Consciousness: here, there and everywhere?

Wednesday 18 November 2015 at 5:16 pm.

Consciousness: here, there and everywhere?

Cite this article: Tononi G, Koch C. 2015

Phil. Trans. R. Soc. B 370: 20140167.
Accepted: 6 January 2015
One contribution of 11 to a theme issue
‘Cerebral cartography: a vision of its future’.
Subject Areas:
neuroscience, cognition
mind body problem, causation, existence,
neuronal correlates of consciousness,
awareness, cerebral cortex
Author for correspondence:
Christof Koch
Consciousness: here, there and
Giulio Tononi1 and Christof Koch2
1Department of Psychiatry, University of Wisconsin, Madison WI, USA
2Allen Institute for Brain Science, Seattle, WA, USA
The science of consciousness has made great strides by focusing on the behavioural
and neuronal correlates of experience. However, while such
correlates are important for progress to occur, they are not enough if we
are to understand even basic facts, for example, why the cerebral cortex
gives rise to consciousness but the cerebellum does not, though it has
even more neurons and appears to be just as complicated. Moreover, correlates
are of little help in many instances where we would like to know if
consciousness is present: patients with a few remaining islands of functioning
cortex, preterm infants, non-mammalian species and machines that are
rapidly outperforming people at driving, recognizing faces and objects,
and answering difficult questions. To address these issues, we need not
only more data but also a theory of consciousness—one that says what
experience is and what type of physical systems can have it. Integrated information
theory (IIT) does so by starting from experience itself via five
phenomenological axioms: intrinsic existence, composition, information, integration
and exclusion. From these it derives five postulates about the
properties required of physical mechanisms to support consciousness. The
theory provides a principled account of both the quantity and the quality
of an individual experience (a quale), and a calculus to evaluate whether
or not a particular physical system is conscious and of what. Moreover,
IIT can explain a range of clinical and laboratory findings, makes a
number of testable predictions and extrapolates to a number of problematic
conditions. The theory holds that consciousness is a fundamental property
possessed by physical systems having specific causal properties. It predicts
that consciousness is graded, is common among biological organisms and
can occur in some very simple systems. Conversely, it predicts that feed-forward
networks, even complex ones, are not conscious, nor are aggregates
such as groups of individuals or heaps of sand. Also, in sharp contrast to
widespread functionalist beliefs, IIT implies that digital computers, even if
their behaviour were to be functionally equivalent to ours, and even if
they were to run faithful simulations of the human brain, would experience
next to nothing.
1. Consciousness: here, there and everywhere?
I know I am conscious: I am seeing, hearing, feeling something here, inside my
own head. But is consciousness—subjective experience—also there, not only in
other people’s heads, but also in the head of animals? And perhaps everywhere,
pervading the cosmos, as in old panpsychist traditions and in the Beatles’ song?
While these kinds of questions may seem scientifically inappropriate, we argue
below that they can be approached in a principled and testable manner. Moreover,
obtaining an answer is urgent, not only because of difficult clinical cases
and in our interactions with other species but also because of the advent of
machines that are getting closer to passing the Turing test—computers programmed
to perform many tasks as well as us, and often far better than
some brain-damaged patients.
& 2015 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution
License, which permits unrestricted use, provided the original
author and source are credited.
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2. Here
That I am conscious, here and now, is the one fact I am absolutely
certain of—all the rest is conjecture. This is, of course,
the gist of the most famous deduction in Western thought,
Descartes’ je pense, donc je suis. Everything else—what I
think I know about my body, about other people, dogs,
trees, mountains and stars, is inferential. It is a reasonable
inference, corroborated first by the beliefs of my fellow
humans and then by the intersubjective methods of science.
Yet consciousness itself—the central fact of existence—still
demands a rational explanation.
The past two centuries of clinical and laboratory studies
have revealed an intimate relationship between the conscious
mind and the brain, but the exact nature of this relationship
remains elusive. Why is the brain associated with consciousness
but not the liver or the heart, as previous cultures
believed? Why certain parts of the brain and not others?
Why is consciousness lost in some stages of sleep? Why
does red feel like red and not like the sound of a violin? Is
consciousness just an epiphenomenon, or does it have a function?
Can computers be conscious? Could a system behave
like us and yet be devoid of consciousness—a zombie?
Such questions seem to resist the empirical, reductionist
approach that has been so successful for other aspects of
the natural world. Nevertheless, thanks to experimental and
theoretical progress in the past decades [1–5], we are in a
better position to understand which systems under which
conditions can be conscious. That is, the study of consciousness
is becoming a science. In doing so, it is leaving behind
the defeatist dictum of the physiologist Emil du Bois-Reymond,
ignoramus et ignorabimus (we don’t know and never
will), espousing instead the upbeat maxim of the mathematician
David Hilbert, Wir mu¨ ssen wissen—wir werden
wissen (we must know and we will know).
3. There
We usually grant consciousness to others—of the same kind
we experience in the privacy of our own mind—if they can
tell us what they feel, or if they look and behave more or
less like us. However, we become less and less confident in
attributing consciousness to those who cannot talk about
their experiences, such as infants or severely brain injured
patients. Many assume that animals closely related to homo
sapiens—apes and other primates—are conscious, though
presumably less than we are, based on the similarity of
their behaviour and their brain. But should we attribute
experience to all mammals,1 to all vertebrates, to invertebrates
such as cephalopods and bees or even to all multicellular
animals? What about cultured organoids that mimic
the cellular organization of the developing human brain
[8]? And finally, what about the sophisticated machines
that run software designed to substitute for conscious
humans in many complicated tasks?
(a) Behavioural correlates of consciousness and
Traditionally, we assess consciousness by observing behaviour
(figure 1a). If someone is awake and acts meaningfully,
we have little doubt he is conscious. If he speaks, and
especially if he can answer questions about what he is conscious
of, we are fully confident. In the laboratory, the
ability to report one’s experiences has become the gold standard
for judging the presence of consciousness. Reportability
is often reduced to a binary forced choice, in which the subject
pushes one of two buttons for ‘seen’ versus ‘not seen’, or
‘angry face’ versus ‘happy face’. One can also ask subjects
how confident they are in their judgements (confidence
rating [10]), ask them to further describe their experiences
( perceptual awareness scale [11,12]) or get them to make an
economic judgement following each response ( post-decision
wagering [13]). These kinds of meta-cognitive and confidence
reports can also be obtained from trained monkeys and other
animals, with so many similarities to our own reports that
there is little doubt as to the presence of consciousness
[14,15] (but see [16]).
But behaviour can be misleading: a person may walk and
speak in her sleep, yet it is quite dubious whether she is
experiencing anything. Or a person can be asleep, immobile,
silent and unresponsive, yet she may be dreaming—vividly
conscious of an imaginary environment. In such cases,
reportability can be used as retrospective evidence of consciousness,
by waking up the sleeper to obtain a ‘dream
report’. However, reportability, too, can be problematic.
Since we obviously experience things in dreams whether or
not we are woken up to report them, we should accept the
possibility that in certain situations consciousness can be present
even if it is not reported [17,18]. Moreover, insisting on
reportability elevates language to a king-maker role, which
makes inferring consciousness in non-verbal infants, preterm
babies, fetuses or animals problematic.2 Clearly, if we want to
understand what is really going on, we must also investigate
the brain mechanisms that underlie consciousness.
(b) Neural correlates of consciousness
The neural correlates of consciousness (NCC) have been defined
as the minimal neural mechanisms that are jointly sufficient for
any one conscious percept, thought or memory, under constant
background conditions (figure 1b) [1,23,24]. The latter are the
distal or proximal enabling factors that must be present for any
conscious experience to occur—the heart must beat and
supply the brain with oxygenated blood, various nuclei in the
midbrain reticular formation and brainstem must be active
[25–27], cholinergic release needs to occur within the corticothalamic
complex [28] and so on.
Every experience will have an associated NCC: one for
seeing a red patch, another one for hearing a high
C. Inducing the NCC by manipulating the relevant neuronal
populations via magnetic stimulation, optogenetics or other
means will give rise to the associated conscious percept.
Interfering with the NCC by disabling the underlying
neural circuits will eliminate the percept.
The NCC are typically assessed by determining which
aspects of neural function change depending on whether a subject
is conscious or not, as established using behavioural
reports. This can be done by considering a global change in
the level of consciousness, as when awareness is lost during
deep sleep or general anaesthesia [29,30]. Or it can be done
by considering changes in a particular content of consciousness,
as when a subject’s awareness of a particular stimulus
is experimentally manipulated (‘seen’ versus ‘not seen’
[31,32]). In optimally controlled experiments, the stimulus Phil. Trans. R. Soc. B 370: 20140167
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and the behavioural report (such as a button press) are kept
constant while the subject sometimes sees the percept and
sometimes does not [3,33,34]. Once a particular NCC has
been sufficiently validated, it can be used to extrapolate to situations
in which reports are not available. Both functional brain
imaging in magnetic scanners and as high-density electroencephalography
(EEG) recordings from outside the skull have
been put to use to track down the footprints of consciousness
in the brain of healthy adult observers. Popular candidates
include strong activation of high level fronto-parietal cortices
(figure 1b), high-frequency electrical activity in the gamma
range (35–80 Hz), and the occurrence of an EEG event known
as the P300 wave [1,3,29]. However, there is still no consensus
on whether any of these signs can be treated as reliable
‘signatures’ of consciousness. In particular, there can be
consciousness without frontal cortex involvement [35–37],
gamma activity without consciousness [38], such as during
anaesthesia [39,40], and consciousness without a frontal P300,
for example, during dreaming sleep [41,42]. Moreover, it is
likely that many of the signatures proposed as possible NCC
may actually be correlates of neural activity that is needed leading
up to a conscious percept [43,44], or for giving a report
following a conscious percept [36,37,44], rather than for having
an experience. A major challenge is to keep constant cognitive
functions such as selective attention, memory, decision making
and task monitoring, in order to isolate the ‘naked’ substrate of
consciousness at the neuronal level [45,46]. Finally, NCC
obtained in healthy adults may or may not apply to braindamaged
patients, to infants, to animals very different from us,
not to mention machines (figure 2).
I see
left eye
I do
2 4 6
conscious of red >
I see
left eye
left eye
Figure 1. Behavioural (BCC) and neuronal correlates of consciousness (NCC). The top row shows a schematic diagram of a binocular rivalry experiment. A horizontal
red grating is shown to the left eye and a vertical green grating to the right eye throughout the experiment (courtesy of Naotsugu Tsuchiya and Olivia Carter). The
subject does not see a juxtaposition of both stimuli but experiences either the red grating or the green one, switching back and forth every few seconds. Even if the
stimuli do not change, what one sees consciously does, as is inferred by the subject’s report. The bottom row shows the results of an experiment using magnetoencephalography
(MEG), in which the red grating was flashed at one frequency and the green one at another. Yellow indicates areas of the cortex (seen from the
top) that had more power at the frequency of the red grating when it was experienced than when it was not. The cyan lines indicate increased coherence (synchronization)
between distant brain regions associated with experiencing the grating (from [9]). Phil. Trans. R. Soc. B 370: 20140167
Downloaded from on November 18, 2015
(c) Patients and infants
Patients with widespread cortical or thalamic damage pose a
poignant challenge. Emergency roompersonnel quickly evaluate
the severity of a head injury behaviourally by assigning a
number to a patient’s auditory, visual, verbal and motor functions
as well as communication and arousal level. Various
NCC, such as the presence of a P300 wave in response to a
non-standard stimulus, are increasingly being used to complement
the behavioural assessment and occasionally modify
the diagnosis. In some cases, NCC can be decisive. Thus, if a
patient who lies mute and immobile can nevertheless respond
to commands by appropriately activating certain brain areas, it
is fair to conclude that she is conscious [47]. Yet most of the proposed
‘signatures’ of consciousness are inadequate. For
example, the P300wave is absent in many minimally conscious
patients and even in some brain-damaged patients who can
communicate [48]. And what should one make of patients in
whom, amidst widespread destruction and inactivity, one or
a few isolated cortical areas may show signs of metabolic activation
and electrophysiological ‘markers’ of consciousness
[49]? Is an island of functioning brain tissue sufficient for generating
a limited kind of awareness, maybe just awareness of
sound or of pain? In other words, ‘what is it like’ to be a
brain island, if it feels like anything at all? And how big must
the island be to qualify?
