Cogn Process (2008) 9:1–17
DOI 10.1007/s10339-007-0185-8
REVIEW
Coherence and recurrency: maintenance, control and integration
in working memory
Gezinus Wolters Æ Antonino Raffone
Received: 15 March 2007 / Revised: 7 June 2007 / Accepted: 4 September 2007 / Published online: 28 September 2007
Ó Marta Olivetti Belardinelli and Springer-Verlag 2007
Abstract Working memory (WM), including a ‘central
executive’, is used to guide behavior by internal goals or
intentions. We suggest that WM is best described as a set
of three interdependent functions which are implemented
in the prefrontal cortex (PFC). These functions are maintenance, control of attention and integration. A model for
the maintenance function is presented, and we will argue
that this model can be extended to incorporate the other
functions as well. Maintenance is the capacity to briefly
maintain information in the absence of corresponding
input, and even in the face of distracting information. We
will argue that maintenance is based on recurrent loops
between PFC and posterior parts of the brain, and probably
within PFC as well. In these loops information can be held
temporarily in an active form. We show that a model based
on these structural ideas is capable of maintaining a limited
number of neural patterns. Not the size, but the coherence
of patterns (i.e., a chunking principle based on synchronous
firing of interconnected cell assemblies) determines the
maintenance capacity. A mechanism that optimizes
coherent pattern segregation, also poses a limit to the
number of assemblies (about four) that can concurrently
reverberate. Top-down attentional control (in perception,
G. Wolters (&)
Department of Psychology,
Institute for Psychological Research, Leiden University,
P.O. Box 9555, 2300 RB Leiden, The Netherlands
e-mail: wolters@fsw.leidenuniv.nl
A. Raffone
University of Rome ‘‘La Sapienza’’, Rome, Italy
A. Raffone
Laboratory of Perceptual Dynamics, RIKEN BSI,
Saitama, Japan
action and memory retrieval) can be modelled by the
modulation and re-entry of top-down information to posterior parts of the brain. Hierarchically organized modules
in PFC create the possibility for information integration.
We argue that large-scale multimodal integration of
information creates an ‘episodic buffer’, and may even
suffice for implementing a central executive.
Keywords Working memory Maintenance
Control of attention Integration Prefrontal cortex
Recurrent networks Synchronization
Introduction
When we return home from work we sometimes find
ourselves deeply engaged in thinking about an unsolved
problem. Yet, at the same time we manage to find our way
home and avoid accidents. This indicates that apparently
we are able to automatically control our behavior without
needing conscious attention. If suddenly an obstacle
appears, however, we are also able to switch attention in a
split second. Given the right cues, we may also remember
that in the morning we planned to do some shopping on the
way home. That too would put solving the problem to a
halt and bring us to figure out the best route to the shop.
This shows that control may be taken over by retrieving a
plan, and that in order to carry it out we need access to a
large database, including a route planner. If later on we
resume thinking about the problem, we may figure out a
solution by combining known elements in a novel configuration, and then start thinking how to use it in the future.
The description given above is an example of the
coordinated and goal-directed thoughts and actions that are
generated in our brains all the time. Clearly, such
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coordinated behavior requires a versatile system of cognitive control for which as yet there does not seem to exist an
adequate concept. Some of the contours of such a control
system, however, are discernable in the example given
above, like the ability to maintain representations, to direct
attention and to combine previously unrelated information.
An early and influential framework for the study of
control and coordination of cognitive behavior is the multicomponent model of working memory (Baddeley and Hitch
1974). Working memory (WM) refers to a limited capacity
system for temporary holding and manipulating information, that is required for performing a wide range of
cognitive tasks such as comprehension, learning and
reasoning. Originally the model distinguished three-components. A general control system, or ‘central executive’,
and two subsidiary slave systems, a phonological loop and a
visuo-spatial sketch pad, each capable of maintaining a
limited amount of information. More recently (Baddeley
2003), an episodic buffer was added to connect working
memory with long-term memory and to allow binding
together information from different sources into integrated
episodes. The central executive is the most important but
least understood component of WM, and it looks conspicuously like a homunculus. Baddeley is well aware of this
criticism, but he defends the central executive concept by
pointing out that it defines a problem area for which the
processes have to be specified.
An important conceptual model was suggested by Norman and Shallice (1986). They proposed a ‘data base
system’ for automatic habitual action routines, containing
well-learned stimulus-response associations and cognitive
and behavioral skills, and a ‘supervisory attentional system’ capable of controlling behavior by selectively biasing
and reconfiguring the available skills and schemas. Automatic control develops gradually through practice as
learning processes create associative pathways between
perception and action. The conscious form of control is
applied when we are confronted by novel and unexpected
stimuli, and when we have the intention to attain specific
goals. In this case, automatic actions to stimulus patterns in
the environment have to be suppressed and replaced by
novel task- and goal-directed actions.
The model of Norman and Shallice aimed to explain
deficits of executive control that are often observed in
patients with damage in prefrontal cortical areas. It is now
generally accepted that the prefrontal cortex (PFC) is of
crucial importance when behavior must be guided and
controlled by internal states and intentions, when automatic
responses have to be suppressed, and when tasks require
the establishment of new or rapidly changing mappings
between perception and action (e.g., Goldman-Rakic 1996;
Miller and Cohen 2001; Smith and Jonides 1999; Wood
and Grafman 2003). Anatomically, the PFC is well
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positioned to coordinate processing in the rest of the brain.
It consists of a number of strongly interconnected areas that
collectively have reciprocal connections with virtually all
other neocortical and subcortical structures. It is also an
area that shows late development, both phylogenetically
and ontogenetically, reaching maturity only in adolescence
(Fuster 2001).
Based on these anatomical considerations, we have
suggested (Phaf and Wolters 1997) that working memory
or, more general, executive functions, may have emerged
as a consequence of an evolutionary development of the
PFC. This development may have created the possibility
for neural processes that could run (partly) independent of
present input and output, but that also could modulate, and
thus control, perception-action relations in the rest of the
cortex. In this view, the role of PFC in controlling behavior
is modulatory rather than transmissive, which is similar to
the role assigned to PFC in several other models (e.g.,
Norman and Shallice 1986; Miller and Cohen 2001;
O’Reilly et al. 1999). Whereas simple adaptive behavior
rests on a cycle of perception, action, and perception-ofaction results, the added PFC would allow an internalization of this loop, freeing the organism of the restriction of
being aware of, or acting upon, physically present objects
or situations only. In fact, it would implement a medium
for creating a virtual world, i.e., manipulating internal
representations that are independent of the present environment (for a similar idea, describing thinking as
simulated behavior and perception, see Hesslow 2002).
