The value of connectionist models in neuropsychology: A case study of
neglect - MSc Thesis
J.H. Fabius
Neuroscience & Cognition (Cognitive Neuroscience), Life Sciences, Utrecht University
Supervisor: J.M.J. Murre
Brain and Cognition, University of Amsterdam
1. Introduction
Neuropsychology is concerned with the relationship
between the injured brain and consequent
abnormal behaviour (Deelman & Eling, 2004;
Heilman & Valenstein, 2011; Lezak, 2004). Recent
developments have contributed to fine-tuned
neuropsychological assessments, in which the
diagnosis, prognosis and rehabilitation program can
be adapted to the clinical picture of an individual
patient (Braun et al., 2011). Ultimately, the goal of
neuropsychological science is to understand the
neurobiology underlying cognitive symptoms
(Lezak, 2004), and to construct effective
rehabilitation programs (Cicerone, 2012). Despite
the availability of cognitive rehabilitation programs
for patients with neuropsychological disorders,
studies of their effectiveness are not unanimously
supportive (Cicerone et al., 2011; Hoffman, Bennett,
Koh & McKenna, 2011; Fierro, Brighina & Bisiach,
2006; Powel et al., 2012; Rohling, Faust, Beverly &
Demakis, 2009).
Arguably, the scarcity of effective
rehabilitation programs is partially the result of an
insufficient understanding of the neurobiology
underlying the disturbed cognitive function
(Cernich, Kurtz, Mordecai & Ryan, 2010). In order to
successfully set up rehabilitation programs, it is
necessary to have a better understanding of how
the brain works under normal conditions and how it
gives rise to cognition (Bressler & Tognoli, 2006;
Goldrick, 2008; Mogensen & Malá, 2009;
Schierwagen, 2012). Unfortunately, although the
way cognition arises from its underlying neurobiological structure is being clarified more and
1
more, modern neuropsychology is still unable to
explain many cognitive disorders from a purely
neurobiological perspective (Bilder, 2011; Vakil,
2012). Therefore, models are being used to be able
to work with patients with neurological damage.
This thesis will elaborate on the contribution that
connectionist models can make to the development
of neuropsychological methods. However, since
appraising
connectionism
for
all
known
neuropsychological disorders would be a very
extensive read, this thesis will be limited to the
neuropsychology of unilateral spatial neglect.
To investigate what connectionism might
be able to contribute, or has already contributed to
the understanding of unilateral spatial neglect, is
interesting for two reasons. First, many aspects of
this disorder are still debated (Corbetta & Shulman,
2002, 2011; Hillis, 2006), yet it is a frequently
observed and seriously disabling disorder
(Bartolomeo, 2007; Gillen, Tennen & McKee, 2005;
Nys et al., 2005; Ringman, Saver, Woolson, Clarcke
& Adams, 2004). Moreover, although progress is
being made (Hesse, Sparing & Fink, 2011), no
effective rehabilitation programs for neglect have
been established yet (Bowen, Lincoln & Dewey,
2002; Bowen & Wenman, 2002; Lincoln & Bowen,
2006). Connectionist models might be able to shed
new light on some of the ambiguities. Second,
models of neglect have not yet been reviewed,
unlike models of disorders in other cognitive
domains such as language or memory (see e.g.
Bechtel & Abrahamsen, 2002; or Thomas &
McClelland, 2008). Hence, since neglect both raises
several important clinical questions and its
connectionist models have not been reviewed
thoroughly, this thesis will focus on this particular
disorder. This will not be a historical overview of all
contributions connectionism has made in the past
to a better understanding of cognitive processes
(for such overviews see e.g. Bechtel & Abrahamsen,
2002, or O’Reilly & Munakata, 2000), but rather a
pragmatic approach of how neuropsychology could
view these contributions, as exemplified with the
case of neglect.
Before the existing models of neglect will
be reviewed, first the current contributions of
connectionism to cognitive neuroscience will be
illustrated. Subsequently, a short introduction to
connectionist modelling will be given, covering
some general concepts that are important to
understand in order to discuss the existing models.
Next, the current understanding of unilateral spatial
neglect will be discussed. Then, the connectionist
models of neglect will be reviewed individually,
followed by a general discussion, in which the
models will be compared and an evaluation will be
given of the overall contribution of the models.
2. Why connectionism?
Since the dynamics of the entire brain are
still unknown, simplified models of cognitive
functions have been used and localized to cortical
areas in order to construct more accurate diagnoses
and prognoses for patients with neurological
damage. Classically, behavioural experiments gave
rise to models of cognitive functions, which were
then applied to patients with neurological damage
(Deelman et al., 2004; Gazzaniga, Ivry & Mangun,
2009a). The models of cognitive functions that are
applied to neuropsychological patients consisted of
boxes and arrows (Bechtel & Abrahamsen, 2002;
Gazzaniga et al., 2009a). At the starting point of a
model is an external input. The input goes into a
(series of) box(es) in which cognitive operations are
performed. The operations, consequently, yield a
new representation, i.e. the output. Bechtel and
Abrahamsen (2002) refer to this approach of
cognition as the ‘symbolic paradigm’, considering
the central role the ‘symbols’ (i.e. the input of a
box) play. The authors argue that with this
approach, a cognitive function is defined by one or
2
several operations that can be performed on a
symbol.
The problem with the approach of boxand-arrow models is that the definitions of different
operations can be quite abstract (e.g. ‘grammatical
encoding’), and, thus, a topic of debate. Moreover,
the abstract definition causes problems in linking an
operation to neuronal activity (Mogensen & Malá,
2009; Price & Friston, 2005). Indeed, a more
modern view of cognitive neuroscience on the
relationship between brain structure and cognition
is shifting from a localization paradigm (i.e. discrete
anatomical modules for different cognitive
functions) to a large-scale network paradigm. The
network paradigm states that the human brain
consists of numerous networks, made up of
multiple cortical areas that are functionally
connected to each other (Bressler & Menon, 2010;
Seghier, Zeidman, Neufeld, Leff & Price, 2010;
Zamora-López, Zhou & Kurths, 2010). Furthermore,
a cognitive module is not (necessarily) represented
by a single anatomical structure. In other words,
there is no one-on-one correspondence between
cognitive and anatomical modularity (Bressler &
Tognoli, 2006; Medler, Dawson & Kingstone, 2005;
Mogensen & Malá, 2009; Price & Friston, 2005). In
fact, exactly how these networks give rise to
cognition has yet to be unravelled. However, even
though this coupling needs further research, it has
been argued that it is highly unlikely that the brain
contains neurons for operations such as
‘grammatical encoding’, and that it should
therefore be the aim of cognitive neuroscience to
elaborate on the contributions of cortical modules
in terms of ‘simple’ manipulations on their input
(Mogensen & Malá, 2009; Price & Friston, 2005).
A subdivision of cognitive neuroscience
that has specialized in elaborating on the bridge
from simple computations to complex behaviour is
connectionism, using artificial neural networks to
model cognitive processes (Bechtel & Abrahamsen,
2002). In its relatively short history as a field of
research within the cognitive neuroscience,
connectionism has shown to be able to provide
arguments in debates on whether or not a set of
assumptions is sufficient to explain a complex
theory. This quality is referred to as existence proof,
meaning that a model does not aim at falsifying a
theory, but rather showing that a certain set of
assumptions is sufficient to address the
characteristics of a concept that a cognitive theory
tries to explain (McClelland, 2008; Meeter, Jehee &
Murre, 2007). Perhaps the best known example of
this has been provided by the model of Rumelhart
and McClelland, on the transformation of English
verbs to their past tense forms (Rumelhart &
McClelland, 1986, in Thomas & McClelland, 2008).
This model shed new light on the at that time
predominant linguistic theory that children acquire
the correct past tense of English verbs by learning a
rule for the regular verbs and by learning the
association between the present and past tense of
irregular verbs by experience. The model showed
that is also possible to learn a correct past tense
transformation without any assumptions on the
distinction between regular and irregular verbs (see
Thomas & McClelland, 2008 for more explicit details
and the consequences of this model for linguistic
theory).
Although this is not the case for the past
tense model, many connectionist models aim to be
biologically plausible, making them particularly
interesting for neuropsychology because then they
might explain or even predict human performance
given a specific configuration (Meeter et al., 2007).
For neuropsychologists, the models would be even
more interesting if they could be lesioned and show
the same pathological behaviour as a particular
group of patients. Thus, the models could give
insight into the specific cognitive processes that
have been damaged in those patients. For example,
connectionists have constructed models of
cognitive disorders such as aphasia (e.g. Dell, Chang
& Griffin, 1999; Plaut, 2002) amnesia (e.g. Meeter &
Murre, 2005; Nadel, Samsonovich, Ryan &
Moscovitch, 2000) and even of complex psychiatric
disorders such as schizophrenia (e.g. Aakerlund &
Hemmingsen, 1998; Cohen, Braver & O’Reilly, 1996;
Cohen & Servan-Schreiber, 1992; Rolls et al., 2008).
With the knowledge provided by these lesioned
A1
A2
0.6
-0.3
-0.3
0.6
B1
0.6
3. Basic
modelling
C1
B2
0.6
principles
of
connectionist
In connectionism an artificial neural network is
constructed of simple units, representing neurons.
Like neurons, the units can be either transmitting
their activity or not (i.e. firing or not), depending on
the input they receive. There are several ways by
which it can be determined whether or not a unit
‘fires’ or not. A simple way is having a unit become
activated when it receives enough input to rise
above a certain threshold. Units are connected to
each other with connections of varying strengths,
called weights. The weight of a connection between
two units can be either positive (excitatory) or
negative (inhibitory), typically ranging from -∞ to
∞. To get a grasp of what a connectionist model
looks like, a simplistic model is depicted in figure
1A. This network consists of five units (A1 ... C1),
spread over three layers (A, B and C) with weights
of either 0.6 or -0.3. The activity of each unit is
updated synchronously per layer. When the sum of
the input exceeds the threshold of a unit, it is
activated. For example, all units have a threshold
(θ) of 0.6. If only unit A1 is activated, then B1 will
A1
Figure 1a. Simple three layer network, containing five nodes.
C1 will only be activated if either A1 or A2 is activated, not both
or neither.
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networks, performance of the patient and network
could be compared. Then, it would theoretically be
possible to define to what extent the network is still
intact, and, subsequently, to construct a
rehabilitation program, focussing on either the
recovery of the damaged operations, or on the
training of the operations that are still intact
(Robertson & Murre, 1999). Which of these two
different rehabilitation approaches would be
favourable for an individual patient could be
determined with knowledge of the network. Hence,
connectionism could contribute to a more
fundamental understanding of neuropsychological
symptoms and, eventually, to the construction of
more effective rehabilitation programs.
A2
0.6
0.3
-0.3
0.3
B1
0.6
C1
B2
0.6
Figure 1b. Network with a design similar to 1a, but with
different weights. This changes the way by which C1 will be
activated.
become activated and consequently C1. B2 will not
be activated because A1 inhibits B2. With this
design, C1 is only activated when either A1 or A2 is
activated, but not when both or neither are
activated. However, if the weights would be
adjusted, C1 could, for example, become active if
either A1 alone or both A1 and A2 were activated,
but not when A2 alone was activated (see fig. 1B).
Adjustment of the weights is central to many
connectionist networks, because this allows the
network to ‘learn’ associations between input and
output patterns. Thus, information can be stored in
the way the units are connected to each other.
For neurobiological plausibility, units
should not be activated indefinitely. Therefore,
once a unit has become activated, there are two
main ways by which it can become inactive again.
First, the unit could become inactive when its input
is below its threshold. Second, similar to real
neurons, the activation level of the unit could decay
over time from the point when it became activated.
Time in this sense is a model of the ‘real’ time. One
unit of time, called an iteration, is defined as a
single update of the activations of the units.
Furthermore, many physiological features of real
neurons can be implemented in the behaviour of
the units, such as the dynamics of an action
potential, instead of having the unit being artificially
‘on’ or ‘off’ (O’Reilly & Munakata, 2000).
Of course, more complex networks can
store more complex information (such as accurate
discriminating between different input patterns). In
the simple model presented in figure 1 only
feedforward connections are implemented.
