The Essence of Emotion:
Knowledge-Making and Entity-Shaping in Scientific Practice
Patrick BeckerUniversity of Maastricht
Technological Culture SpecializationAcademic Year 2002-2003
24.807 Words
1
1. IN SEARCH OF EMOTIONS 4
1.1. INTRODUCTION 41.2. THE AIM OF MY STUDY 61.3. OVERVIEW 10
2. THE ANALYSIS OF KNOWLEDGE-MAKING IN THE SOCIAL BRAIN SCIENCES 13
2.1. REFLECT YOURSELF - REQUIREMENTS FOR AN ANALYTICAL MODEL 142.2. THREE PERSPECTIVES ON THE PRODUCTION OF KNOWLEDGE 152.3. TOWARDS AN ANALYTICAL MODEL FOR KNOWLEDGE FORMATION 222.3. DRAWING THE MODEL TOGETHER 28
3. TALKING OF EMOTIONS: THE CREATION OF A DISCURSIVE SPACE FOR EMOTIONS 32
3.1. EFFECTS OF THE ELUSIVE MIND: THE TRADITIONAL DISCOURSE OF EMOTION 343.2. EXPRESSIONS OF THE EMOTIONAL BRAIN: THE NEW DISCOURSE OF EMOTION 363.3. PUTTING EMOTIONS INTO THE BRAIN - THE CONSTRUCTION OF A DISCURSIVE SPACE FOR EMOTION IN THE NEUROSCIENCES 383.4. THE EPISTEMOLOGICAL AND ONTOLOGICAL POLITICS OF THE EMOTIONAL BRAIN 46
4. THE EXPERIMENTALIZATION OF EMOTIONS 53
4.1. THE EXPERIMENTAL SYSTEM IN NEUROIMAGING RESEARCH 564.2. THE PRINCIPLE OF EXPERIMENTAL AVAILABILITY 574.3. THE DEFINITION AND OPERATIONALIZIATION OF EMOTIONS 604.4. THE RIGHT METHOD TO LOCATE EMOTIONS IN THE BRAIN 624.5. THE RIGHT TOOLS TO DO THE JOB 66
5. INSCRIBING EMOTIONS INTO THE BRAIN 71
5.1. THE PROCESS OF INSCRIPTION IN NEUROIMAGING PRACTICE 72
2
5.2. STEP 1 - SCANNING EMOTIONS 755.3. STEP 2 - DEVELOPING THE IMAGE 775.4. STEP 3 - MAKING ACTIVATIONS SIGNIFICANT 795.5. STEP 4 - PUTTING EMOTIONS ON THE (BRAIN) MAP 835.6. IMAGES OF THE MIND - MAPS OF THE BRAIN 84
6. WHAT ARE EMOTIONS? 93
7. REFERENCES 98
7.1. LIST OF INTERVIEWEES 987.2 . BIBLIOGRAPHY 99
3
1. In Search of Emotions
1.1. Introduction
Since the emancipationist call to “know thyself” has become the marching
order of modern science, few things in its quest to unravel the mysteries of the human
nature could have been more exciting, more challenging and of higher intellectual
attraction than to get a better, a scientific understanding of our own emotions.
Disputably, emotions - both the most personal and most occult aspects of our mind-
are at the core of who we are. It thus comes as no surprise that generations of
philosophers, scientists and scholars from ancient ages onwards have tried to shed
some light on the workings and nature of emotions in order to better understand and
possibly control them.
In the last decade, however, a new ambitious research program into the human
condition in general, and emotions in particular was launched in the context of neural
sciences - the study of the “social” brain. Its proponents argue that our social nature
defines what marks us as human, what makes us conscious and what gave us our large
brains; and, by probing the biological basis of the social in our brains, they hope to
finally understand what makes us uniquely human.
As a new field, the “social brain sciences“ (Adolphs, 2003) cover a wide range
of research topics, from studies in social cognition (as the basis of all social
interaction) and complex decision-making (such as moral decisions), to the mental
representation of other people and oneself. Although the issues and questions that the
social brain sciences are dealing with are thus quite multifaceted, the intuition is that
4
emotion stands in a privileged position to provide an explanation for most if not all of
them. As such, all research efforts are bound together by a common craving: to find
out how the brain makes us “emotional”.
In their quest for the biological-neural underpinnings of emotions, the social
brain scientists are assisted by an exiting new technology - functional brain imaging.
Functional brain imaging, or neuroimaging, is a powerful new investigational method
based on the in vivo visualization of biophysical structures and processes in the living
brain through computer-based scanning devices. For the first time in the history of
medicine, it thus makes the unthinkable possible - to open up a new and clear window
into the inner workings of the living, thinking, feeling brain. Needless to say, these
prospects have caught and fuelled the neuroscientist’s imagination: “The excitement
evident in neural science today is based on the conviction that at last we have in hand
the proper tools to explore the extraordinary organ of the mind, so that we can
eventually fathom the biological principles that underlie human cognition” (Kandel
2000:17)
Among the various neuroimaging modalities in use today, the quick adoption,
exponential growth and widespread application of functional magnetic resonance
imaging (MRI) marks out this method as the undisputed favorite of researchers and
clinicians alike. Less that a decade after the first functional MRI studies appeared,
they now fill the pages of neuroscience journals, proceedings of conferences, and
often enough also the headlines of newspapers and television documentaries.
5
In sum, it seems as if the merger of the social brain sciences with MR-
neuroimaging holds high potentials for the identification and exploration of the neural
mechanisms that underlie emotional behavior and social cognition, and thus to finally
understand the mysteries of our emotional nature. However, the peculiar character of
the relationship between our brains and ourselves -in investigating the brain, we
investigate the self- means that these new research activities also hold the potential to
induce far-reaching shifts in both how we think of and how we behave towards
ourselves: If who we are is solely determined by the neural states of our brain, there
hardly seems to be much more use for the classical idea of a human „mind“ or „soul“
as the final cause of our ability to act freely and consciously, to have feelings, or to
create cultural achievements.
In other words, the social brain sciences not only generate new insights into
the neural basis of our “emotional” human nature - by doing so, they are also about to
reconfigure some of our most central tenets about the human subject and the world it
inhabits: the dualism between mind and body, and the related one between culture and
nature. And, as indicated, the main thrust of their research efforts is to reduce both
‚social’ phenomena - the mind and the culture – to their biological correlates and thus
point out the primacy of the nature pole in both dualisms: That, in the end, the body is
the master of the (emotional) mind, and that nature is clearly supervenient to culture.
1.2. The aim of my study
In my view, the precarious efforts of the social brain scientists to find out what
emotions really are constitute a highly interesting and fascinating topic: it is a story
about true and scientific knowledge, about what makes us human, and about the
crucial role of technology in this process of knowledge-making and entity-shaping. As
6
such, it is a quintessential example of what STS stands for - the study of science,
technology, and their interactions with society.
Abiding by this conviction, my study will focus on two questions: first, how is
the new knowledge about the human brain and its emotions generated in the social
brain sciences and, and second, what are the ontological consequences of their
scientific work for the constitution of the human self?
To answer my questions, I will explore the sites where the social brain
scientists “make” emotions: in their scientific texts that muse about concepts of
emotion, in the experimental settings of the laboratory that generate emotional
phenomena, and in the computerized MR brain images on which emotion comes to be
inscribed. However, I will not make my journey into the inner recesses of
neuroscientific knowledge/entity-making unprepared and without any guidance - with
me, I will take some of the ideas and experiences of other researchers to act as a
counsel whenever I cannot make sense out of the things myself: luckily, I can build
upon a small corpus of existing literature both within and outside STS that has already
dealt with some of the issues and problems raised by my research questions.
Two fields in particular need to be mentioned:1 On the one hand, STS-studies
on the digitalisation and the “pictoral turn” connected to new imaging technologies,
and on the other, anthropological and historical works written about the ontological
reconfigurations that come along with new, technologically mediated insights into the
human body. In the following, I wish to give a short review of the major topics and
findings in each of those fields in question.
1 I will abstain from a wider discussion of the general STS-approaches and views that my study is grounded in, as those will be discussed in detail in the following chapter.
7
1. Digitalisation and pictoralization of science
For some STS-researchers, the increasing use of computerized tools and
instruments in the natural sciences represent a radical shift in the epistemological style
of the current research practices: Keller (2000), for example, sees the advent of a
„cyberscience”, and Lynch (1991) considers „digitalism“ to be a new configuration of
epistemology, representation and laboratory work which stands in stark contrast to the
„opticist“ framework used earlier in biology and other natural sciences: In opticism,
the model of ocular vision supplies a vocabulary and set of conditions for a more
general epistemology - most crucially, the idea of an object „out there“ and an
internal image „inside“ the observer, with a point-by-point correspondence between
internal image and outside object. Digitalism, on the other hand, is an explicitly
simulated or constructed space, and the phenomena in a digital context are not a
representation or point-to-point image of any object. In fact, due to its digital nature,
the data of the object in question could be computed and visually represented in many
different ways, none of them intrinsically more or less close to reality. In such a new
epistemological practice, the correspondence between a simulated model and reality
thus becomes a critical issue for the whole scientific endeavour.
Closely linked to the issue of digitalisation (and its potential to visualize data
of all kinds) is the so-called „pictorial turn“ in scientific practice. In this context, Burri
(2001) and Hagner (1996, 2001) analysed the impact of the pictorial turn in the
neurosciences and found that visualisations serve a wide range of function - most
importantly, they are used as immutable mobiles which aggregate and translate a huge
set of (statistical) data about brain functions into an easily manageable form. Also
taking neuroimaging technology as an exemplary, Beaulieu (2002) illustrates the
central problems and conflicts inherent in such a visual style, namely the questions
8
relating to the epistemic status of the image: MRI pictures of the brain are seen to be
mere statistical maps (not images) by one group of scientists, while another believes
that these images “of the mind” are mimetic, and that they offer an realistic insight
into the cognitive activities of the brain.
2. Ontological reconfigurations of the body
Feminist researchers, anthropologists and historians of medicine have pointed
out that the technologies used to produce new body imagesare no transparent
intermediaries between knowledge and reality, but they change both the knowledge,
and the object of knowledge itself. Duden (1991) illustrates how the perception of and
the sovereignty over the pregnant female body was transformed by the use of
ultrasound images: While previously having been considered as a somewhat
mysterious part of the human body that was accessible only through a women’s
subjective experiences, it became now an open uteral space subjected to the medical
gaze.
In a similar vein, Mol’s (2002) analysis of “the body multiple” highlights the
different ontological politics inherent in medical representations of the body. She
points out that the different practices of representing the body do not simply generate
different perspectives of their object, but instead create different referents -i.e.
different bodies- that do not necessary converge, but might as well clash, or contradict
each other. As such, the body is not a single entity but a multiplicity of objects which
first need to be re-arranged and realigned before they can act as a unitary object with
clear ontological characteristics.
9
1.3. Overview
In the next chapter, I will outline the theoretical-analytical approach that will
guide me through the different episodes of the “making” of emotions in
neuroscientific practice. First, I will present the general methodological
considerations and theoretical positions from which I intend to tackle my research
questions. After that, I will lay out the elements of the analytical model with which I
want to study the process of knowledge production in the social brain sciences. In
summary, it considers knowledge formation as the construction of a continuous chain
of translations from theoretical concepts via experimental phenomena to scientific
objects that become accepted as valid evidence for the actual existence of the item
under investigation.
In the following chapters, I will therefore follow this chain of translation, both
to understand how scientific knowledge about the nature of our emotions is produced,
and what effects it has on the ontological dualism between mind and body.
Chapter 3 will take a closer look at how a new object of inquiry -the emotional
brain- is constructed in the neuroscientific discourse. The analytical focus will be on
the way in which new concepts of emotion are defined, defended and integrated into
the discursive framework of cognitive neuroscience. Besides this, it also examines the
wider epistemological and ontological consequences that are effectuated by the
discourse of the emotional brain.
In Chapter 4, I will examine the specific experimental methods and activities
through which the neuroscientists generate and empirically tackle emotions in the
laboratory. In particular, I will analyze how the different experimental models and
research designs, analytical methods and disciplinary backgrounds are mutually
10
adapted and combined with each other to form an experimental “system” in which the
phenomenon? under investigation (i.e. the neural correlates of emotion) comes into
existence.
Chapter 5 will then look at the translation of these ephemeral experimental
events under the MR-scanner into a solid and universally valid representation of the
emotional brain - the photo-realistic brain activation maps that are the outcome of
every neuroimaging study. Two aspects deserve special attention: on the one hand, I
want to examine which facets an emotional event finally become visualized and
“inscribed” in such a brain map, and which don’t. On the other, I will analyze the role
of the computerized instruments and digital tools used during the construction of such
representations of emotions, and try to uncover the specific epistemological and
ontological effects that come along with their technically mediated transformation.
In the final chapter, I want to return to my original question about how
scientific knowledge about the (neural) nature of our emotions is produced, and what
effects it has on the ontological dualism between mind and body. We will learn that -
in spite of their huge efforts- the social brain scientists are still in doubt about the true
nature of their object of inquiry. However, we will also see that the multiplicity of
understandings of emotions is not a failure, but rather the result of the various
scientific activities undertaken in the social brain sciences. Thus, instead of
conceiving emotion as a universal, singular entity, I argue that we should understand
them as different, multiple objects brought into being by different scientific practices.
11
12
The Analysis of Knowledge-Making in the Social Brain
Sciences
As discussed in the previous chapter, the main aim of the social brain sciences
is to investigate the neural mechanisms that underlie emotional behavior and social
cognition. By identifying the biological basis of the social and emotional in our
brains, researchers hope to solve one of the greatest mysteries of life – to finally
understand what makes us uniquely human.
However, the peculiar -and historically rather controversially discussed-
nature of the relationship between the brain and the mind means that this bold and
ambitious research program into the human condition (which might never be
completely achieved, as even most neuroscientists would admit) holds the potential to
induce far-reaching shifts in both how we think of and how we behave towards
ourselves: If who we are, and what we possibly can become, is solely determined by
the biological matter and the neural states of our brain, there hardly seems to be much
“raison d’être” for the classical idea of a human mind or soul as the final cause of our
ability to act freely and consciously, to have feelings, or to attain cultural and
civilizational achievements. In other words, the social brain sciences not only
generate new insights into the neural basis of human emotions. By doing so, they are
also about to reconfigure two of the most prominent dualisms of modernity with
regard to the understanding of the human subject and the world it inhabits: The
dualism between mind and body, and the related one between culture and nature.
13
2.1. Reflect yourself - requirements for an analytical model
How could we possibly analyze the processes of reconfiguration that are
induced by the neuroscientific quest for the biological basis of our mind? Obviously,
there are two related aspects involved which are in need of a closer study: on the one
hand, we would have to examine how the constitution of a naturalized human self is
effectuated by the scientific work done in the social brain sciences. And on the other
hand, as this reconfiguration is firmly grounded in their newly generated findings and
facts about the human brain, we would need to analyze how this new neuroscientific
knowledge is produced in the first place.
Although both questions sound rather straightforward, formulating an
appropriate theoretical and methodological approach that provides answers to them is
more complicated than it might initially sound. The reasons for that lie in the self-
reflexivity of the epistemological and ontological issues they touch upon: By
questioning how a specific ontological reordering -a change in the way we conceive
the nature of things (in this case, the human self)- can be brought about through by the
scientific activities of a particular community and the knowledge it generates, they
also question the epistemological grounds on which its knowledge is considered to be
valid and truthful. However, in order to critically answer these questions, one cannot
avoid musing on the epistemological grounds according to which one considers one’s
own knowledge claims for valid – and obviously, there isn’t a position of „nowhere“
from which to analyze another group’s ontology and epistemology: no researcher
stands outside the subject matter of adhering to a certain ontology and epistemology
in his/her research, and as such also my analysis also will be made from a certain
perspective.