By the same token, what is it like to be a newborn baby
with an immature brain and restricted connectivity among
cortical structures [50]? Again, considering NCC can be helpful:
for example, a wave resembling the P300 wave has been
reported in six to 16 months old infants, although weaker,
more variable and delayed compared with adults [51]. But
does this mean that newborn and preterm babies or even
fetuses experience nothing because they do not show a P300?
(d) Animals
The problem becomes even more acute when turning to other
species. The study of consciousness in nature has been
hindered for centuries by a strong belief in human exceptionalism.
Yet the range and complexity of animal behaviour has
laid rest to this belief, at least among biologists. This is
particularly true for mammals. In psychophysical tasks involving
simple button presses, trained macaque monkeys act
very similarly to human volunteers, including signalling
when they do not see anything [14]. Visual recognition
of self, meta-cognition (knowing one’s mind), theory of
mind, empathy and long-range planning have all been
demonstrated in primates, rodents and other orders [52].
It is also difficult to find anything exceptional about the
human brain [53]. Its constitutive genes, synapses, neurons
and other cells are similar to those found in many other
species. Even its size is not so special, as elephants, dolphins
and whales have even bigger brains [54]. Only an expert neuroanatomist,
armed with a microscope, can tell a grain-sized
piece of neocortex of a mouse from that of a monkey or a
human. Biologists emphasize this structural and behavioural
continuity by distinguishing between non-human and human
animals [55]. Given this continuity, it seems unjustified to
claim that only one species has consciousness while everybody
else is devoid of experience, is a zombie. It is far
more likely that all mammals have at least some conscious
experiences, can hear the sounds and see the sights of life.
pre-term infant
brain ‘islands’, vegetative patient
Apple Siri
ketamine anaesthesia
Figure 2. Six instances in which it becomes progressively more difficult to
infer the existence of consciousness, since the behavioural repertoire and
the underlying mechanisms (brains) differ substantially from that of typical
persons able to speak about their experiences (figure 1). Phil. Trans. R. Soc. B 370: 20140167
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As we consider species that are progressively further
removed from Homo sapiens in evolutionary and neuronal
terms, the case for consciousness becomes more difficult to
make. Two observations, one relating to complexity of behaviour
and another one to complexity of the underlying nervous
system, are critical. First, ravens, crows, magpies, parrots and
other birds, tuna, coelacanths and other fish, octopuses and
other cephalopods, bees and other members of the vast class
of insects are all capable of sophisticated, learnt, non-stereotyped
behaviours that we associate with consciousness if
carried out by people [56–58]. Darwin himself set out ‘to
learn how far the worms acted consciously’ and concluded
that there was no absolute threshold between ‘lower’ and
‘higher’ animals, including humans, which would assign
higher mental powers to one but not to the other [59]. Second,
the nervous systems of these species display a vast and illunderstood
complexity. The bee contains about 800 000 nerve
cells whose morphological and electrical heterogeneity rivals
that of any neocortical neuron. These cells are assembled in
highly nonlinear feedback circuits whose density is up to ten
times higher than that of neocortex [60]. Thus, neural signatures
of consciousness that have some validity in humans and other
mammals may not apply at all to invertebrates.
On the other hand, the lessons learnt from studying the
behavioural (BCC) and neuronal correlates of consciousness
in people must make us cautious about inferring its presence
in creatures very different from us, no matter how sophisticated
their behaviour and how complicated their brain.
Humans can perform complex behaviours—recognizing
whether a scene is congruous or incongruous, controlling
the size, orientation and strength of how one’s finger
should grip an object, doing simple arithmetic, detecting
the meaning of words or rapid keyboard typing—in a seemingly
non-conscious manner [61–66]. When a bee
navigates a maze, does it do so like when we consciously
deliberate whether to turn right or left, or rather like when
we type on a keyboard? Similarly, consider that an extraordinarily
complicated neuronal structure in our brain, the
cerebellum, home to 69 of the 86 billion nerve cells that
make up the human brain [54], apparently has little to do
with consciousness. Patients who lose part or nearly all of
their cerebellum owing to stroke or other trauma show
ataxia, slurred speech and unsteady gait [67] but do not complain
of a loss or diminution of consciousness. Is the bee’s
brain central complex more like the cerebellum or more like
the cerebral cortex with respect to experience? Thus, the
extent to which non-mammalian species share with us the
gift of subjective experience remains hard to fathom.3
(e) Machines
Difficulties in attributing sentience become even more apparent
when considering digital computers. These have a
radically different architecture and provenance from biological
organisms shaped by natural selection. Owing to the
relentless decrease in transistor size over the past 50 years
and the concomitant exponential increase in computational
power and memory capacity, present-day computers executing
appropriate algorithms outperform us in many tasks that
were thought to be the sole prerogative of the human mind.
Prominent examples include IBM’s Deep Blue that beat the
reigning chess world master in 1997; another IBM computer,
Watson, that can answer questions posed in spoken English
and won the quiz show Jeopardy in 2011; smart phones that
answer questions by speech; Google’s driverless cars that
have logged more than half a million miles on open roads;
and machine vision algorithms for face detection in security
and commercial applications [68]. People playing chess, supplying
meaningful answers to questions, driving a car or
picking out a face are assumed to be conscious. But should
we say the same for these digital creatures?
4. Integrated information theory
Clearly, as we move away from people, BCC and NCC
become progressively less helpful to establish the presence
of consciousness. Even in the normal human brain, we
need to understand why and how certain structures are associated
with experience (the cerebral cortex or, possibly, the
claustrum [69,70]) while others are not (the cerebellum),
and why they do so under certain conditions (wake,
dreams) and not others (deep sleep, seizures). Some philosophers
have claimed that the problem of explaining how
matter can give rise to consciousness may forever elude us,
dubbing it the Hard problem [71–73]. Indeed, as long as
one starts from the brain and asks how it could possibly
give rise to experience—in effect trying to ‘distill’ mind out
of matter [74], the problem may be not only hard, but
almost impossible to solve. But things may be less hard if one
takes the opposite approach: start from consciousness itself,
by identifying its essential properties, and then ask what
kinds of physical mechanisms could possibly account for
them. This is the approach taken by integrated information
theory (IIT) [75–79], an evolving formal and quantitative framework
that provides a principled account for what it takes for
consciousness to arise, offers a parsimonious explanation for
the empirical evidence, makes testable predictions and permits
inferences and extrapolations (table 1).4
(a) Axioms: essential phenomenological properties of
Taking consciousness as primary, IIT first identifies axioms of
experience (figure 3, left), then derives a set of corresponding
postulates (figure 3, right) about its physical substrate [77,80].
The axioms of IIT are assumptions about our own experience
that are the starting point for the theory. Ideally, axioms are
essential (apply to all experiences), complete (include all the
essential properties shared by every experience), consistent
(lack contradictions) and independent (not derivable from
each other). Whether the current set of five axioms are truly
valid, complete and independent remains open.5 The five
axioms are intrinsic existence, composition, information, integration
and exclusion.
(i) Intrinsic existence
Consciousness exists: my experience just is. Indeed, that my
experience here and now exists—it is real or actual—is the
only fact I am immediately and absolutely sure of, as Descartes
realized four centuries ago.Moreover,myexperience exists from
its own intrinsic perspective, independent of external observers.
(ii) Composition
Consciousness is structured: each experience is composed of
many phenomenological distinctions, elementary or higher order, Phil. Trans. R. Soc. B 370: 20140167
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which also exist.Within the sameexperience, for example, Imay
distinguish a book, a blue colour, a blue book and so on.
(iii) Information
Consciousness is specific: each experience is the particular way
it is—it is composed of a specific set of specific phenomenal
distinctions—thereby differing from other possible experiences
(differentiation). Thus, an experience of pure darkness
and silence is what it is because, among other things, it is
not filled with light and sound, colours and shapes, there
are no books, no blue books and so on. And being that
way, it necessarily differs from a large number of alternative
experiences I could have. Just consider all the frames of all
possible movies: the associated visual percepts are but a
small subset of all possible experiences.
(iv) Integration
Consciousness is unified: each experience is irreducible to noninterdependent
subsets of phenomenal distinctions. Thus, I
experience a whole visual scene, not the left side of the
visual field independent of the right side (and vice versa).
For example, the experience of seeing written in the middle
of a blank page the word ‘HONEYMOON’ is irreducible
to an experience of seeing ‘HONEY’ on the left plus the
experience of seeing ‘MOON’ on the right. Similarly, seeing
a blue book is irreducible to seeing a grey book plus the
disembodied colour blue.
(v) Exclusion
Consciousness is definite, in content and spatio-temporal
grain: each experience has the set of phenomenal distinctions
it has, neither less (a subset) nor more (a superset), and it
flows at the speed it flows, neither faster nor slower. Thus,
the experience I am having is of seeing a body on a bed in
a bedroom, a bookcase with books, one of which is a blue
book, but I am not having an experience with less content—
say, one lacking the phenomenal distinction blue/not
blue, or coloured/not coloured; nor am I having an experience
with more content—say, one endowed with the
additional phenomenal distinction high/low blood pressure.
Similarly, my experience flows at a particular speed—each
experience encompassing a hundred milliseconds or so—
but I am not having experience that encompasses just a few
milliseconds or instead minutes or hours.
(b) Postulates: properties that physical mechanisms
must have to support consciousness
To parallel these axioms that capture the essential properties
of every experience, IIT proposes a set of postulates concerning
the requirements that must be satisfied by any physical
system to account for experience (figure 3, right). For simplicity,
physical systems are considered as elements in a state,
such as neurons or logic gates that are either ON or OFF.
All that is required is that such elements have two or more
Table 1. Some terms used in integrated information theory (IIT).
Axioms. Properties of consciousness that are taken as self-evident. The only truths that, with Descartes, cannot be doubted and do not need proof. They
are intrinsic existence, composition, information, integration and exclusion (figure 3, left).
Postulates. Assumptions, derived from axioms, about the physical substrates of consciousness (mechanisms must have cause–effect power, be irreducible,
etc.), which can be formalized and form the basis of the mathematical framework of IIT. It is as yet unproven whether the mapping from axioms to
postulates is unique. There are five postulates, matching the five axioms (figure 3, right).
Element. A minimal component of a system, for example, a neuron in the brain or a logic gate in a computer, having at least two states, inputs that can affect
those states and outputs that depend on them. Strictly speaking, such elements are macro-elements constituted of micro-elements such as molecules, which
are constituted in turn of atoms and so on. IIT predicts that, if neurons are the relevant elements for consciousness, intrinsic cause–effect power within the
system must be highest at the level of such macro-elements rather than at the level of the constituting micro-elements [79].
