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Consciousness
and
Cognition
Consciousness and Cognition xxx (2004) xxx–xxx
www.elsevier.com/locate/concog
Identifying neural correlates of consciousness:
The state space approach
Juergen Fell*
Department of Epileptology, University of Bonn, Sigmund-Freud Street 25, D-53105 Bonn, Germany
Received 22 April 2004
Available online
Abstract
This article sketches an idealized strategy for the identification of neural correlates of consciousness. The
proposed strategy is based on a state space approach originating from the analysis of dynamical systems.
The article then focuses on one constituent of consciousness, phenomenal awareness. Several rudimentary
requirements for the identification of neural correlates of phenomenal awareness are suggested. These
requirements are related to empirical data on selective attention, on completely intrinsic selection and on
globally unconscious states. As an example, neuroscientific findings on synchronized c activity are categorized according to these requirements.
2004 Elsevier Inc. All rights reserved.
Keywords: Neural correlate; Consciousness; State space; Phenomenal awareness; Attention; EEG; c-Activity
1. Introduction
During recent years, neuroscientific research has received increasing attention within the scientific community and the public. In parallel, interest in neuroscientific findings and methodology increased within the philosophical community, yielding to the establishment of the new discipline of
neurophilosophy. Interdisciplinary debate mainly focuses on the problem of finding neural corre*
Fax: +49 228 287 6294.
E-mail address: [email protected].
1053-8100/$ - see front matter 2004 Elsevier Inc. All rights reserved.
doi:10.1016/j.concog.2004.07.001
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lates of consciousness (e.g., Crick & Koch, 1990; Metzinger, 2000). The term neural correlate of
consciousness (NCC) characterizes neural systems and properties of that systems, which are associated with conscious mental states (see Section 2.2). Within the framework of search for NCCs the
‘‘hard problem’’ (Chalmers, 1995), if and how these neural correlates actually produce consciousness, is left untouched. The methodological objective of the present article is to sketch an idealized
strategy for the identification of NCCs. The proposed strategy is based on a state space approach
originating from the analysis of dynamical systems (Section 3). The present article will then focus
on one specific constituent of consciousness, phenomenal awareness. Because of several unsolved
methodological problems associated with the state space strategy (Section 3.4), a rudimentary approach for the identification of neural correlates of phenomenal awareness will be suggested (Section 3.5). One of the most promising candidates for a variable constituting a neural correlate of
phenomenal awareness is synchronized EEG c activity. As an example, neuroscientific findings
on synchronized c activity will be categorized according to the proposed requirements (Section 4).
2. Terminology
2.1. Consciousness, phenomenal awareness, and attention
The term consciousness is used in many fashions. Analogous terms in other languages or for
different cultural backgrounds again have another spectrum of meanings (e.g., Wilkes, 1988).
Here, no complete survey of the historical roots and evolution of the term consciousness or the
analogous German term ‘‘Bewusstsein’’ will be given (see, e.g., Metzinger & Schumacher,
1999). Within the western tradition, a central meaning of the term consciousness, dating back
to Descartes, is notion of oneÕs own mental state. As John Locke phrased, consciousness in this
context is ‘‘the perception (today we would rather say awareness) of what passes in a manÕs own
mind’’ (Locke, 1690). In the following, the term consciousness will accordingly be understood as a
property of mental states and processes. This property can be denoted as awareness of certain aspects of those mental states and processes. Related to those aspects, there are different kinds of
awareness of mental states, which contribute to consciousness.
First of all, there is phenomenal awareness,1 which has been described as the experience of
‘‘what is it like to be’’ in a certain mental state (Nagel, 1974). In other words, phenomenal awareness designates the subjective character of experience (Metzinger, 1996). According to the different
kinds of representational content, phenomenal awareness may be further differentiated into sensory awareness, awareness of motor activity, awareness of thoughts, awareness of feelings, etc.
Besides phenomenal awareness other kinds of awareness contributing to consciousness are, for
example, self-awareness or the meta-awareness about being in a certain mental state.2 The following reflections will be concerned with phenomenal awareness, in particular sensory awareness.
However, the widely established term neural correlate of consciousness (NCC) will still be used,
although its meaning will be confined to phenomenal awareness.
1
For the purpose of the present article the terms phenomenal awareness and phenomenal experience will be treated
as synonymous.
2
Allowing for example differentiation between regular and lucid dreaming (LaBerge & Ornstein, 1985).
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3
In the context of the search for NCCs, it is important to clarify how information processing
within the brain is related to phenomenal awareness. It seems safe to state that there is no phenomenal awareness without information processing within the brain. The mechanism that controls which brain processes enter awareness and which do not, is attention.3 According to
William James (1890) attention is ‘‘the taking possession by the mind in clear and vivid form,
of one out of what seem several simultaneously possible objects or trains of thought.’’ In general,
brain processes, which are at the focus of attention, reach phenomenal awareness (e.g., Näätänen,
1992; Velmans, 1991). With regard to perception, the mechanism of focused or selective attention
allows selection of certain sensory items, which thereby become part of the contents of phenomenal awareness (e.g., Desimone & Duncan, 1995; Driver, 2001).4 In a strict sense, one becomes
aware of a stimulus only slightly after one has selected and processed it, and aware of oneÕs
one response only after it has been initiated (e.g., Libet, 1985; Rugg & Coles, 1995).
Perceptual selection as accomplished by selective attention is triggered by the salience of external stimuli (bottom-up), as well as modulated by task relevant features of those stimuli (topdown) (e.g., Itti & Koch, 2001). However, there are a few cases, where perceptual selection occurs
completely intrinsically in spite of non-varying external stimulation, for example, under conditions of binocular rivalry, multistable perception, or when stimuli are presented at the perception
threshold (see Section 4.1). During the normal waking state the attentional focus in general does
not disappear, i.e., there are always some perceptual items within the focus of attention. There are,
however, some ‘‘globally unconscious’’ mental states, which are not accompanied by phenomenal
awareness at all, for example the portion of deep sleep that is completely unconscious (see Section
4.2). However, during those mental states, not only is phenomenal awareness absent, but also
information processing associated with the waking state.