14
Nevertheless, I will attempt to take a self-reflexive stance in this matter by
clearly laying out the methodological considerations and theoretical positions from
which I intend to tackle my research questions. For this reason, I will begin with a
methodological discussion of the different metaphysical assumptions upon which
scientific accounts -including my own, of course- can be grounded. This is done both
to offer a better understanding of the ontological-epistemological framework that my
object of inquiry -the social brain sciences- is devoted to, as well as to explain my
own analytical position. As such, it also represents the first step in the development of
the explanatory model with which I want to study the process of knowledge
production in the social brain sciences.
2.2. Three perspectives on the production of knowledge
Generally speaking, there are three mutually exclusive packages that combine
a certain set of metaphysical positions to base ones cognition upon: realism,
relativism and (symmetrical) constructivism. They differ both in the ontological and
epistemological resources employed in the process of knowledge formation as well as
in the critical repertoire they can call upon in order to weaken competing claims to
truthful knowledge.
The term „realism“ (at least in the way it is used here) signifies a metaphysical
belief system that underlies most natural and social sciences. Its proponents assume
that all observable natural (say, fermentation) or social phenomena (say, suicide) are
governed by universal laws and have their origin in transcendental entities (either
nature or society) that exist independently of people’s thoughts and perceptions.
Based on this ontological a priori of an objectively existing world, it is further argued
15
that truthful knowledge about it can be gained through systematic observation and
experimentation (i.e., the scientific method)2. These two assumptions of the realists –
first, there is one unique ordering of the natural and social phenomena of the world,
and second, there is a set of procedures to determine what this ordering is – form the
core of arguments for the possibility of universal, objective knowledge about the
world.
At the same time, they also provide the realist with a critical repertoire to put
into question any other kind of knowledge not produced according to the canons of
scientific method - and in consequence, also any other kind of ontological ordering
that is not based on such scientific knowledge. In its most common form, this critical
repertoire is used to explain the “great divide” between our western society and all
others before or still around: whereas “their” knowledge is local, subjective and based
on naïve (but false) beliefs (i.e. myths, religion, tradition, ideology, etc.), “ours” is
universal, objective, and based on the scientific method; whereas “their” outdated
ontologies are contingent and still reflect traditional thinking and the influences of the
(local) socio-cultural context in which it is embedded, “our” modern ontology is both
historically and culturally invariable, as it reflects the true order of things revealed to
us by science.
The set of epistemological and ontological assumptions outlined above has
often be seen as the defining feature, and the major source of strength, of modern
science (and some even say: of modernity itself), as it made it plausible to believe in
both a transcendental nature and society, in the idea of scientific progress and
enlightenment, and in the great divide that distinguishes “us” moderns from any other
culture and their primitive knowledge. It thus comes to no surprise that most
2 This idea of the scientific method has been most thoroughly elaborated in the positivist-empiricist approach of Karl Popper and his “The Logic of Scientific Discovery” (1959).
16
neuroscientists ground their work firmly within the epistemological-ontological
framework of realism / modernity: Based on a positivist-empirist epistemology, they
consider their observed findings about social behavior and cognition as valid
reflections of our human nature (namely, behavior is driven by evolutionary-derived
brain responses to social stimuli), and any ontological reordering of the body-mind (or
nature-culture) dualism effectuated by these new facts would be seen as having their
cause in the - now finally unveiled - true nature of things3.
Among the first to point out problems of the realist position were a group of
scholars that adhered to a metaphysical framework known as relativism.4 Its central
ontological and epistemological tenets sharply questioned the core assumptions of
realism. In their view, there neither exists an absolute or unique (ontological) ordering
of things nor any universal truths, as all knowledge is relative to the culture from
which it comes. Relativism thus negates the existence of a transcendental natural
world as the origin of observable phenomena (or at least, it negates the possibility to
actually prove its causal involvement), and instead stresses that all social structures -
including the one within which modern science is operating - impose irresistible
distortions on all perceptions we might have of the world. Without the possibility to
ever get at the correct ordering of the natural phenomena of the world, all societies
and cultures are thus bound to produce nothing but partial views on nature.
3 Consider the telling quote of one of the “founding fathers” of social brain sciences: “I believe that mind and self-consciousness really are of biological nature. […] It seems to be certain that until 2050, we will have accumulated enough knowledge about those biological phenomena that the old dualisms between body and soul, or brain and mind, will vanish completely.” (Damasio 2002: 8ff)4 Acknowledging that the term relativism is used in many different contexts and disciplinary discourses, I will delimit my understanding and discussion of the relativist position to that prominent in the science studies. In this context, relativism “is the prescription to threat the objects of the natural world as though our beliefs of them are not caused by their existence…This can best be accomplished by treating what seems to exist as being relative to the social group in which it is taken to exist” (Collins 1995:294f)
17
These two assumptions -first, every perception of the world is governed by our
cultural context, and second, as all knowledge is the product of local and contingent
social conditions, it cannot be ranked (at least not along epistemological criteria)- lay
the foundation of the critical repertoire of relativism: stressing the cultural
incommensurability of viewpoints, its proponents argue for the impossibility of
universally valid and objective knowledge, as well as for the dominant influence of
societal structures in the process of knowledge formation. Furthermore, relativism
offers an alternative explanation for the truth or falsehood of knowledge - one that
does not conceives our beliefs of the natural world as being caused by the existence of
an objective nature “out there”. Instead, whatever seems to exist and counts as being
true or false is treated as being relative to the social group in which it is taken to exist.
In other words, the ascription of being “true to nature” (or, for that matter as being
“based on false beliefs”) is seen as the result of a social process, and not of its relation
to the “real” state of affairs. Thus, by inverting the assumptions of realism, relativism
is able to explain the establishment of a view as true without the realists’ fallacy of
referring to what is taken to be true nowadays as its causal factor (i.e. ‘whig history’).
Nevertheless, there is another catch: While realists explained truth (a
posteriori) through its congruence with given natural reality, and falsehood through
the constraint of cultural categories and influences (traditions, ideologies, social
interests, etc.), relativists and constructivists sought to explain the ascription of truth
and falsehood alike through the same cultural influences and social processes. This
methodological principle of explaining both attributions with the same explanatory
repertoire did away with the asymmetrical accounts of realism, and became known as
the “principle of symmetry” (Bloor 1976). Yet the relativist approach is flawed with
an asymmetry itself: By conceiving the ascription of being “true to nature” (or, for
18
that matter as being “based on false beliefs”) as the result of a social process, it
brackets out the natural world, and makes the social world carry the full weight of
explanation. In other words, they are relativist only where nature is concerned, but
realistic about society. A truly symmetrical account of knowledge formation,
however, should not be allowed to resort neither to the realist belief in a universal
nature, nor to this relativist belief in the omnipotence of social structures.
Such a symmetrical framework has been developed by a group of scholars
(most notably, Bruno Latour, Michel Callon, and John Law) in the context of the
actant-network-program. I will use the term “constructivism” to set it off from the
relativism/social realism outlined before.
The main aim of this “symmetrical” constructivism is to offer an explanation
of how knowledge is produced (and an ontological ordering is created) without
referring either to a transcendent nature or to a transcendent society as an explanans,
but to take them both as the outcome of a construction process. Its proponents ground
their argumentation in the insurmountable contradictions regarding the explanatory
role given to nature and culture in both realist and relativist accounts: Either, as in
realism, a universal nature is the cause of all our knowledge on the world - or, as in
relativism, omnipotent but incommensurable cultural and societal contexts are the
cause of it. In order to overcome both of these asymmetries, they propose to conceive
our world as made up of natures-cultures - heterogeneous collectives that combine
cultural/social and natural/material elements. Instead of an ontological ordering built
upon on the nature-culture dualism, symmetrical constructivism thus argues for an
ontology based on hybrid entities that cannot be reduced to either their social or
material aspects.
19
This ontological claim on the hybrid character of all objects greatly broadens
its critical repertoire: As all social and natural objects are members of the same (non-
separable) hybrid community, any apparent ontological ordering (such as the nature-
culture dualism) has to be explained by the divisions constructed by the joint
community itself5. As such, instead of being part of the explanation, both nature and
society now become part of explanandum, that is, as another outcome of the
knowledge formation and construction process that has to be accounted for.6 Due to
this philosophical innovation, the constructivist framework offers an entirely new
analytical approach that can be used to explain any kind of construction process - not
only knowledge formation, but also agents, machines, social institutions and even
ontological orderings can be analyzed as a product or an effect of a network of
heterogeneous (both human and nonhuman) elements. So, how would such an account
for the production of knowledge and the construction of distinct ontological zones
(that comes to be seen as true) look like?
Proponents of symmetrical constructivism argue that both knowledge
formations and ontological orderings are the result of the same epistemological
practice: A process of material mediation (or “translation”) which generates ordering
effects and patterned networks out of the heterogeneous set of hybrid objects that
populate our world. This process of translation starts from irreducible, unconnected
quasi objects and hybrid phenomena (say, observations made inside a vacuum pump)
and tries to connect, juxtapose and organize them -within a continuous chain of
reference- into a stable network. Translation thus is a material matter as well as a
5 Obviously, this is not to say that they socially constructed, as that would be to use one element of the dichotomy that is to be explained - i.e. the difference between the social and the nonsocial/natural – as the starting point of the explanation.6 This view is particularly connected with the writings of Bruno Latour, where he insisted that “nature never is the cause of the outcome of a (knowledge) construction process, but the consequence of it” (1987:99) and thus encourages us to be agnostic about nature as we are of society and instead examine the co-production of both.
20
matter of organizing and ordering those materials - a process of "heterogeneous
engineering" in which bits and pieces from the social, the technical, the conceptual
and the textual are fitted together, and so converted (or "translated") into something
that passes as a single entity (for example, a mathematical table or formula that sums
up the observations). This view on translation implies transformation and the
possibility of equivalence, the possibility that one thing (a mathematical formula) may
stand for another (for instance, a collection of heterogeneous elements and
phenomena). It is based on a semiotic understanding of entity building through the
creation of associations between objects, and is the central practice of any knowledge
production.
However, “modern” scientists usually add a particular procedure to this
translation process: the transformation of such ontological still ‘networky’ /
heterogeneous entities (like a mathematical formula for calculating the exact volume
of gases when compressed) into a pure ontological entity that either is completely
human-made or entirely nonhuman (in this case, the universal /“natural” law for the
behavior of gas under pressure, also known as ‘Boyle’s law’). This representational
practice of “purifiying objects” glosses over the original practice of mediation
(moving from hybrid objects to heterogeneous networks) and removes it from sight.
In fact, it effectuated a complete background-foreground reversal: Instead of taking
them as the partial and purified results of the process of heterogeneous mediation that
is at the center of the knowledge formation effort, the moderns started to attach their
explanations of hybrid phenomena on the two pure ontological zones (free society or
objective nature) they created in this process.7 The “modern” explanation now
7 This appears to be an inherent problem of every practice of mediation and representation: Far from being neutral intermediaries, representations play an influential part in the construction of the reference points to which they claim to correspond, and in this case, the “purified” objects came to be seen as the ultimate referents of two new idealized (anduniversal) entities – nature and society.
21
considered the hybrids as the outcome of a mixture of those two pure forms, and tried
to split this mixture apart in order to extract from them what came from the social and
what came from nature.
Although this reversal had several pragmatic advantages -as it made possible
to construct, believe in and use the realist framework for knowledge formation8 in the
first place- the relativist critique laid open its flaws and made it problematic to uphold
it.
2.3. Towards an analytical model for knowledge formation
Realism, relativism symmetrical constructivism - what does this review of the
different metaphysical frameworks leave us with? For a start, it illustrates once more
that there is no epistemological or ontological position of ”nowhere” from which to
make “objective”, “universal”, or “true“ accounts. And with all positions
presupposing certain assumptions, the choice between them should be guided by a
pragmatic concern to pick the one that seems the most appropriate for the research
question at hand.
This brings us back to the original research interest of this paper - an analysis
of the ontological reconfigurations of the self that are induced by the neuroscientific
quest for the biological basis of our emotions. As mentioned before, two related
aspects are in need of a closer study here: on the one hand, we would have to examine
how the construction of a naturalized human self is effectuated by the scientific work
done in the social brain sciences. And on the other, as this reconfiguration is firmly
grounded in their newly generated findings and facts about the human brain, we
8 See Shapin & Schaeffer (1985) for the history of its genesis
22
would need to analyze how this new neuroscientific knowledge is produced in the
first place.
What would then be a fitting framework to analyze these processes of
knowledge formation and ontological reconfiguration in the neurosciences? As
outlined above, the neuroscientists themselves operate deeply within the realist
framework. Thus, analyzing them with the same framework would be rather
tautological, as it would add no new insights into their processes of knowledge
formation, but would just confirm their own perspective. Examining their work from a
relativist perspective would certainly help to uncover many of the socio-cultural bases
of their knowledge-making effort, (and would be a healthy antidote to their realist
view), but in the end, it would just replace one one-sided account -i.e., the naturalistic
one of realism- with another one -the social realistic view of relativism- without
questioning the underlying ontological dualism both take for granted. In order to
reflexively examine the process of knowledge production and ontological reordering
in the neurosciences, we therefore need a different approach - one that analyses the
reconfiguration of this dualism without advocating a priori the one over the other.
Such an approach couldn’t no longer resort to society and nature as transcendent and
universal categories to explain phenomena and to provide the epistemological
guarantee of “truthful” knowledge formation, but would have to analyze these
ontological entities as an effect generated by scientific practice.
Apparently, then, the symmetrical constructivist approach seems the most
appropriate for the case at hand. However, although it outlines a general framework
for the analysis of knowledge and entity making, nothing had been said about how to
employ it in practice, and what specific concepts and methods could be used for my
empirical study in particular. In the following, I therefore want to give a short
23
overview of the conceptual cornerstones that my analysis will be build upon, and how
they can be integrated into an explanatory model of knowledge formation.
As mentioned, constructivism conceives the production of knowledge as a
sequence of translations that transform heterogeneous bits and pieces from the social
and the non-social, the textual and the material into a provisionally stable entity. In
the center of this epistemic practice thus stands a strategic deployment and
arrangement of associations that converts the hybrid phenomena into scientific objects
that are handle-able, research-able, and immutable enough to be granted an existence
of its own. In particular, three different instantiations of this practice have come into
the focus of theoretical and empirical work, where they have proven to be valuable
concepts for the analysis of scientific knowledge production:
One way of generating such strategic arrangements of associations is through
discursive practices. Authors like Foucault, Derrida, Serres and other post-structuralist
thinkers have demonstrated the far-reaching consequences on knowledge (and
subject) formation that are effectuated by the establishment of new scientific
discourses. By ‘discourse’, Foucault (1975) means a group of statements which
provides the language for talking about -a way of representing the knowledge about- a
particular topic in a particular historical moment. Through the establishment of
interrelated networks of statements and rules that govern their production, a discourse
determines the way the topic can be meaningfully reasoned about: Just as it ‘rules in’
and defines certain ways of talking and constructing truthful knowledge about the
topic, it ‘rules out’, limits and restricts other ways of producing statements and
knowledge claims about it. From a Foucaultian perspective, then, every object of our
24
knowledge must be considered as being defined and produced only within a certain
discursive space.9
With regard to our research question, the methods of discourse analysis can
provide us with an analytical repertoire to examine how a new object of inquiry -the
emotional brain- was constructed in the social brain scientists’ discourse, and to
identify the central discursive elements that defined which questions and statements
about emotions can (or cannot) be formulated in its context.
