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Self-organization in Normal and
Abnormal Cognitive Development
Denis Mareschal
Centre for Brain and Cognitive Development,
Birkbeck College, University of London, Malet Street, London
D.Mareschal@bbk.ac.uk
Michael S. C. Thomas
Neurocognitive Development Unit,
Institute of Child Health, 30, Guilford St., London
M.Thomas@ich.ucl.ac.uk
To appear in: The Handbook of Brain and Behaviour in Human Development.
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Running head: Self-organization in cognitive development
Abstract
This chapter discusses self-organization as a motor for cognitive development. Selforganization occurs in systems with many degrees of freedom and is ubiquitous in the
brain. The principal means of investigating the role of self-organization in cognitive
development is through connectionist computational modeling. Connectionist models are
computer models loosely based on neural information processing. We survey a range of
models of cognitive development in infants and children and identify the constraints on
self-organization that lead to the emergence of target behaviors. A survey of
connectionist models of abnormal cognitive development illustrates how deviations in
these constraints can lead to the development of abnormal behaviors. Special attention is
paid to models of development in autistic children.
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We have come a long way in understanding the processes that underlie brain
development since the days of Piaget’s attempts to relate cognitive development to an
unfolding biological substrate (e.g., Piaget, 1971; 1980). Developmental Cognitive
Neuroscience is a new field of research that addresses that very issue. The aim of this
field is to bridge the gap between children’s cognitive development (as assessed by
behavioral studies) and the underlying development of the brain (Johnson, 1997).
Although the age-old debate concerning the relative importance of nature and
nurture in determining development rages on, it has recently taken a new twist. Few
people now claim that innate knowledge is hard-wired in a priori neural connections
(representational innateness). Rather, it is generally accepted that both nature and nurture
play a role in children’s cognitive development. The pivotal question that remains is the
extent to which plasticity dominates development and the extent to which structural
constraints are genetically determined such that experience only plays a limited role in
fine-tuning these structures (Elman, Bates, Johnson, Karmiloff-Smith, Parisi, and
Plunkett, 1996).
This chapter will explore how the concept of self-organization can provide an
account of behavioral development in infants and children. Along the way it will explore
how constraints (or boundary conditions) guide self-organization. An important tool for
exploring self-organization in cognitive development is connectionist computational
modeling. Connectionist network models (or artificial neural network models) are
computer models loosely based on neural information processing. These models allow us
to explore how different system constraints interact with an environment to give rise to
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observed system behaviors. They also provide a means of exploring how deviations in
self-organization (due to a shift in boundary conditions) can result in the emergence of
abnormal behaviors.
In the rest of this chapter we begin by discussing self-organization in the brain.
We then turn to discussing self-organization in cognitive development. Following this,
connectionist modeling is introduced as a means of investigating self-organization in
development. The two subsequent sections review connectionist models of normal
cognitive development and abnormal cognitive development.
Self-organization in the brain
Many chapters in this handbook provide examples of self-organization. Selforganization occurs when structure emerges in response to a system’s dynamic
interactions with an environment. Self-organization is a fundamental characteristic of the
brain (Willshaw and von der Malsburg, 1976; Grossberg, 1982; Changeux, Heidman and
Patte, 1984; Edelman and Finkel, 1984; von der Malsburg, 1995; Leslo, 1995). It can
occur at several time scales: a learning time scale of hours and days, a developmental
time scale of months and years, but also on the functional time scale of seconds and
minutes. All stages of brain organization involve an element of self-organization (Leslo,
1995; Johnson, 1997). It is unlikely that the genes can (in any direct way) encode the full
information necessary to describe the brain (Elman et al. 1996). Given that the cerebral
cortex alone contain some 1014 synapses, and given the variability of vertebrate brain
structure, it is difficult to see how individual wiring diagrams could be encoded within
the limited coding space of the genome (von der Malsburg, 1995).
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There are many well-studied examples of self-organization in the physical and
biological sciences (Prigogine and Stengers, 1986). Self-organization occurs in systems
with a large number of degrees of freedom (e.g., synapses in the brain). Initially the
system is undifferentiated (randomly organized) but, as a result of small adaptive
changes, an order begins to emerge among the elements of the system. These changes can
self-amplify, resulting in a form of positive feedback. If there is a limitation in resources,
this limitation can lead to competition and selection among the changes. Finally, changes
can co-operate, enhancing the “fitness” of some changes over the others in spite of
competition. With respect to self-organization in the brain, synaptic adjustment rules
(such as the Hebbian learning rule; Hebb, 1949) can lead to ordered connection patterns
that in turn lead to structured behaviors.
A fundamental characteristic of self-organizing systems is that global order can
arise from local interactions. This is extremely important in the brain where local
interactions between cellular neighbors create states of global order and ultimately
generate coherent behavior. However, we should be careful in what we understand by
local here, because nerve cells are connected by long axons. Local neural interactions are
not necessarily topologically arranged (von der Malsburg, 1995). Connected cells can be
neighbors although they are physically located at other ends of the brain. One implication
of this is that some ordered structures in the brain may not initially “look” ordered to our
eyes because we rely heavily on spatial contiguity to perceive patterns.
There are two relevant parameters in network self-organization. The first is the
information or activation transmitted through the network (cf. action potentials). The
second is the connection strength between successive units (cf. synaptic strength).
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Connections control neural interactions and are characterized by a continuous weight
variable. These connections reflect the size of the effect exerted on one unit by another
unit. Organization in the brain can therefore take place at two levels: activity and
connectivity. Changes in activation levels reflect self-organization at the instantaneous,
functional level whereas changes in connectivity correspond to self-organization on a
learning or developmental time scale.
Self-organization in cognitive development
The fundamental question of cognitive development is where new behaviors come
from. Traditionally, developmentalists have looked for the source of these behaviors
either in the organism or in the environment. Perhaps new structures arise as a result of
instructions stored beforehand in some code (e.g., the genes). Or, perhaps new behaviors
are acquired by absorbing the structures and patterns of the environment directly. Oyama
(1985) has suggested that both of these accounts are fundamentally preformationist in
that it is either the genes or the environment that determines the nature of the structures
that are developed. She argues that attributing the origin of structure to the genes or the
environment simply pushes back to another level questions concerning the causal origin
of these structures. Therefore, it fails to answer the fundamental question of cognitive
development. Oyama further suggests that it is the concept of self-organization that
rescues developmentalists from this logical hole of infinite regress. In biological systems,
pattern and order can emerge from the process of interaction without the need for explicit
instructions.
