A Tale of Two Animats: What does it take to have goals?
Larissa Albantakis
Wisconsin Institute for Sleep and Consciousness
Department of Psychiatry, University of Wisconsin, Madison, WI, USA
albantakis@wisc.edu

What does it take for a system, biological or not, to have goals? Here, this question is approached in the context of in
silico artificial evolution. By examining the informational and causal properties of artificial organisms (“animats”)
controlled by small, adaptive neural networks (Markov Brains), this essay discusses necessary requirements for
intrinsic information, autonomy, and meaning. The focus lies on comparing two types of Markov Brains that
evolved in the same simple environment: one with purely feedforward connections between its elements, the other
with an integrated set of elements that causally constrain each other. While both types of brains ‘process’
information about their environment and are equally fit, only the integrated one forms a causally autonomous entity
above a background of external influences. This suggests that to assess whether goals are meaningful for a system
itself, it is important to understand what the system is, rather than what it does.

0. Prequel
It was a dark and stormy night, when an experiment of
artificial evolution was set into motion at the University of
Wisconsin-Madison. Fifty independent populations of
adaptive Markov Brains, each starting from a different
pseudo-random seed, were released into a digital world full
of dangers and rewards. Who would make it into the next
generation? What would their neural networks look like
after 60,000 generations of selection and mutation?
While electrical signals were flashing inside the
computer, much like lightning on pre-historic earth, the
scientist who, in god-like fashion, had designed the
simulated universes and set the goals for survival, waited in
suspense for the simulations to finish. What kind of
creatures would emerge? …

I. Introduction
Life, from a physics point of view, is often pictured as
a continuous struggle of thermodynamically open systems
to maintain their complexity in the face of the second law
of thermodynamics—the overall increase of entropy in our
universe [1–3]. The ‘goal’ is survival. But is our universe
like a game, in which organisms, species, or life as a whole
increase their score by surviving? Is there a way to win?
Does life have a chance if the ‘goal’ of the universe is a
maximum entropy state (‘death’)?
Maybe there is an underlying law written into the
fabrics of our universe that aligns the ‘goal’ of life with the
‘goal’ of the universe. Maybe ‘information’ is fundamental
to discover it [4] (see also Carlo Rovelli’s essay
contribution). Maybe all there is are various gradients,
oscillations, or fluctuations. In any case, looming behind
these issues, another fundamental question lingers: What
does it take for a system, biological or not, to have goals?

To approach this problem with minimal confounding
factors, let us construct a universe from scratch: discrete,
deterministic, and designed with a simple set of predefined,
built-in rules for selection. This is easily done within the
realm of in silico artificial evolution. One such world is
shown in Fig. 1A (see also [5]). In this environment, the
imposed goal is to categorize blocks of different sizes into
those that have to be caught (‘food’) and those that have to
be avoided (‘danger’), limiting life to the essential.
Nevertheless, this task requires temporal-spatial integration
of sensor inputs and internal states (memory), to produce
appropriate motor responses. Fitness is measured as the
number of successfully caught and avoided blocks.
Let us then populate this simulated universe with
‘animats’, adaptive artificial organisms, equipped with
evolvable Markov Brains [5,6]. Markov Brains are simple
neural networks of generalized logic gates, whose inputoutput functions and connectivity are genetically encoded.
For simplicity, the Markov Brains considered here are
constituted of binary, deterministic elements. Over the
course of thousands of generations, the animats adapt to
their task environment through cycles of fitness-based
selection and (pseudo) random genetic mutation (Fig. 1B).
One particularly simple block-categorization environment
requires the animats to catch blocks of size 1 and avoid
blocks of size 3 (“c1-a3”) to increase their fitness.
In silico evolution experiments have the great
advantage that they can easily be repeated many times, with
different initial seeds. In this way, a larger portion of the
‘fitness landscape’, the solution space of the task
environment, can be explored. In the simple c1-a3
environment, perfect solutions (100% fitness) were
achieved at the end of 13 out of 50 evolution experiments
starting from independent populations run for 60,000
generations. In the following we will take a look at the kind
of creatures that evolved.

