MEMORY-BASED EMBODIED COGNITION: A COMPUTATIONAL
ARCHITECTURE
Paul Baxter* and Will Browne
C.I.R.G., *p.e.baxter@reading.ac.uk
ABSTRACT
At its most fundamental, cognition as displayed by
biological agents (such as humans) may be said to
consist of the manipulation and utilisation of memory.
Recent discussions in the field of cognitive robotics
have emphasised the role of embodiment and the
necessity of a value or motivation for autonomous
behaviour. This work proposes a computational
architecture – the Memory-Based Cognitive (MBC)
architecture – based upon these considerations for the
autonomous development of control of a simple mobile
robot. This novel architecture will permit the
exploration of theoretical issues in cognitive robotics
and animal cognition. Furthermore, the biological
inspiration of the architecture is anticipated to result in a
mobile robot controller which displays adaptive
behaviour in unknown environments.
1. INTRODUCTION
The development of artificial agents with autonomous
and adaptive behaviour is an ongoing goal for cognitive
robotics research. Biological agents (most notably
mammals) provide arguably the best examples of these
properties, and so are the source of design concepts and
principles [1]. In addition, the implementation of these
concepts allows an examination of their theoretical
bases in the biological agents themselves. One such
concept obtained from biological theory is that
cognition is fundamentally concerned with the
manipulation and utilisation of memory: this forms the
basis of the present work.
This paper proposes an explicit memory-based
control architecture for a mobile robot based on
functional neurobiological principles. It is proposed that
this will result in an agent capable of autonomous and
adaptive behaviour. Furthermore, it is anticipated that
this architecture will establish a platform for exploring
theoretical issues in both cognitive robotics and animal
behaviour. There is a particular emphasis on allowing
the specific functionality of the architecture to develop
through interaction with the world, and not through an a
priori or otherwise human-centred approach [2, 3].
1.1 Motivation
One of the most widespread approaches to
implementing the desired autonomy and adaptivity in
artificial agents is behaviour-based robotics. For
example, the subsumption architecture [4, 5] has proven
very successful in producing real time and real world
behaviour [6], but is unable to account for biological
observations relating to the flexibility of mammalian
cognition. There have thus been numerous endeavours
to incorporate neurobiological principles and models
into robotics work, with the dual goals of capturing the
desirable behaviours of the biological systems, e.g. [711], and as an alternate means of understanding these
systems and their constituent processes, e.g. [12-14].
Whilst these approaches implement the neural or
behavioural dynamics of the cognitive processes of
interest, the consideration of autonomy and embodiment
as playing a central role in these is often overlooked.
The underlying thesis for this architecture is
relatively uncontroversial. It is that cognitively flexible
agents are constrained both by their physical
instantiation and their environment, meaning that
developed and learned behaviours must emerge from the
interaction of the two [3]. Therefore, it is not enough to
consider the agent, and indeed the development of its
cognitive abilities, as separable from its interaction with
the environment in pursuit of its goals. On this basis, the
presented Memory Based Cognitive (MBC) architecture
is formulated in an attempt to address some of these
difficulties, and provide a means of examining the
issues involved (both theoretical and practical) in the
learning of basic sensorimotor competences, upon
which more complex behaviours may subsequently be
learned [15].
It is hypothesised that a number of benefits exist
through the use of this approach. Firstly, the
biologically inspired approach is envisaged to produce a
behaviourally flexible artificial agent. The resulting
platform will also allow the investigation of theoretical
issues because it brings together both the neural
considerations for a cognitive architecture, and the more
philosophical considerations such as embodiment and
autonomy. Furthermore, the use of explicit
representations of memory (in the form of a rule-base)
affords a detailed analysis of the development of
functionality.
For the remainder of this paper, the following terms
are defined for clarity. “Architecture” is used to refer to
the software-implemented element of the work.
“Model” is analogous to this, but emphasis is on the
theoretical underpinnings. “System” refers to the
ensemble of architecture and the physical mobile robot:
the behaviour of the system is emergent from the
interaction of robot (be it real or simulated), the
architecture, and the environment.
The remainder of this paper is organised into four
sections. In the first, a more detailed overview is given
of the motivation of the novel architecture, and how it
relates to existing approaches. In the following section,
the neuroscientific theory upon which the architecture is
founded is reviewed. Thirdly, the theoretical concepts of
embodiment, autonomy and value (or motivation) are
discussed in relation to the present work. Finally, the
MBC architecture is introduced, with a discussion of its
potential applications to both practical and theoretical
issues.