Mechanism. Any subset of elements within a system, first- and higher order, including the system itself, which has cause–effect power within the system.
Cause–effect repertoire. The probability distribution of potential past and future states of a system as informed by a mechanism in its current state.
Integrated information (w). Information that is specified by a mechanism above and beyond the information specified by its (minimal) parts. w measures
the integration or irreducibility of the cause–effect repertoire specified by a mechanism.
MIP (minimum information partition). The partition that makes the least difference—in other words, the minimum ‘difference’ partition.
Complex. A set of elements within a system that specifies a local maximum of integrated conceptual information Fmax. Only a complex exists as an entity
from its own intrinsic perspective.
Concept. A mechanism and the maximally irreducible cause–effect repertoire it specifies, with its associated value of integrated information wmax. The
concept expresses the cause–effect power of a mechanism within a complex.
Conceptual structure. The set of all concepts specified by a system set with their respective wmax values, which can be plotted as a constellation of
concepts in cause–effect space.
Cause–effect space (or qualia space). A high-dimensional space with one axis for each possible past and future state of the system in which a conceptual
structure can be represented.
Integrated conceptual information (F). Conceptual information that is specified by a system above and beyond the conceptual information specified by its
(minimal) parts. F measures the intrinsic integration or irreducibility of a constellation of concepts (integration at the system level), a non-negative number.
Quale. A conceptual structure specified by a complex in a state that is maximally irreducible intrinsically (synonymous with constellation in qualia space). Phil. Trans. R. Soc. B 370: 20140167
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internal states, inputs that can influence these states in a certain
way and outputs that in turn depend on these states.
(i) Intrinsic existence
A system of mechanisms in a state must exist intrinsically.
Specifically, in order to exist, it must have cause–effect
power, as there is no point in assuming that something
exists if nothing can make a difference to it, or if it cannot
make a difference to anything [88].6 Moreover, to exist from
its own intrinsic perspective, independent of external observers,
it must have cause–effect power upon itself,
independent of extrinsic factors (figure 3, intrinsic existence).
exists intrinsically:
each experience is real,
and it exists from its own
intrinsic perspective,
independent of
external observers (it is
intrinsically real)
to account for experience, a system of mechanisms
in a state must exist intrinsically. To exist, it must
have cause–effect power; to exist from its own
intrinsic perspective, independent of extrinsic
factors, it must have cause–effect power upon itself :
its present mechanisms and state must ‘make a
difference’ to the probability of some past and
future state of the system (its cause–effect space)
consciousness is
structured: each
experience is
composed of
elementary or
higher-order, which
exist within it
blue book
intrinsic existense
higher order mechanism
the system must be
structured: subsets of
system elements
(composed in various
must have cause–effect
power upon the system
[AB] [AC] [BC]
[A] [B] [C]
elementary mechanisms
the system must specify a cause–effect structure
that is the particular way it is: a specific set of
specific cause–effect repertoires—thereby
differing in its specific way from other possible
structures (differentiation). A cause–effect
repertoire specifies the probability of all possible
causes and effects of a mechanism in a state. A
cause–effect structure is the set
of cause–effect repertoires specified
by all subsets of system elements and
expresses how the system gives an
actual form to the space of possibilities
consciousness is
specific: each
experience is the
particular way it is
(it is composed of a
specific set of specific
distinctions), thereby
differing from other
possible experiences
integration partitioned
cause–effect structure
consciousness is
unified: each
experience is
irreducible to noninterdependent
subsets of
the cause–effect structure specified by
the system must be unified: it must be
intrinsically irreducible to that
specified by non-interdependent subsystems
(F> 0) across its weakest
(unidirectional) link:
MIP = minimum information partition
(Fmax) = 1.92
Fmax conceptual structure
jmax of concepts
consciousness is
definite, in
content and
grain: each
experience has
the set of
distinctions it has,
not less or more,
and flows at the
speed it does, not
faster or slower
the cause–effect structure specified
by the system must be definite:
specified over a single set of
elements—not lesss or
more—and spatiotemporal
faster or slower;
this is a cause-effect
structure that is maximally
irreducible intrinsically (Fmax),
called conceptual structure,
made of maximally irreducible
cause–effect repertoires (concepts)
essential properties of every experience
properties that physical systems (elements in a state)
must have to account for experience
cause (past)
effect (future)
cause (past)
cause-effect structure
effect (future)
0.5 ABCc/ABCf
Figure 3. Axioms and postulates of integrated information theory (IIT). The illustration is a colourized version of Ernst Mach’s ‘View from the left eye’ [84]. See also
the mechanisms in figure 4. Phil. Trans. R. Soc. B 370: 20140167
Downloaded from on November 18, 2015
Cause–effect power can be established by considering a
cause–effect space with an axis for every possible state of the
system in the past (causes) and in the future (effects).
Within this space, it is enough to show that an ‘intervention’
that sets the system in some initial state, keeping the state of
the elements outside the system fixed (background conditions),
can lead with probability different from chance to
its present state (cause); conversely, setting the system to its
present state leads with probability different from chance to
some other state (effect).
(ii) Composition
The system must be structured: subsets of the elementary mechanisms
of the system, composed in various combinations, also
have cause–effect power within the system. Thus, if a system
ABC comprises elements A, B and C (figure 3, composition),
any subset of elements, including A, B, C; AB, AC, BC; as
well as the entire system, ABC, can compose a mechanism
having cause–effect power. Composition allows for elementary
(first-order) mechanisms to form distinct higher order
mechanisms, and for multiple mechanisms to form a structure.
(iii) Information
The system must specify a cause–effect structure that is the
particular way it is: a specific set of specific cause–effect
repertoires—thereby differing from other possible ones
(differentiation). A cause–effect repertoire characterizes in full
the cause–effect power of a mechanism within a system by
making explicit all its cause–effect properties. It can be
determined by perturbing the system in all possible ways
to assess how a mechanism in its present state makes a
difference to the probability of the past and future states of
the system. Together, the cause–effect repertoires specified
by each composition of elements within a system specify a
cause–effect structure. Consider for example, within the
system ABC (figure 3, information), the mechanism
implemented by element C, an XOR gate with two inputs
(A and B) and two outputs (the OR gate A and the AND
gate B). If C is OFF, its cause repertoire specifies that, at
the previous time step, A and B must have been either in
the state OFF,OFF or in the state ON,ON, rather than in
the other two possible states (OFF,ON; ON,OFF); and its
effect repertoire specifies that the next time step B will
have to be OFF, rather than ON. Its cause–effect repertoire
is specific: it would be different if the state of C were different
(ON), or if C were a different mechanism (say, an AND
gate). Similar considerations apply to every other mechanism
of the system, implemented by different compositions of
elements. Thus, the cause–effect repertoire specifies the full
cause–effect power of a mechanism in a particular state,
and the cause–effect structure specifies the full cause–
effect power of a system of mechanisms. Note that the
notion of information in IIT differs substantially from that
in communication theory or in common language, but it is
faithful to its etymology: information refers to how a
system of mechanisms in a state, through its cause–effect
power, specifies a form (‘informs’ a conceptual structure) in
the space of possibilities.
(iv) Integration
The cause–effect structure specified by the system must be
unified: it must be intrinsically irreducible to that specified
by non-interdependent sub-systems obtained by unidirectional
partitions. Partitions are taken unidirectionally
to ensure that cause–effect power is intrinsically irreducible—
from the system’s intrinsic perspective—which implies
that every part of the system must be able to both affect
and be affected by the rest of the system. Intrinsic irreducibility
can be measured as integrated information (‘big phi’ or F,
a non-negative number), which quantifies to what extent the
cause–effect structure specified by a system’s mechanisms
changes if the system is partitioned (cut or reduced) along
its minimum partition (the one that makes the least difference).
For example, the system in figure 3 is integrated,
because partitioning it through its weakest link destroys several
cause–effect repertoires and changes others (compare the
cause–effect structure under ‘information’ and under ‘integration’
in figure 3). By contrast, if a system of mechanisms
can be divided into two sub-systems and the partition
makes no difference to the associated cause–effect structure,
then the whole is reducible to those parts. Being intrinsically
irreducible is another precondition for existence having to do
with causation: there is no point in assuming that the whole
exists in and of itself, if it has no cause–effect power above
and beyond its parts. This postulate also applies to individual
mechanisms: a subset of elements can contribute a specific
aspect of experience only if its cause–effect repertoire
within the system is irreducible by the minimum partition
of the mechanism (‘small phi’ or w).
(v) Exclusion
The cause–effect structure specified by the system must be
definite: it is specified over a single set of elements—neither
less nor more—the one over which it is maximally irreducible
(Fmax) from its intrinsic perspective, thus laying maximal
claim to existence. For example (figure 3, exclusion), within
ABCDE, many candidate systems could specify cause–
effect structures, including AB, AC, BC, ABC, ABCD,
ABCDE and so on. Among these, the system that specifies
the cause–effect structure that is maximally irreducible
intrinsically is the set of elements ABC, rather than any of
its subsets or supersets. The exclusion postulate provides a
sufficient reason why the contents of the experience should
be what they are—neither less nor more. With respect to causation,
this has the consequence that the ‘winning’ cause–
effect structure excludes alternative cause–effect structures
specified over overlapping elements: if a mechanism in a
state (say A OFF) specifies a particular cause–effect repertoire
within one system (ABC), it should not additionally specify an
overlapping cause–effect repertoire as part of other, overlapping
systems (say AB or ABCD), otherwise one would be
counting multiple times the difference that mechanism
makes. The exclusion postulate can be said to enforce
Occam’s razor (entities should not be multiplied beyond
necessity): it is more parsimonious to postulate the existence
of a single cause–effect structure over a system of elements—
the one that is maximally irreducible—than a multitude of
overlapping cause–effect structures whose existence would
make no further difference. The exclusion postulate also
applies to individual mechanisms: a subset of elements in a
state specifies the cause–effect repertoire within the system
that is maximally irreducible (wmax), called a core concept, or
concept for short. Again, it cannot additionally specify a
cause–effect repertoire overlapping over the same elements, Phil. Trans. R. Soc. B 370: 20140167
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because otherwise the difference a mechanism makes would
be counted multiple times. A maximally irreducible cause–
effect structure composed of concepts is called a conceptual
structure. The system of mechanisms that specifies a conceptual
structure is called a complex.7 It is useful to think of a
conceptual structure as existing as a form in cause–effect
space, whose axes are given by all possible past and future
states of the complex. In this space, every concept is a point
(star), whose size is given by its irreducibility wmax, and a
conceptual structure is a ‘constellation’ of points, that is, a
form. Finally, this postulate also applies to spatio-temporal
grain. For example, a mechanism cannot have effects at a
fine temporal grain, and additional effects at a coarser
grain, otherwise causal exclusion would be violated. On the
other hand, if the effects at a coarser grain are more irreducible
than those at a finer grain, then the coarser grain of
causation excludes the finer one [79].8
(c) The central identity: experience as a conceptual
Altogether, the elements of a complex in a state, composed
into higher order mechanisms that specify concepts, form a
conceptual structure that is maximally irreducible intrinsically,
also known as a quale. The constellation of all concepts specifies
the overall form or shape of the quale (figure 4).