2

A

B

Environment

Adaptation through mutation and fitness selection

36 units

sensors

Generation #0
Fitness: 47%

Generation #11264
Fitness: 78.9%

Generation #59904
Fitness: 97.7%

hidden
elements
motors

16 units
Fig. 1: Artificial evolution of animats controlled by Markov Brains. (A)Task
The animat
16 by 436 world with
3 is placed in aTask
Task 2
Task 1
periodic boundaries to the left and right. An animat’s sensors are activated when a block is positioned above them,
Catch
3 to the left
6 or right one
regardless
of distance.
1 or 4left. Animats can move
1 one at a time to the right
1 Blocks of different sizes fall
unit per update. (B) An animat’s Markov Brain is initialized without connections between elements and adapts to the task
5
2
3from [7] with permission.
4
2
Avoidthrough 3fitness-based selection and probabilistic
environment
mutation. Adapted

C

Task 1 (“catch size 1, avoid size 3”)
DII. Perfect
fitness—goal achieved?

< )Max >

H

H

H

<#concepts>

Fitness (%)

Task 4 (“catch size 3+6, avoid size 4+5”)
Task 4 (7 fittest)
implemented for fitness selection. It is the animat’s one and
100
6
only ‘purpose’, programmed into
its artificial universe.
4
0.6
Note
also that this description
captures
the animat’s
90 in nature, various possible5 adaptations provide
As
3.5
0.5
behavior
perfectly. After all, it is—literally—determined to
distinct80solutions to the c1-a3 environment.
The animats we
4
3
0.4 the task.
solve
tested in
this environment [5] could develop
a maximal size
70
3
2.5
0.3 Is this top-level description in terms of goals useful
of 2 sensors, 2 motors, and 4 hidden elements, but were
60
2
2 an extrinsic, observer’s
and
is
it
justified?
Certainly,
from
started at generation #0 without any connections between
0.2
perspective,
it
captures
specific
50
them (Fig.
1B). We discovered 13 1out of 50 strains of
1.5 aspects of the animat’s
0.1
universe:
the
selection
rule,
the
fact
that there are blocks of
animats40 that evolved perfect fitness,
using
diverse
0
0
1
0 strategies,
2
4
2
4
0 these 2blocks 4are caught
size 01 and size
3, and
that some of
behavioral
implemented
by 0Markov2 Brains4 with
4
#Generations
#Generations
#Generations
#Generations
#10
#10
by the animat
and some
are
not, etc. But
does it relate
at4 all
different logic
functions and architectures
(see two
1.5
5
5
to intrinsic properties of the animat itself?
examples
12 in Fig. 2).
Task 4
4.5 To approach this question,
4.5
first, one might ask
From
mere
observation
of
an
animat’s
behavior,
it
is
10
whether,
where,
and
how
much
information
about the
4
4
notoriously difficult to compress its behavioral
strategy
into
1
8
environment
is
represented
in
the
animat’s
Markov
Brain.
a simple mechanistic description (see [8] for an example
3.5
3.5
6
The
degree
to
which
a
Markov
Brain
represents
features
of
video). In some cases, an animat might first ‘determine’ the
3
3
0.5
the
environment
might
be
assessed
by
information-theoretic
size and4 direction of the falling block
and then ‘follow’
2.5
2.5
means
[6], for example, as the2 shared entropy between
small blocks
or ‘move away’ from large blocks. Such
2
2
environment states E and internal states M, given the sensor
narratives,
however, cannot cover all0 initial conditions or
0
1.5
1.5
40
60
80 we understand
100
40 an animat
60
80 its 100 states
40 S:
60
80
100
0
0.5
1
1.5
task solutions.
How
can
and
Fitness (%)
Fitness (%)
Fitness (%)
< )Max >
behavior?
𝑅 = 𝐻(𝐸: 𝑀|𝑆).
(1)
On the one hand, the animat’s Markov Brain is
R captures information about features of the environment
deterministic, consists of at most 8 elements, and we have
encoded in the internal states of the Markov Brain beyond
perfect knowledge of its logic structure. While there is no
the information present in its sensors. Conditioning on the
single elegant equation that captures an animat’s internal
sensors discounts information that is directly copied from
dynamics, we can still describe and predict the state of its
the environment at a particular time step. A simple camera
elements, how it reacts to sensor inputs, and when it
would thus have zero representation, despite its capacity to
activates its motors, moment by moment, for as long as we
make > 107 bit copies of the world.
want. Think of a Markov Brain as a finite cellular
For animats adapting to the block-catching task,
automaton with inputs and outputs. No mysteries.
relevant environmental features include whether blocks are
On the other hand, we may still aim for a
small or large, move to the left or right, etc. Indeed,
comprehensive, higher-level description of the animat’s
representation R of these features increases, on average,
behavior. One straightforward strategy is to refer to the goal
over the course of evolution [6]. While this result implies
of the task: “the animat tries to catch blocks of size 1 and
that representation of environmental features, as defined
avoid blocks of size 3”. This is, after all, the rule we
< )Max >

