intellecton/papers/project_paper_2_neuroscience/references/Tononi2016.txt

Consciousness is subjective experience 
— ‘what it is like’, for example, to perceive 
a scene, to endure pain, to entertain a 
thought or to reflect on the experience 
itself 1–3. When consciousness fades, as it 
does in dreamless sleep, from the intrinsic 
perspective of the experiencing subject, the 
entire world vanishes.

Consciousness depends on the integrity 

of certain brain regions and the particular 
content of an experience depends on the 
activity of neurons in parts of the cerebral 
cortex4. However, despite increasingly refined 
clinical and experimental studies, a proper 
understanding of the relationship between 
consciousness and the brain has yet to be 
established5,6. For example, it is not known 
why the cortex supports consciousness 
when the cerebellum does not, despite 
having four times as many neurons7,8, or why 
consciousness fades during deep sleep while 
the cerebral cortex remains active. There are 
also many other difficult questions about 
consciousness. Are patients with a functional 
island of cortex surrounded by widespread 
damage conscious, and if so, of what? Are 
newborn infants conscious? Are animals that 
display complex behaviours, but have brains 
very different from humans, conscious6? Can 
intelligent machines be conscious9?

the brain, leads to testable predictions, and 
allows inferences and extrapolations about 
consciousness.

From phenomenology to physics
The axioms of IIT state that every experience 
exists intrinsically and is structured, 
specific, unitary and definite. IIT then 
postulates that, for each essential property of 
experience, there must be a corresponding 
causal property of the PSC. The postulates 
of IIT state that the PSC must have intrinsic 
cause–effect power; its parts must also have 
cause–effect power within the PSC and they 
must specify a cause–effect structure that 
is specific, unitary and definite. Below, we 
discuss the axioms and postulates of IIT (see 
Supplementary information S1,S2 (figure, 
box)) and describe the fundamental identity 
— between an experience and a conceptual 
structure — that it proposes (FIG. 1).

The first axiom of IIT states that 

experience exists intrinsically. As 
recognized by Descartes13, my own 
experience is the only thing whose existence 
is immediately and absolutely evident, 
and it exists for myself, from my own 
intrinsic perspective. The corresponding 
postulate states that the PSC must also exist 
intrinsically. For something to exist in a 
physical sense, it must have cause–effect 
power — that is, it must be possible to make 
a difference to it (that is, change its state) 
and it must be able to make a difference to 
something. Moreover, the PSC must exist 
intrinsically — that is, it must have cause–
effect power for itself, from its own intrinsic 
perspective. A neuron in the brain, for 
example, satisfies the criterion for existence 
because it has two or more internal states 
(such as active and inactive) that can be 
affected by inputs (causes) and its output 
can make a difference to other neurons 
(effects). A minimal system consisting of 
two interconnected neurons satisfies the 
criterion of intrinsic existence because, 
through their reciprocal interactions, the 
system can make a difference to itself.

The axiom of composition states that 

experience is structured, being composed of 
several phenomenal distinctions that exist 
within it. For example, within an experience, 
I may distinguish a piano, a blue colour, a 
book, countless spatial locations, and so on 

To answer these questions, the 

empirical study of consciousness should 
be complemented by a theoretical 
approach. The reason why some neural 
mechanisms, but not others, should be 
associated with consciousness has been 
called ‘the hard problem’ because it seems 
to defy the possibility of a scientific 
explanation10. In this Opinion article, we 
provide an overview of the integrated 
information theory (IIT) of consciousness, 
which has been developed over the past 
few years1–3,11,12. IIT addresses the hard 
problem in a new way. It does not start 
from the brain and ask how it could give 
rise to experience; instead, it starts from 
the essential phenomenal properties of 
experience, or axioms, and infers postulates 
about the characteristics that are required 
of its physical substrate. Moreover, IIT 
presents a mathematical framework for 
evaluating the quality and quantity of 
consciousness1–3,9. We begin by providing a 
summary of the axioms and corresponding 
postulates of IIT and show how they can be 
used, in principle, to identify the physical 
substrate of consciousness (PSC). We then 
discuss how IIT explains in a parsimonious 
manner a variety of facts about the 
relationship between consciousness and 

OPINION
Integrated information theory: 
from consciousness to its physical 
substrate

Giulio Tononi, Melanie Boly, Marcello Massimini and Christof Koch

Abstract | In this Opinion article, we discuss how integrated information theory 
accounts for several aspects of the relationship between consciousness and the 
brain. Integrated information theory starts from the essential properties of 
phenomenal experience, from which it derives the requirements for the physical 
substrate of consciousness. It argues that the physical substrate of consciousness 
must be a maximum of intrinsic cause–effect power and provides a means to 
determine, in principle, the quality and quantity of experience. The theory leads 
to some counterintuitive predictions and can be used to develop new tools for 
assessing consciousness in non-communicative patients.

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Experience

Identity

Purviewp
Purviewf
Mechanism

1.0
0.5
0.0

1.0
0.5
0.0

1.0
0.5
0.0

1.0
0.5
0.0

Probability of state

000100010
110
001101011111

1.0
0.5
0.0

BCp

ABCp

ABCf

ABCf

ACf

Af

Bf
ABCp

ABp

Ap

ACc

ABc

Cc

Bc

Ac

0.083

0.167

0.25

0.25

0.25

000100010
110
001101011111

φmax of
concept

Conceptual structure

011

011

010

010

110

110

001

001

100

100

101

101
000

000

111

111

B
C

A

Physical substrate

D

MAJ

OR
AND

AND

Φmax = 0.66

A

B
C

AB
AC

Boundary of experience

Concept

Logic gate ON

Probability of past states

Probability of future states

Logic gate OFF

(FIG. 1). Based on this axiom, IIT postulates 
that the elements that constitute the PSC must 
also have cause–effect power within the PSC, 
either alone or in combination (composing 
first-order and higher-order mechanisms, 
respectively).

experience might be composed of seeing a 
book (rather than seeing no book), which 
is blue (rather than not blue), and so on for 
all other possible contents of consciousness. 
The corresponding postulate states that the 
PSC must specify a cause–effect structure 

The axiom of information states that 

experience is specific, being composed of a 
particular set of phenomenal distinctions 
(qualia), which make it what it is and different 
from other experiences. In the example 
shown in FIG. 1, the content of my current 

