2013. Vol.4, No.3A, 363-373
Published Online March 2013 in SciRes (http://www.scirp.org/journal/psych) http://dx.doi.org/10.4236/psych.2013.43A053
Copyright © 2013 SciRes. 363
Re-Evaluation of Attractor Neural Network Model to Explain
Double Dissociation in Semantic Memory Disorder*
Center for Information Sciences, Tokyo Woman’s Christian University, Tokyo, Japan
Received December 20th, 2012; revised January 20th, 2013; accepted February 15th, 2013
Structure of semantic memory was investigated in the way of neural network simulations in detail. In the
literature, it is well-known that brain damaged patients often showed category specific disorder in various
cognitive neuropsychological tasks like picture naming, categorisation, identification tasks and so on. In
order to describe semantic memory disorder of brain damaged patients, the attractor neural network model
originally proposed Hinton and Shallice (1991) was employed and was tried to re-evaluate the model
performance. Especially, in order to answer the question about organization of semantic memory, how our
semantic memories are organized, computer simulations were conducted. After the model learned data set
(Tyler, Moss, Durrant-Peatfield, & Levy, 2000), units in hidden and cleanup layers were removed and
observed its performances. The results showed category specificity. This model could also explain the
double dissociation phenomena. In spite of the simplicity of its architecture, the attractor neural network
might be considered to mimic human behavior in the meaning of semantic memory organization and its
disorder. Although this model could explain various phenomenon in cognitive neuropsychology, it might
become obvious that this model had one limitation to explain human behavior. As far as investigation in
this study, asymmetry in category specificity between animate and inanimate objects might not be ex-
plained on this model without any additional assumptions. Therefore, further studies must be required to
improve our understanding for semantic memory organisation.
Keywords: Attractor Neural Network; Double Dissociation; Category Specificity; Semantic Memory;
Cognitive neuropsychological evidence about semantic
memory disorder have given deep impacts to studies of cogni-
tive science and psychology. Among the cognitive neuropsy-
chological data, disorder about distinction between animate and
inanimate objects is suggestive in order to understand organiza-
tion of our semantic memories. Because patients with semantic
memory disorder often have tendency known as “double disso-
ciation”. Some patients show deficits in identification, naming,
and categorization tasks of animate objects, but their knowl-
edge of inanimate objects (i.e. tools, outdoor objects, jewelries,
body parts, and so on) remains intact (Caramazza & Shelton,
1998; De Renzi & Lucchelli, 1994; Hillis & Caramazza, 1991;
Warrington & Shallice, 1984). On the other hand, there exits
another kind of patients who are not able to identify, to name,
and to categorize inanimate objects. However, their knowledge
about animals remains intact (Hillis & Caramazza, 1991; War-
rington & McCarthy, 1987). Although many studies controlled
for confounding factors such as familiarity and frequency (Ca-
ramazza & Shelton, 1998; De Renzi & Lucchelli, 1994), these
factors failed to explain explain the double dissociation. In the
literature, this double dissociation was first described by Niel-
sen (1946) Capitani, Laiaconna, Mahon, and Caramazza (2003)
reviewed evidences in category specific processing in the hu-
man brain which has selective impairments in recognizing par-
ticular types of objects. Based upon their clinical evidences,
Warrington and her colleagues (Warrington, 1981; Warrington
& McCarthy, 1983; Warrington & Shallice, 1984; Warrington
& McCarthy, 1994) have tried to explain that the structure of
semantic memory and its nature. Would these data suggest that
different contents of semantic memory are localized in the brain
(maybe the left lateral inferior gurus)? Might these data suggest
that the information of these two categories are stored in dis-
tributed manner in the brain? Or might these data emerge from
the inter- and intra-correlations between objects? In this paper,
it was intended to focus upon these questions.
Neuroimaging studies revealed a similar double dissociation.
In a review of functional neuroimaging studies in normal sub-
jects, Martin and Chao (2001), Martin and Caramazza (2003)
mentioned that animate objects had tendency to show peak
activity in both the lateral portion of the fusiform gyrus in both
hemispheres and the right superior temporal sulcus while in-
animate objects had tendency to show peak activity in the me-
dial portion of the fusiform gyrus, the left middle temporal
gyrus, and the ventral premotor and parietal cortex in the left
hemisphere. Similar conclusions have been made in other re-
view papers (Josephs, 2001; Lewis, 2006; Thompson-Schill,
2003). These areas are possible candidates responsible to per-
form semantic memory tasks. However, it is worth noticing that
these findings might be inconsistent with cognitive neuropsy-
chological findings (see the next section).
*The author would like to thank Sachiyo Iwafune for her help.
Cognitive Neuropsychologic al Evide nce
For the most of neuropsychological case studies with seman-
tic memory disorders, the performances of patients to stimuli of
animals were less than those of inanimate objects. It was re-
ported that patients, who have an animate specic disorder in
category judgement, he/she had a tendency to confuse an ani-
mal with another animals more than he/she confused an inani-
mate objects with another inanimate objects (Warrington &
Shallice, 1984). The representation of semantic memory can be
considered that this kind of representation may vary based upon
how they can be retrieved within the same category. Warring-
ton and her colleagues (Warrington, 1981;Warrington & Mc-
Carthy, 1983; Warrington & Shallice, 1984; Warrington & Mc-
Carthy, 1994) insisted that generally speaking animate objects
are stored in the brain as visually resemble features. On the
other hand, inanimate objects have been shared more functional
features than those of animals.