2.2. Neural correlates of consciousness
NCCs are neural systems and properties of those systems, which are correlated with conscious
mental states. This correlation implicates no inference about a causal relationship. The search for
neural correlates therefore determines a research strategy, which is not concerned with the ontological problem of how NCCs actually produce conscious mental states (e.g., Cleeremans & Haynes,
1999). Thus, the ‘‘hard problem’’ of bridging the explanatory gap between neural properties and
phenomenal states is not tackled by the NCC research program (Chalmers, 1995; Levine, 1983).
In a strict sense, the NCC program does not even infer that NCCs actually produce conscious mental states. The aim of the NCC program is confined to identification and isolation of neural systems
3
This is the predominant view in cognitive psychology. Recently, an alternative model has been suggested (Lamme,
2003). According to this model we are conscious of many inputs but, without attention, this conscious experience
cannot be reported and is quickly erased and forgotten, i.e., attention is needed for conscious report, as well as to
transfer the inputs into working memory. However, it has not been demonstrated yet in how far the hypothesis of
conscious experience, which in case of absence of attention is instantaneously forgotten, would be practically relevant or
whether it is falsifiable at all.
4
Of course, as a prerequisite the stimuli have to be suitable for sensory perception, for instance, optical stimuli have
to be in the visual electromagnetic spectrum. In some cases, even such stimuli do not enter awareness, although they are
at the focus of attention. One example is visual masking, i.e., stimulus processing is affected by presentation of
neighboring or immediately following stimuli (e.g., Enns & Di Lollo, 2000).
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and states of those systems, under which conscious mental states occur. The NCC approach does
not offer a solution to the question of how the correlation between neural and phenomenal states
can be explained. Thus, with respect to the different positions of the mind-brain problem, such as
psychophysical parallelism, identity theory, etc., the NCC program holds a neutral position.
A definition of a NCC, which may perhaps be commonly accepted, is the following: a neural
correlate of consciousness is a neural system (S) plus a certain state of this system (NS), which
together are correlated with a certain state of consciousness (C). In mathematical notation this
definition may be written as5:
NCC S NS jNS C
In the following, further clarification will be given for the individual terms of this definition and
the meaning of ‘‘to be correlated.’’
A neural system S may be the human brain, an animal brain or an artificial brain and its compartments. S can be thought as a vector, where Si denotes the different constituents of the neural
system. From a physical point of view, brains are basically dynamical systems. Thus, a state of a
neural system may best be described in accordance to the definition of dynamical systems (e.g.,
Beer, 2000; van Gelder & Port, 1995). In this sense, a neural system can be characterized by a
set of state variables Nj. In principle, each state variable may apply to each of the constituents
Si of the neural system, so that N is a vector with the components Ni, j.6 Similarly, a state of consciousness may be described by a vector C consisting of the components Ck. In order to allow
empirical investigation, we may moreover postulate that each of the components of both state
variables corresponds in a one-to-one relation or a chain of one-to-one relations to a physical
measurement variable (observable) or a operationalized psychological variable, respectively. A
state of a neural system and a state of consciousness can now be specified by certain values of
the components of the state vectors N and C.
Still, the above definition can be regarded as too extensive, since one typically is not interested
in all constituents of a neural system and all neural states that are correlated to a state of consciousness, but more specifically in those particular constituents and states, which are necessary
for the occurrence of a certain state of consciousness. As Chalmers (2000) has pointed out, necessity nevertheless may be a too restrictive condition. For example, if two different neural systems
would be both associated with the same state of consciousness, they could not both be denoted to
be necessary for this state.7 Chalmers therefore has proposed minimal sufficiency as a more adequate requirement for a NCC. With this restriction, system constituents and systems states are
excluded if they are redundant for a state of consciousness to occur. In a more specific sense, a
NCC may thus be defined as the minimally sufficient neural system plus a state of this system,
which are correlated with a certain state of consciousness.
NCC S NS jNS C ^ S NS minimally sufficient for C
5
The meaning of the symbols is: ” is defined as; ¯ combination; j so that; to be correlated with.
In the case of neural measures quantifying the interaction between two constituents of the neural system, the same
formalism may be used. However, the index i then designates not one, but a pair of constituents of the neural system.
7
Of course, it is an open question as to whether two different neural or artificial systems can indeed exhibit exactly
the same state of consciousness.
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5
However, minimal sufficiency is a requirement which is rather difficult to validate experimentally. Even in lesion studies, i.e., in case of experimental separation or disfacilitation of brain
areas, it is rather difficult or impossible to guarantee that the remaining areas of interest still have
an appropriate operational embedding, for instance that they still receive the required input.
Thus, at the time being minimal sufficiency may be regarded rather as a theoretical condition, than
as a practically relevant requirement (see Section 3.4).
After having specified a working definition for a NCC, it remains, however, unclear how to
practically proceed in order to establish evidence for a NCC. In particular, the questions have
to be answered as to how the variables characterizing neural states and states of consciousness
should be chosen, and how a correlation between neural states and states of consciousness may
be detected and proven. These questions will be treated in the following section.
3. How can NCCs be identified?
3.1. The state space strategy
Unlike CPUs of computers, which function with discrete processing steps determined by the
clock rate, brain dynamics evolve continuously in time (van Gelder & Port, 1995). As a result,
brains operate on a range of different timescales, which for example are related to the periods
of the dominant brain rhythms. Thus, we may introduce time as a variable into our definition
of a neural correlate of consciousness.
NCC S NS ðtÞjNS ðtÞ CðtÞ
Although neural action potentials represent a quasi-digital aspect of brain dynamics, brain
states in general change continuously. Physical systems, which change continuously in state
and time are described by dynamical systems theory. Since brain dynamics are essentially non-linear (e.g., Fell, Kaplan, Darkhovsky, & Röschke, 2000), methods developed for the analysis of
non-linear dynamical systems may in particular be useful for the characterization of neural states.