Figure 1 | An experience is a conceptual structure. According to inte-
grated information theory (IIT), a particular experience (illustrated here from 
the point of view of the subject) is identical to a conceptual structure spec-
ified by a physical substrate. The true physical substrate of the depicted 
experience (seeing one’s hands on the piano) and the associated conceptual 
structure are highly complex. To allow a complete analysis of conceptual 
structures, the physical substrate illustrated here was chosen to be 
extremely simple1,2: four logic gates (labelled A, B, C and D, where A is a 
Majority (MAJ) gate, B is an OR gate, and C and D are AND gates; the straight 
arrows indicate connections among the logic gates, the curved arrows indi-
cate self-connections) are shown in a particular state (ON or OFF). The anal-
ysis of this system, performed according to the postulates of IIT, identifies a 
conceptual structure supported by a complex constituted of the elements 
A, B and C in their current ON states. The borders of the complex, which 
include elements A, B, and C but exclude element D, are indicated by the 
green circle. According to IIT, such a complex would be a physical substrate 
of consciousness (Supplementary information S1 (figure)). The conceptual 
structure is represented as a set of stars and, equivalently, as a set of histo-
grams. The green circle represents the fact that experience is definite (it 
has borders). Each histogram illustrates the cause–effect repertoire of a 
concept: how a particular mechanism constrains the probability of past 
and future states of its maximally irreducible purview within the complex 
ABC. The bins on the horizontal axis at the bottom of the histograms rep-
resent the 16-dimensional cause–effect space of the complex — all its 
eight possible past states (p; in blue) and eight possible future states (f; in 

red; ON is 1 and OFF is 0). The vertical axis represents the probability of each 
state (for consistency, the probability values shown are over the states of the 
entire complex and not just over the subset of elements constituting the 
purview). In this example, five of seven possible concepts exist, specified by 
the mechanisms A, B, C, AB, AC (all with φmax>0) in their current state (which 
are labelled as Ac, Bc, etc.). The subsets BC and ABC do not specify any con-
cept because their cause–effect repertoire is reducible by partitions 
(φmax=0). In the middle, the 16-dimensional cause–effect space of the com-
plex is represented as a circle, where each of the 16 axes corresponds to one 
of the eight possible past (p; blue arrows) and eight possible future states 
(f; red arrows) of the complex, and the position along the axis represents 
the probability of that state. Each concept is depicted as a star, the position 
of which in cause–effect space represents how the concept specifies the 
probability of past and future states of the complex, and the size of which 
measures how irreducible the concept is (φmax). Relations between two 
concepts (overlaps in their purviews) are represented as lines between the 
stars. The fundamental identity postulated by IIT claims that the set of con-
cepts and their relations that compose the conceptual structure are identi-
cal to the quality of the experience. This is how the experience feels — what 
it is like to be the complex ABC in its current state 111. The intrinsic irreduc-
ibility of the entire conceptual structure (Φmax, a non-negative number) 
reflects how much consciousness there is (the quantity of the experience). 
The irreducibility of each concept (φmax) reflects how much each 
phenomenal distinction exists within the experience. Different experiences 
correspond to different conceptual structures.

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of a specific form, which makes it different 
from other possible forms. A cause–effect 
structure is defined as the set of cause–effect 
repertoires specified by all the mechanisms of 
a system. A cause–effect repertoire specifies 
how a mechanism in its current state affects 
the probability distribution of past and future 
states of the system.

The axiom of integration states that 

experience is unitary, meaning that it 
is composed of a set of phenomenal 
distinctions, bound together in various ways, 
that is irreducible to non-interdependent 
subsets. For example, I experience a whole 
visual scene and that experience cannot be 
subdivided into independent experiences of 
the left and right sides of the visual field. In 
other words, the content of an experience 
(information) is integrated within a 
unitary consciousness. The corresponding 
postulate states that the cause–effect 
structure specified by the PSC must also 
be unitary — that is, it must be irreducible 
to the cause–effect structure specified by 
non-interdependent subsystems. Note 
that, from the intrinsic perspective of the 
system, integration requires that every part 
of the system has both causes and effects 
within the rest of the system, which implies 
bidirectional interactions. The irreducibility 
of a conceptual structure is measured 
as integrated information (denoted Φ, the 
minimum distance between an intact and 
a partitioned cause–effect structure). The 
integration postulate also requires the 
irreducibility of each cause–effect repertoire 
(denoted φ, the minimum distance between 
an intact and a partitioned cause–effect 
repertoire) and the irreducibility of relations 
among overlapping cause–effect repertoires.

The axiom of exclusion states that an 

experience is definite in its content and 
spatio-temporal grain. For example, in 
the scene depicted in FIG. 1, the content of 
my present experience includes seeing my 
hands on the piano, the books on the piano, 
one of which is blue, and so on, but I am 
not having an experience with less content 
(for example, the same scene in black and 
white, lacking the phenomenal distinction 
between coloured and not coloured) or 
with more content (for example, including 
the additional phenomenal distinction of 
feeling one’s blood pressure as high or low). 
The duration of the instant of consciousness 
is also definite, ranging from a few tens of 
milliseconds to a few hundred milliseconds, 
rather than lasting a few microseconds 
or a few minutes14–16. The corresponding 
postulate states that the cause–effect 
structure specified by the PSC must also 

A set of elements in a state that satisfies 

all the postulates of IIT constitutes the PSC 
and is referred to as a complex (FIG. 1). Thus 
a complex specifies a conceptual structure 
composed of concepts, which can be 
represented as a set of points (shown as a 
constellation of stars in FIG. 1) in cause–effect 
space, in which each axis corresponds to a 
possible past and future state of the system 
and each star corresponds to a concept1 

(FIG. 1). With these notions at hand, the 
fundamental identity of IIT can be stated 
as follows2: an experience is identical to a 
conceptual structure, meaning that every 
property of the experience must correspond 
to a property of the conceptual structure and 
vice versa. Note that the postulated identity 
is between an experience and the conceptual 

be definite. It must specify a definite set of 
cause–effect repertoires over a definite set of 
elements, neither less nor more, at a definite 
spatio-temporal grain, neither finer nor 
coarser. Because a prerequisite for intrinsic 
existence is having irreducible cause–
effect power, the cause–effect structure 
that actually exists, over a set of elements 
and spatio-temporal grains, is that which 
is maximally irreducible (Φmax), called a 
conceptual structure. As a consequence, any 
cause–effect structure overlapping over the 
same set of elements and spatio-temporal 
grain is excluded. The exclusion postulate 
also requires the maximum irreducibility 
of cause–effect repertoires (denoted φmax), 
called concepts, and of relations among 
overlapping concepts.

Glossary

Achromatopsia
A condition in which a person is unable to perceive colours.

Anosognosia
A condition in which a person has a neurological deficit, 
but is unaware of it.

Axioms
Properties that are self-evident and essential; in integrated 
information theory, those that are true of every possible 
experience — namely, intrinsic existence, composition, 
information, integration and exclusion.

Background conditions
Factors that enable consciousness, such as neuromodulators 
and external inputs that maintain adequate excitability.

Cause–effect repertoire
The probability distribution of potential past and future 
states of a system that is specified by a mechanism in its 
current state.

Cause–effect space
A space with each axis representing the probability of each 
possible past and future state of a system.

Cause–effect structure
The set of cause–effect repertoires specified by all the 
mechanisms of a system in its current state.

Complex
A set of elements in a state that specifies a conceptual 
structure corresponding to a maximum of integrated 
information (Φmax). A complex is thus a physical substrate of 
consciousness.

Concepts
The cause–effect repertoires specified by a mechanism 
that is maximally irreducible (φmax).

Conceptual structure
The set of all concepts specified by a system of elements in 
a state with their respective φmax values, which can be 
plotted as a set of points in cause–effect space.

Content-specific NCC
Neural elements, the activity of which determines a 
particular content of experience.

Elements
The minimum constituents of a system that have at 
least two different states (for example, being on or off), 
inputs that can affect those states and outputs that 
depend on them.

Full NCC
The neural elements constituting the physical 
substrate of consciousness, irrespective of its 
specific content.