There are several hypotheses have been proposed so far.
Those are as follows:
1) Modality speci hypothesis (Warrington & Shallice, 1984;
Warrington & McCarthy, 1983, 1987)
2) Organized unitary content hypothesis (Caramazza, Hillis,
Rapp, & Romani, 1990; Hillis & Caramazza, 1991).
3) Sensory in topography hypothesis (Simmons & Barasalou,
4) Hierarchy in Topography hypothesis (Humphreys & Forde,
The facts that each hypothesis has supportive evidences
and/or computational results have to remember while discuss-
ing about the model performances and corresponding pheno-
Warrington and her colleagues (Warrington & Shallice, 1984;
Warrington & McCarthy, 1983, 1987) proposed the perceptual
and functional hypothesis. According to this theory, the cate-
gory specificity can be regarded as our semantic memories are
organized along with both perceptual and functional knowledge.
They advocated that knowledge about musical instruments and
jewelry were similar to animate objects. They also, on the other
hand, insisted that inanimate objects and body parts could be
identified as functional knowledge. According to their percep-
tional/functional hypothesis, the brain damages to the regions
for dealing with perceptual semantic knowledge would cause
the deficits of knowledge about animate objects. In other words,
the difference between animate and inanimate objects might be
different on the loci damaged. This hypothesis was also sup-
ported by the results of the neural network simulation (Farah &
McClelland, 1991). This study by the way of computer simula-
tion revealed that memory about animate objects would suffer
from the brain damage more than that of inanimate objects, if
perceptual memory had more damage than that of functional
memory. It is because the knowledge of animals had been
deeply contributed by perceptual memory.
However, there exist studies that semantic memory about
animal had been damaged without lack of any perceptual
knowledge. There are patients who showed deficits about ani-
mal without any specific disorders of perceptual knowledge
(Caramazza & Shelton, 1998). Can we say that the representa-
tions of perceptual and functional aspects of semantic memory
would differentiate between animate and inanimate objects?
Are the information of perceptual and functional knowledge
stored separately in the brain? And therefore, do local lesions
cause category specific disorders? Can we say that the category
specificity suggests difference in the contents and the structures
Especially, there exists a kind of category specificity without
any semantic memory disorders. A hypothesis has been pro-
posed that each concept in semantic memory has been repre-
sented by activation patterns of micro features, i.e. multidimen-
sional vectors. A similar relationship between concepts could
be regarded as overlapped activation patterns in the micro fea-
It was attempted to represent data on the basis of feature dis-
criminability in this study. It is hypothesized that correlation
matrix among objects could be explained category specificity
and double dissociation between animate and inanimate objects.
This method of memory representation was originally described
by Devlin et al. (1998).
Figure 1 shows the correlation matrix of each item calcu-
lated from data of Tyler et al. (2000). Tyler et al. (2000) con-
trolled their stimuli, where inner correlations among animate
objects (lower right sub-matrix) have higher than those of in-
animate objects (upper left side). Compared upper left with
lower right sub-matrices in Figure 1, it is obvious that the up-
per left sub-matrix (inanimate objects) have less mutual corre-
lation coefficients than those among animate objects (the lower
right sub-matrix). Tyler et al. (2000) insisted that they could
control the stimuli. Figure 1 shows the correlation matrix cal-
culated from the data employed by Tyler et al. (2000). Open
circles in Figure 1 mean positive correlation coefficients, and
filled circles mean negative correlation coefficients as well.
Size of circles indicates correlation strengths. The upper left
sub-matrix of Figure 1 indicates inanimate objects, while the
lower right sub-matrix shows animate objects.
In studies of connectionists’ computer simulations, each
Correlation matrix calculated from the data of Tyler et al. (2000).
Copyright © 2013 SciRes.
concept has been described by micro features, which are com-
posed of multidimensional dichotomous (0 or 1) vectors (Pat-
terson et al., 1996; Plaut & Shallice, 1993; Plaut, MaClelland,
& Seidenberg, 1995; Plaut, 2001; Plaut, McClelland, & Sei-
denberg, 1995; Seidenberg, Plaut, Petersen, McClelland, &
McRae, 1994; Seidenberg, Alan, Plaut, & MacDonald, 1989;
Devlin et al., 1998). It is considered that similar concepts over-
lap their activation patterns of micro features each other. That is,
it is regarded that each concept is represented based upon the
discriminability of micro features. The category specificity
might be explained by the correlation matrix among concepts.
Therefore, representation of semantic memory would constrain
how to retrieve among the same category of the concept. Con-
cept of animal shares more perceptual features than that of in-
animate objects. On the other hand, concept of inanimate ob-
jects shares more discriminative features than that of animals.
Co-occur- rence of micro features might strengthen the rela-
tionship between objects in semantic memory space, which is
defined by micro features. The concept of animal would have
higher correlation coefficients than those of inanimate objects.
Considering the representation of semantic memory described
above, we did not adopt dichotomous definition between ani-
mate and inanimate objects. Also, dichotomous definition be-
tween perceptual and functional aspect of semantic memory
was not adopted. Rather, it was attempted to represent data on
the basis of discriminability.