However, networks could also contain (indirect)
feedbackward connections to provide top-down
feedback, and lateral connections (i.e. connections
within the same layer) to enable ‘competition’
between units. Competition is favourable when, in a
layer only a single unit is supposed to be active,
called a winner-takes-all principle. This, in its
simplest form, implies a gating mechanism which
ensures that the unit (or group of units) with the
highest input will be activated, in contrast to the
units in the same layer with a lower input (Maass,
2000). This is, for example, used to model word
recognition based on the identification of simple
features of letters (see e.g. the network in fig. 2
from the classical work of McClelland & Rumelhart,
1981). The units in the first layer representing the
simple features (i.e. line configuration at a specific
location in the word presented to the model), have
excitatory connections to the letters, represented in
the next layer, that contain those features (in fig. 2
those connections have arrow endpoints) and
inhibitory connections to the letters that do not
contain those features (in fig. 2, circular endpoints).
The units in the second layer have lateral inhibitory
connections. So, when one letter is processed,
some features may activate multiple units in the
second layer, but due to the lateral inhibition, the
unit representing the presented letter will quickly
inhibit the units that correspond only to some of
the features of the presented letter. The same
applies to the third layer. This concludes some of
the basic principles that are found in most of the
models that will be discussed further on.
4. The neuropsychology of unilateral
spatial neglect
4.1 Clinical presentation
Unilateral spatial neglect refers to an
impaired ability to direct attention to the
contralesional space without primary sensory
deficits (Heilman, Watson & Valenstein, 2011).
Patients frequently recover from neglect within the
first twelve months after a stroke (Karnath, Rennig,
Johannsen & Rorden, 2011). Nevertheless, the
implications of neglect are severe: it disrupts daily
Figure 2. McClelland and Rumelharts interactive model of
context effects in letter perception. (Adopted from McClelland &
Rumelhart, 1981).
4
activities (Bartolomeo, 2007) and is one of the best
negative predictors for stroke recovery (Gillen et al.,
2005; Nys et al., 2005). It has been found that the
disorder can potentially affect all sensory
modalities, but it is most frequently observed in the
visual domain, possibly because in that form it is
most disabling (Brozzoli, Dematte, Pavani,
Frassinetti & Farne, 2006; Hillis, 2006). Therefore,
this thesis will focus on unilateral visuospatial
neglect. Hence, from here on, “neglect” will refer to
unilateral visuospatial neglect. In the following
paragraphs the behavioural characteristics and
neurobiology of neglect will be discussed.
4.2 Characteristics of neglect
Clinical assessment tools – In clinical
settings, neglect is most frequently assessed using
one out of two tasks that are easy to conduct: the
cancellation task and the line bisection task (Parton,
Malhotra & Husain, 2004). The typical performance
of a neglect patient is a failure to cancel out the
targets at the contralesional side of a scene and a
deviation to the right on the line bisection task. In
other more experimental tasks, patients with
neglect often fail to fixate attention at targets on
the contralesional side of a scene during a visual
search task, show spontaneous rightward eye
movements, fail to copy the left side of a picture,
orienting task (Bartolomeo, 2007, Corbetta et al.,
2005; Corbetta & Shulman, 2011; Fruhmann-Berger
& Karnath, 2005; Molenberghs & Sale, 2011).
Despite the common use of the line bisection and
cancellation task it is still under debate whether or
not the tasks are sensitive to the same deficit
(Ferber & Karnath, 2010; Molenberghs & Sale,
2011; Molenberghs, Sale & Mattingley, 2012;
Rorden, Fruhmann-Berger & Karnath, 2006).
Attentional gradient – One of the most
accepted characteristics of neglect is that inability
to direct attention to stimuli follows a gradient
across the visual space (Kinsbourne, 1993). This
gradient implies that the probability of selecting a
stimulus is high for stimuli located at the right side
of space (ipsilesional), and decreasing for stimuli
located more to the left (contralesional). However,
the gradient is not monotonically increasing from
5
the extreme left to the extreme right, but rather
has a peak somewhere in the right visual field and
decreases again to the extreme right (Behrmann,
Watt, Black & Barton, 1997; Karnath, Mandler &
Clavagnier 2011; Müri, Cazzoli, Nyffeler &
Pflugshaupt, 2009;).
Frames of reference – Neglect is classically
associated with egocentric frames of reference, i.e.
neglect of stimuli with respect to the body of the
patient (Brozzoli et al., 2006; Corbetta & Shulman,
2011; Hillis, 2006). Nevertheless, patients who
showed neglect within an allocentric Frame of
reference, i.e. neglecting a part of the stimulus,
independent of the egocentric location of the
stimulus, have been observed as well (Brozzoli et
al., 2006; Corbetta & Shulman, 2011; Hillis, 2006).
This has been investigated using cancellation tasks
with stimuli consisting of small circles. The targets
were circles with an opening at either the left or
right side, distractors were closed circles. Targets
and distractors were evenly distributed across the
scene. Some patients omitted targets, independent
of the lateralization of their opening, when they
were located on the contralesional side of the scene
with respect to themselves (egocentric Frame of
reference), whereas other patients omitted the
targets with an opening on the contralesional side,
independent of their egocentric location (allocentric
Frame of reference). The two types of Frames of
reference in neglect seem to be both behaviourally
and anatomically dissociable (Corbetta & Shulman,
2011). In addition, egocentric neglect occurs more
often than allocentric neglect (Marsh & Hillis, 2008).
However, it has been suggested that the
dissociation between egocentric and allocentric
neglect can be explained from a purely egocentric
perspective, considering the attentional gradient
(Driver & Pouget, 2000). By assuming that neglect is
most severe at the egocentric left and decreasingly
less severe toward the egocentric right, the left side
of both the entire visual space and individual
objects would be more neglected than the right.
The theory of Driver and Pouget (2000) has lead to
the hypothesis that the severity of allocentric
neglect is associated with the severity of egocentric
neglect, which has been shown by several studies
(e.g. Karnath et al., 2011; Rorden et al., 2012).
Hence the theory questions the suggested
behavioural
and
anatomical
dissociations.
According to Karnath and Rorden (2012), egocentric
neglect is the core deficit in neglect, and neglect
severity can be estimated by calculating the ‘centre
of cancellation’ on a cancellation task (Rorden &
Karnath, 2010). Nevertheless, different lesion sites
have been suggested to underlie the different
deficits (Chechlacz, Rothstein, Bickerton, Hansen,
Deb & Humphreys, 2010; Yue, Weiqun, Huo &
Wang, 2012).
Extinction
–
Another
attentional
phenomenon associated with neglect is extinction.
Extinction is a condition in which a patient can
direct his/her attention toward stimuli on either
side of his body, but when two equally salient
stimuli are presented simultaneously, the stimulus
on the ipsilesional side of visual space ‘wins’ the
attention competition (Heilman et al., 2011).
Extinction is thought to be associated with a
different type of lesion than neglect, or at least, less
extensive (Karnath & Rorden, 2012; Umarova et al.,
2011; Vossel, Eschenbeck, Weiss, Saliger, Karbe &
Fink, 2011), as will be discussed in the next
paragraph.
4.3 Neurobiology
Right hemisphere lateralization – Neglect
is observed, mainly, after a stroke in the right
hemisphere (Bartolomeo, Thiebaut de Schotten &
Doricchi, 2007; Kleinman, Newhart, Davis, HeidlerGary, Gottesman & Hillis, 2007; Ringman et al.,
2004). It has been estimated that up to 90% of all
patients with neglect has had a right hemisphere
stroke (Corbetta, Kincade, Lewis, Snyder & Sapir,
2005). Moreover, approximately 25% of the
patients with a stroke in the right hemisphere has
been diagnosed with neglect (Ringman et al., 2004;
Vossel et al., 2011). Given that not all patients with
a right-hemispheric stroke suffer from neglect much
research has been done investigating the
anatomical structures that are commonly damaged
in neglect patients.
Major theories – (1) Heilman and Van den
Abell (1980) and Mesulam (1981, 1991) suggest that
parietal and frontal regions in the right hemisphere
direct spatial attention towards both the ipsi- and
contralateral side of space, while the same regions
6
in the left hemisphere direct attention exclusively
to the contralateral space. A right hemispheric
lesion would therefore result in the absence of a
system orienting attention towards the left side of
space. (2) Kinsbourne (1977) argues that both
hemispheres are capable of directing attention to
either side of space, but each hemisphere is biased
to direct attention toward the contralateral side. In
addition, the left hemisphere is more heavily biased
toward the contralesional space than the right
hemisphere, resulting in bias toward the right side
of space in the healthy brain. According to this
theory, a right hemispheric lesion would be
followed by an increased bias to direct attention
toward the right visual field.
Integration of lesions and theories – The
influential works of Corbetta and Shulman (2002;
2011) and Karstner and Ungerleider (2000) state
that both hemispheres contain networks for visual
selection that are biased toward the contralateral
space and that are mutually inhibitory. To
accomplish visual selection, they integrate topdown and bottom-up information to create salience
values for all presented stimuli. These networks are
thought to consist of dorsal frontoparietal areas.
The networks compete with each other to direct
attention to the stimulus with the highest salience
in their receptive field. In contrast to the bilateral
dorsal networks, only the right hemisphere contains
a network consisting of ventral frontoparietal areas
that is specialized for reorientation to novel or
behaviourally relevant stimuli, independent of their
location. However, to direct reorientation of
attention to a novel or relevant stimulus, coactivation of the dorsal network is required. Hence,
it is hypothesized that the ventral network
interrupts the ongoing activity of the dorsal
network, thus forcing the dorsal frontoparietal
network to reorient and select the novel stimulus.
Corbetta and Shulman (2002, 2011)
distinguish between spatial and non-spatial deficits
of neglect. The spatial deficit comprises the
egocentric bias in spatial attention, and arises from
damage to the right dorsal network. As mentioned
earlier, the dorsal networks in both hemispheres
compete with each other in the selection of a visual
stimulus. Both networks are biased to the
contralateral visual field. Hence, when competition
is not equal anymore (as a consequence of a
weakened right dorsal network), the left dorsal
network will almost always ‘win’ the competition.
An interesting line of evidence for this distorted
competition lies in non-invasive brain stimulation. It
has been shown that neglect can virtually be
induced in healthy subjects by stimulating the right
hemisphere with transcranial magnetic stimulation
(TMS; Sack, 2010). Moreover, neglect symptoms
have been shown to be ameliorated when TMS is
performed on the left, intact, hemisphere, and
thereby hypothetically decreasing the superiority of
the left dorsal network in the competition for visual
selection (Fierro, Brighina & Bisiach, 2006; Hesse et
al., 2011; Koch et al, 2012; Oliveri, 2011). The nonspatial deficits are an impaired attentional
disengagement and reorientation, and impaired
detection of behaviourally relevant stimuli.
Damage to the ventral network underlies
the non-spatial deficits of neglect. Over the last
decade there has been a growing consensus, in
favour of the third theory. As shown by lesion
studies, neglect most frequently occurs after lesions
to the right temporoparietal junction (TPJ), the
inferior parietal lobule (IPL), the superior and
middle temporal gyri (STG and MTG, resp.) and the
ventral lateral prefrontal cortex (vlPFC; Bartolomeo
et al., 2007; Corbetta et al., 2005; Karnath &
Rorden, 2012; Molenberghs et al., 2012; Urbanski et
al., 2011). Especially the disruption of the
connections between these areas (indicating
damage to the superior longitudinal fascicle
subunits II/III) could cause the symptoms of neglect
(Bartolomeo et al, 2007). Except for the IPL, all
areas were localized in the ventral frontoparietal
network by Corbetta and Shulman (2002, 2011).
Arguably, the non-spatial deficits that are related to
a lesioned ventral frontoparietal network seem to
correspond with the clinical description of
extinction, which is just what lesion studies suggest.
Several studies have shown that patients with
extinction, but without neglect have more focal
lesions, located in the areas that comprise the
ventral frontoparietal network, while their dorsal
frontoparietal network is relatively spared (Karnath
& Rorden, 2012; Vossel et al, 2011). In contrast, the
spatial deficits of neglect were associated with
damage to the dorsal frontoparietal network
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(Karnath & Rorden, 2012; Umarova et al., 2011;
Vossel et al, 2011).
5. Connectionist models of neglect
In the next section, eight connectionist models will
be summarized and appraised for both the
symptoms of neglect they explain and the method
by which the symptoms are induced. The models
will be presented in a chronological order of
publication, because the authors often seem to be
influenced by each other. By presenting the models
in a chronological order it may become clear how
the models complement or improve each other. If
the authors have not named their model, it will be
referred to as the name of the first author between
brackets (for example, [Fabius]). For each model
several aspects will be discussed. Each description
starts with a brief overview of the architecture of
the model (in the section ‘design’). The technical
details of, for example, update rules and
connections between individual units will not be
discussed unless they have significant importance
for the manifestation of neglect. These details can
be found in the papers cited in the headings of the
model’s paragraph. After the design, the way the
performance of the model is observed will be
discussed (‘performance’). Then, the method by
which the model is virtually lesioned is explained
(‘lesion’), and how that affects the model’s
performance (‘results’). Finally, an appraisal of the
strengths and limitations of the model is given
(‘evaluation’).