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There is now ample evidence of self-organization occurring during development
in both linguistic and cognitive domains. Specific examples include children’s
understanding of balancing relations, (Karmiloff-Smith and Inhelder, 1971), children’s
drawing abilities (Karmiloff-Smith, 1990), and language acquisition (Karmiloff-Smith,
1985). Karmiloff-Smith (1992) suggests that what drives development is the endogenous
principle of “representational re-description”. Even when performance seems to be
adequate, there are pressures arising from within the cognitive system to re-describe
existing knowledge in more abstract and accessible forms. These pressures arise from the
need to make information in one functional module accessible to another functional
module.
There have been several attempts to explain the apparent stage-like growth of
competence in children in terms of self-organizing principles. Van Geert (1991) outlined
a framework for discussing language and cognitive development as growth under limited
resources. He formulated a dynamic systems model of development in terms of logistic
growth equations. This model describes development as a result of supportive and
competitive interactions between “cognitive growers”. Similarly, Van der Maas and
Molenaar (1992) presented an account of stage transitions on conservation tasks in terms
of catastrophe theory. According to this account, discrete and qualitative shifts in
behavior arise as a result in continuous changes in the underlying parameters of a system.
Although these models provide a good account of how new behaviors can emerge
through the continuous adjustment of abstract parameters, and as a result of endogenous
pressures, they rarely relate those parameters to any measurable cognitive quantities. Nor
do they relate them to some underlying neurological substrate. Explicit accounts of self-
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organization in behavior have been limited to describing how actions are elicited in
infancy, or to how separate motor systems become coupled to induce higher levels of
motor action (e.g., Goldfield, 1995). A more effective means of exploring selforganization in cognitive development (and to relate development to neural information
processing) is to construct neurally based computer simulations of cognitive
development.
Connectionist computational modeling
Connectionist models are computer models loosely based on the principles of
neural information processing (Rumelhart and McClelland, 1986; Elman, Bates, Johnson,
Karmiloff-Smith, Parisi, and Plunkett, 1996; McLeod, Plunkett, and Rolls, 1998). They
are information processing models and are not intended to be neural models. They
embody general principles such as inhibition and excitation within a distributed, parallel
processing system. They attempt to strike the balance between importing some of the key
ideas from the neurosciences while maintaining sufficiently discrete and definable
components to allow questions about behavior to be formulated in terms of a high-level
cognitive computational framework.
From a developmental perspective, connectionist networks are ideal for modeling
because they develop their own internal representations as a result of interacting with an
environment (Plunkett and Sinha, 1992). However, these networks are not simply tabula
rasa empirical learning machines. The representations they develop can be strongly
determined by initial constraints (or boundary conditions). These constraints can take the
form of different associative learning mechanisms attuned to specific information in the
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environment (e.g., temporal correlation or spatial correlation), or they can take the form
of architectural constraints that guide the flow of information in the system. Although
connectionist modeling has its roots in associationist learning paradigms, it has inherited
the Hebbian rather than the Hullian tradition. That is, what goes on inside the box (inside
the network) is as important in determining the overall behavior of the networks as is the
correlation between the inputs (stimuli) and the outputs (responses).
Connectionist networks are made up of simple processing units (idealized
neurons) interconnected via weighted communication lines. Units are often represented as
circles and the weighted communication lines (the idealized synapses) as lines between
these circles. Activation flows from unit to unit via these connection weights. Figure 1a
shows a generic connectionist network in which activation can flow in any direction.
However most applications of connectionist networks impose constraints on the way
activation can flow. These constraints are embodied by the pattern of connections
between units.
============== Insert Figure 1 about here ===========
Figure 1b shows a typical feed-forward network. Activation (information) is
constrained to move in one direction only. Some units (those units through which
information enters the network) are called input units. Other units (those units through
which information leaves the network) are called output units. All other units are called
hidden units. In a feed-forward network, information is first encoded as a pattern of
activation across the bank of input units. That activation then filters up through a first
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layer of weights until it produces a pattern of activation across the band of hidden units.
The pattern of activation produced across the hidden units constitutes an internal rerepresentation of the information originally presented to the network. The activation at
the hidden units continues to flow through the network until it reaches the output unit.
The pattern of activation produced at the output units is taken as the network’s response
to the initial input.
Each unit in the network is a very simple processor that mimics the functioning of
an idealized neuron. The unit sums the weighted activation arriving into it. It then sets it
own level of activation according to some non-linear function of that weighted sum. The
non-linearity allows the unit to respond differentially to different ranges of input values.
The key idea of connectionist modeling is that of collective computation. That is,
although the behavior of the individual components in the network is simple, the behavior
of the network as a whole can be very complex. It is the behavior of the network as a
whole that is taken to model different aspect of infant development.
Given the units’ response functions, the network’s behavior is determined by the
connection weights. As activation flows through the network, it is transformed by the set
of connection weights between successive layers in network. Thus, learning (i.e.,
adapting one’s behavior) is accomplished by tuning the connection weights until some
stable state of behavior is obtained. Supervised networks adjust their weights until the
output response (for a given input) matches a target response. The target can be obtained
from an explicit teacher, or it can arise from the environment, but it must come from
outside the system. Unsupervised networks adjust their weights until some internal
constraint is satisfied (e.g., maximally different inputs must have maximally different
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internal representations). Backpropagation (Rumelhart, Hinton, and William, 1986) is a
popular training algorithm for supervised connectionist networks that incrementally
updates the network weights so as to minimize the difference between the network's
output and some desired target output. These networks self-organize in such a way as to
internalize structures in the environment.
Through adaptation, the connection weights come to encode regularities about the
network’s environment that are relevant to a task the network must solve. Networks are
very sensitive to the distribution statistics of relevant features in their environment. A
feedforward network with a single layer of hidden units can approximate arbitrarily well
any finite output response function, given enough hidden units (Cybenko, 1989). Further
details of the similarities between connectionist network learning and statistical learning
procedures can be found elsewhere (e.g., Hertz, Krogh, and Palmer, 1991).