The second instantiation of creating patterned connections and associations
between different components can be found not in the discourses of science, but in the
“experimental systems” within its laboratories. These networks of local knowledge
and activities, scientific instruments and experimental arrangements clustered around
laboratories are yet another (less discursive than material) frame for defining and
producing objects of knowledge - a reasoning machinery in its own right. Different
authors (most notably, Ludwik Fleck, Andrew Pickering and Jörg Rheinberger) -while
not contesting the role of theories/theoretical discourses as patterns that connect- have
pointed out the important role of experimentation for knowledge formation: Instead of
being mere empirical instances in the evaluation of theoretical concepts, experiments
are shown to be crucial events in the discovery of new scientific objects, as it is only
within their complex, tinkered, and heterogeneous settings -irrevocably local and
situated in time and space- that new objects (phenomena or material entities) make
their first appearances. In fact, the very construction of the theoretical concepts is
9 This is not to deny that they might have existed before or outside of it – the crucial point is that that they only become accessible and meaningful to us through discursive means and practices.
25
intertwined with the experimental practices that produce their empirical reference, and
make them function as tools for the production of knowledge in the first place10.
In this view, then, there is a primacy of the experimental situation - it co-
generates both the phenomena or material entities and the concepts they come to
embody. Based on such an understanding of “science as (experimental) practice”, my
own study will therefore examine what specific work has to be done to experimentally
trace emotions in the brain, focusing in particular on the processes through which
experimental phenomena that embody emotions are generated in the laboratory, and
the relationship between these “epistemic things”11 and the (socio-) technical
conditions of their coming into existence (especially, the technical possibilities and
constrains of the current neuroimaging methods).
The third mode of arranging heterogeneous associations is the construction of
a cascade of successive transformations of the epistemic things under investigation
that result in a particular category of objects, called immutable mobiles. Immutable
mobiles are visual displays or graphical articulations (such as a diagram, a graph, a
photograph or a map)12 of the phenomena under investigation. However, as authors
like Latour (1999) or Lynch and Woolgar (1990) have pointed out, they are far more
than just a mimetic likeness or reminder of the original event - quite to the contrary,
they are the outcome of an elaborate sequence of re-representations, with each new
representation being an abstraction of the previous one, which on the one hand
simplifies and concentrates its information, and on the other still refers to (and still
10 The idea that practices and theories become packaged together in the process of experimentation is best explained in Pickering’s (1995) work on the “mangle of practice”.11 Rheinberger coined this nice term to signify “things embodying concepts”, which is admittedly a highly fitting characterization of the newly produced objects in the lab.12 As these examples suggest, most immutable mobiles are two-dimensional (or at least: printable on paper), and of such a size that they can be stored/archived or moved around easily.
26
retains some qualities of) the object that was its point of origin. With each new step in
this chain of representations, one reduces the materiality, locality and particularity of
the original phenomena. But at the same time, one enhances its non-materiality,
mobility, comparability and versatility, because the more abstract the form of the
representation, the easier is it to compare and combine it with other forms that are the
result of a different chain. Moreover, through this process of abstraction and
purification, the phenomenon under observation often reveals entirely new features -
and new relations to other phenomena- that weren’t visible before.
The crucial role of immutable mobiles for knowledge production thus does not
reside in what they depict but by how they work: They fix and purify the transient,
hybrid phenomena, and render them into durable (in a material sense), homogeneous
representations which then can be moved around and inserted into other contexts -
most importantly, into scientific texts. Via immutable mobiles, scientists can literally
“put a finger” on their objects of inquiry and synoptically oversee them, and are now
able to accumulate, process and manipulate them in a simplified and highly effective
manner.13
For my study of the knowledge production in the neurosciences, the concept
of immutable mobiles thus offers me a valuable analytical tool to examine how new
and stable representations of emotions are created - especially those digital (2D/3D)
brain maps that are the central outcomes of affective neuroimaging research efforts.
Two aspects deserve particular attention: First, what has to be done to translate local
phenomena into a universal digital-visual code (for example during the transformation
13 The wider contribution of the immutable mobiles to the scientific effort, then, is that they enable to move everything that is inscribed in them back and forth, from the laboratory (or ”center of calculation”, as Latour (1987) calls it) in which they were created, into the outside world, so that they may have an impact here. However, this further extension of the translation chain into the outside world is a gigantic enterprise that centers around the creation of laboratory conditions in the outside world – in other words, the outside world has made to fit to the new scientific object before it can be safely translated there. Latour’s study on the “Pasteurization of France” (1988) is a case in point for such a process.
27
of an emotional state in the subject’s brain into its computerized visualization on a
brain map), and second, which instrumental set-ups or “inscription devices”14 are used
during the construction of such representations - are they just unassuming
instruments, or do their effectuate certain ontological reconfigurations of the object of
inquiry?
2.3. Drawing the model together
After the presentation of the conceptual cornerstones that my analysis will be
built upon, I hope that it now becomes clear how they can be integrated into an
general model of knowledge formation: The interrelated network of statements in the
text, the complex, tinkered, and heterogeneous experimental arrangements in the
laboratory, and the chain of reference resulting in a durable, materialized and uniform
representation - they are all outcomes of association-building, path-construction, or
order-making, even though they are dealing with different “materials”. If one now
considers all three of them as different instantiations of the same process of
heterogeneous engineering, one does not have to specify if it is texts or objects that
one is analyzing. Such a move gives a new continuity to practices that were deemed
different when one dealt with language and theories, with skills and experimental
work, or with matter and representations.
This idea of knowledge production as a process of heterogeneous engineering
that translates elements from the textual, the social, the technical, and the conceptual
into a provisionally stable entity is thus the conceptual “glue” that sticks those
different discursive, experimental and representational practices together. Obviously,
every one of them put emphasis on different aspects of this translation process, but 14 Latour refers to them as “inscription devices” because they translate the epistemic things into inscriptions that can be readily included in a scientific text.
28
instead of considering them diverging, I see them as complementary to each other.
What makes all three of them count in my account is exactly what makes them
different.
In this view, then, the genesis of a scientific fact can best be understood and
analyzed as the construction of a continuous chain of translations from theoretical
concepts (i.e. “discursive objects”) via experimental phenomena (i.e. “epistemic
things”) to scientific objects and images (i.e. durable and uniform representations /
“immutable mobiles”) that become accepted as valid evidence for the actual existence
of the item under investigation.15 In the following chapters, I want to follow this chain
of translation in the field of social brain sciences, both to understand how scientific
knowledge about the (neural) nature of our emotions is produced, and what effects it
has on the ontological dualism between mind and body.
In the first part of my analysis (Chapter 3), I will have a closer look at how the
new object of inquiry -the emotional brain- becomes constructed in the neuroscientific
discourse. My analytical focus will lie on the way new concepts of emotion are
defined, defended and integrated into the discursive framework of cognitive
neuroscience. Besides this, I also want to examine the wider epistemological and
ontological consequences that are effectuated by the discourse of the emotional brain.
The empirical data to tackle these questions is largely drawn from primary sources,
i.e. from a collection of neuroscientific texts on emotion and social cognition that
15 In this context, it is important to not forget that even after new findings have been discovered, they don’t spread everywhere by themselves - quite to the contrary: in order to make them into incontestable “facts” for science and society, scientists do not only have to convert theories into scientific objects, but also create further translation chains to the world outside the lab. As indicated (cf footnote12), this extension of new objects into the outside world is a gigantic enterprise that tries to make the outside world fit to the fact so that it can easily be inserted, and it obviously is a research topic in its own right. Granting that this translation into the outside world is instrumental for the final effects and consequences that knowledge claims might have, I will therefore delimit myself to an analysis of how they are generated in the lab, as those processes lay the very ground upon which any further translation activities are build upon.
29
outline the principal research interests, dominant theoretical concepts, and accepted
findings within this field. In concrete terms, I will engage in a discourse analysis with
a group of texts and publications written in the last decade which are now considered
(at least, within the social brain) to be part of their foundational corpus of literature.
In the second part (Chapter 4), I will examine the specific experimental
methods and activities through which the neuroscientists generate and empirically
tackle (“mobilize”) emotions in the laboratory. In particular, I will analyze how the
different experimental models and research designs, analytical methods and
disciplinary backgrounds are mutually adapted and combined with each other so that
they form an experimental “system” in which the phenomena under investigation (i.e.
the neural correlates of emotion) comes into existence. In order to get some firsthand
material on this issue, I spend two weeks with a group of social brain scientists at a
neuroscientific research institute at the University of Tuebingen (Institute of Medical
Psychology and Behavioral Neurobiology) that were conducting a series of affective
neuroimaging experiments. Apart form the ethnographic data gained through
participant observation, I also collected a series of semi-structured interviews during
my stay in the field. Both field notes and interview transcripts will serve as the
empirical bases for this part of the analysis.
In a final step (Chapter 5), I will then look at the translation of these
ephemeral experimental events under the MR-scanner into a solid and universally
valid representation (an “immutable mobile”) of the emotional brain - the photo-
realistic brain activation maps that are the outcome of every neuroimaging study. Two
aspects will deserve special attention: on the one hand, I want to examine which
facets of the phenomenon finally become visualized and “inscribed” in such a brain
map, and which don’t - that is to say, which attributes of a emotion become amplified,
30
purified and stabilized during the different stages of the translation chain, and which
attributes gradually fade out and thus are literally left out of the picture. On the other,
I will analyze the role of the computerized instruments and digital tools used during
the construction of such representations of emotions, and try to uncover the specific
epistemological and ontological effects that come along with their technically
mediated transformation. To investigate these issues, I compared different didactic
texts and monographs -mostly aiming at students or researchers in need of an
introduction into the basic principles and applications of neuroimaging methods- and
tried to find out what they considered as valid procedures for data analysis,
interpretation or visualisation with this field, and on which accounts. To complement
and cross-check my textual exegesis, I also lead some focus interviews with
neuroscientists who themselves are engaged in the development of computer-based
methods and procedures for the analysis and visualisation of neuroimaging data.
31
3. Talking of Emotions: The Creation of a Discursive Space for
Emotions
“Everyone knows what emotions are - until they are asked to define them.”
John Le Doux
In the first part of my analysis, I want to take a closer look at how a new
object of inquiry - the emotional brain - is constructed in the neuroscientific discourse.
I will begin with a short review of the way emotions were conceived historically, and
then examine how this traditional discourse of emotion came to be replaced by a new
one – the one of the “emotional brain”. My initial focus will lie on the way the social
brain scientists defined, defended and integrated their new concept of emotion into the
discursive framework of cognitive neuroscience. Based on this, I will then discuss the
wider epistemological and ontological consequences that are effectuated by the
discourse of the emotional brain.
Since the emancipationist call to “know thyself” has become the marching
orders of modern science, few things in its quest to unravel the mysteries of the
human condition could have been more exciting, more challenging and of higher
intellectual attraction than to get a better scientific understanding of our own
emotions. Emotions are at once the most personal and most occult aspects of our
minds - they are the mental states we know best and remember with the greatest
clarity, yet sometimes we do not know where they come from, why they change, or
how we could control them. Clearly, it is hard to imagine life without emotions, and
32
their huge importance for the quality of life becomes most apparent when this part of
the mental life breaks down; in fact, most mental disorders are considered to basically
be emotional disorders. (LeDoux1996:19)
So, emotions are undisputable at the core of who we are, but at the same time
seem to have their own agenda, one often carried out without, or even against our
willful involvement. It thus comes as no surprise that long before the cognitive
neuroscientists took up the subject, generations of philosophers, scientists and
psychologists tried to shed some light on the workings and nature of emotions in order
to better understand and possibly control them. All their thoughts, findings and
writings added up to a rich and multi-layered corpus of ideas and concepts that
constituted different discursive frameworks (in the Foucaultian sense), each of them
‘ruling in’ and determining specific ways of talking and constructing truthful
knowledge about the topic while ‘ruling out’ and limiting alternative knowledge
claims. In other words, any knowledge claim about what emotions are must be
considered as being produced only within a certain, historically contingent discourse
that defines how to think and speak about them. As such, every new statement about
the nature of emotions would either have to follow the (currently dominant) discursive
rules and limits of knowledge production, thus placing it well within the established
boundaries of what can truthfully be said - or one would have to alter the discursive
frame so that it becomes “sayable”, thus challenging and changing the very rules of
the discourse in such a way that the new knowledge claim can no longer be ruled out
as a heterodoxy.
As I will show in the following, it is the latter of these two strategies that the
members of the social brain community pursue in their efforts to turn emotions into an
object of neurocognitive research. To really apprehend the series of transformations
33
through which this new discursive space for reasoning about emotions is created, it is
instructive to first have a look at the dominant discourse of emotion prior to its
“neurocognitive ” turn.
3.1. Effects of the elusive mind: The traditional discourse of emotion16
Since the times of the ancient Greeks, scholars have found it compelling to
separate reason from passion, thinking from feeling, and cognition from emotion.
These contrasting aspects of the mind have often been viewed as waging an inner
battle for the control of the human psyche. Emotions were seen to be wild and
illogical impulses that have to be checked by intellect and reason in order to protect
ourselves from their possibly harmful consequences. This ancient divide between
emotion and reason had an enormous historical impact and would become a central
building (or stumbling) block for any discourse of emotion to follow.17
A similar conceptual cornerstone was provided through Descartes’
philosophical works and his famous “cogito ergo sum”. He redefined the mind to only
include what we are aware and conscious of, making mind and consciousness the
same thing. Consciousness and the ability to reason were thus viewed as a uniquely
human gift, whereas emotions - which could also be found among others mammals -
were relegated to the dismissible realms of unconscious animals and the urges of the
flesh, thus making them a marginal aspect of the human mind. It was only in the
course of the 19th century that emotions became acknowledged as a valid research
object sui generis. Seminal works of Darwin and Freud in particular granted them a
16 See LeDoux (1996) and Damasio (1999) for the details of the following discussion.17 The separation between the “emotional” and “rational” mind is still highly present in our private and public life – just consider our legal system that treats “crimes of passion” differently from premeditated transgression, or take the different kinds of tests to measure the intellectual functioning (the intelligence quotient / “IQ”) vis-à-vis the emotional functioning (such as a person’s emotional quotient “EQ”.)
34
place in the scientific (mostly biological and medical) discourse, and helped to re-
establish emotions as an important aspect of the mind, even if operating largely on a
subconscious (as with Freud) or evolutionary-arrived, “animalistic” (as with Darwin)
level.
However, most of these ideas vanished from sight or their influence went
elsewhere. Throughout most the 20th century, the emotional mind was left out of the
laboratory – emotions were simply seen as too subjective, too elusive and vague to be
the object of rigorous scientific research. This critical stance soon became another
important piece of the discursive frame of emotions, and lies at the bottom of the two
most eminent models of the human mind developed in the last century - behaviorism
and cognitive science. The behaviorists dismissed the whole idea of the either rational
or emotional mind; in their view, inner states of the mind (like perceptions, memories,
emotions) are simply not appropriate research topics, as they cannot be examined
scientifically. Instead, they turned to observable and objectively measurable behavior
(hence the name!), and restrained themselves from using any mental state as
explanans or explanandum. In mid-century, though, the new cognitive sciences
dethroned the behaviorists and brought the mind back into the center of scientific
attention. By conceptualizing the mind as an information-processing machine (not
unlike to a computer), cognitive scientists resurrected the Greek idea of mind as the
seat of reason and logic, but banned the emotional part of the mind from
rehabilitation. Obviously, it was difficult to conceive emotions as part of a logical
reasoning process, and as such, they were left out of the cognitive research program.
Emotions were not rational, and studying them was probably not quite rational either.