More specifically, we suggested that the development of
PFC created the possibility to maintain physically absent
information in an active state by recurrent connections
(loops) between PFC and the rest of the cortex. The
recurrent activity in these loops may affect subsequent
perceptual and motor processing, i.e., it can redirect
attention and control actions by activating or inhibiting
particular motor programs. By assuming interactions and
integration between loops, more complex forms of representation and control may develop. For instance, recurrent
connections with memory systems would allow access to
all stored information, and mechanisms for combining
information in the loops would allow the formation and
updating of future goal states, and ways to achieve them.
A taxonomy of working memory functions
Although there is no generally accepted taxonomy of executive functions, we may speculate which functions would be
required. It can be argued, for instance, that to be able to exert
top-down control over cognitive processes, patterns of
activity representing task relevant information must be
actively maintained in the PFC. Moreover, PFC must be able
Cogn Process (2008) 9:1–17
to generate biasing signals to guide the flow of information
by selectively inhibiting or activating particular representations and pathways in other parts of the brain. This
interaction between PFC and posterior parts of the brain
would also be needed to retrieve any relevant information
stored in memory. Finally, PFC must be able to integrate
information from different sources to implement goal
directed behavior over time. We suggest, therefore, that three
main executive functions have to be distinguished in PFC.
The first function is maintenance, i.e., holding a limited
amount of currently needed information, i.e., all task-relevant information supplied by preceding events, in an active
form. The second function is attentional control, i.e., topdown controlled selective activation of task-relevant stimulus representations and responses. Only selective activation
is required, because in a competitive system this automatically induces selective inhibition of task-irrelevant stimuli
and responses. The third function is integration. This function consists of the ability to flexibly combine and reorganize
information from different sources in the service of controlling task execution. This also includes control over search
in LTM, monitoring and evaluating results of actual or
imagined actions, and sequencing operations that are needed
for planning, decision making and problem solving. In our
view, the central executive operations in PFC are strongly
interdependent and continuously interacting with processing
in other cortical areas (see also Duncan 2001, for emphasis
on interdependence of control, working memory and attention in PFC). A somewhat similar view of PFC functions was
suggested by Smith and Jonides (1999). They proposed a
distinction between short-term storage and two executive
processes, selective attention and task management.
In this paper we will first discuss the neuroanatomical
correlates of these hypothesized interdependent working
memory functions. Then we will present a neural model for
binding and segregation in working memory to simulate the
maintenance function. This model is based on recent ideas
about synchronization of activation patterns in networks
and it is able to explain capacity limitations of maintenance
in WM. Next, extensions of this model will be discussed to
realize the attentional control mechanism. It is suggested
that modulatory effects within PFC may selectively
enhance or suppress representations in posterior cortex by
recurrent connections. Finally, we will present some ideas
on how to realize integration and manipulation functions,
given that large-scale multi-modal integration is necessary
to explain coherence and coordination of behavior.
Neuroanatomical correlates of working memory in PFC
Generally, the brain shows a remarkable specialization of
anatomically distinctive areas performing specific
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cognitive functions. Also the PFC comprises a number of
cyto-architectural distinctive areas or modules that differ in
patterns of connectivity with other brain regions (e.g.,
Miller and Cohen 2001; Wood and Grafman 2003). Much
effort has been invested in finding functional specializations within the PFC, i.e., the extent that different areas or
modules of PFC are selectively involved in encoding particular types of information (e.g., verbal versus visual), or
in implementing various processing functions (e.g., maintenance versus attentional control).
Although this debate is still ongoing, there seems to be
a growing consensus that broadly speaking, three major
areas in PFC seem to be distinguishable in terms of
processing specific types of information (Fuster 2001;
Wood and Grafman 2003), probably with additional subspecializations. First, ventromedial and orbitofrontal
regions (BA 11/12/47) seem specifically involved in
representing and processing reward and affective information, and thus participate in emotionally and
motivationally driven behavior (e.g., Fuster 2001; Rolls
2000). These regions have major reciprocal connections
with the temporal lobe and the amygdale complex. Second, medial regions of the PFC (in particular the anterior
cingulate cortex, BA 24/32) are suggested to be involved
in error monitoring, and in detecting conflicts between
competing stimuli and responses (e.g., Ridderinkhof et al.
2004). Third, lateral and anterior PFC areas (BA 9/10/44/
45/46; see Petrides 2005, for a review of the architectonic
organization of this area) are supposedly involved in all
executive functions that are needed for organized goaldirected behavior, such as the selection of goal-relevant
information, the manipulation and maintenance of information, and monitoring multiple-events (e.g., Miller and
Cohen 2001; Petrides 2000). This area has strong reciprocal connections with parietal and temporal association
areas, the hippocampal formation and with all other PFC
regions. Damage to lateral PFC typically impairs the
ability to formulate and carry out plans and sequences of
actions (Fuster 2001) and to control long-term memory
encoding and retrieval (Blumenfeldt and Ranganath 2006;
Tomita et al. 1999).
There is also increasing evidence, however, that localization of informational content is not precise, but rather a
matter of degree. A review of imaging results showed that
quite different cognitive demands (i.e., response conflict,
task novelty, WM delay and load, and perceptual difficulty)
induced very similar patterns of prefrontal recruitment,
mainly involving lateral and medial PFC areas (Duncan and
Owen 2000). This finding is corroborated by single cell
studies that have shown a substantial adaptability of function at the level of individual neurons. Many lateral PFC
neurons show highly specific activity patterns depending
upon the current task and task conditions (e.g., Asaad et al.
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1998, 2000; White and Wise 1999; Quintana and Fuster
1999; Wallis et al. 2001).
These results led Duncan (2001) to postulate the
‘adaptive coding’ model. According to this model, neurons
in PFC adjust their function to match the requirements of
particular tasks that are carried out. Apparently any given
cell can be driven by many different kinds of input, both
from posterior parts of the brain and from other PFC areas.
The collective responsiveness pattern of all PFC cells thus
reflects the particular task relevant conditions. Duncan also
noted, however, that the potential of cells to process different types of information, does not exclude regional
specializations. This would reconcile results indicating
regional specialization, with results from single cell studies
showing large scale integration and adaptation. For
example, a statistical rather than an absolute specialization
seems to apply for left-hemisphere PFC recruitment for
verbal material and right-hemisphere PFC recruitment for
non-verbal stimuli (see, e.g., Passingham et al. 2002).
A slightly different perspective on functional specialization of PFC areas is to assume that some areas are
relatively specialized, whereas others serve more general
integrative purposes and come into play in almost any task.