5.1 Basis function model (Pouget & Sejnowski,
1996, 1997a, 1997b, 2001).
Design – The basis function model of
Pouget and Sejnowski was originally designed to
model cortical processing of object location by
integrating sensory input of different modalities,
without explicitly encoding frames of references.
The architecture of the model is based on the
observation that the brain integrates information of
different modalities that all code for object position.
In their model, Pouget and Sejnowski used retinal
and eye-position coordinates. The model (see fig. 3)
consists of two input maps, a retinotopic (V1) and
an eye-position map (thalamus). These maps
project to an intermediate map, the ‘basis function
map’ (area 7a of the macaque cortex). This map is
the core of the model. There is one unit in the map
for one of all possible combinations of retinal and
eye-position coordinates. In the first paper (1996)
there is one basis function map. In the third paper
(1997b), it is specifically stated that there is one
basis function map for each hemisphere. The units
in the basis function maps are tuned in such a way
that a contralateral activity gradient is induced, e.g.
in the left hemispheric basis function map, the units
are on average activated most strongly when an
object appears on the right side of the retina (or V1)
and when the eyes are turned to the right. After this
transformation, the basis function map projects to
two motor maps, one to direct saccadic eye
movements based on retinotopic coordinates
(superior colliculus) and another to direct reaching
movements based on head-position coordinates
(premotor cortex).
When multiple stimuli are presented, selection is
based on the salience value of the presented
stimuli. The salience value of a stimulus is defined
as “the sum of activities of all the basis function
units that have their receptive field centered on the
retinal position of the stimulus” (1997b). The
stimulus that will be selected is the one with the
highest salience (i.e. winner-takes-all). At the next
time step, the salience of the previous winner is set
to zero (inhibition of return; for further information
on this principle see box 1.) in the basis function
map. To observe which stimulus had been selected,
the profile of activity of the module representing
superior colliculus (see fig. 3) was monitored, since
the units in that map are activated according to a
mathematically equivalent method. In the third
paper (1997b), reaction times were also monitored
to observe the effect of distractors. Reaction time
was determined by target selection and target
processing. Target selection was arbitrarily set to
take 50 ms per iteration. Target processing was set
to be inversely proportional to the stimulus
salience.
Performance – The goal of the model is to
have the basis function map select a stimulus for
action, i.e. reaching or a saccadic eye movement.
Lesion – In the first paper (1996), neglect is
induced by tuning more neurons to right retinal and
eye-position coordinates. However, since in the
third and fourth paper (1997b), both basis function
maps are already biased to the contralateral side,
the entire right basis function map is removed,
leaving the model in the same state as in the first
paper, i.e. one map with a contralateral activity
gradient.
Figure 3. The basis function model. Each dot represents a unit.
Input enters the model in the retinotopic map, which represents
V1. The eye position cells provide another input, representing
the thalamus. (Adopted from Pouget & Sejnowski, 2001).
8
Results – Neglect behaviour was observed
in a line cancellation (1996, 1997b) and a line
bisection task (1997b). (a) In the line cancellation
the model was presented with a stimulus display
consisting of multiple small lines. A cancellation was
modelled with the selection of a line. Due to the
activity gradient in the basis function map, only the
lines on the right half of the display were selected.
Stimulus selection started on the outer most right
side of the display (as a consequence of the higher
salience of the right stimuli and the winner-takes-all
principle), progressing to the left (as a consequence
of the inhibition of return). How far the stimuli to
the left of the display will be selected depends on
the inhibition of return recovery rate. Which the
authors, apparently, set to a value that when the
Box 1. Inhibition of return
In many of the models described in this thesis, the
authors have implemented an ‘inhibition of return’
principle. Which most frequently refers to oculomotor
inhition of return, rather than the classical inhibition
of return, as proposed by Posner and his colleagues
(Gazzaniga et al., 2009b; Klein, 2000; Smith &
Henderson, 2011; Wang & Klein, 2009).
Inhibition of return implies that fixations,
due to saccadic eye-movements during the scanning
of a visual scene, are not able to return to the
previous point of fixation. One mechanism on which
this principle is thought to be grounded is the visual
short term memory (VSTM; Corbetta & Shulman,
2011). The VSTM temporarily stores the locations of
fixation and thereby decreasing the salience of those
locations, ensuring that they will not be visited until
they are no longer stored in the VSTM. However, it
has also been argued that this oculomotor inhibition
of return is not so much an inhibition of the salience
of the previous location of fixation, but rather a result
of a bias of saccades to be made in the same direction
as the previous saccade (Smith & Henderson, 2011).
However, as will be seen, the implementation of this
principle varies over the models.
basis function map selected lines at the centre of
the display, the salience of the stimuli on the outer
right side was higher than the salience of the
stimuli to the left of the centre.
(b) In the line bisection, the centre of the
line was estimated by computing the centre of mass
of activity in the basis function map after stimulus
presentation. The activity gradient induces an bias
in the estimation of the line centre, leading to a
‘response’ that is slightly to the right of the centre
of the line. This error increased proportionally with
line length, and a cosine function of line orientation,
when the line was rotated.
(c) In a third experiment, the model was
presented with four stimuli, one was the target, the
other three distractors. The stimuli were
horizontally arranged, such that the target was
either to the left or the right of the distractors. Due
to the activity gradient in the basis function map,
the target was fastest detected when it was located
on the right side of the distractors, independent of
the retinal lateralization of the target. However, the
9
salience of the stimuli increased monotonically (due
to the gradient) when their location moved from
the left to the right.
(d) To investigate the frames of reference
used in neglect, the eye-position coordinates were
replaced by head-position coordinates. That is, the
output of the superior colliculus (used in the
majority of the simulation) are coded using
retinotopic coordinates, provided by the basis
function map. The premotor cortex of the basis
function model (see fig 3.), extracts head-centered
coordinates from the basis function map. So, in this
final simulation, the authors monitored the output
of the premotor cortex instead of the superior
colliculus. Following this adjustment, the model was
presented with a stimulus to the left or right of the
centre of the display, with the head oriented
straight ahead or turned to the right. Stimulus
identification was modelled as the total amount of
activity in the basis function map evoked by the
stimulus. The amount of activity was higher for the
right than for the left stimulus, independent of head
orientation. Additionally, The amount of activity
was higher when the head was oriented toward the
right, independent of the retinal position of the
stimulus.
Evaluation – (Strengths) The basis function
model has a neurobiological background, that is
based on the theory of sensorimotor integration in
the parietal cortex (Pouget & Sejnowski, 1997a),
although mainly inspired by the parietal areas of the
macaque, the authors state that the same
integration has been shown in human parietal
cortices. However, it is not one of the major goals
of this model to be fitted exactly with
neurobiological principles. As is consistent with
human data (although far from generally accepted,
see e.g. Corbetta & Shulman, 2011), damage to the
modelled parietal cortex leads to neglect like
behaviour, as shown by the performance on the
cancellation and line bisection tasks. Furthermore,
the model elegantly addresses the problem of the
frames of reference in neglect. It does not explicitly
implement the frames of reference, but they are an
emerging property of the input transformations in
the basis function map. As a result, effects of
neglect can be found in both egocentric and
allocentric frames of reference, with the allocentric
symptoms being more severe in the contralesional
egocentric space. This finding is congruent with the
patient data of Karnath, Mandler and Clavagnier
(2011). However, the two types of neglect (i.e. alloand
egocentric
neglect)
rarely
co-occur
simultaneously and are thought to be the
consequences of lesions to different anatomical
regions (Corbetta & Shulman, 2011). Interestingly,
as a result of the same lesion that was used to
model neglect, the model would show extinction.
This extinction behaviour was in an allocentric form,
as shown by the third experiment (i.e. when two
stimuli are presented to the model), the stimulus
located most the right is always selected,
independent of the egocentric locations of the
stimuli. In the version of the model with two basis
function maps, the neuronal activity gradient
increasing from the ipsi- to the contralateral side,
implemented in the ‘healthy’ model, gracefully gets
around the problem of a gradient lesion (a type of
lesion found in some of the following models to
account for the attentional gradient). Consequently,
the entire right parietal cortex is removed, which is
more biologically plausible than inducing a gradient
lesion (as will be discussed later on with some of
the other models), for which there seems to be no
mention in the literature.
(Limitations) Removing the entire right
basis function map is used to simulate damage to
the right parietal cortex, inducing left-sided neglectlike behaviour, congruent with human neglect
patients. However, removing the left basis function
map would just as easily result in right-sided
neglect, which is inconsistent with the observations
that 90% of the neglect patients have right
hemisphere damage, resulting primarily in
contralateral neglect (Corbetta et al., 2005).
Another limitation is that the lesioned model will
always initially select the stimulus located far to the
right of the display, since, when all stimuli are
identical, the salience of the targets most to the
right is the highest, due to the activity gradient of
the basis function map. The next stimulus that is
selected will always be more to the left of the
former target. However, there is no clear indication
of this kind of sequentially organized behaviour
found in patient data (Kettunen, Nurmi, Dastidar &
Jehkonen, 2012). Furthermore, the principle of
inhibition of return, as argued by the authors, is
10
necessary for the basis function map to produce
human like behaviour, i.e. to not select the same
stimulus twice in a row. Nonetheless, the artificial
way by which their inhibition of return is
implemented has consequences for the lesioned
model. That is, the time (i.e. number of iterations) it
takes before the salience of the initially selected
target is recovered enough to be selected again,
depends on the size of a recovery factor. This size,
regrettably, is not justified in any of the papers, and
specified only in the fourth paper. One could
imagine that, if indeed there is an inhibition of
return recovery factor in humans, its value would
probably vary among neglect patients and thus be a
contributing factor to neglect severity. Whether this
is actually the case has yet to be investigated, for
example with eye-tracking methods. With the initial
cancellation being the stimulus most to the right,
the inhibition of return principle leads to the
observation that saccades (or cancellations) are
always made to the left of the previous stimulus.
This pattern is repeated until the salience of the
stimulus that was selected at first (at the right side
of the display) is recovered. These patterns are
somewhat similar, though not identical to patient
data (Husain et al. 2001; Mannan et al., 2005; Müri
et al. 2009; Nys, Stuart & Dijkerman, 2010; Parton
et al., 2006). Moreover, since the centre of
cancellation is supposed to be a measure of neglect
severity (Rorden & Karnath, 2010), the severity of
neglect in the basis function model depends on the
speed of recovery of inhibition of return and the
activity gradient in the basis function map.
Therefore, the severity of neglect as modelled in
the third article seems arbitrary.
5.2 MORSEL (Mozer, Halligan & Marshall, 1997;
Mozer, 2002).
Design – MORSEL (short for “Multiple
Object Recognition and attentional SELection”) was
originally designed to analyze complex scenes with
multiple visual stimuli, more specifically letters and
words, and to account for multiple psychological
data, such as perceptual errors, facilitating effects
of context, and attentional phenomena (Mozer,
1990). The modules in MORSEL (see fig. 4) are
explicitly not based on cortical structures, but
rather considered inevitable to model the intended
psychological data. The input enters the model
through the ‘retina’. In this module, there are 5
feature detectors per retinal location, 4 for line
orientation and 1 for line termination. The activity
pattern of the retina projects to ‘BLIRNET’, a
collection of processing modules, originally
designed for letter and word identification. Each
module processes a different property of the retinal
input. BLIRNET modules in turn projects to the ‘pull
out network’, a module that selects activity
patterns of BLIRNET with a semantic content. The
module of MORSEL that is central to modelling
neglect is the attentional mechanism (AM). The AM
has one unit per retinal location. It serves to gate
the input of the BLIRNET, by selecting the spatial
locations of the input that are needed for
processing. It receives input from the retina and
from higher cognitive areas. These are not
‘physically’ modelled in MORSEL, but rather
implemented as an artificial bias to the units in the
AM. Active units in the AM bias the transmission
from the retina to the BLIRNET by mediating the
probability by which activity from the retina is
transmitted to BLIRNET. The presence of the AM is
necessary because BLIRNET has a limited
processing capacity.
decreasing gradient, most severe at the left and
least severe at the right side. Damage to the
connections leads to a decrease in the probability
that the retinal input is transmitted to BLIRNET.