2. THE DEVELOPMENT OF A MEMORYBASED COGNITIVE COMPUTATIONAL
ARCHITECTURE
When incorporating biologically-inspired functionality
into robotics work, two broad approaches have been
used: neural modelling, and behavioural modelling.
In the first, a model of neural connectivity serving
the function of interest is created, and implemented as
an artificial neural network. This approach is essentially
one of learn-by-building: further understanding of the
biological system of interest may be gained through the
implementation of biologically-plausible neural
mechanisms for performance of a behavioural task. For
example, a model of the mammalian nervous system has
been implemented [16], in which a number of brain
regions are simulated. The architecture as a whole is
embedded in a mobile robot which interacts with the
real world [17], which allowed the successful learning
of a spatial navigation task. This approach has allowed
the functional characterisation of a number of
hippocampal pathways [18], which demonstrates its
utility both to the understanding of biological systems,
but also to the creation of behaviourally flexible
artificial agents.
In the second approach, computational architectures
are implemented based on higher level cognitive
psychological theories. This approach emphasises the
behavioural functionality over the specific neural
implementation. For example, Kawamura et al. [19-21]
have implemented a model of human working memory
in an upper-torso humanoid robot, with the aim of
providing cognitive control. Whilst a more engineering
solution to the problem of intelligent/cognitive control
of mobile robots (no claims are being made concerning
the biological theory upon which it is based), this
example demonstrates the applicability of biologicallybased theories to robot control problems.
Both of these approaches collectively stress the need
for embodiment in the real world as a prerequisite for
the emergence of intelligent behaviour through learning.
However, this form of embodiment does not necessarily
place the constraints on the computational architecture
that the 'body' of a biological agent would place on its
nervous system – in general, in these approaches the
body provides the necessary sensory and motor spaces,
but the relation between the software architecture and
hardware body is wholly defined by the designer, rather
than providing mutual constraints. Furthermore, in
terms of autonomy of learning and behaviour, the predefined nature of the chunks of information in the
second approach proves problematic if the task
environment changes significantly. Finally, it may be
argued that the transparency (in terms of ability to
explain the causes of produced behaviour) of neural
system-based modelling approaches, such as that
presented in the first approach, is reduced as the fidelity
(and hence complexity) of the model increases. As a
general observation, these approaches tend to focus on
what may be described as higher-level cognitive
functions.
The novel MBC architecture allows these issues to
be addressed by incorporating two considerations.
Firstly, the utility of neural systems-based architectures
for the potential elucidation of biological mechanisms
(at least in terms of function). Secondly, the theoretical
and philosophical considerations of autonomy and
embodiment that describes the conditions under which
the biological agents have developed – an aspect
frequently overlooked in current robotics work. By
combining these two elements, the architecture my be
used to explore issues which arise at the intersection of
the two approaches discussed, and more theoretical
considerations.
The following sections describe the basis of the
MBC architecture. Firstly, the network memory theory
is reviewed as a broad neuroscientifically-supported
theory of human cognition, upon which the main
functional mechanisms of the architecture may be
established. Since the theoretical considerations are of
central importance in addressing these issues, these are
discussed afterwards. Finally, these two strands are
brought together with a discussion of the MBC
architecture, its structure, and functionality.
3. THE NETWORK MEMORY THEORY
Based on neuroanatomical and neuroscientific evidence,
Joaquin Fuster has proposed the “Network Memory”
theory [22]. This theory of human cortical and subcortical organisation and functioning is based largely on
single-unit recording studies conducted on primates,
which on an anatomical level display many similarities
with human neuroanatomy [23]. It is of particular
applicability to the present work as it provides a wide
ranging theory of human cognition. The MBC
architecture is based upon these principles in order to
create a biologically non-implausible framework within
which it can take shape.
There are three central ideas which underlie the
Network Memory theory:

that memory is at its most basic an associative
process

that memory is not localised in specific regions of
the brain, but distributed across the cortex (and to a
lesser extent sub-cortical regions)

that the organisation of these distributed memories
is roughly hierarchical.