The essential diagnostic tool of non-linear dynamics is the state space, meaning a multidimensional representation of system states. The state space is defined by the set of all states, which
can be reached by a certain class of systems. Hence, each dimension corresponds to a different
system variable. The system variables are incorporated in the state vector, so that each component
of the state vector stands for another variable.
Such a state space approach has for example been proposed by Churchland (1989, 1995) for the
characterization of neural states related to sensory processing. Color perception has been described by three-dimensional vectors in a state space, where each dimension corresponds to activity rates in one of three classes of photoreceptors present in the human retina. With regard to
artificial neural networks a state space approach has for instance been suggested for the representation of abstract semantic concepts (Brown, Evans, Sales, & Aleksander, 1997). Hereby, the
affinity of different abstract concepts is expressed by their proximity in phase space. Allan Hobson
and colleagues (Hobson, 1995, 2001; Hobson, Pace-Schott, & Stickgold, 2000) have introduced a
three-dimensional state space model (the so-called AIM model) for the classification of mental
states during sleep and wakefulness, as well as altered states of consciousness. In this model
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the system variables are given by the activation, i.e., the information processing capacity of the
system, the information flow, i.e., the degree to which the information processed comes from
the outside world, and the way in which the information is processed (mode). Waking state
and REM sleep for example would be characterized by similarly high activation levels, but by different levels for information flow and by different processing modes. In contrast to the more general approach described in the present article the three variables of the AIM model are thought to
represent the same dimensions in both, the psychological (phenomenal) and the neurobiological
(neural) domain. The activation variable would for instance stand for the complexity of cognitive
processing in the psychological domain, as well as for the firing level in the neural domain.
Keeping with the aforementioned more general strategy, we may construct a neural and a phenomenal state space with different neural and phenomenal state variables. The state vectors are
given by the formally introduced neural and phenomenal states evolving in time. For example,
in the case of three constituents of a neural system and two state variables the neural state vectors
for the time points ti (i = 1, . . . , n) would look like:
Nðti Þ ¼ ðN1;1 ðti Þ; N1;2 ðti Þ; N2;1 ðti Þ; N2;2 ðti Þ; N3;1 ðti Þ; N3;2 ðti ÞÞ:
Similarly, state vectors describing states of consciousness for the same time points ti can be constructed. Further on, the focus will be on phenomenal awareness and phenomenal states as one
aspect of consciousness, which nevertheless itself may be composed of several variables. For
example, with 4 variables defining the phenomenal state, the state vectors would be:
Cðti Þ ¼ ðC1 ðti Þ; C2 ðti Þ; C3 ðti Þ; C4 ðti ÞÞ:
Each vector-point in state space now designates a momentary neural or phenomenal state,
respectively. The set of states reached from a certain initial state is called the trajectory. So-called
limit sets describe the long-term behavior of the trajectories in state space. Since neural and phenomenal state spaces cannot be regarded as dissipative (i.e., non-energy-preserving), these limit sets
are not necessarily equivalent to attractors, which can be observed under the condition that the volume occupied by the states in state space shrinks as time goes towards infinity. Thus, the evolution
of states in neural and phenomenal state spaces may be better called limit sets or just sets of states.
According to van Gelder (1998) two hypotheses underlying the dynamical approach in cognitive science must be differentiated. The ‘‘nature hypothesis’’ claims that cognitive systems described within the framework of state space are dynamical systems, i.e., the nature hypothesis
implicates an ontological statement. This hypothesis can be accepted as true with regard to the
neural state space, since brains without doubt, are physical systems. However, this hypothesis
is questionable with regard to the phenomenal domain. The ‘‘knowledge hypothesis,’’ on the other
hand, claims that cognitive science can and should be understood by a dynamical systems approach, i.e., the knowledge hypothesis incorporates an epistemological statement. The present
proposal is in accord with this hypothesis, in the sense that the state space approach may be a
useful tool in the search for NCCs, although it is not justified in a strict sense to speak of dynamical systems with regard to both the neural and the phenomenal domain.
3.2. Psychoneural homeomorphism
What is the exact form of the correlation between neural and phenomenal states? Psychophysiological research is based on the implicit model of a one-to-one correspondence between neural
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and phenomenal states, an idea, which has been termed psychoneural isomorphism (e.g., Scheerer,
1994).8 Mathematically, the term isomorphism implies not only a one-to-one correspondence, but
also a linear transformation between both domains, which is not obvious. Therefore, in a strict
sense psychoneural one-to-one correspondence is a more correct characterization. The hypothesis
of a psychoneural one-to-one correspondence goes back to Fechner (1860) and Mach (1866) and
has received particular appreciation by the protagonists of Gestalt psychology (e.g., Köhler,
1929). Although the alternatives, that one neural state may be correlated with two (or more) different phenomenal states or, that one phenomenal state may be correlated with two (or more) different neural states appear awkward, they cannot be rejected per se. Thus, as Köhler (1929) has
emphasized, psychoneural one-to-one correspondence is not an axiom, but a hypothesis, which is
not immune against falsification and has to be validated by empirical tests. However, since the
identification of neural and phenomenal states depends on the chosen description, for example
on the construction of state spaces, it may be impossible to unequivocally prove, whether oneto-one correspondence is valid or not. Because of this methodological problem and following
‘‘OccamÕs razor,’’9 it seems advisable to start with psychoneural one-to-one correspondence as
the most economical hypothesis.