Moreover, also the “subjective”-argument precluded their deeper inquiry: emotions
were seen as subjective states of consciousness, and insofar as cognitive science was
35
the science of logical information processing, rather that conscious content, they were
seen as mental states that fall outside a cognitive explanation.
Taken together, all the motives and topics outlined above subjected emotions
to a discourse that stressed the subjective, irrational and non-conscious (and thus:
elusive) character of its object, making it rather difficult to produce objective, rational
and scientifically valid statements about them. In other words, the dominant discourse
provided little space for any scientific efforts to speak and reason about emotions.
To turn emotions into an valid object for their research, then, the social brain
scientists had to change the discursive frame and re-define emotions in their sense:
instead of taking emotions to be subjective, they had to show that they are objective;
instead of accepting them as elusive, they had to make them identifiable and
measurable; and instead of considering them to be irrational and non-cognitive, they
had to show how well they could be conceived as integral parts of the cognitive
processes in the mind.
3.2. Expressions of the emotional brain: The new discourse of emotion
This “scientification” of emotions is exactly what the social brain scientists
started to do since their first publications in the early 1990’s. The focal point in their
discursive efforts to move emotions into the realm of the scientifically researchable
was an ontological re-centering of their object of study: Instead of continuing to treat
emotions as phenomena produced by the mind, they re-located them into the human
body, and conceived them as phenomena generated by the brain - the “organ” of the
mind. As such, emotions were no longer taken to be the outcome of some vague
mental processes largely independent of the physical machinery which executes them,
36
but -quite to the contrary- the actual results of specific physiological operations
embedded in and carried out by the brain. This switch from the elusive, subjective,
emotions “in the mind” to the tangible, objective emotions “in the brain” provided the
social brain scientists with an Archimedean point around which their re-ordering of
the old discourse of emotions could revolve, as it enabled them to approach emotions
in a totally new way. Now they could be placed under the authority of an
acknowledged and accepted scientific discipline - neural science, the science of the
brain: “The proper study of the mind begins with the study of the brain…Such an
approach depends on the view that all behavior – not only simple motor behaviors
such as walking or eating, but also complex actions that we believe are
quintessentially human, such as speaking, feeling, creating works of art – are a result
of brain function. The task of neural science is to explain them in terms of the
activities of the brain”, reads a passage from the introduction of an standard textbook
(Kandel 2000: 4f) of neuroscience, and gives a telling example of how emotions were
turned into a scientifically researchable and explainable phenomena.
In historical hindsight, then, it was the transformation of the elusive emotional
mind into the tangible emotional brain that made it possible to grant emotions finally
a (discursive as well as physical) space of their own within the scientific discourse.
But this directly opens up another question – how did the social brain scientist
manage to successfully ground emotions in the brain? Why is the brain the proper
place for the study of emotions?
From their own point of view, the answer seems self-evident - emotions are
conceived and examined as part of the brain because this is what they are: “I believe
that mind and self-consciousness really are of biological nature…I therefore argue
37
that the biological processes that we previously considered as mere correlates of
mental processes are in fact the mental processes themselves. […] It seems to be
certain that until 2050, we will have accumulated enough knowledge about those
biological phenomena that the old dualisms between body and soul, or brain and
mind, will vanish completely.” (Damasio 2002: 8ff) So, for the scientists the idea that
emotions are nothing but states of brain will win through because it is true to nature,
and –given time – will be supported by enough scientific evidence to rule out any
remaining doubts.
3.3. Putting emotions into the brain - the construction of a discursive space for
emotion in the neurosciences
Appealing as this answer may seem, our analysis cannot stop here. In the
previous chapter, we found that the “ being true to nature”-argument to legitimize
your views is a difficult one - at least if you look at it from a constructivist point of
view, which reminds us not to resort to nature (or society) as an external reference for
the statements. To explain how this new discourse came to be dominant and accepted
as true, we will therefore have to take a different route: According to my theoretical
model, the successful establishment of the “emotional brain” as a dominant concept
for speaking and reasoning about emotion can be explained through strategic
associations between different - and in this case: mostly discursive - elements (i.e.,
theories, ideas, ideologies) that come to form a coherent network of interrelated
statements. In order for this network of statements - or: discursive frame - to displace
the existent one, it must not only provide an alternative way of reasoning about
emotions, but it also has to be bigger, stronger, and more encompassing than the older
38
one.18 In the following, then, I want to reconstruct some of the chains of associations
and arrangements of elements that created this new discursive network for the
“emotional brain”. Particular attention will be paid to the operations through which
the social brain scientists secured and strengthened their discursive frame and
legitimated a neuroscientific view of emotions.
A comparative analysis of some of the foundational texts in the social brain
sciences reveals a common set of statements around which they all revolve. In
particular, five interrelated principles seem to form the core of their discursive net:
1. ‘Emotions are (embodied) in the individual’
This statement primarily inserts and reaffirms a discursive rule in the context
of emotion which has been prominent in modern science from its very beginning – its
strict dedication to a methodological individualism19. It defines the individual as the
only carrier of our personality, and as such, demands us to start searching here for any
explanation of what makes us human, and more specifically, what makes us
emotional. This idea is so integral to scientist thinking that it no longer needs to be
spoken out explicitly, but it can be found everywhere: When neuroscientists declare
that “the proper study of the mind begins with the study of the brain” (Kandel 2000:4)
they of course imply that it is a single brain which is to study, and if they propagate
that “the task of neural science is to explain behavior in terms of the activity of the
brain” (Kandel 2000:5), it is obviously the behavior, or emotion of the individual in
which the single brain is contained that has to be explained.18 As Latour (1988:206ff) pointed out, it is not so much the power of logical coherence that gives such a network its strength and displacing power, but mostly a matter of how far its chains of association reach out, and how much elements it thus could muster /enroll to sustain and defend them.19 However, as Foucault (1975) has illustrated, the concept of knowing subjects as the carriers of agency (and the central object of investigation in the drive to “know thyself”) is not as universal and self-evident as it may seem, but in fact was a historically contingent development. As such, it is principally subject to change or undoing.
39
Grounded in this dedication to individualism, the social brain scientists can
easily ‘ rule out’ every other way to account for emotions, especially those which
conceive them as products of divine or otherwise spiritual entities (for example, in
creationist science) or as emergent phenomena - “social constructions that happen
between rather than within individuals” (Le Doux 1996:23) - like the “collective
unconscious” in the psychoanalytical tradition.
2.’Emotions are part of our organism’
The second principle of the brain scientists is that emotions cannot be
separated from our physical body, but must be conceived as intimately connected to
it. Two discursive elements in particular are mustered to support and legitimize their
view:
First, emotions are considered to be part of the regulatory system that controls
the organic functions of the living body: “Emotions in the broad sense are part of the
bioregulatory devices which with we come equipped to maintain life and survive…
Emotions are collections of chemical and neural responses; all emotions have a role to
play in maintaining the life of the organism” (Damasio 1999:14/51). In a similar
manner, Le Doux (one of the founding fathers of the social brain sciences) establishes
a connection between emotion and bodily processes and enrolls another scientific
authority to support this linkage: “The response of the body is an integral part of the
overall emotion process. As William James, the father of American psychology, once
noted, it is difficult to imagine emotions in the absence of their bodily expressions.”
(Le Doux1996:40)
This argumentation is further solidified by placing it well within an
evolutionary perspective, which serves as the second discursive resource for
40
transforming emotions into an obvious part of our physical body: “All animals,
including people, have to satisfy certain conditions to survive in the world and fulfill
their biological imperative to pass their genes onto their offspring…Each of the
diverse groups of animals has neural systems that accomplish these behavioral goals.
And within the animal group that have a backbone and a brain, it seems that the
neural organization of particular emotional behavioral systems – like the system
underlying fearful or sexual behavior – is pretty similar across species. Our
understanding of what it means to be human involves an appreciation of the ways in
which we are like other animals as well as the ways in which we are different.”
(Damasio 1999:17). According to Damasio, then, emotions are best conceived as the
product of different stages in the evolution of the neural system, both in our human
and animal predecessors: “Emotions are biologically determined processes, laid down
by a long evolutionary history” (Damasio 1999: 51) To further immunize his
arguments from any criticism, he reminds us that “neuroscience cannot proceed as if
Darwin never existed” (Damasio 1999:39), enrolling yet another founding father -
this time, of evolutionary biology - as a supporter for the social brain scientists’ cause.
In sum, by associating them with our evolutionary history and homeostatic
regulation system, emotions became firmly connected to our organism. Most
importantly, this discursive operation opened up (“ruled in”) the possibility to relate
mental emotional states to their biophysical expressions, which could be more easily
subjected to systematic observation.
3. ‘Emotions are valid objects of research, as long as feelings are kept out of it’
The third theme follows from the second: If emotions have biophysical
counterparts, those can be scientifically examined. As such, emotions are no longer
41
some vague, elusive, subjective phenomena, but become objectively researchable
events: “If, indeed, emotional responses are effects caused by the activity of a
common underlying (neural) system, we can then use the objectively measurable
emotional responses to investigate the underlying mechanisms.” (Damasio 1999:18)
First, however, the scientists should be clear about their real object of inquiry:
“The conscious feeling that we know our emotions are red herrings in the scientific
study of emotions…Conscious emotional experiences are but one part, and not
necessarily the central function, of the system that generates them. If we are going to
understand where our emotional experiences come from, we have to reorient our
pursuit of them. From the point of view of trying to understand what a feeling is, why
it occurs, or where it comes form, the feeling itself may not have much to do with it at
all.” (Damasio 1999:20) For this reason, Damasio advocates a principled distinction
between the term “emotion” and “feeling”, as these two terms indicate two sets of
phenomena: “The term ‘emotion’ should be used to designate all the physiological
responses that are triggered by a certain neural system. The term ‘feeling’, as a
shorthand for “feeling of emotion”, should be reserved for the private, mental
experience of an emotion. In practical terms that means that you cannot observe [and
thus, further analyze! P.B.] a feeling, only the emotion (or some aspects of it) that
gave rise to it.“ (Damasio 2000:15)
Obviously, then, this definition of ‘emotions’ and ‘feelings’ re-orders the
entire discursive frame in which the social brain scientists conceive their object of
inquiry: It is not the emotion that is vague, subjective, and elusive (as it always has
been in the previous discourses of emotion), but only our conscious experience of it -
and for this reason, the social brain sciences could and should deal only with
emotions, not personal experiences and‘ feelings’.
42
4. ‘Emotions are cerebral functions and can be studied as such’
This statement places emotions (that is, emotion minus‘ feelings’) even further
in the realm of the scientific research-able: If emotions are the expression of
evolutionary derived, biological processes instantiated in the neural system of the
brain (as proposed in statement 2), it follows that the emotional system originated and
further differentiated in the context of the general structural and functional
development of the brain. As such, it has to be conceived and investigated as a
specialized cerebral function located in some parts of the brain20: “The brain has
distinct functional regions…affective traits and aspects of personality are also
anatomically localized in the brain. Although emotional aspects have not been
precisely mapped (yet), distinct emotions can be elicited by stimulating specific parts
of the brain in humans or experimental animals. The localization of affect has been
dramatically demonstrated in patients with certain language disorders and those with a
particular types of epilepsy.” (Kandel 2000:14)
However, this view of emotions as – localizable - functions of the brain is also
important in another regard: it opens up the possibility to differentiate normal
emotions from abnormal ones by comparing their underlying cerebral mechanisms,
thus offering a new understanding of psychological disorders: “All the behavioral
disorders that characterize psychiatric illness – disorders of affect and cognition - are
disturbances of normal brain functions” (Kandel 2000:5) Moreover, by associating
pathologic emotional states with disturbed brain functions, the social brain scientists
also imply a new approach to their clinical treatment – one that directly aims at
20 However: The exact principle of structural and functional organization of the brain is still under debate – there is a (dominant) segregationalist discourse (“neo-prenology”) and a connectivist discourse that questions the regional location of brain functions. I will come back to this point.
43
abnormal brain functions instead of their behavioral expressions. Lane et. al outlines
the therapeutic potential of such a perspective of emotions-as-brain-functions for
patients with autism, sociopathy and similarly impaired empathic awareness:
“Learning the details of how the capacity for empathy fails to develop, and creating
effective intervention methods to improve normal function in this area would have an
important impact on the sense of well-being on an individual level or the eradication
of prejudice and discrimination on a broader social scale” (Lane et al. 2000: 409).
Ingeniously, Lane et al.’s argument does not only link their discourse of emotion with
the diagnostic and therapeutic aims of the clinical profession -that is, learning more
about, and possibly treating a serious disorder -, but even enrols future patients and
the society at large for their new view, which will raise their well-being and reduce
their societal exclusion!
5. ’Emotions are integral to reasoning’
According to Damasio (2000:13) the opposition between cognition and
emotion is an ‘artificial’ one, and also other social brain scientists question whether
emotional behavior can be separated from behavior considered more ‘cognitive’: “The
fact that some components of an emotion can be triggered before full awareness of its
cause does not conflict with a cognitive view. Although they may have many
problematic effects, none of these phenomena require an extra-cognitive
explanation…When is cognition implicated in emotion – always, sometimes, or
never? Our answer, of course, is always!” (Clore/Ortony 2000:55ff)
For the social brain scientists, then, emotions are best studied as if they were
another cognitive phenomenon. Consequently, emotional processes can (and should)
be integrated into the neurocognitive framework, opening up the possibility to tap
44
their established explanatory and methodological repertoire in the study of emotions:
“Questions such as the fundamental organization of emotion fit well into the cognitive
neuroscience research program and do not limit it in any way. Methods and strategies
used to answer questions about emotion can be enriched by the approaches taken in
other domains of cognitive neuroscience.” (Lane et al. 2000:408)
Evidently, then, a main motivation of the discursive operation to make
emotions ‘cognitive’ was to integrate them in the cognitive neuroscientific discourse
so that the social brain scientists could associate their approaches with those of an
established discipline – one whose scientific authority was undisputed and thus could
help to legitimize their own efforts. Particularly, they hoped to exploit the accepted
neurocognitive linkage of the mind and the brain: in fact, the cognitive neuroscientists
themselves have just successfully established the idea of the ‘cognitive brain’ by
combining the classical cognitive scientific questions -the study of the reasoning
mind- with neural sciences -the science of the brain - to study the physical
embodiments of cognitive processes in the brain. (Kandel 2000:5). The social brain
scientist had good reasons to believe that their concept of an ‘emotional brain’ could
be turned into a valid scientific object in a similar fashion. But to do so, they
obviously first had to prove that emotion is not different from cognition, but could be
conceived as a cognitive phenomenon as well.
This implosion of the cognition-emotion-boundary was thus the coup de grâce
against the old discourse of emotions, and at the same time an important maneuver
with which the social brain scientists solidified their own discursive net around which
the ‘emotional brain’ revolved.
45
3.4. The epistemological and ontological politics of the emotional brain
Taken together, the five principles of the new discourse of emotion form a
coherent framework, within which a completely new understanding of emotions is
possible - one that turns them into a clearly delineated scientific object that is worth of
neuroscientific inquiry: “ Emotions are specific and consistent collections of
physiological responses (see statement 3) triggered by certain brain systems (see
statements 2 & 4) when the organism represents certain situations…In a typical
emotion, then, certain regions of the brain, which are part of a largely preset neural
system (see statement 4) related to emotions, sends commands to other regions of the
brain and to most everywhere of the body proper.…both the body proper and the
brain are largely and profoundly effected by the set of commands (see statement 2) ,
although the origin of those commands was circumscribed to a brain system (see1)
that was responding to a particular set of sensory patterns (see statement 3)…Thus,
emotion is best studied as if it were another cognitive phenomenon (see statement 5).”
