5. Neuroscientific window to consciousness
It is sincere and honest to admit that we do not know how it is possible that such a physical
system like brain gives rise to subjective mental phenomena, including perception, emotions,
thinking, etc., consciousness being at the top of them. All we can do so far, is to try to reveal
regularities and possibly rules that exist between particular neural processes and particular
mental processes. However, we can not speak about causal laws yet, what causes what and
how. Neural processes that are being assumed to accompany given mental phenomena are
called neural correlates or neural mechanisms of these mental phenomena.
Present neuroscience has a good reason to assume that mechanisms of reflective
consciousness are derived from the mechanisms of perception (SINGER 1999a). Thus, in
building the picture of neural correlates of reflective consciousness we will proceed through
assumed neural correlates of sensory awareness (primary, phenomenal consciousness).
Fig. 5.1. Corticocortical connections
between the posterior parietal cortex and
the main subdivisions of the frontal cortex
(taken from KANDEL et al. 1991). Arabic
numerals
denote
corresponding
Brodmann’s areas. Although the arrows are
unidirectional,
the
interconnecting
pathways are reciprocal. Illustrated areas
showed increased coherence within the 40
Hz band in the Rodriguez et al.‘s
experiment on recognition of Mooney faces
(RODRIGUEZ et al. 1999).
When a
human face was recognized, transient
coherence occurred in the time window of
180360 ms after the beginning of the
picture presentation.
5.1
Neural correlates of sensory awareness
Currently, transient (100 – 200 ms) synchronous gamma oscillations are being studied as the
promising candidate for the mechanism of binding many elementary features belonging to
one object to one transient whole corresponding to one‘s percept of that object (SINGER
1999b, ENGEL et al. 1999) (see also section 4.3 of this text). Such synchronized activity
summates more effectively than nonsynchronized activity in the target cells at subsequent
processing stages. If so, synchronization could increase the effect that a selected population of
neurons has on other populations with great temporal specificity (in the range of miliseconds).
There is also evidence that synchrony is important for inducing changes in synaptic efficacies
and hence facilitate transfer of information into memory. Different objects in one scene may
be associated with different phase-locked synchronous oscillations within the 40 Hz
frequency band. Thus, increased coherence between brain areas confined to a narrow band
around 40 Hz may denote a holistic perception of a complex stimulus. Based on experimental
findings, crucial neural conditions for a consciouss percept to be experienced is (SINGER
1994, KOCH – CRICK 1994, CRICK – KOCH 1995, KOCH 1996, RODRIGUEZ et al.
1999):
28

Over the chain of higher-order sensory areas with areas that have direct connections to the
frontal cortex being at the end of this chain (e.g. the posterior parietal cortex), and over the
evolutionary youngest cortical areas, i.e. the frontal and prefrontal cortex, certain
suprathreshold quantity (number) of neurons must be coherently active for a certain time
of 100-200 miliseconds (see Fig. 5.1).
Let us go through these conditions one by one. Why higher-order sensory areas? Because they
code for invariant object features, and thus come closer to an invariant object identification
(CRICK - KOCH 1995, ENGEL et al. 1999). Lower-order sensory areas also take part in the
chain, their activity however may not be crucial for the build up of a conscious percept. With
respect to the quantitative condition, that a certain suprathreshold number of neurons must be
synchronized within relevant cortical areas: Electrophysiological measurments on
blindsighted monkeys and fMRI on blindsighted humans have shown that besides the superior
colliculus, also the hierarchically higher visual cortical areas remain responsive to visual
stimuli when V1 is inactivated or damaged (STÖRIG et al. 1997, SAHRAIE et al. 1997).
Thus, blindsight seems to be mediated by both, intact relays within the extra-geniculostriate
pathway which leads to superior colliculus, and also by the sparse and spared relays within
the retino-geniculate-cortical pathways themselves. However, neither subcortical structures
nor an insufficient number of active cortical neurons can lead to a consciouss percept. There
is also an intuition from theory: in order for a large synchronization to occur in some physical
system, a certain threshold number of elements must start the process, otherwise
synchronization does not spread over distance.