In other words, Tyler et al. (2000) did not consider that the
category specificity (the difference between concepts of ani-
mate and inanimate objects) might emerge from the localized
lesions in the brain. They might think the category specificity
as the result of learning each concept of various objects. This
learning might inevitably give rise to category specificity, be-
cause the double dissociation between animate and inanimate
objects must emerge from the correlation matrix. Here, ex-
plaining category specificity from the viewpoint of computer
simulations of a neural network model was attempted.
In explanation of category specificity from the viewpoint of
neural networks, patterns of correlation coefficients between
micro features may play an important role in order to under-
stand category specificity (Plaut & Shallice, 1993). The re-
searchers in this field have been seeking for origin of the cate-
gory specificity and the double dissociation of semantic mem-
ory between animate and inanimate objects.
Attractor Neural Network Model
Several computational models have been proposed in order to
explain category specific deficits so far (Hinton & Shallice,
1991; Farah & McClelland, 1991; Plaut & Shallice, 1993; Plaut,
1995; Devlin et al., 1998; Bullinaria, 1999; Perry, 1999). How-
ever, it is worth noticing that Bullinaria (1999) tested and got
negative conclusions in neural network models.
Tyler et al. (2000) adopted a three layered network known as
“perceptron” model to deal with the data described above. Al-
though this type of neural network model is sufficient to ac-
count for the double dissociation between animate and inani-
mate objects, the attractor neural network seems to have more
advantages than perceptron in order to describe some charac-
teristics in semantic memory disorders. For example, the num-
ber of iterations between output and cleanup layers (Figure 2)
until reaching the threshold of output criteria can be regarded as
the prolonged reaction times of brain damaged patients.
Attractor neural network model pro- posed by Hinton and Shallice
(1991) and Plaut and Shallice (1993).
Plaut, McClelland, and Seidenberg (1995) and Plaut (2001)
adopted the attractor networks and tried to account for semantic
dyslectic and compound errors from both visually and seman-
tically. In their neural networks, basic processing units are con-
nected mutually. Upon this multidimensional space consisted of
activation values of processing units, the networks can change
and retrieve contents of adequate memories. In other words,
when the network was given random initial values, the acti-
vation values of each processing unit would transit from value
to value in semantic memory space. The behavior of this net-
work could be absorbed in an “attractor”. There are many
attractors corresponded to each memory object. If the set of
initial values may be changed, the state of this attractor network
might be absorbed in a correct “point” attractor. Thus, it is pos-
tulated that “basins” of each attractor are different each other.
Each basin corresponds to correct concept of an object.
Plaut and Shallice (1993) tried to explain the semantic errors,
visual errors, and compounded both semantic and visual errors
by using attractor networks. In their neural networks, in general,
units are connected mutually causing interactions among units.
This interaction of activation patterns of each unit can be iden-
tified as the states of activation patterns of units. The activa-
tions of the units are transited from one to another as the mem-
ory retrievals. The transition from arbitrary initial states to
some attractors are called the “absorb-ability” of attractors.
Therefore, it could be considered that different basins for each
word are composed throughout learning.
In case of attractor neural network, each attractor corre-
sponds to each concept, and its basin represents its range to be
absorbed in. Even if the state of the network defined by the
activations of each unit would be changed on influences either
noises or perturbations to the network, the state would stay
within its basin. This means that we could get to the correct
concept no matter how high the noises or perturbations are.
In addition, if damages in attractor networks would destroy
positions of point attractors, the same stimuli might fall into
incorrect attractors due to transformations of size and shapes of
basins. Therefore, it requires more time to fall in correct attrac-
tors than the normal attractor network does (see Figure 3).
Each neuron, or unit, x
U has an output function
which is a sigmoid function, as follows,
Copyright © 2013 SciRes. 365
Schematic description of basins of attractor neural network model
and its modification by damages against the model.
Throughout the numerical experiments in this study, it was
fixed a constant . The units in the hidden layer = 4.0a
can be expressed as follows:
where, i means -th connection weight, i
U means an
output value of the -th input unit, and means a threshold
value in the unit , the subscription means the output
values in the units of input layer.
A unit in the output layer
U and a unit in the cleanup
U are denoted as (3) and (4);
where, and in the equations denote threshold values in
the output and the cleanup layers respectively. The states in
units both the output and the cleanup layers were updated re-
peatedly until the convergence criterion had been reached or
until the maximum numbers of iterations ().
In the learning phase, the mean square error can be defined
where, i indicated an i-th teacher signal. Actual learning of
connection weights of each unit can be obtained by partial dif-
ferential as follows:
where, indicates a learning rate fixed as throug-
out this study.
The initial values of and θ were assigned in accordance
with an uniform random value generator .
Abilities of Attractor Neural Network
Attractor networks show rather higher performances than the
perceptrons. In general, it is said that three layered perceptron
can be regarded as the function approximator in arbitrary preci-
sion, when attractor neural network model has plenty of units in
the hidden layer.
Attractor neural network model, however, show good per-
formances even with the limitation of units in hidden and
cleanup layers. A good example is the exclusive OR problem.