Gruber and von Cramon (2003), for instance, found both
modality specific and amodal areas in PFC in verbal and
visuo-spatial working memory tasks. Other findings suggest that many cells in ventrolateral areas of PFC are
specifically sensitive to maintaining representations of
single stimuli (e.g., objects or locations), whereas cells
sensitive to maintaining complex integrated stimuli and
elaborative rehearsal are found more often in dorsolateral
areas (Prabhakaran et al. 2000; Owen 2000; Wagner et al.
2001). Wallis and Miller (2003) presented evidence suggesting that orbitofrontal cortex, which is specifically
involved in reward and affect processing, passes on this
information to dorsolateral areas of PFC where it is combined with other information to control behavior. Similarly,
Ridderinkhof et al. (2004) concluded from a review that
detection of response conflicts and response errors elicits
overlapping foci of activation in medial PFC areas, and that
this activation serves as a signal that engages regulatory
processes in lateral PFC areas.
Learning and working memory
Our view of working memory is that its informational
content consists of the active part of long-term memory
representations that is available at any point in time for
controlled processing (e.g., Cowan 1999; O’Reilly et al.
1999). These representations presumably are stored in the
temporal, parietal and occipital cortex, so the functions of
working memory (maintenance, selection and integration)
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that apply to these representations are realized by an interaction between PFC and posterior cortical areas. In this
view, PFC is not itself involved in storing long-term memory information. Its representational role would be limited to
temporarily maintaining and integrating information stored
elsewhere. This view seems to be endorsed by the fact that
the neural circuits and pathways assumed to be involved in
the two main learning processes (Eichenbaum and Cohen
2001), and the corresponding procedural and declarative
memory systems (Squire and Zola-Morgan 1991), do not
include the PFC as a representational medium.
The suggestion that PFC implements WM functions, but
is not itself involved in associative learning, would fit the
requirement that it is maximally flexible in the service of
generating novel combinations of representations that are
stored somewhere else. There are no indications, however,
that the neural architecture and processes of the PFC differ
fundamentally from those in posterior cortex which
intrinsically generate a learning capacity. Therefore, some
learning (and representational) capacity in PFC cannot be
ruled out a priori.
Some evidence suggests that the information used in
working memory tasks does not have to be stored in PFC.
First, prefrontal patients generally perform quite well in
tasks requiring the retrieval of previously learned procedural and declarative knowledge. However, they are
typically impaired in episodic memory tasks requiring for
instance elaboration of stimuli, ordered recall and source
monitoring. So the problem with prefrontal damage does
not seem an inability to store information in memory, but an
inability to control encoding and retrieval processes (e.g.,
Shimamura 1995). Second, several recent studies have
linked activity in PFC areas with promoting effective LTM
formation (e.g., Blumenfeld and Ranganath 2006; Buckner
2003). They suggest, however, that the information itself is
not stored in PFC. Instead, the interaction between PFC and
posterior cortex controls what is selectively activated,
maintained and elaborated, and what is subsequently stored
as a novel representation via the medial temporal lobe and
the hippocampus (see, e.g., Ranganath et al. 2005). Third,
an increasing number of studies showed that the activation
patterns of many single neurons or neuron ensembles in
PFC quickly and flexibly adapt to represent completely
arbitrary rules (Asaad et al. 2000; Wallis et al. 2001; White
and Wise 1999), or to arbitrary action sequences (Averbeck
et al. 2006). Although in all of these studies a number of
learning trials precedes the development of task specific
activation patterns, the speed and flexibility of the adaptation process seems more suggestive of an adaptive coding
than of an associative learning process.
Other authors have suggested that learning in PFC does
occur. O’Reilly et al. (1999), for example, suggested that
the information that is maintained in WM consists of
Cogn Process (2008) 9:1–17
activation patterns in PFC that are sustained by strong
mutual excitation of the neurons involved. To explain the
flexibility in combining these representations without losing their specificity, they assumed that PFC representations
had to be relatively isolated from each other. Therefore,
learning in PFC was supposed to be slow, taking place over
many years, and eventually producing a rich palette of
independent PFC representations that enable flexible
problem-solving skills. Such representations might have a
hierarchical structure with an increasing level of abstraction along a posterior-anterior PFC axis, probably with the
most posterior PFC representations being closely connected to detailed memory representations in posterior
cortex.
A worked out version of this model by Rougier et al.
(2005) showed that extensive training of their model with
various tasks resulted in the development of abstract rulelike PFC representations that supported flexible generalization in novel tasks. The PFC representations developed
slowly, but once learned adaptive behavior was mediated
by a search for a task appropriate pattern of activity (cf. the
adaptive coding mechanism of Duncan 2001), rather than
the need to update connection weights. Although the simple tasks simulated in this model may also apply to more
realistic and complex rules, the authors point out that it still
leaves unexplained how PFC representations can be
dynamically recombined and can interact with other systems (such as episodic memory, language and affect).
It remains to be seen whether the supposed PFC learning
mechanism is viable. Over brief periods of time, learning
probably does not play a major role, but a role of learning
over extensive periods cannot be ruled out.
Evidence for three interdependent working memory
functions in PFC
Maintenance
Neurophysiological studies have established persistent
activity during delays (i.e., after stimulus offset) as the
main candidate for a neural substrate of the maintenance
function of WM. Single cells showing persistent firing
during a delayed-matching task were first discovered in
PFC, and later in other neocortical areas like inferotemporal (IT) cortex (Fuster and Alexander 1971; GoldmanRakic 1996; Nakamura and Kubota 1995).
Different areas, especially in posterior parts of PFC,
may be involved in maintaining modality specific information. Smith and Jonides (1999), for instance, suggested a
relatively specialized role of left and right PFC hemispheres in maintaining verbal and visual information,
respectively. Moreover, they proposed that maintenance of
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spatial information may involve more dorsally located
regions than maintenance of object information (see also
Baddeley 2003).
In principle, there are two ways to maintain activity in
neurons during delays. One is to assume that input activation creates an activation pattern in a network of cells
that is maintained within the network by auto-association
(Amit 1995; Deco and Rolls 2003), and possibly augmented by a dopamine gating mechanism (O’Reilly et al.
1999). The other possibility is that the activation pattern
oscillates in a recurrent loop between different networks of
cells.
There is much evidence that supports an important role
of recurrent loops in maintaining information during
working memory tasks. Strongest evidence is that during
delays continued activity is not only found in PFC, but also
in modality specific areas in IT cortex when coding for
objects (Fuster et al. 1985; Miller et al. 1993; Tomita et al.