On this basis, the central identity of IIT can be formulated
quite simply: an experience is identical to a conceptual structure
that is maximally irreducible intrinsically. More precisely, a conceptual
structure completely specifies both the quantity and
the quality of experience: how much the system exists—the
quantity or level of consciousness—is measured by its Fmax
value—the intrinsic irreducibility of the conceptual structure;
which way it exists—the quality or content of consciousness—
is specified by the shape of the conceptual structure. If a
system has Fmax ¼ 0, meaning that its cause–effect power
is completely reducible to that of its parts, it cannot lay
claim to existing. If Fmax . 0, the system cannot be
reduced to its parts, so it exists in and of itself. More generally,
the larger Fmax, the more a system can lay claim to
existing in a fuller sense than systems with lower Fmax.
According to IIT, the quantity and quality of an experience
are an intrinsic, fundamental property of a complex of mechanisms
in a state—the property of informing or shaping the
space of possibilities (past and future states) in a particular
way, just as it is considered to be intrinsic to a mass to
bend space–time around it.9
At any given time, then, consciousness is supported by a
set of neuronal elements forming a complex of high Fmax that
specifies a conceptual structure that is maximally irreducible
intrinsically. The particular set of neurons that form the major
complex, the one of highest Fmax in the brain, may change to
some extent from moment to moment, as well as their state—
which neurons are firing and which are not. For example, let
us assume that while I watch a scene of a movie containing
the actress Jennifer Aniston (JA), the major complex in my
brain is made up of neurons within certain parts of the cerebral
cortex.10 Every neuron within the complex necessarily
shapes the probability of possible past states (causes) and
future states (effects) of the complex, depending on how it
is connected to the other neurons and on its state (say
firing strongly for 100 ms). Thus, a neuron firing strongly in
a certain visual area may specify as more likely those past
states of the complex that are compatible with the invariant
concept ‘J.A.’s face’, as well as certain appropriate future
states. Another neuron firing strongly in another visual area
may specify that there likely was a horizontal edge in a certain
position of the visual field, and so on. Yet other
neurons that are part of the complex but are silent may
specify that certain past (and future) states are unlikely to
have occurred (or to occur), such as those having to do
with the invariant concepts ‘book’, ‘square’ and so on. Moreover,
combinations of neurons may specify higher order
concepts, such as ‘J.A. with a red hat sitting on the couch
on the left’. Note that all the concepts are specified by
elements of the complex, specify cause–effect repertoires
over elements of the complex, and acquire meaning intrinsically,
in relation to the other concepts in the quale, and not
by referring to external inputs (J.A. is just as meaningful
when daydreaming about her, or in a dream) [80].
In principle, then, the postulates of IIT offer a way to analyse
any system of mechanisms in a particular state and
determine whether it constitutes a complex, over which
spatial and temporal grain,11 and which quale it specifies.
Furthermore, while in practice it is not possible to determine
the quale and Fmax precisely for a realistic system, it is
already possible to employ IIT for prediction, explanation
and extrapolation.
(d) Predictions
A straightforward experimental prediction of IIT is that the
loss and recovery of consciousness should be associated
with the breakdown and recovery of the brain’s capacity for
information integration. This prediction has been confirmed
using transcranial magnetic stimulation (TMS) in combination
with high-density EEG in conditions characterized
by loss of consciousness [95,96]. These include deep sleep,
general anaesthesia obtained with several different agents
and brain-damaged patients (vegetative, minimally conscious,
emerging from minimal consciousness, locked-in). If
a subject is conscious when the cerebral cortex is probed
with a pulse of current induced by the TMS coil from outside
the skull, the cortex responds with a complex pattern of reverberating
activations and deactivations that is both
widespread (integrated) and differentiated in time and
space (information rich) [95]. By contrast, when consciousness
fades, the response of the cortex becomes local (loss of
integration) or global but stereotypical (loss of information).
The perturbational complexity index (PCI), a scalar measure of
the compressibility of the EEG response to TMS inspired
by IIT, decreases distinctly in all the different conditions of
loss of consciousness and, critical for a clinically useful
device, is high instead in each conscious healthy subject or
neurological patient tested so far [96].
A theory is the more powerful the more it makes correct
predictions that violate prior expectations. One counterintuitive
prediction of IIT is that a system such as the cerebral
cortex may generate experience even if the majority of its pyramidal
neurons are nearly silent, a state that is perhaps
approximated through certain meditative practices that aim
at reaching ‘naked’ awareness without content [97,98]. This
corollary of IIT contrasts with the common assumption that
neurons only contribute to consciousness if they are active
in such a way that they ‘signal’ or ‘broadcast’ the information
they represent and ‘ignite’ fronto-parietal networks [3]. Phil. Trans. R. Soc. B 370: 20140167
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That silent neurons can contribute to consciousness is
because, in IIT, information is not in the message that is
broadcast by an element, but in the form of the conceptual
structure that is specified by a complex. Inactive elements
of a complex specify a cause–effect repertoire (the probability
of possible past and future states) just as much as active ones
(think of the dog that did not bark in the famous Sherlock
Holmes story). Conversely, if the same neurons were not
merely inactive, but pharmacologically or optogenetically
inactivated, they would cease to contribute to consciousness:
even though their actual state is the same, they would not
specify a cause–effect repertoire, since they do not affect
the probability of possible past and future states of the
Another counterintuitive prediction of IIT is that if the
efficacy of the 200 million callosal fibres through which the
two cerebral hemispheres communicate with each other
were reduced progressively, there would be a moment at
which, for a minimal change in the traffic of neural impulses
across the callosum, there would be an all-or-none change in
consciousness: experience would go from being a single one
to suddenly splitting into two separate experiencing minds
(one linguistically dominant), as we know to be the case
with split-brain patients [101,102]. This would be the point
at which Fmax for the whole brain would fall below the
value of Fmax for the left and for the right hemisphere
taken by themselves.
More generally, IIT predicts that, whatever the neural correlate
of consciousness (NCC) turns out to be—whether it is
global or local within the cortex, anterior or posterior, medial
or lateral, whether it includes primary areas or not, the
thalamus or not, whether it encompasses neurons in supragranular,
infragranular layers of cortex or not—it should be
a local maximum of F, and thus of a maximum of intrinsic,
irreducible cause–effect power. IIT also predicts that the
NCC is not necessarily fixed, but may expand, shrink and
even move within a given brain depending on various conditions.
In fact, there may even be multiple NCCs in a
single brain, as shown by split-brain patients, in which case
there should be multiple local maxima of integrated information.
Finally, IIT makes precise predictions about the
physical elements that constitute the NCC and the time intervals
and levels of activity at which they operate [77,79]: they
should have a spatial scale that achieves the highest value of
F, as opposed to finer or coarser grains (say, either individual
neurons or local groups of neurons rather than neuronal compartments
or brain areas); they should operate most
effectively (highest value of F) at the time scale of consciousness,
as opposed to finer or coarser scales (say, hundred
milliseconds rather than a millisecond or ten seconds); and
the activity states that make the most difference to the NCC
should be the ones that support phenomenological distinctions
(say, bursting, high mean firing, low mean firing). In
short, the general rule is that the NCC must always
correspond to a maximum of intrinsic, ireducible cause–
effect power.
(e) Explanations
IIT offers a coherent, principled account of the NCC—which
it identifies with the major complex in a particular state—and
of many disparate empirical observations. For example, why
is consciousness generated by the cerebral cortex (or at least
some parts of it), but the cerebellum does not contribute to
it, despite the latter having even more neurons; [103]? Why
does consciousness fade early in sleep, although the brain
remains active? Why is it lost during generalized seizures,
when neural activity is intense and synchronous? Why is
there no direct contribution to consciousness from neural
activity within sensory pathways (the retina) and motor pathways
(the motoneurons in the spinal cord), or within neural
circuits looping out of the cortex into subcortical structures
and back, despite their manifest ability to influence the
content of experience?
These and other well-known facts find a parsimonious
explanation based on the postulates of IIT. Thus, a prominent
feature of the cerebral cortex, which is responsible for the content
of consciousness, is that it is composed of elements that
are functionally specialized and at the same time can interact
rapidly and effectively. This is the kind of organization that
yields a comparatively high value of Fmax. Instead, the cerebellum
is composed of small modules that process inputs
and produce outputs largely independent of each other
[104,105]. Simulations also show that input and output pathways,
while capable of affecting the major complex and being
affected by it, can remain excluded from it, because they are
not part of a local maximum of integrated information. The
same applies to loops that may exit the major complex and
reenter it. Other simulations show that Fmax is low when
the effective connectivity among a set of elements is weak
or is organized in homogeneous manner. Indeed, as was
mentioned above, when consciousness fades during deep
slow wave sleep or in certain states of general anaesthesia,
the interactions among different cortical regions become
weaker or highly stereotypical, as they do during generalized
epileptic seizures.
(f ) Extrapolations
Finally, the more the postulates of IIT are validated in situations
in which we are reasonably confident about whether
and how consciousness changes, the more we can use the
theory to extrapolate and make inferences about situations
where we are less confident—brain-damaged patients, newborn
babies, alien animals, complicated machines and other
far-fetched scenarios, as we shall consider next.
5. Everywhere?
In the ‘Canticle of the Creatures’, Saint Francis addressed animals,
flowers and even stones as if endowed with soul, and
praised them as mother earth, brother sun, sister moon, the
stars, the air, water and fire. And he was not alone. Some
of the brightest minds in the West embraced some form of
the ancient philosophical doctrine of panpsychism, starting
with the Presocratics and Plato. The Renaissance philosophers
Patrizi, Bruno, Telesio and Campanella took the
position that matter and soul are one substance. Later, Spinoza,
Leibniz, Schopenhauer and, closer to modern times,
James, Whitehead, Russell, and Teilhard de Chardin
espoused panpsychist notions [106,107]. Strawson [108,109]
is a well-known contemporary defender of panpsychism.
Eastern traditions, such as Buddhism, have always emphasized
the continuity of consciousness across life.
Materialism, or its modern offspring, physicalism, has
profited immensely from Galileo’s pragmatic stance of Phil. Trans. R. Soc. B 370: 20140167
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removing subjectivity (mind) from nature in order to describe
and understand it objectively—from the extrinsic perspective
of a manipulator/observer. But it has done so at the cost of
ignoring the central aspect of reality from the intrinsic perspective—
experience itself. Unlike idealism, which does
away with the physical world, or dualism, which accepts
both in an uneasy marriage, panpsychism is elegantly unitary:
there is only one substance, all the way up from the
smallest entities to human consciousness and maybe to the
World Soul (anima mundi). But panpsychism’s beauty has
been singularly barren. Besides claiming that matter and
mind are one thing, it has little constructive to say and
offers no positive laws explaining how the mind is organized
and works.
IIT was not developed with panpsychism in mind (sic).
However, in line with the central intuitions of panpsychism,
IIT treats consciousness as an intrinsic, fundamental property
of reality. IIT also implies that consciousness is graded, that it
is likely widespread among animals, and that it can be found
in small amounts even in certain simple systems. Unlike
panpsychism, however, IIT clearly implies that not everything
is conscious. Moreover, IIT offers a solution to several
of the conceptual obstacles that panpsychists never properly
resolved, like the problem of aggregates (or combination problem
[107,110]) and can account for its quality. It also
explains why consciousness can be adaptive, suggesting a
reason for its evolution.