In the natural extension of an exclusive OR problem, there is a
parity bit problem. This problem is more difficult than exclu-
sive OR problem. And this problem is more general than exclu-
sive OR problem. The attractor neural network model can solve
4 bits parity problem. The number of units in input layer is 4.
The number of learning patterns to be learnt is 16. The 8 bits
parity problem where the number of units in the input layer is 8,
and the total number to be learned is 256 with the minimum
hidden layer, 1 unit. Figure 4 shows a solution, which can solve
8 bits parity problem with 1 hidden unit and 1 cleanup unit.
Furthermore, the attractor network with only one hidden
layer unit and only one cleanup layer unit could solve the cate-
gory condition in the data of Tyler et al. (2000). The architec-
ture of the network was exactly the same as the Figure 4.
Application of Attract or Neural Netw orks
Hinton and Shallice (1991) and Plaut and Shallice (1993) showed
that their attractor network could reproduce symptoms of a kind
of dyslexia. According to their simulations, by means of the
operation of semantic memory structure, they succeeded to
account for the double dissociation between concrete and ab-
stract words (Plaut, McClelland, & Seidenberg, 1995; Plaut,
2001). They constructed the semantic memory that the repre-
sentations of concrete words have more micro features than
those of abstract words. They postulated when the degree of the
brain damages would be moderate, concrete words would show
lighter deficits than abstract words. Further, if the degree of the
brain damage would be severe, the concrete words would have
more severe deficits than the abstract words.
In this study, the dichotomous taxonomy, such as animate/
inanimate objects classification, was not adopted. Rather, the
data on the basis of the discriminability and correlation was
Computer simulations were conducted under the three condi-
tions described below. After learning completed, the effect of
A set of connection weights which could solve a 8 bit parity
Copyright © 2013 SciRes.
brain damages were intended to mimic by removal of units in
hidden and cleanup layers. In each brain damaged simulation,
numbers of iteration were postulated to identify prolonged re-
action times of patients with semantic memory disorders. Then,
the effect of relearning was investigated.
Tyler et al. (2000) adopted the isomorphic mappings in order
to train their networks. In other words, their networks had to
learn the output pattern identical to the input patterns. In this
condition, the network must acquire the reproduction of the
input pattern. However, it is possible to consider two more
conditions (teacher signals in this case). One is that the target
matrix (teacher signals) being the identity matrix, having 16
rows and 16 columns, all the diagonal elements being 1 and all
the non-diagonal elements being 0. Another is that the matrix
having 16 rows times 2 columns, where the elements of this
matrix consisting (1, 0) when the item is an animate object, and
(0, 1) when the item is an inanimate object. To summarize these
Category condition: the target matrix is a 16 rows × 2 col-
umns matrix, where the targets to be learned are animate ob-
jects, the output vectors are (1, 0). Otherwise (inanimate objects)
the output vectors are (0, 1).
Diag condition: the target matrix is an unitary matrix of 16
rows × 16 columns, where diagonal elements are 1 and other
elements in this matrix are 0.
Same condition: the target matrix is a 16 rows × 24 columns
matrix. This target matrix is the same as the matrix of the input
signals. This condition is the one which Tyler et al. (2000)
The category condition can be regarded as the category
judgement task in neuropsychological test. Under this condition,
the neural network model must learn and discriminate both
animate and inanimate concepts. This means that the network is
required to learn higher concepts than each item to be learned
as Tyler et al. (2000) suggested. In the diag condition, the net-
work must learn precise knowledge of each member in the in-
put patterns. The unitary matrix in this condition means that
each item can play a roll to form the identical matrix. In the
same condition, the network is required to learn the precise
knowledge of each member in the input patterns.
The number of units in the hidden layer was set to be 10, and
the number of units in the cleanup layer to be 1. The reason for
determining the number of units in the cleanup layer to be 1 is
based on the preliminary experiment.
The maximum iteration numbers between the output and the
cleanup layers was set to be 10 for each item. If the error of this
attractor network did not reach the convergence criteria, de-
fined by the sum of squared errors being less than 0.05 for each
item. Within the maximum number of iterations between the
output and the cleanup layer, the program gave up to let the
networks learn this item, and was given the next item to be
learned. The order of the items to be learned was randomized
within each epoch. This procedure was repeated until the net-
work learned all the items. The initial values of the connections
are decided by using a random number generator whose range
were from −0.15 to +0.15 in accordance with uniform random
The convergence criteria were set that all the sum of squared
errors are below 0.05 throughout in this study. The network
was given the input signals and teacher signals at a time to learn
the output patterns. At first, the output values were calculated
from the input patterns to the units in the output layer. Then
iterations between the output and the cleanup layers started
until the output values have reached the criteria, or the iteration
numbers have been exceeded 50 times.
Mean Conver g e n ce and Indi v i du a l C onvergence
Computer simulations of neural networks, in general, have been
considered that the convergence criteria have often been set as
the mean square errors (MSE, hereafter) computed from the
data set of the whole stimulus. When the MSE of the system
outputs would reach the point blow the criteria, it is considered
that the system (or the neural network model) could learn the
given task. However, in case of both the data set of Tyler et al.
