ArticlePDF Available

Freud and the algorithm: neuropsychoanalysis as a framework to understand artificial general intelligence


Abstract and Figures

The core hypothesis of this paper is that neuropsychoanalysis provides a new paradigm for artificial general intelligence (AGI). The AGI agenda could be greatly advanced if it were grounded in affective neuroscience and neuropsychoanalysis rather than cognitive science. Research in AGI has so far remained too cortical-centric; that is, it has privileged the activities of the cerebral cortex, the outermost part of our brain, and the main cognitive functions. Neuropsychoanalysis and affective neuroscience, on the other hand, affirm the centrality of emotions and affects—i.e., the subcortical area that represents the deepest and most ancient part of the brain in psychic life. The aim of this paper is to define some general design principles of an AGI system based on the brain/mind relationship model formulated in the works of Mark Solms and Jaak Panksepp. In particular, the paper analyzes Panksepp’s seven effective systems and how they can be embedded into an AGI system through Judea Pearl’s causal analysis. In the conclusions, the author explains why building a sub-cortical AGI is the best way to solve the problem of AI control. This paper is intended to be an original contribution to the discussion on AGI by elaborating positive arguments in favor of it.
This content is subject to copyright. Terms and conditions apply.
Freud and the algorithm: neuropsychoanalysis as a
framework to understand articial general
Luca M. Possati1
The core hypothesis of this paper is that neuropsychoanalysis provides a new paradigm for
articial general intelligence (AGI). The AGI agenda could be greatly advanced if it were
grounded in affective neuroscience and neuropsychoanalysis rather than cognitive science.
Research in AGI has so far remained too cortical-centric; that is, it has privileged the activities
of the cerebral cortex, the outermost part of our brain, and the main cognitive functions.
Neuropsychoanalysis and affective neuroscience, on the other hand, afrm the centrality of
emotions and affectsi.e., the subcortical area that represents the deepest and most ancient
part of the brain in psychic life. The aim of this paper is to dene some general design
principles of an AGI system based on the brain/mind relationship model formulated in the
works of Mark Solms and Jaak Panksepp. In particular, the paper analyzes Panksepps seven
effective systems and how they can be embedded into an AGI system through Judea Pearls
causal analysis. In the conclusions, the author explains why building a sub-cortical AGI is the
best way to solve the problem of AI control. This paper is intended to be an original con-
tribution to the discussion on AGI by elaborating positive arguments in favor of it. OPEN
1University of Porto, Porto, Portugal. email:
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
The thesis
of this paper is that neuropsychoanalysis and
affective neuroscience can provide a new paradigm for AI,
particularly for articial general intelligence (AGI). I
especially refer to the works of Mark Solms and Jaak Panksepp.
Neuropsychoanalysis and affective neuroscience give us a precise
answer to the enigma of the mind/brain dualism by highlighting
the constant interaction of these two dimensions. I will try to
show how this approach can give us a new key for the con-
ceptualization of AGI. This is a preliminary communication in
which I treat the problem in a broad outline and the rst step in a
research project on the possibility of AGI. Obviously, the topics
covered are all controversial and would ideally be given a detailed
analysis in several papers.
The structure of the paper is as follows. In section Neu-
ropsychoanalysis: an introduction, I give a denition of the
fundamental aspects of neuropsychoanalysis and its relationships
with affective neuroscience. In Sections The neuropsychoanalytic
model of the mindand The primitive affective states, or the
basic human values, I illustrate the neuropsychoanalytic model
of the mind/brain relation. In particular, I analyze Panksepps
theory of the basic affective states. In section A Freudian com-
puter: sketches,Idene some basic design principles of an AGI
model based on neuropsychoanalysis and affective neuroscience. I
answer the question: How can we translate Panksepps theory of
the basic affective states into algorithms?
Why do we need a new approach to AGI?
I answer this question with two remarks. Firstly, an essential
point that articial intelligence (AI) research must consider is that
a method based merely on the physical imitation of the brain is
wrong. This is for two reasons: the rst is that our knowledge of
the brain is still very limited, and the second is that even
assuming that we could properly reconstruct each cell of our
brain and its functioning, something would still be missing,
namely the mind. We, therefore, need a model that can hold these
two dimensions together, mind and brain. The imitation of
anatomical mechanisms and the psychological expression of these
mechanisms must go hand in hand.
Secondly, so far, research in AGI has mainly focused on the
activities of the cerebral cortex and the main cognitive functions
(language, logic, memory, cognition, etc.). The time has come to
think and develop an AGI of the subcortical. Neuropsychoana-
lysis and affective neuroscience afrm the centrality of emotions
and affecti.e., the subcortical area of the brain where primary
processes (instincts, emotions, feelings) are located in psychic and
cognitive activity. My hypothesis is that an AGI system inspired
by neuropsychoanalysis and affective neuroscience must be based
on the modeling and simulating of the seven basic affective states
analyzed by Panksepp. Panksepps workscrucial also for neu-
ropsychoanalysisgive us a theoretical framework on which to
develop this hypothesis.
An important clarication should be made. Today, there is
much discussion of affective computing. The relationship
between emotion and AI is a vast research eld, beginning with
the important and controversial book by Rosalind Picard (1997).
What does it mean for a computer to have emotions? In gen-
eral, when we talk about effective computing, we mean three
connected things: (a) the way in which a computational system
can recognize and instantiate human emotions; (b) the way in
which a computational system can respond to human emotions;
and (c) the way in which a computational system can express
emotions spontaneously or use them in a positive way in the
decision-making process (see Schuller and Schuller, 2018; Erol
et al., 2019; Shibata et al., 1997; El Nasr et al., 2000; Fogel et al.,
2018). More specically, affective computing involves the
recognition, interpretation, replication, and potentially the
manipulation of human emotions by computers and social
robots(Yonck, 2017, p. 5). Experts agree that articial emotional
intelligence is a continuously developing research eld and that it
will have a decisive importance in the future economy and
society. However, articial emotional intelligence will also pose
new ethical and legal problems. There are new dangers, such as
psychological manipulation (see Picard, 1997, chapter 4). The
study of biomimetics and hybrid systems (biological and tech-
nological) that analyze the possibility of building robots capable
of reproducing the versatility of the human organism (see Pre-
scott et al., 2018) is also connected to this immense research eld.
How is my research different from the affective computing
approach? This paper does not intend to provide an overview of
the debate on affective computing. The scope of that subject
would merit an entire book unto itself. However, I will develop
some critical considerations of Picards concept of emotions and
feeling. In my opinion, Picard remains too tied to a cognitivist
conception of mind, preventing her from considering emotion as
such. Following Panksepp (1998) and Panksepp and Biven
(2012), I hold that emotion is an intrinsic function of the brain,
not the reection or derivative of the higher cognitive functions.
There exist basic instinctual systems that are phylogenetic
memories that we have inherited as evolutionary tools for living.
If we do not fully understand these systems, we cannot under-
stand the brain/mind relationship, or the BrainMind,as
Panksepp terms it. Human emotionality has an intelligence, a
structure; it is not only the mechanical answer to a series of
random situations. A subcortical AGI should be capable not only
of reproducing the basic human affective systems but also of
using them to build the most elaborate cortical functions, such as
learning and language. Therefore, three aspects characterize my
approach: a) emotions are not reducible to cognitive activities; b)
cognitive activities arise from emotions; c) emotions are analyzed
from a neuropsychoanalytic point of view.
From this point of view, an AGI system that is able to
instantiate these basic affective systems or even the Freudian
unconscious must be thought of in a way radically different from
classical methods.
What is AGI?
The dream of creating machines perfectly capable of reproducing
human intelligence is very old. The investigations of Turing, von
Newmann, Shannon, and many others have radically revolutio-
nized this idea, opening an entirely new eld of research (Dyson,
2012). Today, AI is an ever-expanding sector in which philoso-
phy, technology, design, storytelling, sci-dystopian stories, and
speculations of all kinds are continuously intertwined (see
Amoore, 2009; Apaydin, 2016; Baldwin, 2016; Le Cun, 2019;
Colvin, 2015). A classic denition is that of one of the great
pioneers of AI, Marvin Minsky: AI is the science of making
machines do things that would require intelligence if done by
men(Bolter, 1986, p. 193). As Fjelland (2020, p. 1) underscores,
this is what we call weak AI,that is, a type of AI capable of
performing only some specic human tasks (seeing, manipulating
objects, classifying, etc.).
The concept of AGI is essentially different from that of weak
AI because it denotes a type of AI capable of fully simulating
human intelligence, not just a part of it. It is the intelligence of a
machine that is capable of learning and understanding any
human intellectual activity, a machine with general-purpose,
adaptive intelligence(Shanahan, 2015, p. 3). It is therefore a
generalist intelligence, capable of adapting and creating new
forms of behavior with a degree of ability similar to that of a
human being. However, research on AGI still appears to be very
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
limited. As Wikipedia says, MIT presented a course in AGI in
2018, organized by Lex Fridman and featuring a number of guest
lecturers. However, as yet, most AI researchers have devoted little
attention to AGI, with some claiming that intelligence is too
complex to be completely replicated in the near term.
My thesis in this paper is that an AGI is possible but must be
conceived starting from the basic affective states. I will use the
expression AGIas the equivalent of strong AI,even if some
differences can be found between the two. The expression strong
AIcould, in fact, also be interpreted as superintelligence”—that
is, intelligence that wants to overcome humans and control them
(Bostrom, 2016).
There are many difculties related to the concept of human-
like AI (see Russell, Norvig, 2016). First of all, there is the obvious
difference between algorithms and the human way of thinking:
Penrose (1989,1994) and Dreyfus (1972) have demonstrated, in
different ways, the abyss between the human way of reasoning
and computation. Dreyfus, in particular, holds that computers,
who have nobody, no childhood, and no cultural practice, could
not acquire intelligence at all (Dreyfus and Dreyfus, 1986;
Fjelland, 2020).
The thesis of the present paper is twofold: (a) our AGIs do not
have a childhood, or practical culture because they do not have a
crucial element of human evolutioni.e., emotions and affects
and (b) the large masses of data (the so-called Big Data) and the
statistical techniques now available allow us to instantiate human
emotions and affects. From a neuropsychoanalytical point of
view, affects are the basis of intelligence and consciousness.
Furthermore, this paper wants to show that claiming that AGI is
possible does not at all mean overestimating technology and
underestimating human intelligence.
Neuropsychoanalysis: an introduction
The main advocates of the neuropsychoanalytic point of view
(Solms, Kaplan-Solms, Turnbull) argue that their perspective on
the mind/brain question is the same as Freuds and call this
approach dual-aspect monism.Their thesis is that the mind is a
unique reality. Nonetheless, we cannot directly access it. To
describe and understand the mind, we must draw inferences (to
build models) based on two limited forms of experience: rst-
person subjective experience (psychology) and third-person study
of brain structures and functions (neuroscience). In more formal
terms, the rst form is interoceptive,while the second is
These two forms of experience are independent observational
perspectivesand have the same value, but are not able to explain
this unique reality, which can be called the mind/brain, in a
complete way. If we look at it with our physical eyes, we see a
brain, biological organ-like many others. If we look at it with the
eyes of our subjective consciousness, we come into contact with
mental states such as sadness, desire, and pleasure. It is therefore
necessary to keep both points of view (subjective and objective)
open and build dynamic parallelism between them. We will never
nd a thought, a memory, or an emotion in a piece of brain
tissue; we will nd brain cells, nothing else. Meanings and
intentionality are not reducible to neurons. According to dual-
aspect monism, the mind can be distinguished from the brain
only from the perceptual perspective. If we admit a single entity
Xbehindthe terms mindand brain,then we can say that
(a) the mind is X perceived subjectivelythat is, through ones
own consciousnessand (b) the brain is X perceived objectively
that is, through external perception and objectifying methods
of sciences.
Neuropsychoanalysis tries to connect the X-object to the
X-subject. In this way, neuropsychoanalysis does not intend to
reduce the mind to the brain; even if it has been accused of
biologism, it does not intend to reduce everything to biochemical
processes and anatomy. All mental phenomena require a biolo-
gical correlate; this is indisputable. This does not mean, however,
entirely reducing the mental phenomena and their meaning to
supposed biological correlates. Biological and psychological
dimensions must be kept together; they must be considered two
sources of information of the same value.
Neuropsychoanalysis does not intend to prove that Freud was
always right. Instead, it claims to nish the work started by Freud.
Indeed, Freud began his career as a neuroscientist and neurologist
(see Sulloway, 1979, chapter 1). He had a specic and broad
scientic program, but it was largely conditioned by the limits of
the neuroscientic methods available at the time. For Freud,
psychoanalysis is not only a hermeneutics of mental life. The
separation between psychoanalysis and neuroscience was for him
only a pragmatic, strategic, and temporary solution; it was
motivated by the lack of knowledge about the brain at the time.
However, as Freud repeats in several passages, the inevitable
progress of neuroscience would sooner or later lead to a bridging
of the gap between the two disciplines and to an organic basis for
the discoveries of psychoanalysis (Solms and Turnbull, 2002). In
other words, Freud was dissatised with the clinical-anatomical
method of his time and therefore developed his analytical method
independently of neuroscience from 1895 to 1939. He eagerly
awaited the progress of neuroscience and biology, and for this
reason, he sought confrontation, dialog, and cooperation with
these sciences (Solms and Saling, 1990).
Since Freuds time, things have changed a great deal. We can
now verify the validity of Freuds basic statements through
appropriate scientic observations. The knowledge and methods
for studying the brain are much more developed and therefore
allow us to improve and nish Freuds endeavor. In the past
twenty years, neuroscience has not only experienced exponential
growth but also changed its character, thanks to technological
advances. In particular, the critique of the behavioristic (focused
only on the observable patterns) and cognitive (the thesis that the
human mind is essentially information processing, and so per-
ception and learning) models of mind has led to a broader vision
that includes emotions and feelings, the connection to a body that
acts and perceives within a social and technological environment.
Both the behavioristic and cognitive models undermine the
importance of emotions and feelings.
This turning point can be found in numerous works: Benedetti
(2010), Damasio (1994), Decety and Ickes (2009), Gallese (2009),
LeDoux (1996), and Panksepp (1998). Furthermore, Lurias
(1976) important work also demonstrated the possibility of
renewing the psychoanalytic method through neuroscience. In
particular, Solms (2000) and Kaplan and Solms (2000) underlined
the importance of Lurias method, which entails the abandon-
ment of a rigid localization of cognitive functions in favor of a
much more integrated approach to the mind. This is the so-called
dynamic localization methodaccording to which complex
mental activities (memory, imagination, thought, etc.) cannot
each be located rigidly in a single area of the brain. On the
contrary, many areas of the brain activate at once, each time in a
different way.