Mozer and his colleagues (1997) state that
damaging the units in the AM directly, instead of
their input, would yield the same deficit.
Results – In the first paper (1997), the
model is used to explain line bisection data. In the
second paper (2002), a large body of other
experimental paradigms were used to explain the
frames of reference in neglect based on the
lesioned MORSEL. In the line bisection task, the line
is assumed to be transected at the centre of the
active AM units. Because the lesion is modelled as a
graded impairment and the activity of the units in
the AM depends on their retinal input, the
activation of their neighbours and the mean activity
of the AM, the lesion results in the transmission of
more than half of the line. Due to the linearly
Performance – The goal of the AM is to
select spatial regions of the retinal input that have
to be processed by BLIRNET. The update rule for
the units in the AM depends on three factors: the
input by the spatially congruent retinal location,
the activity of their neighbours, their relative
activation as compared to the mean activity of the
AM (i.e. “units below the mean activity are
encouraged to turn off, units below the mean
activity are encouraged to turn on”; Mozer et al.,
1997). This update rule ensures that the AM
selects the spatial region that consists of the
location of an object. The update rules for the
units in BLIRNET and the pull out network are not
discussed in the papers because they have no
influence on neglect, the way it is modelled with
MORSEL. Thus, performance of MORSEL is observed
through the activity patterns of the AM.
Lesion – To induce neglect in the
performance of MORSEL, the connections from the
retina to the AM are damaged with a monotonically
11
Figure 4. MORSEL. For each retinal location the retina contains five feature
detectors, depicted as five separate layers. BLIRNET and the pull out network
were not used in the simulation of neglect. (Adopted from Mozer, Halligan &
Marshall, 1997).
damaged connections to the AM, the lines were
bisected to the right of the true centre by the
lesioned model, because the activity transmission
from the retina to the AM was opposite to the left
part of the line. When a line is present on the retina
of the ‘healthy’ model, a 0.9 probability of
transmission from a retinal position to a congruent
AM position is assumed to account for varying
responses in healthy human subjects. Thus, the
variability of the responses of the healthy model
increases when line length increases. This increase
in the variability of the response as a function of
line length is also observed in the lesioned model.
To model the cross-over effect found in
neglect patients, an additional term was used in
determining the centre of the line, as judged by the
AM. This effect describes the phenomenon where
neglect patients with a rightward bias on the line
bisection task, show a leftward bias when the line is
of a short length (Marshall & Halligan, 1989;
Savazzi, Posteraro, Veronesi & Mancini, 2007). The
additional term used for this effect consists of a
small, fixed leftward bias, which is overruled by the
rightward bias (that depends on line length), when
the line length is sufficiently large.
Furthermore, neglect in the line bisection
task was allocentric, as pointed out by positioning
the line at different locations across the retina. At
all positions the right side of the line was processed
by the AM, in contrast to the left side. Although
neglect appeared to be less severe for positions on
the right of the retina. In the second paper (2002),
this emerging property (i.e. allocentric neglect) of
MORSEL was further elaborated. The lesioned model
was subjected to a set of experiments by Behrman
and Tipper (1994, 1996, 1999, in Mozer 2002),
Pavlovskaya, Glass, Soroker, Blum and Groswasser
(1997), Arguin and Bub (1993, in Mozer 2002) and
Driver and various colleagues (1991, 1994, 1999, in
Mozer 2002). The experiments aimed to elaborate
on the frames of reference in neglect. They used
rotated, rotating, cued, and differently lateralized
targets. With the exception of some effects found in
the original experiments that “could readily be
incorporated in the model” (Mozer, 2002), the
majority of the data could be replicated by the
lesioned model without making any adjustments to
it.
12
Evaluation – (Strengths) The design of
MORSEL is well suited to explain the behavioural
phenomena of neglect, as indicated by the large
bulk of experimental data of neglect patients that
can be explained by lesioning the model. Moreover,
no adjustments had to be made to the original
design of MORSEL in order to replicate the data.
Among the tasks on which MORSEL can replicate
human neglect, the line bisection task is particularly
interesting since it is used in many clinical settings
as an indicator for neglect. Hence, as stated by the
authors, it may be possible to use the model as a
diagnostic tool to indicate the severity of neglect,
by the means of a transmission probability curve
(Mozer et al., 1997). Furthermore, MORSEL
addresses the allocentric frame of reference in
neglect without it being explicitly implemented, but
rather with it being an emergent property of the
egocentric frame of reference. Although this is, as
mentioned with the basis function model, not
conform patient data, since it is thought that the
two forms of neglect rarely co-occur.
(Limitations) As it is not an objective of
MORSEL to be biologically plausible, there are some
questions about neglect that cannot be answered
by MORSEL. It assumes a graded lesion, which is
justified by the authors because it is present in
behavioural data. Although lesioning the model this
way is effective in explaining behavioural data, it
does not address the origin of this lesion. On the
other hand, this issue could be dismissed by noting
that the gradients are already present in the healthy
brain, cf. the basis function model of Pouget and
Sejnowski (1996, 1997a, 1997b, 2001). Nonetheless,
it would then still remain unexplained why 90% of
the patients with neglect have right hemisphere
damage (Corbetta et al., 2005). Summarized,
MORSEL does fairly well in explaining behavioural
data. It is probably also able to predict performance
of neglect patients in various new tasks. Therefore,
the model could be used as a clinical instrument to
indicate the severity of neglect. However, it does
not address the origin of neglect, other than in a
conceptual manner. Questions such as why neglect
occurs mostly after right hemisphere damage, or
how the attentional gradient in neglect arises
remain unanswered.
5.3 [Hilgetag] (Hilgetag, 2000; Hilgetag, Kötter &
Young, 1999).
Design – [Hilgetag] was designed to explain
the paradoxical effect on visual orienting, of
contralesional lesioning (Sprague effect). This effect
has been investigated mostly in cats. Hence, the
architecture of [Hilgetag] is based on the
anatomical connectivity of the superior colliculus of
the cat. Moreover, the connectivity and physiology
of the midbrain structures of the cat have been
explored quite thoroughly (Hilgetag, 2000).
Additionally, when lesioned, these structures
produces symptoms similar to the symptoms
observed in human patients suffering from neglect.
The model consists only of two retinotopic
modules, Ml and Mr (M = midbrain, l/r = left/right).
Each module covers the entire retinal input, which,
in the model, ranges from -90° to 90° visual angle.
As can be seen in figure 5, both modules are tuned
with a skewed Gaussian distribution, in favour of
the visual hemifield contralateral to each module.
Furthermore, the modules mutually inhibit each
other in a mirrored fashion (see fig. 5).
Performance – The input of the model was
very basic. There was a stimulus either to the left,
to the right, or in both hemifields. When input was
presented to the model, a saccade was assumed to
be made to a new location, determined by the
average input pattern of the two modules. The
activity of Ml promotes a rightward saccade, Mr a
leftward saccade. The amount of activity per
module determined the final length of the saccade
is the result of the amount of activation of the
highest activated module.
Lesion – To induce neglect, the input of Mr
was reduced with 90%. If the input to Mr was
reduced with 70%, the model exhibited extinction.
Results – The severe lesion (90% reduced
input) prevented Mr to increase its activation to a
level, high enough to produce a leftward saccade,
because the activity of Mr did not rise above its
threshold when a stimulus was presented in the left
visual field. The activity level of Mr did not rise
above the threshold due to a combination of the
reduced input and the inhibition of Ml. However, if,
following the lesion to Mr, Ml was also lesioned,
stimuli close to the central visual field (i.e. were the
responsiveness of the two modules is the highest),
appropriate saccades could be made again. Because
the activity level of Mr was no longer inhibited by
Ml. This resembles the Sprague effect, observed in
cats and perhaps also in a human patient (Weddell,
2004). When the inputs of Mr were reduced only
70% a leftward saccade could be made to a left
stimulus, however, only in the absence of a right
stimulus and, again, when the stimulus was close to
the central visual field.
Evaluation – (Strengths) The model is
based on anatomical, physiological and behavioural
Figure 5. [Hilgetag]. ML and MR represent the left and the right part of a Midbrain structure of the cat. The numbers ranging from -90 to 90
correspond to the retinal location that is represented by that location in the midbrain structure. The ‘I’-s stand for Input, where the length of
the arrow indicates the relative sensitivity of the midbrain structure to input at that specific location. The mutually, reciprocal inhibitory
connections between the two halves of the midbrain are indicated with kC/K-C. (Adopted from Hilgetag, 2000).
13
data of the cat. Although the model is quite small,
its biological plausibility is relatively high, as
compared to the previous two models, but it should
be noted that this plausibility is when the model is
compared to the brain of a cat, not a human.
Moreover, the monotonic neuronal gradient
assumed in the previous two models, is in [Hilgetag]
a skewed Gaussian distribution, which is more
biological plausible, as shown by the data from the
cat, and human neglect patients, as will be
discussed with the next model (the ISO-map of
Niemeier and Karnath, 2002). Additionally, the
model explains effects of various types of lesions
(Hilgetag et al., 1999). The strongest point of this
model is that it accounts for the Sprague effect,
which can probably be extrapolated to the effects
of TMS in humans (Dambeck et al., 2006; Fierro et
al., 2006). By damaging the healthy left midbrain
structure (Ml), when the right structure has already
been damaged, leads to partial recovery of the
neglect symptoms. This effect is not only in found in
animal lesion studies, but it also seems to be
present following TMS of the left parietal cortex in
humans (Fierro et al., 2006; Hesse et al., 2011; Koch
et al., 2008; Koch et al., 2012; Oliveri, 2011).
Moreover, the behavioural result of a bilateral
lesion (i.e. relatively normal attention to targets in
the central visual field), is quite similar to the
symptoms found in a case study of a neglect patient
with a bilateral stroke (Cazzoli et al., 2012).
(Limitations) – The design of [Hilgetag] is
based on the anatomy and physiology of the brain
of the cat. However, these data are only used as
guidelines and greatly simplified in the model.
Moreover, given that the model is based on the
brain of the cat leaves the question whether this
model also applies to human neglect unanswered.
On the other hand, the behavioural results of
[Hilgetag]
bear
resemblance
with
the
aforementioned case study. Furthermore, in
contrast with the number of lesion effects that are
explained, the number of behavioural tasks that
have been presented to the model is restricted to
one. However, the authors argue that it the
explanation of neglect could easily be extended to
the human performance on the line bisection task.
On the issue on which frames of reference neglect
operates, [Hilgetag] makes use of only egocentric
coordinates. To be fair, the authors (1999) mention
14
that the model only uses egocentric coordinates,
because there were no known experimental
paradigms for addressing allocentric frame of
reference in a cat. Still, this is in contrast with the
basis function model and MORSEL, in which the
frames of reference were both ego- and allocentric.
5.4 ISO-map (Niemeier & Karnath, 2002).
Design – The integrated space (ISO) map is
the only module in the model of Niemeier and
Karnath (2002). Input and output modules are not
explicitly modelled, but could be incorporated
easily. The model offers an explanation of the
process by which allocentric neglect can be
ameliorated by the egocentric positions, an
empirical finding described by the authors. The
units in the ISO-map integrate object-based (i.e.
allocentric) and trunk-centered (i.e. egocentric)
coordinates. As can be seen in figure 6, a stimulus
always covers the entire ‘allocentric axis’, but only a
part of the ‘egocentric axis’. That part is
proportional to the size of the stimulus. Each ISOunit assigns a salience value to a point of the
presented stimulus, with salience being the
probability of orienting to a certain location.
Performance – The task that was used to
observe the performance of the model was a visual
Figure 6. The ISO-map. Retinal input is mapped onto this ISOmap. The object covers the entire vertical axis, and only a
portion of the horizontal axis (depending on its size). (Adopted
from Niemeier & Karnath, 2002. Note: the image was also blurry
in the original paper).
search task, similar to task used to test the basis
function map. The goal was to have the ISO map
select a stimulus for action. For simulation
purposes, action was modelled by an additional
algorithm, in order to generate saccades. This
algorithm was based on the algorithm used by
Pouget and Sejnowski (1996, 1997a, 1997b, 2001) in
the basis function model. Hence, saccades followed
a winner-takes-all and inhibition of return principle,
similar to the winner-takes-all and inhibition of
return principles in the basis function model. The
location for the next saccade was the location with
the highest salience. The salience of the previous
location was temporarily reduced. With the
algorithm, the model selects locations for fixation
based on the salience value, assigned by the ISOmap. The model was presented with three stimuli, a
visual scene without a target, a visual scene with a
target area to the right of the centre, and a target
area somewhat further to the right.