<#concepts>

E

3

above, is related to task fitness, the measure R itself does
not capture whether or to what extent the identified
representations actually play a causal role in determining an
animat’s behavior1.
Machine-learning approaches, such as decoding,
provide another way to identify whether and where
information about environmental variables is present in the
evolved Markov Brains. Classifiers are trained to predict
environmental categories from brain states—a method now
frequently applied to neuro-imaging data in the
neurosciences [9,10]. Roughly, the better the prediction, the
more information was available to the classifier. Just as for
R, however, the fact that information about specific stimuli
can be extracted from a brain’s neural activity does not
necessarily imply that the brain itself is ‘using’ this
information [11].
What about our animats? As demonstrated in Fig. 2,
the c1-a3 block-categorization task can be perfectly solved
by animats with as few as 2 hidden elements. Their capacity
for representation is thus bounded by 4 bits (2 hidden
elements + 2 motors). Is that sufficient for a representation
of the goal for survival? At least in principle, 4 binary
categories could be ‘encoded’. Yet, in practice, even a
larger version of animats with higher capacity for
representation (10 hidden elements) only achieved values
on the order of R = 0.6 bits in a similar block-catching
environment [6]. To solve this task, the animats thus do not
seem to require much categorical information about the
environment beyond their sensor inputs.
While this lack of representation in the animats may
be due to their small size and the simplicity of the task,
there is a more general problem with the type of
information measures described above: the information that
is quantified is, by definition, extrinsic information.

A

Feed-forward network
with self-loops

B

Feed-back network

Any form of representation is ultimately a correlation
measure between external and internal states, and requires
that relevant environmental features are preselected and
categorized by an independent observer (e.g. to obtain E in
eq. 1, or to train the decoder). As a consequence, the
information about the environment represented in the
animat’s Markov Brain is meaningful for the investigator.
Whether it is causally relevant, let alone meaningful, for the
animat is not addressed.2

III. Intrinsic information
To be causally relevant, information must be
physically instantiated. For every ‘bit’, there must be some
mechanism that is in one of two (or several) possible states,
and which state it is in must matter to other mechanisms. In
other words, the state must be “a difference that makes a
difference” [12,13].
More formally, a mechanism M has inputs that can
influence it and outputs that are influenced by it. By being
in a particular state m, M constrains the possible past states
of its inputs3, and the possible futures states of its outputs in
a specific way. How much M in state m constrains its inputs
can be measured by its cause information (ci); how much it
constrains its outputs is captured by its effect information
(ei) [13].
An animat’s Markov Brain is a set of interconnected
logic elements. A mechanism M inside the Markov Brain
could be one of its binary logic elements, but can in
principle also be a set of several such elements4. In discrete
dynamical systems, such as the Markov Brains, with
discrete updates and states, we can quantify the cause and
effect information of a mechanism M in its current state 𝑚,
within system Z as the difference D between the
constrained and unconstrained probability distributions over
Z’s past and future states [13]:

sensors

(2)

𝑒𝑖(𝑀 = 𝑚, ) = 𝐷 𝑝 𝑧,63 |𝑚, , 𝑝 𝑧,63

(3)

where 𝑧,23 are all possible past states of Z one update ago,
and 𝑧,63 all possible future states of Z at the next update.
For 𝑝 𝑧,23 , we assume a uniform (maximum entropy)
distribution, which corresponds to perturbing Z into all

hidden
elements

motors

2

Fitness: 100%

Fitness: 100%

Fig. 2: Example network architectures of evolved
Markov Brains that achieved perfect fitness in the c1-a3
block-catching task. Adapted from [5] with permission.
1

𝑐𝑖(𝑀 = 𝑚, ) = 𝐷 𝑝 𝑧,23 |𝑚, , 𝑝 𝑧,23

Furthermore, representations of individual environmental
features are typically distributed across many elements [6], and
thus do no coincide with the Markov Brain’s elementary (micro)
logic components.