1999; Ungerleider et al. 1998), and in parietal cortex when
coding for locations (Curtis and d’Esposito 2003; Rowe
et al. 2000; Sakai et al. 2002). Moreover, localized cooling
of either PFC or IT cortex interferes with activity in the
other area and causes behavioral deficits in a working
memory task (see Fuster 2001). From a review of the literature, Ranganath (2006) concluded that information in
visual WM tasks is maintained through persistent activity
in visual cortical areas (e.g., inferotemporal and parietal
areas) that is promoted by top-down input from PFC. So
according to Ranganath, maintenance would be an interaction between PFC and modality or object specific
posterior areas. An additional loop involving the medial
temporal lobe (i.e., the hippocampus and related areas)
may be required to maintain complex novel stimuli, and to
quickly create new long-term representations (e.g., Ranganath et al. 2005).
Ranganath (2006) is somewhat unclear about the nature
of the feedback from PFC in maintenance. As a further
specification we would suggest a fixed connection scheme
(probably shaped by long-term learning) between posterior
brain areas, containing long-term memory or newly created
representations of stimuli, and adjacent areas in PFC.
These pathways convey to PFC what information is being
maintained. This may also explain the finding that during
maintenance, activation in posterior areas is more vulnerable to distraction than activation in PFC (Miller et al.
1993; O’Connor et al. 2002). Maintenance may resist such
distraction because PFC activation can reinstate activation
of corresponding lower level representations. Such reinstatement probably also underlies memory retrieval and
mental imagery. It has been shown, for example, that in
tasks requiring participants to imagine faces or buildings,
category specific regions in the IT cortex became activated
(e.g., Ishai et al. 2002; O’Craven and Kanwisher 2000).
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As was argued by O’Reilly et al. (1999), there are two
seemingly incompatible core requirements of working
memory. The first is to robustly maintain goals and task
instructions over time even when faced with strong interference. The second is to rapidly and flexibly update goals
and tasks if circumstances change. Their solution to combine robust maintenance with flexible updating has been to
assume a dopamine gating mechanism. Under tonic dopamine levels a strong mutual excitation within a cell
assembly would lead to sustained activity and resistance to
interference by irrelevant input. Phasic shifts of dopamine
levels induced by significant events (e.g., success or failure
in a task or novel stimuli predicting reward) would enhance
the strength of afferent input and cause updating of the
activation state, which subsequently may lead to representing novel strategies, rules, goals or task states.
Although a dopamine-gating mechanism is an interesting possibility, we believe that re-entry of activation
through recurrent neuronal circuits is the main mechanism
for the maintenance function of WM. It is likely that there
are many of such recurrent loops for different types of
information. These loops link the perceptual, memory and
motor representational areas in posterior cortex to PFC.
They feed information into PFC and, in turn, are activated
by the recurrent activation from PFC. Modulating activity
in these loops by other PFC sources would modulate the
top-down recurrency. We also suggest and that these loops
form a hierarchy, at the lowest level maintaining simple
stimuli or features and at higher levels maintaining
increasingly complex stimulus relations and rules. We will
show how this idea can be implemented and explore the
feasibility of such an implementation. We agree, however,
that especially for the highest and most global or integrative levels of maintaining information, a dopamine gating
mechanism for auto-associative maintenance cannot be
ruled out.
Cogn Process (2008) 9:1–17
Reynolds et al. 2000; Gazzaley et al. 2005). According to
such a model, top-down control activates corresponding
representations that are then in a better position to compete
with irrelevant information for perceptual awareness and
control of motor behavior. Such top-down attentional
modulation of neural responses, i.e., relative enhancement
of neuronal responses to task-relevant stimuli and relative
suppression of neuronal responses to task-irrelevant stimuli, has been shown throughout the visual system as early
as the primary visual cortex (e.g., Desimone and Duncan
1995; Reynolds et al. 1999), and the exact areas that are
biased depend on the task that is performed (e.g., Kastner
et al. 1999).
Top-down attentional bias not only pre-activates specific
target representations but also spreads activation to related
representations. Distractors with visual similarities to targets also attract attention (are positively biased), as well as
distractors with semantic or associative links to the target
(Moores et al. 2003). Task specific activity in PFC also
generates top-down signals involved in the selection of
actions, and in long-tem memory retrieval (Hasegawa et al.
1998) and storage (Brewer et al. 1998; Kyd and Bilkey
2003; Blumenfeld and Ranganath 2006; Wagner et al.
1998). This perspective thus suggests a single underlying
mechanism of cognitive control by the PFC, namely a topdown biasing effect on processing in specialized sensory,
motor and memory systems responsible for task performance (see, e.g., Miller and Cohen 2001).
We will explore the feasibility of a mechanism for
attentional control based on modulating feedback signals
from PFC in the recurrent circuits that are used for maintenance. We suggest that attentional control is mediated by
biasing the recurrent loops between PFC and posterior
cortex. In this view, the biasing signals stemming from
higher integrative areas in PFC would modulate the
recurrent feedback to posterior cortical areas (see also
Deco and Rolls 2003).
Attentional control
Integration
Attentional control by executive functions in PFC requires
top-down effects on local information processing in posterior brain areas (e.g., Desimone and Duncan 1995; Miller
and Cohen 2001; Shimamura 2000). Evidence for such a
role comes, for instance, from findings showing that an
important characteristic of behavioral deficits following
damage to the PFC is extreme distractability (i.e., an
inability to suppress interfering information) and disinhibition (i.e., an inability to suppress inappropriate
responses).
A mechanism for top-down attentional control is suggested by the ‘biased competition model’ (Chelazzi et al.
1993; Desimone and Duncan 1995; Downing 2000;
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The importance of a large-scale integrative system for the
coordination of behavior has been widely acknowledged.
Such a system would provide the combination and integration of all information necessary for controlling taskrelevant behavior, i.e., it would comprise all task-relevant
information supplied by the present context and goals, and
by knowledge retrieved from memory. Baddeley (2000),
for instance, suggested an episodic buffer as an additional
subsystem in his WM model which is capable of integrating and maintaining information from different
sources. In a similar vein, Miller and Cohen (2001) argued
for a cognitive control system in the PFC that represents
Cogn Process (2008) 9:1–17
goals and the means to achieve them. They suggest that this
system integrates converging inputs from many sources. It
exerts control by divergent feedback signals to sensory,
memory and motor areas in posterior cortex which mediate
directed attention, response selection, and guide retrieval
from LTM.
A high level integrative control system was also proposed by Koechlin et al. (2003). They suggested a cascade
model, consisting of three nested levels of cognitive control. At the top of this cascade, located in rostral (anterior)
parts of the PFC, they assume an episodic control system
involved in selecting task-set representations ‘according to
events that previously occurred or to ongoing internal
goals’ (p. 1181). The view that progressively ‘higher’
neural areas support functions that are increasingly more
integrative also has been endorsed also by Fuster (2001).