Lesion – The lesion, used to model neglect,
induces a Gaussian distribution of salience values
along the egocentric axis and a linear graded
salience distribution along the allocentric axis
(monotonically increasing from the left to the right;
see fig. 6).
Results – The authors made a distinction
between the global and local exploration of the
model, in order to be able to make statements
about ego- and allocentric neglect. Global refers to
the exploration of the entire visual scene. Local
refers to the exploration of a specific area of that
scene. Globally, the saccades produced by the
lesioned model tended to be clustered within the
right visual field. Locally, the right part of an area
was investigated. However, the more the
investigated area was to the right, the more the left
part of that area was investigated.
Evaluation – (Strengths) The model seems
to behave similar to the basis function map of
Pouget and Sejnowski (1996, 1997a, 1997b, 2001),
although a bit more simplified, as the allocentric are
explicitly modelled, in contrast to the basis function
map. The most notable difference between the
basis function map and the ISO-map is the lesion.
The lesion of the ISO-map produces behaviour that
better resembles patient data, as shown by directly
15
comparing the performance of the ISO-map to the
performance four neglect patients. The distribution
of saccades produced by the model was similar to
the distribution of saccades produced by the
patients. Karnath and Niemeier (2002) note that
this type of performance results from a Gaussian
distribution of salience values in egocentric
coordinates, and will never result from a linear
lesion (cf. basis function map and MORSEL).
(Limitations) Except for the shape of the salience
curve induced by the lesion, the model provides no
additional insights to the previous models.
Moreover, by explicitly modelling the allocentric
coordinates without stating how they might arise in
the human cortex, the ISO-map is less elegant than
the basis function map. It could be argued that by
tuning the units in the basis function map, not
according to a sigmoid, as it was tuned by Pouget
and Sejnowski, but to a Gaussian function, the basis
function map would probably perform just as good
as the ISO-map.
5.5 [Deco] (Deco & Rolls, 2002; Deco & Zihl, 2004;
Heinke, Deco, Zihl & Humphreys, 2002).
Design – [Deco] was originally designed to
construct a biological plausible model for visual
attention, accounting for the ventral and dorsal
pathways (Goodale & Milner, 1992). The dynamics
of the model are based on the physiological
dynamics of neuronal pools. Input is presented to
the model by the retina (or ‘visual field’). The model
first receives the input in V1, a retinotopic module.
For each retinal location, there are twenty-four
feature sensitive units in V1, with three spatial
frequencies and eight orientations. V1 has
bidirectional connections with the parietal module
(PP), which represents the dorsal stream. Units in
PP represent the spatial location of a stimulus in the
scene presented to the object. When a unit in PP is
active, the retinal location it represents can be
thought of as being paid attention to. Because the
PP not only receives input from V1, but also
projects back to V1, a certain location can be biased
by directing spatial attention toward that location.
V1 also has bidirectional connections with a module
for object recognition, called the inferior temporal
cortex (IT), which represents the ventral stream. IT
contains one unit per object that can be presented
to [Deco]. It should be noted that the connections
from IT back to V1 were absent in most simulations,
in order to account only for bottom-up effects of
spatial attention. As can be seen in figure 7, the
model also contains several ‘inhibitory pools’. When
these inhibitory pools are activated they inhibit the
pool they are connected to (i.e. V1, PP or IT). The
more active V1, PP or IT is, the more their inhibitory
pool is activated, and thus, the inhibition of V1, PP
or IT is increased. Consequently, only the most
active units will survive the inhibition, which will
thus be able to activate their connected units in the
other modules. The inhibitory modules are not
present in the first paper (Deco & Rolls, 2002), in
which [Deco] is used specifically to model
allocentric neglect, because the inhibitory pools are
causing only one stimulus to be selected by PP.
Therefore, Deco and Rolls (2002), provided V1 and
PP with local lateral inhibition, which produces local
peaks of activity in both maps. These multiple, local
peaks are necessary to account for allocentric
neglect when multiple stimuli are presented.
Performance – The inputs consisted of visual
scenes containing a stimulus, located at different
positions within the scene (in Deco & Zihl, 2004;
and Heinke, Deco, Zihl & Humphreys, 2002), or two
stimuli at various distances from each other (in
Deco & Rolls, 2002; and Deco & Zihl, 2004). To
observe how the model performed, activated
locations of PP were registered. According to the
authors, these locations can be thought of as ‘paid
attention to’. Hence, by observing the active PP
locations, one can see which locations are attended
and which are neglected. The model also accounts
for extinction. To accomplish this, a visual
discrimination task was used in which either one or
two stimuli were presented. One stimuli was the
target, the other a distractor. The difference
between target and distractor was defined as a topdown bias from IT to V1. This task consisted of four
conditions (1) target in the ipsilesional hemifield; (2)
target in the ipsilesional hemifield and distractor in
the contralesional hemifield; (3) target in the
contralesional hemifield; (4) target in the
contralesional hemifield and distractor in the
ipsilesional hemifield.
Lesion – Two types of lesions were used to
induce neglect in [Deco], one for allocentric neglect,
and one for egocentric neglect/extinction. To
Figure 7. [Deco]. The arrows indicated the directions of the connections. Only connections with inhibitory pools are
inhibitory, all others are excitatory. The dots represent single units, but there is no one-on-one correspondence between
the number of dots depicted and the number of units modeled. (Adopted from Deco & Zihl, 2004).
16
induce allocentric neglect, the activity of the units in
PP was reduced. The magnitude of the activity
reduction was graded linearly, increasing from the
left to the right of PP (i.e. comparable to the way in
which neglect was induced to MORSEL). In order to
induce egocentric neglect/extinction, PP was
divided into two halves. The activity of the right half
was reduced with 40% (i.e. a unilateral damage).
Results – The gradient lesion over the
entire PP accounted for allocentric neglect. When a
stimulus was presented, the PP units that
represented the location of the stimulus became
active, only when they represented the right half of
the stimulus. The PP units that represented the left
half of the stimulus were inhibited as the activity of
the inhibitory pools increased along with the overall
activity of the PP. This was observed independent of
the egocentric location of the stimulus. The overall
activity of the PP was larger when the stimulus
appeared at the right side (ipsilesional) of the
scene, as compared to stimuli on the left side
(contralesional).
In the first paper (Deco & Rolls, 2002),
allocentric neglect was also observed when two
stimuli were presented simultaneously, at either a
short or a long distance from each other. Note that
in that paper the inhibitory pools (as depicted in fig.
7) were replaced by local lateral inhibition. When
the two stimuli were at a short distance from each
other, the PP units representing the right side of the
right stimulus, inhibited all other units that
represented either the left part of the right
stimulus, or any part of the left stimulus, as a result
of the local lateral inhibition in combination with
the gradient lesion. However, when the stimuli
were at sufficient distance of each other, the local
lateral inhibition was not strong enough to inhibit
the activity of the units representing the left
stimulus. Consequently, the units representing the
right side of both stimuli and, due to the gradient
impairment, inhibited the units representing the
left sides. The unilateral lesion accounted for
egocentric neglect, as shown by the activity of the
PP units when a stimulus was presented at different
sites in the visual field. When the stimulus appeared
at the right side the PP units became active. When
it appeared at the left side, no PP units were
activated. The ‘egocentric lesion’ combined with
17
the top-down bias accounted for extinction. The
top-down bias was strong enough to overrule the
inhibition when a target was presented in the
contralesional hemifield. However, the bias was not
strong enough to overrule the inhibition when a
distractor was present in the ipsilesional hemifield.
Evaluation – (Strengths) The authors have
implemented several physiological features of
spatial processing that have been found to be
present in the human brain, including topographical
organization of cortical areas and the spiking
behaviour and receptive fields of individual neurons
(Fitzpatrick, 2008). The search behaviour produced
by the [Deco] is quite similar to the search
behaviour observed in humans. This makes [Deco],
in its non-lesioned form the most biological
plausible model, as compared to the previous
models.
Later,
the
neurophysiology
was
implemented to an even further extent in a small
model of Mavritsaki, Heinke, Deco and Humphreys
(2009). This model will not be discussed in this
thesis, because it mainly elaborates on the function
of the healthy model, but not on neglect.
(Limitations) The number of different
lesions to the same network, that is required to
induce allo-, egocentric neglect and extinction is the
main limitation of [Deco]. On the other hand, as
mentioned before, it has been stressed that indeed
these symptoms are clinically and anatomically
dissociated (Corbetta & Shulman, 2011; Karnath &
Rorden, 2012; Umarova et al., 2011; Vossel et al,
2011). Nonetheless, these different lesions are not
different lesions to the same structure, but rather
the same lesions to different structures. Although
the healthy model functions quite well in simulating
human behaviour in visual search tasks, the model
requires additional assumptions about the type of
lesion or top-down bias to mimic symptoms of
neglect. Furthermore, the search tasks used to test
the model do not correspond to the tasks often
used to diagnose neglect, in contrast with MORSEL
for example. Moreover, as with most of the
previous models, [Deco] does not account for the
high prevalence of neglect after right hemisphere
damage or the ameliorating effects that TMS has on
neglect. If TMS would be simulated by a lesion to
the left PP, when the right PP was already
unilaterally lesioned, PP would show no activity at
all, unless IT provides a top-down bias. In sum,
[Deco] takes into account many neurophysiological
data, which makes the non-lesioned model fairly
biological plausible for human visual attention.
However, to induce neglect a number of
assumptions had to be made, which lower the
explanatory power (concerning neglect) of this
model.
5.6 SAIM (Heinke & Humphreys, 2003).
Design – The selective attention for
identification model (SAIM) was designed to
simulate ‘translation invariant object recognition’,
not be confused with view-invariant recognition.
Translation invariant means: high-order object
recognition irrespective of the original retinal
location of the object. SAIM consists of six modules
(see fig. 8) The retina is the module to which input
is presented. The information is then transmitted to
the contents network (a matrix specifying all
possible combinations between the retina and the
following module, the FOA) and the selection
network. The selection network gates activity of the
contents network, resulting in a single stimulus to
be transmitted from the contents network to the
FOA. The units in the FOA (focus of attention) are
Figure 8. Architecture of SAIM. (Adopted from Heinke & Humphreys, 2003).
18
activated bottom-up. The FOA contains a single
stimulus, that has to be transmitted to the
knowledge network. The knowledge network is
comparable to IT in [Deco] and the pull-out network
in MORSEL, i.e. it contains template units. These
units contain representations of all known stimuli,
based on the stimulus feature. The knowledge
network generates a top-down bias, modulating the
activity of the selection network in favour of stimuli
that are ‘known’ to the network. When a unit in the
knowledge network is activated, it becomes
temporarily inhibited, in order to simulate a shift in
attention from an identified to an unidentified
object. The last module is the location map, a
retinotopic map that has bidirectional connections
with the selection network. Units in this map
represent the retinal locations that are activated by
a stimulus. The number of active units in the
location map decrease according to a winner-takesall principle, ultimately resulting in the activation of
only a single unit. This unit corresponds to the
location of the target. When only one unit is active,
the location map temporarily inhibits the
corresponding units in the selection network,
leading to an inhibition of return principle, as seen
in the basis function model and the ISO-map.
Performance – The performance of the
healthy model was evaluated thoroughly by the
authors before a lesion was implemented in the
model. This evaluation was done using either one or
two stimuli (a ‘+’ and a ‘2’), located at varying
positions on the retina. The stimuli that were used
were equal, in the sense that the number of pixels
per stimulus was identical. The performance of the
model was monitored by looking at the activation
pattern of the FOA. To model reaction time, the
number of iterations before a stable state was
reached by the network, was monitored. The
performance of the lesioned model was monitored
with the same task. In addition, a line bisection task
was presented to the model. The bisection point
was determined by the part of the line that was
represented in the middle of the FOA.
Lesion – In order to induce neglect in SAIM,
the selection network was lesioned. The authors
described three types of lesions. The first lesion
damaged the bottom up connections from the
retina to the selection network with a linear
gradient. In line with their visualization of SAIM, this
lesion was called the ‘vertical lesion’. The vertical
lesion did not entirely remove the connections, but
reduced their strength. The second lesion, the
horizontal lesion, damaged the connections that
project to the right side of the FOA (i.e. the side of
the FOA representing the left side of the visual
input). The third lesion was a combination of the
first two lesions, i.e. both linearly graded damage to
the connections projecting to the selection network
and unilateral damage to the selection network
itself.