In addition to this, Fuster postulates the presence of
“phyletic memory”, in other words, memory of the
species – that which is genetically encoded [24]. This
type of memory is the basis of all of the initial
associative units, however, as it is similarly encoded
neurally, it is subject to the same potential for change as
subsequently learned information.
The first point is not a controversial one in itself as it
is a widely held view. It is held that a process akin to
hebbian learning (such as long-term potentiation [25]) is
the underlying process of memory formation.
The second point is in contrast to traditional theories
of memory, which, inspired by the Turing machine
metaphor, views brain organisation as essentially
modular, where specific memories are locatable in
particular parts of the cortex. In the network memory
theory, it is postulated that through the use of the
associative processes, incident sensory (or indeed
afferent motor) information causes the formation of
'units' of memory which store that particular sensory (or
motor) experience. Known as “cognits” [26], these units
may be associated with one another as required through
experience to form further associative units. These
cognits are not spatially located in particular regions or
consist of single neuronal units – rather, they are made
up of networks of neurons (where individual neurons
may be a part of multiple cognits) which are distributed
across the cortex [27]. Consequently, cognits are not
fundamentally discreet, as there may be significant
overlap (in terms of neural substrate) between cognits.
Thus cognits are distributed, overlapping neural
representations. Another important point made in the
Network Memory theory is that memory and perception
share the same neural substrate. This is an extension of
the cognit idea, where cognits are created or activated
when their 'conditions' (i.e. the conditions in which they
were created) are met, which is the process of
perception.
The third central idea of the Network Memory
theory is the self organisation of cognits into loose
hierarchies: one for sensory-based associations, and one
for motor-based associations. These two hierarchies are
not however entirely dissociable: whereas somewhat
separable neural substrates may be identified, there are
multiple bi-directional interconnections, and a common
apex. Based on neuroanatomical evidence [28], the
prefrontal cortex (PFC) has been suggested to be this
common apex – the main function ascribed to it is the
temporal integration of behaviour [29], which allows
novel goal directed behaviours to be concatenated from
previously learned behaviours. Koechlin and associates
have provided neuroimaging evidence in support of a
functional model of PFC functionality in line with this
proposal [30]. Note that the Network Memory theory
proposes that well-rehearsed (or otherwise automatic)
actions may be executed without the mediation of the
hierarchy apex, occurring as a result of activity in the
lower echelons of both the sensory and motor
hierarchies, and with the involvement of sub-cortical
regions [29].
3.1. Related work to the Network Memory theory
There exist an increasing number of theories that
emphasise the widely distributed nature of memory and
cognitive processes over the competing modular view
(indeed, the network memory theory was itself inspired
by the work of Lashley, e.g. [31], who proposed the
principles of mass action and equipotentiality). As
examples of this trend, Postle [32] and Chadderdon and
Sporns [33] have recently presented theories of Working
Memory (WM) which emphasise the coordination of
distributed brain regions to provide the observed
functionality, rather than proposing a specialised neural
system, as occurs in more traditional psychological
theories of WM. The approach of the present work in
emphasising the distributed nature of memory and
cognition is thus justified.
4. THEORETICAL AND PHILOSOPHICAL
CONSIDERATIONS
4.1. Autonomy and ‘Emotion’
Autonomy is considered to be a desirable trait for
artificial systems, despite differences over precise
definition, as it broadly implies the ability to act without
explicit direction by the human designer [34].
constrained by the particular physical instantiation of
the architecture (and therefore the interaction with the
environment). The resultant system is then dependant on
the interaction of the embodiment of the architecture
with the functionality of the architecture itself.
5. THE MBC ARCHITECTURE
5.1. An Overview
The creation of autonomous systems, or systems in
which there is a significant autonomous component, has
thus been the subject of much work, e.g. [35, 36].
However, it has been argued that since autonomous
systems result from processes which maintain the
identity of the system, autonomy can not be modelled as
a function – consequently, approaches that do so are
inherently problematic [37]. An alternative approach
would be the introduction of homeostatic processes, or
‘emotion’, into artificial systems, thereby providing the
necessary link (in terms of autonomy) between system
behaviours (or ‘cognition’) and system implementation
(or ‘embodiment’) [37, 38].