Lateral
Medial
8B
8Ad
9
9/46d
4
8B
6
9
8Av
9/46v
46
CC
44 6
24
10
32
10
45A 45B
25
47/12
45A
47/12
10
13
11
14
14
Orbito-frontal
Fig. 5.2. Human prefrontal cortex. Lateral (from outside), medial (from inside) and orbital (from
below) view at the left hemisphere. The same divisions hold also for the right hemisphere (from
ROBERTS et al. 1998.)
Generating sensory awareness involves some form of attentional mechanism. Several areas in
the prefrontal cortex are crucially involved in attention, namely areas 8Av (major connections
with the visual system), 8Ad (major connections with the auditory system) and 8B (major
connections with the limbic system) (see Fig. 5.2, ROBERTS et al. 1998). Attentional
selection may depend on appropriate binding (coherence) of neuronal discharges in sensory
areas in two simultaneously active directions: an attentional mechanism in prefrontal cortex
could induce synchronous oscillations in selected neuronal populations (top-down
29
interaction), and strongly synchronized cell assemblies could engage attentional areas into
coherence (bottom-up interaction) (SINGER 1994).
Another prefrontal areas activated during sensory perception include areas 9, 10, 45,
46, 47 (see Fig. 5.2). These prefrontal areas are known to be involved in an extended action
planning. These prefrontal areas plus the posterior parietal cortex are known to be involved
in the working memory. Posterior parietal cortex is known to be involved in mental imagery.
For planning of actions it is necessary to keep track of at least one sequence of partial actions,
hence the overlap between planning and memory mechanisms. It might be, that sensory
contents reach awareness only if they are bound to prefrontal areas via the posterior parietal
cortex and, thus have a possibility to become part of the working memory and action planning
(ENGEL et al. 1999). In turn, action planning may influence organization of attentional
mechanisms and thus what is being perceived.
Coherences in the involved areas are generated internally within the cortex and
although they are phase-locked, they are not stimulus locked. They are superimposed upon
global thalamocortical (TC) gamma oscillations which are generated and maintained during
cognitive tasks (RIBARY et al. 1991). TC oscillations may provide the basic oscillatory
modulation of cortical oscillations. Other cortical mechanisms are then responsible for a
precise phase-locking of internal cortical synchronous oscillations. In particular, these are
lateral inhibitory and excitatory interactions, regularly bursting layer V pyramidal cells, and
spike-timing dependent rapid synaptic plasticity. In the latest, synapses and thus the inputs
which do not drive the postsynaptic cell in synchrony are temporarily weakened.
5.2
Neural correlates of reflective consciousness
Since early childhood, we are engaged in learning, first through nonverbal and later through
verbal communication, to assume what is going on inside of other people. Our reflective
consciousness and our self-reflection develop gradually, step by step. Due to learning to
assume what is going on inside of other people, we can learn to assume what is going on
inside of ourselves. Self-reflection is possible only thanks to communication and social
interaction. However, it seems that our brains already possess certain structures that have
been prepared and selected for this task – these are mirror neurons (RIZZOLATTI et al.
1996) and mentalization module (FRITH 2001). In 1996, scientific community got stirred by
the discovery of mirror neurons. G. Rizzolatti et al. (1996) recorded activity of neurons in the
ventral premotor cortex of macaque monkeys. They have found out that these neurons are
active not only when the monkey prepares (contemplates) for its own actions, but also when
she watches others, either monkeys or humans, to perform a given action. Examples of these
actions may be grasping a peanut, peeling off banana, and so on. Thus, these mirror neurons
follow or "imitate" what others are doing. They may form a neural basis for learning by
imitation. In humans, the ventral premotor area also includes the Broca’s area (area 44),
which is a specific cortical area associated with expressive and syntactical aspects of
language. Thus maybe, evolution of the ventral premotor area with its mirror neurons played a
determining role in evolution of language. It is also intriguing to see areas responsible for
contemplation of actions and areas processing language being at the same place in the brain.
