult brain (Gopnik et al., 1999). No wonder Churchland
could say, ‘The synaptic adjustments undergone by any normal infant make a series
of 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 very
strong 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 connections
are broken. This synaptic ‘pruning’ eliminates weaker neural contacts while strong
connections are not only retained but also strengthened. Our experiences deter-
mine which synaptic connections will be strengthened and retained and which will
be pruned. Those connections which are activated most frequently are the ones
most likely to be secure, and the ones most likely to be preserved are those which
serve an ongoing function or purpose. Those which do not are at some point in
time for pruning; the connection is lost and so that particular neural pathway is
closed off (Tortora & Grabowski, 1996). It is this plasticity of the brain that
permits the opening of new and the closing of old synaptic connections—we learn
new things and remember them if they retain an ongoing function, old things are
long forgotten, so we recall some childhood experiences but have no recollection
of others. Over a person’s lifetime the number of cells, or neurons, change as old
ones die and new ones are formed. While it would appear that new cells cannot
emerge 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 important
for memory and learning, where neurogenesis (new cell growth) does occur (Gould
et 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 explanation
of 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 …



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 other
things not? At a superficial level, we can point to the synaptic weights, for these
control the strength of the neural signals from one neuron to the next. What we
presently are unable to do is explain how the current synaptic weights have been
arrived at and how new sensory inputs can lead to changes in synaptic weights and
hence how the input signals (what a teacher says) are processed through neural
layers to be transformed into neural outputs (what a child says in response). If we
knew this then we would have a far more powerful empirical handle on how
children learn, the required conditions for learning, and the most effective and
efficient 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 new
experience. Assimilation is the integration of new learning with that already learned
in a relatively straightforward way: neurologically, this is accounted for by the
forward movement of data from the first layer sensory cells moving along existing
ascending pathways to the second layer neurons and by the backward movement
of data along descending pathways from the second layer cells to bring prior
experience to bear on current experience in a manner which requires very little
modification of synaptic connections and weights. This entails a minimum of con-
ceptual redeployment of existing concepts constituted by current neural contact
with the second layer (e.g. lemon taste cell, apple taste cell) and the neural pathway
connections. When our existing conceptual apparatus is unable to assimilate new
experience, accommodation is acquired either by revising that which has already
been learned or by creating new conceptual structures: some second layer cells
acquire content (e.g. banana taste developed upon eating a banana for the first
time) and/or new synaptic connections are formed either by revised weightings or
new dendritic linkages for the redeployment of concepts. Once these target cells
have 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 assimilate
similar 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 we
know about neurology, at least in broad terms. Brains consist of neurons set in a
network; so too is the neural network built on units and their connections. Neural
networks are well suited to solving problems which require complex parallel
processing of information for their solutions. Neural networks also handle concepts
which have blurred edges allowing for exceptions, whereas the computational
model, based on algorithmic rules, has difficulty doing so. Thus, connectionism is