Results – Obviously, when one stimulus
was presented to the healthy model it was selected
for attentional processing and identified correctly.
When two stimuli were presented to the healthy
model, the ‘+ ‘ was selected by the FOA, over the
‘2’. This was the case because the pixels of the ‘+’
are spread more uniformly with respect to its
centroid, which results in faster identification,
enabling top-down modulation, in favour of the ’+’
to quicken the competition of the two stimuli.
Lesioning the model with the vertical lesion had no
effect on the mapping of that stimulus on the FOA,
also when the position of the stimulus was on the
most affected side. However, when two stimuli
were presented, the stimulus that was presented in
the least affected field always won the competition,
indicating extinction. This was observed for both
the ‘2’ and the ‘+’, however, extinction could be
ameliorated when the damage was not severe and
the stimulus on the most affected side was more
salient (e.g. larger) than the stimulus on the least
affected side.
The presence of the knowledge network
ameliorated the neglect symptoms as well. When
the model was lesioned using the horizontal lesion,
the centre of the stimulus was mapped too far to
the left of the FOA, missing the pixels at the left.
This represents allocentric neglect because missing
the left part was independent of the retinal location
of the stimulus. The authors explain the rightward
deviation on the line bisection task of neglect
patients with this horizontal lesion, because with
this lesion the left part of the line is not mapped
onto the FOA. Finally, by combining the horizontal
and the vertical lesion, the model shows extinction,
but also allocentric neglect. Moreover, the model
19
can simultaneously show both left-sided extinction
and right-sided allocentric neglect.
Evaluation – (Strength) As the authors
note, SAIM shows several emergent properties of
features that have been found to be of influence on
human behaviour. Among these features are
stimulus familiarity, stimulus salience and the
number of presented stimuli. These properties of
SAIM also lead to a naturalistic presentation of
extinction. As shown by both SAIM and experiments
with human patients, extinction can be ameliorated
by prior knowledge of the stimuli and the salience
of it (Driver & Vuilleumier, 2001). Furthermore, in
contrast with [Deco], SAIM needs lesions at
different locations to show symptoms of extinction
or neglect, which, as mentioned before, have been
shown in patients. Additionally, like [Hilgetag], it
seems that SAIM is able to explain the ameliorating
effects of TMS on extinction (Oliveri, 2011). By
damaging the contralesional connections to the
selection network, or
the contralesional
connections to the FOA, the connections are equally
strong again, and thereby eliminating the bottomup effects of neglect.
(Limitations) On the basis of this model lie
several case studies of a patients with bilateral
damage, who showed left-sided egocentric neglect,
and at the same time right-sided allocentric neglect.
As has been mentioned, SAIM is capable of
simulating these deficits. However, these are not
common symptoms when neglect is observed after
a bilateral stroke (Cazzoli, et al., 2012). Another
limitation of the case studies as a basis for SAIM, is
that the lesions used do not explain egocentric
neglect, the core deficit of neglect (Karnath &
Rorden, 2012), nor do they explain that the severity
of allocentric neglect varies with the egocentric
position of a stimulus (Driver & Pouget, 2000;
Karnath et al., 2011). Moreover, most tasks used to
observe neglect symptoms of SAIM differ from the
tasks used in clinical neuropsychology, except for
the line bisection task. Not that this is a limitation
per se, but, for example, MORSEL demonstrates its
strength by explaining many data of neglect
patients. Furthermore, as with all previous models
the lesions can be applied just as easy to the right
side of the model as to the left side, in contrast with
the prevalence of neglect (Corbetta et al., 2005).
Finally, as noted by the authors themselves, the
behaviour of individual units is very basic, in
contrast with for example [Deco] or the spiking
search over time and space model (not described
here; Mavritiski, Heinke, Deco & Humphreys, 2009).
In sum, SAIM simulates extinction quite elegantly,
and unlike most of the other models, it can
potentially explain the effects of TMS (at least
partially), however, its explanation of neglect is very
limited.
5.7 [Monaghan] (Monaghan & Shillcock, 2004).
Design – The model of Monaghan and
Shillcock was constructed in explicit response to all
previously summarized models. The authors argued
that one of the most striking features of neglect
remained inexplicable by all the other models. This
feature, as mentioned before, is the observation
that neglect occurs much more often after right
hemispheric damage than after left hemispheric
damage. All the models described above have not
attempted to make a distinction between a lesion
to the left or right hemisphere. For [Monaghan] on
the other hand, explaining this difference is its
primary goal. The authors investigate three
potential hemispheric asymmetries. The model
consists of four modules: a retina, a left hemisphere
(LH), a right hemisphere (RH) and an output module
(see fig. 9). Input is presented to the retina. As can
be seen in figure 9, due to the design of the retina,
only horizontal lines can be ‘perceived’. The retina
projects to both hemispheres. The hemispheres are
comparable to the basis function maps of the basis
function model. Each hemisphere is tuned to
toward the contralesional space, similar to the
central modules of the basis function model,
[Hilgetag] and the ISO-map. The output module
consists of a module with a size identical to the
retina. The potential hemispheric asymmetries are
modelled by sequentially implementing three
differences between the LH and the RH. The first
difference concerns the steepness of the
contralateral gradient. As proposed by Pouget and
Sejnowski (2001), the gradient by which the LH is
tuned toward the contralateral hemifield is steeper,
as compared to the gradient in the RH. With the
second asymmetry, the units in the LH have narrow
20
receptive field, whereas units in the RH have broad
receptive field, based on the concept of local versus
global processing (Gazzaniga, Ivry & Mangun,
2009c). With the third asymmetry, the RH was
tuned to both hemifields, while the LH kept its
contralateral bias, based on the theory of Heilman
and Van den Abell (1980, in Hillis, 2006) and
Mesulam (1981, 1991, in Hillis, 2006). The
implementation of this third asymmetry was based
on a mathematical model of line bisection
behaviour in neglect patients (Anderson, 1996). The
three asymmetries were modelled separately. Their
performances were compare by the authors.
Performance – [Monaghan] was designed
to be able to perform the line bisection task, and to
explain several distinctive features of a right or left
hemispheric lesion on the line bisection task (see
the features of neglect described in table 1). The
bisection point was determined to be the centre of
the active output units. The lines used in the task
were of varying lengths, ranging from 2 to 16 (i.e.
the entire retina). Thus, on average the activity
pattern of the output module of the healthy model
is identical to activity pattern of the retina (the
input module).
Lesion – The lesion applied to the model
was a reduction the activity of either the RH or the
LH with 10, 50 or 100 percent. The learned weights
of the connections projecting to and from the
Figure 9. [Monaghan]. The input and output layer contain units that
can be either activated or inactive (i.e. 1 or 0). The input layer can be
considered as a representation of the retina, the output layer as a final
internal representation of the visual input. The layers ‘left hidden’ en
‘right hidden’ both encode the entire visual input. (Adopted from
Monaghan & Shillcock, 2004).
Table 1. The authors summarized their simulations in the this table. Y = behaviour reproduced. N = behaviour not reproduced. (N) =
not reproduced with conservative parameter (see original paper for details). (Adopted from Monaghan and Shillcock, 2004).
1.
2.
3.
4.
5.
6.
7.
8.
Feature of neglect
RH lesions reduce bisection accuracy
RH lesions produce contralateral neglect
RH lesions cause larger displacements and more deviation for longer
stimuli. No relationship for LH lesions
RH lesions crossover effect
RH lesions cause larger bisection errors than LH lesions
LH lesions cause greater variance in bisections
RH lesions cause greater cuing effect
RH lesions result in slower recovery
lesioned hemisphere remained intact.
Results – In their article, Monaghan and
Shillcock provided a table that summarizes the
features of neglect that the different asymmetries
accounted for (see table 1). As can be seen, the
different sizes of receptive fields per hemisphere
accounted for all the feature that the authors tried
to explain, whereas the two other asymmetries
lacked the explanation of approximately two
features. In sum, the model showed neglect when
the RH of model with different receptive field sizes
was lesioned. When the LH of the same model was
lesioned, the variability of the response increased.
Evaluation – (Strengths) [Monaghan] is the first
model that elaborated on the hemispheric
asymmetries of neglect. In doing so, it adopted
some features of the previously described models
(e.g. the neuronal gradient of the basis function
map). Furthermore, it did not implement one
theory, but rather compared three of the main
theories on hemispheric asymmetry and compared
the results. Thus, simulations with [Monaghan]
argue in favour of the theory concerning differing
sizes of receptive fields between the two
hemispheres and against both the plausibility of the
theory of Heilman and Van den Abell and Mesulam
(Hillis, 2006) and the theory on the steepness of the
neuronal gradient of Pouget and Sejnowski (2001).
In addition, like MORSEL, [Monaghan] explained
many features of line bisection behaviour as seen in
neglect patients. In addition to the simulations with
MORSEL, [Monaghan] did not require an additional
assumption in order to account for the cross-over
effect.
(Limitations) On the other hand not all line
bisection data were simulated entirely accurately.
[Monaghan] showed decreasing variability in the
21
Receptive field
Y
Y
Model
Steeper LH
gradient
Y
Y
Bilateral RH
Y
Y
Y
Y
Y
Y
Y
Y
N
Y (N)
Y
N
Y
Y (N)
N
Y
Y
N
Y
Y
performance of the model when the RH was
damaged, whereas the MORSEL and human data
show that variability increases with line length. The
main limitation of [Monaghan] is its small size.
Beside the limited biological plausibility, the limited
size of the model disables it to explain only more
data than that of the line bisection task. Actually,
that is the only task it can replicate. The line
bisection, although frequently used in clinical
settings, is a frequent topic of debate in whether or
not it is truly sensitive for neglect (Ferber &
Karnath, 2010;
Molenberghs & Sale, 2011;
Molenberghs et al., 2012; Rorden et al., 2006).
Whether the model can be extended to explain
more
behavioural,
human
data
remains
unanswered.
Furthermore, in contrast to most of the
previous models, the current model did not aim on
elaboration of the frames of reference damaged in
neglect. However, it would have been enlightening
if the simulations would have included this topic,
because literature on neglect patients with left
hemispheric lesions report that there seems to be a
difference in the frequency of allocentric and
egocentric neglect between patients with a left, or a
right hemispheric lesion (Kleinman et al., 2007;
Molenberghs & Sale, 2011). It has been observed
that patients with a left hemispheric lesion suffer
from allocentric neglect twice as much as
egocentric neglect, whereas this ratio seems to be
inversed in patients with right hemispheric lesions.
Perhaps, the lack of an explanation for these data is
because simulation of the performance of patients
with a left hemispheric lesion was largely based on
a single study (i.e. Mennemeier, Vezey, Chatterjee,
Rapcsak, & Heilman, 1997). In sum, the explanatory
value of [Monaghan] seems small, however, it
provides an interesting view on neglect in relation
to the difference
hemispheric lesions.
between
left
and
right
5.8 [Lanyon] (Lanyon & Denham, 2004a, 2004b,
2010).
Design – [Lanyon] was originally designed
to simulate the effects of attention on visual search,
based on neuroanatomical and -physiological data
of a macaque (2004a, 2004b). In the third article
(2010), the model was lesioned in order to replicate
neglect. The authors examined the difference
between the effects of frontal and parietal lesions
on visual search behaviour. [Lanyon] contains eight
modules (see fig. 10), mainly named after
anatomical structures of the macaque brain: the
retina, V1, V4, the inferior temporal area (IT), the
ventral prefrontal cortex (vPFC), the covert spatial
attention window (AW), the parietal module (in the
first two articles this module was named
after the lateral bank of the intraparietal
sulcus; LIP), and the orbitofrontal cortex
(OFC). The design and dynamics are fairly
comparable to the basis function model and
[Deco]. Input is presented to the retina,
where form and colour processing takes
place by simple centre-surround processing
and concentric opponent processing,
respectively, as modelled by Grossberg and
Raizada (2000, in Lanyon & Denham, 2004a).
For simulation purposes, the size of the
retina is variable, and all subsequent
retinotopic modules have the same size as
the retina. However, in most simulations,
the size of the retina was fixed.