There are two levels of knowledge in these networks. The connection weights
encode generalities about the problem that have been accumulated over repeated
encounters with the environment. One can think of this as a form of long-term memory or
category specific knowledge as opposed to knowledge about a particular task or object. In
contrast, the pattern of activation that arises in response to inputs, encodes information
about the current state of the world. Internal representations are determined by an
interaction between the current input (activation across the input units) and previous
experience as encoded in the connection weights.
Many connectionist networks are very simple. They may contain some 100 units
or so. This is not to suggest that the part of the brain solving the corresponding task only
has 100 neurons. It is important to understand that most connectionist models are not
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intended as neural models, but rather as information processing models of behavior. The
models constitute examples of how systems with similar computational properties to the
brain can give rise to a set of observed behaviors. Sometimes, individual units are taken
to represent pools of neurons or cell assemblies. According to this interpretation, the
activation level of the units corresponds to the proportion of neurons firing in the pool
(e.g., Changeux and Dehaene, 1989).
Connectionist models of cognitive development
Infancy provides an excellent opportunity to model self-organizing processes
because behavior is closely tied to perceptual-motor skills. We begin this section by
describing two models of infant cognitive development and then turn to describing selforganizing models of children’s cognitive development.
Many infant categorization tasks rely on preferential looking or habituation
techniques, based on the finding that infants direct more attention to unfamiliar or
unexpected stimuli (Reznick and Fagan, 1984). Connectionist autoencoder networks
have been used to model the relation between sustained attention and representation
construction (Mareschal & French, 1997; Mareschal & French, 2000; Mareschal, French,
& Quinn, submitted; Schafer & Mareschal, in press). An autoencoder is a feedforward
connectionist network with a single layer of hidden units (Figure 1b). It is called an
autoencoder because it associates an input with itself. The network learns to reproduce on
the output units the pattern of activation across the input units. The successive cycles of
training in the autoencoder are an iterative process by which a reliable internal
representation of the input is developed.
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This approach to modeling novelty preference assumes that infant looking times
are positively correlated with the network error. The greater the error, the longer the
looking time, because it takes more training cycles to reduce the error. The degree to
which error (looking time) increases on presentation of a novel object depends on the
similarity between the novel object and the familiar object. Presenting a series of similar
objects leads to a progressive error drop on future similar objects.
An unusual asymmetry has been observed in natural category formation in infants
(Quinn, Eimas, & Rosenkrantz, 1993; Quinn & Eimas, 1996). When 3- to 4-month-olds
are initially exposed to a series of pictures of cats, they will form a category of cat that
excludes dogs. We can tell this because, after habituating to a series of pictures of cats,
they nevertheless show a novelty response to a picture of a dog. However, when they are
exposed to a series of pictures of dogs, they will form a category of dog that does include
cats. Thus, there would be no novelty response to a picture of a cat after a series of dogs.
We used the autoencoder network above to explain this behavior. The original cat and
dog pictures were measured along 10 dimensions and presented to the networks for
categorization. The same presentation procedure was used as with the infants. These
networks developed CAT and DOG categories with the same exclusivity asymmetry as
the 3- to 4-month-olds. Moreover, the model predicted that learning DOG after CAT
would disrupt the prior learning of CAT whereas as learning CAT after DOG would not
disrupt the prior learning of CATS. A subsequent study with 3- to 4-month-olds found
this to be true of infants as well (Mareschal, French, and Quinn, submitted).
The asymmetry was explained in terms of the distribution of cat and dog feature
values in the stimuli presented to the infants. Most cat values fell within the range of dog
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values but the converse was not true. Thus for a system that processes the statistical
distribution of features of a stimulus, the cats would appear as a subset of the dog
category. Further analyses revealed that these networks could parse the world into distinct
categories according to the correlation of feature values in the same way as 10-montholds have been shown to do (Younger, 1985; Mareschal & French, 2000). In short, this
model demonstrates that categorical representations can self-organize in a neural system
as a result of exposure to familiarization exemplars.
We now turn to discussing object-directed behaviors in infancy. Newborns
possess sophisticated object-oriented perceptual skills (Slater, 1995) but the age at which
infants are able to reason about hidden objects remains unclear. Using manual search to
test infants’ understanding of hidden objects, Piaget concluded it was not until 7.5 to 9
months that infants can understand that an object continues to exist beyond direct
perception. He concluded this because infants younger than this age fail to reach
successfully for an object hidden behind an occluding screen (Piaget, 1952, 1954). More
recent studies using a violation of expectancy paradigm have suggested that infants as
young as 3.5 months do have some understanding of hidden objects. These studies rely
on non-search indices such as surprise instead of manual retrieval to assess infant
knowledge (e.g., Baillargeon, Spelke, & Wasserman, 1985; Baillargeon, 1993). Infants
watch an event in which some physical property of a hidden object is violated (e.g.,
solidity). Surprise at this violation (as measured by increased visual inspection of the
event) is interpreted as showing that the infants know: (a) that the hidden object still
exists, and (b) that the hidden object maintains the physical property that was violated
(Baillargeon, 1993). The nature and origins of this developmental lag between
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understanding the continued existence of a hidden object and searching for it remains a
central question of infant cognitive development.
An initial attempt to account for this behavior in terms of the principles of selforganization was put forward by Munakata, McClelland, Johnson, and Siegler (1997).
These authors reported on a connectionist model that learned to keep track of the
potential reappearance of a hidden object. The model experienced a number of events in
which a screen would move past a stationary object, thereby hiding the object. With
experience, the model learned to predict when the object would reappear. Knowledge
about the hidden object was gradually encoded in the network connection weights in
response to experience with the environment. Similar accounts of infant performance on
delayed response tasks that involved reaching for a hidden object, accounts also based on
the interaction and competition between neural systems, have been proposed (Deheane
and Changeux, 1989; Munakata, 1988).
Mareschal, Plunkett, and Harris (1999) describe a model also designed to address
this question (Figure 2). This model embodies more neurophyisiological constraints than
the previous models. Anatomical, neurophysiological, and psychophysical evidence
points to the existence of two processing routes for visual object information in the cortex
(Ungerleider & Mishkin,1982; Van Essen, Anderson, & Felleman, 1992; Goodale, 1993;
Milner & Goodale, 1995). Although the exact functionality of the two routes remains a
hotly debated question, it is generally accepted that they contain radically different kinds
of representations. The dorsal (or parietal) route processes spatial-temporal object
information, whereas the ventral (or temporal) route processes object feature information.