Integrated information
(Denoted Φ). Information that is specified by a system that 
is irreducible to that specified by its parts. It is calculated 
as the distance between the conceptual structure specified 
by the intact system and that specified by its minimum 
information partition.

Mechanism
Any subset of elements within a system that has  
cause–effect power on it (that is, that constrains its 
cause–effect space).

Neural correlates of consciousness
(NCC). The minimum neuronal mechanisms jointly 
sufficient for any one specific conscious experience.

Postulates
Properties of experience that are derived from the axioms 
of integrated information theory and that must be 
satisfied by the physical substrate of consciousness — 
namely, to be a maximum of irreducible, specific, 
compositional, intrinsic cause–effect power (intrinsic 
cause–effect power for short).

Purviews
The subsets of elements of a complex, the past and future 
states of which are constrained by a mechanism specifying 
a concept.

Qualia
The qualitative feeling of phenomenal distinctions within an 
experience (for example, seeing a colour, hearing a sound 
or feeling a pain).

Relations
Maximally irreducible overlaps among the purviews of two 
or more concepts.

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structure specified by the PSC, not between 
an experience and the set of elements in 
a state constituting the PSC (FIG. 1). The 
quality or content of consciousness — which 
particular way the system exists for itself — 
corresponds to the form of the conceptual 
structure. The quantity of consciousness 
— how much the system exists for itself — 
corresponds to its irreducibility Φmax.

The PSC within the brain
Experimental evidence currently suggests 
that the neural correlates of consciousness 
(NCC) are likely to be located in certain 
parts of the cortico-thalamic system5, but 
it is not known specifically which cortical 
areas, layers or neuronal populations are 
involved, whether the relevant units are 
neurons or groups of neurons, and which 
aspects of their activity matter5. It is also 
not known whether the neural substrate 
of consciousness is anatomically fixed or 
can shrink, expand and move. IIT offers 
theoretical clarity on the empirical notion 
of the NCC5. Specifically, it states that 
the content-specific NCC correspond to the 
neural elements of the PSC in a particular 
state (activity pattern), which specify a 
particular phenomenal content; the full 
NCC correspond to the neural elements 
constituting the PSC irrespective of their 
particular state; the background conditions 
are factors that enable consciousness, such 
as neuromodulators and external inputs 
that maintain adequate excitability, which 
are kept fixed when evaluating the Φ value 
of the PSC. Most importantly, the axioms 
and postulates of IIT can be used to provide 
a single, general principle for identifying 
the PSC in the brain — namely that the 
PSC must correspond to a complex of 
neural elements with maximum intrinsic 
cause–effect power.

Elements of the PSC. What is the spatial 
scale of the neural elements that support 
consciousness: synapses, neurons, 
neuronal groups, local fields or perhaps 
all of these? According to IIT, the neural 
elements of the PSC are those, and only 
those, that support a maximum of cause–
effect power, as determined from the 
intrinsic perspective of the system itself. 
Importantly, and contrary to common 
reductionist assumptions17, cause–effect 
power can be higher at a macro-level than 
at a micro-level18. For example, a system 
of neuron-like micro-elements may have 
less cause–effect power than the same 
system coarse-grained at the macro-level of 
neuronal groups (FIG. 2a). In general, whether 

both individual neurons and groups of 
neurons, an experimenter could thus assess 
at which grain size the network has most 
cause–effect power from its own intrinsic 
perspective — that is, at which level it 
makes the most difference to itself. IIT 
predicts that the elements of the PSC are 
to be found at exactly that level and not at 
any finer or coarser grain, a prediction that 
is empirically testable: does the firing of 
a single neuron make a difference21 to the 
content of experience, or only the average 
activity of a cortical mini-column22?

Timescale. Which timescale of neuronal 
activity is important for consciousness: 
a few milliseconds, tens of milliseconds, 
hundreds of milliseconds, or perhaps 
all of these? Again, IIT predicts that the 
relevant time interval should be that 
which makes the most difference to the 
system, as determined from its intrinsic 
perspective. Once more, depending on 
the specific mechanisms of a system, some 
macro-temporal grain may have a higher 
cause–effect power than both finer and 
coarser grains (FIG. 2b). Whatever timescale 
turns out to have the maximum cause–effect 
power within the relevant brain regions, it 
should be consistent with estimates of the 
timescale of experience14–16.

State of the elements. An external observer 
can choose to analyse brain states at any 
level of detail. For example, some neu-
rophysiologists may be interested in the 
effects of the timing of individual neuronal 
spikes on brain function, others in the 
effects of broad fluctuations in the activity 
of populations of neurons. In fact, it is 
likely that almost any change in the state 
of any neurobiological variable will have 
some effect somewhere in the brain21. 
According to IIT, the neural states that are 
important for consciousness are only those 
that have maximum cause–effect power on 
the system itself. For example, assume that, 
from the intrinsic perspective of the system, 
maximum cause–effect power was achieved 
when coarse-graining firing states into 
low, high and burst firing (FIG. 2c). In this 
case, IIT predicts that finer grained neural 
states, despite their demonstrable neuro-
physiological effects, make no difference 
to the content of experience. Note that 
spatio-temporal grain and the relevant 
activity states of the elements specifying 
the PSC could change according to brain 
region, developmental period, species, 
neuromodulatory milieu and even the task 
being performed.

the macro or micro grain size has higher 
cause–effect power depends on how intra- 
and inter-group connections are organized 
and the amount of indeterminism (noise) 
and degeneracy (multiple ways of obtaining 
the same effect18).

An exhaustive evaluation of cause–

effect power at multiple levels is only 
possible in small simulated networks19. 
In a real network20, we could start by 
assessing the cause–effect repertoire of 
individual neurons. For example, if a 
neuron is firing a burst of spikes, its cause 
repertoire is the probability distribution 
of past network states that would have 
caused it to burst (for example, firing 
patterns of its afferent neurons within 
the previous 100 ms). Similarly, its effect 
repertoire is the probability distribution 
of future network states given that the 
neuron is bursting. Experimentally, we 
could obtain an estimate of such cause–
effect repertoires by stimulating one 
or more neurons optogenetically while 
simultaneously recording the firing activity 
of a population of neurons via two-photon 
calcium imaging (keeping the background 
conditions constant, such as the level of 
arousal and sensory input) (FIG. 2a). Next, 
we would need to test for the irreducibility 
of the cause–effect repertoires, which 
can be achieved by noising connections 
(that is, enforcing firing at chance levels) 
across a partition of the network. Doing so 
would establish which subset of incoming 
connections makes the most irreducible 
difference (φmax) to the firing of the 
observed neuron1 (and this could be carried 
out analogously for outgoing connections). 
A similar procedure should then be 
repeated for subsets of two neurons, three 
neurons, and so on, because combinations 
of neurons can also have irreducible 
cause–effect repertoires (defined as higher 
order mechanisms). Such experiments 
would provide an estimate of maximally 
irreducible cause–effect repertoires at the 
level of neurons.