It should never be forgotten, however, that the debate on
neuropsychoanalysis is broad and complex. Much research in
neuroscience claims that the Freudian dream theory (but not only
this) is completely wrong (Hobson, 2007). There are also many
psychoanalysts (see Blass and Carmeli, 2007, Edelson, 1986,
Pulver, 2003) according to whom neuroscience is irrelevant to
psychoanalysis, and this is because neuroscience has nothing to
say about our mental meanings and their interpretation, which
are the domains of psychoanalysis. Knowing the biological basis
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
of mental processes explains nothing of the meanings that make
up our lives; it would be like wanting to explain software based on
knowledge of hardware. I will not analyze these criticisms in the
present paper.
The neuropsychoanalytic model of the mind
Neuropsychoanalysis proposes a general model of how the
human mental apparatus, as conceived by psychoanalysis, can be
represented in brain tissues. It is a hypothetical model based on
current knowledge of the brain and on a still limited amount of
empirical data. The theoretical points of reference for this
operation are mainly Lurias work and Freudian metapsychology.
The main thesis is that mental functions are not rigidly localized
in individual areas of the brain but are statistically distributed in
several areas. Each area contributes in its own way. Information
processing is a dynamic process that involves many areas of the
brain in ever-changing ways.
At the center of the model is the Pcpt-cs system, namely, the
perceptual consciousness. This system has two surfaces: an
external one, directed toward the world around the brain (it is
divided into different areas of specialization: sight, hearing,
kinesthesia, and tactile sensation) and an internal one, directed
toward the processes taking place inside of the body. The rst
surface is located in the posterior part of the cortex (although
numerous subcortical structures contribute to the processing of
the stimulus in a dynamic way). The second surface is connected
to the limbic system and to a series of deeper, subcortical brain
structures that represent the oldest part of the brain. These are the
only two sources of stimuli, or data, that our brain possesses,
namely, external reality and internal reality. For neuropsychoa-
nalysis, therefore, consciousness is nothing abstract or metaphy-
sical. It is the set of our external and internal perceptionsthe
connection between the data we have about the external world
and the way in which these data modify us.
According to Solms (2008), the intermediate zone between the
internal and the external perception corresponds to areas of the
brain that lter, record, and structure information by using
connections, associations, and classications. These areas are
located in the posterior parts of the cortex. Associations and
connections can be of different types, depending on the type of
memory involved in the process, such as working memory, epi-
sodic memory, procedural memory, and semantic memory. The
information is recorded in different ways. Through memory, the
brain develops intentionality, the ability to plan its actions and
therefore to act in the world. In addition, it develops the ability of
thoughtthat is, deferring the action or satisfaction of the need at
a given moment.
Now, Solms links this area to the Freudian notion of ego. In
Freudian metapsychology, in fact, the ego is that instance that
must mediate between external and internal reality; it is precisely
that part of the id that has been modied by external reality
(natural and social) in the course of evolution. Freud also saw in
the ego a series of memory structures through which experiences
are connected and recorded. These connections are not pre-
determined; they develop over time. The progressive stabilization
of the connections gives rise to the main cognitive functions, such
as thought, language, logic, attention, calculation, and imagina-
tion. Two important articles by Kandel (1979,1983) explain the
way in which these processes develop at the cellular level. The ego
is a continuous, dynamic connective process whose constant
evolution depends on numerous variables. The crucial function of
the ego is to work as a barrier to the stimuli. If there were no ego
to lter and organize information, the human brain would be
overwhelmed by stimuli and therefore would be in a state of
perennial excitement.
The id is our deep, visceral biological dimension, which is also
called internal milieu(milieu intérieur) and includes different
systems of our body, such as the musculoskeletal system, the
immune system, the endocrine system, the chemical processes,
and organic cycles. The way the brain perceives changes occur-
ring in this biological system is what neuropsychoanalysis calls
internal perception”—this is the immense eld of instincts,
feelings, and emotions to which psychoanalysis gives a pre-
dominant role in the psychic activity. Internal perception consists
of the activation of deep and ancient brain structures (the limbic
system and subcortical brain structures) connected to the biolo-
gical dimension of the body and the mechanisms of adaptation. It
is important to note that the neurons that make up the limbic
system and the subcortical brain structures work very differently
than the neurons of the perceptive-mnestic systems of the cortex
(Solms, 1996). These neurons generate not only discrete stimuli
but also gradual state changes.
The ego also mediates between the id and the super-ego.
According to Solms (1996), the super-ego can be connected to
some regions of the prefrontal lobe and precisely to those regions
that connect the prefrontal part of the brain with the limbic
system. These regions act as a lter, as censorship toward the
needs of the instinctual pole of the mind. Their type of memory is
called semantic memoryand mainly concerns social conven-
tions. In line with what Freud says, the super-ego arises from the
internalization of behavior and value schemes in the social
Before this section is concluded, one puzzle must be solved.
The source of activation of internal perception is the id, the vital
biological systems that compose our organism. Does this mean
that the id is conscious, that we have the perception of the id? Is
the unconscious conscious? Solms claims that the id is the source
of all forms of consciousness: This constant presenceof feeling
is the background subject of all cognition, without which con-
sciousness of perception and cognition could not exist(Solms,
2013, p. 16). Solms and Friston (2018) stress this point: con-
sciousness is mostly interoceptive: The primary function of
consciousness is not to register states of the external world but
rather to register the internal states of the experiencing subject.
[] conscious qualia arise primarily not from exteroceptive
perception (i.e., vision, hearing, somatic sensation, taste, and
smell), and still less from reective awareness of such repre-
sentations, but rather from the endogenous arousal processes that
activate them(23). This means that external perceptions
become conscious and subjective only when they are connected to
and activated bydeeper and internal arousal processesthat
is, the affect and instinct systems.
Therefore, affects and instincts compose the rst form of
consciousness, which is the condition of all the others. Where,
then, does repression arise? In the transition from one system to
another. Where does what we call properly unconscious originate,
in a Freudian sense? The basic form of affective consciousness is
not fully translated into the more complex systems of the ego and
the superego and therefore remains invisible. If we retain Freuds
view that repression concerns representational processes, it seems
reasonable to suggest that repression must involve withdrawal of
declarative consciousness(Solms, 2013, p. 17). In a nutshell, the
id has no access to declarative consciousness.
Now, if consciousness is essentially founded on affects, what
are affects properly?
The primitive affective states, or the basic human values
The organism of mammals is generally composed of a series of
structures in relation to each other. Homeostasis is the set of
coordinated and partly automatic physiological, biological, and
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
chemical processes that are indispensable for maintaining the
state of the organism stable and thus guaranteeing survival,
including the regulation of temperature and heart rate, the con-
centration of oxygen in the blood, the structure of the muscu-
lature, the skin tone, and the metabolism. According to Damasio
(1999), emotions are closely connected to homeostasis; they are
biological phenomena produced by neuronal congurations in
order also to guarantee homeostasis. The brain inuences and
modies the body by regulating it. The aim is adaptation and
survivalthat is, to create advantageous conditions for the
organism in certain situations. For example, fear causes the
acceleration of heart rate in dangerous conditions. An external
situation (the danger) activates some regions of the brain that
produce, through the release of chemicals or neurotransmitters, a
series of modications of the body (the acceleration of the
heartbeat, the movement of the legs, etc.). In response to the
brain, the body changes its internal regulation mechanisms and
adapts to the new situation, and survives. Damasio distinguishes
primary emotions (joy, sadness, fear, anger, surprise, and disgust)
from secondary emotions, which are more complex. Then, there
are the background feelings, such as well-being, malaise, calm,
and tension, expressed in the details of posture and in the way of
moving the body. With the somatic marker hypothesis, Damasio
has shown that emotions play a role of primary importance in
cognitive processes (Damasio, 1994, pp. 4549). Furthermore,
consciousness itself is closely connected to emotion, feeling, and
homeostasis; it is a more rened and effective form of realizing
homeostasis in the face of the challenges posed by the sur-
rounding environment (see Damasio, 1999, Chapter 10; see also
Damasio, 2003 and Damasio, 2010).
Based on the study of animals and the comparison between
animals and humans, Panksepp (1998) offers us a much more
elaborate and complete theory of emotions than Damasio.
According to Panksepp, Damasio is still a victim of cognitivist
prejudice because he still thinks that emotions are a variant of
higher cognitive processesi.e., the results of a sort of re-read-
ingof them by the cortex. Cognitive prejudice can also be found
in Rolls (1999,2005): there are no basic affective states; emotions
are the products of the cognitive activityfor example, the ability
to verbalize or conceptualize assessments is considered a neces-
sary condition for emotional experience. For Panksepp, these
theories are full of problems and contradictions: How can a
cognitive state give rise to an affective experience?
In contrast, Panksepp, who moves closer to neuropsychoana-
lysis than Damasio, has identied the existence of an ancestral
core of emotional states that underlie any form of psychic activity,
unconscious or conscious. Panksepp argues that emotions are
intrinsic functions of the sub-cortical brain that humans have in
common with animals. Emotions, or affects, are ancient brain
processes for encoding valueheuristics of the brain for making
snap judgments as to what will enhance or detract from survival
(Panksepp and Biven, 2012, pp. 3132). These basic affective
systems are not cognitive at all; they are made up of neuroa-
natomies and neurochemistries that are remarkably similar across
all mammalian species(Panksepp and Biven, 2012, p. 4).
In general, Panksepp distinguishes three levels of brain activity:
a. The primary process, which includes the most basic affects;
b. The secondary process, such as learning and behavioral and
evolutionary habits;
c. The tertiary process, which includes executive cognitive
functions (thoughts and planning).
The primary process activities are organized into three areas:
emotional affects, homeostatic affects, and sensory affects. The
homeostatic effects concern internal biological cycles (the need to
defecate or eat, for example) that allow homeostasis. Sensory
affects are reactions to sensations experienced from the outside;
they are exteroceptive, sensory-triggered pleasurable, and
unpleasurable/disgusting feelings. Emotional affects (also called
also emotion action systems,or intentions-in-actions) are the
oldest and most complex. Panksepp organizes these affects into
and PLAY (he uses capitalization to distinguish these primary
emotional brain systems from the use of the same terms in
common language). These systems are described by Panksepp as
real physical circuits present in the most ancient and deep parts of
the brain, the subcortical area, which activates certain reactions
and behaviors (for example, the rat escapes the smell of predators,
and this pushes it to look for another ground to feed) and
therefore forms of learning. They are instinctive (automatic
reactions) and evolutionary (the result of a long natural selection
process). They are networks of causal processes, as I will
show later.
Panksepp argues that raw affects are the fundamental basis of
any brain activity; the mind is essentially emotional, and raw
affects tend to shape any other cognitive activity. Most promi-
nently, it looks like the SEEKING urge may be recruited by the
other emotional systems. It is required for everything the animal
does; one could conceptualize it in psychoanalytic terms as the
main source of libidinal energy(Panksepp, 2008, p. 165). For
Panksepp, the study of the constitution of these systems is
essential for understanding our own affects and for developing
better psychiatric treatments for emotional imbalancesbut it
would require further causal preclinical research into our
ancestral subcortical primary process emotional brain systems
(Davis and Montag, 2019, p. 2). In other words, raw affects are
ancient brain processes for coding values, which are heuristic
operations of the brain used to make rapid assessments of what,
in the real situation, increases or decreases the chances of survi-
val. They can interact with and be inuenced by cognitive states,
often in very complex ways, but they do not presuppose them.
They are, using a Panksepp expression, aexible guide for liv-
ing(Panksepp and Biven, 2012, p. 43).
As I have just said, the crucial point of Panksepps approach is
that basic emotions have nothing cognitive and therefore cannot
be understood from a cognitive point of view. They must be dealt
with on their own terms. The Pankseppian affective neuroscience
principle is that the neocortex is fundamentally tabula rasa at
birth,Latin expression for blank slate(Panksepp and Biven,
2012, p. 427). Therefore, the widespread claim that affects are
just a variant of cognitions seems little more than a word game to
me, even though I certainly accept that the many (good and bad)
feelings of the nervous system are always interacting with cog-
nitions (imagination, learning, memory, thoughts) within the full
complexities of most human and animal minds(Panksepp and
Biven, 2012, p. 489). The point is that it is through experience
that the neocortex is programmed(likely through interactions
with subcortical regions) to acquire its capacities that as we reach
maturity to come to seem like hard-wiredbrain functions
(Davis and Montag, 2019, p. 4). With maturation, these phy-
sically, as well as evolutionarily separate brain regions, develop a
reciprocal seesaw like the relationship to weigh whether a life
event should trigger or inhibit the expression of a primary
emotion with imbalances in either direction potentially becom-
ing dysfunctional(Davis and Montag, 2019, p. 5). The neo-
cortex is organized by the subcortical functions of the brain.
These latter guide the neocortex in acquiring and processing
information. For instance, Johnson and Horn (1986,1988)
clearly demonstrated it by studying chicks. Alberini (2010)
proved that all long-term memory has an emotional component:
traumatic events create very strong memories, or they can lead to
partial or total memory loss. One of the basic functions of the
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
primary-process emotional memory systems is arousal and the
associated drawing attention to specic events that can facilitate
the formation of memories for important life events. These
memories in turn can subsequently inform how we respond to
future life events.
Panksepp claims that cognitions are often handmaidens,or
emissaries, of the affects, not the opposite. Cognition
emerges from the neocortex, which is the brains outermost
layer and the part that is evolutionarily newest. This
indicates that the capacity for affective experience evolved
long before the complex cognitive abilities that allow
animals to navigate complex environmental situations. It is
also noteworthy that the deeper evolutionary location of the
affective systems within the brain renders them less
vulnerable to injury, which may also highlight the fact that
they are more ancient survival functions than are the
cognitive systems. (Panksepp and Biven, 2012, pp. 4344)
Affects are automatic, instinctual, and innate processes; indi-
vidual behavior, education, and culture cannot change them. In
all the mammalians, the two brains(neocortical and sub-
cortical) communicate but are fundamentally different. (On the
distinction of two brains,see Kahneman, 2011). Yet, we cannot
understand secondary and tertiary functions if we do not
understand primary functions rst. This is also conrmed by
other data: subcortical neurons function very differently from
those of the regions of the neocortex (see Panksepp and Biven,
2012, p. 50).
This distinction between the two brains is important, and it is
the reason that leads me to criticize Picards point of view. Like
Damasio, Picard remains too tied to a cognitive conception of
emotions and affects. According to Picard, emotions are gener-
ated by a cognitive activity (a thought, the knowledge of a state of
things, etc.) (see Picard, 1997, pp. 6566). With this, in my view,
Picard does not grasp the essence of human emotional life. This
point of view implies that emotion is not something intrinsic to
the human brain, but something built from cognitive reections
operated by a human or a machine. On the other hand, Panksepp
says that emotion is intrinsic to the brain, and it is the brain that
produces the physiological reactions of the body.