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The Mareschal et al. model is more complex than the simple autoencoder
networks described above. Rather than drawing individual units (as in Figure 1), each box
represents a layer of units and each arrow represents a full set of connections between
successive layers. The dotted lines delimit separate modules. Information enters the
networks via a simplified retina. The Object Recognition Network develops a spatially
invariant feature-based representation of the object (cf. the functions of the ventral
cortical route) whereas the Trajectory Prediction Network develops a spatial temporal
representation of the object (cf. the functions of the dorsal cortical route). The Response
Integration Network recruits and co-ordinates these representations as and when required
by an active, voluntary response.
============= Insert Figure 2 about here ========
Like infants, the model showed a developmental lag between expectancy and
retrieval. Active tasks such as retrieval of a desired hidden object required the integration
of information across the multiple object representations, whereas surprise or
dishabituation tasks may only require access to one of the representations separately. A
developmental lag appeared between retrieval and surprise-based tasks because of the
added cognitive demands of accessing two object representations simultaneously in an
active response task. The model predicted that dishabituation tasks requiring infants to
access cortically separable representations would also show a development lag, as
compared to tasks that only required access to one cortical representation.
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We now turn to considering models of cognitive development in children rather
than infants. The recent revival in connectionist modeling of cognitive development was
triggered by the need to provide a mechanistic account of Piagetian cognitive
development. Piaget described the motors of development in terms of assimilation (that
new information is changed to match existing knowledge better), accommodation (that
existing knowledge is changed to match new information better), and equilibration (a
combination of assimilation and accommodation, Piaget, 1977). However, it was never
entirely clear what these terms referred to (Boden, 1980). The first connectionist models
of children’s cognitive development attempted to couch connectionist modeling in terms
of assimilation and accommodations (Plunkett and Sinha, 1992). So, for example, the
pattern of activation produced by the presentation of an input to a network was described
as assimilation, while weight adjustment in response to an error signal was seen as
accommodation (McClelland, 1989). In an alternative account, weight adjustments were
seen as assimilative learning and the addition of new units (the growth of the networks)
was seen as accommodation (Mareschal, 1991; Shultz, Schmidt, Buckingham and
Mareschal, 1995). As a result of the focus on Piagetian development, many of the early
models tried to capture the trademark of Piagetian theory: stage-like development.
Perhaps the first connectionist model of cognitive development was Papert’s
(1963) Genetron model. Papert used a series of simple neural networks (Perceptrons) to
model how children learn to assess which of two sticks is the longest. He found that, like
the children, perceptrons would focus on one end of the stick before considering both
ends in conjunction. In his model, development was viewed as the hierarchical
integration of successive perceptron modules.
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A classic post-piagetian problem-solving task on which children show stage-like
development is Siegler’s (1976) balance-scale task. Siegler examined children’s
developing abilities to predict which side of a balance scale would tip when a set of
weights was positioned at varying distances on either side of the fulcrum. Detailed
analyses of behaviors across of a battery of balance-scale problems revealed that children
progressed through 4 stages in the development of balance scale understanding. In stage 1
children always chose the side with the most weights as the one that would go down. In
stage 2 children would attend predominantly to the weight dimension, but if there were
equal weights on both sides, they would chose the side with the greatest distance as the
one to go down. In stage 3, children would succeed on weight problems, but would guess
in conflict problems where there were more weights on one side but greater distance on
the other side. Finally, in stage 4 children would solve all problems by implicitly
computing the torque on each side of the fulcrum.
Performance on this task has been modeled by presenting feedforward
connectionist networks with a series of balance scale problems (McClelland and Jenkins,
1991; Shultz, Mareschal and Schmidt, 1995). Networks were presented with weight and
distance information for the left and right sides of the fulcrum and had to learn to predict
which way the balance scale would tip. The networks developed through a series of
stages analogous to the children. Development was accounted for in terms of the selforganizing properties of weight adjustment and the possible addition of new units to the
network. The macroscopic behavior of stage development emerged as a result of finegrained, local adjustments to the weights. An important assumption of these models was
that the children’s learning environment was biased towards learning from problems
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where weight was the important dimension. That is, the dimension of weight was made
more important in the training set. Thus, self-organization in these networks reflected the
distribution of problems that children learn from.
Mareschal and Shultz (2000) describe a connectionist model of the development
of children’s Seriation (or sorting) abilities. Seriation is a task originally developed by
Piaget (1965) to probe children’s developing transitive reasoning skills. Piaget identified
4 stages of development. In stage 1, young children made no effort to sort a series of
sticks when asked to do so. In stage 2, they would group sticks according to local size
comparisons, but were unable to extend this order to the entire array. Their sorted series
consisted of small groups of consistently sorted sticks but would not extend over the
entire set of sticks. In stage 3, children successfully sorted the entire array of sticks, but
used a slow and fastidious empirical trial and error strategy. Finally, in stage 4, children
quickly and efficiently sorted the entire series by systematically choosing the smallest unordered stick at each move.
Mareschal and Shultz modeled this task by decomposing the Seriation task into
the two subtasks. These were: (1) identifying where a stick should be moved to and (2)
identifying which stick need to be selected. Each task was learnt by a distinct
connectionist module. There was no initial architectural difference between modules that
biased a network to learn one task better than the other task. However, one module
received feedback about the correct stick to move and the other module received
feedback about the correct location that a stick needed to be moved to. In response to this
feedback, the two modules self-organized differently and in such a way as to encode the
order relationship that ultimately enabled them to accomplish their individual subtasks.
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During this process the whole network progressed through the same behavioral stages
(described above) as do the children. Again, the macroscopic stage-wise developmental
profiles could be attributed to fine-grained changes in the connection weights between
units in the sub-networks. Also of interest was the fact that the observed level of
competence of a given network resulted from the interacting competence of the two
modules developing at their own distinct rates. Observed sorting errors could be
explained in terms of an interaction between errors on selecting a stick and errors in
placing a stick in a partially ordered series.