He argued that a cascaded control model, assuming several
nested levels of control, with a highly integrative system at
the top, may explain neuropsychological results showing
that damage to the top system only affects tasks that require
a high degree of information integration (e.g., planning and
problem solving), but does not interfere with tasks that can
be executed at a lower level of control.
Potentially the highest level of integration is attained in
the anterior PFC (aPFC), in humans corresponding to
BA10. In a recent discussion, Ramani and Owen (2004)
pointed out this area as a likely candidate for being the
apex of a hierarchical system of PFC modules, because it is
predominantly (or even exclusively) reciprocally connected to other supramodal PFC areas. Therefore, it is in
the position to integrate everything the brain is capable of
representing. Moreover, the aPFC has anatomical characteristics (a high dendritic spine density combined with a
low density of cell bodies) that make it likely to be
involved in integration. The aPFC has been suggested to be
specialized for processing internal mental states and
introspective evaluation (Christoff and Gabrieli 2000),
monitoring successful retrieval (Ranganath and Paller
2000), management and monitoring of goals and sub-goals
(Koechlin et al. 2000; Braver and Bongliatti 2002), integration of information over time (Braver et al. 2001;
Koechlin et al. 2003; Sakai et al. 2002), and manipulation
of relational knowledge (Kroger et al. 2002).
A hierarchical model of integration based on the convergence of input from lower level specialized PFC
modules on higher integrative levels, is consistent with
claims that a distinction can be made between PFC areas
involved in simple maintenance, and others involved in
maintenance of complex information and executive processes (D’Esposito et al. 1999; Sakai et al. 2002). For
example, several findings suggest a distinction between
ventrolateral (vlPFC) and dorsolateral (dlPFC) areas in
terms of the level of abstractness of information processed
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(e.g., Koechlin et al. 2003; O’Reilly et al. 2002). Tasks
requiring processing of single words or concepts mainly
seem to involve vlPFC (e.g., Wagner et al. 2001), whereas
in more complex tasks, like sentence processing, enhanced
involvement of dlPFC has been found (e.g., Hashimoto and
Sakai 2002; Kerns et al. 2004b). A relatively high integrative role for the dlPFC (as compared to vlPFC) is also
suggested by findings showing this area to be involved in
tasks that go beyond simple maintenance, such as elaborative rehearsal (Blumenfeldt and Ranganath 2006),
processing relations between stimuli (Kroger et al. 2002),
and solving conflicts that are signalled by medial areas of
PFC (Egner and Hirsch 2005; Kerns et al. 2004a).
We endorse the idea that the PFC is hierarchically
organized, with subordinate modules being (relatively)
specialized in processing simple aspects of tasks, and
super-ordinate modules, located in dlPFC and frontopolar
cortex, being specialized in large scale integration (i.e.,
binding) of inputs from different sources. At the apex of the
supposed hierarchy, the aPFC might integrate all goal
related information over space and time (see Fig. 1). Here,
emotional and motivational evaluations (from orbitofrontal
areas), results of memory retrieval, language and rule-like
representations (from dlPFC), and anticipated action
effects, response conflicts and bodily states (from medial
PFC areas) converge. So the aPFC would be crucial for
integrating multiple forms of information in the pursuit of
general goals (e.g., Ramnani and Owen 2004), or when
information from temporally dispersed events has to be
integrated (e.g., Koechlin et al. 2003, 2006). Conversely,
the ensuing activation patterns in aPFC set the stage for
task and goal directed behavior, with control being exerted
by top-down modulation of subordinate modules. In this
aPFC
oPFC
Pm cortex
dlPFC
vlPFC
mPFC
Parietal/temporal cortex
(perception, memory, perception-action links)
input
output
Subcortical centres
Thalamus, amygdala
Basal ganglia
Fig. 1 A schematic view of the hierarchical structure of PFC. Many
details regarding sensory input and motor output processing in
subcortical centres and cerebellum are not shown. Double arrowheads indicate recurrent connections. Connections and modules
shown are not suggested to be anatomically fully complete and
accurate. aPFC anterior PFC; dlPFC dorsolateral PFC; vlPFC
ventrolateral PFC; oPFC orbital and ventromedial PFC; mPFC
medial PFC (anterior cingulate cortex); pm cortex premotor cortex
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8
way a hierarchy of top-down coordinated control is
implemented by entraining successively lower order areas,
eventually biasing perceptual processing, memory search,
and action selection.
A neurocomputational model of maintenance,
control and integration
Elsewhere (Raffone and Wolters 2001), we have presented
a model for the temporary holding in (visual) working
memory of a limited number of neural patterns, simulating
either single features or integrated objects. The model
implemented a cortical mechanism of maintenance in a
network of model neurons with biologically plausible
parameters. Although the model implemented a visual
working memory system, the principles may be applicable
to any form of information or type of working memory.
In the model WM was assumed to be based on recurrent
connections between IT cortex containing representations
of objects or features, and corresponding neurons in PFC.
The IT representations were modelled as strongly associated neural assemblies that generate synchronized firing
patterns when activated by external input. The simultaneous activation of independent assemblies in IT causes
competition via inhibitory interneurons. Due to the neuron
characteristics, this leads to desynchronization among the
activation patterns of competing assemblies resulting in a
sustained phase-locked activation of multiple assemblies
over time.
Maintenance in cortical circuits of visual working
memory was shown to be possible in terms of oscillatory
reverberations between PFC and IT modules. Firing rate
oscillations induced during stimulus presentation were
maintained after stimulus offset by active feedback from
prefrontal areas. Neurophysiological plausible model
parameters enforced a limitation of about three to four
independent assemblies that could be maintained in this
way. This number closely coincides with recent estimates
of the maintenance capacity of WM (e.g., Cowan 2001).
The same mechanism that optimizes coherent pattern
segregation, also poses a limit to the number of assemblies
(about four) that can concurrently reverberate. The model
thus indicated that selective synchronization and desynchronization of feedback-based oscillatory reverberations
creates a suitable medium for a visual working memory.
Simulations showed that the model was able to explain
both the existence of severe limits in the number of
assemblies (stimuli) that can be held (e.g., Luck and Vogel
1997; Luck and Beach 1998), and the absence of a limit on
the size of assemblies, i.e., representing either simple
stimuli or complex chunks (e.g., Ericsson and Delaney
1999). We introduced the concept of ‘chunking fields’ to
123
Cogn Process (2008) 9:1–17
account for the creation of more complex neural assemblies
(e.g., higher order information units or chunks) through
previous Hebbian learning (e.g., Hummel and Biederman
1992; Singer 1995). The model can potentially account
for different degrees of within-object feature integration
(Olson and Jiang 2002) in terms of graded synchrony
between neurons coding for features of the same object.