To evaluate cause–effect power at the 

macro-level, we could then repeat the 
same stimulation–recording–noising 
procedure by considering subsets of 
neurons as distinct macro-groups and 
mapping micro-states onto macro-states. 
For example, we could take all pyramidal 
neurons in each mini-column as a distinct 
group and define the group state as low 
firing, high firing or bursting, depending 
on the overall firing rate of the neurons 
over 100 ms. By estimating the φmax value 
of cause–effect repertoires at the level of 

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Trial 1

a

b

c

Trial 2
Trial 3

Recording
Recording

10 ms

100 ms

10 ms

100 ms

10 ms

100 ms

N1

N2

N3

N4

N1

N2

N3

N4

N1

N2

N3

N4

N1

N2

N3

N4

N1

N2

N3

N4

N1

N2

N3

N4

60 Hz
250 Hz

Recording
Recording

N4
N4
N4

60 Hz
250 Hz
1 Hz
60 Hz
250 Hz
1 Hz

N1
N1
N1

N2
N2
N2

N3
N3
N3

N4
N4
N4

N1
N1
N1

N2
N2
N2

N3
N3
N3

Low
High
Burst

1 Hz

Firing rate unchanged
Firing rate decreases
Firing rate increases
Burst firing
Optogenetic stimulation

Constitution of the PSC. Assume that we 
have determined that the elementary units of 
the PSC are local groups of cortical neurons, 
over a time interval of ~100 ms, with three 
relevant states (low, high and burst firing) 

(FIG. 3a). Next we must determine, at the 
system level, which particular subset of 
neuronal groups constitutes the PSC for a 
particular experience. IIT addresses this 
question from first principles — it predicts 
that the PSC is the set of neuronal groups that 
has maximally irreducible cause–effect power 
on itself, specifying a conceptual structure 

differentiation)23; and integration, using 
measures of functional or effective 
connectivity among brain regions24,25. In 
addition, large-scale computer simulations 
based on the known anatomy and 
physiology of cortical circuits26 can be 
used to assess cause–effect repertoires, 
test their irreducibility and estimate 
conceptual structures. Crucially, if the 
evidence thus obtained indicates that the 
PSC does not correspond to a maximum 
of intrinsic cause–effect power, IIT would 
be invalidated. A related prediction is 

with the highest value of Φ1 (FIG. 3b). Ideally, 
systematic manipulation and recording of this 
particular set of neuronal groups would show 
that it has the maximum value of Φ, whereas 
any other assortment of neuronal groups in 
the brain has a lower value of Φ.

Although such an exhaustive evaluation 

of Φ is not currently feasible, neuroimaging 
studies can evaluate two key requirements 
for a high Φ value: information, using 
measures that reflect the size of the 
repertoire of neural states the system 
can have (that is, neurophysiological 

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that any perturbation of the PSC at the 
appropriate spatio-temporal grain should 
produce a change in experience, whereas 
any perturbation that does not alter the PSC 
should not.

Can the PSC change? An important issue 
is the extent to which the set of neural 
elements that constitute the PSC is fixed. 
Clearly, if a cortical area is inactivated (by a 
lesion, for example) it will no longer be part 
of the PSC and the phenomenal distinctions 
contributed by that area will no longer be 
available. For example, if cortical areas 
responding to colour are inactivated (FIG. 3c), 
experiences will not only lack colour, but 
patients would not even understand what is 
lacking (as reported in cases of achromatopsia 
with anosognosia27).

It is an open question whether the PSC 

can shrink, expand or move during normal 
wakefulness, possibly through attentional 
modulation of excitability and functional 
connectivity. For example, when we are 

experiences of pure thought that have 
minimal perceptual content may be caused 
by slow waves that inactivate the posterior 
cortex, and be specified by a PSC that is 
considerably different from the PSC for 
purely perceptual experiences31 (FIG. 3d). 
At other times, transient, local slow waves 
(indicative of an off-period) in colour areas 
may cause the PSC to shrink and lead to 
brief episodes of achromatopsia. Novel 
methods that allow the transient inactivation 
of specific cortical areas in humans, such 
as transcranial magnetic stimulation or 
focused ultrasound, would be ideal for 
evaluating the contribution of those areas to 
conscious content.

Multiple complexes. According to IIT, 
two or more non-overlapping complexes 
may coexist as discrete PSCs within a 
single brain1, each with its own definite 
borders and value of Φmax. The complex 
that specifies a person’s day-to-day stream 
of consciousness should have the highest 
value of Φmax — that is, it should be the 
‘major’ complex. In some conditions, for 
example after a split-brain operation, the 
major complex may split (FIG. 3e). In such 
instances, one consciousness, supported 
by a complex in the dominant hemisphere 
and with privileged access to Broca’s area, 
would be able to speak about the experience, 
but would remain unaware of the presence 
of another consciousness, supported by a 
complex in the other hemisphere, which 
can be revealed by carefully designed 
experiments32,33. An intriguing possibility 
is that splitting of the PSC may also occur 
in healthy people during long-lasting 
dual-task conditions — for example, when 
driving in an auto-pilot like manner on a 
familiar road while listening to an engaging 
conversation (FIG. 3f). Splitting into separate 
maxima may also occur through functional 
disconnections caused by pathological 
conditions, such as conversion and 
dissociative disorders34.

Another intriguing possibility is that 

multiple conscious streams may coexist 
within a single brain in daily life. For 
example, the grid-like architectures in the 
colliculus and related mesencephalic regions, 
which are adept at multimodal integration 
within a spatial framework, may support a 
separate minor complex. Some examples 
of high-level cognitive performance such 
as judging whether a scene is congruous 
or incongruous35,36 — that appear to 
be carried out unconsciously from the 
perspective of the major complex — may 
support a separate minor complex (FIG. 3e,g). 

engrossed in an action movie and not 
engaged in self-reflection, the activity in 
prefrontal areas decreases28. Does this mean 
that the PSC shrinks, like when colour 
areas are inactivated, or that brain regions 
supporting self-reflection remain inside the 
PSC but are inactive, in the same way that 
colour areas are inactive when watching a 
black and white movie? The location and 
size of the PSC is likely to change during 
sleep, during seizures, in patients with 
conversion and dissociative disorders, and 
possibly during hypnosis. During slow wave 
sleep, for example, neurons are bistable and 
show off-periods during which they become 
hyperpolarized (down-states) and silent29. 
However, these off-periods are usually not 
global, but affect local subsets of brain areas 
at different times30. Hence it is possible that 
during slow wave sleep the PSC may become 
smaller and reconfigure substantially. 
Sustained inactivation of certain areas 
during sleep may make dreaming patients 
incapable of reflective thought. Similarly, 