Note that this holds, even if we could evaluate the correlation
between internal and external variables in an observer-independent
manner, except then the correlations might not even be meaningful
for the investigator.
3
If M would not constrain its inputs, its state would just be a
source of noise entering the system, not causal information.
4
Sets of elements can constrain their joint inputs and outputs
in a way that is irreducible to the constraints of their constituent
elements taken individually [13]. The irreducible cause-effect
information of a set of elements can be quantified similarly to
Eqn. 2-3, by partitioning the set and measuring the distance
between 𝑝 𝑧,±3 |𝑚, and the distributions of the partitioned set.

4

possible states with equal likelihood. Using such systematic
perturbations makes it possible to distinguish observed
correlations from causal relations [14]5. By evaluating a
causal relationship in all possible contexts (all system
states), we can obtain an objective measure of its specificity
(“Does A always lead to B, or just sometimes?”) [13,15].
Likewise, we take 𝑝 𝑧,63 to be the distribution obtained
by providing independent, maximum entropy inputs to each
of the system’s elements [13]. In this way, Eqn. 2 and 3
measure the causal specificity with which mechanism M in
state 𝑚, constrains the system’s past and future states.
A system can only ‘process’ information to the extent
that it has mechanisms to do so. All causally relevant
information within a system Z is contained in the system’s
cause-effect structure, the set of all its mechanisms, and
their cause and effect distributions 𝑝 𝑧,23 |𝑚, and
𝑝 𝑧,63 |𝑚, . The cause-effect structure of a system in a
state specifies the information intrinsic to the system, as
opposed to correlations between internal and external
variables. If the goals that we ascribe to a system are indeed
meaningful from the intrinsic perspective of the system,
they must be intrinsic information, contained in the
system’s cause-effect structure (if there is no mechanism
for it, it does not matter to the system).
Yet, the system itself does not ‘have’ this intrinsic
information. Just by ‘processing’ information, a system
cannot evaluate its own constraints. This is simply because
a system cannot, at the same time, have information about
itself in its current state and also other possible states. Any
memory the system has about its past states has to be
physically instantiated in its current cause-effect structure.
While a system can have mechanisms that, by being in their
current state, constrain other parts of the system, these
mechanisms cannot ‘know’ what their inputs mean6. In the
same sense, a system of mechanisms in its current state
does not ‘know’ about its cause-effect structure; instead, the
cause-effect structure specifies what it means to be the
system in a particular state7. Intrinsic meaning thus cannot
arise from ‘knowing’, it must arise from ‘being’.
What does it mean to ‘be’ a system, as opposed to an
assembly of interacting elements, defined by an extrinsic
observer? When can a system of mechanisms be considered
an autonomous agent separate from its environment?
5
By contrast to the uniform, perturbed distribution, the
stationary, observed distribution of system Z entails correlations
due to the system’s network structure which may occlude or
exaggerate the causal constraints of the mechanism itself.
6
Take a neuron that activates, for example, every time a
picture of the actress Jennifer Aniston is shown [22]. All it
receives as inputs is quasi-binary electrical signals from other
neurons. The meaning “Jennifer Aniston” is not in the message to
this neuron, or any other neuron.
7
For example, an AND logic gate receiving 2 inputs is what it
is, because it switches ON if and only if both inputs were ON. An
AND gate in state ON thus constrains the past states of its input to
be ON.