(thinking of thinking)
Normal people have the ability to explain and predict others‘ behavior in terms of their
presumed thoughts and feelings. The ability to attribute mental states to others and to
ourselves is called "mentalization" or "theory of mind". Noninvasive brain imaging has shown
that the ability to attribute various mental states, desires and beliefs to others and also to
ourselves depends upon full functioning of a specific neurocognitive module (FRITH 2001).
30
The mentalization module includes in both hemispheres: (i) the medial prefrontal cortex (area
32), in particular the most anterior part of paracingulate cortex, a region on the border
between anterior cingulate and medial prefrontal cortex (very medial), (ii) the temporalparietal junction at the top of the superior temporal gyrus (stronger on the right), and (iii) the
temporal poles adjacent to the amygdala (somewhat stronger on the left). Neural activity in all
three or at least in the prefrontal part of this mentalization module as revealed by the brain
imaging is significantly lower in autistic people (FRITH 2001). Autistis people are not able
to "read out" neither the mind of others nor the mind of themselves, while there is a whole
spectrum of severity of autistic disorder. Mentalization module overlaps substantially with the
brain higher-order emotional system.
Medial parts of the prefrontal cortex (area 32) and orbitofrontal parts (i.e. areas 10, 11,
12, 13, 14) are evolutionary younger parts of the brain emotional system (DAMASIO 1994).
These medial and orbital prefrontal areas are thought to be responsible for the so called
secondary emotions. Secondary emotions are emotional feelings based on learned variety of
associociations between primary emotions and life situations. Hierarchy of these associations
involves planning and strategies related to one’s social role and personal goals in relation to
the past and future. Evaluation and planning and feelings in the social and emotional spheres
are therefore linked to be processed by the same structures in the prefrontal cortex, and these
overlap with the mentalization module. According to Damasio (1994), the feeling of self, and
consequently the awareness of self, would depend also on the intactness of the somatosensory
system, on the signaling from the cortex down to the body and back.
As we have said, it might be, that sensory contents reach awareness only if they are
temporarily synchronized with activity in the prefrontal areas, thus displaying a highly
coherent joint activity. In such a way they can become part of conscious working memory and
action planning. According to Singer (1999a) reflective consciousness would be based upon
the same processes, i.e. highly coherent activity, happening over the prefrontal areas involved
in planning and working memory and between areas devoted to representations of our inner
world. Secondary consciousness or meta-awareness would result from iteration of the very
same processes that support primary consciousness, except that they are not applied to the
signals arriving from the sensory organs, i.e. from the outer world, but to the outputs of
previous cognitive operations (SINGER 1999a).
By means of recording electromagnetic activity of the brain it is possible to capture
and visualize the fast semiglobal coherent activity of the brain that accompanies conscious
perception of a stimulus (TONONI – EDELMAN 1998). It is almost a magical view, because
this semiglobal coherent activity changes with time as a burning fire which is boosted from
the centre of the brain and its flames transiently engage currently synchronized brain areas
(see Fig. 5.3). Why is the fire boosted from the centre of the brain? Clinical research has
revealed that damaging intralaminar nuclei in the thalamus leads to the loss of consciousness
and to coma. Neurons in intralaminar nuclei possess dense reciprocal connections to and from
the brain cortex. Intralaminar nuclei are the source of arousal, without which the cortex
cannot function.
G. Edelman and G. Tononi (2000) call this ever changing semiglobal coherent
activity, the dynamic core. The dynamic core corresponds to a large (semiglobal) continuous
cluster of neuronal groups that are coherently active on a time scale of hundreds of
miliseconds. Its participating neuronal groups are much more strongly interactive among
themselves than with the rest of the brain. The dynamic core must also have an extremely
high complexity as oppossed to for instance convulsions. Each roughly 150 ms, a pattern of
semiglobal activity must be selected within less than a second out of a very large, almost
infinite, repertoire of options. Thus, the dynamic core changes in composition over time. As
suggested by imaging, exact composition of the core related to particular conscious states
31
vary significantly not only over time within one individual, but also vary significantly across
individuals.