(Damasio 2000:16ff)
If we follow Foucault in his view that a discourse determines the ways a topic
can be meaningfully reasoned about, we have to keep in mind that such a discursive
framework not only empowers a speaker to formulate certain questions and
statements about its object of inquiry, but also rules out certain others which are not
accepted (‘sayable’) in its context. With regard to the new discourse of emotion, the
question thus arises which research trajectories and knowledge claims are already
implied in the ‘emotional brain’, and which alternative ways of understanding
emotions are deliberately left out.
46
Generally speaking, two different discursive effects can be observed. On the
one hand, the concept of the “emotional brain” effectuates a profound redefinition of
the nature of emotions by assigning or negating them certain qualities; on the other,
the social brain scientist’s discourse of emotion also contains a certain
epistemological-disciplinary politic that delimits which knowledge about emotions
can be accepted as (scientifically) valid, and the appropriate ways to arrive at it. I
want to review each of these processes in turn.
As indicated, the first effect is a far-reaching ontological reconfiguration of
what emotion - and in fact, the human mind in general - really is: by referring only to
the neural aspects of our mind in the explanation of emotional phenomena, a
naturalized human self (i.e., a self in which everything is explained in naturalistic
terms) is generated. Only those ontological qualities of the self that can be accessed
neuroscientifically and explained without an extra-cognitive explanation are accepted
as existent, whereas those which imply a non-biological origin are ruled out. In the
following, I would like to illustrate these ontological reconfigurations with some
examples extracted from texts and my own interviews with researchers in the fields of
social brain science.
As mentioned by Damasio, questions about subjective phenomena like
“feelings” or “conscious emotional experiences” are somewhat difficult to address,
but also not relevant for a neurocognitive understanding of emotions. It is therefore
suggested that the scientists best bracket these issues out (at least for the time being)
and try to explain human behavior as much as possible without them. However, this
approach is not as unproblematic as it might seem, as one interviewee explained:
“There are a lot of scientists who believe that we don’t have to deal at all with such
47
subjective experiences when we study emotions. Yet, to focus only on neural
correlates of emotions without giving attention to one’s subjective sensations is highly
reductive, as it ignores the crucial role of personal experiences and feelings in the
execution of cognitive and emotional responses.” (Junior researcher, behavioral
scientist by training)21
Another researcher reflects the ontological consequences of the dismissal of
conscious awareness and feelings in the explanation of our emotions: “There is a
dilemma in our research: On the one hand, we must focus on the biophysical basis of
the mind and our emotions - it is only because of this reductionism that we are able to
talk about and deal with subjective experiences in an intelligible manner. On the other
hand, with every experiment that shows that the subjective is only a biological process
in the brain, we create the impression that the human being can be understood as a
mere robot or zombie - that is, a being which is able to think, act, or display emotions,
but finally lacks the ability to feel something.” (Senior researcher / Head of unit, M.D.
by training). What is left out in the social brain scientist’s view on the emotional brain
is thus much more than “just the idea that our behavior might still be influenced by
conscious “feelings” and other subjective phenomena22 - in the end, it suggests that
we should abolish the idea that there is any part of the human self which does not
function according to some fixed biological principles. Or, as a young researcher in
the Institute of Medical Psychology put it: “Neurocognitive science conceives the
human brain - the organ of the mind - as a highly complex reasoning machine that is
completely determined by the rules and laws of nature…the ultimate goal of all their
21 As indicated in the previous chapter, most of interviewees were part of different neuroscientific research groups in Germany. As such, the interviews were conducted in German, and so this and all following quotes are based on my subsequent translation into English. For the sake of clarity, I tried to stick to the original expressions as closely as possible.22 As, for example, in the psychoanalytic view on mental life, which conceptualizes and explores the relation between emotion and cognition just the other way around - here, unconscious and largely non-cognitive factors (sexual desires etc.) determine our behavior.
48
efforts would be: How can I describe the entire brain with a single formula, from
which then everything else can be deduced?” (Junior researcher, physicist by training)
In a discourse that tries to naturalize every aspect of the human mind (or
brackets it out if this is not possible), the traditional philosophical ideas of a “free
will”, “body-mind-dualisms” or the “eternal soul” of human beings appear to be
rather pointless. But it is not only our traditional self-concept which is put into
question, but also some of the most central societal practices and institutions that are
built upon them – as the neuroscientists themselves do admit: “Everything that we
usually attribute to the mind in dualistic mind-body-models actually has a biological
cause. We consider ourselves as being free in out actions, but from a neurobiological
standpoint, this volition simply does not exist. Likewise, the construct of an
“immortal soul” is not bearable from a scientific point of view. These changes in our
traditional self-understanding of ourselves are painful, even for the researchers
themselves…And just think of its implications for education and jurisdiction: How
can I teach someone to take responsibility for his choice of actions – or punish him if
he intentionally transgresses a societal rule – if there is, strictly speaking, no such
thing as a free will or volition? Moreover, what would it mean for our communal live
if the Christian belief in a life after death becomes totally obsolete and socially
discriminated?” (Singer 2002:32f)
The ontological reconfigurations of the social brain scientists – i.e., the
naturalization of every aspect of human emotion - are further solidified by a certain
epistemological politic that is embedded in their discourse of the emotional brain. It
delineates the epistemological standards and disciplinary practices that are ruled in to
49
authoritatively speak and to generate valid knowledge about emotions, and
marginalizes those who speak outside of this framework.
One example of such epistemological “boundary-work”23 - that is, the
separation between what constitutes a valid method of producing knowledge, and
what doesn’t - is the discredit of the subject as the person who knows best about his
emotions and feelings, instead giving the neuroscientist the sole authority to speak
about the human mind and its emotions: “The neuroscientist’s claim to objectively
measure emotions puts into question the certainty and ability of the individual to
know best what he is feeling. It suggests that the scientist might be able to explore,
unveil and possibly control his innermost secrets - the way he feels and thinks – better
than he can do it himself. In other words, both the subjective and the subject is
demystified, objectified and devaluated.” (Senior Researcher / Head of department,
M.D. by training)
In a similar manner, the neuroscientific discourse of the emotional brain also
rejects any other scientific or medical frameworks to approach the human mind, such
as psychoanalysis, hypnosis, etc. No matter what findings their alternative
epistemological-disciplinary practices might produce, they are considered to be
invalid or seriously flawed (and thus cannot provide a sound and reliable footing for
further research, clinical diagnoses or therapeutic work) because of their inclusion of
factors that are outside the neurocognitive explanatory repertoire. So, whether they
are alternative accounts or the patients’ subjective experiences of emotions, both
should be ruled out in favor of a neurocognitive view, as “none of these phenomena
require an extra-cognitive explanation”. (Clore/Ortony 2000:55).
23 I owe this expression to Thomas Gieryn and his analyses on the “cultural boundaries of science” (1999).
50
By establishing and enforcing knowledge-making practices of their own, the
social brain scientist thus institutionally secures their ontological reconfigurations of
the emotional brain. In fact, with each and every scientific work that conforms with
their epistemological principles, the existence of a naturalized human self is
(implicitly or explicitly) instantiated anew – not only because of the actual findings
(which well might be inconclusive), but because their generation according to the
discursive rules of the emotional brain.
I want to finish my discussion of the ontological and epistemological
reconfigurations with a short but illustrative example – the naturalization of fear in the
neurocognitive discourse.
Early on in the short history of the social brain sciences, fear was picked out as
one of the basic emotions that can be found both in animals and humans24. As such,
fear was conceived as an evolutionary arrived, automatic response reaction to a
threatening stimulus that is processed in a circumscribed (and evolutionary old)
region of the brain, the amygdala. Animal studies provided further evidence that fear
can be understood as a physiological phenomena that - at least to a great extent- is not
under conscious control, both in animals and humans: “When the stimulus occurs, the
organism (snail, rat, person) reacts the way its species normally respond to danger. No
conscious awareness is needed in the snail or the human” (Le Doux 2000:543). Based
on this understanding of fears as a automatic behavioral reaction, it was then argued
that also social fears (such as being afraid of talking in front of a lot of people) can
best be understood as just a special case of fear – namely, where a specific emotional
response is triggered by a certain fearful social stimulus or social situation. But if
24 Indeed, some of the pioneers and leading experts of the emotional brain (among them Le Doux) actually started their career in the field of animal studies, using classical fear conditioning to study emotions in the brain.
51
social stimuli are processed the same way as any other stimuli, then abnormally strong
fear reactions, such as social phobias, are best conceived to be the result of an
impairment in the brain’s ability to distinguish and normally process fearful and non-
fearful social stimuli.
If this new understanding becomes dominant, it might have far reaching
consequences both on a personal level -especially for those who suffer from such
social phobias- as well as in the wider medical or social context. In the end, it negates
the role of cultural or socio-psychological factors (such as stress and anxieties caused
by experiences of poverty, social deprivation, violation, etc.) in the explanation of
such psychiatric disorders like sociophobia or sociopathy, and instead suggests that
their real causes (and possible starting points for a therapy) lie in structural or
functional abnormalities of the brain.
52
4. The Experimentalization of Emotions
“It is quite obvious to me how dangerous it is to try to demonstrate a theoretical
proposition directly by experiments”
Friedrich Schiller, responding to J. W. Goethe and his essay
‘The Experiment As Mediator between Object and Subject.’
In this part of my analysis, I wish to take a closer look at the specific
experimental methods and activities through which neuroscientists generate and
empirically tackle (‘mobilize’) emotions in the neuroimaging laboratory. I will begin
with the description of an affective neuroimaging study about media-visual violence.
Then, I will critically analyze the canonical experimental model according to which
such studies are conducted, and compare this “textbook procedure” with the actual
experimental activities in the violence study. This comparison will point out the many
indeterminacies inherent to the “textbook model” of neuroimaging, and illustrate the
role of local knowledge and pragmatic adjustments in the actual realization of a
neuroimaging experiment.
The previous chapter described how the new discourse of emotion places
emotions well within the cognitive neuroscience framework and its overall research
program. However, the concepts and theories of the emotional brain do not further
specify how to produce empirical evidence for its existence - i.e., the material-
biological reification of an emotion in a real brain. So, while the new discourse
provides the social brain scientists with a general frame of reference in which the
53
emotional brain can come into being, more work has to be done to make it physically
appear and researchable as a real scientific object.
Much of this work takes place in the experimental setting of MRI centers and
similar neuroimaging laboratories, which is why I want to bring these into closer
focus now. In the following, I will therefore examine the specific experimental
practices through which social brain scientists generate and empirically tackle the
emotions in their neuroimaging labs. In particularly, I want to analyse how the
different experimental designs, research methods, technical instruments and
disciplinary traditions are mutually adapted and combined with each other to form an
experimental “system” in which the phenomenon under investigation (i.e. the neural
correlates of emotion) comes into existence.
To adequately analyze this materially heterogeneous framework for producing
objects of knowledge, I will adopt a twofold approach: on the one hand, I want to
(discursively) examine the principal components and basic procedural steps -the
‘textbook ingredients’, so to say- of the experimental system that is considered to be
canonical in neuroimaging practice. On the other hand, I want to (ethnographically)
contextualize these standard practices by discussing a real example of their
instantiations in an experiment the design, development and realization of which I
have followed during my fieldwork in a neuroimaging lab.
The main motive for this juxtaposition is the contention that in order to fully
understand experimental practices, one has not only to focus on the structural
characteristics of the network that makes up the experimental system, but also on the
ways it is modified and put into operation in a local setting: it is only through their
mutual adaptation that the assembled research methods, scientific instruments and
local knowledge practices evolve into a productive and smoothly working machinery
54
for defining and producing objects of knowledge – or, as Rheinberger (1999: 224) put
it, “a reasoning machinery in its own right”.
Before commencing with the analysis, however, I want to give a short description
of the neuroimaging study that serves as the empirical point of reference for my
reflections.
nn
55
The empirical case study
The central goal of the study in question was to explore the “effect of medial representations of violence on brain and behavior” (also the working title) in three different subject groups: criminal sociopaths, sociophobes, and a normal control group. Within every group, one half of the subjects (4-5 persons) would be confronted with some form of medial representation of violence (such as: playing an explicitly violent computer game, like the ego-shooter game “Quake”), whereas the other half would be presented with a similar medial stimulus without violent contents (in this case, a non-violent computer game). After that, every subject would take part in a behavioral experiment that was deliberately designed to evoke aggressive reactions. During the realization of the experiment, the subjects’ brain activity would be constantly measured (‘scanned’) via functional magnet resonance imaging, supplemented by some basic physiological measurements (heart rate, respiration, etc).
It was assumed that the medial display of violence would effectuate a more aggressive behavior in the members of the sociopath group and the control group, but a less aggressive reaction in the sociophobic group (always in comparison to those group members who were not exposed to the violent medial stimulus). Based on this expectation, it was then hypothesized that in connection with this rise (or reduction) in aggression, different activation patterns in the prefrontal cortex – the outer layer of the brain - would be detectable. Moreover, it was suggested that the three groups might also differ significantly in the extent to which certain cortical and subcortical areas of the brain -namely those supposably responsible for the regulation of fear and aggression- are active during the performance of the experimental tasks. As stated in the project proposal, the researchers hoped that the results of their experiment would indicate some neurobiological explanations for the different effects medial violence has on different groups.
(Source: Die Wirkung von medialer kriegerischer Gewalt auf Gehirn und Verhalten. Studienprotokoll, Institut für medizinische Psychology und Verhaltensneurologie, Universität Tübingen, 2003)
As will be illustrated in the following, this original plan would be hotly
debated, modified, and redrafted several times before the experiment finally was
conducted in the MRI lab. These shifts and changes, however, lie at the heart of what
experimental practice is all about - getting an experimental model system to work in
your local setting. It is to this standard model -and its local adaptation- that I want to
turn now.
4.1. The experimental system in neuroimaging research
The current experimental approach in neuroimaging has its origins in the
seminal PET- and fMRI-studies conducted by Petersen et al (1988) and Ogawa
(1992). While Petersen et al.’s earlier PET-experiments were important for the
development of the overall methodological framework, it was Ogawa’s ingenious
application of MRT scanning technology which made functional imaging so
appealing for neuroscientific research, as MRI scanners didn’t demand the injection of
radiation tracers in the subject’s brain before the scan (thus making multiple sessions
possible), were less expensive (making them much more widely available – almost
every hospital has one), and had a better spatio-temporal resolution than PET
scanners. Nowadays, neuroimaging studies (mostly conducted via MRI) fill the pages
of every neuroscience journal and proceeding or conferences, and their overall layout
still closely mirrors the conceptual assumptions, experimental designs and research
methods first introduced a decade ago (Gazzania et al 2002:138). This indicates a
rather continuous experimental system for neuroimaging research. In the following,
its constitutive ingredients shall be discussed in more detail.
56
4.2. The principle of experimental availability
An obvious element (or even: a prerequisite) of the experimental system in
neuroimaging is the possibility that the phenomenon under investigation can actually
be made experimentally available, that is, it can be occasioned at specific times, with
identifiable beginnings and ends, ideally in a repetitive manner and without any other
concurring phenomena.
The social brain scientists already took this requirement into account in the
way emotions were re-defined in their discourse. Based on the understanding of
emotions as “evolutionary-arrived, physiological responses to certain situations”, they
now can be further transformed into simple stimulus-response-pairings that lend
themselves easily to experimentation, because stimuli can be defined, designed and
controlled, and responses can be observed and measured in a systematic manner in the
laboratory setting. However, not every emotional phenomenon complies with these
conditions and thus not all of them are equally amenable to experimental replication.
In some cases, experimental availability is generally taken for granted (for example,
simple emotional conditioning), whereas in others it is contestable, either in principle
or in practice.