Figure 2 | Identifying the elements, timescale and states of the physical substrate of conscious-
ness (PSC) from first principles. It is possible to determine maxima of cause–effect power within 
the central nervous system by perturbing and observing neural elements at various micro- and 
macro-levels18. High cause–effect power is reflected in deterministic responses and low cause–
effect power is reflected in responses that vary randomly across trials. a | To identify the spatial grain 
of the elements of the PSC supporting consciousness, a schematic example shows how optogenetic 
perturbation and unit recording could be applied to a subset of neurons (here, 3 out of 36 neurons) 
to establish maxima of cause–effect power. For each of three trials, the left panel shows the effects 
of the perturbation on the entire system at the micro-level. Grey neurons are unaffected, blue neu-
rons decrease their firing rates, red neurons increase their firing rates and purple neurons respond 
with burst firing. The right-hand panel shows the effects of the perturbation at the macro-level after 
coarse-graining of the 36 neurons into nine groups of four cells each. Macro-states are defined 
according to the rule that if ≥50% of the neurons in the group are in a given micro-state (such as low 
firing, high firing or bursting), then the group is considered to be in that state at the macro-level. In 
this example, the macro-level (groups of neurons) has higher cause–effect power than the micro-
level (single neurons), because the response is deterministic at the macro-level (as evidenced by the 
consistent colour scheme), whereas there are variations between trials at the micro-level (incon-
sistent colours). b | To identify the temporal grain of neuronal activity supporting consciousness, a 
possible experimental setup would be one in which one neuron (the top trace) is optogenetically 
excited while recording from other neurons (labelled N1–N4) across three trials, shown in the upper 
panel at the 10 ms timescale (micro-scale). Grey shading indicates no effects on neuron firing in the 
10 ms following the stimulation compared with the 10 ms before the stimulation, blue shading indi-
cates decreased firing and red shading indicates increased firing. The lower panel shows the same 
data after temporal coarse-graining over 100 ms intervals. Macro-states are defined according to the 
rule that if a neuron increases (or decreases) its firing rate by >50% within 100 ms post-stimulus 
compared with the baseline, the macro state is considered to be high (or low) firing. In this example, 
the macro-level (100 ms intervals) has higher cause–effect power (more deterministic responses) than 
the micro-level (10 ms intervals). c | To identify the neural states that support consciousness, optoge-
netic perturbations could be used to drive one neuron to fire either at low frequency, high tonic 
frequency or bursting (top trace) resulting in spectral peaks at 2 Hz (green), 50 Hz (red) and 150 Hz 
(yellow) for neurons N1–N4 (data are shown as a firing rate histogram). For each trial, the upper panel 
shows the responses of the other four neurons to each stimulation frequency at the micro-scale level 
in the spectral domain (micro-bins, only a few of which are represented). The coloured bars indicate 
coincidence, within a micro-bin, between the frequency of stimulation and the spectral peak of the 
responses. The lower panel of each trial shows the effect of the perturbation at the corresponding 
macro-level after spectral coarse-graining. Macro-states map into micro-states as indicated below 
the frequency bins. Here, spectral coarse-graining (binning firing rates into three levels, low, high 
and burst firing) results in higher cause–effect power (responses that are more deterministic) than 
at the micro-level.

◀

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50 μm
100 ms

Space
Time
State

a  Macroelements, macrointervals 
     and macrostates

Low

High

Burst

b  The major complex
c  Shrinking of the major complex

Major 
complex

High firing
Low firing
Burst firing

Minor 
complex

d  Movement of the major complex
f  Functional splitting of 
     the major complex
g  Coexistence of the major complex 
     with minor complexes
e  Anatomical splitting of 
     the major complex

Alternatively, some of these functions may 
be mediated by feedforward circuits37 that 
have Φmax=0 because they lack integration 
and therefore are strictly unconscious1. 
An important question for the future is 
whether automatic, unconscious behaviours 
are mediated by specific cell types within 
the cortex, such as subcortical projection 
neurons of layer 5B38, that are different from 
other cell types that support consciousness.

Information capacity of consciousness
The information-processing approach 
common in psychology estimates 
the information capacity of human 
consciousness to be at around 7 ± 2 items39 
or ≤40 bits per second39,40. In the classic 
Sperling task41, for example, participants 
are presented with a set of 12 letters for 

of the Sperling display during the delay 
period, they can report three letters of any 
row; moreover, they can report the colour 
diversity of unattended letters at no cost 
to the identification of the cued letters50. 
Likewise, change blindness may be due 
not to a failure to experience, but to a lack 
of memory for the experience51. Similarly, 
low-level phenomenal features may be 
difficult to report because they vary rapidly 
and may be forgotten before they can be 
accessed from top-down mechanisms; 
pre-categorical stimuli, such as irregular 
scribbles, may be phenomenally salient but 
hard to describe in words.

IIT claims that human consciousness has 

a high capacity for integrated information 

(BOX 1). Even for a simple experience, such 
as seeing the Sperling display, the elements 

300 ms, of which, after a mask and a delay, 
they can report at most four (FIG. 4). The 
inference from such experiments is that the 
information content of consciousness is 
extremely limited, as is also suggested by the 
attentional blink and related psychophysical 
paradigms42,43. For example, in change 
blindness, a major modification in a visual 
scene may go undetected if a blank is 
interposed between the two images44. In this 
view, the content of consciousness is limited 
to what can be accessed and reported, 
despite our phenomenal impression of 
richer content42,45,46. By contrast, others 
argue that phenomenal consciousness (what 
it is like to have an experience) has far 
greater capacity than access consciousness 
(what can be reported)47–49. For example, 
if participants are cued to a particular row 

Figure 3 | Identifying the physical substrate of consciousness (PSC) 
from first principles. The complex of neural elements that constitutes the 
PSC can be identified by searching for maxima of intrinsic cause–effect 
power. a | For example, assume that the elements, timescale and states at 
which intrinsic cause–effect power reaches a maximum have been identified 
using optogenetic and unit recording tools (FIG. 2). Here, the elements are 
groups of neurons, the timescale is over 100 ms and there are three states 
(low, high and burst firing). b | In a healthy, awake participant, the set of neural 
elements specifying the conceptual structure with the highest Φmax is 
assumed, based on current evidence, to be a complex of neuronal groups 
distributed over the posterior cortex and portions of the anterior cortex5. 
Empirical studies can, in principle, establish whether the full neural corre-
lates of consciousness5 correspond to the maximum of intrinsic cause–effect 
power, thereby corroborating or falsifying a key prediction of integrated 

information theory. c | The boundaries of the PSC (green line) may change 
after cortical lesions, such as those causing absolute achromatopsia, result-
ing in a smaller PSC. d | The PSC boundaries may also move as a result of 
changes in excitability and effective connectivity, as might occur during pure 
thought that is devoid of sensory content. e | The PSC could also split into 
two large local maxima of cause–effect power (represented here by green 
and blue boundaries) as a result of anatomical disconnections, such as in 
split-brain patients, in which instance each hemisphere would have its own 
consciousness. f | The PSC may also split as a result of functional disconnec-
tions, which may occur in some psychiatric disorders and perhaps under 
certain dual-task conditions — for example while driving and talking at the 
same time. g | The coexistence of a large major complex with one or more 
minor complexes that may support sophisticated, seemingly unconscious 
performance could be a common occurrence in everyday life.