IV. To be or not to be integrated
Living systems, or agents, more generally, are, by
definition, open systems that dynamically and materially
interact with their environment. For this reason, physics, as
a set of mathematical laws governing dynamical evolution,
does not distinguish between an agent and its environment.
When a subsystem within a larger system is characterized
by physical, biological, or informational means, its
boundaries are typically taken for granted (see also [16]).
Let us return to the Markov Brains shown in Fig. 2,
which evolved perfect solutions in the c1-a3 environment.
Comparing the two network architectures, the Markov
Brain in Fig. 2A has only feedforward connections between
elements, while the hidden elements in Fig. 2B feedback to
each other. Both Markov Brains ‘process’ information in
the sense that they receive signals from the environment
and react to these signals. However, the hidden elements in
Fig. 2B constrain each other, above a background of
external inputs, and thus from an integrated system of
mechanisms.
Whether and to what extent a set of elements is
integrated can be determined from its cause-effect structure,
using the theoretical framework of integrated information
theory (IIT) [13]. A subsystem of mechanisms has
integrated information Φ > 0, if all of its parts constrain,
and are being constrained by, other parts of the system.
Every part must be a difference that makes a difference
within the subsystem. Roughly, Φ quantifies the minimal
intrinsic information that is lost if the subsystem is
partitioned in any way. An integrated subsystem with Φ > 0
has a certain amount of causal autonomy from its
environment8. Maxima of Φ define where intrinsic causal
borders emerge [17,18]. A set of elements thus forms a
causally autonomous entity if its mechanisms give rise to a
cause-effect structure with maximal Φ, compared to smaller
or larger overlapping sets of elements. Such a maximally
integrated set of elements forms a unitary whole (it is ‘one’
as opposed to ‘many’) with intrinsic, self-defined causal
borders, above a background of external interactions. By
contrast, systems whose elements are connected in a purely
feedforward manner have Φ = 0: there is at least one part of
the system that remains unconstrained by the rest. From the
intrinsic perspective, then, there is no unified system, even
though an external observer can treat it as one.
So far, we have considered the entire Markov Brain,
including sensors, hidden elements, and motors, as the
system of interest. However, the sensors only receive input
from the environment, not from other elements within the
system, and the motors do not output to other system
elements. The whole Markov Brain is not an integrated
system, and thus not an autonomous system, separate from
8
This notion of causal autonomy applies to deterministic and
probabilistic systems, to the extent that their elements constrain
each other, above other background inputs, e.g. from the sensors.

5

its environment. Leaving aside the animat’s ‘retina’
(sensors) and ‘motor neurons’ (motors), inside the Markov
Brain in Fig. 2B, we find a minimal entity with Φ > 0 and
self-defined causal borders—a ‘brain’ within the Markov
Brain. By contrast, all there is, in the case of Fig. 2A, is a
cascade of switches, and any border demarcating a
particular set of elements would be arbitrary.
Dynamically and functionally the two Markov Brains
are very similar. However, one is an integrated, causally
autonomous entity, while the other is just a set of elements
performing a function. Note again that the two systems are
equally ‘intelligent’ (if we define intelligence as task
fitness). Both solve the task perfectly. Yet, from the
intrinsic perspective being a causally autonomous entity
makes all the difference (see here [13,19]). But is there a
practical advantage?

V. Advantages of being integrated
The cause-effect structure of a causally autonomous
entity describes what it means to be that entity from its own
intrinsic perspective. Each of the entity’s mechanisms, in its
current state, corresponds to a distinction within the entity.
Being an entity for which ‘light’ is different from ‘dark’,
for example, requires that the system itself, its cause-effect
structure, must be different when it ‘sees’ light, compared
to when it ‘sees’ dark. In this view, intrinsic meaning might
be created by the specific way in which the mechanisms of
an integrated entity constrain its own past and future states,
and by their relations to other mechanisms within the entity.
The animat ‘brain’ in Fig. 2B, constituted of the 2
hidden elements, has at most 3 mechanisms (each element,
and also both elements together, if they irreducibly
constrain the system). At best, these mechanisms could
specify that “something is probably this way, not that way”,
and “same” or “different”. Will more complex
environments lead to the evolution of more complex
autonomous agents?
In the simple c1-a3 environment, animats with
integrated brains do not seem to have an advantage over
feedforward architectures. Out of the 13 strains of animats
that reached perfect fitness, about half developed
architectures with recurrent connections (6/13) [5].
However, in a more difficult block-catching environment,
which required more internal memory (“catch size 3 and 6,
avoid size 4 and 5”), the same type of animats developed
more integrated architectures with higher Φ, and more
mechanisms (one example architecture is shown in Fig.
1B). The more complex the environment, the more
evolution seems to favor integrated structures.
In theory, and more so for artificial systems, being an
autonomous entity is not a requirement for intelligent
behavior. Any task could, in principle, be solved by a
feedforward architecture given an arbitrary number of
elements and updates. Nevertheless, in complex, changing

environments, with a rich causal structure, where resources
are limited and survival requires many mechanisms,
integrated agents seem to have an evolutionary advantage
[5,20]. Under these conditions, integrated systems are more
economical in terms of elements and connections, and more
flexible than functionally equivalent systems with a purely
feedforward architecture. Evolution should also ensure that
the intrinsic cause-effect structure of an autonomous agent
‘matches’ the causal structure of its environment [21].
From the animats, it is still a long way towards agents
with intrinsic goals and intentions. What kind of causeeffect structure is required to experience goals, and which
environmental conditions could favor its evolution, remains
to be determined. Integrated information theory offers a
quantitative framework to address these questions.