(2000) adopted and the three conditions described above, it
might be something strange when the mean convergence crite-
ria was employed. For example, when we on the supposition
that the MSE would be 0.06 when they know “lion”, and that
the MSE would be 0.04 when they know “cheater”. In this case,
the average MSE would be 0.05, and then the learning must be
regarded to complete. However, it seems to be difficult to imag-
ine that a man would know lions uncertainly and he would
know cheaters certainly simultaneously. Ordinary persons, in
general, have knowledge about both lions and cheaters are
predatory animals and live in Africa. Here, in view of this rea-
son, we decided to adopt the convergence criteria as the indi-
vidual convergence. It means that the MSE for each item to be
learned must be reached blow the point (0.05 in this study). But
the mean convergence criteria were adopted in the category
condition. Because the correct output of the first item is (1, 0)
and the correct output of the second item is also (1, 0). It cannot
be distinguished between these two items. For the same reason,
from the fist item to the 8-th item, the correct output patterns
are all the same (1, 0), also from the 9-th to 16-th patterns the
outputs are (0, 1) as well. Therefore, it would not be able to
discriminate the outputs of the neural network systems con-
structed for this study could be produced from which output
pattern. In case of category judgement tasks for actual human
subjects, when the subjects would be asked to answer whether
animals or not, they would answer the same way like neural
network systems would, whether the object is a lion or a cheater.
In this reason, it is adequate that we employed the mean con-
vergence criteria for the category condition. On the other hand,
the diag and same conditions have different situations. The
correct answer for the first item matches only the first output.
Therefore, we adopted the individual convergence criteria for
these two conditions as it seems to be a natural interpretation
like human subjects do.
Comparison among Conditions
We investigated the mean iteration numbers between the output
Copyright © 2013 SciRes. 367
and the cleanup layers. These numbers indicate the times that
the initial value is absorbed in an attractor when the initial
value was located within a basin of an attractor (Figure 5).
This figure shows the mean iteration numbers for each con-
dition. The category condition was the least among three condi-
tions. This might come from that the system was required to
discriminate between only two options in the category condi-
tion. There, in this condition, were eight objects of (1, 0) and
other eight objects of (0, 1). Other two conditions require that
16 objects must discriminate into 16 options. This simplicity of
the output manner in the category condition might cause a kind
of easiness of learning. In other words, category judgement task
might be easy because of the small number of options.
Effect of Damage
In order to investigate the effect of damages, we removed the
units after the system completed to learn the data set. Removal
of units in the hidden layer caused severe disorders. The system
failed to answer all the trials in all the conditions. The system
had to relearn in order to get the correct answer again. This
symptom might resemble that patients often would show severe
declines of performance just after brain damage. The result of
relearning is shown in the Figure 6.
The horizontal axis in Figure 6 is the number of units re-
moved. So, this axis can be considered as the severity of dam-
The mean iteration numbers for learning completion.
It shows the iteration numbers that each MSE reach-
ed below 0.05. The whiskers indicate the standard
A simulation of brain damages, the removal of the
hidden units after the learning completed. The hori-
zontal axis shows the number of units removed. The
vertical axis indicates percent correct (n = 100).
age. In this figure, the results of diag and same conditions are
indicated. The system could easily recover from damages in
category condition. Even if the rest of unit become 1, the sys-
tem could recover 100% correct. So, we could not draw any
curves in the figure. That is to say that the attractor neural net-
work model have enough ability to solve this category judge-
ment task. The figure also shows that the system was robust
against damages in diag and same conditions. The system
maintained rather good performance against damages. The
performance declined suddenly when the number of units in the
hidden layer were 2 or 3.
In order confirm these findings above, we conducted another
experiment with 5 units in the hidden layer and 2 units in the
cleanup layer. The result shows in Figure 7. This figure reveals
that the system showed relatively higher performance in cate-
gory condition. The other two conditions, diag and same, were
indicated that the performance of the system fell down sud-
denly when damages became severe.
It could be said that the system has an ability for relearning
in category judgement task. On the other hand, object identifi-
cation task (same condition) and naming task (diag condition)
are difficult to recover when damages are severe.
Iteration Number between Output and C l ea n u p
Iteration number between output and cleanup layers were in-
vestigated. Attractor neural network model is a generalized
model which includes three layered perceptron in the special
case. If the organization of network is enough in order to solve
given tasks, we could predict the iteration number between
output and cleanup layers would be 0. Then, this iteration might
apply to tasks which are required to use attractors. There were
many cases of no iteration between output and cleanup layers in
all conditions. After damages, the system needs to iterate in
order to utilize attractors. Figure 8 shows one of the results.
After learning completed, units in hidden layer were re-
moved. The horizontal axis shows the number of units removed.
Therefore, the number in the horizontal axis can be regarded as
severity of brain damage. The vertical axis indicates iteration
numbers between output and cleanup layers (n = 100). As it can
be seen in the figure, the system had to use interaction between
output and cleanup layers. This was the same in all the three
conditions. If we could consider these iterations as delays of
Simulation of brain damage, removal of units in
hidden layer after completion of learning. The hori-
zontal axis indicates the number of units removed.
The vertical axis indicates percent correct (n = 100).
Copyright © 2013 SciRes.
latencies in reading, naming, and identification tasks, attractor
neural network model could succeed to simulate task perform-
ance of brain damaged patients, because more iteration times
were required to respond in all the three conditions.