In continuing with the general trend to model Piagetian tasks, Shultz (1998)
described a connectionist model of conservation behaviors in children. Conservation is
the understanding that some quantities are preserved during some perceptual
transformations (e.g., the total amount of clay is preserved when a lump of clay is
stretched out). The model shows a shift from attending to perceptual information to
transformation information as a result of self-organization in the network.
Tasks on which children’s development appears not to show stages have also been
modeled. Sirois and Shultz (1999) described a model of discrimination shift learning in
children. Discrimination shift tasks represent a basic form of concept learning in which
the subjects must shift the criteria for categorization during the task. Manipulations of the
amount of experience the networks had with the task simulated the developmental
phenomena. This suggests that human developmental differences in shift learning arise
from spontaneous over-adaptation in older participants.
Connectionist models have also been used to investigate processes of early
language development (see Plunkett, 1997, for a review). These include models of early
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phonological development in learning to categorize speech sounds (Nakisa and Plunkett,
1998, Schafer and Mareschal, in press), learning to segment the speech stream into
discrete words (Christiansen, Allen, and Seidenberg, 1998), vocabulary development
(Gasser and Smith, 1998; Plunkett, Sinha, Muller, and Strandsby, 1992), the acquisition
of inflectional morphology, for instance in forming the past tense of verbs (Daugherty &
Seidenberg, 1992; Juola & Plunkett, 1999; MacWhinney and Leinbach, 1991; Plunkett
and Marchman, 1991, 1993, 1996; Rumelhart and McClelland, 1986) and in forming the
plural of nouns (Forrester and Plunkett, 1994; Juola & Plunkett, 1999; Plunkett and
Nakisa, 1997), and models of the acquisition of syntax (Elman, 1990, 1992, 1993).
In all of these models, an initial network structure is postulated by the modeler,
but the connection weights are randomized, so that the model has no initial content. The
model is then exposed to language input of some sort, and required to adjust its weights,
either to form concise representations with an unsupervised algorithm (e.g. Nakisa and
Plunkett’s model of early phonological development) or to learn a mapping between two
domains using a supervised algorithm (e.g., in Christiansen et al.’s (1998) model, the
network had to learn a mapping between the current phoneme and the next phoneme in
the speech stream; in Rumelhart and McClelland’s (1986) model, the network had to
learn a mapping between the stem and the past tense form of verbs). During training, the
network weights self-organize to reflect the structure of the language task. Thus in the
lexical segmentation model, Christiansen et al. showed how the model could learn to
combine probabilistic cues about phonemes, relative lexical stress, and boundaries
between utterances, to predict word boundaries. Each of these cues on its own is an
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unreliable indicator, yet after training on a large corpus of child-directed speech, the
model was able to use these cues to reliably identify word boundaries.
These models have been successful in showing how distributional information in
the child’s language input can be very useful in helping the child to learn language. It is
the latent structure in the input that allows these self-organizing models to form
structured representations appropriate to the linguistic task (Redington and Chater, 1998).
The fact that such information can be extracted from the linguistic environment has led to
a reconsideration of the degree to which aspects of language must be innately coded into
the developing language system (Elman et al., 1996). However, the difficulty of
extracting information which is not readily apparent in the language input, such as
underlying grammatical structure, continues to drive claims that significant aspects of our
knowledge of the structure of language are innately specified (see e.g. Chomsky, 1986,
1988, 1995; Pinker, 1994, 1999). This remains a very active area of research (see e.g.
Henderson and Lane, 1998, for recent work on the acquisition of grammatical structures
in connectionist networks).
In summary connectionist models provide concrete examples of how the
processes of self-organization can account for many aspects of behavioral change during
cognitive development. Moreover, connectionist models are loosely based on the
principles of neural information processing and thereby provide a means of linking
Behavioral development with neural or brain development.
Connectionist models of developmental disorders
The ability of self-organizing connectionist models to capture changes during
development also presents us with the opportunity to investigate disorders of
23
development. Connectionist models have been used to explore a range of developmental
disorders, including autism (Cohen, 1994, Gustafsson, 1997), developmental dyslexia
(Harm and Seidenberg, 1999; Manis, Seidenberg, Doi, McBride-Chang, and Peterson,
1996; Plaut, McClelland, Seidenberg, and Patterson, 1996; Seidenberg and McClelland,
1989; Zorzi, Butterworth, and Houghton, 1998), Specific Language Impairment
(Hoeffner and McClelland, 1993), the development of morphology in a damaged
language system (Marchman, 1993), and language processing in Williams syndrome
(Thomas & Karmiloff-Smith, 1999).
Connectionist models of normal development carry with them a number of
assumptions about constraints (boundary conditions). These include (1) the initial state of
the network, in terms of the number of units, layers, connections, and the pattern of
connectivity, (collectively known as the network architecture); (2) the way a particular
cognitive problem is presented to the network, in terms of the input and output
representations; (3) the dynamics of activation changes in the network; (4) the learning
algorithm that the network will use to change its connection weights (and potentially, its
architecture); and (5) the regime of training which the network will undergo.
Models of developmental disorders have assumed that one or more of these
elements is atypical in the disordered system. In particular, the following initial changes
have been proposed to simulate each disorder: ‘too few’ hidden units (developmental
dyslexia, autism), ‘too many’ hidden units (autism, Williams syndrome), ‘too much’
lateral inhibition in the output layers of self-organizing feature maps (autism), alterations
in input and output representations (developmental dyslexia, Williams syndrome,
Specific Language Impairment), and elimination of intermediate layers of units
24
(developmental dyslexia, Williams syndrome). We will return to consider what
constitutes ‘too few’, ‘too many’, or ‘too much’ later. For the time being, the key aspect
to note with these models is that, with the exception of Marchman (1993), who explored
the ability of a network to recover from damage at different points in development, these
models all postulate differences in the system prior to training. That is, the system
undergoes a process of development under a set of constraints that is different right from
the start. As a consequence, the developmental profile that we find in these systems may
be qualitatively different from the normal case, rather than simply being a delayed
version, or a version of development which is normal but terminates at an earlier stage.
As we shall see, connectionist models provide us with a new type of vocabulary to
consider the nature of this disordered development.
The application of connectionist modeling methods to developmental disorders is
still very new. However, enough work has been done to outline the potential contribution
of this approach, as well as its possible weaknesses. In the following section, we will
consider one example in more detail, that of autism.