Here, we will explore an extension of the model of
Raffone and Wolters (2001), simulating not only maintenance, but also a selective attention mechanism and a
particular characteristic of an integration mechanism. The
network architecture presented here to model these functions, is composed of three modules, which we assume to
correspond to an IT module, a ventrolateral prefrontal
module (vlPFC), and a dorsolateral prefrontal module
(dlPFC), respectively (see Fig. 2). We assume that visual
features are coded by individual assemblies of neurons in
IT, which are ‘‘matched’’ to one assembly in vlPFC in a
recurrent circuit. Moreover, we assume that different subsets of four vlPFC assemblies coding for given visual
chunks, are bi-directionally connected to dlPFC assemblies
(one for each set of four vlPFC assemblies).
In the IT module, strong connections are implemented
within and weak connections between assemblies coding
different ‘stimuli’. We also implemented a global inhibition (competition) mechanism between IT assemblies. This
circuitry, in which a given assembly is inhibited by the
firing of the other assemblies in the IT module, mediates an
active desynchronization mechanism.
Stimulus input to the IT module is given by stochastic
spike trains from (not explicitly modeled) lower visual
areas, during a limited onset-offset period. During stimulus
presentation all stimulus specific IT neurons received a
high frequent train of spikes as input, which was added to a
continuous stochastic low frequency spike input to all IT
neurons. The vlPFC module has a coding structure
‘‘matching’’ the structure of the IT module (see Fig. 2).
Each IT assembly is recurrently connected with one vlPFC
assembly, with a signaling delay of 15 ms in both directions. Although we implemented a monosynaptic feedback
circuit, in real cortical networks this delayed feedback is
likely to be mediated by multisynaptic circuitries of the
‘synfire’ type, with a stable transmission along multiple
diverging/converging synaptic links (Abeles et al. 1993a,
b; see also Villa and Fuster 1992). In vlPFC and dlPFC
there are no inhibitory neurons and no inter-assembly
connections.
Although stimuli are coded by large assemblies of
neurons in real cortical networks, in the present model
individual stimuli are coded by single neurons. Our simulations will show that the functional processes we have
investigated previously at the level of assemblies of neurons (Raffone and Wolters 2001) also hold at the level of
Cogn Process (2008) 9:1–17
9
Fig. 2 Scheme of the cortical network architecture. In the IT module,
20 neural assemblies code for 20 hypothetical visual features or
separate representational elements. The figure shows the case with
five four-feature chunks, with synchronizing connections between the
assemblies coding the features of the same object (depicted as
diamond-like configurations). The IT module also comprises an
assembly of globally inhibitory neurons, which are implicitly
modeled through inhibitory postsynaptic potentials (IPSPs). The
vlPFC module is a set of 20 assemblies of neurons, with a coding
structure ‘‘matching’’ the IT module structure. For simplicity, each IT
assembly is recurrently connected with one vlPFC assembly, with a
signaling delay of 15 ms in both directions. The vlPFC assemblies
coding for a given chunk are reciprocally connected with a dlPFC
assembly, which propagates synchronous firing before Hebbian
learning takes place in the IT module
single neurons, thus pointing out the robustness of the
observed effects. The use of single neurons instead of
assemblies of tens of neurons, is also motivated by the sake
of running relatively fast simulations with a smaller
time-step in numerical integration of neuronal equations
(higher computational accuracy) than in our previous
investigations.
The model, of course, is very simplified with respect to
the complexity of the real cortical networks. Feedback from
the vlPFC module does no more than maintaining the
oscillatory state of IT assemblies after the stimulus offset.
More realistic network versions might include ‘‘closed’’
reverberatory circuits within prefrontal areas, making prefrontal assemblies independent from the IT assemblies in
maintaining the delay activity. This would allow modality
shifts by activating other prefrontal assemblies, which in
turn would trigger reverberatory circuitries in lower cortical
areas, in the absence of actual physical stimulation. Moreover, we used a simplified one-to-one matching between IT
and vlPFC assemblies, whereas it seems more realistic to
assume that prefrontal assemblies are relatively non-specific, thus being connected to multiple sets of neurons in
posterior areas. Instead of the one-to-one matching of
assemblies, dlPFC neurons might simply send back activation to all vlPFC neurons, and these in turn to all IT
neurons from which they receive activation. However, the
present simple architecture is sufficient for demonstrating
the functional principles we have specified earlier.
Simulations
Simulating maintenance in working memory
We first replicated our earlier results (Raffone and Wolters
2001) with stimulus features coded by individual neurons,
rather than by sets of neurons, as well as with a higher
temporal resolution of the simulations (computational
accuracy) and a more realistic global inhibition mechanism. In this simulation the weights of the synchronizing
inter-neuron connections in IT were set to zero, so all
neurons code for different independent features. As shown
in Fig. 3, about four elements are retained in the network
after offset of the stimuli, which is in accordance with
actual capacity limitations (e.g., Cowan 2001; Luck and
Vogel 1997). The frequency of the reverberatory oscillations is approximately 30 Hz. Figure 3a and b show the
retention of three out of four retained items, Fig. 3c and d
the retention of four out of four, and Fig. 3e and f the
retention of four out of eight items. Note the automatic
phase-segregation due to the mutual inhibition desynchronizing effects (see Raffone and Wolters 2001, for a
systematic investigation of retention capacity with different
network parameters). The missed retention of some items
(Figs. 3a, b, e, f) is due to the transient inhibition propagated by the firing of other competing neurons in IT. This
inhibition ‘‘counteracts’’ the feedback input from the matched vlPFC neuron, thus interrupting the reverberatory
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10
Cogn Process (2008) 9:1–17
Fig. 3 Limited retention
capacity related to between-item
segregation. Due to mutual
inhibitory activity, the neuronal
action potentials (spikes)
become spaced in the oscillatory
phase, thus allowing a markedly
discriminative oscillatory
reverberation and retention of
the coded items. Often not all
reverberations survived the
stimulus offset. The panels
show the fluctuating membrane
potentials of neurons, as well as
their spikes by short vertical
bars above the membrane
potentials. The panels a, c,
and e shows the dynamic
behavior of IT neurons, and the
panels b, d and f the responses
of respectively matched vlPFC
neurons. In (a, b), three out of
the four neurons remained
active. In (c, d), all the
reverberations remained active,
with all the four items being
retained. In (e, f), four out of
eight neurons maintained their
sustained oscillation. Note that
the oscillatory reverberations
tend to be optimally spaced in
the phase-lag, depending on the
number of reverberating
neurons
cycle after stimulus offset. Such an interruption is more
likely to occur with multiple reverberatory activities and
stronger inhibition, as in that case the probability of the
simultaneous arrival of strong inhibition and feedback
signals increases.