As the evidence of increasing of within category error, the neu-
ral network system had suffered removals of hidden units. The
system consisted of 10 units in the hidden layer and 1 unit in
the cleanup layer. After learning completed, 3 out of 10 units in
the hidden layer were removed. Confusion matrices were cal-
culated from activation values of 7 units in the hidden layer and
1 unit in the cleanup layer. Figures 9-11 show the results.
An obvious difference can be recognized when we compare
these figures with Figure 1. The confusion matrix in category
condition indicated that correlation coefficients within category,
which means 8 × 8 upper left corner and 8 × 8 lower right cor-
ner in this matrix, became higher each other than those in Fig-
ure 1. This might be analogous that most brain damaged pa-
tients with semantic disorder showed error like mistaking lion
On the other hand, in diag condition (naming task) and in
same condition (object identification task), confusion matrices
had tendencies that there were high confusion values inter
category. This might be supposed a kind of reason that brain
damaged patients often show difficulty in naming and identifi-
cation tasks. Further, this result could be considered that these
Simulation of brain damages.
A confusion matrix in category condition.
A confusion matrix in diag condition.
A confusion matrix in same condition.
confusion matrices would cause visual and semantic errors.
Categor y Sp ecificity
We observed the performance of the attractor neural network
when we removed the units in the hidden layer and the cleanup
layer. Because the ability of re-learning or the ability of recov-
ery of the attractor neural network model is excellent, this sys-
tem can recover immediately from the damage, which we re-
moved 1, 2, or 3 units in the hidden layer. Brain damage, in
general, might be considered that the system would fall into an
unrecoverable status when it would be suffered damages. In
order to express this kind of status, in addition to the removal of
the hidden units, we tried to fix the connection weights from the
units in the hidden layer to the units in the output layer, and
tried to let the system relearn. The relearning in this case would
be expected to occur only units between output and cleanup
layers. In this result, the performances in all the conditions did
not recover completely. It means that the learning times reached
the maximum iteration numbers in all the conditions. Figure 12
shows that the correlation coefficients calculated from the acti-
vation values among units in hidden and cleanup layers. Figure
12 was calculated from a result of the system which has 10
Copyright © 2013 SciRes. 369
A visualization of a matrix of correlation coefficients
among objects to be learned, calculated from the
hidden and cleanup layers after relearning.
units in hidden layer and 1 unit in cleanup layer. After learning
completed, 3 units in the hidden layer were removed. Compare
Figure 1 with Figure 12. Comparison between figures indi-
cates that the correlation coefficients are relatively higher in
Figure 12 than in Figure 1. It is possible to interpret that this
result might cause confusions among objects. For example,
brain damaged patients with animate specific disorder may
confuse lion as cheater. The system may confuse objects in the
data set as well.
Removal of Units in Cleanup Layer
We set the number of units in the cleanup layer as two and train
the system, then we removed one of the units in the cleanup
layer. We varied the initial values and performed simulations.
The results are shown blow. Each line indicates each result.
There are 16 items to be learned. The first 8 columns indicated
by the digits from 0 to 7 mean inanimate objects, and the last 8
columns indicated by the digits from 8 to 15 means animate
objects. Parentheses “()” indicate that the system failed to reach
the correct answer within the limited iterations between output
and cleanup layers. Each digit shows the number of items
which the system produced (Table 1).
This results might mean that brain damages would transform
the basins. Therefore, it could be pointed out that a kind of
confusion among other items occurred. Compared with animal
objects, the system did not make any mistakes about inanimate
objects. It is supposed that the correlation coefficients between
inanimate objects were relatively smaller than those of animates.
Table 2 shows the iteration numbers when the system suffered
damage: removal of units.
The iteration numbers between output and cleanup layers
were increased in animate objects. If we could identify these
iteration numbers as reaction times which brain damaged pa-
tients show, the attractor neural network can be regarded as the
model of semantic memory disorder to explain category speci-
As an analysis of the types of error, objects are close each
other in the data set of Tyler et al. (2000). So, if the system
would suffer injuries or damages, it would give rise to mistakes
the most likely objects. In fact, when we conducted a multidi-
mensional scaling analysis to the data of Tyler2000, its result
showed as Table 3. The coordinate values were calculated until
Example of the outputs when one of the units in the cleanup layer was
0 1 2 3 4 5 6 7 (5) 9 (5) 11 (5) (5) (5) (5)
0 1 2 3 4 5 6 7 (7) (7) (7) (7) 12 (7) (7) 15
0 1 2 3 4 5 6 7 (6) (6) (6) (6) (6) (6) (6) (6)
0 1 2 3 4 5 6 7 (1) (1) (1) (1) (1) (1) 14 (1)
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
0 1 2 3 4 5 6 7 (1) (1) 10 (1) (1) 13 (1) (1)
0 1 2 3 4 5 6 7 8 (1) 10 11 (1) (1) (1) (1)
0 1 2 3 4 5 6 7 (4) (4) (4) 11 12 (4) 14 15
0 1 2 3 4 5 6 7 (7) 9 10 11 (7) (7) (7) (7)
Example of the iteration numbers (max = 20) when one of the units in
the cleanup layer was removed.