Autism is a developmental disorder characterized by a central triad of deficits.
These deficits are in social interaction, communication, and imagination (Wing and
Gould, 1979). In addition, other features have been associated with the syndrome. These
include a restricted repertoire of interests, an obsessive desire for sameness, savant
abilities, excellent rote memory, a preoccupation with parts of objects, improved
perceptual discrimination, and an impaired ability to form abstractions or generalize
knowledge to new situations (see Happé, 1994, for a review). Autism has multiple
biological causes (Gillberg and Coleman, 1992), and a disposition to the disorder is
25
probably inherited (Simonoff, Bolton, and Rutter, 1998). Autism appears to have multiple
Behavioral subgroups, so that it is perhaps best characterized as a spectrum of disorders
rather than a single disorder (Wing, 1988).
Cohen (1994) focused on the pattern of improved perceptual discrimination and
impaired generalization found in the syndrome. He concluded that evidence from
neuropathological investigations of the brains of affected individuals was suggestive of
abnormal wiring patterns in various brain regions, perhaps caused by deficits in neuronal
migration during fetal development (Piven et al., 1990), by curtailment of normal
neuronal growth (Bauman, 1991), and/or by aberrant development (Courchesne et al.,
1993). In comparison with the normal brain, in Cohen’s view, the structural deficits were
consistent with too few neurons in some areas, such as the cerebellum, and too many
neurons in other areas, such as the amygdala and hippocampus (see also Bauman, 1999).
Cohen took a simple three-layer feedforward network and trained it on a classification
task using Backpropagation. The network had two output units for the binary
classification task, and 11 inputs units on which to present exemplars of the two classes.
Cohen explored the effect of varying the number of units in the hidden layer between 1
and 13, firstly on the ability of the network to learn the classification in the training set,
and secondly on its ability to generalize this classification to a new test set.
Cohen found that with one hidden unit, performance on both the training set
(examples previously presented to the network) and generalization set (novel examples)
was poor. With 13 hidden units, the network quickly and accurately learnt the
classification for the training set. Early on in training, generalization was also good but
with further training, generalization then declined, as the network was subject to over-
26
learning. In over-learning, the network’s representations become increasingly focused on
details of the training set. Cohen suggested that his networks captured different
characteristics found in autistic subjects. In some cases, autistic children have trouble
acquiring simple discriminations and attend to a restricted range of stimuli
(corresponding to too few neurons). In others, autistic children have good discrimination
and indeed very good memory, but this relies on representing too many unique details of
stimuli, details that subsequently interfere with generalization (corresponding to too many
neurons).
This study illustrates some of the advantages and disadvantages of using
connectionist networks to model disordered development. Let us begin with the
disadvantages. Using a modeler’s rule of thumb, Cohen decides that a network with three
hidden units will be the ‘normal’ case (p.12). Less than this and learning suffers, greater
than this and generalization suffers. But where does the rule of thumb come from? Well,
connectionist modelers have traditionally attempted to set the number of hidden units
precisely to achieve optimal learning on the training set and optimal generalization.
Cohen can provide us with no independent justification for the ‘normal’ set of parameters
in the network. And this holds true for all of the models mentioned above. Thus far, we
simply don’t have solid grounds for saying why some set-up of architecture,
representations, and training regime should represent the ‘normal’ case for development –
other than it seems to capture the normal pattern of development. On the other hand, it is
worthwhile to show that a single model can account for normal development using one
set of parameters, and atypical development using a second set. This suggests we may be
on the right track. Nevertheless, work remains to be done to ground these parameter
27
settings. In Cohen’s case, this might be done by pursuing the hypotheses that certain
patterns of neural connectivity are a sufficient condition for autism, and that variation in
this connectivity can account for the various sub-groups of autism.
On the plus side, Cohen’s model does generate some interesting new hypotheses.
One of these is that the apparent regression in functioning that some autistic children
show between 18 and 30 months of age could be explained by over-learning in systems
which have too many neurons. The second is that Behavioral therapy may be effective
because its training regimes weaken previously established, idiosyncratic connections in
the networks which have too many neurons, and strengthen connections for knowledge
that forms a better basis for generalization. These are useful unanticipated hypotheses that
have arisen from the modeling process.
Gustafsson (1997) followed a similar line of thought in proposing that the
capacity of autistic individuals to form representations of sensory experience may be
impaired. Gustafsson suggested that their cognitive impairment arose in the development
of ‘cortical feature maps’. These maps are thought to develop in systems using
unsupervised learning, where the aim is for a set of processing units to self-organize such
that they form a concise representation of the information they receive. This concise
representation may then be used to drive higher-order processes. In Gustafsson’s view,
the formation of such feature maps is damaged in autism due to too much lateral
inhibition in this layer of units. The idea can be explained as follows. A simple,
unsupervised network has two connected layers of units, the input units and the output
units. On a given learning trial, the output units compete with each other to become the
one that will represent the pattern of activation arriving from the input units. Through
28
exposure to many different patterns, areas of neighboring output units come to specialize
in representing certain patterns.
Lateral inhibition is the process that mediates the competition between the output
units. Now, if there is ‘too much’ competition between these units, the output units may
come to represent very precise details of particular input patterns. For example, instead of
a unit coming to represent a book, it might represent the book on a shelf at a certain angle
with another book next to it. While such fine-grained information would be useful in
classification tasks (such as spotting the book on the shelf), this context-bound
information is a poor basis for generalization (e.g. recognizing the book over a range of
situations). In relation to autism, fine-grained information would explain good perceptual
discrimination abilities. The inability to form abstractions of sensory data might explain
impairments to higher order processes, since such processes would have to rely on “raw
data” or rote memory (Hermelin and O’Connor, 1970).