(a) Consciousness is a fundamental property
The axioms and postulates of IIT say that consciousness is a
fundamental, observer-independent property that can be
accounted for by the intrinsic cause–effect power of certain
mechanisms in a state—how they give form to the space of
possibilities in their past and their future. An analogy is
mass, which can be defined by how it curves space–time
around it—except that in the case of experience the entities
having the property are not elementary particles but complexes
of elements, and experience comes not in two but in
a trillion varieties. In this general sense, at least, IIT is not
at odds with panpsychism.
(b) Consciousness comes in various qualities
Unfortunately, panpsychism is mute when it comes to
explaining the way any one conscious experience feels—
why the perception of red feels different from that of blue
and why colours are experienced as different from tones.
Instead, at least in principle, IIT says exactly what determines
the quality of an experience—what makes it the
particular way it is: an experience is a maximally irreducible
conceptual structure or quale—a shape in a fantastically highdimensional
cause–effect space specified by a complex of
neurons in a particular state. This is the constellation of concepts
through which the neurons of the major complex, in
various combinations, give form to the space of its possible
past and future states (figure 4). Different experiences—
every different scene in a movie or in a dream—correspond
to different shapes, with some shapes being measurably
closer (red and blue) and some more distant within
the space (a black screen and a city scene). Indeed, there is
much scope for future research to begin mapping psychophysics,
for example, the circular nature of colour space,
onto the geometry of shapes in cause–effect space—except
that a shape in cause–effect space, unlike the shape of an
object in 3D space, is the shape within, the shape of experience
itself. It is the voice in the head, the light inside the skull.
(c) Consciousness is adaptive
IIT takes no position on the function of experience as such—
similar to physics not having anything to say about the function
of mass or charge. However, by identifying
consciousness with integrated information, IIT can account
for why it evolved, another aspect about which panpsychism
has nothing to say. In general, a brain having a high capacity
for information integration will better match an environment
with a complex causal structure varying across multiple time
scales, than a network made of many modules that are informationally
encapsulated. Indeed, artificial life simulations
(‘animats’) of simple Braitenberg-like vehicles that have to
traverse mazes and whose brains evolve by natural selection
over 60 000 generations show a monotonic relationship
between (simulated) integrated information and adaptation
[111,112]. That is, the more adapted individual animats are
to their environment, the higher the integrated information
of the major complex in their brain. Similar animats, evolved
to catch falling blocks in a Tetris-like scenario, demonstrate
that increased adaptation leads to increased number of concepts
in the major complex and an associated increase in
integrated information that depends on the complexity of
the animats’ environment [113]. Thus, evolution by natural
selection gives rise to organisms with high Fmax because,
given constraints on the number of elements and connections,
they can pack more functions per element than their less integrated
competitors and thus are more adept at exploiting
regularities in a rich environment.
(d) Consciousness is graded
IIT does side with the panpsychist intuition that consciousness
may be present across the animal kingdom, and even
beyond, but in varying degrees. Everything else being
equal, integrated information, and with it the richness of
experience, is likely to increase as the number of neurons
and the abundance of their interconnections grow, although
sheer number of neurons is not a guarantee, as shown by
the cerebellum. It is also likely that consciousness is graded
across the lifetime of any one organism. In us it becomes
richer as we grow from a baby to an adult whose brain has
fully matured and becomes more functionally specialized. It
can also wax and wane when we are highly alert or
drowsy, intoxicated by drugs or alcohol, or become demented
in old age. This is illustrated schematically in figure 5a,
where a set of ‘cortical’ areas is integrated into a major complex
of ‘high’ Fmax when the inter-areal connections are
strong, undergoes a reduction in Fmax when connection
strength is reduced by neuromodulatory changes (simulated
as an increase in noise), and finally breaks down into small
complexes of low Fmax.
A corollary of IIT that violates common intuitions is that
even circuits as simple as a ‘photodiode’ made up of a sensor
and a memory element can have a modicum of experience
[80] (see also figure 5a, right panel). It is nearly impossible
to imagine what it would ‘feel like’ to be such a circuit, for
which the only phenomenal distinction would be between
‘this rather than not this’ (unlike a photodiode, when we
are conscious of ‘light’ or of ‘dark,’ our experience is what Phil. Trans. R. Soc. B 370: 20140167
Downloaded from on November 18, 2015
cause repertoires
effect repertoires
complex: a physical substrate
of experience
concept: a maximally irreducible
cause–effect repertoire
past states future states
conceptual structure (‘quale’): a cause–effect structure in cause–effect space
made of concepts that is maximally irreducible intrinsically
quantity of experience:
intrinsic irreducibility (Fmax)
of the conceptual structure quale
Fmax= 1.92
(jmax= 0.5)
of state
111 101
past states future states
p = 1
p = 1
quality of experience:
(‘form’) of the conceptual structure
Figure 4. A didactic example of how to calculate the quality and quantity of consciousness given a system of elements in a state. On the upper left are three gates
with binary states (either ON or OFF: ABC ¼ 100; see also figure 3) that are wired together as shown. An analysis based on the postulates of IIT [80] reveals that the
system forms a complex. The complex in its present state specifies a quale—a conceptual structure that is maximally irreducible intrinsically. The quale is presented
both as the set of maximally irreducible cause–effect repertoires (concepts) specified by each mechanism (top) and as a two-dimensional projection in which each
concept is a ‘star’ in cause–effect space (bottom). Cause–effect space or qualia space is a high-dimensional (here, 2  8 dimensions) space in which each axis is a
possible past (in blue) and future (in green) state of the complex, and the position along the axis is the probability of that state. Each concept is a star whose
position indicates how a mechanism composed of a subset of elements affects the probability of past and future states of the system (its cause–effect repertoire,
which specifies what the concept contributes to experience) and whose size (wmax) measures how irreducible the concept is (how much it contributes to experience).
In IIT, Fmax—a non-negative number—measures the intrinsic irreducibility of the entire quale, how much consciousness there is—the quantity of
experience. The ‘form’ or shape of the quale (constellation of stars) is identical to the quality of the experience. Different shapes correspond to different experiences:
they feel the way they do—red feeling different from blue or from a headache—because of the distinct shapes of their qualia. Phil. Trans. R. Soc. B 370: 20140167
Downloaded from on November 18, 2015
it is because it includes scores of negative concepts, such as
no colours, no shapes, no thoughts and so on, that are all
available to us). But consider that normal matter at
2272.158C, one degree above absolute zero, still contains
some heat. However, in practice its temperature is as cold
as it gets. Similarly, there may well be a practical threshold
for Fmax below which people do not report feeling much of
anything, but this does not mean that consciousness has
reached its absolute minimum, zero. Indeed, when we fall
into a deep, dreamless sleep and don’t report any experience
upon being awoken, some small complex of neurons within
our sleeping brain will likely have a Fmax value greater
than zero, yet that may not amount to much compared to
that of our rich, everyday experience.
(e) Multiple consciousnesses
IIT also allows for the possibility of two or more complexes
coexisting within a single system [80]. Depending on the
exact connectivity, these are likely to have quite different
values of Fmax. Indeed, in the brains of both vertebrates
and invertebrates, there may well exist, at least under some
conditions, a major complex and one or more minor complexes.
In humans, the complex that supports our day-today
stream of conscious experience should have by far the
highest value of integrated information—it should be the
major complex. In split-brain patients the speaking, major
complex is unaware of the presence of another consciousness,
one that typically lacks speech, but which can be revealed by
clever experimental paradigms [102,114]. It is conceivable
that at least some cases of ‘high-level’ performance found
in normal subjects [64,115]), while unconscious from the perspective
of the major complex, may be due to the presence of
minor complexes (of course, some of these behaviours may be
mediated by purely feed-forward circuits). This counterintuitive
scenario of ‘many conscious minds within a single brain’
could be assessed, at least in principle, by measurements of
integrated information at the neuronal level. Major and
minor complexes may also occur in patients with Marchiafava–
Bignami disease [116] and other disconnection
syndromes, in patients with identity and conversion disorders
[63], and in other neurological and psychiatric
(f ) Aggregates are not conscious
‘Take a sentence of a dozen words, and take twelve men and
tell to each one word. Then stand the men in a row or jam
them in a bunch, and let each think of his word as intently
as he will; nowhere will there be a consciousness of the
whole sentence’. This is how William James illustrated the
combination problem of panpsychism [110]. Or take John
Searle: ‘Consciousness cannot spread over the universe like
a thin veneer of jam; there has to be a point where my consciousness
ends and yours begins’ [117]. Indeed, if
consciousness is everywhere, why should it not animate the
United States of America? IIT deals squarely with this problem
by stating that only maxima of integrated information
exist. Consider two people talking: within each brain, there
will be a major complex—a set of neurons that form a maximally
irreducible cause–effect structure with definite borders
and a high value of Fmax. Now let the two speak together.
They will now form a system that is also irreducible (F .
zero) due to their interactions. However, it is not maximally
irreducible, since its value of integrated information will be
much less than that of each of the two major complexes it
contains. According to IIT, there should indeed be two separate
experiences, but no superordinate conscious entity that is
the union of the two. In other words, there is nothing-it-islike-
to-be two people, let alone the 300 plus million citizens
making up the USA.13 Again, this point can be exemplified
schematically by the system of figure 5a, right panel. While
the five small complexes do interact, forming a larger integrated
system, the larger system is not a complex: by the
exclusion postulate, only the five smaller complexes exist,
since they are local maxima of integrated information
(Fmax ¼ 0.19), while the larger system is not a complex
(F ¼ 0.03).Worse, a dumb thing with hardly any intrinsically
distinguishable states, say a grain of sand for the sake of the
argument, has no experience whatsoever. And heaping a
large number of such zero-F systems on top of each other
would not increase their F to a non-zero value: to be a
sand dune does not feel like anything either—aggregates
have no consciousness.
(g) Complicated systems can be unconscious
A second class of zero-F systems are purely feed-forward
computational networks in which one layer feeds the next
one without any recurrent connections. In a feed-forward network,
the input layer is always determined entirely by
external inputs and the output layer does not affect the rest
of the system, hence neither layer can be part of a complex,
and the same is true recursively for the next layers downstream
and upstream. According to IIT, then, a feedforward
network does not exist intrinsically—for itself—but
is a zombie—carrying out tasks unconsciously [118]. Yet
from the extrinsic perspective of a user, feed-forward networks,
like those used in deep learning, perform plenty of
useful computational functions, such as finding faces or
cats in images [119], labelling images, reading zip codes
and detecting credit card fraud.
This has a rather startling consequence. Consider that any
neural network with feedback circuits can be mapped onto a
purely feed-forward network in such a manner that the latter
approximates its input–output relationships (for computations
bounded by a maximal time step [120]). That is, for
the same inputs, the two networks will yield the same
output (in general, the equivalent feed-forward network
will have many more nodes and connection than the feedback
network). Therefore, a purely feed-forward system
able to replicate the input–output behaviour of the human
brain (under the limited time-step constraint) would be behaviourally
indistinguishable from us, and certainly capable
of passing the Turing test, yet it would have zero F and
would thus be a ‘perfect’ zombie. A simple example of two
functionally equivalent systems, one with recurrent connections
and non-zero F, and one purely feed-forward with
zero F, is shown in figure 5b [80].
In people and organisms that evolved through natural
selection, input–output behaviour provides a good first
guess about the presence of consciousness. However, as
demonstrated by the example in figure 5b, this may not
always be the case for radically different computational architectures.