The involvement of emotion and homeostatic
processes with cognition is now generally accepted,
largely due to [39]. Indeed, there is a growing consensus
that these processes are not independent from cognition,
but rather intrinsically integrated [40]. Robotics work
has followed this trend, allowing an advance towards
autonomy in artificial agents, and providing an insight
into the nature of emotions themselves in the process
[41]. The central role of an ‘emotion’ system for
supporting autonomy of the system is thus established.
The MBC architecture is implemented on the basis of
the discussed characteristics of the Network Memory
theory, which essentially provide the framework and
fundamental functional guidelines. It is memory-based
in the sense that its functionality revolves around the
acquisition and manipulation of information gained
through experience in its environment. Whilst there is
no information on the environment set a priori, any
information which is embedded in the architecture (such
as the architecture framework for example), is
analogous to the phyletic memory proposed by the
network memory theory.
The association hierarchy is based in the particular
sensory and motor spaces of the physical device in
which the architecture is embodied, Figure 1. These
associations are encoded in the form of ‘rules’ – which
are Learning Classifier System [45] -inspired constructs
which explicitly encode a relationship (as defined by
temporal or spatial adjacency during operation) between
two sensory and/or motor space elements. This
organisation is viewed to be an abstraction of the
‘cognit’ concept introduced by the Network Memory
theory: where a cognit is the fundamental unit of
memory in the biological neural system, so is the ‘rule’
4.2. Embodiment and Environmental Interaction
This emphasis on homeostatic (and emotion) processes
underlines the requirement for the strong embodiment of
artificial systems, as a means of integrating the
‘cognitive’ part of the system with the physical system
in which it is instantiated. A discussion of the
requirement and meaning of embodiment is relevant.
Whilst numerous concepts of embodiment exist,
with some definitions being far more restrictive than
others [42], it has been argued that even if the most
restrictive definition holds (organismic embodiment,
which holds that only living organisms can be truly
embodied), there remains utility in examining ‘weakly’
embodied systems as if they were ‘strongly’ embodied
[43]. With respect to the discussion of autonomy,
embodiment is proposed to ground cognition – cognitive
processes are inherently body-based [44].
In line with this, embodiment of the MBC
architecture is required to define sensory and motor
spaces upon which the development of environmental
‘knowledge’ is founded. This development is thus
Figure 1. Functional diagram of MBC architecture. The
sensory and motor spaces tightly integrate the
architecture with its physical instantiation. The credit, or
value, system plays a central role in the development of
the hierarchy of associations.
proposed here the fundamental unit of memory and
association in the MBC architecture. In particular, the
functional properties of the cognit are of importance: the
overlapping nature, the manner in which cognits may be
combined to form other cognits, and the way in which
cognits form the basis of memory and perception.
Furthermore, where a cognit can be activated, so too
does a 'rule' have an associated activation value.
Whilst the property of distributed basis (in terms of
neural substrate) is also central to the cognit idea, for the
‘rules’ here proposed, this does not apply since the
intention is to capture the essential functional nature of
cognits, and so no claims are being made regarding their
fundamental neural properties.
5.2. Implementational Details
The architecture is implemented using the objectoriented programming language C++. The intention is to
create a generic library which can be used to easily
create instantiations of the MBC architecture for
operation with different hardware platforms. At the
present time, this library is subject to extension,
particularly in the areas outlined below. A high-level
description of the main system execution steps in the
current version of the architecture may be seen in Figure
2. This execution scheme applies irrespective of the
hardware embodiment.
After an initialisation stage that defines the sensory
and motor spaces (thus tying the architecture to the
physical embodiment), each iteration of the loop
performs operations from sense to act. This loop is
closed through interaction with the environment. At the
current stage of development, this loop incorporates
relatively simplistic incarnations of the functional
aspects presented in Figure 1. A number of extensions
may be made to these mechanisms, which are outlined
below. The following points refer to the labelled stages
in Figure 2:
Figure 2. High- level program flow indicating six main
stages of processing.
Within the functional architecture framework, Figure
1, the development of the hierarchy of associations is
constrained by the particular physical embodiment. In
other words, since each mobile robot in which the
architecture is embodied has (although perhaps very
subtle) differences in its array of sensors and effectors
(either physical differences in the units or the
topological arrangement thereof), the associations that
are formed would differ. This also indicates the
necessity for interaction with the environment to form
the associations in the first place.