Yet, one can think of more intuitive requirements for the relation between neural and phenomenal states, than just one-to-one correspondence. These requirements were already formulated by
Müller (1896) in his psychophysical axioms (see also Cleeremans & Haynes, 1999): ‘‘1. Every state
of consciousness is based upon a material process, a so-called psychophysical process, which is a
prerequisite of the occurrence of this state of consciousness. . . 2. To an identity, similarity, difference in the constitution of sensations. . . corresponds an identity, similarity, difference of the structure of the psychophysiological processes, and vice versa. . . 3. If the changes through which a
sensation passes have the same direction, or if the differences which exist between series of sensations are of the same direction, then the changes through which the sensation passes, or the differences of the given psychophysical process have the same direction. . .’’ Besides the one-to-one
correspondence, MüllerÕs postulates implicate, that, if there are continuous transitions between
phenomenal states, there should be also continuous transitions between the corresponding neural
states, and vice versa. In other words, this postulate means that the principle that ‘‘natura non
facit saltus,’’10 simultaneously holds for both the phenomenal and the neural domain. Translated
into mathematical terms, this requirement means that there should be a homeomorphic11 relationship between phenomenal and neural states, i.e., a continuous one-to-one relationship. In this
8
Mathematically, an isomorphism is a linear bijective function, i.e., a transformation, which maps one set in a oneto-one fashion onto another set.
9
The epistemic principle attributed to the medieval philosopher William of Occam (1280–1347) demanding
ontologically sparse explanations: ‘‘Entia non sunt multiplicandam praeter necessitatem (the number of entities
(required to explain anything) should not be multiplied, beyond what is necessary).’’
10
This phrase goes back to the Swedish botanist and physician von Linné (1751). In modern science, this principle
has been proven wrong in the domain of quantum mechanics, where leaps can occur between discrete energy levels.
However, single quantum events are not likely to have an impact on brain dynamics (e.g. Fell et al., 2000; Tegmark,
2000).
11
A homeomorphism is a continuous one-to-one transformation between two sets (of states) with a likewise
continuous inverse transformation.
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Fig. 1. State space approach: as an example, phenomenal states and simultaneous neural states are characterized by
points in three-dimensional state spaces.
sense, it may be more appropriate to speak of psychoneural homeomorphism than of psychoneural isomorphism.
Coming back to the concept of a state space representation of neural and phenomenal states,
the hypothesis of psychoneural homeomorphism asserts that the sets of neural and corresponding
phenomenal states are topologically equivalent. Topological equivalence means that coming from
the neural state space, topological structures are preserved in the phenomenal state space, and vice
versa. For example, two states which are separated in one of the state spaces must also be separated in the other state space. This implies, for instance, that if there are no trajectory crossings in
one of the state spaces, there should also be no crossings in the other state space. Moreover, the
flow of trajectories should be preserved, in the sense that a continuous one-to-one correspondence
between neural and phenomenal trajectories exists. The following paragraph outlines how the idea
of psychoneural topological equivalence may be exploited in the search for NCCs.
3.3. Constructing neural and phenomenal state spaces
Pursuing the state space approach, an idealistic procedure for the identification of NCCs could
consist of the following steps. First, the variables of the phenomenal domain, i.e., the coordinates
Ck of phenomenal state space have to be chosen. The rationale underlying the selection of phenomenal variables is, that phenomenal states that are experienced as different are separated in
state space, and that phenomenal states that are experienced as identical are also identical in state
space.12 Furthermore, it seems advisable that the total number of phenomenal variables should be
minimized, under the above conditions. As an example, Fig. 1 illustrates phenomenal states and
simultaneous neural states in three-dimensional state spaces. Each state relates to a specific point
in time. The time intervals between subsequent neural and phenomenal states do not necessarily
have to be equidistant. With respect to the phenomenal content of a visual percept, for instance,
12
The methodological problem of subjective identification of states is being discussed in the following section.
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the three phenomenal variables might be prevalence of a certain color, a certain form and a certain direction of motion of a perceived object.
Now, the variables defining neural state space Ni, j have to be selected in a way that the same
‘‘graining of states’’ is achieved in the neural as in the phenomenal domain (e.g., Scheerer, 1994).
That means, based on the assumption of psychoneural one-to-one correspondence, states which
are separate in phenomenal state space, should correspond to separate states in neural state space,
and states which are identical in phenomenal state space, should correspond to identical states in
the neural domain. In the above case, a three-dimensional neural vector might, for instance, be
constituted by the firing rate within three subareas of the visual system. Under the assumption
of topological equivalence, structures of state distribution in phenomenal state space should be
preserved in neural state space. If there are, for example, several clusters of states in phenomenal
state space, these clusters should also be recognizable in neural state space.
If a very large number of phenomenal states has been observed, further quantitative state space
analysis is possible. For example, a generalized dimension of the set of all states could be estimated in both state spaces.13 Under the premise that the assumption of psychoneural topological
equivalence is correct, the resulting dimension values in both domains should be equal. With this
quantitative requirement, a minimal set of variables defining neural state space could be chosen,
so that the dimension estimates are still equal in both domains. Furthermore, not only dimension
estimates, but also values of measures quantifying the flow of trajectories14 should in principle
yield identical results for both domains. However, quantification of those measures would only
be possible for uninterrupted long-term recording of phenomenal and corresponding neural
states.
3.4. Methodological problems
Clearly, the previously described state space approach for the identification of neural correlates
of consciousness outlines an idealized strategy. In practice, several methodological problems may
complicate this approach. The first issue is the definition of variables spanning the phenomenal
state space. As discussed in the previous section, the minimal requirement for the embedding
of phenomenal states is that states, which are subjectively experienced as different, are also represented as separate vectors in state space. However, still multiple state space representations fulfilling this requirement may be possible, so that an agreement upon the variables defining the
phenomenal state would be needed.
The variables spanning phenomenal state space are subjective variables, i.e., they are based on
first person experience. Since there exists no ‘‘consciousness meter’’ (Chalmers, 1998), these variables have to be translated into operationalized objective measures in order to be empirically
accessible. For example, selective attention may be inferred from the correctness of responses
13
The dimension of a set of points means the dimension of a manifold (a multidimensional body), on which these
points are distributed. For an explanation of the calculation of generalized dimensions, for instance the so-called
correlation dimension, see, e.g., Grassberger, Schreiber, and Schaffrath (1991) or Kantz and Schreiber (1997).
14
For example entropies and Lyapunov-exponents (see, e.g., Grassberger et al., 1991; Kantz & Schreiber, 1997). In
this case phenomenal and neural states should be acquired in a way, that the time intervals between subsequent states
are equidistant.