The latter, more practical problem of replication is caused mainly by the
physical and psychological confines of the MRI-instrument. During the experiment,
the subject’s head is rigidly fixated, and his entire upper body completely immersed
into a narrow tube around which a strong magnetic field is first built up and then
permanently measured with a rather unsettling loud sound. Needless to say, these
technical restrictions alone prevent a wide range of possible experimental designs (for
example, those demanding a face-to-face interaction between two persons), and make
57
others quite difficult to realize (for example, experiments that depend on the subjects’
movements, or his auditory capacities). In addition to that, the artificial and rather
claustrophobic nature of the MR-scanner makes it nearly impossible to elicit certain
kinds of emotional reaction. The principal researcher of the aforementioned
aggression experiment explained to me that “because of the aversive nature of such an
environment, we usually cannot evoke positive emotions like happiness or love, or at
least not in those subjects who are not already well acquainted with these kind of
experiments.” (Senior researcher, M.D. by training)
The second problem with the experimental reproduction of emotions is a more
fundamental one: in a lot of cases, it is not clear whether a reliable, valid way to
stimulate the reaction exists at all. Although there is consensus that certain culturally
invariant stimuli reliably and validly elicit a set of basic emotions (anger, fear,
disgust, happiness, sadness and surprise) in nearly every culture around the world, it is
questionable whether similarly universal stimuli (or combinations thereof) exist for
the more complex kinds of emotional reactions that are involved in social cognition.
But even if such universally valid social stimuli were available - would the subjects
always be able (or willing) to respond to them as intended?
To sum it up, the social brain scientists are often confronted with experimental
situations in which it is difficult to either choose the right stimuli, or to reliably
generate the intended emotional state in their subjects. Moreover, exactly those
phenomena that matter most to the social brain scientists -complex social and
emotional behavior- appear to be the least suitable for experimental replication and
analysis, and thus have to be approached under epistemological uncertainties (at least
with regard to their experimental validity).
58
This ethnographic example highlights some of the problematic aspects
inherent in the experimental system - the uncertain status of the emotions replicated in
the neuroimaging lab: can the experiment really occasion the emotions in question?
However, the example also indicates how this problem is solved in the local setting:
Whether certain situations and emotions are considered to be practically do-able in a
concrete experiment is often rather a matter of pure “scientific” reasoning than of
personal judgment and experience, disciplinary reasoning, or institutional politics!
As we will see in the next paragraph, a similar problem-and-solution pattern
emerges in the context of the second element of the experimental model - the
definition of the emotional function under investigation and the design of a
corresponding experimental task that operationalizes it.
4.3. The definition and operationaliziation of emotions
Ideally, the definition and operationalization of a specific emotional function
can be deduced from an overarching theoretical framework. It would specify what
59
Fieldnote No. 1
It shouldn’t be surprising that the problems of experimental feasibility and validity were also prominent in the “violence”-study. Although it seemed logical and inventive to use a medial-visual stimuli (like a violent computer game) to provoke aggressive behavior (indeed, this setting was seen as a good replication of a situation in real life), a debate among the researchers ensued how to practically implement this experimental condition: Should the subjects play before the actual experiment starts, or better during its execution? And what difference would this make?
Before these discussions could come to an end, however, the head of the institute (professor of medical psychology) intervened and removed the computer play condition altogether, as he considered it to be a experimental condition which was too unspecified and contained too many uncontrollable stimuli. Instead, he proposed a different group sampling in which one half would consist of subjects who characterized themselves as regularly playing violent computer games, and the other half made up of people who reported to not play such games at all.
events occasion the function in question, which mental and physiological processes
are involved in its execution, and what observable behavior is effectuated.
Unfortunately, the reality in the social brain science is different: For one thing, the
scientist cannot yet refer to such a broad and detailed “theory of emotion”; instead
there are many different “theories of the middle range” which are quite indeterminate
with regard to their possible operationalizations. Moreover, he is confronted with the
fact that at any given point usually more than one theoretical approach is considered
viable, and each conceptualizes a specific function differently (Papanicolaou
1998:101).
Faced with the double problem of theoretical pluralism and operational
indetermination, most brain scientists resort to a largely data-driven research strategy.
Typically, the definition of emotional functions is no longer primarily informed by
theory, but is based on their possible operationalizations, i.e. a emotion gets re-
defined by the experimental stimuli and conditions under which it can be produced in
the lab, and its function thus is to respond to those stimuli (Kosik 2003). Moreover,
the specific set-up of an experiment is strongly guided by exemplary studies or
paradigmatic experimental designs, and most research hypotheses have their origins
rather in previous experimental findings (and the new questions they generate) than in
theoretical models. Commenting on the last point, a young researcher told me that in
his research group, “the hypotheses are formulated in a quite pragmatic manner. In
my last experiment, the hypotheses were deduced from an experiment of a colleague
of mine. In fact, it was just a replication of her experiment with some different
modalities…In some studies, however, one really wonders if they were only
formulated ‘ex post’ to match the experimental results, as are all grounded in different
theoretical strands without any discernible conceptual coherence or common
60
theoretical frame. Personally, I consider such a purely data-oriented approach as not
really scientific.” (Junior researcher, neurocognitive scientist by training)
In sum, the experimental operationalisation of emotions -which in principle,
seems to be quite a difficult and problematic affair- usually is accomplished in a quite
straightforward and pragmatic way in neuroscientific practice. This can also be seen
in the design-process of the “violence”- study:
61
Fieldnote No. 2
As mentioned, the original intention of the study was to examine the influence of violent medial-visual stimuli on a subjects’ disposition to react aggressively. As indicated , the problems with the definition and practical realization of a ‘violent medial-visual stimuli’ proved too complex so that this part of the experiment was dropped and simply replaced with a different group sample. This left the researchers with the problem of defining and operationalizing the ‘aggressive reaction’ they wanted to analyze via fMRI.
Initially, it was planned to generate aggression with classical aversive (fear) conditioning. This way of operationalizing aggressive emotions was generally accepted in the scientific community (conditioning paradigms have been standard tools since Pavlov’s classical conditioning experiments), and some members of the research group had already used the same experimental design successfully in similar neuroimaging studies. After the new principal researcher (M.D, physician by training) joined the group, however, this design was put into question, as he wanted the experiment to be as straightforward as possible, both because he felt this would make journal submissions more successful -“peer reviewers like it simple and easily understandable…if the description of the research design takes more than a page, they tend to reject it”- and due to his own disciplinary background: “Because of our clinical training, physicians like me are more inclined to use ‘quick-and dirty’ models that deliver easily interpretable and applicable results”.
In consequence, an alternative way of occasioning aggression was searched for in the literature - and finally found in the form of a ‘competitive reaction time task’ that had been designed for a psychological experiment that also examined the effects of violent computer games on behavior. The new design seemed to be much more fitting to the emotional phenomena under investigation than the previous one, and it proved to be quite compatible with the requirements of the fMRI-setting. Subsequently, the entire experiment would be extensively re-modeled, until it was more or less a neuroimaging ‘replica’ of the original psychological experiment.
Once again, the case study illustrates the crucial role of local experimental
“cultures” and practices in the definition of what an emotional function (‘aggression’)
is or constitutes in a concrete experiment. The preference for simplicity and
practicability in definition and design is grounded both in specific disciplinary
approaches towards modelling, as well as in practical considerations and previous
experiences concerning the successful publication of the results.
Continuing our list of ingredients for the experimental system, we now come
to its third component: A method that connects the emotional event with the brain
activation measured by fMRI.
4.4. The right method to locate emotions in the brain
The final goal of the social brain scientists is to identify the mechanisms
responsible for emotions within the anatomical and functional structure of the brain.
To do so, they would need to know when a specified emotion (say, fear) begins and
ends, and would have to show that all experimentally performed tokens of this
emotional event are correlated with a certain set of unique brain activations, located
either in the same part of the brain, or in a specific connective pattern across it. Since
Petersen et al.’s seminal PET-studies (1988), the so-called subtractive method has
become the standard method to single out individual mental functions and to relate
them to recorded brain activations. In this approach, fMR-activation images obtained
in two states -usually referred to as the control state and the task state- are subtracted
from one another to create a difference image. This image identifies the areas of the
human brain that differ between the ‘task’ and ‘control’ state. Usually, but not
necessarily, the activation images of the ‘control state’ are recorded during a simple
resting condition (i.e. the subject is supposed to do nothing), while the ‘task state’
62
images are obtained during the actual execution of the experimental conditions. As
such, it is reasoned, the subtraction image would only depict those brain activations
unique to the mental processes during the experimental condition.25
Although the subtraction approach appears to be quite straightforward and
self-evident, it is not as unassuming as it seems. Indeed, it has been the subject of
constant debate since its introduction. Because it is one of the neural points of the
entire experimental system, I shall address these issues here in some more detail.
Its crucial contribution to the entire neuroimaging effort is grounded in its
congenial combining of two already established but separate research methods -one
stemming from the cognitivist tradition, the other from the neuroscientific one- to
create a conceptually simple yet effective method to explore and locate the mind in
the brain: It linked up the cognitivist idea of the decomposition of mental operations
with the neuroscientific principle of functional segregation in the brain and thus made
it possible that any mental task could become correlated to a certain brain region
through the subtraction of two activation images (for as long as those two images
differed only in the inclusion or exclusion of the decomposed mental task under
consideration).
The critics of the subtraction method consider it flawed exactly because of
these conceptual linkages, and question its validity and usability on three accounts:
first, the principle of cognitive decomposition is based on largely uncertain
assumptions about the nature of mental processing; second, the view that the brain is
functionally segregated can be countered by an equally valid concept of functional
25 Through additional data processing and analysis, the subtraction images can be further used to specify the location and shape of the regions, and make it possible to quantify the magnitude of the change in activation. (? See chapter 5 for a longer discussion of the image-making procedure).
63
integration, and third, an activation image only represents brain activity during the
task execution, and not necessarily a mental state, let alone a mental function.
The last point resonates a problem mentioned earlier, namely the problems of
appropriately defining and operationalizing an emotional function. By defining
emotions in an experimental manner, the social brain scientists were able to
operationalize them, but at the same time this transformation prevents them from
making uncontested claims of having found mental states or functions in the brain.
Seen from an epistemological standpoint, an activation image cannot be directly
correlated to a real function, exactly because of what it represents - namely, the brain
activation evoked by a certain experimental condition, nothing else. (Kosik 2003)
The main point of criticism, however, is the concept of cognitive
decomposition upon which the subtraction idea is based26: According to one of the
central tenets of cognitive science, every mental process is composed of separate and
successive (cognitive/emotional) operations; as such, even highly complex mental
processes can be decomposed into their constitutive parts. On the basis of cognitive
decompositions already known, one can therefore design specific experimental task
conditions that singularly and precisely involve the mental operation under
investigation.
As such, the subtraction depends critically on the construction of experimental
tasks for which researchers already have a plausible cognitive decomposition that
guides the interpretation of their imaging results. But these decompositions
themselves are contested, especially by advocates of dynamical systems models who
see behavior as an emergent product of highly distributed processes in the brain, not
as the result of successive stages of processing. Other researchers, while principally
accepting the concept of cognitive decomposition, consider the dominant model to be 26 See Bechtel (2001:68ff) for a more detailed discussion on this problem.
64
overly simplistic, as it doesn’t take into account any possible feedback-loops or
interactions between the different components: “The crucial problem with the idea of
cognitive decomposition is its assumption that mental processes are made up of
separate cognitive components which exist separately and thus can be singled out
piecemeal through subtraction, because there are no interactions or feedback-loops
involved. The subtraction approach thus is not just simply a mathematical operation,
but a theoretical model. And it is this model -or the assumptions it is based upon-
which seem highly unrealistic.” (Junior reseacher, physicist by training)
The scientific controversies surrounding the concept of cognitive
decomposition are further stimulated by their connections to a fundamental debate
about the proper way to conceive the functional organization of the brain27: on the one
hand, there is the idea of “functional segregation”, according to which every brain
area has its specific function. Obviously, the idea of segregation is congenial to the
idea of cognitive decomposition, as both explain the mental processes as being made
up of separate components. On the other hand, there is the view that functions emerge
from interactions between many brain areas, which -like the dynamic models of
cognition- stresses the distributed yet connected nature of brain activities. According
to this principle of “functional integration”, a circumscribed area can have no function
of its own, and in consequence, any efforts to correlate a mental function with a
circumscribed brain region by subtraction is quite futile. In their view, the subtraction
method (and the segregationalist results it produces) is logically flawed, as its findings
are strongly predetermined by its underlying assumptions of segregation and
decomposition: If one models mental functions in such a decomposite way, every
subtraction of two ‘task states’ can produce nothing but a regional activation image,
because all those elements that would evoke a more distributed activation (interaction, 27 See Kandel (2000) for an extensive discussion.
65
feedback, complexity, etc.) have already been ruled out by the way the experimental
tasks are designed. Also some of my interviewees pointed out this self-fulfilling
character: “By definition, the subtraction method only produces two kinds of results -
either, there is a local activation, or there is nothing at all. It is obvious that if you do
not look for interactions - because they cannot be modelled within the subtraction
method - you cannot find them.” (Junior researcher, behavioral scientist by training)
Accordingly, social brain scientists are still in doubt about whether
physiological changes in brain activation are really better analyzed through cognitive
decomposition and functional segregation or in terms of dynamic interactions and
distributed patterns. But even if one accepts that there is some sort of decomposition
(and by consequence, segregation) of mental processes in the brain, there remains the
question of exactly which decompositions (and local activations) the brain performs.
And as long as this question remains unanswered, any imaging study / subtraction
image is only as good as the assumption of decomposition of processing components
on which it relies (Bechtel 2001:70).
4.5. The right tools to do the job
Any discussion of the experimental model in the social brain sciences would
be incomplete without an examination of the element that is the main raison d’être of
the entire neuroimaging approach – the high-tech instrumentation that makes the
measurement and visualization of brain activity possible in the first place. Indeed, its
crucial role for knowledge formation in this new field is widely acknowledged (and
always stressed) by the scientists themselves, and also from my constructivist
viewpoint these technologies represent a central element in the whole neuroscientific
knowledge making effort. In fact, their specific epistemological and ontological
66
effects will also be the main topic of the following chapter. For this reason, I will
abstain from their detailed analysis for the moment and restrict my discussion here to
the practical aspects that govern their use in an experimental setting.
In general, the neuroimaging instrumentation consists of two separate
components: On the one hand, you need a device to register the signals of brain
activation during the experiment, and on the other, you need an instrument that further
processes and transforms the signals into a visual displays or graphical articulations of
the brain phenomena under investigation. So connected with each other, the
instrumentation produces a certain kind of re-representation of the original
phenomenon -an “inscription” or “immutable mobile” in Latourian terms- which then
can be moved around and inserted into other contexts - most importantly, into
scientific texts.
To illustrate the working of such an instrumental set-up (or “inscription
device”) in practice, I will retrace the steps by which it was put into use in the
‘violence’ study.
67
Fieldnote No. 3
Prior to the actual experiment, the ‘subject’ and the researcher first meet outside the MRT laboratory for a short briefing. The conversation touches on a short introduction to the experimental paradigm, the kind of task that the subject needs to perform, and some information about the potential risks of the examination due to the exposure to strong magnetic fields during the MR-scanning. The subject is then prepared for the scanning, that is, he has to remove all metal and magnetizable items such as watches, credit cards, or keys, and is put on a sled that is moved into the MR-scanner. After the subject has been pushed into the scanner, a short test stimulus is presented, and final adjustments to the magnetic field distribution and other scanner settings are made.