From the discussion of autonomy and value systems
in the previous section, and given the requirement of the
architecture to autonomously develop, the value/credit
system indicated has central importance. Because the
development of the association rules is initially
essentially random (see Implementational details), this
system is required for the evaluation of associations for
the creation of behaviour, e.g. [46]. The information
contained within this value system is implemented as
part of the framework of the architecture, and is thus
defined explicitly. However, this is goal and
environment independent, thus maintaining the
autonomy of the system in this respect.
1) Given a motor command executed on the previous
time-step, this stage evaluates the effect on the
system, and updates the value element of the
relevant associations. Whilst in the current
implementation this update concerns only that
association which was selected, this may be
extended to related associations (on the basis of
similarity). Additionally, the value system may
itself be extended to account for a number of
biological theories of emotion, e.g. [47].
2) Associations in MBC architecture are explicitly
represented by an object containing two elements,
which may be either sensory or motor, or indeed
another association. In this way, the loose
hierarchies may be formed in line with the proposal
of no explicit layers in the network memory theory.
An open question remains as to which associations
are formed on a given time-step – as the number of
sensory and motor spaces increase, the number of
possible associations increases combinatorially,
which leads to the potential for exceeding available
computational power. A possible solution to this
would be to take into account spatial arrangement
and type of sensors in order to reduce this number.
In the current version of the architecture, where the
mobile robot used allows small sensory and motor
spaces, all possible associations are encoded on
each time-step, and no such problems are
anticipated.
3) Because the association rules have a coupled
activation value, this stage updates them according
to the recency of last use. The activation is in this
way a time decaying function, which is allocated
not just to those associations which exactly match
the incident sensory states, but also those adjacent
(in a scaled fashion). In a similar manner, a scaled
activation level may be spread to related
associations.
4) The associations whose activation values exceed a
certain threshold form the association match set.
This is in accordance with a number of biological
action selection schemes, such as the affordance
competition hypothesis [48]. Because this selection
is based on activation and not directly the sensory
state, there is a possibility that rules not directly
related to the current sensory state are part of the
action set, thereby allowing the potential for
chaining of rules.
5) The action selection procedure operates on the
match set produced in the previous stage. In the
current version of the architecture, the action
selection procedure selects the associations for each
system effector with the highest value element.
However, a number of other action selection
schemes may be implemented in the future –
indeed, the architecture may serve as a platform for
comparing alternative actions selection schemes.
6) From the selected rules, the motor commands are
executed, and preparations are made for the next
time-step.
As the implemented system is merely an abstraction
of the natural neural system from which it is inspired,
the relevant neural dynamics (for example the
suppression of competing activation profiles) must be
explicitly implemented. In this way, the action selection
procedures (as discussed in points four and five) may be
viewed as explicit representations of functions which
are performed neurally, even though no claim is made as
to their particular neural implementation.
on each time-step.
This use of explicit representations of association
also has an inherent cost relating to computational load.
Because associations are encoded on each time step
from the systems' experience, the number of rules would
increase rapidly, especially given more complex sensory
and motor spaces, thus requiring greater computational
power. In this way, the system may lag in terms of realtime performance. However, a number of alternative
schemes for the creation of associations, and indeed
their removal once created, would alleviate the problem.
This need not reduce the biological plausibility of the
architecture, as such schemes may be implemented in
accordance with current knowledge of nervous system
operation: for example, strong attention and affective
effects in memory processes, e.g. [49, 50], and the largescale synaptic pruning which occurs in early childhood
[51] may provide suitable mechanisms.
The placement of the architecture in the trade-off
between speed of operation (as may be afforded by an
equivalent ANN implementation) and explanatory
power (afforded by this rule-based setup) is justified
given the purpose of the model: to provide adaptive
behaviour in unknown environments, but also for use as
a platform for investigating theoretical issues, for which
explanatory power is desirable. The utility of the
architecture is thus maintained despite the potentially
slow real-time behaviour.
For the development of cognitive robotics, as in the
present work, the emergent behaviour is of critical
importance. Techniques from artificial ethology [52] are
thus required to provide metrics for the characterisation
of the behaviour. This ability to compare the internal
processes with the resulting behaviour is one which is
not (currently at least) possible with biological agents,
so the use artificial agents in this regard is a major
advantage to the study of general principles of cognition
and intelligence [53], and thus the applicability to the
present work.