Another crucial aspect that emerges from Panksepps research
is the complexity of emotions and the brain. We cannot reduce
emotions and affects to simple bipolar systems based on pairs of
opposites such as charge/discharge and pleasure/displeasure. It is
much more complex; it cannot be reduced to the on/off
mechanics of neurons. The simple-minded neurone-doctrine
view of brain function, which is currently the easiest brain model
to apply in AI/robotics, under-represents what biological brains
really do(Panksepp, 2008, p. 163). Each basic affective system
acts in a different way according to very complex chemical and
neurochemical dynamics and equilibria, which we do not yet fully
know. Emotions cannot be explained in a dualistic way according
to a series of oppositions arranged on three levels: energetic,
perceptive, and motor. Each of the fundamental affective systems
generates positive or negative states, but, in reality, the distinction
between pleasure and displeasure is not clear-cut. There are many
intermediate or even superimposed states (so that the same
situation generates pleasure in one case and displeasure in
Now, I hold that the crucial assumption of an AGI based on the
neuropsychoanalytic model of mind is the design of a computa-
tional system capable of simulating the seven basic affective
systems analyzed by Panksepp. The instantiation of human raw
affects must be the fundamental basis of AGIin the sense that
any other activity of the system must be based on them.
Let us see how.
A Freudian computer: sketches
Panksepps topography of emotions gives us a clear indication of
what an emotion is and how basic affective systems and sub-
cortical brain work. How can we translate these indications into
an AGI system?
6/1 The Solms-Friston model. As stated above, at the root of our
AGI system, there must be seven systems that would be able to
instantiate the seven basic affective systems in mammals. In
order to simulate the operations of the human mind, we must
consider both the genetic and epigenetic construction of the
human brain. We must be clear about what is genetically fun-
damental and what is epigenetically derivative(Panksepp, 2008,
p. 149). Each basic affective system can be described in terms of
state-spacesthat regulate information-processingalgorithms
(Panksepp, 2008, p. 149). Can such affective-emotional properties
of biological brains be emulated by machines? Only future work
can tell(Panksepp, 2008, p. 149). For Panksepp, simulating the
ancient visceral nervous system is problematic: a deep under-
standing of the subcortical tools for living and learning is the
biggest challenge for any credible future simulation of the fuller
complexities of the mind. The cognitive aspects may be com-
paratively easy challenges since many follow the rules of propo-
sitional logic(Panksepp, 2008, p. 150). The crucial question is
whether and how an algorithm can instantiate complex sub-
cortical circuits. What kind of logic should we follow? This issue
may require a complete re-thinking of where we need to begin to
construct the ground oor of mind(Panksepp, 2008, p. 152).
Panksepp comes to express skepticism about the possibility of
accomplishing this feat. I have no condence that the natural
reality of those processes can be computed with any existing
procedures that articial intelligence has offered for our con-
sideration(Panksepp, 2008, p. 152; see the rst attempt of this
project: Dietrich et al., 2007).
Is Panksepps skepticism justied? Today, the tools of statistical
rationality and the evolution of technology can drastically change
the situation. The last twenty years have been marked by what can
be called Bayesian turn.In particular, the application of
Bayesian formulations to the study of perception and other
processes described as problems of inference has generated a huge
literature, highlighting a large interest in Bayesian probability
theory for the study of brains and minds(Bruineberg et al., 2020).
Solms and Friston (2018) demonstrate that it is possible to
create a statistical modelization of affects following the indica-
tions of neuropsychoanalysis and computational biology. The
Solms-Friston model is based on the hypothesis that the self-
organization of autonomous organisms can be represented in
statistical terms. The key idea is that a biological system is able to
adapt to its environment and predict possible future states in
order to maintain homeostasis. This ability can be described as a
statistical procedure of evaluation and inferencein other words,
a Markov blanket. Markov blanket denes the boundaries of a
system in a statistical sense. It is a statistical partitioning of a
system into internal states and external states, where the blanket
itself consists of the states that separate the two(Kirchhoff et al.,
2018, p. 1). The most intuitive example is that of a cell; the
boundaries between the cell and its environment can be described
by a Markov blanketthat is, as a set of variables separated from
another set of variables called external statesthat are
independent of each other. Here statesmean any variable
that locates the system at a particular point in state-space; for
example, the position and momentum of all the particles
constituting a thermodynamic systemright through to every
detail of neuronal activity that might describe the state of the
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
This entails that
The external states are conditionally independent of
internal states, and vice versa; thus, internal and external
states can inuence each other only via sensory and active
states. External states cause sensory states that inuence,
but are not inuenced by, internal states. These latter cause
active states that inuence, but are not inuenced by,
external states. The distinction between external and
internal states involves a process called active inference,
which tries to predict the external states in correspondence
to the internal states. The active inference can be described
in terms of approximate Bayesian inference and probabil-
istic beliefs that are implicit in a systems interactions with
its local surroundings(Kirchhoff et al., 2018, p. 2; for the
general concept of inference in statistics, see Bruineberg
et al., 2020, pp. 510); therefore, the functioning of the
Markov blanket must be interpreted as a probabilistic
inference (Bayesian inference) in which the variables
correspond to the levels of condence in the occurrence
of an event.
The goal of active inferenceis the minimization of free
energy, i.e., the energy available to the system to do useful
work, and therefore the reduction of entropy (uncertainty,
surprise, etc.) within the system. Thus, the Solms-Friston
model presents a teleological interpretation (the goal is the
minimization of free energy) of Bayesian inference.
Therefore, biological systems have a capacity to maintain low
entropy distributions over their internal states (and their Markov
blanket) despite living their lives in changing and uncertain
circumstances(Kirchhoff et al., 2018, p. 3). This scheme should
not be considered as a xed structure. A system can have many
Markov blankets, the boundaries of which are neither xed nor
stable(2). Furthermore, Kirchhoff et al. (2018) distinguish two
different types of active inference: mere active inference and
adaptive active inference. Only the latter enables autonomous
organization. This aspect is very important: even a cell-like any
other autonomous biological systemis capable of biological and
unconscious inference in order to maintain its integrity. Thanks
to the Markov blanket, we can build a mathematical modelization
of this process.
Solms and Friston (2018) apply this general scheme to the
psychoanalytic model of the mind. The main thesis is that we can
explain consciousness and affects through the principle of free
energy minimization and the concept of the Markov blanket, as
dened before. Then, the goal of the psychic system is to
minimize the amount of free energy used to reduce entropyi.e.,
to maintain homeostasis. The ideal state corresponds to a
situation where the free energy is zeroa state without entropy.
The possible outcomes of the active inference in the psyche are
three: (a) to change the sensation that comes from the outside; (b)
to change the internal representation of the sensation and
therefore the prediction of future sensations; and (c) to try to
match prediction and sensation more and more, improving the
confrontation between reality and expectationsthe optimization
of precision with respect to free energy. In short, consciousness is
an inferential processa self-evaluation processthat aims to
predict changes in the use of free energy and formulate strategies
against entropy. This process takes place simultaneously on
different levels: sensory, motor, internal, external, etc. What we
describe is an elemental form of a self-maintaining mechanism
that takes more complex forms in more complex biological
systems (like vertebrates)(Solms and Friston, 2018, p. 28).
Qualitative uctuations in felt affect arise continuously from
periodic comparisons between the sensory states that were
predicted (based upon a generative model of the viscera and
the world and samples of the actual sensory states)(28).
Consciousness comes from this biological mechanism of affective
In other terms, to maintain its integrity, the organism responds
to the external stimulus (sensory state) by changing its internal
state and its environment. This process can be interpreted in
terms of a Bayesian inference that aims at inferring the most
probable, hidden causes of sensory signals in terms of expectations
about states of the environment(Kirchhoff et al., 2018,p.4;my
emphasis). In other words, the organism tries to predict the cause
of a sensory state in relation to its expectations and then produces
an active state in order to minimize free energy. In the Solms-
Friston model, free energy corresponds to prediction errors; the
recurrent assessment of sensory states only gives rise to changes
in subjective quality (i.e., precision and feeling) when the
amplitude of prediction errors changessignaling a change in
uncertainty about the state of affairs and, in particular, the
consequences of action(Solms and Friston, 2018, p. 28). In this
context, the concept of hidden cause
is essential: What is seen
does not cause what is felt. Both have hidden causes. Conscious-
ness (both exteroceptive and interoceptive) involves the quest for
these unitary hidden causes, which must be inferred from the two
sets (i.e., modalities) of data and explain them both(29).
Is this scheme really satisfying to describe Panksepps
emotional systems? In my opinion, it is not. If we take Panksepps
seven systems as a point of reference, none of them can be
explained only on the basis of the Solms-Friston model and
Bayesian networks. In the next section I want to formulate some
criticisms of the Solms-Friston model and propose a new model
inspired by Pearls theory of causality.
6/2 How Panksepps systems can be organized and embedded
in AGI. At the beginning of the eighties, Panksepp (1982) was
convinced that there were at least four biological brain-based
emotional action systems, which were Expectancy, Rage, Fear,
and Panic. In the nineties, especially with the publication of
Affective Neuroscience (Panksepp, 1998), Panksepp expanded his
list of primary emotions to seven primary-process emotional
command systems: SEEKING/Expectancy, RAGE/Anger, FEAR/
Anxiety, LUST, CARE/Nurturing, PANIC/Sadness, and PLAY/
Social Joy. The core of his approach was mapping of the seven
primary emotional systems by means of electrical stimulation of
the mammalian brain, including pharmacological challenges and
brain lesions(Davis and Montag, 2019, p. 2). The ESB and DBS
techniques have shown that all mammalian brains work in a very
similar way; distinct affects can be linked to pretty much the same
areas of the brain and the same type of electrical or pharmaco-
logical stimuli. Panksepps hypothesis is that (1) imbalances in
these primary emotional systems are strongly linked to psychia-
tric disorders, such as depression or suicidal thoughts, (2) we can
act on these systems to modify and cure these imbalances. One of
the most important conrmations of this was the Affective
Neuroscience Personality Scale (ANPS), according to which the
primary-process emotions constitute the psychobiological foun-
dations of personality (Davis and Montag, 2019). The study of
primary affective systems has allowed us to better understand
how the personality is born and evolves, what are its main dis-
orders, and how we can cure them. Panksepp has also made
several predictions about psychiatric treatments. For instance,
[he] predicted that autistic children might have dysfunctional
brain opioid systems resulting in excess endogenous opioid
levels(Davis and Montag, 2019, p. 24). Following this approach,
Montag et al. (2017) explained depression, and Yovell et al.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
(2016) explained suicidal thoughts. Many other research works
conrmed the value of Panksepps work.
Now, the questions I want to ask are the following: (1) How do
these ancestral emotional systems work? (2) How can we translate
them into algorithms? This second question implies another one:
Is the SolmsFriston model able to fully formalize the ancestral
emotional systems, or do we need other theoretical tools?
Carrying out a detailed analysis of ancestral emotional systems
would require not a simple paper but several books. Here I will
analyze only the SEEKING system becauseas Panksepp states
this system is the oldest and most general; it always continues to
operate in the background in all our brains activities. When the
SEEKING system is very active, rats move with a specic purpose,
vigorously snifng and exploring where they are, even making an
inaudible sound. The same thing happens in humans when they
experience feelings of anticipatory craving; they feel as though
they are effective agents in the world and are happy with what
they do. The SEEKING system causes a positive sense of wanting
and being able to do. Therefore, it produces expectations about
what can be done and how it can be done and, ultimately, pushes
us to act in a certain way. It is your subcortical SEEKING system
that helps energize your neocortexyour intellectand prompts
you to do things like buy this book and also to learn from books,
if they are engaging(Panksepp and Biven, 2012, 102). When this
system is underactive, mammals feel depressed and hopeless; or,
due to its hyperactivation, they can become psychotic. It is
evident that the SEEKING-EXPECTANCY system is a general-
purpose system for obtaining all kinds of resources that exist in
the world, from nuts to knowledge, so to speak(Panksepp and
Biven, 2012, p. 103).
The activation of the SEEKING system can take place in two
ways: due to homeostatic imbalances or more complex negative
emotions, such as loneliness or pain. In the rst case, some nerve
cells located in the ancient regions of the brain or in some body
organs (Panksepp and Biven, 2012) register homeostatic
imbalances (thirst, hunger, cold, etc.), which are a problem. The
SEEKING system works not only to respond positively to the
problem but also to give us hope, i.e., a positive purpose, and
push us to act. It is not simply a positive reaction to an external
stimulus but something much more complex. It is the reaction to
discomfort and a precise plan of action. If it is cold, the mammal
immediately seeks a suitable solution to that situation: a shelter,
or a blanket. In other cases, in humans, low levels of endogenous
opioids (such as endorphins) can activate the SEEKING system,
whereas variations in hormone levels favor the activation of the
LUST system.
Panksepps research shows that ancestral emotional systems
are not simple automatic ways of reacting to certain stimuli. They
are much more. The SEEKING system is driven by brain
dopamine, but it is much more than just the creation of that one
energizing neurotransmitter. It is a complex knowledge-
generating and belief-generating machine(Panksepp and Biven,
2012, p. 103). The complexity of the SEEKING system is
conrmed, for example, by its connection with the sense of time.
As Panksepp and Biven (2012, p. 138) explain, The dopamine-
containing neurons of the SEEKING system have such endogen-
ous pacemakers that normally keep them ring at a stable
monotonous rate, like the ticking of a clock, especially when
nothing special is happening to an animal; these neurons even
keep ring when animals are asleep, but the background activity
is not normally attended by the release of dopamine.The
ancestral affective systems are not automatic ways of reacting to
certain stimuli but complex and dynamic causal networks.
Now, is there a whole structure of the SEEKING system that
can also be identied in the other ancestral affective systems? In
other words, is there a common structure shared by the emotional
systems? In the SEEKING system, we can identify four crucial
phases: (a) the generation of emotion for internal or external
reasons, i.e., the biological reaction; (b) the evaluation of the
emotion, which can be positive or negative (pleasure or
unpleasure); (c) the anticipation, in the sense that our emotional
brain anticipates reality and creates emotional memories and
projections of what might happen (for example, when we raise
our arms to protect ourselves even if there is nothing threatening
us) that can later be processed by other parts of the brain in an
ever more rened way; (d) the action, i.e., the production of a
series of actions in accordance with the previous moments. This
cycle (emotion, evaluation, anticipation, and action) constitutes
the rst form of learning in mammals. If the action conrms the
prediction and evaluation, mammals learn to link the reward to
that behavior. Otherwise, they learn that it does not lead to any
reward. This improves their ability to adapt to the environment.
As I said, this cycle has a causal structure: Emotion causes
evaluation and anticipation, which then causes action; but the
action can also modify anticipation, evaluation, and emotion.
The same structure can be found in another basic emotional
system, the RAGE. Rage is not meant to punish; anger, hatred,
and revenge, but also remorse and forgiveness, are cognitive
elaborations of rage that many animals do not possess. If some
areas of the amygdala, hypothalamus, and periaqueductal gray are
electrically activated, a human being clenches their jaw and
experiences a feeling of rage without knowing why. The causes
that can trigger rage are many (homeostatic imbalances, external
factors, other emotional systems, etc.). There are also several
chemicals that regulate rage: testosterone, norepinephrine,
glutamate, acetylcholine, etc., which behave differently depending
on the part of the brain in which they act. However, even in this
case, we can distinguish at least four phases in the functioning of
the system: (1) the generation of an emotion (with the release of
some chemicals), (2) the evaluation of the emotion (pleasure or
displeasure), (3) the production of more or less cognitively
elaborated anticipations, and (4) the action. For example, (1)
hunger and scarcity of resources fuel the release of certain
chemicals that produce rage; (2) rage makes you feel bad, as it is a
negative feeling; (3) this negative feeling can trigger delusional
fantasies of persecution and revenge, and, nally, (4) some actions
that will either tend to eliminate the origin of the rage (hunger) or
those fantasies. Again, the system is a complex causal network.