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to certain target stimuli and also from response times. Or, in binocular rivalry paradigms, the
dominant percept may be inferred from the direction of the eye movement. Another option is direct information about the phenomenal state of the subject by verbal report or button press (e.g.,
Lehmann, Strik, Henggeler, König, & Koukkou, 1998; Lutz, Lachaux, Martinerie, & Varela,
2002). When a translation of phenomenal variables into empirically accessible psychological measures is not possible, those phenomenal variables are not useful in the search for NCCs. Another
crucial problem is the exact determination of the point of time, at which a phenomenal experience
occurs (for an inventive experimental solution see, e.g., Libet, 1985). In any case, it seems to be
impossible, to reach the same time resolution in the phenomenal domain as can be reached in
the neural domain, because of the subjective fusion of experiences occurring within a certain time
window (e.g., Ruhnau, 1995).15 Furthermore, to allow a detailed analysis of state space structures,
phenomenal variables should in principle be measurable on an almost continuous value range.16
Since no commonly accepted agreement exists, either on the definition of phenomenal variables,
nor on their translation into empirically accessible measures, there is without doubt a lack of an
adequately elaborated phenomenology (Cleeremans & Haynes, 1999).
In the neural domain, a multitude of different measurement techniques exist, for example direct
electrophysiologic methods like EEG and MEG or indirect imaging techniques like fMRI and
PET measuring cerebral blood flow. Furthermore, there are different measurement levels, for
example in the electrophysiological domain intracellular spike recordings, local field potentials
or neural mass activity measured by EEG. With regard to the state space strategy, it is necessary
to find a level of description in the neural domain, which establishes a one-to-one correspondence
or a homeomorphic relation between neural and phenomenal states. In other words, both neural
and phenomenal state space representations should exhibit the same graining (e.g., Scheerer,
1994).
This problem involves not only the search for appropriate neural measures, but also for the
compartments of the brain, whose activation is minimally sufficient for consciousness. But, because of the hierarchical and massively interconnected structure of the brain, it is difficult or
impossible to isolate certain brain areas. It is, for example, a methodological problem of lesion
studies, that not only the lesion areas, but also all interconnected areas are affected. Moreover,
the function of higher-order brain areas can only be accessed under proper input from the lower-order areas. Thus, it is, for instance a matter of debate, whether or not the activity of primary
visual cortex is necessary for visual awareness (Crick & Koch, 1995). An elegant approach to separate earlier perceptual processing stages, which are possibly not necessary for phenomenal
awareness, from later processing stages, is the investigation of intrinsic perceptual selection under
constant external input (see Section 4.1).
Besides finding the appropriate set of neural variables, another fundamental problem related to
the graining of states exists on the phenomenal side. It is not clear, whether two phenomenal
15
This so-called fusion threshold was estimated, for instance, to be around 4 ms for auditory sensations and around
25 ms for visual sensations (Pöppel, 1994).
16
Dynamical state space methods are based on the premise, that the state space variables have continuous value
ranges. The postulate, that phenomenal variables should in principle be measurable on an almost continuous value
range, does not imply, that all possible values may actually occur. The same, of course, is the case for the neural
measures.
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states, which are associated with different subjective experiences can indeed always be subjectively
identified as different. It is for example well-known, that 10–20 times more colors can be differentiated, when directly compared, as can be identified, when individually presented (e.g., Raffman,
1995). One interpretation is that in both cases, sensory experience corresponds to identification of
that experience, and that the experimental situation and resulting attentional processes make the
difference. The other interpretation is, that sensory experience and identification of that experience dissociate. Under this view, a sensory experience of slightly different colors and the associated mental states, although indeed different, would not be subjectively identified as different.
Since there is no other authority concerning subjective experiences than the subject herself/himself, this could pose a serious methodological problem. A way out of this dilemma could be that
the experimenter informs the subject about different neural measurement values, so that the subject could learn to distinguish the associated phenomenal states. However, this solution instigates
a ‘‘vicious circle’’ and is thus methodologically questionable.
Finally, it seems straightforward to acquire evidence for the validity of a NCC through repeated observation of the simultaneous occurrence of a certain neural state and the corresponding
phenomenal states. But this simple approach is only feasible for intra-individual data. Because of
the different neuroanatomical equipment of different subjects, identical neural states cannot be
established inter-individually in a strict sense. Of course, this is also the case in the phenomenal
domain. Thus, evidence can only be provided that inter-individually similar neural states are correlated to inter-individually similar phenomenal states. The previously described state space approach, therefore, could be applied in an exact sense only to intra-individual data. But, even in
the same subject, the neural correlate of a certain phenomenal content may change over time because of neural plasticity and learning.
It has to be remarked, that the described methodological problems are not so severe that they
completely shipwreck the proposed state space approach for the identification of NCCs. From a
practical point of view, the most crucial methodological problems are the specification of appropriate neural levels of description and the functional separation of different brain areas. The
remaining issues mainly apply to a degree of precision of NCC resolution, which is far from being
achieved today. In fact, present neuroscientific research can already be considered to be consistent
with a reduced state space approach. However, in the majority of studies only one or two phenomenal or information processing coordinates, and therein only two or a few clusters of states,
are investigated. Accordingly, in the neural domain typically only one or very few variables are
registered and analyzed.
3.5. A rudimentary approach
As described above a full-blown state space approach for the identification of NCCs is hardly feasible because of several, as yet unresolved, methodological problems. However, in the following,
some rudimentary requirements are formulated in order to pursue a reduced analysis. These
requirements will then be applied to one of most promising candidates for a NCC variable, which
is synchronized c activity, i.e., high-frequency EEG oscillations (above 20 Hz) exhibiting a constant
phase relationship between the involved neural assemblies (e.g., phase differences close to zero). Evidence for the hypothesis, that synchronized c activity is one of the variables constituting a neural
correlate of phenomenal awareness, will be categorized according to the proposed requirements.