0 0 0 0 0 0 0 0 2 2 2 2 1 2 2 2
2 0 0 0 0 0 0 0 (20) 2 (20) 2 (20) (20) (20) (20)
0 0 0 0 0 0 0 0 (20) (20) (20) (20) 2 (20) (20) 2
0 0 0 0 0 0 0 0 (20) (20) (20) (20) (20) (20) (20) (20)
0 0 0 0 0 0 0 0 (20) (20) (20) (20) (20) (20) 3 (20)
0 0 0 0 0 0 0 0 2 2 2 2 3 2 2 2
0 0 0 0 0 0 0 0 (20) (20) 2 (20) (20) 2 (20) (20)
0 0 0 0 0 0 0 0 2 (20) 2 3 (20) (20) (20) (20)
0 0 0 0 0 0 0 0 (20) (20) (20) 2 2 (20) 2 3
0 0 0 0 0 0 0 0 (20) 2 2 2 (20) (20) (20) (20)
Two dimensional values of the result of MDS for each object.
objects Dimension 1 Dimension 2
1 −0.000000 −0.968246
2 −0.000000 −0.968246
3 −0.000000 −0.968246
4 −0.000000 −0.968246
5 −0.000000 −0.968246
6 −0.000000 −0.968246
7 −0.000000 −0.968246
8 −0.000000 −0.968246
9 −1.414214 0.968246
10 −1.414214 0.968246
11 −1.414214 0.968246
12 −1.414214 0.968246
13 1.414214 0.968246
14 1.414214 0.968246
15 1.414214 0.968246
16 1.414214 0.968246
Copyright © 2013 SciRes.
two dimensional. The upper 8 rows indicates the coordinate
values of inanimate objects. The lower 8 rows show the coor-
dinate values of animate objects. The result insisted that the
data employed in this study could not discriminate in the
meaning of multidimensional scaling. Therefore, in case that
there is an object near another object, this object might be a
possible candidate of the nearest solution. If we could consider
the obtained result as described above, it could explain that
intra and inter category errors might occur upon the attractor
neural network model. If we can modify the data set more real-
istic, result obtained might differ. Further works need to answer
the question about the double dissociation which showed brain
damaged patients in real.
Interpretation of Each Condition
If the attractor network can be regarded as a concept formation
model of human brain, then the diag condition can be regarded
as a model of recognition when a shape of dog was exposed in
retina, we can recognise this retinal image as “dog”. The cate-
gory condition might be considered that subjects and/or patients
can recognize this visual image of dog as animal, analogous to
category judgement task. The same condition can be considered
such that subjects or patients recognize a “dog” per se. In this
way, the three conditions adopted in this study can be inter-
preted as models of the brain. The results showed that the at-
tractor neural network might utilize the loop between output
and cleanup layers for problem solving. In addition, we ob-
served the effect of category specific disorders in the destruc-
tion experiment which destroyed the mutual connections be-
tween output and cleanup layers. This results should not be
considered as accidental artifacts of the computer simulations.
Although the results here showed the category specificity in
animate objects, it might not be explained another kind of
specificity for inanimate objects or inanimate specific category
disorder. If our semantic memory could be consisted of micro
features like presented in this study, the correlation matrix
among objects calculated from the micro features is the one and
the only one source for explaining the category specificity. If so,
it might be difficult to explain inanimate specific disorders
without any additional assumptions.
Comparis o n with Previ ous Studies
Hinton and Shallice (1991) and Plaut and Shallice (1993) in-
troduced the same attractor neural network model as this study.
They investigated types of errors the model produced. Here, the
four points enumerated below must be taken into consideration:
1) The task: input and output pairs the network trained on.
2) The network architecture: type of unit used in simulation,
the way of organisation into groups, and manner of groups
3) The training procedure: examples presented to the network,
the procedure to adjust the weights to accomplish the task, and
the criterion for halting training.
4) The testing procedure: the performance of the network to
be evaluated, the way of lesions carried out to the network, and
the way of interpretation of the damaged network in terms of
overt responses which can be compared with those of patients.
The same data set developed by Tyler et al. (2000) was em-
ployed in this study. Therefore, the conclusion also corresponds
to this study, while the network architecture was different from
the one they employed. They employed the three layered per-
ceptron, on the other hand, the attractor neural network was
employed in this study. Tyler et al. (2000) claimed that the
distinctiveness of functional features correlated with perceptual
features varies across semantic domains. They also insisted that
category structure emerges from the complex interaction of
these variables. The representational assumptions that follow
from these claims make predictions about what types of seman-
tic information are preserved in patients with category specific
deficits. The model showed, when damaged, patterns of pres-
ervation of distinctive and shared functional and perceptual
information which varies across semantic domains. The data
might be interpreted that dissociation between knowledge about
animate and inanimate objects. According to their claim, the
category specific deficits can emerge as a result of differences
in the content and structure of concepts in different semantic
categories rather than from broad divisions of semantic memory
in independent stores. In this framework, category specific
deficits are not necessarily the result of selective damage to
specific stores of one or other type of semantic information.