This is even more evident in the LUST system. Sexual stimulation
increases the production of testosterone (in males), or estrogen
(in females), and generates a feeling of general well-being, as well
as leading to the activation of bodily systems and certain types of
behavior (for example, a sexually receptive body posture, a
particular kind of smell, or erection, copulation, and courting)
(see Panksepp and Biven, 2012, p. 235). Furthermore, sexual
desire is closely related to the SEEKING system that is recruited
for the task of seeking a sexual partnerone system affects the
other by conditioning it. An important example is that of
sadomasochism: a painful and unpleasant stimulus triggers
emotion, i.e., sexual desire, and, therefore, the release of
hormones (Panksepp and Biven, 2012, p. 245).
The example of sadomasochism illustrates an important point.
The distinction between pleasure and unpleasure in the second
phase (evaluation) is very vague in the sense that it can vary. Rage
is a perfect example of this: There are people for whom rage is a
positive feeling in the sense that they feel good when they feel
anger and attack others (even if, in the long run, rage and anger
have very negative effects and are unsustainable). As Panksepp
points out (see Panksepp and Biven, 2012, p. 148), the boundary
between pleasure and unpleasure, as well as the nature of
anticipations and actions, depends on (1) which other areas of the
brain are acting at that moment, (2) which other affective systems
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
are interacting with each other, and (3) which chemicals trigger
the whole process. These are very complex dynamics. Let us
consider, for example, the system of FEAR. An emotion triggers
anxiety and, therefore, a series of anticipations (fantasies,
memories, etc.) and actions (increased heart rate and blood
pressure, sweating, ight, etc.). However, anxiety is not
necessarily a negative feeling that causes pain. When we watch
a horror movie, for example, we seek that situation; we want to
feel fear, and this gives us pleasure. The relationship between
pleasure and unpleasure varies according to the structure of the
affective system or the relationships of an affective system with
the others. It varies according to the type of causal network
involved. Every emotional system is a set of causal networks that
are not organized by the dualism of pleasure and unpleasure.
We now come to the second question that we started with:
How can we translate ancestral emotional systems into
Here, I propose to integrate the SolmsFriston model with
Pearls causal calculus. I think that Pearls causal calculus is a
more plastic tool and that, precisely for this reason, it allows one
to better explain (and, therefore, model) the complexity of
ancestral emotional systems. In the following parts, I will justify
this idea.
Is the SolmsFriston model really satisfying for describing
Panksepps ancestral emotional systems? In my opinion, it is not
for two main reasons:
1. The model remains too tied to the Freudian dualism of
pleasureunpleasure, which becomes a general scheme used
to explain all effects and emotions. However, as shown by
Panksepps research, the differences between pleasure and
unpleasure are never clear-cut and can often vary between
the different affective systems and within the systems
themselves. I think that the free energy principle is more
useful for describing what Panksepp calls homeostatic
affects and sensory affects. More complex and plastic
models are needed in order to describe emotional affects
that are networks of cause-effect relations. In short, the
pleasureunpleasure couple does not capture the essence of
2. The SolmsFriston model is based on the concept of
Markov Blanket, which presupposes that of Bayesian
network. My question is this: Do these concepts really
have anything to do with causation? With regards to this
point, Pearls critique of the Bayesian network seems very
compelling. Pearl argues that we cannot dene causality
purely in probabilistic terms, i.e., A is the cause of B because
it increases the probabilities of B. Probability and causality
are different concepts. If we follow Pearls critique and his
concept of the Ladder of Causation(Pearl and Mackenzie,
2018, pp. 2829) (more on this below), we have to
recognize that the SolmsFriston model still stands at the
rst rung of the ladder and cannot move toward the upper
rungs. The model explains causation in purely probabilistic
terms, i.e., as a correlation. In a nutshell, it confounds
causation and correlation.
I will try now to justify these two theses. The concept of
causality plays a central role in the SolmsFriston model (see
Friston 2009). According to Friston, biological self-organizing
systems are based on a Markov blanket and, for this reason, they
are capable of active inference; in other words, The partition of
states implied by the Markov blanket endows internal states with
the apparent capacity to represent hidden states probabilistically
so that they appear to infer the hidden causes of their sensory
states(Friston, 2013, p. 6). Thanks to the Markov blanket, a
circular causality(Friston, 2013, p. 6) takes place in the system.
This circular causality connects sensory states and active states;
sensory states depend on active states rendering inference active
or embodied(Friston, 2013, p. 6). This circular causality allows
one to limit the surprise in the system, gradually adapting
expectations and conrmations, and, therefore, it allows one to
limit the free energy and resist entropy, i.e., the dispersion of the
system. Homeostasis is informed by internal states, which means
that active states will appear to maintain the structural and
functional integrity of biological states(Friston, 2013, p. 6).
As is evident from these passages, the active inference is a type
of Bayesian inference. My question is the following: Is the circular
causality, which, according to Friston, is the core of active
inference, really a causality or only a correlation between
variables? As mentioned above, I refer to Pearls critique of the
Bayesian networka concept created by Pearl himselfwhich is
the basis of the concepts of active inference and the Markov
blanket. All current machine learning systems are based on
Bayesian networks (Pearl and Mackenzie, 2018, pp. 122128).
Nonetheless, according to Pearl, the Bayesian network alone does
not grasp the essence of causality and cannot express it.
Let us now discuss some characteristics of a Bayesian network.
Then, I will introduce the core of Pearls critique. Thanks to the
Bayesian network, it is possible, starting from a certain set of data
(probabilities), to calculate (a) the probability of the causes (e.g.,
symptoms disease) and, therefore, (b) the recurrence of other
similar events in the future. For this reason, Bayesian networks
are used to develop machine learning algorithms. The essence of
the Bayesian network is the calculation of inverse probability:
Starting from one set of probabilities (effects), we arrive at
another set of probabilities (causes). Now, the Bayesian network
is highly dependent on the available dataon a particular set of
data. It does not involve the formulation of a hypothesis, i.e.,
general models on causality; it does not apply a model to the data
and only calculates the probability of unknown events (causes)
starting from the available data (effects). This means that, in the
Bayesian network, causality amounts to the increase of the
probability of the effect. However, this equivalence (A causes B
because it increases the probability of B) is wrong because the
increase in the probability of B is not a sufcient criterion to
make A the cause of B. Indeed, alone, the increase in the
probability of B does not allow us to understand if there are
hidden causes of B, or indirect causes, or even backgrounds
factors that inuence B and its probability. This is the so-called
problem of the confounder.Causation implies increasing the
probability of the effect but is not limited to that. To determine
the cause, we need a hypothesis, i.e., a theoretical model that must
be tested and that is independent of the data.
This is the essence of Pearls argument against the Bayesian
network: In both a cognitive and a philosophical sense, the idea
of causes and effects is much more fundamental than the idea of
probability(Pearl and Mackenzie, 2018, p. 46). According to
Pearl, the Bayesian network is essential to understand causality,
and, yet, it is not enough. Causation requires going beyond the
data, hence creating more complexity. For this reason, Pearl
develops specic tools such as causal diagrams and do-calculus.
These mathematical methods solve the problem of confounding
and related paradoxes. Whereas a Bayesian network can only tell
us how likely one event is, given that we observed another []
causal diagrams can answer interventional and counterfactual
questions(Pearl and Mackenzie, 2018, p. 130).
Let us now have a closer look at Pearls theory of causation.
Pearl describes three levels of causal inference, what he calls the
ladder of causation(Pearl and Mackenzie, 2018, pp. 2829):
1. Association: being able to nd phenomena that are related;
most animals can do this to some extent, and most machine
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
learning models are trained to learn associations between
variables. Example: What is the expected lifespan of
somebody who is vegetarian and does not smoke? This level
is that of the simple observation of facts and their
correlation. It answers questions such as What if I see?
2. Intervention: being able to guess what the effect will be if
one performs an action, i.e., it changes the value of a
variable. Such higher-level understanding is typical of more
intelligent animals and is related to the topic of reinforce-
ment machine learning. Example: How would my expected
lifespan change if I become a vegetarian? What if I ban
cigarettes? This level is that of the action changing the facts.
It answers questions such as What if I do?
3. Imagining: being able to reason about hypothetical situa-
tions, things that could happen and not that have already
happened. Imagining is typically done in intellectual
activities, such as performing thought experiments, making
up a story, etc. Example: Would Kennedy be alive if Oswald
had not killed him? Would my grandfather still be alive if he
did not smoke? This level is that of the imagination and
possible worlds. It answers questions such as What if I had
These are fundamentally different concepts, which require
different mathematical tools to be described. The rst level,
dealing with associations, is studied using the rules of the
probability theory and can be learned from data using statistical
methods. The second level deals with interventions. To assess the
effect of interventions, one either has to perform a suitable
experiment (which might be expensive or even not possible) or be
able to determine the causal relations among the variables of the
system. Data alone cannot help us answer action-related
questions at the second level of the ladder. If you have a database
of high school pupils with their curricula and test scores, you
could easily see that pupils who follow advanced math courses
tend to score better on a standardized mathematics test. Would
enlisting all students in such advanced math courses improve
their mathematics understanding? Not necessarily. It is possible
that students enrolled in such classes are naturally more gifted in
mathematics. Adding pupils who do not have a knack for
mathematics would, in this case, not help; or worse, it could
demotivate them, resulting in an even worse grade. We must go
beyond the data to understand what the exact dynamics of the
facts and the causes of the processes are.
The nal level is even more challenging, as it deals with reality
as it would be if the circumstances were different. In this case,
there is no data available, nor could we ever perform experiments.
The results of the queries at this level are called counterfactuals.
To make statements about such hypothetical situations, we need
an intricate understanding of the system and how all its parts are
linked together. If we return to the example of mathematics
education, I could determine what my score would be if I had
taken the advanced mathematics course. This would not only
account for all the things that I would have learned during such a
course but also involve backtracking who I might have met there,
what inuence this could have had on my other activities, etc.
In other words, Pearl argues that there is a radical difference
between simple association prediction and causation. In the rst
case, the question is What is the probability of x given the
presence of y?This is what a machine learning system does with
the data in its possession and what the active inference of the
SolmsFriston model does too. Starting from the observation of
one fact, we can calculate the probability of another fact on the
basis of the data in our possession. In the second case, the
question is What is the probability of x if I change the value of
y?In order to calculate how the change introduced in the data
inuences the probability of x, I cannot use the same
mathematical tools as in the rst case. Why? The reason is that
as I said beforelimiting myself to nding associations could
lead me down the wrong path and make me believe that the
probability of x is inuenced by a variable that, in reality, has no
causal function. If there is a correlation between x and the
appearance of y, that correlation is not necessarily causal; it could
be caused by hidden common causes or background factors. I
simply know that, when I look at y, x also appears. But there
could be a hidden cause that inuences both x and y, or, maybe,
there are several different causessome direct, and others
indirect. Knowing a correlation is, in itself, passive knowledge.
In order to understand the causal connection between data and to
plan my actions, in reality, I need conceptual tools that allow me
to distinguish between association and causation, as well as the
different types of causation. The causal diagrams and the do-
calculus describe this normal activity of the human brain. Thanks
to these tools, we are able to modify the laws of statistics in order
to statistically determine correlation and causality. Without these
tools, as Pearl shows, it is impossible to solve the problem of
confounding and some of the paradoxes that arise precisely from
the confusion between correlation and causality, such as the
Simpsons Paradox or the Berksons Paradox (I cannot go into
detail here; I just refer to Pearl and Mackenzie, 2018, pp.
197211). That is the reason for distinguishing between Bayesian
networks and causal diagrams: Bayesian networks inhabit a
world where all questions are reducible to probabilities or degrees
of association between variables; they could not ascend to the
second or third rungs of the Ladder of Causation(Pearl and
Mackenzie, 2018, p. 51). The main point is this: While
probabilities encode our beliefs about a static world, causality
tells us whether and how probabilities change when the world
changes, be it by intervention or by an act of imagination(Pearl
and Mackenzie, 2018, p. 51).
Let us be even more precise in distinguishing between a
Bayesian network and a causal diagram. How are these two
theoretical structures distinguished? According to Pearl, A
causal diagram is a Bayesian network in which every arrow
signies a direct causal relation, or, at least, the possibility of
one, in the direction of that arrow. Not all Bayesian networks are
causal, and in many applications, it does not matter(Pearl and
Mackenzie 2018,p.95).However,if you ever want to ask a
rung-two or rung-three query about your Bayesian network, you
must draw it with scrupulous attention to causality(Pearl and
Mackenzie, 2018, p. 95) and transform your Bayesian network
into a causal diagram.
An objector might ask, at this point, what causation is from
Pearls perspective. What is the general concept of the cause that
guides Pearlsanalysis?Pearls guiding idea comes from the
philosopher David Kellog Lewis and his theory of counter-
factuals. Lewis claims that we think of a cause as something
that makes a difference, and the difference it makes must be a
difference from what would have happened without it. Had it
been absent, its effectssome of them, at least, and usually all
would have been absent as well(Lewis, 1973,p.161).Iwould
say that the ladder of causality itself is a denition of causality. It
can be conceived as a test to answer the question Can A be the
cause of B?A variable has a causal inuence on another if and
only if (1) it is observable when the other variable is present
(association), (2) it makes a difference, in the sense that, if its
value changes, the value of the other variable also changes
(intervention), or (3) the difference does not occur without it
(counterfactual). The do-calculus is a mathematical formaliza-
tion of this fundamental idea, which is actually an instinctive
process in the human beingwe are instinctively led to
recognize causality. In a nutshell, causal diagrams and the do-
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
calculus allow us to identify and mathematically dene what
really makes the differencein a complex of changing variables.
Here is the difference between the active inference of the
SolmsFriston model and Pearls techniques. If we take seriously
Pearls critique of the Bayesian network, we have to admit that
active inference remains a classic model of machine learning
based on association prediction, which is unable to properly
analyze causality. It remains on the rst rung of the Ladder. In
other words, Bayesian networks and active inference fail to grasp
what makes the difference.Pearls aim is to understand how to
describe what makes differencein probabilistic terms. As I said
above, I think that the Solms-Friston model can describe better
the homeostatic and sensory affects, and not the emotional ones,
following Panksepps terminology.
These considerations lead us to the answer to the second
question that we started with: How can we translate the
emotional systems described by Panksepp into algorithms?