684 John Clark

able to recognise patterns not easily reduced to rules. Finally, connectionist models
have made some rapid advances in demonstrating the power of neural networks to
master cognitive tasks. Sejnowski and Rosenberg’s (1987) early work on a neural
net (NETtalk) that could read English text coupled to phonics and a speech
synthesiser 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 Dictate
is now so sophisticated that it can be trained in minutes for voice recognition to
turn spoken English into very accurate written English and vice versa.
But the connectionist programme is not without its problems. There is a marked
tendency 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 activated
flow from inputs to hidden units and on to output units. More realistic models of
the brain would trace the many layers of hidden units and connections back down
which are required to explain short-term memory. Neural networks also seem to
require 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 connected
computer power is available to run neural networks. But whether computers can
fully model human learning is problematic—humans can learn from a single expe-
rience, but computers seem incapable of doing so. Lastly, connectionism does have
difficulty handling rule-based learning, which computationalism is more suited to.
However, there are several reasons for thinking that brains are not like computers
and 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 lights
go out). But the human brain works in parallel with many operations performed in
tandem which allows for complex performances. Further, if one part of the system
should close down or malfunction then in many cases new neural pathways can be
generated to maintain the performance. Second, brains are not like computer
hardware which runs all sorts of software programmes. Rather, each neuron is like
a small microcomputer wired up to perform a particular task. However the neuron
itself is not the basic computational unit; rather, it is the parts of the neuron which
matter as they change and interact with other neurons. Learning is not akin to the
running of new software, but occurs when there are changes to the hardwiring (i.e.
to the synapse weightings). Learning can be characterised in terms of changes in
synaptic weights in the neural network and the subsequent reduction of error in
network output. Third, the computational account relies on syntax, hence sen-
tences, or the manipulation of propositions, for learning to happen. But animals
and pre-linguistic children (new-born babies) learn but do not use sentences in
their learning, so pre-linguistic learning in young children cannot be sentential.
Some other non-syntactical account of learning is therefore required. And even if
sentential processing is part of brain functioning, it does not explain some brain
functioning (e.g. pattern recognition, physical skills) which are non-linguistic.
Finally, if our learning is algorithmic then it is very evident that in areas where
algorithms matter the most (mathematics, logic) children (and adults) fail to perform



well in algorithmic computations. This suggests that some other basis of learning
is required; one possibility lies in pattern recognition which very young pre-linguistic
children appear to do so well in. Thus, connectionism rejects the computational
account based on formal systems, sentences containing mental representation and
syntactical rules in favour of a neural, electro-chemical, pattern recognition theory
of learning.
These considerations lead to questions about the relationship between the com-
putational and connectionist approaches. One possibility is implementation
connectionism which seeks an accommodation between the two. The brain is con-
ceptualised as a symbol processor, as a neural network but processing symbolic
information at a higher level, so research is aimed at explaining how neural net-
works support symbolic processing. On the other hand, radical connectionism
rejects symbolic processing which is unable to explain many of the features of
human learning captured by neural networks. It thus advocates the elimination of
mental explanation, including folk psychology. How this tension will work itself out
is far from clear, as the example of systematicity reveals. Fodor and Pylyshyn
(1988) identify a feature of human intelligence called systematicity—the ability to
understand some sentences is intrinsically connected to the ability to understand
other sentences with a similar structure. They deny that connectionist models can
do this; only computational models can. But as others (e.g. Aizawa, 1997) point out,
computationism is no better at performing this task. Neither computationism nor
connectionism alone seem able to do so. Some combination of the two may be required.

Conclusion
If learning is to be explained then clearly it is not going to be explained by
conceptual analysis, behaviourism or constructivism. None of these have the
explanatory 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 already
have or make the necessary adjustments required when the new is inconsistent with
the old. Cogitive science, which makes extensive use of the epistemic resources of
philosophy and science, may offer a way forward, although for this to happen the
conﬂict between the computational and connectionist approaches will need to be
resolved, 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 introduced
into schools and classrooms on a large scale. For many, the computational model
of learning sits comfortably with the growing emphasis on digital literacy and
technological competence, thus giving it legitimacy. But the wholesale adoption of
computationalism is no guarantee that it really does explain learning. Given some
of the successes of connectionism, it is very likely that it does not. Neurophilosophy
is a viable alternative theory of learning. Rather than pouring all of the available
educational resources into constructivist/computational approaches to learning, at
the expense of connectionism, it would not be unreasonable to also direct resources


686 John Clark

to establishing a sound research programme in neurophilosophy as well, since it
has 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 those
currently supporting computationalism/constructivism eventually discover they
were backing the wrong horse. Against the odds, for my part, as a sound empirical
conjecture open to empirical refutation, I shall back neurophilosophy which, if it
turns out to be the best explanatory theory of learning available, will carry with it
a very large pay-back indeed, in terms of how teachers understand and promote
children’s learning. And that, as they say, is what really matters!

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