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of the PSC specify a rich conceptual 
structure (high Φmax) composed of a very 
large number of concepts and relations. 
These correspond to all the phenomenal 
distinctions that make that experience what 
it is and thereby different from countless 
others11 (FIG. 4). It is useful to distinguish 
between low- and high-order concepts, 
depending on how many PSC elements are 
contained in their purviews. For example, 
a concept specifying the presence of an 
oriented edge at a particular location in 
the visual field has a low-order purview, 
whereas a concept specifying the extent 
of the entire visual field has a high-order 
purview. Concepts can also have low- and 
high invariance; for example, the concept 
for the letter A has high invariance 
because its purview specifies a high-order 
disjunction of states of the PSC elements (a 
specific arrangement of oriented edges in 
any of a large number of possible locations). 

concepts, such as letters in the Sperling 
paradigm. However, we could undoubtedly 
report many more concepts than just the 
identity of a few letters. For example, we 
could report that there are many black 
symbols, that they are arranged in three rows 
and four columns, in a rectangular array, 
within a rectangular display, over a white 
homogeneous background that is spatially 
extended, being composed of a multitude 
of distinguishable locations, each with its 
specific neighbours, and so on. We can 
also report many negative concepts — for 
example, that the Sperling display did not 
include a face, a tree, an animal, a house, and 
so on — for the thousands of high invariance 
concepts we possess that happen to be 
negative for this particular image. Finally, we 
can report how all these concepts are bound 
together within the same experience in a 
complex pattern of relations — for example, 
we see the letter A as an invariant that is 
nevertheless located at a particular spatial 
location, that is composed of two oblique 
edges and a horizontal edge in between, that 
is capital, printed in black and located on 
the rightmost column in the upper row of 
the array, and so on. According to IIT, this 
dynamic binding of phenomenal attributes56 
occurs if, and only if, in cause–effect space 
the corresponding concept purviews are 
related, meaning that they refer to an 
overlapping set of PSC elements and jointly 
constrain their past or future states.

In short, the information that 

specifies an experience is much larger 
than the purported limited capacity 
of consciousness57. Although we are 
accustomed to summarizing what we 
see by referring to a few positive, high 
invariance concepts (for example, in FIG. 4 
bottom panel, a participant may state: “I 
see the letters O, S and A”), we would not 
see what we see without the contribution 
of a large number of other concepts — low 
and high order, low and high invariance, 
positive and negative — and relations, 
which make the experience what it is 
(information) and thereby different from 
others (differentiation; FIG. 4). Consider 
what it would be like to look at the Sperling 
display not as a human, but as a machine 
implementing an efficient feedforward 
algorithm for letter recognition. The 
machine could certainly report three 
letters (in fact, all 12). However, such a 
machine could not see the scene and would 
understand virtually nothing because it has 
no other concept apart from the letters, not 
for the letter combination OSA, the array, 
the display, a face, an animal, and so on. 

Mechanisms specifying invariant concepts 
form a hierarchy going from low- to 
high-level areas of the cerebral cortex, 
as indicated by experimental data52 and 
consistent with computational models for 
the recognition of objects53, places, events54 
and spatial reference frames55. A concept 
can have low or high selectivity, depending 
on how strongly the state of its mechanism 
constrains its cause–effect repertoire. In 
the brain, the adaptive bias towards sparse 
firing makes it likely that the neurons 
would fire strongly when specifying a 
high invariance, high selectivity concept, 
such as the presence of the letter A (that 
is, a positive concept), and be silent when 
specifying its low selectivity counterpart, 
such as the absence of the letter A (that is, a 
negative concept) (FIG. 4).

In experimental settings, the content of 

experience is typically probed by asking the 
participant about high invariance, positive 

Box 1 | Consciousness, integrated information and Shannon information

The term information is used very differently in integrated information theory (IIT) and in Shannon’s 
theory of communication1, and confusing the two meanings can cause misunderstandings80. The 
word information derives from the Latin verb informare, which means ‘to give form’. In IIT the 
information content of an experience is specified by the form of the associated conceptual 
structure (the quality of the integrated information) and quantified by Φmax (the quantity of 
integrated information). In IIT, information is causal and intrinsic: it is assessed from the intrinsic 
perspective of a system based on how its mechanisms and present state affect the probability of its 
own past and future states (cause–effect power). It is also compositional, in that different 
combinations of elements can simultaneously specify different probability distributions within the 
system. Moreover, it is qualitative, as it determines not only how much a system of mechanisms in a 
state constrains its past and future states, but also how it does so. Crucially, in IIT, information must 
be integrated. This means that if partitioning a system makes no difference to it, there is no system 
to begin with. Information in IIT is exclusive — only the maxima of integrated information are 
considered. By contrast, Shannon information is observational and extrinsic — it is assessed from 
the extrinsic perspective of an observer and it quantifies how accurately input signals can be 
decoded from the output signals transmitted across a noisy channel. It is not compositional nor 
qualitative, and it does not require integration or exclusion1.

When averaged over many different states of the physical substrate of consciousness (PSC), we 

can think of the integrated information Φmax as a measure of the intrinsic phenomenal capacity of 
the conceptual structures specified by the PSC. By contrast, Shannon information can be used to 
measure the extrinsic access capacity of a channel that runs from a subset of elements of the PSC to 
Broca’s area and from there to the motor neurons that ultimately convey the report (FIG. 4). In IIT, the 
experience of seeing the Sperling display is identical to a particular conceptual structure — it is a 
form in cause–effect space with a high value of integrated information Φmax, as specified by its PSC 
(FIG. 4). The average value of Φmax for different states of the PSC measures its intrinsic phenomenal 
capacity. The figure also shows a neural information channel from the PSC to Broca’s area, formed 
dynamically by top-down attentional mechanisms located in the prefrontal cortex, which select 
which subset of elements of the PSC should drive the report (FIG. 4). This channel conveys extrinsic 
information and has a low Shannon capacity (only four letters at a time can be reported), which 
corresponds to the mutual information between its inputs and outputs. Seen in this way, it becomes 
obvious that the extrinsic information that can be selected through attention, kept in working 
memory and channelled out for report is only a partial read-out of the intrinsic information that is 
specified by the PSC over its own cause–effect space. Although at any given time we can access and 
report the state of a few elements of the PSC, and that of some other elements at another time, it is 
not possible to dump the state of all elements through a limited capacity channel. It is certainly not 
possible to transmit a conceptual structure (intrinsic information) through a channel (extrinsic 
information)—phenomenal capacity, properly understood, truly exceeds access capacity. Likewise, 
conscious information is not something that is transmitted or broadcast from one part of the brain 
to another77,78 (Supplementary information S5 (box)).

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Boundary of 
experience

Conceptual structure
Experience

Identity

Past
Future

‘OSA’

PFC

Broca

Physical substrate

‘No face’

‘A’

‘Top right corner’

‘Report seen letters’

High firing

Low firing

Burst firing

Indeed, if there were a face, an animal, or 
anything else in the middle of the display, it 
would do its best to categorize it as a letter.

Explanations
IIT provides a principled explanation for 
several seemingly disparate facts about 
the PSC. For example, IIT can explain 
why the cerebral cortex is important 
for consciousness, but the cerebellum 
is not. In general, the coexistence of 
functional specialization and integration 
in the cerebral cortex is ideally suited to 
integrating information (Supplementary 
information S3 (figure)). Specifically, the 
grid-like horizontal connectivity among 
neurons in topographically organized 
areas in the posterior cortex, augmented by 
converging–diverging vertical connectivity 
linking neurons along sensory hierarchies, 
should yield high values of Φmax. By 
contrast, cerebellar micro-zones that 
process inputs and produce outputs that 
are feedforward and largely independent 
of each other cannot form a large complex; 
nor can they be incorporated into a cortical 
high Φmax complex, even though each 
cerebellar micro-zone may be functionally 
connected with a portion of the cerebral 
cortex (Supplementary information S3 
(figure))1. In principle, these differences 
in organization can explain why lesions 
of the cerebellum, which has four times 
more neurons than the cerebral cortex58, 
do not seem to affect consciousness7,8. 
Furthermore, circuits providing inputs 
and outputs to a major complex may not 
contribute to consciousness directly. This 
seems to be true with neural activity in the 
peripheral sensory and motor pathways, 
as well as within circuits looping out and 
back into the cortex through the basal 
ganglia59–61, despite their manifest ability 
to affect cortical activity and thereby 
to influence the content of experience 
indirectly (Supplementary information S3 
(figure)).