In the general case, and certainly with machines,
it becomes essential to consider the internal circuitry—not
just what the machine does, but how it does it. This also Phil. Trans. R. Soc. B 370: 20140167
Downloaded from on November 18, 2015
Fmax= 10.56
≥1 Fmax= 0, no concepts
Fmax= 0.76
17 concepts
integrated system
2 2 1 0 0 2 2 1
979 date: 9 Nov 2007
EHT = 5.00 kV
100 nm
S/D Silicide
(likely NiSi) STI
W metal ‘0’ used as
local interconnect
in some regions
NMOS transistor
uses replacement
metal gate (gate-last)
0 0 0 0 0 2 2 1
O2 ≥1
J t-3
≥2 ≥2
I2 t-2 H2 t-2
H2 t-1
I2 t-1
[0, 1, 2]
... ...
... ...
2 2 1 0 0 2 2 1
[0, 1, 2] [0, 1, 2]
0 0 0 0 0 2 2 1
[0, 1, 2]
I1 I2
D ≥1
D2 t-1
D2 t-2
D2 t-3
t4 t10
feed-forward system
t4 t10
Fmax= 3.22
(a) 0.3 noise
(i) (ii)
0.48 noise
Fmax= 0.19
F= 0.19
F= 0.03
Figure 5. IIT makes several predictions about which systems can experience anything—how much and in which way—and which systems, even complicated ones,
have no experience, remaining ‘in the dark’. IIT implies that consciousness is graded (a); that aggregates are not conscious (a, right panel); that strictly feed-forward
systems are not conscious (b, right panel), even if they are functionally equivalent in terms of their input–output operations to feedback networks that are conscious
(b, left panel); that even accurate biophysical simulations of the human brain running on digital machines would not be conscious like us, but would be mere
aggregates of much simpler systems (transistors and the like) having minimal Fmax (c). The last row (c) shows, from left to right, a human brain (Allen Institute),
the IBM Blue Gene P supercomputer, a columnar model of mouse cortex (Blue Brain Project) and a scanning electron micrographic cross-section of 4 NMOS INTEL
transistors in a grid. Phil. Trans. R. Soc. B 370: 20140167
Downloaded from on November 18, 2015
means that there cannot be an ultimate Turing test for consciousness
(although, there may be some practical
CAPTCHA-like tests [121]). According to many functionalist
notions [122], if a machine reproduces our input–output behaviour
in every circumstance, it would have to be granted
consciousness just as much as us. IIT could not disagree
more—no Turing test (e.g. Samantha in the Hollywood
movie She) can be a sufficient criterion for consciousness,
human or otherwise.
(h) Simulations of conscious neural systems can be
Finally, what about a computer whose software simulates in
detail not just our behaviour, but even the biophysics of neurons,
synapses and so on, of the relevant portion of the human
brain [123]? Could such a digital simulacrum ever be conscious?
Functionalism again would say yes, even more
forcefully. For in this case all the relevant functional roles
within our brain, not just our input–output behaviour,
would have been replicated faithfully. Why should we not
grant to this simulacrum the same consciousness we grant
to a fellow human? According to IIT, however, this would
not be justified, for the simple reason that the brain is real,
but a simulation of a brain is virtual. For IIT, consciousness
is a fundamental property of certain physical systems, one
that requires having real cause–effect power, specifically the
power of shaping the space of possible past and future
states in a way that is maximally irreducible intrinsically. In
the same way, mass is an intrinsic property of systems of particles,
a property that has real causal power, specifically that
of bending space–time. Therefore, just like a computer simulation
of a giant star will not bend space–time around the
machine, a simulation of our conscious brain will not have
consciousness.14 Of course, the physical computer that is running
the simulation is just as real as the brain. However,
according to the principles of IIT, one should analyse its
real physical components—identify elements, say transistors,
define their cause–effect repertoires, find concepts, complexes
and determine the spatio-temporal scale at which F
reaches a maximum. In that case, we suspect that the computer
would likely not form a large complex of high Fmax, but
break down into many mini-complexes of low Fmax. This is
due to the small fan-in and fan-out of digital circuitry
(figure 5c), which is likely to yield maximum cause–effect
power at the fast temporal scale of the computer clock.15
6. Conclusion
In summary, there are some aspects of IIT that definitely do
not fit with panpsychism, and others that vindicate some of
its intuitions. In this respect, it is natural to consider how
one should regard some of the inferences derived from IIT
for which it is hard even to imagine a direct test at the present
time. Our position is that, as is often the case in science,16 a
theory is first tested and validated in situations that are
close to ideal, and then extrapolated to more remote cases.
Ideally, whether consciousness varies with integrated information,
and other predictions of IIT, would first be
validated here—on my own consciousness: for example,
does Fmax collapse when I undergo general anaesthesia or
a seizure, or when I fall into dreamless sleep, and return to
high values when I dream? Does my experience change if
one temporarily inactivates a region of my brain that is part
of the major complex, but not one that is outside it? Does it
change if one succeeds in connecting a neuromorphic microcircuit
that becomes part of my major complex and not
otherwise? Then one can extrapolate to there, at first in situations
involving other healthy humans, then in slightly
more difficult cases, say monkeys with a brain similar to
ours who are trained to give reports similar to ours. Finally,
insofar as the theory has been validated and has shown
good predictive and explanatory power, one can try and
extrapolate to everywhere, unresponsive patients with just a
small ‘island’ of functioning brain tissue, newborn babies,
animals very different from us, photodiodes, machines, and
computer simulations. After all, often in science the most
we can do is to draw our best inferences about unknown
instances based on a theory that works well in many
known instances. And that is much better than to make
arbitrary claims or to draw no inference whatsoever.
Acknowledgements. We thank Larissa Albantakis, Melanie Boly, Chiara
Cirelli, Lice Ghilardi and Marcello Massimini for their many contributions
to the work presented here.
1Note that we consider reflective–consciousness, highly developed in
adult humans, to be a subclass of conscious experiences. Likewise, the
feeling of freely willing an action—such as raising one’s arm—sometimes
also referred to as agency [6,7]—is another subclass of
conscious experiences. While their content differs from the content
associated with feeling pain or seeing red, subjectivity is common
to all.
2Consciousness can be dissociated from many other cognitive processes
that have traditionally been closely linked to it, including
memory, emotions and selective attention (for reviews see [19,20]).
It can persist if the recall of long-term memories is impaired, it can
be present in patients who lack affect, and it can be dissociated
from attention. The last point is particularly counterintuitive but is
well supported—subjects can attend to invisible objects [21]. The
extent to which it is possible to become conscious of something without
also attending to it is more controversial [21,22].
3Not to mention the question of whether it feels-like-something to be
a Venus flytrap or a single-cell organism.
4If it is not outright wrong, IIT most likely will have to be refined,
expanded and adjusted. However, in its current form (IIT 3.0), it
explains and predicts a wide range of phenomena, including a
number of counterintuitive predictions amenable to empirical falsification.
For the latest formulation of the theory, see [80]; for earlier
versions, see [76,77,81,82]; for a literary account, see [77,83]. The
main differences between IIT 3.0 and earlier versions are listed in
the appendix of [80].
5For instance, the unified nature of conscious experiences has been
questioned by psychophysical experiments demonstrating temporal
asynchrony [85,86]. See also [87].
6For example, the notion of the aether was introduced in the late nineteenth
century to explain the propagation of light. When more and
more experiments concluded that, whatever the aether might be, it
had no effects whatsoever, it finally fell under Occam’s razor, and
it plays no role in modern physics.
7Importantly, thismay be amacro- rather than a micro-spatio-temporal
scale [79]. For example, the relevant level for human consciousness is
likely to be neurons at the scale of 100 ms, rather than molecules at
the nanosecond scale. Note that it is possible for a single physical
system, such as the brain, to contain two or more independent yet causally
interacting complexes, each with their own Fmax (see section on
multiple consciousnesses). Indeed, it is even possible for a physical
system to contain complexes at different spatio-temporal grains, such
as a mitochondrion forming a complex inside a neuron, as long as
there is no causal overlap at the relevant scales. Phil. Trans. R. Soc. B 370: 20140167
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8Requiring that only the maximum of F over elements, spatial and
temporal grain must be considered is not exceptional in science:
many of the laws of physics are formulated as extremum principles,
e.g. the principle of least action.
9IIT postulates that experience is a fundamental, intrinsic property of
the world. Different experiences must be specified by different physical
substrates, although different physical substrates may specify the
same experience (for example, by differing in micro-properties that
are causally irrelevant at the macro-scale that achieves a maximum
of cause–effect power, see also metamers) [89]. Note that IIT is compatible
with quantum mechanics. In principle, F and related
quantities can be assessed also in quantum system, although it has
been suggested that at the quantum level F values may be very
small [90].
10Here we do not elaborate about particular cortical areas, cortical
layers or particular population of neurons.
11The exclusion postulate requires that the set of mechanisms that
specify one particular experience do so over the time window at
which F reaches a maximum. If the next experience involves an overlapping
set of mechanisms, it would seem that, to avoid multiple
causation, it should be specified over a non-overlapping time
window. Accordingly, the seemingly continuous ‘stream’ of consciousness
would actually be constituted by a discrete succession of
‘snapshots’, in line with some psychophysical evidence [91–94].
Note that each snapshot has motion and other dynamic percepts
associated with it.
12It is instructive to consider ‘the perfect experiment’ hypothesized
by Cohen & Dennett [99]: a subject looks at a red apple, and neurons
in his cortical colour region (say V4) fire. However, imagine one
could selectively block their projections to further cortical regions, so
that the subject cannot access and report the colour of the apple.
According to Cohen and Dennett, any theory claiming that
‘phenomenal’ consciousness can be dissociated from cognitive
access would have to claim that, as long as the colour neurons are
active, the subject would be conscious of a red apple, while at the
same time he would be denying that it is red. According to IIT,
however, blocking the projections of V4 neurons destroys their
cause–effect repertoires no matter whether they are firing or not,
leading to the collapse of the ‘colour section’ (Q-fold) of the conceptual
structure that corresponds to the subject’s experience. As a
consequence, the subject would not be conscious of colours (cerebral
achromatopsia) and would not even understand what he has lost
(colour anosognosia), just like a patient described by von Arx et al.
13By the same token, the exclusion postulate predicts a scenario that
is the mirror image of the prediction that consciousness will suddenly
split in two when the corpus callosum is ‘cooled’ below a critical
point: if two people speaking were to increase their effective causal
interactions by some, yet to be invented, direct brain-to-brain connectivity
booster, to the point where the Fmax of the two interacting
brains would exceed Fmax of the individual brains, their individual
conscious mind would disappear and its place would be taken by
a new U ¨ ber-mind that subsumes both.
14A similar point was made by John Searle with his Chinese Room
Argument [124] and by Leibniz 300 years earlier with his mill [125].
15Ultimately, any digital computer running software can be
mimicked by a Turing Machine with a large state-transition matrix,
a moving head that writes and erases, and a very, very long
memory tape—in that case, cause–effect power would reside in the
moving head that follows one out of a few instructions at a time.
On the other hand, there is no reason why hardware-level, neuromorphic
models of the human brain that do not rely on software
running on a digital computer, could not approximate, one day,
our level of consciousness [126].
A related question has to do with the Internet and whether it could
be conscious [127]. One way to think about this is to assume that each
computer connected to the Internet is an element having real cause–
effect power at a macro-level (by ‘black-boxing’ its internal mechanisms).