The retina projects to V1, a
retinotopically organized module with 4
feature detectors per retinal location, 2 for
line orientation and 2 for colour. In order to
process line orientation, simple cells were
used, for colour, double opponent cells, as
modelled by Grossberg and Raizada (2000, in
Lanyon & Denham, 2004a). In double
opponent cells, the centre of the receptive
fields is excitatory when its preferred colour
is presented (red or green in this model), the
surrounding receptor area is inhibitory when
22
the same colour is presented to it.
After V1, information is processed in a
model of the dorsal and a model of the ventral
route (Milner & Goodale, 2008), where the dorsal
stream is mainly concerned with object location,
while the ventral stream more with object identity.
The dorsal route consists of the AW, the
parietal module and the OFC. The AW modulates
the form V1 to the parietal module by adjusting the
retinal image according to the stimulus density
around the point of fixation, i.e. a larger part of the
scene if transmitted to the parietal module when
stimulus density is low. The parietal module is a
retinotopically organized module, which receives
retinal input, modulated by the AW. The function of
the parietal module is to select a location for the
next fixation, based on bottom-up information
(through the AW) and by top-down bias (from the
OFC and V4). The OFC generates temporarily
Figure 10. Architecture of [Lanyon]. The broad arrows indicated connections between
units in different modules but at similar retinal locations. (Adopted from Lanyon &
Denham, 2010).
inhibits the current location of fixation in the
parietal module, thus creating inhibition of return.
Beside, the current location of fixation all adjacent
location are also, though to a lesser degree,
temporarily inhibited. The OFC stores the inhibition
values in a retinotopic map. The inhibition values of
a visited location decrease with every new fixation.
The ventral stream, comprising V1, V4, IT
and the vPFC, is implemented for object
identification and to generate a top-down bias in
visual search. V1 projects to V4, a retinotopically
organized module with 4 feature detectors per
retinal location, one for each of the 4 stimuli that
can be presented to the model (described in
‘Performance’). Two sets of inhibitory neurons
mediate the competition within V4, one set for the
competition of stimulus orientation, and another
for stimulus colour. V4 projects, as mentioned, to
the parietal module to generate a top-down bias,
based on stimulus features. Conversely, V4 receives
a spatial bias by the parietal module, enhancing the
processing of stimuli in the attended region.
Furthermore, V4 has bidirectional connections with
IT. IT contains 4 units (one for each possible
stimulus) for object identification. The vPFC can
select one of the stimuli as a target, by biasing IT. In
turn, IT biases stimulus processing in V4 in favour of
the target that was selected by the vPFC.
Performance – [Lanyon] was designed to
simulate visual search task, and the influence of
visual attention on that task. The task consisted of a
large scene in which numerous small bars were
present. The bars varied in orientation (horizontal
or vertical) and colour (red or green). The task for
the model was to identify all the bars that were
instructed as target (e.g. all red horizontal bars). In
order to do so the parietal module and V4
cooperate extensively. When a scene is presented
the initial fixation point is at the centre of the
scene. Then, a part of the scene, with the size of the
retina, is transmitted further into the network. The
parietal module operates on a part of the retinal
selection, because of the modulation of the AW. V4
also shows increased activation of the area selected
by the AW, due to the extensive interaction with
the parietal module. In V4, the units that represent
the behaviourally relevant stimuli (i.e. targets) are
becoming more and more active, due to interaction
23
with IT and indirectly the vPFC. Finally, the location
of a target will be most active in the parietal cortex,
and is thus the location for the next fixation. When
multiple stimuli are equally relevant, the probability
by which they will be selected for the next fixation
is also equal. In the next iteration, the previously
fixated location is inhibited by the OFC, making it
highly unlikely that the previous target will be
selected for the next fixation.
Lesion – As mentioned before, one of the
goals of Lanyon and Denham was to model the
effects of different lesion locations. Hence, the
authors lesioned the OFC uni- and bilaterally, and
the parietal module, unilaterally and with a linear
gradient. The uni- and bilateral damage to the OFC
implied that none of the units in the affected area
could be activated. The same follows for the
unilateral lesion to the parietal module. The
gradient lesion to the parietal module is
comparable to the lesions in MORSEL, [Deco] and
SAIM. The damage ranges from 100 to 0 % from the
left to the right.
Results – The unilateral parietal lesion
resulted in a complete neglect of the left visual
hemifield. Since the left part of the parietal module
had been lesioned, the location of the next fixation
was always a location to the right of the current
fixation. This lead to a scanpath that consisted
solely of rightward saccades. Hence, the scanpath
became stuck at the right border of the scene.
Therefore, a reset saccade was implemented,
bringing the fixation back to the centre of the
scene. When the gradient lesion was applied, the
more a stimulus was located to the left, the more
severe neglect of that stimulus was (i.e. the less
likely it was that a stimulus was selected for
fixation). When the OFC was damaged unilaterally,
the scanpath showed the a neglect pattern similar
to the pattern following a parietal lesion, although
less severe. A bilateral lesion completely impaired
the inhibition of return, thus, disabling the model to
completely explore the scene. Locations with a
target were frequently revisited. Lesions to the
parietal module lowered the activity in V4 and V1,
whereas lesions to the OFC left the activity of those
modules unaltered.
Evaluation – (Strenghts) [Lanyon] covers
many of the symptoms that are thought to be
central to neglect, as proposed by Corbetta and
Shulman in their model (2002, 2011). Namely, the
model distinguishes different lesion locations for
spatial (neglect of the left hemifield) and nonspatial (frequent re-fixation) deficits, a dissociation
that has also been found in human patients (Husain
et al., 2001; Karnath & Rorden, 2012; Molenberghs
et al., 2012; Umarova et al., 2011; Vossel et al,
2011). Although it should be noted that the
impairments of model, following lesions to the OFC,
were more severe than the impairments following
damage the corresponding regions in humans
(Hodgson, Mort, Chamberlain, Hutton, O’Neill &
Kennard, 2002; Husain et al., 2001) Moreover, the
model captures several well-established features of
visual search behaviour of healthy humans,
including spatial and temporal characteristics of the
relationship between saccades and stimuli
abundance or salience (for details see Lanyon &
Denham, 2004a). Also, many neurobiological data
were implemented in [Lanyon], similar to [Deco],
but more focussed on cortical visual processing.
Another interesting feature of [Lanyon], is that it
shows that parietal lesions alter the activity in V4
and V1, which has also been observed in neglect
patients (Corbetta et al., 2005). Furthermore, the
ameliorating effects of TMS, could arguably be
reproduced by [Lanyon], by lesioning the right half
of the parietal module. However, visual search
would be quite uncontrolled, since the parietal
module and OFC could not exercise their effects on
the search behaviour, thus leaving only the ventral
areas to the task. However, the disadvantageous
effects of a unilateral or gradient lesions would be
ameliorated
(Limitations) Like, [Deco] the model
captures many aspects of human visual search
behaviour, but simulating neglect behaviour raises
some problems. In the first place, the authors test
the effects of both a unilateral and a gradient
lesion. The gradient lesion was used to make
neglect less severe than following a unilateral
lesion. This distinction, however, is not useful. Since
there is no biologically founded justification for a
gradient lesion, the gradient of neglect could arise
from a neuronal tuning gradient that is present in
healthy brains, as argued by Driver and Pouget
24
(2000), Pouget and Sejnowski (1997) and Niemeier
and Karnath (2002). Thus, a unilateral lesion leads
to a gradient impairment. Second, following a
unilateral parietal lesion (and in a milder form
following a gradient lesion), the model only made
rightward saccades. Although it has been noted that
neglect patients tend to make spontaneous
rightward saccades (i.e. seemingly in the absence of
salient stimuli), this does not indicate an inability to
make a leftward saccade (Fruhmann-Berger &
Karnath, 2006; Husain et al., 2001). To overcome
this problem, the authors implemented an artificial
reset saccade. Third, the model only captured a
basic visual search task to investigate neglect
behaviour, in contrast to the basis function model,
MORSEL, [Deco] and SAIM, that have all been shown
to be able to explain a variety of tasks of which
impaired performance is associated with neglect.
With the visual search task used with [Lanyon],
egocentric neglect is produced. Whether or not the
neglect simulated by the model also applies to
allocentric neglect or other clinical assessment
tools, such as the line bisection task, remains
untested. Furthermore, like many previous models,
[Lanyon] does not account for the hemispheric
asymmetry of neglect lesions.
6. General discussion
Connectionism is a relatively young field of
research that has been able to provide significant
contributions to debates within the cognitive
neurosciences, as illustrated by the example of the
past tense model of Rumelhart and McClelland
(Thomas & McClelland, 2008). In thesis the value of
connectionist models for neuropsychology has been
appraised, with the hypothesis that connectionist
models can also contribute to the understanding of
neuropsychological
disorders,
arising
from
neurological damage. Eight connectionist models
that explain several features of neglect have been
reviewed. In table 2 a summary is given of those
aspects. In the table the models are ordered
according to the number of features of neglect they
account for. It is then visible that, remarkably, the
basis function model is the most comprehensive
model, yet also the oldest model reviewed here.
Table 2. Accounts of the models for features of neglect described in section three. 0 = the model did not account for this feature. 1 = the model
accounted for the feature. Explanation of the features: 1. The probability by which a stimulus is neglected decreases from the left to the right. 2.
TMS ameliorates neglect in human patients. 3. The models explanation of neglect has been shown to be applicable to more than one task. 4. Either
two different lesions account for neglect and extinction, or extinction is a milder form of neglect. 5. Does the model correspond to visuospatial
processing in healthy human subjects? 6. Does the lesion require other assumptions (e.g. graded damage) than the degradation of a specific
module or connections? 7. The model explains the allo- and egocentric frames of reference of neglect with a single lesion. 8. Neglect occurs more
often after a right than after a left hemispheric stroke. *BF-model = basis function model
BFmodel*
[Hilgetag]
SAIM
[Monaghan]
[Deco]
MORSEL
[Lanyon]
ISO-map
Total number
of models
Attentional
gradient1
TMS
effects2
Multiple
clinical data
explained3
Extinction4
Plausibility
healthy
model5
Plausibility
of the
lesion6
1
1
1
1
1
1
1
1
8
0
1
1
1
0
0
1
0
4
1
0
1
0
1
1
0
0
4
0
1
1
0
1
0
0
0
3
1
0
0
0
1
0
1
0
3
1
1
0
1
0
0
0
0
3
However, conclusions from table 2 should
be drawn carefully, because whether a model is a
‘good’ model depends on the features accounted
for by the model are considered ‘good’ or relevant,
which in turn depends on the interest of the reader.
Therefore, in this discussion, mainly the number of
features that can be explained by a model will be
considered, leaving the reader to assign his/her
value to a model, considering the specific features.
As can be seen in the last row, where it is
shown how many models account for a specific
feature, all models accounted for the attentional
gradient, which is not very surprising since it is a
cardinal symptom of neglect. Nevertheless, only the
basis function model, [Hilgetag] and [Monaghan]
did so using a lesion that requires no assumption
about a gradient in the lesion itself, for which, as
mentioned in the individual analyses, is no
indication in the literature. Therefore, the
attentional gradient of neglect is best accounted for
by unilateral (and uniform) damage to a single
hemisphere containing neurons tuned toward the
contralateral side of egocentric space. This is of
course in accordance both with the theory of
Heilman and Van den Abell, and Mesulam, and with
the theory of Kinsbourne (Hillis, 2006). Monaghan
and Shillcock (2004) explicitly state that they used
Kinsbournes approach as the base of their model.
However, it would not be fair to lower the
explanatory value of a model on the type of lesion,
without taking a closer look at the justification for a
gradient lesion. As the basis function model,
[Hilgetag] and [Monaghan] showed, when each
hemisphere contains a retinotopic map that is
25
Multiple
FoR with
single
lesion7
1
0
0
0
0
1
0
0
2
Hemispheric
asymmetry8
Total number
of features
explained
0
0
0
1
0
0
0
0
1
5
4
4
4
4
3
3
1
tuned in favour of the contralateral hemifield,
unilateral damage results in a single map tuned in
favour of the contralateral field, directly
corresponding to the attentional gradient of neglect
patients. Since the other models did not implement
a contralateral tuning, they were thus forced to
introduce the gradient in the form of the lesion, in
order to account for the attentional gradient.