Although Gustafsson did not run any computer simulations, Oliver, Johnson,
Karmiloff-Smith, and Pennington (2000) have examined the ways in which just such a
process of feature map formation can be disrupted by changes in the initial properties of a
self-organizing connectionist network. Moreover these authors did so in an attempt to
develop a more explicit conceptual framework in which to consider developmental
disorders. Oliver et al. employed a model based on that proposed by Miller, Keller, and
Stryker (1989) to explain the emergence of ocular dominance columns in the primary
visual cortex of mammals. Ocular dominance columns are sets of neurons tuned to
respond to information coming from only one eye. Initially, neurons in this area of visual
cortex are not specialized, responding to both eyes. However, through a process of self-
29
organization, the sheet of neurons segregates itself into strips that respond only to one eye
or the other. These strips of neurons supply information that can be used in subsequent
computations involved in stereovision. Miller et al.’s neural network model had an output
layer which received input from two retinas. In the output layer, output neurons competed
with each other to respond to input patterns. Miller et al. showed how this network,
constructed using biologically plausible principles, could self-organize its output layer to
segregate into areas responding only to input from one or other of the retinas.
Similarly, Oliver et al. employed a two-layer network, whereby an output layer of
30x30 units received information from a single input retina of 30x30 units. The network
was shown a set of 4 stimuli, in the form of bars lying across the input retina. The bars
had a certain similarity structure, whereby A was similar to B, B was similar to C, and C
was similar to D. Oliver et al. showed that, using their initial parameter set, the output
layer formed a topographic map of the possible inputs. That is, certain areas of the output
layer specialized in responding to input A, other areas specialized in responding to input
B, other areas to C, and so on. Moreover, the similarity structure of the input was
reflected in the topographic map: the area responding to A was next to that responding to
B, the area for B was next to that for C, and so on.
Oliver et al. then went on to disrupt the network in various different ways. In each
case they performed the manipulation prior to exposing the network to the training
stimuli. They varied the threshold of the output units, disrupted the connectivity between
the input and output layers, disrupted the connectivity responsible for lateral inhibition in
output layer, and changed the similarity of the input stimuli to each other. These
manipulations demonstrated that small initial differences in the constraints under which
30
the model developed could have significant effects on the outcome of development. The
resulting topographic map suffered a range of disruptions, including output units failing
to specialize at all or simply turning off, specialization emerging but not in organized
areas, and organized areas emerging but without adjacent areas representing similar
looking bars.
Three points are of particular note here. Firstly, loss of function in the final
performance of the network was not due to removing specific parts of the network
responsible for that function. Final performance was the outcome of a developmental
process. Secondly, changes in the initial network conditions altered the trajectory of
development that the network followed. Disordered development was qualitatively
different.
Oliver et al.’s model demonstrates that Gustafsson’s conjecture is a plausible one.
Changes in factors such as the initial levels of lateral inhibition in the output layer may
well disrupt the featural representations subsequently formed by the network, and
indirectly, any processes that use these representations. This leads us to the third point.
The outcome of this differential development may not simply be a generalized
impairment, but a pattern of strengths and weaknesses. The representations may be
changed in ways that are good for some tasks but bad for others. Thus, context-bound
features will support superior discrimination, but will lead to inferior generalization. Even
abilities that seem unimpaired (sometimes referred to as ‘intact’ or ‘spared’ abilities)
may, as a consequence of disordered development, be achieved by qualitatively different
cognitive processes (Karmiloff-Smith, 1998).
31
This explanation of autism is exciting in that it attempts to build bridges between
levels of description, from its basis in biological principles through computational
modeling to Behavioral outcome. However, the explanation is somewhat limited. Both
Cohen and Gustafsson’s models tackle associated perceptual impairments of the disorder.
But neither addresses the central triad of features that is generally taken to characterize
the syndrome – deficits in socialization, communication, and imagination. It is far from
clear that their biologically constrained network models can be straightforwardly
extended to account for deficits in, say, play behavior (Thomas, 2000). Currently, when
we extend connectionist principles to investigating high level behavior of this sort, these
principles serve less as biologically motivated constraints and more as an extended
metaphor, a set of conceptual tools to formulate new ideas that themselves must be
empirically tested (Thomas and Stone, 1998). Perhaps it is the case that context-bound
sensory feature maps impair imagination, socialization, and so on. If so, we must derive
testable hypotheses and collect empirical support for this proposal. For example, we
might identify a particular social task, hypothesize how such a task might be achieved
using ‘social’ features, and then predict how context-bound social features would lead to
certain kinds of deficits in this task. Support for a hypothesis of this type would
strengthen Cohen and Gustafsson’s respective claims that something akin to learning with
too many neurons or learning with unusual levels of lateral inhibition could account for
the range of deficits associated with autism.
It is likely that the application of connectionist modeling to developmental
disorders will have at least two implications for this field. The first will be to favor
dynamic rather than static accounts of selective deficits. The second will be to force
32
clarification of the relationship of individual differences within normal development to
atypical development.
With regard to the first point, it is important to note that in recent times, it has
been common to characterize developmental disorders as if they were equivalent to cases
of adult brain damage – as if they represented a normal cognitive system largely ‘intact’
but for the loss of some specialized functional module. This is nicely illustrated by one of
the leading explanations of the triad of deficits in autism, the “theory of mind” hypothesis
(Baron-Cohen, Leslie, and Frith, 1985). Under this hypothesis, autistic people lack the
specific ability to understand and reason about mental states. For instance, they would not
be able to understand other people’s behaviors in terms of their (potentially false) beliefs
and desires. This inability to ‘think about thoughts’ leads to specific impairments in
social skills, communication, and imagination. Leslie (1987) has suggested that a
particular, discrete functional component of the cognitive system might be responsible for
this ability (the ‘Theory of Mind Module). Moreover, Baron-Cohen suggests that this
module is innately built into the human mind (e.g. Baron-Cohen, 1998). Autism, in this
view, is the consequence of highly selective damage to one component of an otherwise
normally developing cognitive system. Similar explanations of behavioral deficits in
developmental disorders can also be found for Specific Language Impairment and for
Williams syndrome (Clahsen and Almazan, 1998; Pinker, 1991, 1994, 1999).
As discussion of Oliver et al.’s (2000) model demonstrates, connectionist
accounts of developmental disorders place much greater emphasis on the process of
development itself. That is, the selective behavioral deficits found in the end state of an
atypically developing system are the outcome of a developmental process which, due to
33
differing initial constraints, has followed an alternative trajectory. The system is
qualitatively different, not a normal system with some elements missing. This view is
consistent with recent criticisms of the static adult brain damage account of
developmental disorders (Bishop, 1997a; Karmiloff-Smith, 1998). The adult brain
damage account is only feasible if one of two rather unlikely assumptions turn out to be
true. These are either that the computational modules comprising the cognitive system
develop independently, or that the content of these modules is static and innately
specified (as implied by Baron-Cohen, 1998). However, Bishop (1997b) has argued that
interactivity rather than independence is the hallmark of early development; and a
number of researchers have argued that functional modules are the outcome of a
developmental process, rather than being a precursor to it (Elman et al., 1996; KarmiloffSmith, 1992).