(a)
(b)
Fig. 5.3. (a) Illustration of the dynamic core, a changing coherent semiglobal activity of the brain,
which is supposed to be a neural correlate of consciousness. The flames of the core dance as fire
flames. One configuration of the core lasts for about 150 ms. (b) Interpretation of the dynamic core as
an N-dimensional neuronal reference space, where each axis (dimension) denotes some group of
neurons which encodes (represents) a given aspect of the conscious experience. Each axis can be
broken down into more elementary axes. There can be hundreds of thousands of dimensions.
According to Edelman and Tononi (2000), the dynamic core consists of a large
number of distributed groups of neurons which enter the core temporarily based on their
mutual coherence. Connecting groups of neurons into temporarily synchronized whole
requires dense recurrent connections between brain areas, along which a reiterated reentry of
signals occurs. Neural reference space for any conscious state may be viewed as an abstract
N-dimensional space, where each axis (dimension) stands for some participating group of
neurons that code for (represent) a given aspect of the conscious experience. There can be
hundreds of thousands of dimensions. The distance from the beginning of the axis represents
the salience of that aspect. It may, for instance, correspond to the number of neurons within a
given group that codes for it.
What would be, in this theory, a neural basis for unconsciousness? The same group of
neurons may at times be part of the dynamic core and underlie conscious experience, while at
other times it may not be part of it and thus be involved in unconscious processing. Koch and
Crick (1994) have proposed that those active neurons which are not at the moment taking part
in the semiglobal activity, keep processing their inputs, and results of this processing may still
affect behaviour.
We would like to mention also the explanation of neural correlate of qualia, according
to Edelman and Tononi (2000). Qualia are specific qualities of subjetive experiences, like
redness, blueness, warmth, pain, and so on. According to the dynamic core hypothesis, pure
redness would be represented by one particular state of the dynamic core, that is by one and
only one point in the N-dimensional neural space. This core state would certainly include
large participation of neurons that code for the red colour and a small participation of neurons
that code for other colours and for anything else. Coordinates of a point in the N-dimensional
reference neural space are determined by activities of all neuronal groups that are at the
32
moment part of the core. And these activities vary in time and across individuals. Thus, the
subjective experience of redness will be different in different people and can be different for
the same individual for instance in the morning and in the evening. The wholiness of the
dynamic core corresponds with the psychological wholiness of the content of consciousness.
Consciousness during sleep.
Sleep research has revealed that during sleep, humans normally go through two-three cycles
of two sleep phases. One of these two phases is the so called REM sleep, according to the
accompanying Rapid Eye Movements. EEG activity of the brain during the REM phase is
very similar to the EEG activity of the awake brain during cognitive activity. Hence the term
paradoxical sleep for the REM sleep phase, as it was not a sleep at all. We dream only during
REM sleep phases. When awakened during the REM phase, we can recall the content of a
dream. When awakened around at the end of the REM phase, we can remember that we
dreamt, not knowing about what. When awakened during the non-REM sleep phase, we deny
any experience of dreaming. The non-REM sleep phase is also called the deep sleep, and the
brain activity occurs in typical slow large regular waves.
In our dreams, we play the main role, it is us to whom all those peculiar things happen. We
experience self-awareness when we dream but not when we are in the deep sleep (LLINÁS RIBARY 1994). Thus "I" is preserved during dreaming as well as the awake-like EEG
activity of the brain.
Another noticeable findings are, that cognitive deficits caused by the brain damage, manifest
themselves also in dreams. For instance, patients with such damage to one hemisphere that
they do not perceive one half of their visual field, do not see this neglected half neither in
their dreams. Similarly, people in dreams of prosopagnostic people have no faces. A man who
selectively lost his colour vision, due to the V4 damage, lost colour also in his dreams
(SACKS 1995).
6. Conceptual spaces
We think there may be a close correspondence between the theory of dynamic core
and the Gärdenfors‘ theory of conceptual spaces (GÄRDENFORS 2000). First, we will
introduce basic ideas of this theory. One of the central problems in cognitive science is how
representations should be modeled. Either symbolic or connectionist modes of modeling
representation are usually used. Gärdenfors offers a new general theory of representation
called conceptual spaces that is based on a geometrical mode of representation. One of his
aims is to make a bridge between neural networks and models of mental processes (for
instance symbolic models).