Hence, at least in the cases of MORSEL, SAIM and
[Lanyon], this issue could probably be solved if the
neuronal gradients would be implemented into the
healthy model. In contrast, the lesions used in
[Deco] and the ISO-map required additional
assumptions beside the gradient, concerning
different types of lesions to the same module to
induce allo- and egocentric neglect. Although
empirical studies are ambiguous about whether or
not the different frames of reference of neglect
arise from different lesions (Chechlacz et al., 2010;
Corbetta & Shulman, 2011; Karnath & Rorden,
2012; Umarova et al., 2011; Vossel et al, 2011; Yue
et al., 2012), it is implausible that they arise from
different types of lesions at the same anatomical
location. It should be noted that this argument only
holds if the damaged module directly corresponds
to a single anatomical structure. This one-on-one
correspondence is actually suggested by the
authors of both models.
Nonetheless, it is a frequent topic of
debate in which frames of reference neglect is
presented (only in egocentric coordinates? Only in
allocentric? Or in both?). The table might give a
flawed view of the number of models addressing
this topic, since only the models that accounted for
both allo- and egocentric neglect using a single
lesion received a “1”. Despite the “0”-s, SAIM,
[Deco] and the ISO-map also address this issue, but
they use different types of lesions to a single
module to account for the different frames of
reference. Moreover, in the case of the ISO-map,
two different lesions have been applied to the same
module simultaneously. How this is compatible with
neurological data remains unanswered by the
authors of those models. Conversely, the facevalidity of the method of lesioning and stimulus
processing used in the basis function model, to
account for the different frames of reference with a
single lesion is relatively large.
The method was later formalized by Driver
and Pouget (2000), and elaborated on by the ISOmap. The ISO-map was designed with the primary
goal to elaborate on the shape of the attentional
gradient, which has shown to be more similar to a
Gaussian function than a linear one. This was based
on the finding that, in human patients, neglect did
not continually decrease from the left to the right,
but started increasing again when stimuli appeared
in the right peripheral field. Hence, the other
models might be more accurate if they adopted this
shape of neuronal tuning from the ISO-map.
However, it should be noted that this tuning
distribution applies to a visual scene ranging from 140° to 140° in egocentric coordinates, whereas
most models were designed to apply to visual
scenes that are in sizes comparable to the sizes of
most tasks used in clinical settings.
Another topic of debate in the
neuropsychology of neglect is the phenomenon of
extinction (i.e. is it part of the same disorder, but in
milder form? Or is a distinct disorder?). Extinction
has been accounted for by three models ([Hilgetag],
SAIM and [Deco]). They all accounted for extinction
as being a milder form of unilateral neglect. [Deco]
most elegantly explains extinction as a milder form
of neglect, by attributing the difference between
them to a less extensive lesion. [Deco] states that
the egocentric damage in patients with neglect or
extinction is the same, but in patients with
extinction a top-down bias can still bias visuospatial
processing, whereas this bias is diminished in
patients with neglect (Deco & Zihl, 2004). The other
two models, [Hilgetag] and SAIM, accounted for
26
extinction by lowering the intensity of the damage.
Although this may be appropriate to describe
extinction at a behavioural level, it is hard to
imagine how this represents neurological damage,
since milder damage in neurology usually implies a
less extensive region that has been affected, not
damage to an area of similar size but just less
damage (?) to individual neurons.
The number of clinical tasks that can be
performed by a model, yielding the same
performance as neglect patients, is appraised in the
fourth column: “multiple clinical data explained”. All
models explaining the data of more than one task
were assigned a “1”. The other models explained
the data of only a single task, and received a “0”.
This does of course not imply that their explanation
was inadequate, but rather that their explanation of
neglect data has not (yet) been demonstrated to be
generalizable to other tasks. Particularly, Heinke
and Humphreys (2003), and Mozer (2001) described
the performance of SAIM and MORSEL on a broad
variety of tasks. They explain why neglect patients
can fail on both the line bisection task and
cancellation tasks, following a single lesion. No
model provided an explanation for the apparent
dissociation between the lesions that impair
performance on the line bisection task, but not on a
cancellation task, or the other way around (Ferber
& Karnath, 2010; Rorden et al., 2006).
The main difference between the
simulations of SAIM and MORSEL was that SAIM was
mainly tested to explore the characteristics of its
own performance, whereas MORSEL was more
explicitly tested in its ability to describe human
performance in a wide variety of tasks to which
human neglect patients have been submitted.
Although, also with MORSEL, not only quantity, but
also quality of its performance was monitored,
especially for the performance on the line bisection
task. Which resulted in the suggestion of Mozer and
his colleagues (1997) to directly apply MORSEL in
clinical settings, in order to characterize the
transmission probability curve of an individual
patient. This curve shows the probability by which a
stimulus at different retinal locations will be
detected and processed. Conversely, MORSEL has
not been attempted to be made biologically
plausible,
e.g.
by
implementing
many
neurophysiological or -anatomical data. Indeed, it
might be concluded that MORSEL only describes the
behavioural characteristics of neglect, rather than
explaining how they arise from cerebral damage.
In contrast, [Deco] and the basis function
model have clear neurobiological foundations but
they are limited in the level of details of the
explanation of neglect behaviour, as compared to
MORSEL. Whether or not a model was considered to
be biologically plausible depends on the number of
neurophysiological or -anatomical features that
have been implemented in the model. For example,
[Deco] and [Lanyon] provide very detailed
characteristics of small pools of neurons, such as
taking into account the known characteristics of
receptive fields of neurons in V1. The model of
visuospatial processing by Mavritsaki, Heinke, Deco
and Humphreys (2009), not discussed here, even
takes into account characteristics of different
neurotransmitters and their receptors are
implemented. Pouget and Sejnowski (1997b, 2001)
did not describe the individual neurons in the basis
function model to this detail, but they named the
modules in their model after anatomical structures
that have been shown to contain neurons that carry
out the same functions as the units in their
modules, which has been argued to be more
favourable than simulating the cellular features of
neurons (like the model of Mavritsaki et al., 2009),
given the goal of the basis function model, namely
explaining behaviour following neurological damage
(Meeter et al., 2007). As for the basis function
model, this also applies to [Hilgetag]. However, this
model was based on the neuroanatomy of a cat,
making it difficult to extrapolate its explanations to
human patients.
[Hilgetag] does have another, interesting
property, namely mutually inhibitory hemispheres.
With this property it is able to explain the
ameliorating effects of TMS over the unlesioned
hemisphere (Fierro et al., 2006; Hesse et al., 2011;
Koch et al, 2012; Oliveri, 2011), as explained in its
individual evaluation. These effects really add
something to the other models, because, as can be
seen in the table, four out of the eight models
cannot account for this effect. By taking a closer
look at the dynamics of the modules of those
models, it can be concluded that the model will
27
perform even worse when both hemispheres would
be damaged (as is opposite of what the effect of
TMS is thought to result in). As for SAIM,
[Monaghan] and [Lanyon], the authors do not state
anything about the effects of TMS, but by analyzing
the dynamics of these models, it can be concluded
that damaging the left half of the damaged module
might decrease the symptoms of neglect, when the
right half is damaged.
Damaging the right or left half in most
models is arbitrary and exchangeable. However, this
is actually one of the striking features of neglect: it
occurs mainly after right hemisphere damage. The
only model that takes this feature into account is
[Monaghan]. Although this model is limited in
explaining many of the other features, it does
address the hemispheric asymmetry. However, the
asymmetry concerning receptive field sizes (LH fine
coded, RH coarse coded), which the authors
appraised as most powerful, still yielded data that
was inconsistent with human data, but perhaps this
is consequence of the limited size of the model. It
could be quite interesting to see the what would
happen to the other models when they would
incorporate a hemispheric difference like this.
Pouget and Sejnowksi (2001) state that the basis
function model could easily be updated with
hemispheric asymmetry, instantly accounting for
the hemispheric asymmetry. Though, they did not
provide any simulations to support their claim.
7. Conclusion
The connectionist models of neglect
demonstrate a trade-off between explaining the
characteristics of neglect from a neurobiological
perspective and being able to accurately simulate
the behavioural data of neglect patients. However,
as claimed by some of the authors, improvements
could be made to the models, combining the power
of different models and thus increasing the
explanatory value of the model (Pouget &
Sejnowski, 2001). Yet, so far, no model has been
made using combinations of several of the models
described here. Nonetheless, the models have
shown to be able to address for example how it is
possible that neglect seems to manifest in two
different frames of reference, while only the
egocentric coordinates need to be neurally encoded
(Mozer, 2002; Pouget & Sejnowski, 1997b, 2001), or
why neglect arises more frequently after right than
left hemisphere damage (Monaghan & Shillcock,
2004).
Still, as mentioned, all models consider
only some aspects of neglect, while disregarding
others. However, this should actually be regarded
as a virtue, according to McClelland (2008). He
states that, when appraising connectionist models,
one must view simplicity of models as a virtue,
because then they are able to breakdown complex
concepts into comprehensible subconcepts. On the
other hand, with increasing simplicity models may
lose explanatory value, thus becoming less
interesting to (in this case) neuropsychologists.
Currently, neuropsychology has shown
moderate interest in the models, considering the
number of times the models are cited in other
articles (ranging from 13 to 1 time(s) per year).
Moreover, the models are often referred to with
phrases, such as: “[...] as is consistent with
computer simulations” (Bartolomeo, 2007, pp. 384),
i.e. without elaborating on the design of the model.
However, if empirical studies would use the
existence proof, as provided by the models, as a
basis for constructing hypotheses, the strength of
the models could be confirmed or refuted.
Moreover, the model would instantly provide an
explanation for the data. See for example the study
of Karnath and colleagues (2011) on the frames of
reference of neglect. Unfortunately, such studies
are quite rare.
As for the models discussed in this thesis,
they are able to provide some hypotheses. The
basis function model, MORSEL and the ISO-map
predict that the degree of allocentric neglect
depends on the severity of egocentric neglect.
Moreover, a single lesion would account for both
types of neglect, according to the basis function
model en MORSEL. Even more, the models make
predictions about tasks used in the clinical
assessment of neglect (see the [Monaghan], the
basis function model and MORSEL). Also, eyemovement patterns are well predicted by the
models (though not necessarily 100% accurate).
28
[Deco] and SAIM, for example, state that a partially
neglected object would be more extensively
explored when it is familiar to the patient, as
compared to unfamiliar objects. Another interesting
prediction comes from [Hilgetag], which states that
the performances depending on targets in the right
(i.e. unaffected) visual field should be better-thannormal performances. Because it is mainly the left
hemisphere that is involved in processing that
target, and the right hemisphere can no longer
exert its inhibition upon that hemisphere (since it is
lesioned), the left hemisphere would experience
less interference in the lesioned model than in the
healthy model.
In
sum,
it
seems
as
though
neuropsychology uses connectionist models
primarily as an addition to their experimental data
in explaining cognitive disorders or symptoms.
However, as shown by the models presented in this
thesis (i.e. concerning neglect), connectionism could
be of more value when more attention to the
models would be paid. Models could direct the
formation of hypotheses and the interpretation of
behavioural data. On the other hand, connectionist
models that elaborate on neuropsychological
disorders are not a majority of all models. Perhaps,
because lesioned models are only of interest to
neuropsychology, whereas intact models can be of
interest to both cognitive science and computer
science (Abrahamsen & Bechtel, 2002). Therefore,
neuropsychology has only limited options when
they want to ‘consult’ a model. However, if models
aim to be biologically plausible, lesioning them is an
excellent method to test them, just like inferring the
function of a brain area by lesion-mapping (Rorden
& Karnath, 2004). [Lanyon] provides a good
example to illustrate this. It was constructed to
model the effects of attention on human visual
search behaviour. When intact, the model could
fairly well replicate this behaviour, but when
lesioned to induce neglect, the model could only
make rightward saccades. This finding is not
congruent with the search behaviour of neglect
patients, so the authors implemented a reset
saccade when the model reached the border of a
scene. A reset saccade was an artificial way to
overcome their problem, but it was also an
additional assumption to their model, decreasing
the biological plausibility of [Lanyon]. If these
limitations could be improved in future
developments, perhaps with the help of
neuropsychological data, the models biological
plausibility and thereby its explanatory or predictive
value would increase. Hence, both fields could
benefit from a more extensive cooperation
between neuropsychologists and connectionists.
For future directions, lesioned models,
explaining or describing features of neglect, may
also be combined with connectionist approaches to
model rehabilitation. Although so far, these
approaches with connectionist models are still very
limited (Harley, 1996; Plaut, 1996). As for
neuropsychological research, studies could be
performed, testing the hypotheses that were
provided by the models. Thus, the models could
increase the understanding of the deficit and thus
revealing new targets for rehabilitation (Raymer et
al., 2008), and contribute to the aforementioned
goal of neuropsychology.
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