VI. Conclusion
Evolution did produce autonomous agents. We
experience this first hand. We are also entities with the right
kind of cause-effect structure to experience goals and
intentions. To us, the animats appear to be agents that
behave with intention. However, the reason for this lies
within ourselves, not within the animats. Some of the
animats even lack the conditions to be separate causal
entities from their environment. Yet, observing their
behavior affects our intrinsic mechanisms. For this reason,
describing certain types of directed behaviors as goals, in
the extrinsic sense, is most likely useful to us from an
evolutionary perspective. While we cannot infer agency
from observing apparent goal-directed behavior, by the
principle of sufficient reason, something must cause this
behavior (if we see an antelope running away, maybe there
is a lion). On a grander scale, descriptions in terms of goals
and intentions can hint at hidden gradients and selection
processes in nature, and inspire new physical models.
For determining agency and intrinsic meaning in other
systems, biological or not, correlations between external
and internal states have proven inadequate. Being a causally
autonomous entity from the intrinsic perspective requires an
integrated cause-effect structure; merely ‘processing’
information does not suffice. Intrinsic goals certainly
require an enormous amount of mechanisms. Finally, when
physics is reduced to a description of mathematical laws
that determine dynamical evolution, there seems to be no
place for causality. Yet, a (counterfactual) notion of
causation may be fundamental to identify agents and
distinguish them from their environment.

Acknowledgements
I thank Giulio Tononi for his continuing support and
comments on this essay, and William Marshall, Graham Findlay,
and Gabriel Heck for reading this essay and providing helpful
comments. L.A. receives funding from the Templeton World
Charities Foundation (Grant #TWCF0196).

6

References
1. Schrödinger E (1992) What is Life?: With Mind and
Matter and Autobiographical Sketches. Cambridge
University Press.

12. Bateson G (1972) Steps to an Ecology of Mind.
University of Chicago Press.

2. Still S, Sivak DA, Bell AJ, Crooks GE (2012)
Thermodynamics of Prediction. Phys Rev Lett 109:
120604.

13. 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.

3. England JL (2013) Statistical physics of self-replication.
J Chem Phys 139: 121923.

14. Pearl J (2000) Causality: models, reasoning and
inference. Cambridge Univ Press.

4. Walker SI, Davies PCW (2013) The algorithmic origins
of life. J R Soc Interface 10: 20120869.

15. Ay N, Polani D (2008) Information Flows in Causal
Networks. Adv Complex Syst 11: 17–41.

5. 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.

16. Krakauer D, Bertschinger N, Olbrich E, Ay N, Flack JC
(2014) The Information Theory of Individuality The
architecture of individuality.

6. Marstaller L, Hintze A, Adami C (2013) The evolution of
representation in simple cognitive networks. Neural
Comput 25: 2079–2107.
7. Albantakis L, Tononi G (2015) The Intrinsic CauseEffect Power of Discrete Dynamical Systems—From
Elementary Cellular Automata to Adapting Animats.
Entropy 17: 5472–5502.
8. Online Animat animation.
http://integratedinformationtheory.org/animats.html
9. Quian Quiroga R, Panzeri S (2009) Extracting
information from neuronal populations: information
theory and decoding approaches. Nat Rev Neurosci 10:
173–185.
10. King J-R, Dehaene S (2014) Characterizing the dynamics
of mental representations: the temporal generalization
method. Trends Cogn Sci 18: 203–210.
11. Haynes J-D (2009) Decoding visual consciousness from
human brain signals. Trends Cogn Sci 13: 194–202.

17. Marshall W, Albantakis L, Tononi G (2016) Blackboxing and cause-effect power. arXiv 1608.03461.
18. Marshall W, Kim H, Walker SI, Tononi G, Albantakis L
How causal analysis can reveal autonomy in biological
systems.
19. Tononi G, Boly M, Massimini M, Koch C (2016)
Integrated information theory: from consciousness to its
physical substrate. Nat Rev Neurosci 17: 450–461.
20. Albantakis L, Tononi G (2015) Fitness and neural
complexity of animats exposed to environmental change.
BMC Neurosci 16: P262.
21. Tononi G (2015) Integrated
Scholarpedia 10: 4164.

information

theory.

22. Quiroga RQ, Reddy L, Kreiman G, Koch C, Fried I
(2005) Invariant visual representation by single neurons
in the human brain. Nature 435: 1102–1107.