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Within the landscape of NCC theories the hypothesis of synchronized c activity being a NCC
can be categorized as a ‘‘process theory,’’ in contrast to the so-called ‘‘vehicle theories’’ (Atkinson,
Thomas, & Cleeremans, 2000). According to Atkinson and colleagues vehicle theories, like for
example OÕBrian and OpieÕs representational connectionism (OÕBrian & Opie, 1999), put greater
importance on implementation, while process theories are more neutral concerning the nature of
the material substrate of consciousness. Thus, process theories, like the synchronized c hypothesis
or the ‘‘dynamical core model’’ (Edelman & Tononi, 2000), more or less support the view, that
any system implementing these computations might exhibit consciousness.
What minimal requirements should a neural correlate of phenomenal awareness fulfill? A starting point to look at is the significance of the potential neural correlate for information processing
in the normal waking state. Accepting the aforementioned relationship between information processing and phenomenal awareness (Section 2.1) (i.e., the model that certain items of information
processing are accompanied by phenomenal awareness; in general those which are within the focus of attention), basically two relations are possible: (a) The NCC variable is specifically correlated with phenomenal awareness, but is not generally correlated with waking-state-like
information processing. (b) The NCC variable is generally correlated with waking-state-like information processing; the NCC variable is correlated with phenomenal awareness only with respect
to certain modules of the brain, and/or with another value range or strength than for information
processing without awareness17; the NCC variable is therefore specific for phenomenal awareness
only in the previous sense. In the case of synchronized c activity option (b) appears to be valid,
i.e., synchronized c activity seems to be generally essential for brain processing. This idea is supported by a large amount of empirical data, which indicate that phase synchronization of c activity is a mechanism underlying sensory feature binding and, in general, transient coupling of neural
assemblies (for overviews see e.g., Engel & Singer, 2001; Keil, Gruber, & Müller, 2001; TallonBaudry & Bertrand, 1999; Varela, Lachaux, Rodriguez, & Martinerie, 2001).
Based on the hypothesis of a psychoneural one-to-one correspondence, the candidate NCC
should at least be able to distinguish states, which are clearly separated in phenomenal state space
due to different phenomenal content. Here, focused on the state space variable phenomenal
awareness, three major classes of phenomena will be treated, all of which are associated with
markedly separated states in phenomenal space. (1) Selective attention: As outlined in Section
2.1. phenomenal awareness and selective attention are densely inter-related. In general, the stimuli
which reach awareness are at the focus of attention. Thus, a neural correlate of phenomenal
awareness should correlate with selective attention. (2) Intrinsic perceptual selection: In some
cases, completely intrinsically induced changes in phenomenal awareness occur under non-varying
external stimulation. Examples are experiments implementing binocular rivalry, bistable percepts,
or presentation of stimuli at the perception threshold. Data on intrinsic perceptual selection enable a clear separation between intrinsically modulated phenomenal awareness and external influences. (3) Globally unconscious mental states: during some mental states, waking-state-like
information processing and phenomenal awareness are generally absent. The most prominent
example of such a state is the portion of deep sleep that is completely unconscious. This state corresponds to a cluster in a region of mental state space, where phenomenal awareness disappears.
17
Greenfield (2000) for example has suggested, that consciousness depends on the number of activated neurons,
which are transiently recruited into a global assembly.
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A neural correlate of phenomenal awareness should thus be significantly altered during this state
compared to the normal waking state. In the following section empirical data relating synchronized c activity to these three classes of phenomena will be addressed.
4. Synchronized c activity: a neural correlate of phenomenal awareness?
4.1. Selective attention and intrinsic perceptual selection
Several studies point to an involvement of synchronized c activity in attentional processes (see
also, Fell, Fernández, Klaver, Elger, & Fries, 2003). For example, with respect to visual selective
attention an enhancement of scalp recorded c activity was reported when subjects attended to a
certain stimulus or when they perceived a Gestalt (Gruber, Müller, Keil, & Elbert, 1999; Müller,
Gruber, & Keil, 2000). In intracranial recordings from area V4 in monkeys, increased c range synchronization and reduced low-frequency synchronization in case of attended compared to unattended visual stimuli was observed (Fries, Reynolds, Rorie, & Desimone, 2001). For many
recording sites reported in this study the enhancement of c synchronization was not accompanied
by simultaneous increases of firing rates. Therefore, synchronization of spike discharges in the c
range seems to be a mechanism supporting selective attention that is independent of firing rate
modulation. Increased phase synchronization in the c range has also been reported between prefrontal and parietal areas in a selective somatosensory attention task (Desmedt & Tomberg,
1994). Furthermore, shifts in attention between a visual and a tactile task have been observed
to be correlated with changes in the degree of synchronization of neural firing within the somatosensory cortex of monkeys (Steinmetz et al., 2000).
Data on the relevance of synchronized c activity for completely intrinsic perceptual selection
have for example been supplied by studies on binocular rivalry. In the case of binocular rivalry
the images presented to the two eyes are incoherent and cannot be fused to one percept (e.g., Engel, Fries, König, Brecht, & Singer, 1999). Under these conditions only information corresponding to one of the two eyes is selected and perceived, whereas information from the other eye is
suppressed. The perceived image thus alternates between both eyes. Since these perceptual shifts
occur without changes in stimulus presentation, experiments of binocular rivalry are particularly
revealing for the study of intrinsically induced perceptual selection. Several studies in monkeys
investigated whether response selection in binocular rivalry is achieved by changes of neural firing
rates. It was found that firing rate changes, which are correlated to the dominant percept, mainly
occur in the higher areas of visual processing, whereas the correlations with discharge rates within
the lower visual processing areas are weak and can be both positive or negative (Leopold & Logothetis, 1999; Sheinberg & Logothetis, 1997). However, it was shown that an increase of phase synchronization of c-oscillations in lower visual areas (V1 and V2) of cats strongly correlates with the
dominant percept under interocular rivalry in strabismic cats (Fries, Roelfsema, Engel, König, &
Singer, 1997, 2002). Together with the findings on firing rates, this could mean that synchronized c
activity may emerge in lower visual areas as a first step. It has been experimentally demonstrated
that correlated firing of neural assemblies can reliably trigger certain target areas (Alonso, Usrey,
& Reid, 1996; Roy & Alloway, 2001; Usrey, Alonso, & Reid, 2000). Since synchronized c activity
is associated with precise spike timing (Chrobak & Buzsáki, 1998; Engel & Singer, 2001; Fries,
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Neuenschwader, Engel, Goebel, & Singer, 2001), the involved assemblies may afterwards trigger
target neurons in higher visual areas and cause changes of firing rates in these areas (Abeles, 1982;
König, Engel, & Singer, 1996; von der Malsburg, 1999).