IIT also accounts for the fading of 

consciousness during slow wave sleep 
when cortical neurons fire but, as a result 
of changes in neuromodulation, become 
bistable — that is, any input quickly triggers 
a stereotypical neuronal down-state, 
after which neurons enter an up-state 
and activity resumes stochastically29. 
Bistability implies a generalized loss of 
both selectivity (causal convergence or 
degeneracy) and effectiveness (causal 
divergence or indeterminism)18 that results 
in a breakdown of information integration 
(Supplementary information S3 (figure)). 

consciousness fades despite the increased 
level of activity and synchronization that 
occurs early during generalized seizures63.

IIT also provides a plausible account as 

to why conscious brains might have evolved. 
The world is immensely complex, at multiple 
spatial and temporal scales, and organisms 
with brains that can incorporate statistical 
regularities that reflect the causal structure 
of the environment into their own causal 
structure have an adaptive advantage for 
prediction and control2. The IIT framework, 
which emphasizes the information 
matching between intrinsic and extrinsic 
causal structures, has both similarities 
and differences with Bayesian approaches 
(for example, see REF. 64). According to 
IIT, given the constraints on energy and 

Findings from a study that used intracranial 
stimulation and recordings in patients with 
epilepsy are consistent with this account 
(Supplementary information S4 (box))62. 
During wakefulness, electrical stimulation of 
the cortex triggered a chain of deterministic 
phase-locked activations, whereas during 
slow wave sleep the same input induced a 
stereotyped slow wave that was associated 
with a cortical down-state (that is, a 
suppression of power ≥20 Hz). The cortical 
activity resumed to wakefulness-like levels 
after the down-state, but the phase-locking 
to the stimulus was lost, indicative 
of a break in the cause–effect chain 
(Supplementary information S4 (box)). 
Similar considerations would explain why 
information integration is impaired when 

Figure 4 | Phenomenal content and access content. The content of an experience is much larger 
than what can be reported by a subject at any point in time. The left-hand panel illustrates the Sperling 
task41, which involves the brief presentation of a three by four array of letters on a screen, and a par-
ticular row being cued by a tone. Out of the 12 letters shown on the display, participants correctly 
report only three or four letters — the letters cued by the tone — reflecting limited access. The top 
middle panel illustrates a highly simplified conceptual structure that corresponds to seeing the 
Sperling display, using the same conventions as outlined in FIG. 1. The myriad of positive and negative, 
first- and high-order, low- and high invariance concepts (represented by stars) that specify the content 
of this particular experience (seeing the Sperling display and having to report which letters were seen) 
make it what it is and different from countless other experiences (rich phenomenal content). The 
bottom panel schematically illustrates the physical substrate of consciousness (PSC) that might cor-
respond to this particular conceptual structure (its boundary is represented by a green line). The PSC 
consists of neuronal groups that can be in a low firing state, a high firing state or a bursting state. Alone 
and in combination, these neuronal groups specify all the concepts that compose the conceptual 
structure. Stars that are linked to the PSC by grey dashed lines represent a small subset of these con-
cepts. The PSC is synaptically connected to neurons in Broca’s area by means of a limited capacity 
channel (dashed black arrow) that is dynamically gated by top-down connections (shown as solid black 
arrows) originating in the prefrontal cortex to carry out the instruction (that is, to report the observed 
letters ‘OSA’).

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space, organisms with brains of high Φmax 
should have an adaptive advantage over less 
integrated competitors because they can fit 
more concepts (that is, functions) within a 
given number of neurons and connections. 
Simulated organisms (known as animats), 
whose ‘brains’ evolve by natural selection, 
show a monotonic relationship between 
integrated information and adaptation when 
placed in a maze65. Similarly, in the brain of 
animats that evolved to catch falling blocks in 
a simulated two-dimensional environment, 
both Φmax and the number of concepts 
increased as a function of how well the 
animats performed on the task. Although in 
simpler environments animats with modular 
feedforward brains can catch blocks just as 
well, only animats with a high Φmax evolve to 
adapt to more complex environments66.

Predictions
At the most general level, IIT predicts 
that the PSC in the brain — that is, the 
major complex — must be a maximum 
of intrinsic cause–effect power, regardless 
of the particular set of neurons that 
constitute it (FIG. 3). IIT also predicts that 
the spatio-temporal grain of the physical 
elements specifying consciousness is that 
yielding the maximum Φ (FIG. 2). Testing 
these predictions experimentally is 
challenging but not impossible.

During the initial formulation of 

IIT, a systematic set of experiments was 
designed to test its specific prediction that 
consciousness requires both integration 
and differentiation67. An empirical 
measure, the perturbational complexity 
index (PCI), which can gauge the intrinsic 
cause–effect power of the cortex, has been 
introduced as a practical proxy for Φmax 

(REF. 68). Calculating the PCI involves two 
steps: perturbing the cerebral cortex using 
transcranial magnetic stimulation to engage 
deterministic interactions among distributed 
groups of cortical neurons (integration) 
and measuring the incompressibility 
(algorithmic complexity) of the resulting 
responses (information). The PCI is high 
only if brain responses are both integrated 
and differentiated, corresponding to a 
distributed spatio-temporal pattern of causal 
interactions that is complex and hence not 
very compressible. So far, studies using PCI 
have confirmed the prediction of IIT that 
the loss and recovery of consciousness is 
associated with the breakdown and recovery 
of the capacity for information integration. 
This relationship holds true across different 
states of sleep69 and anaesthesia (using 
different agents with various mechanisms of 

the organization of experience into distinct 
modalities (such as sight, hearing and 
touch) and submodalities (such as colour, 
shape and motion within the modality of 
sight) should correspond to the presence, 
within a conceptual structure, of distinct 
sets of concepts with extensively overlapping 
purviews within each set, but much less 
across sets2. IIT further predicts that the 
binding56 of phenomenal distinctions, such 
as seeing a blue book on the piano on the 
left, should correspond, in the conceptual 
structure, to an overlap in the purview 
of the respective concepts (a relation). 
Also, differences between experiences 
should correspond to distances among 
conceptual structures in cause–effect space 
and dissimilarities among phenomenal 
distinctions within an experience should 
correspond to distances between concepts. 
The refinement of experience that occurs 
through learning (for example, learning to 
discriminate the taste of different wines) 
should be reflected in a refinement of shapes 
in cause–effect space as a result of the 
addition and splitting of concepts.

IIT also predicts that the spatial 

structure that characterizes much of our 
daily experience should be reflected in 
features of conceptual structures that are 
specified by connections among neurons 
arranged in two-dimensional grids. For 
example, horizontal connections within 
topographically organized visual areas 
would be needed to experience visual space 
from the intrinsic perspective, rather than 
merely serving to mediate modulatory 
contextual effects. This also implies that 
local strengthening or weakening of such 
horizontal connections in topographic 
areas should lead to a local distortion of 
experienced visual space, even though the 
feedforward mapping of visual inputs from 
the world remains unchanged.