In this simulation, we did not replicate our previous
demonstration (Raffone and Wolters 2001) that the
123
capacity of the reverberatory circuit is independent of the
size of the IT representations. As we showed there, larger
cell assemblies of connected neurons with fast-signaling
positive weights, quickly synchronize and behave as single
units. We demonstrated that the maintenance capacity of
our model is not a function of the size of a cell assembly,
but of the presence and strength of associations between
Cogn Process (2008) 9:1–17
the units of an assembly. This probably mimics the fact that
working memory capacity is not a function of the amount
of information per se, but of the level of organization or
chunking of the material to be maintained (Miller 1956).
Simulating attentional control in working memory
Given the severe limitation of the capacity of working
memory, neural mechanisms are necessary to restrict
access to it, depending on the behavioral relevance of the
information. As shown by Asaad et al. (1998, 2000), prefrontal neurons of monkeys performing a ‘delayedmatching-to-sample’ task exhibited a higher firing rate
when they coded for a rule-defined target-object. Such
neurons were suggested to be involved in both the selection
and maintenance of behaviorally relevant information.
To model a selective attention mechanism by which
selection and retention is controlled by working memory
cortical circuits, a top-down input to IT neurons has to be
supplied by a prefrontal source involved in supervisory
control rather than in mere information maintenance. A
similar assumption was made in a model for attentional
control by Deco and Rolls (2003). Such a mechanism
implements the top-down biasing of the competition
between neural assemblies in posterior cortex as suggested
by Desimone and Duncan (1995). In our model this
supervisory control signal could be supplied through the
dlPFC and vlPFC modules.
We first considered an additive (voltage-independent)
input to a subset of four (the bottom four in Fig. 4a) out of
eight independent IT neurons all receiving external input.
This additional input was modeled in terms of additional
spikes with an excitatory postsynaptic potential (EPSP)
amplitude equal to the external input signals and a spiking
probability equal to 0.04. As can be seen in Fig. 4a, the
four items receiving top-down input exhibited a higher
(subthreshold) membrane potential before the stimulus
onset. This top-down bias was crucial in selecting the items
to be retained in terms of reverberatory oscillations. All
four biased (and one unbiased) item were maintained after
stimulus offset.
We also implemented a voltage-dependent modulatory
input to IT neurons. A voltage-dependent gain effect simulates the possible role of NMDA receptors (i.e., receptors
that activate a neuron only if a signal arrives at an already
depolarized synapse). NMDA-based gain effects are suggested to play a crucial role in cognitive coordination and
control (Phillips and Silverstein 2003, see also Raffone
et al. 2003), and such gain effects are often modelled as a
multiplication of input signals. This multiplicative effect
was modeled in terms of the product of the voltage-independent external input (VI) and voltage-dependent
11
top-down signals (VD) with amplitude equal to 0.5/5, and
spiking probability equal to 0.25, according to the following equations
Neti ¼ VIi 1 þ VDi
VDi ¼ VDi ðui VTÞ
ð1Þ
if ui VT
else VDi ¼ 0
ð2Þ
Following suggestions of Tononi et al. (1992), the product
of equation (2) was set equal to 0 when a membrane
potential (ui) was less than a voltage threshold VT (set
equal to 0.5). In that case, only a voltage-independent
stimulus-related input determines the net input to the
neuron, without any amplification.
As can be inferred from Fig. 4b, the attentional bias
(attentional input for four out of eight items) in the voltage
dependent condition is expressed after the stimulus-input
onset, in terms of an amplification effect. Also in this
simulation all four biased (and one unbiased) items were
maintained after stimulus offset. We believe that voltagedependent input to high-level areas of the visual system
may enable an effective top-down control mechanism,
since it produces strong amplification effects, but only in
the presence of relevant stimulus-input (see also Hirsch and
Gilbert 1991; Tononi et al. 1992, about voltage-dependent
signaling in the visual system). This multiplicative signaling effect would prevent spurious activation of
representations in the visual system in the absence of any
bottom-up sensory evidence.
A series of follow-up simulations showed that the
probability of item retention depends on the relative biasing top-down input for a given item. A strong attentional
input to one or two items may ‘narrow’ visual working
memory capacity to one or two highly focused elements,
due to the higher firing rate (e.g. bursting) of the neurons
coding for the focused items.
Mechanisms for integration in working memory
Any domain-specific account of functions or representations in the brain ultimately implies a binding problem,
since the specialized neural representations need to be
dynamically bound to enable the creation (and a conscious
readout) of coherent and integrated representations of
complex events. These binding processes may occur in
terms of the selective synchronization of reverberating cell
assemblies, as shown in our simulations. In this framework,
segregated neural representations may be bound via reciprocal synchronizing connections that originate within the
prefrontal cortex (for a discussion of this and other forms
of binding, see Murre et al. 2006; see also Engel et al.
2001; Varela et al. 2001).
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Cogn Process (2008) 9:1–17
Fig. 4 Attentional modulation
of reverberatory maintenance.
Four out of the five maintained
activities are of neurons (the
first four neurons from below in
the panels) the activities of
which are selectively enhanced
either by additive (voltageindependent) modulation (a),
or by voltage-dependent
(multiplicative) signals (b).
Note that with additive
modulation the membrane
potentials of the four ‘focused’
neurons are higher before the
stimulus onset (a), whereas the
amplification mostly takes place
after stimulus onset in the
voltage-dependent modulation
case (b)
In our model, dlPFC can play a crucial role in what we
call ‘‘associative control’’, i.e. in selecting meaningful or
currently salient conjunctions of otherwise separated representational elements held in a temporarily active state
within vlPFC-IT circuits. We have shown that reciprocal
fast signaling between a dlPF neuron and its corresponding
vlPFC neurons may ‘‘entrain’’ all these vlPFC neurons
coding for different features or units to fire in a nearly
synchronous manner. This synchronous firing is propagated
to IT neurons, which in turn fire almost coincidently before
123
the action of the mutual inhibitory signals within the IT
module (Fig. 5a, b). We suggest that such a top-down
controlled selective synchronization of independent representations is a likely candidate of the process underlying integration in PFC. This mechanism ensures the
simultaneous and coherent activation of independent representations that may be widely dispersed over various
brain areas. It may cause, for instance, the large-scale taskdependent synchronization in the gamma band, as observed
by Rodriguez et al. (1999).