For example, each computer could send an ON signal when it
is ON and an OFF signal when it is OFF. One could then make sure
that each computer increased or decreased the likelihood of being
ON depending on how many ON signals it receives. In principle,
this kind of organization could be arranged so that it gives rise to a
complex of high F, although this is certainly not the way the Internet
works right now. On the other hand, if one considers the microelements
inside each computer (say its transistors) as having real
cause–effect power, we are back to the situation in which they
most likely would not form any large complex within each computer,
let alone across connected computers.
16A well-known instance of such an extrapolation is the inference of
singularities in space–time due to the extreme mass of a stellar
object. Such black holes were pure conjectures, based on a solution of
Einstein’s theory of General Relativity, until they were subsequently
confirmed observationally.
1. Koch C. 2004 The quest for consciousness: a
neurobiological approach. Englewood, CO: Roberts
and Co.
2. Baars B, Gage NM. 2010 Cognition, brain, and
consciousness. New York, NY: Academic Press.
3. Dehaene S, Changeux J-P. 2011 Experimental and
theoretical approaches to conscious processing. Neuron
70, 200–227. (doi:10.1016/j.neuron.2011.03.018)
4. Boly M, Baars B, Seth AK, Wilke M, Ingmundson P,
Laureys S, Edelman D, Tsuchiya N. 2013
Consciousness in humans and non-human animals:
recent advances and future directions. Front.
Psychol. 4, 625. (doi:10.3389/fpsyg.2013.00625)
5. Laureys S, Tononi G. 2009 The neurology of
consciousness: cognitive neuroscience and
neuropathology. Amsterdam, The Netherlands:
Academic Press.
6. Fried I, Katz A, McCarthy G, Sass KJ, Williamson P,
Spencer SS, Spencer DD. 1991 Functional
organization of human supplementary motor cortex
studied by electrical stimulation. J. Neurosci. 11,
7. Wegner DM. 2002 The illusion of conscious will.
Cambridge, MA: MIT Press.
8. Lancaster MA et al. 2013 Cerebral organoids model
human brain development and microcephaly.
Nature 501, 373–379. (doi:10.1038/nature12517)
9. Tononi G, Srinivasan R, Russell DP, Edelman GM.
1998 Investigating neural correlates of conscious
perception by frequency-tagged neuromagnetic
responses. Proc. Natl Acad. Sci. USA 95, 3198–
10. Cheesman J, Merikle PM. 1984 Priming with and
without awareness. Percept. Psychophys. 36, 387–395.
11. Ramsoy TZ, Overgaard M. 2004 Introspection and
subliminal perception. Phenomenology Cogn. Sci. 3,
12. Sandberg K, Timmermans B, Overgaard M, Cleeremans
A. 2010 Measuring consciousness: is one measure better
than the other? Conscious Cogn. 19, 1069–1078.
13. Persaud N, McLeod P, Cowey A. 2007 Post-decision
wagering objectively measures awareness. Nat.
Neurosci. 10, 257–261. (doi:10.1038/nn1840)
14. Cowey A, Stoerig P. 1995 Blindsight in monkeys.
Nature 373, 247–249. (doi:10.1038/373247a0)
15. Kepecs A, Uchida N, Zariwala HA, Mainen ZF. 2008
Neural correlates, computation and behavioral
impact of decision confidence. Nature 455, 227–
231. (doi:10.1038/nature07200)
16. Kornell N. 2014 Where is the ‘meta’ in animal
metacognition. J. Comp. Psychol. 128, 143–149.
17. Lamme VAF. 2006 Towards a true neural stance on
consciousness. Trends Cogn. Sci. 10, 494–501.
18. Block N. 2007 Consciousness, accessibility, and
the mesh between psychology and neuroscience.
Behav. Brain Sci. 30, 481–499, discussion
19. Koch C, Tsuchiya N. 2012 Attention and
consciousness: related yet different. Trends Cogn. Sci.
16, 103–105. (doi:10.1016/j.tics.2011.11.012)
20. Tononi G, Koch C. 2008 The neural correlates of
consciousness: an update. Ann. NY Acad. Sci. 1124,
239–261. (doi:10.1196/annals.1440.004) Phil. Trans. R. Soc. B 370: 20140167
Downloaded from on November 18, 2015
21. Tsuchiya N, Koch C. 2014 On the relationship
between consciousness and attention. In The
cognitive neurosciences (ed. MS Gazzaniga).
Cambridge, MA: MIT Press.
22. Cohen MA, Cavanagh P, Chun MM, Nakayama K.
2012 The attentional requirements of consciousness.
Trends Cogn. Sci. 16, 411–417. (doi:10.1016/j.tics.
23. Koch C, Crick F. 1990 Some reflections on visual
awareness. Cold Spring. Harb. Symp. Quant. Biol. 55,
953–962. (doi:10.1101/SQB.1990.055.01.089)
24. Crick F, Koch C. 1998 Consciousness and
neuroscience. Cereb. Cortex 8, 97–107. (doi:10.
25. Moruzzi G, Magoun HW. 1949 Brain stem reticular
formation and activation of the EEG.
Electroencephalogr. Clin. Neurophysiol. 1, 455–473.
26. Parvizi J, Damasio AR. 2003 Neuroanatomical
correlates of brainstem coma. Brain 126, 1524–
1536. (doi:10.1093/brain/awg166)
27. Zeman A. 2001 Consciousness. Brain 124, 1263–
1289. (doi:10.1093/brain/124.7.1263)
28. Perry E, Ashton H, Young A (eds). 2002
Neurochemistry of Consciousness. Amsterdam, The
Netherlands: John Benjamins.
29. Tononi G, Laureys S. 2009 The neurology of
consciousness: an overview. In The neurology of
consciousness: cognitive neuroscience and
neuropathology (eds S Laureys, G Tononi), pp. 375–
412. Amsterdam, The Netherlands: Academic Press.
30. Baars BJ, Ramsoy TZ, Laureys S. 2003 Brain, conscious
experience and the observing self. Trends Neurosci. 26,
671–675. (doi:10.1016/j.tins.2003.09.015)
31. Myerson J, Miezin F, Allman JM. 1981 Binocular
rivalry in macaque monkeys and humans: a
comparative study in perception. Behav. Anal. Lett.
1, 149–159.
32. Baars BJ. 1988 A cognitive theory of consciousness.
Cambridge, UK: Cambridge University Press.
33. Logothetis NK. 1998 Single units and conscious
vision. Phil. Trans. R. Soc. Lond. B 353, 1801–1818.
34. Mudrik L, Koch C. 2013 Differential processing of
invisible congruent and incongruent scenes: a case
for unconscious integration. J. Vis. 13, 24. (doi:10.
35. Mataro´ M, Jurado MA, Garcı´a-Sa´nchez C, Barraquer
L, Costa-Jussa` FR, Junque´ C. 2001 Long-term effects
of bilateral frontal brain lesion: 60 years after injury
with an iron bar. Arch. Neurol. 58, 1139–1142.
36. Goldberg II, Harel M, Malach R. 2006 When the
brain loses its self: prefrontal inactivation during
sensorimotor processing. Neuron 50, 329–339.
37. Fra¨ssle S, Sommer J, Jansen A, Naber M, Einha¨user
W. 2014 Binocular rivalry: frontal activity relates to
introspection and action but not to perception.
J. Neurosci. 34, 1738–1747. (doi:10.1523/
38. Engel AK, Singer W. 2001 Temporal binding and the
neural correlates of sensory awareness. Trends Cogn.
Sci. (regul. ed.) 5, 16–25. (doi:10.1016/S1364-
39. Imas OA, Ropella KM, Ward BD, Wood JD, Hudetz
AG. 2005 Volatile anesthetics enhance flash-induced
gamma oscillations in rat visual cortex.
Anesthesiology 102, 937–947. (doi:10.1097/
40. Murphy MJ et al. 2011 Propofol anesthesia and
sleep: a high-density EEG study. Sleep 34, 283–
41. Cote KA, Etienne L, Campbell KB. 2001
Neurophysiological evidence for the detection of
external stimuli during sleep. Sleep 24, 791–803.
42. Takahara M, Nittono H, Hori T. 2002 Comparison of
the event-related potentials between tonic and
phasic periods of rapid eye movement sleep.
Psychiatry Clin. Neurosci. 56, 257–258. (doi:10.
43. Aru J, Axmacher N, Do Lam ATA, Fell J, Elger CE,
Singer W, Melloni L. 2012 Local category-specific
gamma band responses in the visual cortex do not
reflect conscious perception. J. Neurosci. 32,
14 909–14 914. (doi:10.1523/JNEUROSCI.2051-12.
44. Pitts MA, Martı´nez A, Hillyard SA. 2012 Visual
processing of contour patterns under conditions of
inattentional blindness. J. Cogn. Neurosci. 24, 287–
303. (doi:10.1162/jocn_a_00111)
45. Chalmers DJ. 2000 What is a neural correlate of
consciousness? In Neural correlates of consciousness:
empirical and conceptual questions (ed. T Metzinger),
pp. 17–40. Cambridge, MA: MIT Press.
46. Miller SM. 2007 On the correlation/constitution
distinction problem (and other hard problems) in
the scientific study of consciousness. Acta
Neuropsychiatrica 19, 159–176. (doi:10.1111/j.
47. Owen AM, Coleman MR, Boly M, Davis MH, Laureys
S, Pickard JD. 2006 Detecting awareness in the
vegetative state. Science 313, 1402. (doi:10.1126/
48. King JR, Sitt JD, Faugeras F, Rohaut B, El Karoui I,
Cohen L, Naccache L, Dehaene S. 2013 Information
sharing in the brain indexes consciousness in
noncommunicative patients. Curr. Biol. 23, 1914–
1919. (doi:10.1016/j.cub.2013.07.075)
49. Schiff N et al. 2002 Residual cerebral activity and
behavioural fragments can remain in the
persistently vegetative brain. Brain 125, 1210–
1234. (doi:10.1093/brain/awf131)
50. Lagercrantz H, Changeux J-P. 2009 The emergence
of human consciousness: from fetal to neonatal life.
Pediatr. Res. 65, 255–260. (doi:10.1203/PDR.
51. Kouider S, Stahlhut C, Gelskov SV, Barbosa LS, Dutat
M, de Gardelle V, Christophe A, Dehaene S,
Dehaene-Lambertz G. 2013 A neural marker of
perceptual consciousness in infants. Sci. Am. Assoc.
Adv. Sci. 340, 376–380. (doi:10.1126/science.
52. Smith JD, Couchman JJ, Beran MJ. 2014 Animal
metacognition: a tale of two comparative
psychologies. J. Comp. Psychol. 128, 115–131.
53. Hawrylycz MJ et al. 2012 An anatomically
comprehensive atlas of the adult human brain
transcriptome. Nature 489, 391–399. (doi:10.1038/
54. Herculano-Houzel S. 2012 The remarkable, yet not
extraordinary, human brain as a scaled-up primate
brain and its associated cost. Proc. Natl Acad. Sci.
USA 109(Suppl. 1), 10 661–10 668. (doi:10.1073/
55. Huxley TH. 1863 Evidence as to mankind’s place in
nature. London, UK: Williams & Northgate.
56. Dawkins MS. 1998 Through our eyes only? Oxford,
UK: Oxford University Press.
57. Griffin DR. 2001 Animal minds. Chicago, IL:
University of Chicago Press.
58. Edelman D, Seth AK. 2009 Animal consciousness: a
synthetic approach. Trends Neurosci. 32, 476–484.