It is this graphical output of the computer program (see Table 1) which is
usually presented in an article and set into wider circulation (in neuroimaging journals
and elsewhere in the world outside the laboratory). As such, they represent a prime
example of what Latour (1987: 68) coined as an inscription -“a visual display that
constitutes the uppermost layer of an scientific text”.
68
Then, the experimental scanning sessions begin. The subject is exposed to a short visual stimulus upon which he is required to display a certain response. In dependence to that response, he is further asked to inflict a short painful stimulus, or he receives one himself. Each time this stimulus-response reaction happens, the scanner registers physical parameters of the subject’s brain, such as the difference between oxygenated and de-oxygenated blood in a certain brain area. The recorded physical data -the contrast between oxygenated and de-oxygenated hemoglobin in regional blood flows- provides the basis for calculating the neural activity during the actual experimental task.
When the scanning session is completed, many megabytes of raw data have been recorded as large time series carrying information about the appropriate time and place of the activity in the brain. They are now available for further processing, computation and manipulation on a computer. In fact, the actual calculations and analyses to extract and visualize the brain activations are quite demanding, and can only be performed with the help of sophisticated software programs. There are many different program packages available on the market, and they have by and large replaced the first simple programs developed by each lab in the early days of neuroimaging. However, due to the user-oriented design of these software packages, these processes are now almost entirely black-boxed, i.e. most of the work of the analysis (including the actual computation, all the methodological assumptions, model specifications, and other analytical details) is done automatically.
Proceeding with the neuroimaging analysis, the researcher’s main task now is to choose between various parameters and menu options, and to decide which model should be used to fit the data. Then the computer grinds a while to test the data against the different models and finally indicates those volumes of the brain where the synchronicity between a model of the brain activity assumed to be provoked by the task and the actual MR-measurements exceeds a chosen (statistical) threshold. The final results of the analysis consists of a table of brain coordinates with concurrent statistical significant of activations, which are conventionally displayed as a pattern of color-coded dots and areas superimposed on a standardized 2D/3D representation on a brain - the so-called “brain activation map” or just “brain image”.
Now, what do these insights into the constitutive elements and practical
operations of the experimental system in the social brain sciences leave us with?
First of all, we learned that to make an experimental system work in practice,
it needs more than the accumulation of all the ingredients that a “textbook approach”
tells you to gather: the standard experimental procedures and methods it specifies are
rife with indeterminacies, problematic assumptions, and inconsistencies which make
it impossible to simply transfer them into practice. Thus, to put it into operation, the
social brain scientists first have to bridge these gaps and through a pragmatic
adaptation and modification of the standard procedures. In this context, my
ethnographic examples have illustrated the crucial role of local experimental
‘cultures’ and disciplinary practices in the actual realization of an neuroimaging
experiment: it is only through their mutual adaptation that the assembled research
methods, scientific instruments and the local knowledge practices evolve into a
working experimental system in which the phenomenon under investigation can come
into being.
Second, we also learned that the process of re-defining ‘emotions’ is not
finished with their discursive transformations discussed in the last chapter. Quite to
the contrary, by entering the laboratory, emotions were further reconfigured by
experimental practices: In order to generate and measure emotions in an experimental
situation, they were redefined in an operational manner, that is, by the tasks that were
designed to make them appear during an experiment. Only through this
transformation of emotional states into experimental tasks, the experimental subject
and his/her brain could then be stimulated, and the resulting neural effects be
physically traced with neuroimaging instruments.
69
This brings me to my last point - the central role of the hightech
instrumentation to measure and visualize the emotions. Here, it seems as if the
computerized programs in particular constitute a vital component of the entire
experimental system: Scientist do not only depend on their algorithms to analyze and
make sense out of the data, but also rely on their versatile processing power for the
visualization and final inscription of their findings. At the same time, however, their
application is not unproblematic. For one, these computer tools are much more that
just ‘unassuming instruments’: most of today’s software packages work as true “black
boxes”, and the assumptions and procedures embedded in them can neither be
controlled nor changed by the ordinary user. Moreover, the availability of such
standardized tools has made the analysis not only easier, but also enforces the
establishment of certain methodological community standards and a certain
“neuroimaging culture” in general.28
Especially in view of the latter comments, there seems to be ample reason -and
enough open questions- to have a closer second look at the ensemble of neuroimaging
tools and practices that are involved in the generation of brain images. As already
indicated, the finer details of this complex and fragile transformation from the first
MR signal to the final inscription are therefore the main topic of the next chapter.
28In fact, neuroimaging research groups can be distinguished by the specific software package they prefer to use, and this choice might have quite some consequences both for their findings and their acceptance in the field. One of my interviewees explained that “there are some well-known and widely accepted software packages available, and you should better use one of them. Because if you use your own programs and algorithms, you always have to explain why you did neglect standard tools and methods. This quickly raises doubts about the validity of your findings, and people suspect that you only did use your own methods because you want to hide or make up something which wouldn’t be possible with a normal program.” (principle researcher, M.D. by training)
70
5. Inscribing emotions into the brain
“Images scatter into data, data gather into images”
Peter Galison
As seen in the last chapter, the empirical traces of the emotional brain that
appear in the neuroimaging laboratory are the product of a highly complex and locally
contingent process. Many indeterminacies and (contested) assumptions underlie the
experimental system in the social brain sciences, and its successful operation crucially
depends on its computer-based tools to analyze, visualize and make sense out of its
experimental data. The finer details of this complex and fragile transformation from
the first MR signal to the final inscription on a brain activation map are therefore the
main topic of this chapter.
I shall begin with a closer study of the transformation processes that converts
the ephemeral experimental phenomena under the MR-scanner into a solid and
universally valid representation (an “immutable mobile”) of the emotional brain - the
brain activation map. Two aspects deserve particular attention: first, what procedural
steps have to be taken to visualize and inscribe emotions on such a brain map, and
second, what influence does the computer-based neuroimaging technology exert on
the translation - which particular epistemological and ontological consequences arise
out of such a complex technical mediation? By following the translation chain, we
will see that -from the first signal detection through image construction and analysis
to the final inscription- one is permanently facing situations of uncertainty and
underdetermination. Depending on the assumptions held, and choices made in these
situations, different aspects of the phenomenon under observation will get translated,
71
so that one will end up with two different kinds of representation: Brain activation
maps as mimetical images, or statistical correlates of emotional processes.
These two contrasting views of what is inscribed on the map will then be
analyzed in some more detail. By doing so, it will become clear that they are
grounded in different epistemic positions vis-à-vis the veracity of the inscriptions and
the computer-based modeling and imaging processes that constructed them. Instead of
advocating one of these representations of emotions over the other, however, I want to
illustrate how the practice of translation might offer an explanation for their legitimate
parallel existence.
5.1. The process of inscription in neuroimaging practice29
As has been shown in the theoretical chapter, an immutable mobile is the result of a
cascade of successive transformations of the phenomenon under investigation. In
standard neuroimaging practice, these transformation comprehend (1) the conversion
of a brain activation into functional MR-signals, (2) their transformation into a visual
model (the MR-image) that can be subjected to (3) further processing and analysis in
the computer, and finally is (4) condensed into a single inscription - the brain
activation map30. These different stages will be discussed in the following. For their
better understanding, however, a short primer into the general principles of magnetic
resonance imaging should be given beforehand.
29 See Henning (2001) and Papanicalaou (1998) for the details on the neuroimaging methods and techniques that are discussed in this chapter.30 As has been mentioned in Chapter 2, the entire process of translation starts already much earlier, with the first tentative definition of an emotional function, and the design of the experimental task. Some of the discursive and experimental practices of transforming emotions have been examined in the previous chapters. The reason for focusing on these neuroimaging practices in particular is that from this point onwards, the translations takes place within a large black-box, the inner workings of which are crucial for the conversion of the experimental phenomenon into a immutable mobile.
72
The physical principle underlying magnetic resonance imaging (MRI) is
nuclear magnetic resonance (NMR). In a magnetic resonance (MR)-scanner, various
magnetic fields are used to generate the NMR signal from the hydrogen nuclei of
water molecules in the body. Each and every soft tissue and element in the body
(blood, fat, muscles, etc) emits a unique temporal and intensity NMR-signal pattern if
stimulated by a corresponding magnetic pulse. Other magnetic fields are used to
record these NMR signals from various positions in the three-dimensional space of
the body. Provided the recording frequency is set to the right parameters, the
individual spatiotemporal signals can be accurately located, discriminated and
measured, thus permitting the creation of a volumetric model of the resonance data –
the MR image. Usually, this three-dimensional set of signal intensities is visualized in
series of two-dimensional slices. Although this partition could be made in any
direction, it is typically made along sagittal or coronal sections of the brain.31
Thus, at first sight the MR-image looks quite similar to other medical images
of the body, like those obtained by X-ray, CT or PET. However, in some important
aspects MRI differs fundamentally from all previous imaging technologies. These all
relied on the principles of optical representation, which generates an image through
the recording of the linear radiation path that an imaging media (rays of light, X-rays,
or other radioactive rays) traveled during the imaging process. Moreover, the image is
always based on the bodies’ absorption or reflection of the imaging media. Either
way, the interaction between the imaging media and bodily matter is predetermined
by the physical characteristics of the imaging media, and therefore, every such image
could only convey a rather circumscribed type of information.
31 This special form of depiction is a leftover from anatomical practice (which used to cut the brain into pieces along these lines in their dissections) and classical radiological viewing traditions, and not related to any MRI-characteristic.
73
In contrast, MRI is not based on any of these optical principles: the recorded
data is not a radiation trace of any imaging media, but a wave signal pattern which
must be subjected to further calculation in order to infer a visual model of the data –
the resonance image. In other words, the constructed image is the indirect result of the
application of some modeling processes in the computer, not a point-to-point
correspondence based on a linear radiation path from the body to the imaging media.
This autarchy from optical principles offers some obvious practical
advantages. As the imaging process is based on a purely mathematical-logical
development process, it offers a much wider range of possible conversions of data
into images that material transformations (for example, the chemical-mechanical
development processes in X-ray photography) can offer. Depending on the algorithms
used, one can easily cut, copy and paste parts of the image, or further enhance and
warp it to offer new perspectives.
However, even more importantly than the increased potentials of data
visualization, the content of the MR-image too has become much more variable. In
fact, the full range of information that can be produced by NMR is still not fully
explored. The reason for this is that the hydrogen nuclei not simply resonate with
every magnetic pulse, thus saying “here I am!”. Instead, each measurement sequence
is like a game of question-and-answer, that is, depending on the pulse and recording
frequency used, you can extract very different information. Obviously, this variety
can also be quite challenging, as it presupposes that you know the right sequence with
which to “question” the object under the MR-scanner. Moreover -as the scanner
operates on the nuclear level- one needs a profound understanding of molecular
74
mechanisms in the body to translate these primarily physical information into
physiologically or clinically relevant data.
As we will see in the following, this understanding is still somewhat wanting
in the context of the BOLD-effect upon which the functional MR image is based.
5.2. Step 1 - Scanning emotions
According to the basic tenets of the social brain scientists, every emotional
state is supervenient to a neural activity in the brain. By design, however, the MR-
scanners are not able to detect directly either the flows of neurons or the synaptic
metabolism of nerve cells, both of which are seen as the constitutive elements of such
neural processes.
To acquire data about neural brain activity via MR technology, one therefore
resorts to the measurement of an indirect effect of neural activity, namely the changes
in blood-flow with which it is associated. It makes use of the fact that despite the
substantial increase in blood flow after neural activity, there is a much smaller
increase in oxygen utilization. This leads to a decrease in the concentration of
deoxygenated hemoglobin in the venous blood, which in turn effectuates a decrease in
the local distortion of the magnetic field that can be detected by specific MR-
measurement sequences. These changes are called the blood oxygen level-dependent
(BOLD) effect, and currently constitute the main mechanism for detecting the
hemodynamic changes connected to neural activity.
This translation of neural activities into a BOLD-effect, and further into a set
of magnetic resonance signals that can be detected and recorded by a corresponding
measurement sequence, already contains quite a profound transformation of the
75
original phenomenon. First of all, it is not just every brain activation of the
experimental subject that is of interest, but only the neural ones. In spite of this, it is
not the neural activation which will subsequently be observed, but a physiological
correlate (i.e., the BOLD-effect) to which it is related. What finally gets recorded (and
as such provides the basis for any further analyses), however, is not even the BOLD-
effect itself, but a set of magnetic signal values detected in a highly complex scanning
device.
In other words, what enters the MR-scanner -the experimental subject and his
emotional brain-, and what leaves it -a data matrix that contains a spatiotemporal
series of his MR-evoked BOLD signals- is quite different, and the chain of translation
that connects both ends is not logically conclusive but based on certain assumptions
and associations made between the different stages. In fact, the continuity, stability
and validity of the translation crucially depend on these premises: question and
withdraw your confidence in them, and the entire chain may go bankrupt; stick to
them, and you will gain completely new insights into the emotional brain. A case in
point is the proposition to use the BOLD-effect as an indicator for neural activity: As
mentioned, the hemodynamic change is widely considered to be related to the
metabolic and neuronal activities. However, the exact details of the (molecular)
mechanisms underlying this effect are still unknown, and besides the established but
yet unproven view that BOLD-effects appear on, and thus mark, the very spot where
neural activity takes place, there have been findings that suggest that they might mark
a quite different (but still neurally relevant) spot - the target region of the neural
activity, but not its original source. This uncertainty resulted in a considerable debate
within the neuroimaging community, up to the point where a well-known researcher
proposed a moratorium of fMRI-analyses until the underlying mechanisms of the
76
hemodynamic response are discovered and a consensus on the significance of the
BOLD-effect is reached.32 Although this proposal was rejected, and the confidence in
the association between blood oxygen levels and neural activity was upheld, this
instance shows that the validity of this step in the translation chain is not beyond
doubt and cannot be taken for granted.
Obviously, despite all these uncertainties, the scientists have still good reasons
to sustain the crucial link between neuronal and hemodynamic activities - not
necessarily because it is so compelling, but because it enables them to approach the
emotional brain in a completely new manner: given this link, MR-scanners can be
used to make cheaper, safer, longer and more numerous recordings of neural activities
in a better spatiotemporal resolution than any other existing neuroimaging technology.
Moreover, because of the universal numerical code in which the signal data is
recorded, it lends itself easily to further computer-based modeling, processing and
analysis.
5.3. Step 2 - Developing the image
It follows from the previous description that the recorded spatiotemporal
distribution of the MR-signals can be conceived as a three-dimensional matrix of
intensity values representing the relative activations of different regions (“voxels”33)
of the brain within a given interval. “Developing” the functional images on the basis
of such a distribution is a rather straightforward matter - in principle, the activation
image is just a mathematical-logical transformation of the recorded MR-data into a
32 Compare Logothetis (2001) and the ensuing discussions in Nature (2001) No 412 ff.33 Voxels are the three-dimensional volume elements in which the entire MR-field is compartimentalized; in a typical scan, they are between 1 and 2 mm3 .in size.
77
volumetric model. There are, however, several practical challenges in developing an
informative fMRI image.
One such challenge is the elimination of artifacts created during the recording:
involuntary movements of the subject’s head, local magnetic field inhomogeneities,
white noise fluctuations and similar imperfections in the measurement process lead to
artifacts and distortions in the recorded images, which in turn could seriously affect
the subsequent analyses, either by making up false activations or by concealing the
real ones. Because of the ubiquity of such errors, fMRI-data is always subjected to a
process of image correction and cleaning before being used any further. These
corrections -usually performed automatically by computerized routines and
algorithms- comprehend spatial realignment of the recorded data (to correct for head
movements), changes in the image resolution, and other complex mathematical
transformations (e.q., Fourier functions) of the data to sharpen the contrast between
activated and non-activated regions (to correct for white noise).