Conceptual space is defined as a set of N quality domains. Each quality domain can be
broken down to a set of elementary quality dimensions, each with its distinct geometrical
structure. Inherent geometrical structure of individual quality dimensions can be linear (like
time or distance) or nonlinear, continuous or discrete. Function of quality dimensions is to
represent various qualities of objects. Each quality domain integrates  1 inherently related
qualities into one domain. For instance the domain colour, consists of the three basic colour
quality dimensions, i.e. red, blue and yellow. Quality domains can be pictured as axes in the
N-dimensional abstract space (see Fig. 6.1). At once, we can point out that the relation of this
N-dimensional abstract space with the N-dimensional neural reference space from Fig. 5.3b is
straightforward. Gärdenfors himself says that behind every quality dimension, we can
33
imagine some brain area or neural network, in which a given quality of object is being coded
(GÄRDENFORS 2000).
(a)
(b)
(c)
Fig. 6.1. (a) In the theory of conceptual spaces, an object corresponds to one point in a given
conceptual space delineated by its quality domain axes. Each quality domain can be composed of a set
of more elementary quality dimensions. (b) A concept corresponds to a closed concave region, which
unifies geometrically close points in the conceptual space5.1. Property is a special concept defined only
along one quality axis (domain, dimension). (c) A prototype is an existing or non-existing object that
corresponds to some average, i.e. the center of the concept-region.
Quality dimensions and domains can have a phenomenological (psychological) or scientific
(theoretical) interpretation. Phenomenological interpretation includes phenomenological
qualities of cognitive structures of humans and other organisms (like phenomenological
qualities of percepts, memories, feelings, etc.). Scientific interpretation, on the other hand,
treats dimensions as a part of a scientific theory. For example, without a scientific theory we
would not know about the wavelength as a one quality dimension of light.
In the theory of conceptual spaces, an object corresponds to one point in a given
conceptual space (Fig. 6.1a). Object’s conceptual space is determined by axes of its quality
dimensions (domains). In the interpretation of neural networks, an object corresponds to one
particular configuration of active neurons across brain areas that represent object’s features.
One object is one point in the state space of neural networks. Each state of neural networks
corresponds to one particular distribution of activity. Back to the conceptual space: values of
coordinates of an object reflect the salience of that quality. From these geometrical
interpretations, we can derive a one-to-one relationship between the geometric distance of
points (objects) in the conceptual space, in the state space of neural networks, and between
their similarity in the mental space. Thus, objects that are similar are represented as close
points in the conceptual space.
A concept corresponds to the closed concave region in the conceptual space that
unifies together points (objects) that are geometrically close (Fig. 6.1b). Objects that fall
within a concave region of one concept have similar properties. By properties we mean closed
concave regions along particular individual domains. Objects belonging to one concept have
similar properties, that is, their qualitative characteristics have close values from one
particular interval of values along the quality axis. A prototype then would be an existing or
5.1
We picture concepts as oval regions. However, mathematically more appropriate would be to picture them as
regions of the so-called Voronoi mozaic (GÄRDENFORS 2000).
34
non-existing object with average properties, i.e. the point in the centre of the concave concept
region (Fig. 6.1c).
Semantics deals with relations between linguistic expressions and their meanings.
According to Gärdenfors, meaning is a conceptual structure (point, region, their
combinations) in conceptual spaces. Thus, he argues that semantic meaning can be modeled
independently of its communicative use. The idea is that the conceptual structures of different
individuals will become attuned to each other, otherwise linguistic communication will break
down. Grasping the meaning of linguistic expressions is a combination of having the right
association from the words to a conceptual structure and havig a viable conceptual structure.
Semantics of words should be given primacy to the semantics of sentences. In turn, semantics
of sentences should be primary to syntax.