More generally, IIT predicts that 

changes in the efficacy of the connections 
among elements of the PSC should lead 
to changes in experience even when these 
changes are not accompanied by changes 
in activity. A counterintuitive consequence 
of this prediction is that a brain area 
could contribute to an experience even if 
it is inactive but not if its connections or 
neurons are inactivated. Thus topographic 
visual areas would create visual space 
even in the absence of spiking activity but 
not if the horizontal connections within 
those areas are inactivated. Similarly, if the 
connections of neurons in colour areas 
are intact, the neurons would contribute 
to experience even if they are silent, by 

action)70 and in patients with brain damage, 
at the level of single subjects68. Importantly, 
once PCI is validated in participants that 
can report on whether they were conscious 
or not, the index can be used to assess the 
capacity for information integration in 
patients who are unresponsive (such as those 
in a vegetative state) or cannot report (such 
as newborn infants and non-human species).

Another approach to estimating 

differentiation and integration in practice 
is to investigate the average properties of 
neural interactions based on a representative 
sample of neural states that span many 
regions of cause–effect space, such as those 
triggered by a movie sequence23. The data 
from a candidate set of neural elements 
(for example, functional MRI blood oxygen 
level-dependent values) can then be analysed 
using measures of differentiation and 
integration based on the postulates of IIT23. 
It is also possible to obtain an indication of 
information capacity from the dynamics 
of spontaneous activity26,71,72. Some studies 
in rats73, monkeys74 and humans75 have 
confirmed that the differentiation of blood 
oxygen level-dependent activity patterns 
decreases when consciousness is lost. A 
similar approach can be used to evaluate 
information matching — how well the 
intrinsic cause–effect structures specified 
by the brain fit the causal structure of the 
environment2,23.

Similar approaches could also be used 

to test the prediction that consciousness 
should split if a single major complex splits 
into two or more complexes, and that the 
split should happen precisely when two 
maxima of integrated information supplant 
a single maximum. For example, we 
could progressively reduce the efficacy of 
transmission in the callosal fibres by cooling 
or by the use of optogenetics. IIT predicts 
that there would be a moment at which, 
as a result of a minor change in the traffic 
of neural impulses across the callosum, 
a single consciousness would suddenly 
split into two. As discussed earlier, a split 
from a single major complex into two or 
more might also be observed in functional 
blindness (when a patient claims to be 
blind but may purposefully avoid obstacles) 
and other dissociative disorders, perhaps 
even in healthy participants under certain 
circumstances (such as during autopilot-like 
driving while having a conversation) (FIG. 3f).

Turning to the contents of consciousness, 

the fundamental identity of IIT implies 
that all qualitative features of experience 
correspond to features of the conceptual 
structure specified by the PSC. For example, 

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specifying negative colour concepts, such 
as when seeing a picture in black and white. 
However, if the connections are damaged, 
they would not specify any colour concepts, 
as with certain achromatopsic patients who 
do not even understand that the picture 
is missing colour27 (FIG. 3c). Similarly, 
IIT predicts that the cerebral cortex as a 
whole may support experience even if it is 
almost silent, a state which may perhaps 
be reached through meditative practices 
designed to achieve ‘naked awareness’ 
without content76. This contrasts with the 
common assumption that neurons only 
contribute to consciousness if they are 
active and ‘broadcast’ the information 
they represent77,78 (Supplementary 
information S5 (box)). States of naked 
awareness could be compared with states 
of unawareness that occur, for example, 
during deep sleep or anaesthesia, when the 
cause–effect repertoires of cortical neurons, 
regardless of the level of neuronal activity, 
are disrupted as a result of bistability79.

Conclusions
In summary, IIT is a theory of consciousness 
that starts from the self-evident, essential 
properties (axioms) of experience and 
translates them into the necessary and 
sufficient conditions (postulates) for the 
PSC. The axioms are intrinsic existence (my 
experience exists from my own intrinsic 
perspective); composition (it has structure), 
information (it is specific), integration (it 
is unitary) and exclusion (it is definite). 
The corresponding postulates state that 
the physical substrate of an experience 
must have cause–effect power upon itself 
(intrinsic existence); its parts must have 
cause–effect power within the whole 
(composition); and the cause–effect power 
of the PSC must be specific (information), 
irreducible (integration) and maximally 
so (exclusion). The fundamental identity 
of IIT states that the quality or content of 
consciousness is identical to the form of the 
conceptual structure specified by the PSC, 
and the quantity or level of consciousness 
corresponds to its irreducibility (integrated 
information Φ).

The assessment of the identity between 

experiences and conceptual structures as 
proposed by IIT is clearly a demanding 
task, not only experimentally, but also 
mathematically and computationally. 
Evaluating maxima of intrinsic cause–effect 
power systematically requires going through 
many levels of organization, at multiple 
temporal scales, in many sets of brain 
regions, while performing an extraordinary 

Christof Koch is at the Allen Institute for Brain Science, 
615 Westlake Ave N, Seattle, Washington 98109, USA.

Correspondence to G.T. 

gtononi@wisc.edu

doi:10.1038/nrn.2016.44 

Published online 26 May 2016

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number of perturbations and observations. 
Hopefully, heuristic approaches will be 
sufficient to make a strong case that the 
PSC is constituted of some particular neural 
elements, timescales and activity states. It will 
then be essential to test the prediction that 
any manipulation that affects the PSC at the 
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Importantly, the more convincingly 

IIT can be validated under conditions 
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computers running complex programs 
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a principled, empirically testable and 
clinically useful account of how three 
pounds of organized, excitable matter 
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subjective experience. Time will tell whether 
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Giulio Tononi is at the Department of Psychiatry, 

University of Wisconsin, 6001 Research Park 
Boulevard, Madison, Wisconsin 53719, USA.

Melanie Boly is at the Department of Psychiatry, 

University of Wisconsin, 6001 Research Park Boulevard, 
Madison, Wisconsin 53719 USA; and at the Department 

of Neurology, University of Wisconsin, 1685 Highland 

Avenue, Madison, Wisconsin 53705, USA.

Marcello Massimini is at the Department of Biomedical 
and Clinical Sciences ‘Luigi Sacco’, University of Milan, 

Via G.B. Grassi 74, Milan 20157, Italy; and at the 
Instituto Di Ricovero e Cura a Carattere Scientifico, 

Fondazione Don Carlo Gnocchi, Via A. Capecelatro 66, 

Milan 20148, Italy.

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Acknowledgements
The authors thank L. Albantakis, C. Cirelli, L. Ghilardi, 
W. Marshall, W. Mayner, A. Mensen, M. Oizumi, U. Olcese, 
B. Postle, S. Sasai and other colleagues for their various con-
tributions to the work presented here. This work was sup-
ported by the Templeton World Charity Foundation, the 
McDonnell Foundation and the Distinguished Chair in 
Consciousness Science (University of Wisconsin) (to G.T.), and 
by the James S. McDonnell Scholar Award 2013 (to M.M.).

Competing interests statement
The authors declare no competing interests.

FURTHER INFORMATION
Integrated Information Theory:  
http://www.integratedinformationtheory.org

SUPPLEMENTARY INFORMATION
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S5 (box)

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