The basic assumption based upon this study was the same as
the one of Tyler et al. (2000). That is the patterns of correlation
over features, the semantic neighborhood of concepts in the
different domains plays a part in determining the probability of
errors of different types. For animate objects, within category
errors are likely because concepts within these categories are
Neural Correlates of the Model
As mentioned in introduction, a lot of neuroimaging studies
related to this study were conducted so far. The findings about
neural correlates of the model, or the responsible areas which
might cause category specificity must be taken into considera-
tion. The possible candidates might be the fusiform gyrus and
the left lateral temporal gyrus (Martin & Chao, 2001; Martin &
Caramazza, 2003; Josephs, 2001; Lewis, 2006; Thompson-
Schill, 2003). However, as mentioned in the former section,
there is no need to postulate the independent area to process the
information from one category selectively. Rather, it can pos-
tulate that category errors might occur the correlation matrix
based upon the similarity. If so, we would rather consider a
wide spread expression of category information in the brain.
This might be the reason why neuroimaging studies revealed
that there are many areas related in the category specificity. The
distributed manner of expression of micro features as inputs to
the neural network system might be interpreted as a basic idea
to process information in the brain. The neural network study
must play an important role to understand such situations.
Limitation and Prospect
The model succeeded in explaining robustness against damages
(see Figures 6-8). On the other hand, the model did not succeed
in explaining the double dissociation between categories. This
dissociation might be considered to be reasonable when the
origin of this effect would depend on the input signals and their
similarity. The attractor neural network model per se could not
explain the inanimate specific category disorder without any
additional assumptions, while this model can easily explain the
animate specific category disorder. Taking into account the
Copyright © 2013 SciRes. 371
results obtained in neuroimaging studies and clinical neuro-
psychology, the computational approach using neural network
model must be worth considering. Hereafter, it is tried to de-
scribe the relationship to the areas of cognitive neuropsychol-
ogy and neural network.
Contributio n to Cog ni tive Neuropsych ology
The attractor neural network model employed in this study was
originally developed with an intention to explain neuropsy-
chological evidence (Hinton & Shallice, 1991; Plaut & Shallice,
1993). Therefore, the model can apply directly to the data in
neuropsychology. The model could explain three different tasks:
categorisation, naming, and identification tasks (see the condi-
tions section in numerical experiments). This is one of the
promising ways to bring our knowledge to further understand-
ings. The more phenomenon which the model can explain, the
better in the sense of parsimony.
Contributio n t o Neural Netw ork
The model employed in this study was one of applications of
the generalised neural network model. The method of learning
was also the general one known as the generalised delta rule
(e.g. the back propagation method). The relation between the
generalised model and its application to the particular area or
evidence would make fruitful discussion to understand the
Bridge between Neuroimaging and
Synthesis between neuroimaging and neuropsychological stud-
ies must be required. While neuroimaging studies reveal that
there are many related areas in the brain for category specificity,
neuropsychological studies have tendency to emphasize the
asymmetry or the double dissociation between animate and
inanimate objects. Both findings must be explained simultane-
ously based upon one integrated model. The value of the model
employed in this study can exist in this point of view. This
study was conducted to try to explain along with this point of
Finally, what the author is thinking is enumerated as follow:
1) The disorder in semantic memory might reflect the struc-
ture of the semantic memory.
2) This disorder might emerge neuropsychological level,
which means that it occurs as the size of gyri and sulci. It is
neither individual neuron nor whole system levels.
3) Attractor neural network can be considered as a model for
semantic memory disorder. It might be a useful tool to investi-
gate category specificity.
4) Synthesis between heterogeneous (category specific) and
homogeneous (no neuroanatomical specialisation) point of view
is possibly a promising way to describe phenomenon.
In spite of the simplicity, the attractor neural network could
describe at least three cognitive neuropsychological tasks;
categorisation, identification, and naming tasks. This is one of
major advantages of this model. The model could succeed in
predicting patients’ behaviour with animate specific memory
disorder, however, the model could not explain inanimate spe-
cific memory disorder without any additional assumptions. So,
the possibility for this model to explain the double dissociation
between animate and inanimate objects should be discussed
further in separate papers. However, there still are possibilities
for this model to account for the double dissociation between
animate and inanimate objects. In this study, non-dichotomous
memory representation like Figures 1 and 9 was adopted as the
data set to be learned. The model’s behaviour depends on both
its network architecture and its input data representation, which
is defined by micro features. This micro feature constrains the
model’s behaviour through the correlation matrix among ob-
jects. The difference between intra- and inter-correlations shown
in Figure 1 might cause the category specificity, because one
category has higher inner-category correlations than that of the
other category. The representations could be considered such
that there needs no local representations to deal with both ani-
mate and inanimate objects in our brains. On the contrary,
category specificity might emerge necessarily and naturally as
consequences of exposure of both categories. In addition to this
consideration, these object representations adopted in this study
might also produce category specific memory disorders when
the system suffered damages. Therefore, the attractor neural
network could be considered as the one of possible candidates
to explain various cognitive neuropsychological phenomena.
This model also provides useful suggestions about our semantic
memory organisation. However, the model failed in explaining
patients’ behaviour with inanimate specific memory disorder,
while this model succeeded in explaining patients behaviour
with animate specific disorder. It is obvious that the model has
both advantage and shortcoming. The fact that three kinds of
tasks could be explained by this model is clearly one of mani-
fest advantages of this model. Further studies must be con-
ducted to reveal the shortcoming. It is also obvious that the
model might not be able to explain this shortcoming without
any additional assumptions or modification of network archi-
tecture. However, it can be considered that this study would be
valuable because the model succeeded in showing clear insight
about a direction of studies in the future.
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