Through causal diagrams and the do-calculus, we can formalize
and algorithmize the complex behavior of all emotional systems
because we can formalize and algorithmize the causal networks
that these involve. The do-calculus and the causal diagrams allow
us to recognize the causal relationships between data and to
separate them from the simple associations, therefore allowing us
to intervene effectively, i.e., to plan the causal action.
Now, in the emotional systems described by Panksepp, we can
identify at least four types of causal relations: (a) those internal to
the system (among the four variables we have described); (b)
those between the system and external reality; (c) those between
the system and the other emotional systems; (d) those between
the system and the rest of the brain. These four types of
relationships can be translated into a set of causal diagrams. The
diagrams sort the input data according to possible causal chains,
then the do-calculus operates on them to set them in motion,
transform them, and calculate the consequences (the output data)
of their transformation according to the data. Each emotional
system can therefore be translated into a series of causal diagrams
that allow the analysis and interpretation of data. For example,
several causal diagrams can be drawn to simulate the behavior of
oxytocin, a crucial hormone in the LUST system. All these causal
diagrams constitute the memory of the system. One or more
processors operate the do-calculus on this memory. The result is a
system that can think causally. The plasticity of this model is
guaranteed by two factors: (a) the causal diagrams can be varied
and interchangeable according to the different situationsthey
do not depend on a single set of data like Bayesian networks; (b)
the calculus can modify the diagram, adapting it to the needs of
the situation.
There are two fundamental conditions for the realization of an
AGI system based on this type of model: (a) The design of the
system will presuppose an enormous research work on animals
and humans and use this to construct the most exact causal
diagrams to model the behavior of the different subcortical
; b) it will be essential to set the right conditions for the
evolution of the computational system that instantiate the
emotional systemswe are used to thinking of the AGI in terms
of adult human beings, but this is completely wrong.
There are other two big benets of following Pearls
indications. As I mentioned, the do-calculus can also give a
mathematical representation of the counterfactual inference (see
Pearl and Mackenzie, 2018, pp. 269280). Now, counterfactual
reasoning involves imagination and other complexes/cortical
cognitive functions because it involves not only the ability to
think of the world in a different way, i.e., an alternative world, but
also the ability to reect on ones actions. Counterfactual
reasoning is a form of self-awareness. The do-calculus, therefore,
offers us a unique mathematical language that allows us to (a)
think causally and (b) reect on ones own causal chains. This is
an important point: The do-calculus also allows us to explain how
an emotion (understood and formalized in a causal way) can give
rise to an elementary form of consciousness and the development
of complex/cortical cognitive functions. This conrms one of the
fundamental ideas of Panksepps neuroscience: Emotion is the
basis of consciousness and higher cognitive processes.
The second benet is that Pearls do-calculus is logically
complete, as has been shown by several groups of researchers.
Completeness in mathematics means that an axiom system has
the property that the axioms sufce to derive every true statement
in that language(Pearl and Mackenzie, 2018, p. 237).
6/3 How to build the system: a concrete example. Do-calculus is
essentially a mathematical technique to treat causality in prob-
abilistic terms avoiding the problem of confounding, that is,
mixing correlation and causality in a group of variables.
It is an
axiomatic system that allows for the examination of a causal
diagram and to purifyit of any possible spurious correlations
identifying causal connections and translating them into prob-
abilistic terms. It is a method that can, astoundingly, tease out
causal information from purely observational data(Pearl et al.,
2016, p. 55). In more precise terms, do-calculus is an axiomatic
system for replacing probability formulas containing the do-
operator with ordinary conditional probabilities. It consists of
three axiom schemas that provide graphical criteria for when
certain substitutions may be made(Hitchcock, 2018).
To better understand how to translate Panksepps seven
systems into algorithms we need to better understand Pearls
causal theory and do-calculus. According to Pearl, causation can
be interpreted as increasing the probability of the effect; however,
in order to so, we need mathematical tools that are able to resolve
the difculties that probabilistic causation has encountered in the
past and claries what relationships exist between probabilities
and causation.
Let us say that a conditional probability such as P(Y=yX=x)
P(Y=yX=x) gives us the probability that Ywill take the value
y, given that Xhas been observed to take the value x. Do-calculus
allows us to predict the value of Ythat will result if we intervene
to set the value of Xequal to some particular value x. Pearl writes:
P(Y=ydo(X=x))P(Y=ydo(X=x)) to characterize this prob-
ability. The do-operator identies the intervention. When we
intervene on a variable in a model, We x its value; this means
that: We change the system, and the values of other variables
often change as a result(Pearl et al., 2016, p. 54). The do-
operator allows us to introduce the intervention in the causal
diagram: When we intervene, we override the normal causal
structure, forcing a variable to take a value it might not have
taken if the system were left alone. Graphically, we can represent
the effect of this intervention by eliminating the arrows directed
into the variable intervened upon. Such an intervention is
sometimes described as breakingthose arrows(Hitchcock,
2018). For example, if I write:
j¼x;do Z ¼zðÞ
I assume that the action do(Z=z) is being performed in the
actual world; hence, I observe the values that other variables take
(X=x) in the same world that the intervention takes place.
Graphically, the structure of the diagram changes in the sense
that any arrow that goes toward the node that represents the
intervention is eliminated. No arrow leads to this knot, which is a
Starting from these considerations, I afrm that an emotional
system can be formalized in Fig. 1:
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
This is the basic form of our articial general intelligence (AGI)
system. The system analyzes a set of data (D) that can derive from
external reality, from other emotional systems, and, nally, from
other parts of the brain and body. Using this set of data, the
system elaborates causal hypotheses (H) based on some
fundamental assumptions or beliefs. For example, in the case of
the SEEKING system, in a situation of danger, a fundamental
assumption would be to seek adequate shelter,or, in a situation
of hunger, to seek food resources.Hypotheses are elaborated
based on these assumptions and the data available, and have the
following form: What happens if I do x?”“What is the effect of x
on y?The system is activated when the data indicate a situation
of danger or discomfort and immediately elaborates hypotheses.
The heartof the system is the evaluation (E), or inference,
which is realized through the causal diagram (CD) and the do-
calculus (DC). These two tools act simultaneously and allow us to
analyze and interpret data and variables, and thus identify causal
connections in probabilistic terms. These tools allow for the
elaboration of predictions (P) and actions (A)the evaluation
can be represented in a more complex and articulated way by the
diagram of the inference engine described in Pearl and Mackenzie
(2018, p. 12). Two aspects must be underlined: (a) the causal
diagrams and the do-calculus are continuously updated in
relation to the new data available, therefore to the success or
failure of the chosen strategy; (b) the system must elaborate and
analyze many interlinked hypotheses at the same time, and
therefore elaborate many evaluations, and often also reect on
what happened in counterfactual terms (C).
Let us take a very simple example. A man is dying of thirst and
is lost in the middle of a forest. The SEEKING system is activated
to x the situation. The man has two paths in front of him, one of
which leads to a river, thus to survival. According to his memory
of the forest, almost certainly at least one path leads to the center
of the forest, and therefore walking it would mean losing any
possibility of reaching the river. Also based on his memory, there
is another path that leads to the river and to salvation, but it could
be too long; if it is, the man risks dying of thirst. Moreover, there
are two variables to consider: (a) a friend who often passes by the
forest could help him choose the right pathor even give him
water; therefore, the best choice would be to wait for him; (b) that
the climate gets hotter and warmer.
A very simple example of a causal diagram could be this (see
Fig. 2):
The causal diagram describes a world. It is a way to represent
and sort the data we have and dene probabilities. In this case,
data is about the relation between the system and the external
reality. According to Pearl, our brains use exactly this type of
representation: Humans must have some compact representation
of the information needed in their brains, as well as an effective
procedure to interpret each question properly and extract the right
answer from the stored representation(Pearl and Mackenzie,
2018, p. 39). The arrows can be translated into probabilistic
formulas and be modied by inserting the do-operator. Behind
the arrows, there are probabilities. When we draw an arrow from
Xto Y, we are implicitly saying that some probability rule of a
function species how Ywould change if Xwere to change(Pearl
and Mackenzie, 2018, p. 45).
Let us now apply the do-operator to our diagram; this means
that we introduce an intervention into the diagram, an action. For
example, our man thinks that path 1 is correct and chooses to
walk along it to arrive at the river. However, this choice implies
three other variables: the conditions of the ground could render
the path impervious; ferocious animals could attack him, or
traveling in this way could take too much time. The objective of
the SEEKING system is to calculate the causal relationship
between path 1 and the arrival at the river, hence survival. In
doing this, the system must calculate the weight of the three
aforementioned variables. Considering the variable time is a
characteristic of the SEEKING system, as we have seen before.
The variable concerning animal attacks obviously comes from the
FEAR system that interacts with the SEEKING system.
Let us see how the diagram changes through the introduction
of the do-operator (Fig. 3):
To evaluate what the man should do, the SEEKING system
searches for the best course of action. To do this, the system must
Fig. 1 The formalized structure of an emotional system in our AGI. The diagram respects the four distinct phases previously identied: (a) the generation
of emotion for internal or external reasons; (b) the evaluation of the emotion; (c) the anticipation-prediction; (d) the action, i.e., the production of a series
of actions in accordance with the previous moments.Our AGI system analyzes a set of data (D) that can derive from external reality, from other emotional
systems, and from other parts of the brain and body. Using this set of data, the system elaborates causal hypotheses (H) based on some fundamental
assumptions or beliefs. The heartof the system is the evaluation (E), or inference, which is realized through the causal diagram (CD) and the do-calculus
(DC). The evaluation allows for the elaboration of predictions (P), actions (A), and counterfactuals (C).
Fig. 2 The man has two paths in front of him, one of which leads to a
river, thus to survival. A friend who often passes by the forest could help
him choose the right pathor even give him water; therefore, the best
choice would be to wait for him. However, the climate gets hotter and
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
evaluate the weight of the variables and identify the more likely
causal connection; that is, what really makes the differencein
this situation. Using other terminology, the system must identify
the confounders,i.e., those variables that can produce spurious
correlations and prevent the identication of causal relationships,
as mentioned above. In this situation, the ground,animals, and
time can be considered as confounders. (It should be noted;
however, that there are many strategies for recognizing a
confoundersome researchers may dene a confounder as
something that others do not).
The purpose of do-calculus is to identify what Pearl calls the
deconfounders; that is, the variables that allow identication
of the confounders to clearly distinguish the causal connections
(the interventions that have real causal potential) and non-
causal effects. Pearls idea is that identifying a group of
the probability of the causal effect of each variable. If you have
identied a sufcient set of deconfounders in your diagram,
gathered data on them, and properly adjusted for them, then
you have every right to say that you have computed the causal
effect XY(provided, of course, that you can defend your
causal diagram on scientic ground)(Pearl and Mackenzie,
2018,p.139).Pearlproposestwodeconfounding techniques:
using a back-door criterion or a front-door criterion, which
allow, provided the data are available, to identify the
deconfounders. Do-calculus overcomes them and goes further:
its three axioms allow identication of the deconfounders even
without having experimental data.Inspired by the ancient
Greek geometers, we want to reduce the problem to symbol
manipulation and in this way wrest causality from Mount
Olympus and make it available to the average researcher(Pearl
and Mackenzie, 2018, p. 233). In other words, applied to the real
world, do-calculus makes it possible to identify the most
effective action to achieve a goal through a simple combination
of symbols (obviously, only if our causal diagram is correct).
The three axioms of do-calculus (Pearl and Mackenzie, 2018,p.
236) allow the system to (a) analyze the transformations of the
probabilities, (b) identify causal relationships, (c) make predic-
tions, and (d) design new action plans. This is carried out
through a simple combination of symbols.
This is exactly what happens in our trivial example. The man
wants to understand the effect of x(walking path 1) on y(getting
to the river and drinking). The problem is whether xis a
confounder, i.e., a false cause, or not. What can he do? Find new
data on xand analyze new variables (ground,animals, and time).
For example, by advancing a little along path x, the man could
nd out that the way is crossed by a shortcut that leads directly to
the river. The probabilities in the diagram change; thus, the
diagram itself changes.
As I said, my example is trivial. Pearl applies his
deconfounding techniques to much more serious problems,
such as the use of fertilizers, the link between smoking and
cancer, or global warming. However, my thesis is that our
emotional systems work this way; deconfounding techniques
represent the fundamental ways of action and learning of our
limbic system. This is a basic level of learning. Therefore, it is
possible to represent and interpret the behavior of each
emotional system through causal diagrams and the do-
calculus. Panksepps seven emotional systems are nothing more
than sets of patterns of action and learning. The advantage of
using causal diagrams and do-calculus is that these tools can be
applied to all four types of causal relationships that I have
distinguished. Causal diagrams can be continuously trans-
formed based on the data.
In summary, translating an emotional system into an AGI
Translating causal diagrams and do-calculus into
Dening a data classication system that may concern (a)
internal states of the system, (b) relations between the
system and external reality, (c) relations between the
system and other emotional systems, and (d) relations
between the system and other parts of the brain.
Dening exactly the principles of each system, i.e., the
objectives (for instance: to seek a solution to a problem, to
satisfy sexual desire, etc.).
The heart of the system will be a processor that must be
able to produce causal diagrams and interpret them
through do-calculus, which adapts to new situations and
learns from them: it has to be able to develop the ability to
drawincreasingly complex diagrams on its own and self-
The set of causal diagrams should be classied in relation to
the different types of data, as I discussed in the previous
The system must produce counterfactuals, i.e., a type of
reection and retro-action on the system itself. Is it a real
human-like consciousness? I do not know. But, as Russell
(2019) writes, for AI purposes this makes no
An objector might ask: How do the causal diagrams and the
do-calculus we use to describe emotional systems differ from
those we can use to describe and model cortical cognitive
systems? The difference is in the type of causality we use. In the
following, I will clarify two points:
The distinction between basic emotions and cognitive-
oriented emotions.
The distinction between the do-calculus describing the
cognitive/cortical processes and the do-calculus describing
the subcortical/affective processes.
In their seminal book, Collins et al. (1994)dene emotions as
valenced reactions to events, agents, or objects, with their
particular nature being determined by the way in which the
eliciting situation is construed(13) Thus, the particular
emotion a person experiences on some occasion is determined
by the way he construes the world or changes in it(13). Within
this context, evaluation depends on what the authors call the
knowledge representation system(54). Let us focus on three key
terms contained in this denition: reaction, value, and inter-
pretation. Emotions are reactions to a situation that result in the
individual attributing value to that situation based on their
interpretation of it. In other words, emotion is the effect of a
Fig. 3 How the diagram changes through the introduction of the do-
operator. This diagram describes the behavior of the affective systems
involved in the situation.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
cause, and that cause is both interpreted and evaluated.
Interpretation and evaluation, therefore, presuppose causal
reasoning (i.e., identication of the cause of an effect).