Cogn Process (2008) 9:1–17
13
Fig. 5 Associative control on
vlPFC neuronal activities by
dlPFC neurons. The panels a, c,
and e shows the dynamic
behavior of IT neurons, and the
panels b, d and f the responses
of respectively matched vlPFC
neurons. As shown in panels
a, b, inter-neuron
synchronization of the four
vlPFC neurons is induced by a
dlPFC neuron (activity not
shown), and is then propagated
to the matched IT neurons. As
shown in panels c, d, the joint
effect of dlPFC feedback and
Hebbian associative signals in
IT may induce a higher firing
rate of neurons in IT and vlPFC.
Panels e, f show that a stable
synchronization of IT and
vlPFC neurons is observed after
Hebbian strengthening of IT
chunking synapses, with
feedback from the dlPFC
neuron being switched-off
The same mechanism may allow Hebbian learning to
occur between meaningful or behaviorally-salient conjunctions of independent elements (see Miltner et al. 1999).
In order to account for the retention of integrated units in
visual working memory, in previous simulations we
defined units by assuming already established chunking
fields, i.e. pre-existing associative connections between IT
neurons, coding different features of the same unit. A
major problem, however, is how ‘chunking fields’ or
integrated representations in long-term memory are
formed, i.e. how associative Hebbian learning may take
place in the simulated cortical circuits of (visual) working
memory. This learning process should operate against the
phase-segregation tendency due to mutual desynchronizing
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14
inhibition. In the present model, the dlPFC module may
perform this temporary synchronization control processes.
In a series of simulations, we used the entrainment of
vlPFC and IT neurons by a top-down dlPFC signal and
combined it with a timing-dependent Hebbian learning rule
in IT (simulating timing-dependent plasticity of synapses,
see e.g., Körding and König 2000; Song et al. 2000, see
Fig. 5c, d). These simulations showed that the synchronous
firing in IT, induced by a top-down recurrent signal, causes
the weights between the IT neurons to increase, resulting in
a ‘chunked’ representation of initially independent features. As synaptic weights increased according to the
timing-dependent learning rule, the synchronization among
related IT neurons becomes gradually less dependent on
the feedback action of dlPFC neurons on related vlPFC
neurons, ultimately leading to an ‘‘automatic’’ withinchunk integration within the IT module, in the absence of
any control feedback from the dlPFC module (Fig. 5e, f).
Control simulations with the same model and learning
parameters, showed that a relatively high number of
simultaneous presentations of initially independent elements, may slowly give rise to novel chunks with a
variable degree of stability, even in the absence of feedback from dlPFC to vlPFC. This slow chunk-learning
process may be related to an implicit learning process,
rather than the more rapid process of controlled chunk
formation by feedback from dlPFC.
Discussion
Miller and Cohen (2001) concluded that PFC is critical
when top-down control of processing is needed for the
guidance of behavior by internal states or intentions. In
this paper we conjecture that such control is possible by
assuming that the PFC is a brain system that is reciprocally connected to posterior parts of the brain and
therefore capable of modulating direct perception-action
relations. We argued that three interdependent functions
are required for such control: maintenance of activation
patterns even in the face of distraction, large scale integration of information from different sources, and topdown selective attention. We also showed that these
functions can be simulated, at least in principle, with a
biologically plausible model assuming recurrent connections and a hierarchical structure of PFC. Importantly, the
functions were simulated using a single framework consisting of a hierarchy of recurrently connected modules.
This framework proved capable of maintaining a limited
number of mutually desynchronized patterns, of controlling selective attention by top-down modulatory signals,
and of high level integration by top-down synchronization
of the activity of independent patterns.
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Cogn Process (2008) 9:1–17
Of course, the model suggested here is extremely simplified and it does not capture many of the complexities and
intricate details of the real system. For instance, we did not
try to incorporate modulatory effects of neurotransmitters
and we have not discussed the many possible interactions
of the PFC with subcortical systems, like the basal ganglia
and amygdala, and the cerebellum. It may also be noted
that we discussed, but did not try to incorporate, the possibly important role of a dopamine gating system for
maintaining high level integrative activation states
(O’Reilly et al. 1999, 2002).
One of our aims to develop the model was to try to find
out how the structural and functional characteristics of the
PFC may explain the central executive component of
working memory assumed by Baddeley (2003). In the
latest version of this model an additional component, an
episodic buffer, was proposed. This component was
thought necessary to explain interactions between working
memory and LTM, for example to explain how chunking
may supplement the capacity for immediate serial recall. A
continuous interaction between working memory and LTM
is a basic assumption in our model, and we have shown that
chunking is a natural consequence of this interaction.
According to Baddeley (2003), the episodic buffer
allows information from different systems to be integrated,
and it may be regarded as the ‘storage component of the
executive’. Moreover, Baddeley suggested that the buffer
is a separate temporary store in which long-term memory
information is downloaded in order to be manipulated and
used for creating new representations. In our view, the
structure of such an episodic buffer would actually be a
hierarchy of PFC modules maintaining and integrating
information at successively higher levels. Information is
not copied and downloaded in PFC, but selected task-relevant perceptual and memory information in posterior
cortical areas is kept in an active state by recurrent loops.
We assume that at the highest integration levels, presumably located in anterior and dorsolateral areas of PFC,
information can be integrated over space and time. At this
level, general goals, and plans to achieve them, would be
generated. We suggest that this may be the implementation
of an episodic buffer and even of a central executive. The
creation of short-term and long-term goals at high levels of
integration, allows to control behavior by top-down regulation of activation in subordinate modules and eventually
by modulating perception-action systems. Current goals
and tasks would have to be maintained here in order to
control ongoing behavior. More distant goals, however,
may be stored in LTM to be retrieved later on.
We do not imply that control is always hierarchical.
Because the PFC is assumed to be a modulatory system,
simple tasks may be executed automatically without
PFC involvement. Only controlled processing requires
Cogn Process (2008) 9:1–17
modulation of automatic processes by PFC feedback. Also
here, however, control does not need to be completely
hierarchical. Depending on the type and complexity of a
task, control may be executed by specialized modules at
subordinate levels. Automatic processing and controlled
processing under the guidance of subordinate levels in
PFC, is a requirement to free higher integrative areas to
engage in the kind of simulated actions and perceptions,
and using anticipated outcomes, that are the contents of
conscious thought and planning.
So there are things we can do automatically, using
previously established associations between perceptual
events and effective responses, and there are things we can
do in a controlled way, using the possibility of PFC to
modulate and thus control perception and action. The
possibility of PFC to work off-line, to use and integrate all
present and past knowledge for creating virtual worlds and
for making plans and carry them out if conditions are
appropriate, has generated a tremendously flexible potential for control. The model we propose is vastly insufficient
to simulate such cognitive feats that we engage in daily.
We believe, however, that the principles for a controlling
system we have suggested may be a first step towards a
better understanding of what is until now a cognitive Terra
Incognita.
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