4.2. Unconscious deep sleep
Another approach to gain direct evidence for the significance of synchronized c activity for phenomenal awareness may be the investigation of mental states where phenomenal awareness is
completely absent. The most common example for such a state is deep sleep, i.e. sleep during
the slow-wave stages III and IV.18 However, phenomenal awareness seems not always to be absent during classically defined slow-wave sleep. In about 50% of laboratory awakenings from slow
wave sleep some sort of subjective experience is reported (e.g., Nielsen, 2000). These so-called
sleep mentations may for instance comprise imagery, thinking, reflecting, bodily feelings or vague
and fragmentary impressions. On the other hand, the classically defined slow-wave stages, which
are commonly assigned to epochs of 30 s duration, are not necessarily homogeneous mental states.
According to Rechtschaffen/Kales criteria (Rechtschaffen & Kales, 1968) during deep sleep slow
oscillations with a frequency below 2 Hz and an amplitude of more than 75 lV should occupy
(only) at least 20% (stage III) or 50% (stage IV) of the respective EEG epochs. Two arguments
militate in favor of the hypothesis that the complete absence of phenomenal awareness during
unconscious or what for the present purpose may be called ‘‘core’’ deep sleep is related to the
occurrence of high-voltage slow oscillations. First of all, average mentation recall rates were
found to be significantly higher during sleep stage III than during stage IV (e.g., Foulkes, 1982;
Moffitt et al., 1982; Pivik & Foulkes, 1968). Moreover, for many anesthetics deep general anesthesia is characterized by slow wave activity similar to that occurring during deep sleep (e.g., Amzica
& Steriade, 1998).
Intracranial and scalp EEG studies have demonstrated cortically generated slow oscillations of
around and below 1 Hz during deep sleep, which show distinct oscillatory properties (Achermann
& Borbely, 1997; Amzica & Steriade, 1998). These slow d oscillations have the ability to trigger
and group cortical network firing in the sigma (12–16 Hz) and c range (e.g., Steriade, Amzica,
& Contreras, 1996). c-Activity was shown to be amplitude modulated by the phases of the slow
oscillations during deep sleep. Besides amplitude modulation it has been observed that cortical c
activity reaches its lowest overall level during deep sleep, while d oscillations reach their maximum
level (e.g., Gross & Gotman, 1999; Mann, Bäcker, & Röschke, 1993; Mann & Röschke, 1997; Llinas & Ribary, 1993). It has moreover been speculated that the stereotyped firing pattern during
deep sleep may disturb phase synchronization of c oscillations (Tononi & Edelman, 1998). In cats
indeed a lack of precise synchronization of neural firing within visual cortex during states of low
arousal accompanied by d waves has been reported (Herculano-Houzel, Munk, Neuenschwander,
& Singer, 1999). Thus, the absence of phenomenal awareness could be caused by two different aspects of c dynamics, which are noticed during deep sleep: the overall minimum of c amplitude or
18
It should be noted that different sleep stages as characterized according to standard criteria (Rechtschaffen &
Kales, 1968) in a strict sense are neural states, since their classification is based on physiological data. Here, the term
deep sleep or slow wave sleep is used as a kind of makeshift to narrow down a mental state, where phenomenal
awareness is absent.
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the forced modulation of c amplitudes by the slow oscillations, which may also be associated with
a disturbance of synchronization of neural firing.
4.3. Applying the state space approach
As outlined above empirical data indicate that synchronized c activity is correlated with selective
attention, intrinsic perceptual selection, and the transition from deep sleep to waking state. However, there are other potential neural variables, which have been proposed to correspond to these
axes of the phenomenal domain. As already described, the occurrence of synchronized d oscillations
is the most prominent marker for the transition from waking state to deep sleep. In studies on binocular rivalry and perception threshold the presence and amplitude of certain event-related potential (ERP) components was found to be associated with conscious perception (e.g., Kaernbach,
Schroger, Jacobsen, & Roeber, 1999; Ojanen, Revonsuo, & Sams, 2003; Wilenius-Emet, Revonsuo,
& Ojanen, 2004).19 In this context, it may be noted that amplitude changes of ERP components have
been found to be associated with modulations of amplitude and phase synchronization of c oscillations (e.g., Bertrand, Tallon-Baudry, Giard, & Pernier, 1998; Tomberg & Desmedt, 1998; Fell, Hinrichs, & Röschke, 1997; Marshall, Mölle, & Bartsch, 1996). Furthermore, a oscillations (around
10 Hz) were suggested to play an important role in selective attention and sensory awareness
(e.g., Herrmann & Knight, 2001; Ray & Cole, 1985; Sewards & Sewards, 1999; Ward, 2003). Several
studies indicate that the occurrence of a oscillations is associated with the attentional suppression of
cortical processes related to an irrelevant task or distractor, thereby facilitating the process of focusing attention on the relevant task or target (Cooper, Croft, Dominey, Burgess, & Gruzelier, 2003;
Foxe, Simpson, & Ahlfors, 1998; Fu et al., 2001; Worden, Foxe, Wang, & Simpson, 2000).
Thus, when taking into account only oscillatory neural variables, at least d and a activity may
additionally be included into a state space description of the neural domain, as illustrated in Fig.