Now, for Collins et al. (1994), emotions are always determined
by our representation of knowledge (i.e., cognitive activities),
suggesting a cognitive theory of emotions. For Panksepp, on the
other hand, it is necessary to distinguish pure or basic emotions
from emotions connected to and transformed by cognitive
activities. This point clearly distinguishes Panksepps approach
from that of Collins, Ortony, and Clore. As noted in Section The
primitive affective states, Panksepp criticizes the cognitive
interpretation of emotions. He further defends the idea of basic
emotions,which Collins et al. (1994, pp. 2627) regard as too
vague. However, Collins et al. (1994) do not exclude the concept
completely: We claim that some emotions are more basic than
others because we can give a very specic meaning to it, namely
that some emotions have less complex specications and eliciting
conditions than others(28).
If we follow Panksepps logic, we must not only believe (a) in
the existence of a pure core of basic emotions distinct from
cognition (i.e., biological emotions) but also (b) in the emotional
origin of cognitive activities and cognitive-oriented emotions.
Panksepp proposes a model based on difference and continuity:
the difference between emotion and cognition, but continuity via
cognition that arises from emotion.
How can we express this double relation through the do-
calculus? There must be a difference between the do-calculus that
describes cognitive/cortical processes and the do-calculus that
describes affective/subcortical processes. My claim is that while
affective processes must be represented as interventions (i.e., the
second rung of Pearls ladder of causation), cognitive processes
must be represented as counterfactuals (i.e., third rung). As
discussed in the last paragraphs of the previous Section, counter-
factual reasoning makes it possible to abstract from the individual
causal connection or reaction and generate imaginative variations
that allow for more elaborate evaluations and anticipations.
Therefore, cognitive processes derived from affective processes use
different libraries of diagrams and calculations accumulated over
time. They are, by extension, on a different level of abstraction.
Let us now consider the formal structure of the counterfactual
as described by Pearl and Mackenzie (2018, p. 278). We can
schematically distinguish two stages: (a) transformation of the
initial model or diagram through the do-operator (i.e., the
imaginative variation) and (b) statistical prediction of the
consequences of model transformation and information related
to it. Let us observe what happens, for example, in a poem, which
can be considered the expression of an emotion.
The model or
diagram representing the initial causal connection (i.e., the initial
emotion) is modied through an imaginative act (i.e., the do-
operator). Herein lies the abstraction. The modied model is then
used to predict a new emotion: specically, a reaction in the
audience. Thus, the poem aims to build a new causal connection
and invites the reader to analyze its possible consequences. It is an
example of counterfactual reasoning, which can be translated into
formal terms through the do-calculus. This line of reasoning is
arguably also compatible with Collins, Ortony, and Clores
appraisal structure model (1994, pp. 5058). Therefore, Pearls
ladder of causation gives us a unique model for explaining the
continuity and difference between emotion and cognitive activity
or, perhaps, even consciousness.
What is the unconscious then? The unconscious is repressed.
But what is repression here? Memories connected to traumatic
emotions are repressed. We can interpret repression in a causal
way. In our AGI system the causal diagrams related to traumatic
emotions, those that involve an excessive waste of energy, are
repressed. Repression, in this case, means that those diagrams (or
part of them) are blocked or bypassedby other diagrams, even
if they remain in the memory of the system. More complex
functions such as imagination, language, etc. cannot act on these
diagrams. As I said, diagrams related to an emotional system can
be arranged on several levels, in a hierarchical manner; then only
some levels will be repressed and not others. The system defends
itself by blocking or bypassing the diagrams (or part of them)
related to traumatic emotions. The return of the repressedcan
be due to a miscalculation, i.e., a problem of de-confounding, or
other reasons.
6/4 Reply to Dreyfusclassical argument. In the previous sec-
tions, I have shown how Panksepps topography of emotions can
be organized and embedded in AGI. I have (1) discussed the
Solms-Friston model and (2) proposed a new model based on
Pearls theory of causation. In this section, I intend to reply to
some criticisms of AGI.
Analyzing all the arguments that have been produced against
AGI amounts to writing a book and not a paper. Here I want to
focus only on Dreyfuscriticisms contained in his important book
What Computers CantDo(1972). I will try to briey reconstruct
the structure of Dreyfusargument on AGI and then formulate
some criticisms.
Dreyfuss critique of AGI is inspired by Heideggers phenom-
enological research. Dreyfus criticizes what, according to him, are
four wrong assumptions of AGI research: (a) the assumption that
the brain and mind are analogous to hardware and software; b)
the assumption that the mind works computationally; (c) the
assumption that all human activities can be formalized and
calculated; (d) the assumption that reality consists of a series of
facts. Following Heidegger, Dreyfus holds that human existence is
a specic being-in-the-world dened by a horizon of possibility
(see Coeckelbergh, 2020, p. 47). In Being and Time, Heidegger
claims that the being-in-the-world is an ontological structure
dened by the category of care,which develops in two
directions: the belonging to the world and the relationship with
others. Care is above all a set of possibilities whose ultimate
horizon is temporality and death. This ontological structure
cannot be formalized or reduced to computation because, as
Heidegger claims, science does not think,in the sense that it is
capable only of thinking about entities, physical things, not being.
Inspired by Merleau-Pontys work, Dreyfus emphasizes in
particular that our being-in-the-world is based on our body. As
embodied, we are part of the social world and have tacit
knowledge and skills (dispositions, tendencies) which cannot be
formalized or expressed in a language (see Dreyfus, 1972, pp.
2434). Dreyfus argued that human skills and competence
depend mainly on our background sense of context,that is
the ability to identify what is important and interesting in a given
I want to make four criticisms of Dreyfus.
The rst concerns emotions. According to Heidegger, one of
the central dimensions of the being-in-the-world is emotionality.
The human being is always immersed in a certain emotional
situation that denes him/her. Now, neuroscience conrms this
point but also demonstrates that human emotions (a) have
nothing mysterious but can be perfectly explained in physical and
computational terms, (b) is very similar to the emotions of all
other mammals. Therefore, neuroscience shows that at least a
part of the human being-in-the-world can be translated into
computational terms. Heideggers criticism of science is based on
his own romantic pre-judgment.
Second criticism: it is not true that AGI is based on the
assumption that all reality consists of facts. Dreyfuss arguments
cannot be applied to current AI. Precisely the introduction of the
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Bayesian Networks with Pearl in the 1990s introduced an elegant
way of treating probability (therefore the dimension of the
possibility) in AI. So, if Heideggers being-in-the-world is a
horizon of possibilities, then we can formalize at least part of this
The third criticism of Dreyfus concerns the concept of
embodied. Machines also have a body that constitutes a set of
tacit knowledge and skills related to the social world. The concept
of the design illustrates exactly this point. My computer has a
body, a material shape designed by professional designers based
on certain social needs (Vial, 2013). My computer is a design
object and not a stone or piece of wood found in the woods.
Through design, my computer conveys a set of meanings and it is
part of the human world. Moreover, the act of design is always a
utopian act, which looks to the future of society, which sets a new
way of being in the world. As Findeli says, the end or purpose of
design is to improve or at least maintain the habitabilityof the
world in all its dimensions(2010). Through design, the
computer also possessesimplicit knowledge and skills that are
not translated into propositions. For instance, the ability to
transmit a vision of the world, society, and the future.
The fourth criticism concerns the distinction between
knowing-how and knowing-that and the primacy of intuition.
Dreyfus argues that intuition cannot be translated into formal
propositions, therefore into programs. Knowing-how cannot be
reduced to knowing-that. A possible answer could be that, in
reality, the only difference between knowing-how and knowing
that is the speed with which the data is processed. Therefore, it is
not important whether formal propositions are at the basis of the
process or not. Intuition is just a much faster knowledge than the
Fjelland (2020) has recently drawn on Dreyfuscriticism of
AGI. Moreover, Fjelland uses Pearls theory of causation in order
to claim that AGI is impossible: computers cannot understand
causal connections because they do not have a model of reality.
As we can read:
According to Pearl and Mackenzie, the root of the problem
is that computers do not have a model of reality. However,
the problem is that nobody can have a model of reality. Any
model can only depict simplied aspects of reality. The real
problem is that computers are not in the world because they
are not embodied. (Fjelland, 2020,p.6)
This argument does not make sense. First of all, this is not the
position of Pearl and Mackenzie. The opposite is true: Pearl and
Mackenzie show that human understanding of causation can be
translated into algorithms and softwareit is the outcome of the
causal revolutionthat the causeceases to be a vague and
imprecise concept and acquires a precise mathematical status.
Pearl responds positively to the question Can we make machines
that think?:I believe that strong AI with causal understanding
and agency capabilities is a realizable promise(Pearl and
Mackenzie, 2018, p. 367). Second, it is not true that computers are
not in the world. Computers act in the world just like human
agents, animals, plants, and the rest of things. They have a
physical body just like us. They are social agents exactly like us.
The objector might reply that computers do not have semantic
intelligence; they do not understand the meaning of what they do.
This, however, is an ambiguous answer, for two reasons, one
positive and one negative.
First, the notion of meaning is ambiguous in itself.
Secondly, the notion of meaning can be also understood in an
evolutionary sense: as a layering of networks of affects, emotions,
memories connected to layered networks of sounds, images,
neural connections, etc. In short, what distinguishes us from the
machines would not be a special human qualitythat machines
do not possess, but the fact that machines are still at the
beginning of their evolution. I think that the evolution factor and
integration with the surrounding environment are two crucial
elements for achieving AGI. However, this is the responsibility of
humans, not of the machine. Just as the healthy growth of the
child from an emotional point of view is the responsibility of the
good enoughmother (Winnicott, 1988), not of the child. We
are used to thinking of AI as if it were an adult human being. In
reality, the opposite is true: many AIs behave like children, and
the child needs time and an ongoing personal environment to
become a person. Without the care of a parent, this development
cannot take place.
Pearl denes intelligence as the ability to pass the mini-Turing
test by answering questions about causality (Pearl and Mackenzie,
2018, pp. 3646). For psychoanalysis and affective neuroscience,
intelligence is emotional maturity, which is the result of the
individuals emotional growth. Emotional maturity is the ability
to manage ones emotions, overcoming conicts with the
surrounding environment. Can we build machines that can
experience emotional growth? This paper replies positively by
indicating the fundamental basis for this undertaking.
6/5 A body for AGI. As I mentioned before, Dreyfus afrms that
computers, which do not have a body, a childhood, and a culture,
cannot acquire intelligence in the proper sense. Dreyfus (1992)
argues that much of human knowledge is tacit and therefore
cannot be articulated into a program. The project of a strong AI is
therefore impossible.
On this point, however, more advanced research could
transform the situation again. I am not talking about biorobotics
or biomedical engineering research, but about the creation and
development of the rst biological robots, made of programmable
biological matter. In this regard, Kriegman et al. (2019) open a
completely new path. They present the results of research that led
to the creation and development of Xenobots, the rst tiny robots
made entirely of biological tissues. Xenobots are a new life on our
planet. Researchers used an evolutionary algorithm to simulate
the design of robots. They then selected the best models. These
models have been tested to make them increasingly capable of
adapting to real situations; the researchers subjected them to large
quantities of noise in order to understand if, in a normal
situation, they would have maintained the intended behavior or
not. The transition from the design to the implementation phase
took place through the use of embryonic stem cells of Xenopus
laevis, a type of frog. The cells were assembled and developed by
the computer and then programmed to perform some functions.
Programmedmeans that cells were assembled into a nite
series of congurations to which certain movements and
functions correspond in an aqueous environment. These micro-
organisms are neither animals nor traditional robots. They have a
heart and skin. If damaged, they can repair themselves and
survive for at least ten days. They are assembled by the computer
and programmed to behave according to the models. Xenobot is
an organism in all respects but based on an articial design. As it
has a body, it has bodily senses, and thus it has homeostatic and
sensory affects. This solves many of the problems associated with
robot embodiment (see Dietrich et al., 2008, p. 150).
From our point of view, the principle of biological program-
ming could be used to program the seven basic affective systems
theorized by Panksepp into the cells (or groups of cells). Powerful
learning and evolutionary algorithms would allow us to under-
stand how these cells evolve and whether they develop feelings
and thoughts like humans or other animals.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
6/6 can AI sleep? The AGI model outlined in this paper may
seem too ego-centered. In fact, we have not mentionedexcept
brieytwo key elements of Freudian psychoanalysis: dreaming
and repression. So far, we have essentially described a machine
with instincts. As I said before, If we follow the reasoning
developed in the previous sections of the paper, we must afrm
that the repressed is a causal diagram whose access has been
blocked by internal or external events, for example an eccessive
waste of energy, or the interaction with another diagram. How-
ever, the locked diagram keeps acting in the system memory. In
this section, I re-interpret the concepts of dreaming and repres-
sion starting from a case study.
Can an AI system sleep? Theoretically, machines do not
understand things such as sleep or rest. A machine can continue
to work continuously without ever having to stop if provided a
constant source of energy. However, in June 2020, researchers
from the National Laboratory of Los Alamos made an important
discovery. They realized that a neural network system for
unsupervised learning became more and more unstable if left to
work for too long. The solution was to put the system to sleep.
We study spiking neural networks, which are systems that learn
much as living brains do,said Los Alamos National Laboratory
computer scientist Yijing Watkins. We were fascinated by the
prospect of training a neuromorphic processor in a manner
analogous to how humans and other biological systems learn
from their environment during childhood development.
Watkins and her research team found that neural network
learning to see became unstable after continuous and intense
periods of unsupervised learning. Faced with this difculty, they
decided to put the system to sleep: When they exposed the
networks to states that are analogous to the waves that living
brains experience during sleep, stability was restored,explains
the note from the laboratory. It was as though we were giving the
neural networks the equivalent of a good nights rest,said
The researchers used spiking neural networks (SNNs), which
are computational models that mimic biological neural networks.
Compared with articial neural networks (ANN), SNNs
incorporate integrate-and-re dynamics that increase both
algorithmic and computational complexity(Watkins et al.,
2020). Neuromorphic processors have tested that try to simulate
the behavior of the brain and the human nervous system. These
processors are made of special materials that are able to best
reproduce the plasticity of the human brain. Deep neural network
software is then run in these processors.
The discovery came about as the research team worked to
develop neural networks that closely approximate how
humans and other biological systems learn to see. The
group initially struggled with stabilizing simulated neural
networks undergoing unsupervised dictionary training,
which involves classifying objects without having prior
examples to compare them to. The issue of how to keep
learning systems from becoming unstable really only arises
when attempting to utilize biologically realistic, spiking
neuromorphic processors or when trying to understand
biology itself,said Los Alamos computer scientist and
study coauthor Garrett Kenyon. The vast majority of
machine learning, deep learning, and AI researchers never
encounter this issue because in the very articial systems
they study they have the luxury of performing global
mathematical operations that have the effect of regulating
the overall dynamical gain of the system.
As stated earlier, the researchers solved the instability problem
by making the system sleep.They did that by introducing noise.
The machine sleepsand, thanks to this sleep,manages to
regain equilibrium, exactly as the human body does.