Indeed connectionist modeling thus far tends to lend support to the idea that
damage to a system prior to development has a quite different effect to damage of the
endstate system (Elman et al. 1996; Thomas, 2000). The same kind of pre-training
network manipulations that were used to model developmental disorders have also been
used after network training is complete, but now to capture a range of acquired disorders,
including Alzheimer’s disease, Parkinson’s disease, epilepsy, phantom limbs, stroke,
frontal lobe damage, prosopagnosia, semantic memory deficits, acquired dyslexia, alexia,
schizophrenia, and unipolar depression (see Reggia, Ruppin, and Berndt, 1996, for a
selection of such models. See Marchman, 1993, for an investigation of the effect of
lesioning a network while development is still under way).
34
The second implication of connectionist models for the study of developmental
disorders is with regard to the relation of atypical development to individual differences.
McLeod, Plunkett and Rolls (1998) have suggested that connectionist models may be
able to capture individual differences between people’s cognitive processes by varying
network parameters such as learning rate, number of hidden units, and initial weight
configurations. Given that the same types of manipulations have been used to capture
disordered development, this suggests that (albeit implicitly) connectionist theorists
support the view that developmental disorders and individual differences lie on a
continuum. However, this view has been strongly contested. Developmental disorders are
frequently thought to involve organic damage, and there is evidence that atypical
development is qualitatively different from the development found at the lower end of the
normal distribution of typical development (Bennett-Gates and Zigler, 1998). Work
remains to be done to explore the extent to which initial differences in constraints overlap
for typical and atypical groups in development and the extent to which these constraints
are different.
Discussion and Future Directions
This chapter has illustrated how models can be used to investigate selforganization in cognitive development. When artificial neural network models are used,
developmental phenomena can be related to processes of self-organization in the brain.
This offers one way of addressing the central question of Developmental Cognitive
Neuroscience (Johnson, 1997); namely, the relation between brain development and
cognitive development. The models also provide a means of exploring the cause of
35
abnormal development in terms of divergent self-organization. A number of outstanding
questions still need to be addressed.
The biological plausibility of connectionist models has been questioned (O’Reilly,
1999). Although these models are functionally equivalent to some of the features of
neural information processing, many other processes exist in the brain. These other
processes (e.g., diffusion of neural modulators) may radically effect the self-organizing
properties of these systems. Such processes must be incorporated in any future modeling
work. One example is synaptogenesis, the creation of new synapses. Until recently,
synaptogenesis was not thought to play an important role in later brain development.
However, Quartz and Sejnowski (1995) have argued that synaptogenesis and arborization
(the development of new dendritic trees on which new synapses may be formed) play an
important part in increasing the computational power of natural neural networks.
Although these ideas have been incorporated in some models of cognitive development
(see Mareschal and Shultz, 1996, for a review) they still remain relatively rare. The
realization that synaptogenesis may play an important role in some aspects of cognitive
development begs the question of which processes are best accounted for in terms of
synaptogenesis and which are best accounted for in terms of self-organization in systems
with fixed numbers of synapses. Shultz and Mareschal (1997) provide one tentative
answer by suggesting that abilities that emerge early in development and that are
culturally invariant should be modeled with static network models, whereas later
developing and culturally specific abilities should be modeled with a synaptic growth
process.
36
Many of the models described above speak of cognitive modules. These are
components dedicated to processing certain types of information. While there is no
denying that modularization exists both in the brain and in cognition (Karmiloff-Smith,
1992), the origin of these modules is still unresolved. Again, the modules may be prewired, or they may emerge though self-organizing processes such as competition (e.g.,
Jacobs, 1999; Jacobs, Jordan, and Barto, 1991).
Throughout this chapter we have restricted our review to a single class of
connectionist models (see Grossberg, 1982) for an example of an other class of models).
The appropriateness of this class of model for capturing self-organization in cognitive
development has been brought into question. Raijmaker, van Koten, and Molenaar (1996)
have suggested that existing connectionist models do not capture dynamic aspects of selforganization sufficiently well. They suggested that temporal oscillations in fully
connected recurrent networks are the appropriate markers of self-organization in neural
systems (see also, Molenaar, 1986).
Finally, the models described in this chapter make no attempt to account for
explicit or metacognitive knowledge. That is, knowledge that can be verbalized and that
can be subjected to syntactic logical operations. Because of this shortcoming, it has been
suggested that connectionist networks cannot reveal anything of interest about cognitive
development (Marcus, 1998). What this issue really raises is the degree to which explicit
and metacognitive knowledge is important in the observed development of behavior. It
also suggests that modeling work needs to be done in order to understand how the brain
might instantiate these abilities and how they might emerge as self-organizing processes.
37
Further Reading
Elman, J. L., Bates, E. A., Johnson, M. H., Karmiloff-Smith, A., Parisi, D., & Plunkett,
K. (1996). Rethinking innateness: A connectionist perspective on development. MIT
Press, Cambridge (Mass).
Johnson, M. H. (1997). Developmental cognitive neuroscience. Blackwells, Oxford, UK.
Karmiloff-Smith, A. (1998). Development itself is the key to understanding
developmental disorders. Trends in Cognitive Science, 2, 389-398.
Keslo, S. (1995). Dynamic Pattern: The Self-organization of Brain and Behavior. MIT
Press, Cambridge (Mass).
Siegler, R. S. (1997). Children’s Thinking (3rd Ed). Prentice Hall, London, UK.
Thelen, E, and Smith, L. (1994). A dynamic systems approach to the development of
cognition and action. MIT press, Cambridge (Mass).
Keywords: Self-organization, connectionism, cognitive development, developmental
disorders, children, infants
38
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Figure 1. Schema of (a) a generic and (b) a feedforward connectionist network.
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Figure 2. Schema of Mareschal, Plunkett, & Harris, (1999) object processing model.