Conceptual structures are embodied. All meanings (conceptual structures), all along
with conceptual spaces with their dimensions ontologically originate in perception, motorics
and emotions. The latter three are the basic building blocks of our mental and neural
development and thus all conceptual spaces must build upon them and their neural
representations. All quality dimensions (and domains) are either inborn or learned. Learning
of new concepts is often accompanied by enlargement of one’s conceptual spaces by means of
adding new quality dimensions. Gärdenfors considers adding new quality dimensions to be
equivalent to a scientific discovery. Thus, new scientific concepts correspond to introduction
of new dimensions and new conceptual spaces into the cognition of an individual. It is an
analogical process to the introduction of new dimensions and new conceptual spaces during
the cognitive development of children. The primary scientific activity is to come up with new
relevant dimensions for explaining things. Theoretical concepts are new dimensions.
(a)
(b)
(c)
Fig. 6.2. Illustration of generalization (inductive reasoning) in the theory of conceptual spaces. (a) and
(b) Process of assignment of a particular observation to an existing concept according to the
(geometrical) distance between an observation and the centre of a region. (c) Birth of a new concept
out of several close observations.
One of the most robust features of our cognitive processing is the ability to perform inductive
reasoning or generalization. Often with overwhelming confidence, we are ready to generalize
from a very limited number of observations. Induction is going from single observations to
generalizations. In the theory of conceptual spaces, an observation can be defined as an
assignment of a location in a conceptual space (coordinates along axes) to an object. In
geometrical interpretation, generalization proceeds like this: let us suppose we have a new
observation (object). After evaluation of its distances from the centers of all concepts in a
given conceptual space, an object-observation is included into the region of the closest
35
concept (Fig. 6.2a and Fig. 6.2b). If our observations are very close to each other and at the
same time distant from the centers of existing concepts, a new region-concept is created (Fig.
6.2c). Such explanations of induction and generalization are arbitrary as any other. However,
it has a nice neural implementation by means of selforganizing maps (SOMs) that are trained
by unsupervised Kohonen’s algorithm based on the statistics of inputs. SOMs form Voronoi
mosaics, and the centers of clusters (regions, concepts) adopt new close inputs. Formation of
new concepts corresponds to the process of clustering or re-clustering (KOHONEN 1995).
Another way of generalization is by means of methafors. According to Gärdenfors,
the locus of a methafor is not in language at all, but in thought, in the way we conceptualize
one mental domain in terms of another. A methafor expresses an identity in topological or
geometrical structure between different domains. A word (expression) that represents a
particular structure in one domain (set of domains) can be used as a methafor to express the
same structure in another domain (set of domains). Methaforical mappings preserve the
cognitive topology of the source domain in a way consistent with the inherent structure of the
target domain.
Gärdenfors‘ theory of conceptual spaces is a novel way of modeling representations. It
is noteworthy that its geometrical interpretation nicely corresponds (according to us) to the
abstract interpretation of the dynamic core (Fig. 5.3b). We are attracted also to the possibility
of treating and exploring conceptual spaces as the state spaces of neural networks. It is also
possible to test the theory of conceptual spaces by means of psychophysical experiments. An
example of how to arrange such an experiment is the psychophysical experiment with the
perceptual classification of shell shapes (GÄRDENFORS 2000).
To conclude, we will mention drawbacks associated with this theory. While its
principles are relatively simple and straightforward, concrete implementations whether in
neural networks or psychophysical experiments are far non-trivial. Gärdenfors himself lists
these problems: which dimensions one should choose to represent any particular cognitive
process? It turns out that this is hard to decide already for perception and motorics. What
would be the geometrical structure of these dimensions? How to assess their geometrical
structure? This can be done only by extensive experimentation. Which dimensions are inborn
and which are learned? How one acquires new dimensions, that is how one makes a
discovery? What would be the hierarchical build up of conceptual spaces? How to deal with
the complexity of conceptual spaces when dealing with thousands of dimensions? Etc.
Since, however this theory is very new, we are looking forward to its further
development. It would be cool to discover that the geometry of state spaces of neural
networks is equivalent to the corresponding cognitive "geometry".
36