The researchers characterize the decision to expose the
networks to an articial analog of sleep as nearly a last ditch
effort to stabilize them. They experimented with various
types of noise, roughly comparable to the static you might
encounter between stations while tuning a radio. The best
results came when they used waves of so-called Gaussian
noise, which includes a wide range of frequencies and
amplitudes. They hypothesize that the noise mimics the
input received by biological neurons during slow wave
sleep. The results suggest that slow-wave sleep may act, in
part, to ensure that cortical neurons maintain their stability
and do not hallucinate. The groupsnext goal is to
implement their algorithm on Intels Loihi neuromorphic
chip. They hope allowing Loihi to sleep from time to time
will enable it to stably process information from a silicon
retina camera in real time. If the ndings conrm the need
for sleep in articial brains, we can probably expect the
same to be true of androids and other intelligent machines
that may come about in the future.
This experiment can benet from the integration of the results
of Hobson and Fristons research on dreams. Hobson and Friston
(2012) demonstrate the essential function of the dream in relation
to the free-energy principle. Sleep implies optimization processes
that are perfectly consistent with the free energy principle. In
particular, Hobson and Friston emphasize the connection
between homeothermy, sleep, and consciousness. Sleep is
connected with homeostatic processes, especially temperature
control, which are necessary for consciousness. In particular,
Hobsn and Friston hold a conception of the dreaming brain as a
simulation machine or a virtual reality generator that seeks to
optimally model and predict its waking environment and that
needs REM sleep processes (particularly PGO waves) to do so.
The basic idea is that the brain comes genetically equipped with a
neuronal system that generates a virtual model of the world
during REM sleep because REM sleep processes are essential to
optimize this generative model. In other words, as the brain is a
virtual reality machine or prediction error device (as we saw
above) in order to minimize free energy, sleep is a particular way
to achieve this goal. From this point of view, the experiment of
the National Laboratory of Los Alamos is very interesting because
it shows a profound analogy in the functioning of the brain and a
deep neuronal network: both need an off-linephase in order to
ensure the equilibrium of the system. This also conrms what has
been said above: the free energy principle is a useful model to
describe and explain above all homeostatic processes, but not
emotions. Homeostatic imbalances can activate or inuence
emotional systems. However, homeostatic processes remain
something different from emotions. Furthermore, the homeo-
static processes active during sleep cannot fully explain the
emotions experienced during the sleep or the contents of the
dream itself.
What conclusions can we draw from these considerations? I
have identied two. The rst is that an advanced AGI system
presents much more complex behavior than expected and,
therefore, requires cycles of activity and rest. The other is that
in an advanced AI system, the simulation of human cognitive
activities (language, logic, memory, learning, etc.)what we
would call secondary processesin Freudian termsrequires the
simulation of primary processes(sleep is only one example; we
could also mention instincts or emotions) as well. Here, I want to
avoid confusing sleep and dreaming; obviously, I am not implying
that an AI system can dream. The point is that a cortical AI
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
system needs subcortical AI. In the case we examined, it is the
machine that experiences this need.
However, what exactly is sleepingfor an AI or AGI system?
The essence of Freuds theory is that in sleepand, in particular,
in that phase of sleep in which dreams occura regression takes
place; the ego (the center of cognitive activities) is inhibited and
the id (the unconscious, the set of drives) takes over (see The
Interpretation of Dreams, Chapter 7). Sleep is then a fundamental
observatory in which primitive drive states are more evident. Can
we apply the Freudian notion of regression to AGI? My
hypothesis is that there exist forms of regressions in information
as well. In AGI, the regression goes from information to noise.
The regression to noise is essential to information. In every
information process, there are forms of regression to different
types of noise.
Freud distinguished three types of regression: (a) topicalfrom
one psychic system to another; (b) temporalthe regression
toward older psychic formations; and (c) formalthe return of
primitive modes of expression and representation. I claim the
same about information. The regression to noise can be of three
types: (a) topicaltoward information of different types (data
that must be coded in another way); (b) temporalthat is, toward
more ancient information; and (c) formaltoward data without
conguration (pure noise). The Los Alamos Lab experiment
proves exactly that information needs regressions to noise, to
forms of stabilization and iteration.
Returning to our AGI model, we can connect the regression
from information to noise to the need to maintain homeostasis.
Noise is all that threatens homeostasis and increases entropy; on
the contrary, information ensures homeostasis and decreases
entropy. Noise is the set of data that threaten homeostasis and are
therefore split from information while remaining in the systems
memory. This is a very important point: the unconscious is not a
part of the psyche, but a quality of the processes of the psyche.
Therefore, all internal affective processes that cause an increase of
the free energy must be repressed; they remain in the state of a
pre-mental, pre-cognitive consciousnessthey remain raw data
or noise. The distinction between information and noise arises
from the general need of the system to keep the level of free
energy as low as possible. This distinction corresponds to
Freudian repression. As I said, the Solms-Friston model is not
at odds with Pearls causation theory; they can be held together as
complementary, one to represent homeostatic affects while the
other to represent emotional affects in Panksepps terminology.
I consider the theses developed in this paper to be the beginning
of a research program on the possibility of an AGI based on the
simulation of the subcortical areas of the brain. Only future
investigations will be able to establish the merits and demerits of
the ideas developed here. The central theoretical hypothesis of
this paper is that AGI is possible only if the main cognitive
functions are based on computational systems capable of ade-
quately simulating the raw affective systems that humans share
with other mammals. AGI must not be based on the imitation of
the behavior of humans, but on the modelization of their seven
basic affective systems described by Panksepp within a psycho-
analytic framework. The purpose of this paper was to show how
to organize and embed the seven emotional systems dened by
Panksepp into a computational system. With this in mind, I also
analyzed and criticized the position of Dreyfus, who launched a
famous critique of the AGI project from a Heideggerian point
of view.
In conclusion, I would like to formulate a last thesis: devel-
oping an AGI based on the fundamental human emotional
systems, i.e., capable of producing its own emotional life similar
to the human one, is the best way to solve the problem of AI
The problem of AI control can be formulated as follows: If we
build machines to optimize objectives, the objectives we put into
the machines have to match what we want, but we do not know
how to dene human objectives completely and correctly (Russell,
2019, p. 170; my emphasis). The problem of AI control is to
design machines with a high degree of intelligenceso that they
can help us with difcult problemswhile ensuring that those
machines never behave in ways that make us seriously unhappy
(Russell, 2019, p. 171). The future of humanity is tied to the
future of AI and how humans will be able to integrate AI systems
into their world (Elliott, 2018). This is the reason why it is so
essential to develop machines that are able not only to know
human desires and needs but also to understand them, interpret
their changes and share them. As Russell (2019, p. 11) says:
Machines are benecial to the extent that their actions can be
expected to achieve our objectives.Only in this way, humans will
avoid becoming the second intelligent species on the planet.
The objectives we put into the machines: this is exactly the
problem. Humans want the machine to do what they want, but
we do not know how to dene human objectives completely and
correctlyand we often act in ways that are contrary to our own
preferences. What are the human goals with respect to AI? How
can we clarify them? What do we want from machines? Does an
AI need to be able to recognize human unconscious dynamics so
that it can always act for the best of humansthat best that not
even humans often know? Emotional neuroscience can give an
answer by identifying the DNA of human needs, objectives, and
thoughts. Panksepps emotional systems are the fundamental
schemes of action and learning, the foundation of our
whole being.
Now, a subcortical AGI, like the one we have described in this
paper, would solve the problem of AI control. An AGI system
based on the seven systems of Panksepp would share with the
human beings the fundamental schemes of action and learning,
and therefore the essential needs and desires. This would be
possible without sacricing the computational power of AGI. Nor
would it be necessary to introduce increasingly complex sets of
rules in the system.
What would happen if the AGI system developed wrong
emotions and behaviors? It is a legitimate question. Here an
educational problem arises. If we want to create super-intelligent
systems capable of understanding and supporting our objectives,
and also sharing their emotions with us, we must be able to
educate them, to follow them in their growth, as if they were
children. For this reason, an AI psychoanalysis, that is, psycho-
analysis of AGI systems could play a key role in the future of
Data availability
All data generated or analyzed during this study are included in
this article.
Received: 14 July 2020; Accepted: 18 May 2021;
1 This paper is a development and a creative transformation of Possati (2021, chapters
4 and 5).
2Formally, a Markov blanket renders a set of states, internal and external states,
conditionally independent of one another. That is, for any variable A, A is
conditionally independent of B, given another variable, C, if and only if the
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
probability of A and B were given C can be written as p(AjC) and p(BjC). In other
words, A is conditionally independent of B given C if, when C is known, knowing A
provides no further information about B. [] The cell is an intuitive example of a
living system with a Markov blanket. Without possessing a Markov blanket a cell
would no longer be, as there would be no way by which to distinguish it from
everything else(Kirchhoff et al., 2018, p. 3).
3Hidden causes are called hidden because they can only be seenindirectly by
internal states through the Markov blanket via sensory states. As an example,
consider that the most well-known method by which spiders catch prey is via their
self-woven, carefully placed and sticky web. Common for web- or niche-constructing
spiders is that they are highly vibration sensitive. If we associate vibrations with
sensory observations, then it is only in an indirect sense that one can meaningfully
say that spiders have accessto the hidden causes of their sensory worldi.e., to the
world of ies and other edible critters’” (Kirchhoff et al., 2018, p. 4).
4 An objection could be advanced here: Does not reduce affective systems to causal
probabilistic models imply a cognitive interpretation of affects, as in Picard? This is
an important objection. However, I believe that the reference to Pearls work gives us
a way to respond to it. Pearls causal models are not mere sets of data computations
but involve an interpretation of data that is based on concrete experiencethe
intuition of causality and apprehension of reality that precedes causality and any
other fact or concept.
5 In more technical terms, Pearl denes confounding through two interrelated notions:
incomparability and a lurking third variable (see Pearl and Mackenzie 2018, p. 151).
This passage is essential: []the noncausal paths are precisely the source of
confounding. Remember that I dene confounding as anything that makes P(Y | do
(X)) differ from P(Y | X). The do-operator erases all the arrows that come into X, and
in this way, it prevents any information about X from owing in the noncausal
direction. Randomization has the same effect. So does statistical adjustment, if we
pick the right variables to adjust(Pearl and Mackenzie, 2018, p. 157; emphasis
6 For an elementary introduction to Pearls theory of causality, see Pearl et al., 2016.An
important source of information is also: See
also Tucci, 2013.
7 It is also important to read the rest of the passage: First, let us rephrase the task of
nding the effect of X on Y using the language of proofs, axioms, and auxiliary
constructions, the language of Euclid and Pythagoras. We start with our target
sentence, P(Y | do(X)). Our task will be complete if we can succeed in eliminating the
do-operator from it, leaving only classical probability expressions, like P(Y | X) or P
(Y | X, Z, W). We cannot, of course, manipulate our target expression at will; the
operations must conform to what do(X) means as a physical intervention. Thus, we
must pass the expression through a sequence of legitimate manipulations, each
licensed by the axioms and the assumptions of our model. The manipulations should
preserve the meaning of the manipulated expression, only changing the format it is
written in(Pearl and Mackenzie 2018, p. 233).
8 The nature of poetry and the role of affect in it has been a hotly debated topic
throughout literary history. To give one example, Stephen Halliwell (2017) discusses
the ancient Greco-Roman origins of this debate, which is also the origin of the
modern philosophical tradition. He also touches on more modern branches of this
debate, including the Romanticsdenition of poetry as an expression of emotion and
the Modernistsrejection of affective poetry.
Alberini C (2010) Long-term memories: the good, the bad, and the ugly. Cerebrum
Amoore L (2009) Algorithmic war: everyday geographies of the war on terror.
Antipode 41:4969
Apaydin E (2016) Machine learning. The new AI. MIT Press
Baldwin R (2016) The great convergence: information technology and the new
globalization. Harvard University Press
Benedetti F (2010) The patients brain. Oxford University Press
Blass R, Carmeli Z (2007) The case against neuropsychoanalysis: on fallacies
underlying psychoanalysislatest scientic trend and its negative impact on
psychoanalytic discourse. Int J Psychoanal 88:1940
Bolter D (1986) Turings man. Western culture in the computer age. Penguin
Books, London
Bostrom N (2016) Superintelligence: paths, dangers, strategies. Oxford University
Bruineberg J, Dewhurst J, Dolega K, Baltieri M (2020) The Emperors new Markov
Coeckelberg M (2020) Introduction to the philosophy of technology. Oxford
University Press
Collins G, Ortony A, Clore A (1994) The cognitive structures of the emotions.
Cambridge University Press
Colvin G (2015) Humans are underrated: what high achievers know that brilliant
machines never will. Penguin, New York
Damasio A (1994) Descarteserror. Putnam, New York
Damasio A (1999) The strange order of things. Pantheon, New York
Damasio A (2003) Looking for Spinoza. Heinemann, London
Damasio A (2010) Self comes to mind: constructing the conscious brain. Random
House, New York
Davis K, Montag CH (2019) Selected principles of pankseppian affective neu-
roscience. Front Neurosci 12:1025
Decety J, Ickes WJ (2009) The social neuroscience of empathy. MIT Press
Dietrich D, Fodor G, Kastner W, Ulieru M (2007) Considering a technical reali-
zation of a neuro-psychoanalytical model of the mind - A theoretical fra-
mework. 5th IEEE International Conference on Industrial Informatics.
Dietrich D, Fodor G, Zucker G, Bruckner D (eds) (2008) Simulating the mind: a
technical neuropsychoanalytical approach. Springer, Berlin
Dreyfus HL (1972) What computers cant do. Harper & Row, New York
Dreyfus HL (1992) What computers still cant do. MIT Press
Dreyfus HL, Dreyfus SE (1986) Mind over machine. Basil Blackwell, Oxford
Dyson G (2012) Turings cathedral. Random House, New York
Edelson M (1986) The convergence of psychoanalysis and neuroscience: illusion
and reality. Contemp Psychoanal 22:479519
El-Nasr MS, Yen J, Ioerger TR (2000) FLAME: fuzzy logic adaptive model of
emotions. Autonom Agent Multi-Agents Syst 3:219257
Elliott A (2018) AI culture: everyday life and the digital revolution. Routledge,
London-New York
Erol B, Majumdar A, Benavidez P, Rad P, Choo KR, Jamshidi M (2019) Toward
articial emotional intelligence for cooperative social humanmachine
interaction. IEEE Trans Computat Soc Syst 7(1):234246
Findeli A (2010) Searching for design research questions: some conceptual clar-
ications. In:Chow R, Jonas W, Joost G (eds) Questions, hypotheses, and
conjectures: discussions on projects by early stage and senior design
researchers. IUniverse, Bloomington, pp. 3448
Fjelland R (2020) Why general articial intelligence will not be realized. Humanit
Soc Sci Commun 7:19
Fogel A, Kvedar J (2018) Articial intelligence powers digital medicine. Digital
Med 1(5):2345
Friston K (2009) Causal modelling and brain connectivity in functional magnetic
resonance imaging. PLoS Biol 7(2):220225
Friston K (2013) Life as we know it. J R Soc Interface 10:20130475
Gallese V (2009) The two sides of mimesis: Girards mimetic theory, embodied
simulation and social identication. J Conscious Stud 16(4):2144
Halliwell S (2017) The poetics of emotional expression. Steiner, Stuttgart
Hitchcock C (2018) Probabilistic Causation. Stanford Encyclopedia of Philosophy
Hobson JA (2007) Wake up or dream on? Six questions for Turnbull and Solms.