59. Darwin C. 1881 The formation of vegetable mould
through the action of worms: with observations on
their habits. Cambridge, UK: Cambridge Library
60. Koch C, Laurent G. 1999 Complexity and the
nervous system. Science 284, 96–98. (doi:10.1126/
61. Hassin RR, Uleman JS, Bargh JA. 2005 The new
unconscious. Oxford, UK: Oxford University Press.
62. Kouider S, Dehaene S. 2007 Levels of processing
during non-conscious perception: a critical review of
visual masking. Phil. Trans. R. Soc. B 362, 857–
875. (doi:10.1098/rstb.2007.2093)
63. Berlin HA. 2011 The neural basis of the dynamic
unconscious. Neuropsychoanalysis 13, 1–68.
64. Mudrik L, Breska A, Lamy D, Deouell LY. 2011
Integration without awareness: expanding the limits
of unconscious processing. Psychol. Sci. 22, 764–
770. (doi:10.1177/0956797611408736)
65. Sklar AY, Levy N, Goldstein A, Mandel R, Maril A,
Hassin RR. 2012 Reading and doing arithmetic
nonconsciously. Proc. Natl Acad. Sci. USA 109,
19 614–19 619. (doi:10.1073/pnas.1211645109)
66. Hassin RR. 2013 Yes it can: on the functional
abilities of the human unconscious. Perspect.
Psychol. Sci. 8, 195–207. (doi:10.1177/
67. Lemon RN, Edgley SA. 2010 Life without a
cerebellum. Brain 133, 652–654. (doi:10.1093/
68. Kurzweil R. 2012 How to create a mind: the secret of
human thought revealed. New York, NY: Viking.
69. Crick FC, Koch C. 2005 What is the function of the
claustrum? Phil. Trans. R. Soc. B 360, 1271–1279.
70. Koubeissi MZ, Bartolomei F, Beltagy A, Picard F.
2014 Electrical stimulation of a small brain area
reversibly disrupts consciousness. Epilepsy Behav.
37, 32–35. (doi:10.1016/j.yebeh.2014.05.027)
71. Chalmers DJ. 1996 The conscious mind: in search of
a fundamental theory. New York, NY: Oxford
University Press. Phil. Trans. R. Soc. B 370: 20140167
Downloaded from on November 18, 2015
72. Block NJ, Flanagan OJ, Gu¨zeldere G. 1997 The
nature of consciousness. Cambridge, MA: MIT Press.
73. Shear J. 1999 Explaining consciousness. Cambridge,
MA: MIT Press.
74. Mcginn C. 2000 The mysterious flame: conscious minds
in a material world. New York, NY: Basic Books.
75. Tononi G. 2004 An information integration theory of
consciousness. BMC Neurosci. 5, 42. (doi:10.1186/
76. Tononi G. 2008 Consciousness as integrated
information: a provisional manifesto. Biol. Bull. 215,
216–242. (doi:10.2307/25470707)
77. Tononi G. 2012 The integrated information theory of
consciousness: an updated account. Arch. Ital. Biol.
150, 56–90.
78. Tononi G. 2012 Phi: a voyage from the brain to the
soul. New York, NY: Pantheon Books.
79. Hoel EP, Albantakis L, Tononi G. 2013 Quantifying
causal emergence shows that macro can beat micro.
Proc. Natl Acad. Sci. USA 110, 19 790–19 795.
80. Oizumi M, Albantakis L, Tononi G. 2014 From the
phenomenology to the mechanisms of
consciousness: integrated information theory 3.0.
PLoS Comput. Biol. 10, e1003588. (doi:10.1371/
81. Balduzzi D, Tononi G. 2008 Integrated
information in discrete dynamical systems:
motivation and theoretical framework. PLoS Comput.
Biol. 4, e1000091. (doi:10.1371/journal.pcbi.
82. Balduzzi D, Tononi G. 2009 Qualia: the geometry of
integrated information. PLoS Comput. Biol. 5,
e1000462. (doi:10.1371/journal.pcbi.1000462)
83. Tononi G. 2015 Integrated information theory.
Scholarpedia 10, 4164. (doi:10.4249/scholarpedia.
84. Mach E. 1959 The analysis of sensations and the
relation of the physical to the psychical [Transl. 1st
German Ed. by CM Williams. Rev. and supplemented
from the 5th German edn, 1906 by Sydney
Waterlow. With a New Introd. by Thomas S. Szasz].
Mineola, NY: Dover Publications.
85. Moutoussis K, Zeki S. 1997 Functional segregation
and temporal hierarchy of the visual perceptive
systems. Proc. R. Soc. Lond. B 264, 1407–1414.
86. Zeki S, Moutoussis K. 1997 Temporal hierarchy of
the visual perceptive systems in the Mondrian
world. Proc. R. Soc. Lond. B 264, 1415–1419.
87. Seth AK, Baars BJ, Edelman DB. 2005 Criteria for
consciousness in humans and other mammals.
Conscious. Cogn. 14, 119–139. (doi:10.1016/j.
88. Bateson G. 1972 Steps to an ecology of mind.
Chicago, IL: University of Chicago Press.
89. Palmer SE. 1999 Vision science: photons to
phenomenology. Cambridge, MA: MIT Press.
90. Tegmark M. 2014 Consciousness as a state of
matter. (
91. Crick F, Koch C. 2003 A framework for
consciousness. Nat. Neurosci. 6, 119–126. (doi:10.
92. Stroud JM. 1956 The fine structure of psychological
time. In Information theory in psychology: problems
and methods, pp. 174–207. New York, NY: Free Press.
93. VanRullen R, Koch C. 2003 Is perception discrete or
continuous? Trends Cogn. Sci. 7, 207–213. (doi:10.
94. VanRullen R, Zoefel B, Ilhan B. 2014 On the cyclic
nature of perception in vision versus audition. Phil.
Trans. R. Soc. B 369, 20130214. (doi:10.1098/rstb.
95. Massimini M, Ferrarelli F, Huber R, Esser SK, Singh
H, Tononi G. 2005 Breakdown of cortical effective
connectivity during sleep. Science 309, 2228–2232.
96. Casali AG et al. 2013 A theoretically based index of
consciousness independent of sensory processing
and behavior. Sci. Transl. Med. 5, 198ra105. (doi:10.
97. Sullivan PR. 1995 Contentless consciousness and
information-processing theories of mind. Philos.
Psychiatry Psychol. 2, 51–59.
98. Blackmore S. 2009 Ten Zen questions. Oxford, UK:
Oneworld Publications.
99. Cohen MA, Dennett DC. 2011 Consciousness cannot
be separated from function. Trends Cogn. Sci. 15,
358–364. (doi:10.1016/j.tics.2011.06.008)
100. von Arx SW, Muri RM, Heinemann D, Hess CW,
Nyffeler T. 2010 Anosognosia for cerebral
achromatopsia: a longitudinal case study.
Neuropsychologia 48, 970–977. (doi:10.1016/j.
101. Sperry RW. 1974 Lateral specialization in the
surgically separated hemispheres. In Neuroscience
3rd Study Program (eds FO Schmitt, FG Worden).
Cambridge, MA: MIT Press.
102. Gazzaniga MS. 2005 Forty-five years of split-brain
research and still going strong. Nat. Rev. Neurosci. 6,
653–659. (doi:10.1038/nrn1723)
103. Herculano-Houzel S. 2009 The human brain in
numbers: a linearly scaled-up primate brain. Front.
Hum. Neurosci. 3, 31. (doi:10.3389/neuro.09.031.2009)
104. Cohen D. 1998 Patches of synchronized activity in
the cerebellar cortex evoked by mossy-fiber
stimulation: questioning the role of parallel fibers.
Proc. Natl Acad. Sci. USA 95, 15 032–15 036.
105. Bower JM. 2002 The organization of cerebellar cortical
circuitry revisited. Ann. NY Acad. Sci. 978, 135–155.
106. Skrbina D. 2009 Mind that abides: panpsychism in
the new millennium. Amsterdam, The Netherlands:
John Benjamins.
107. Chalmers D. 2013 The combination problem for
panpsychism. Panspsychism on the Reef. (http:// )
108. Strawson G, Freeman A. 2006 Consciousness and its
place in nature: does physicalism entail panpsychism?
Exeter, UK; Charlottesville, VA: Imprint Academic.
109. Strawson G, Freeman A. 2006 Consciousness and its
place in nature: does physicalism entail
panpsychism? Exeter, UK: Imprint Academic.
110. James W. 1890 The principles of psychology.
New York, NY: H. Holt and Company.
111. Edlund JA, Chaumont N, Hintze A, Koch C, Tononi G,
Adami C. 2011 Integrated information increases
with fitness in the evolution of animats. PLoS
Comput. Biol. 7, e1002236. (doi:10.1371/journal.
112. Joshi NJ, Tononi G, Koch C. 2013 The minimal
complexity of adapting agents increases with
fitness. PLoS Comput. Biol. 9, e1003111. (doi:10.
113. Albantakis L, Hintze A, Koch C, Adami C, Tononi G.
2014 Evolution of integrated causal structures in
animats exposed to environments of increasing
complexity. PLoS Comput. Biol. 10, e1003966.
114. Bogen JE. 1986 Mental duality in the intact brain.
Bull. Clin. Neurosci. 51, 3–29.
115. Mudrik L, Faivre N, Koch C. 2014 Information
integration without awareness. Trends Cogn. Sci. 18,
488–496. (doi:10.1016/j.tics.2014.04.009)
116. Kohler CG et al. 1998 Marchiafava-Bignami disease:
literature review and case report. Neuropsychiatry
Neuropsychol. Behav. Neurol. 13, 67–76.
117. Searle J. 2013 Can information theory explain
consciousness? In New York Review of Books,
vol. 60, pp. 30–41. New York, NY.
118. Koch C, Crick F. 2001 The zombie within. Nature
411, 893. (doi:10.1038/35082161)
119. Le QV, Ranzato MAA, Monga R, Devin M, Chen K,
Corrado GS, Dean J, Ng AY. 2011 Building high-level
features using large scale unsupervised learning.
120. Hornik K, Stinchcombe M, White H. 1989 Multilayer
feedforward networks are universal approximators.
Neural Netw. 2, 359–366. (doi:10.1016/0893-
121. Koch C, Tononi G. 2011 A test for consciousness.
How will we know when we’ve built a sentient
computer? By making it solve a simple puzzle. Sci.
Am. 304, 44–47. (doi:10.1038/
122. Dennett DC. 1993 Consciousness explained.
Harmondsworth, UK: Penguin.
123. Markram H. 2006 The blue brain project. Nat. Rev.
Neurosci. 7, 153–160. (doi:10.1038/nrn1848)
124. Searle JR. 1980 Minds, brains, and programs. Behav.
Brain Sci. 3, 417–424. (doi:10.1017/
125. Leibniz GW, Montgomery GR, Leibniz GW. 2005
Discourse on metaphysics, and the monadology.
Mineola, NY: Dover Publications.
126. Schmuker M, Pfeil T, Nawrot MP. 2014 A
neuromorphic network for generic multivariate data
classification. Proc. Natl Acad. Sci. USA 111, 2081–
2086. (doi:10.1073/pnas.1303053111)
127. Koch C. 2014 Is consciousness universal? Sci. Am.
Mind 25, 26–29. Phil. Trans. R. Soc. B 370: 20140167
Downloaded from on November 18, 2015

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