2. The two dimensions on the phenomenal side represent the axes awake vs. ‘‘core’’ deep sleep and
attended vs. unattended condition. Since the ability to selectively attend to certain brain processes
is absent during ‘‘core’’ deep sleep, in a coarse approach three separate clusters of states may be
distinguished in the phenomenal state space. In the neural domain the three state variables represent the amount of synchronized c, a, and d oscillations. According to the empirical data described above, d activity is significantly larger for ‘‘core’’ deep sleep compared to waking state
and a activity decreases from the unattended towards the attended condition. However, neither
does d activity play a crucial role in selective attention, nor does a activity with regard to the transition from waking state towards deep sleep.20 Synchronized c activity was reported to signifi19
For example, in perception threshold experiments a negative frontocentral ERP component was found to
accompany stimuli, which reach visual awareness (Ojanen et al., 2003; Wilenius-Emet et al., 2004). Interestingly, in
another perception threshold study awareness of somatosensory stimuli was reported to be associated with increased c
synchronization within primary somatosensory cortex as revealed by intracranial recordings in epilepsy patients
(Meador, Ray, Echauz, Loring, & Vachtsevanos, 2002).
20
This statement refers to ongoing d activity. One may argue, that stimulus related d oscillations indeed play a major
role in selective attention, since they are the dominant constituent of the event-related P3 component, which is
associated with target identification. In this context, the interaction between the P3 component and c oscillations has
been suggested to represent a mechanism underlying the closure of a cognitive operation (Fell, Klaver, Elger, &
Fernández, 2002; Tomberg & Desmedt, 1998).
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Fig. 2. Example for three corresponding state clusters within a two-dimensional phenomenal and a three-dimensional
neural state space. The two axes in the phenomenal domain are related to the transition between waking state and
unconscious (‘‘core’’) deep sleep and between attended and unattended mental processes. The three variables in the
neural domain represent the amount of (synchronized) d, a, and c activity.
cantly increase from ‘‘core’’ deep sleep towards the waking state, as well as from the unattended
towards the attended condition (during waking state). Thus, only synchronized c activity appears
to be correlated with shifts on either of the phenomenal axes. Technically, only this neural variable would be sufficient to separate the three state clusters in the neural domain.
5. Discussion
In the present article, the state space approach has been proposed as a strategy for the identification of neural correlates of consciousness. Based on the assumption of psychoneural homeomorphism, the aim of the state space approach is to construct a neural state space so that
topological structures of states in the phenomenal domain reappear in the neural domain. The
article then has focussed on one constituent of consciousness, phenomenal awareness. It has been
argued that, as a minimal requirement, neural correlates of phenomenal awareness should reveal
significant differences between the attended and unattended condition in experiments on selective
attention, between those percepts that reach awareness compared to those that do not in experiments on intrinsic perceptual selection, as well as between waking state and unconscious deep
sleep.
As an example, neuroscientific findings on synchronized c activity, one of the most promising
candidates for a neural correlate of phenomenal awareness, have been described and categorized
according to these requirements. Phase synchronized c activity has been reported to correspond to
shifts on each of the three suggested axes in the phenomenal domain. Therefore, although other
neural variables like d and a oscillations have been observed to correspond to one of those axis,
technically, only synchronized c activity would be sufficient for a description of the neural domain
corresponding to a coarse clustering of states in the phenomenal domain. However, the conclusion that synchronized c activity may be the exclusive variable constituting a neural correlate
of phenomenal awareness (in the strict sense of minimal sufficiency) would in principle require
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to demonstrate that the state transitions in phenomenal space still occur when d and a oscillations
somehow have been disabled (without disturbing c activity).
It has been emphasized that the state space strategy is based on the idea to find a level of
description in the neural domain, which establishes the same graining of states as observed in
the phenomenal domain. Under this view, action potentials of single neurons probably represent
a level of description, which is too fine grained when comparing it with the phenomenal domain.
On the other hand, low-frequency EEG oscillations, which are most closely correlated with the
global state of consciousness may represent a too coarse grained neural level. Thus, an intermediate level of description would be desirable, which nevertheless should interact with the other
electrophysiological levels. Empirical data indicate, that this may be the case for c band EEG
oscillations. On the one hand, c activity has been found to be amplitude-modulated by the slow
oscillations occurring during deep sleep (Steriade, Nunez, & Amzica, 1993), as well as by the hippocampal theta rhythm (Buzsáki, 1996; Chrobak, Lorincz, & Buzsáki, 2000). On the other hand,
it was observed that the timing of neural action potentials is highly correlated with the phase of c
oscillations (Chrobak & Buzsáki, 1998; Engel & Singer, 2001; Fries, Neuenschwader, et al., 2001).
Thus, one may argue that c activity represents an intermediate level of neural processing, which
interacts with both, low-frequency EEG rhythms, as (in a certain sense) the uppermost level, and
neural firing, as the most basal level.
Until now, empirical data point to the view that synchronized c activity is not only specifically
relevant for phenomenal awareness, but is also generally relevant for information processing (with
and without awareness). A theory claiming that synchronized c activity is a neural correlate of
phenomenal awareness thus has to explain—at least in the case where the same brain areas are
involved—how this mechanism can subserve both functions. A possible explanation is that synchronized c activity triggers higher-level target areas in a cascade-like fashion. Only above a certain level of activation, might triggering of those areas be possible, whose activity, either alone or
together with some or all lower level areas, is associated with phenomenal awareness. Again, this
hypothesis may be difficult or impossible to validate—but supposed it would be correct, the firing
rate within certain higher-level areas, for instance, rather than synchronized c activity might actually represent a neural correlate in the specific sense, i.e., a neural correlate, which is minimally
sufficient for phenomenal awareness.
Acknowledgments
This article is based on parts of a philosophical thesis advised by Prof. Thomas Metzinger at the
University of Mainz (Germany). I am grateful to Prof. Metzinger for many ideas and suggestions.
Furthermore, I would like to thank Rüdiger Brennecke, Pascal Fries, Peter Klaver, Klaus Lehnertz, Laura Parkes, Antti Revonsuo, and two anonymous referees for helpful comments.
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