Cortex 43:11131115
Hobson JA, Friston K (2012) Waking and dreaming consciousness: neurobiological
and functional considerations. Prog Neurobiol 98(1):8298
Johnson M, Horn G (1986) Dissociation of recognition memory and associative
learning by a restricted lesion of the chick forebrain. Neuropsychologia
Johnson M, Horn G (1988) Development of lial preferences in dark-reared chicks.
Anim Behav 36:675683
Kandel ER (1979) Psychotherapy and the single synapse. New Engl J Med 301
Kandel ER (1983) From metapsychology to molecular biology: explorations into
the nature of anxiety. Am J Psychiatry 140(10):12771293
Kahneman D (2011) Thinking fast and slow. Penguin Books, New York
Kaplan K, Solms M (2000) Clinical studies in neuro-psychanalysis. International
Universities Press, Madison
Kirchhoff M, Parr T, Ensor P, Friston K, Kiverstein J (2018) The Markov blankets
of life: autonomy, active inference, and the free energy principle J R Soc
Interface 15:20170792
Kriegman S, Blackiston D, Levin M, Bongard J (2019) A scalable pipeline for
designing recongurable organisms Proc Natl Acad Sci USA 117
Le Cun Y (2019) Quand la machine apprend. La revolution des neurons articiels
et de lapprentissage profond. Odile Jacob, Paris
LeDoux J (1996) The emotional brain. Simon & Schuster, New York
Lewis D (1973) Counterfactuals. Wiley&Sons, New York
Luria AR (1976) The working brain. Basic Books, New York
Montag C, Widenhorn-Müller K, Panksepp J, Kiefer M (2017) Individual differ-
ences in Affective Neuroscience Personality Scale (ANPS) primary emotional
traits and depressive tendencies. Comp Psychiatry 73:136142
Panksepp J (1982) Toward a general psychobiological theory of emotions. Behav
Brain Sci 5:407467
Panksepp J (1998) Affective neuroscience: the foundations of human and animal
emotions. Oxford University Press
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Panksepp J (2008) Simulating the primal affective mentalities of the mammalian
brain: a fugue on the emotional feelings of mental life and implications for
AI-Robotics. In: Dietrich D, Fodor G, Zucker G, Bruckner D (eds) Simulating
the mind: a technical neuropsychoanalytical approach. Springer, Berlin
Panksepp J, Biven L (2012) The archeology of mind: neuroevolutionary origins of
human emotions. W. W. Norton, New York
Pearl J, Glymour M, Jewell PR (2016) Causal inference in statistics. Wiley, New
Pearl J, Mackenzie D (2018) The book of why: the new science of cause and effect.
Random, New York
Penrose R (1989) The Emperors new mind: concerning computers, minds, and the
laws of physics. Oxford University Press
Penrose R (1994) Shadows of the mind: a search for the missing science of con-
sciousness. Oxford University Press
Picard R (1997) Affective computing. MIT Press
Possati LM (2021) The algorithmic unconscious. how psychoanalysis helps in
understanding AI. Routledge, London
Prescott T J, Lepora N (2018) Living machines: a handbook of research in bio-
mimetics and biohybrid systems. Oxford University Press
Pulver SE (2003) On the astonishing clinical irrelevance of neuroscience. J Am
Psychoanal Assoc 51:755772
Rolls ET (1999) The brain and emotion. Oxford University Press
Rolls ET (2005) Emotion explained. Oxford University Press
Russell S (2019) Human compatible. AI and the problem of control. Random, New
Russell S, Norvig P (2016) Articial intelligence: a modern approach. Pearson,
Shanahan M (2015) The technological singularity. MIT Press
Shibata T, Yoshida M, Yamato J (1997) Articial emotional creature for human-
machine interaction. In: 1997 IEEE International Conference on Systems,
Man, and Cybernetics. Computational Cybernetics and Simulation, Orlando,
pp. 22692274
Schuller D, Schuller BW (2018) The age of articial emotional intelligence.
Computer 51(9):3846
Solms M (1996) Towards an anatomy of the unconscious. J Clin Psychoanal 5
Solms M (2000) Freud, Luria and the clinical method. Psychoanal History 2:76109
Solms M (2008) Repression: a neuropsychoanalytic hypothesis.
Solms M (2013) The conscious Id. Neuropsychoanalysis 15(1):519
Solms M, Saling M (eds) (1990) A moment of transition. Two neuroscientic
articles by Sigmund Freud. The Institute of Psychoanalysis, London
Solms M, Friston K (2018) How and why consciousness arises: some considera-
tions from physics and physiology. J Conscious Stud 25(56):202238
Solms M, Turnbull O (2002) The brain and the inner world: an introduction to the
neuroscience of subjective experience. Other Pr. Llc
Sulloway F (1979) Freud: biologist of the mind. Harvard University Press
Tucci R (2013) Introduction to Judea Pearls Do-Calculus. https://www.
Vial S (2013) Lêtre et lécran. Puf, Paris
Yonck R (2017) Hearth of the machine: our future in a world of articial emotional
intelligence. Arcade, New York
Yovell Y, Bar G, Mashiah M, Baruch Y, Briskman I, Asherov J (2016) Ultra-low-
dose buprenorphine as a time-limited treatment for severe suicidal ideation: a
randomized controlled trial. Am J Psychiatry 173:491498
Watkins Y, Kim E, Sornborger A, Kenyon GT (2020) Using Sinusoidally-
Modulated Noise as a Surrogate for Slow-Wave Sleep to Accomplish Stable
Unsupervised Dictionary Learning in a Spike- Based Sparse Coding Model.
Working paper, Computer Vision Foundation. https://openaccess.thecvf.
Winnicott D (1988) Human nature. The Winnicott Trust
Competing interests
The author declares no competing interests.
Additional information
Correspondence and requests for materials should be addressed to L.M.P.
Reprints and permission information is available at
Publishers note Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional afliations.
Open Access This article is licensed under a Creative Commons
Attribution 4.0 International License, which permits use, sharing,
adaptation, distribution and reproduction in any medium or format, as long as you give
appropriate credit to the original author(s) and the source, provide a link to the Creative
Commons license, and indicate if changes were made. The images or other third party
material in this article are included in the articles Creative Commons license, unless
indicated otherwise in a credit line to the material. If material is not included in the
articles Creative Commons license and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly from
the copyright holder. To view a copy of this license, visit
© The Author(s) 2021
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Terms and Conditions
Springer Nature journal content, brought to you courtesy of Springer Nature Customer Service Center GmbH (“Springer Nature”).
Springer Nature supports a reasonable amount of sharing of research papers by authors, subscribers and authorised users (“Users”), for small-
scale personal, non-commercial use provided that all copyright, trade and service marks and other proprietary notices are maintained. By
accessing, sharing, receiving or otherwise using the Springer Nature journal content you agree to these terms of use (“Terms”). For these
purposes, Springer Nature considers academic use (by researchers and students) to be non-commercial.
These Terms are supplementary and will apply in addition to any applicable website terms and conditions, a relevant site licence or a personal
subscription. These Terms will prevail over any conflict or ambiguity with regards to the relevant terms, a site licence or a personal subscription
(to the extent of the conflict or ambiguity only). For Creative Commons-licensed articles, the terms of the Creative Commons license used will
We collect and use personal data to provide access to the Springer Nature journal content. We may also use these personal data internally within
ResearchGate and Springer Nature and as agreed share it, in an anonymised way, for purposes of tracking, analysis and reporting. We will not
otherwise disclose your personal data outside the ResearchGate or the Springer Nature group of companies unless we have your permission as
detailed in the Privacy Policy.
While Users may use the Springer Nature journal content for small scale, personal non-commercial use, it is important to note that Users may
use such content for the purpose of providing other users with access on a regular or large scale basis or as a means to circumvent access
use such content where to do so would be considered a criminal or statutory offence in any jurisdiction, or gives rise to civil liability, or is
otherwise unlawful;
falsely or misleadingly imply or suggest endorsement, approval , sponsorship, or association unless explicitly agreed to by Springer Nature in
use bots or other automated methods to access the content or redirect messages
override any security feature or exclusionary protocol; or
share the content in order to create substitute for Springer Nature products or services or a systematic database of Springer Nature journal
In line with the restriction against commercial use, Springer Nature does not permit the creation of a product or service that creates revenue,
royalties, rent or income from our content or its inclusion as part of a paid for service or for other commercial gain. Springer Nature journal
content cannot be used for inter-library loans and librarians may not upload Springer Nature journal content on a large scale into their, or any
other, institutional repository.
These terms of use are reviewed regularly and may be amended at any time. Springer Nature is not obligated to publish any information or
content on this website and may remove it or features or functionality at our sole discretion, at any time with or without notice. Springer Nature
may revoke this licence to you at any time and remove access to any copies of the Springer Nature journal content which have been saved.
To the fullest extent permitted by law, Springer Nature makes no warranties, representations or guarantees to Users, either express or implied
with respect to the Springer nature journal content and all parties disclaim and waive any implied warranties or warranties imposed by law,
including merchantability or fitness for any particular purpose.
Please note that these rights do not automatically extend to content, data or other material published by Springer Nature that may be licensed
from third parties.
If you would like to use or distribute our Springer Nature journal content to a wider audience or on a regular basis or in any other manner not
expressly permitted by these Terms, please contact Springer Nature at
ResearchGate has not been able to resolve any citations for this publication.
Full-text available
[A heavily rewritten version of this paper has been published in BBS in 2021] Markov blankets have been used to settle disputes central to philosophy of mind and cognition. Their development from a technical concept in Bayesian inference to a central concept within the free-energy principle is analysed. We propose to distinguish between instrumental Pearl blankets and realist Friston blankets. Pearl blankets are substantiated by the empirical literature but can do limited philosophical work. Friston blankets can do philosophical work, but require strong theoretical assumptions. Both are conflated in the current literature on the free-energy principle. Consequently, we propose that distinguishing between an instrumental and a realist research program will help clarify the literature.
Full-text available
The modern project of creating human-like artificial intelligence (AI) started after World War II, when it was discovered that electronic computers are not just number-crunching machines, but can also manipulate symbols. It is possible to pursue this goal without assuming that machine intelligence is identical to human intelligence. This is known as weak AI. However, many AI researcher have pursued the aim of developing artificial intelligence that is in principle identical to human intelligence, called strong AI. Weak AI is less ambitious than strong AI, and therefore less controversial. However, there are important controversies related to weak AI as well. This paper focuses on the distinction between artificial general intelligence (AGI) and artificial narrow intelligence (ANI). Although AGI may be classified as weak AI, it is close to strong AI because one chief characteristics of human intelligence is its generality. Although AGI is less ambitious than strong AI, there were critics almost from the very beginning. One of the leading critics was the philosopher Hubert Dreyfus, who argued that computers, who have no body, no childhood and no cultural practice, could not acquire intelligence at all. One of Dreyfus’ main arguments was that human knowledge is partly tacit, and therefore cannot be articulated and incorporated in a computer program. However, today one might argue that new approaches to artificial intelligence research have made his arguments obsolete. Deep learning and Big Data are among the latest approaches, and advocates argue that they will be able to realize AGI. A closer look reveals that although development of artificial intelligence for specific purposes (ANI) has been impressive, we have not come much closer to developing artificial general intelligence (AGI). The article further argues that this is in principle impossible, and it revives Hubert Dreyfus’ argument that computers are not in the world.
The Patient’s Brain describes and explains recent advances within neuroscience that enable us to describe and discuss the biological mechanisms that underlie the doctor-patient relationship, how this new scientific knowledge can be put to great practical use, and the doctor-patient relationship can be subdivided into at least four steps: feeling sick, seeking relief, meeting the therapist, and receiving therapy.
The free energy principle, an influential framework in computational neuroscience and theoretical neurobiology, starts from the assumption that living systems ensure adaptive exchanges with their environment by minimizing the objective function of variational free energy. Following this premise, it claims to deliver a promising integration of the life sciences. In recent work, Markov Blankets, one of the central constructs of the free energy principle, have been applied to resolve debates central to philosophy (such as demarcating the boundaries of the mind). The aim of this paper is twofold. First, we trace the development of Markov blankets starting from their standard application in Bayesian networks, via variational inference, to their use in the literature on active inference. We then identify a persistent confusion in the literature between the formal use of Markov blankets as an epistemic tool for Bayesian inference, and their novel metaphysical use in the free energy framework to demarcate the physical boundary between an agent and its environment. Consequently, we propose to distinguish between ‘Pearl blankets’ to refer to the original epistemic use of Markov blankets and ‘Friston blankets’ to refer to the new metaphysical construct. Second, we use this distinction to critically assess claims resting on the application of Markov blankets to philosophical problems. We suggest that this literature would do well in differentiating between two different research programs: ‘inference with a model’ and ‘inference within a model’. Only the latter is capable of doing metaphysical work with Markov blankets, but requires additional philosophical premises and cannot be justified by an appeal to the success of the mathematical framework alone.
This book applies the concepts and methods of psychoanalysis to the study of artificial intelligence (AI) and human-AI interaction. It develops a new, more fruitful approach for applying psychoanalysis to AI and machine behavior. It appeals to a broad range of scholars: philosophers working on psychoanalysis, technology, AI ethics, and cognitive sciences, psychoanalysts, psychologists, and computer scientists. The book is divided into four parts. The first part (Chapter 1) analyzes the concept of "machine behavior." The second part (Chapter 2) develops a reinterpretation of some fundamental Freudian and Lacanian concepts through Bruno Latour's actor-network theory. The third part (Chapters 3 and 4) focuses on the nature and structure of the algorithmic unconscious. The author claims that the unconscious roots of AI lie in a form of projective identification, i.e., an emotional and imaginative exchange between humans and machines. In the fourth part of the book (Chapter 5), the author advances the thesis that neuropsychoanalysis and the affective neurosciences can provide a new paradigm for research on artificial general intelligence. The Algorithmic Unconscious explores a completely new approach to AI, which can also be defined as a form of "therapy." Analyzing the projective identification processes that take place in groups of professional programmers and designers, as well as the "hidden" features of AI (errors, noise information, biases, etc.), represents an important tool to enable a healthy and positive relationship between humans and AI. Psychoanalysis is used as a critical space for reflection, innovation, and progress.
For many decades, the proponents of `artificial intelligence' have maintained that computers will soon be able to do everything that a human can do. In his bestselling work of popular science, Sir Roger Penrose takes us on a fascinating tour through the basic principles of physics, cosmology, mathematics, and philosophy to show that human thinking can never be emulated by a machine. Oxford Landmark Science books are 'must-read' classics of modern science writing which have crystallized big ideas, and shaped the way we think.