6. DISCUSSION
7. APPLICATIONS AND FURTHER WORK
‘Rules' are abstracted analogues of cognits, and
explicitly encode associations. This has a notable
advantage over artificial neural network (ANN)
approaches in the transparency of operation: internal
methods and operations are easier to interrogate, thus
affording a more detailed analysis of the functionality of
the architecture. Whereas similar methods exist for
artificial nervous system ANN's (such as the backtrack
method proposed in [18]), these may generally only be
applied retrospectively on recorded data, whereas with
the rule-base system here proposed, it may be conducted
on-line (i.e. during run time) for an analysis of the
internal events which result in the observed behaviour.
This analysis may of course also be conducted
retrospectively given a suitable recording of the events
One of the central tenets of the MBC architecture is that
no a priori knowledge of the environment is required
for the system to produce a 'useful' behaviour. In this
context, 'useful' would in the first instance refer to the
ability to develop the necessary sensory-motor
contingencies to move around the environment without
collisions or becoming trapped. While this in itself is a
constructive behaviour from a real world application
point of view (autonomous rescue vehicles for
example), it is hypothesised that the development of
these most basic of behaviours would be prerequisite for
more complex cognitive abilities, as postulated by the
fields of developmental and epigenetic robotics [3, 54],
and particularly the concept of ongoing emergence [15].
The application of the MBC architecture to this task,
and an analysis of the manner in which successful
performance is attained, would then provide a proof-ofconcept experiment. This would demonstrate the utility
of this approach, and the MBC architecture in particular,
by showing how general behaviours can be developed
from individual instances, over the course of experience.
One particular application envisaged for the MBC
architecture is an issue in animal (and to a lesser extent,
human) cognition: the question of whether the
representation of knowledge for spatial information is
represented topologically (i.e. through relations, or
associatively) or in a euclidean manner (i.e. an explicit
map). For example, it is widely held that place cells in
the hippocampi of rodents encode an explicit
representation of space, or a cognitive map [55].
However, a competing view exists which states that
rather than being an explicit map, place cells actually
encode the associative properties of spatial locations
[56]. When place cell functionality is implemented, or
observed to emerge, in robotics work through the
implementations of ANN's, the euclidean view is
implicitly assumed, e.g. [12]. These studies therefore do
not address the possibility that the observed place cell
behaviour encodes not a specific spatial location in an
environment-grid (as it is commonly seen), but the
sensory (and possibly also motor) associations
applicable when the agent is in a specific place – indeed,
it would be very difficult to do so in an ANN. This is
where the rule-based implementation of the MBC
architecture demonstrates its utility. If through
experience in the environment, higher level associations
are observed to be applicable (i.e. activated) only in
specific spatial locations, then this would lend support
to the topological view of hippocampal place cells. It
would not deny the existence of explicit maps, but
would lend support to the alternative paradigm.
For this application, a suitable spatial task would be
an adaptation of the Morris water maze [57], in which
an agent attempts to learn the position of a hidden (i.e.
detectable only when the agent is actually there)
location within an enclosed arena, with static spatial
cues. This setup has been applied to previous robotics
work, for example [18, 58].
The processing stages of the currently implemented
version of the architecture are in some respects
simplistic. An ongoing task is the extension of the
architecture to incorporate further biological evidence to
further expand its biological plausibility, and hence its
explanatory power. Particular regard is first to be made
to the action selection mechanism.
developmental cognitive architecture, with a framework
which incorporates the central role that autonomy and
embodiment play in these processes. As such, this
architecture is based on neuropsychologically plausible
mechanisms, which are provided by the Network
Memory theory of cognition. Furthermore, an analysis
of the theoretical issues of autonomy and embodiment
has demonstrated their applicability to the study of
cognitive architectures in general. These elements
combined provide the foundation of the MBC
architecture. The resulting system is to be used as a
platform for investigating the interplay of embodiment
with cognition (or more specifically, the development of
cognition), in addition to providing a model of
autonomous behaviour for application to artificial
agents.
A novel memory-based cognitive architecture has
been introduced which will allow the investigation of
theoretical issues in cognitive robotics and animal
cognition, in addition to providing an autonomous and
adaptive mobile robot controller.
Acknowledgements: The authors would like to thank
Prof. Richard Sibly (School of Biological Sciences,
University of Reading) and Dr. Phil Beaman
(Department of Psychology, University of Reading) for
their helpful comments and discussions.
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