Once again, these conversions of the original data are not logically conclusive,
but based on certain -mostly mathematical- assumptions. There is no guarantee that
the images they produce are really free of any (remaining or newly made) errors.
Moreover, as most of the techniques employed to transform the images are quite
black-boxed, highly complex, and unconstrained from any optic principles, it is
sometimes doubted whether the emerging image is nothing but a contingent result of
some methods and techniques which just produce their own set of artefacts. Either
way, it seems as if blind faith in the outcome of these translations would be quite
unwarranted.
78
However, if you have enough confidence in their fidelity (and the validity of
the mathematical assumptions in which they are grounded), they provide you with
some fascinating possibilities and insights: with the help of such correction routines,
the experimental impurities and particularities of the raw MRI-data will get sorted
out, while the specific features of interest can be further enhanced and purified.
Moreover, through this process of abstraction and purification, the phenomenon under
observation often reveals entirely new features - and new relations to other
phenomena- that weren’t visible before.
In sum, then, this part of the translation sequence can be approached from two
different angles: in one view, the more computation, conversion and correction, the
better it is for the final result, and the more powerful it becomes. In the other, it is just
the other way around - the less complex, “artificial”, and (mathematically) abstract the
imaging and image correction process, the more credible and faithful it remains to the
original phenomenon. These contrasting views (on how to translate) will also become
visible in the following stages of the image construction.
5.4. Step 3 - Making activations significant
The second issue in the development of the functional MR-image revolves
around the best way to arrive at what the imaging process is all about: the correct
identification and valid assignment of a local activation in the brain. The first part of
the problem is to decide how to separate and extract episodes of activations (the
experimentally created phenomena) from local background activation; the second one
concerns the methods to statistically validate the identified activations.
It is important to note that due to the overall baseline activation of the brain, it
is usually impossible to visually detect the small activation changes that a single
79
emotional event effectuates on a fMR-image. Its specific activation pattern is buried
in the seemingly random temporal variation of the brain. Just by comparing two
images, one thus cannot identify which one was obtained during the emotional event,
and one has to make repeated measurements to detect the relative activation increase
of a brain region (i.e., relative to the overall baseline activation level).
But given these repeated measurements, one faces another, much more
fundamental problem: from every distribution of signals, a variety of activation
images can be developed, dependent on how one decides to define activation. One the
one extreme, you may chose to consider signals that are much beyond the normal
range of intensities as indicative of activation (considering all the rest background
“noise”); on the other extreme, you might decide to consider signals slightly beyond
the range of background variation as indicative of activation. Depending on the
choice, a different image of the same activation will emerge. Provided that there is no
independent information to guide us in selecting the proper threshold and that we do
not know what activation profile to expect, it is unclear which of any two images
represents more accurately the actual activation profile.
The typical way to solve this problem -or actually, to bypass it- is to resort to
statistical tests that help decide whether the identified activation profile is just an
accidental variation of the background activation or not. In fact, the different software
packages offer a wide collection of statistical models to separate significant from non-
significant activations, and choosing the right one is a crucial task: Whereas great
activations are usually detected by even the simplest models, the detection of a
smaller activation often depends on the models used, and a change in the model
specification might easily result in the failure to detect the activation under
investigation.
80
In the center of the entire neuroimanging effort -the identification of brain
activations- we therefore find another strategic translation: The biological problem of
defining a real activation is translated into the statistical one of finding the right model
to detect it, so that the question of a relevant activation threshold now becomes a
matter of statistical rather that biological reasoning. However, this delegation of the
definition and identification of relevant activations to statistical computation has its
own share of problems. For one, statistical model assumptions gain crucial
importance for the analysis, but at the same time are so complex that they are usually
back-boxed (or condensed into a menu option) in the software. The computer
programs are therefore much more that just unassuming instruments, and have certain
assumptions of the human brain activation and function already built in.34 Moreover,
the epistemological and ontological status of a significant activation is obscure (“the
tip of a conceptual iceberg”, as one researcher put it), even to some of the
neuroimagers themselves, who obviously find it difficult to base their understanding
of relevant activations entirely on statistics: “What are the relevant brain areas
activated in a given task? The complete answer to this question must not be limited to
the brain areas that happen to exceed some arbitrary, practical threshold for detection
by our current MRI-machines.” (Savoy 2001:30)
As with every other translation before, one has to put confidence in the
plausibility and authenticity of the transformation, which -once again- cannot be
legitimized by logical necessity but only by its pragmatic utility, that is, by the new
34 In a personal communication with the creators of one neuroimaging-software package, it was indicated to me that one can even identify different “schools” or “epistemic communities” which are defined by both their use of certain imaging-tools/programs and the acceptance of specific conceptual assumptions that go with them. See also Roepstorff (2002) for a more detailed discussion about the general role of these software programs in the neuroimaging community.
81
possibilities it offers to deal with the phenomena under investigation. And it does
make new, exiting things possible: it marshals a huge and powerful number of
activation events on demand. Instead of having a single activation image, you now
have long series of them, thus enabling to synoptically oversee and compare them.
Moreover, there is another strength in large numbers - they can provide you with
overwhelming statistical significance, thus enrolling all the power of elegant
statistical demonstration, inference, and persuasion for your cause.
In sum, then, the identification and validation of activations can be conceived
and accomplished in two different ways. One is based on the conviction that the less
complex the statistical models, and the simpler and robust the algorithms involved in
the analysis, the more valid the resulting activation image becomes. In this view, the
brain researchers often place too much emphasis on sophisticated statistical analyses,
and such a naïve confidence in statistical numbers might easily lead to
misinterpretations. Therefore, one should always inspect and verify the identified
activations visually (for example by cross-checking the threshold pictures with the
original, raw fMRI-data). The second approach, in contrast, considers it problematic
to rely on the visual impression of brain images. It stresses that -despite its photo-
realistic appearance-, the activation image actually is nothing but a quantitative
distribution of data points, and not a photograph of the brain. For them, it is a statistic
given a graphical form - and as such, its visual inspection can never be as good as a
powerful statistical analysis that is able to detect things hidden even to the most
experienced eye.
82
5.5. Step 4 - Putting emotions on the (brain) map
The final challenge in the process of image development is the actual
inscription of the functional activations into the physical structure of the subject’s
brain. Usually this “brain activation map” is created by superimposing the functional
on a structural image, that is, a high-resolution MR-scan of the anatomical structures
of the brain in which activations were detected. (See Figure 2 for a selection of
different brain activation maps)
A major problem related to the inscription process is the exact location of
these activated regions on the basis of the current neuroanatomical reference systems:
the ever-increasing spatial resolution of fMR-images makes it more and more difficult
to locate and depict them in the standard reference system of neuroscience -the
Talairach system- which is simply too imprecise to provide an adequate framework
for their accurate representation. Several new reference systems are therefore in the
process of development, but so far none of them is as firmly established in the field as
the old one35.
The issues of neuroanatomical reference, however, go also far beyond the
problems of precisely classifying the activations found, as such a common reference
system is also integral for any comparisons of activation pattern across the subjects of
a fMRI study. Obviously, their individual brains do not look the same, so one must
first place them in a comparable framework of standardized coordinates before they
can be subjected to any further analysis. This task is accomplished by mathematically
turning, twisting and stretching the presentation of each brain so that the result of this
normalization fits, in certain fixed points, the representation of a standard reference
brain. But, as there currently is no universally accepted reference system available,
35 See Beaulieu (2000) for a closer study of the problems surrounding the search for such a new standard system/standard brain.
83
this often ends up in brain activation maps that are not easy comparable with those of
other studies.
The final step of the translation sequence thus reveals some serious problems
with an adequate and universally comparable representation of the study results. For
one, the transformation of the individual brain activation images into a universal
representation is difficult because it is currently not clear which reference system to
use. Moreover, also the mathematical procedures and algorithms for transforming the
individual brains into the reference brain (however conceived) are not yet
standardized.
Seemingly, also the final step in the process of inscription mirrors the
ambiguous nature of the translation process: Although the normalization algorithms
enhance the comparability of findings, they are also known to produce errors and
distortions. Likewise, the transformation of activation images into a standard
reference system universalizes results, but it also makes it more difficult to consider
these generalizations and abstractions as still supported by the empirical data. In other
words, there are good and valid reasons to approach it from either way, but none of
them are compelling enough to refute the opposite side.
5.6. Images of the mind - maps of the brain
The previous analysis has shown that the development of a fMR-image centers
around a highly complex sequence of transformations. Many of these are based on the
massive possessing power of modern computers and their software programs, and it is
fair to say that the potentialities arising out of this technical-digital mediation are what
makes MR-neuroimaging such a crucial tool for the social brain scientist’s (or in fact,
84
the entire neuroscientific) research effort. These great new possibilities however, have
their price - the many uncertainties and unsolved question about the validity of the
performed translations. My analysis illustrated that on each step of the translation
chain, it is unclear how to further transform the data (either preserving its original
form and visual gestalt, or statistically purifying and condensing its abstract essence)
and, in consequence, why to believe in the veracity of the resulting image (either
because of its fidelity to the original phenomenon, or because of the powerful
mathematical transformations and statistical tests it has been subjected to). These
inherent tensions about the epistemological character of the brain image lead to an
icono-“clash”36 between different groups in the neuroimaging community: for some,
the brain activation images represent mimetical images of the underlying mental
processes (“images of the mind”), while others consider them to be visualized
numbers that represent statistically significant brain activations accompanying certain
mental tasks (“statistical activation maps”).37
It is important to point out that these contrasting views on what is inscribed in
a brain image are not just a matter of purely academic concern, but come with some
real and far reaching consequences: For one, if the brain images are real images, then
their meaning is an emergent property that needs a trained and knowing eye to extract,
analyze and interpret it. Of course, the interpretation of images is a concept deeply
entrenched into medical practice, and the important role of the medical gaze has been
a prominent one for the longest part of modern medicine history38. As such, by
conceiving brain maps as images, the role (and status) of the classical medical
36 Latour (2001) introduced this term to describe what happens when there is confusion and conflict about the exact role of the mediating image in the process of translation.37 Beaulieu (2002) illustrates nicely these iconographic and iconoclastic urges that are related to the use of digitalized images in brain research.38 Foucault’s (1975) analyses are a case in point.
85
observer -the physician- is further strengthened. If, however, the brain images are
nothing but maps made up of numerical data, then it should be possible (at least in
principle) to develop algorithms that systematically examine and objectively interpret
them in terms of their clinical relevance. In the end, this might lead to a largely
automatized diagnosis of the brain, performed by sophisticated computer programs
that replace the physician and all the individual idiosyncracies inherent to personal
evaluations. In this view, then, the carrier of the medical gaze is no longer the clinical
expert, but the computer and those who develop and know how to use the imaging
software.
Besides these (professional) consequences for the clinical applications of
neuroimaging, the iconoclash might also have a fundamental effect on the way normal
emotional functions and pathologies of affective behavior might in future be defined,
identified, and distinguished from each other.
If our emotions can be mimetically imagined, the definition and classification
of affective pathologies could easily be re-centered around their visual appearance
and gestalt on the brain map - deviant minds will come to be judged and classified on
deviant looks of their brain maps, just like in the physiognomic system of Lavater. As
such, the distinction between a normal emotion and a pathological one would be
written in, and could be read out of the morphological expression of the individual
brain (image). The examination of abnormalities would center on the visual
comparison of the case at hand with images of ideal types or typical cases and is a
matter of qualitative assessment of the categorical differences between the normal and
the pathological. In this view, then, images of emotion will become an important
element and referent for our perception of psychological illnesses and conditions, but
86
the identification and ascription of pathologies still depends on the subjective
judgments of an outside viewer.
If brain maps are visualized numbers, on the other hand, then their content is
thoroughly quantifiable, and any irregularities that would not be evident to the naked
eye of the observe now become apprehensible and can be discovered through
statistical analysis. The classification and definition of normal and pathological
emotions is no longer based on visual appearance but on their statistical-quantitative
essence. As Beaulieu (2000) has illustrated, this leads to a radically altered
(“statistically enhanced”) definition of normality: instead of choosing a typical or
ideal representation of a normal brain, it is possible to collect a huge sample of
individual brain scans and statistically average their features into a probabilistic atlas
that not only shows the „average“, normal brain, but also the typical range of
deviations from this standard that can be expected in a normal brain. As such, normal
and pathological emotions become redefined in relation to a statistical probability
distribution and its significance thresholds. From being discrete entities that are
categorically different, their difference now becomes a matter of degree. In this view,
the identification and ascription of aberrant affective functions is no longer performed
by the subjective observer (and his ability to qualitatively differentiate the normal
from the abnormal); but instead becomes an outcome that imposes itself through
statistical sampling.
Unmistakably, then, it does matter how you represent emotions: If you turn
emotions into images, then there are normal emotions and there are pathological ones,
and they are categorically different because they look categorically different. If you
translate them into statistical maps, however, then normal and pathological emotions
are not necessary distinguishable by their looks, and their difference does not lie in
87
their (categorically) different nature, but in their relation to their probabilistic
distribution in the population. Both approaches obviously imply contrasting
understandings and concepts of conceiving, diagnosing, and treating emotional
illnesses.
These examples were only meant to illustrate some of the issues that are at
stake in the debate about the best representation of emotion, both for the researchers
themselves and the wider societal sphere. The finer details of this controversy have
been discussed elsewhere (Beaulieu 2002). Here, I’d rather shed some light on the
underlying motives that inform these opposing views, and want to present my own
explanation for their (legitimate) parallel existence.
One of the main reasons for the different understanding of the image is that
due to the complex conditions of their coming into existence, there is uncertainty as to
what reality these representations actually refer to. As has been mentioned, for
phenomena happening in the scanner, there is no “natural” image, as the MR-
technology does not work according to optical principles. The recorded signals -the
voxels- do not resemble anything in reality but are abstract pieces of information that
can be transformed into all kinds of images. Each of these images thus is some kind of
simulation, that is, a model which tries to visualize some features of the original
phenomenon. As such, for fMR-imaging (much more than for optical imaging), the
question of how to conceive the epistemological relation between the model and the
reality it tries to simulate moves center stage. Within the neuroimaging community,
two different positions regarding this relationship can be identified:
88
The first is based on the conviction that there is an objective reality “out
there”, and the ideal model would mimetically correspond with this reality. For those
scientists, the main concern in the process of translation is to best preserve the form of
things as they occur in the world, and not to subject them to endless statistical
computation. Their view can be summed up as follows: “Too much emphasis has
been placed on sophisticated statistical computation, and not enough on common
sense. If no difference is seen visually or graphically, then it either does not exist, or
is too small compared with methodologic error, to have great significance” (Aine, in
Beaulieu 2000:109)
The second view principally questions any model’s ability to adequately
portray reality. Instead of believing in the realistic qualities of the models, they stress
their pragmatic function as reliable if simplified guides to reality. Scientist of this
conviction give up the focus on the perfect translation of the individual occurrence
and sacrifice the details of the single event for the stability of the many, thus obtaining
strong quantitative arguments for the existence of effects: “We have maintained the
tradition of quantification, even if it is statistical level quantitative, and SPM [SPM is
the standard software program for statistical brain activation analysis, P.B.] has been
critical to that. There is no question we would simply inspect images to make
diagnoses or conclusions” (Beaulieu 2000:108). In this view, then, the ideal model is
the one that enables one to navigate most successfully within reality, not the one
which looks most similar to it.
These contrasting views illustrate that the models used in fMR-imaging -far
from being neutral intermediaries- are crucial to what is seen as its outcome:
depending on how the epistemological relation between model and reality is
conceived, the resulting brain activation images either directly refer to, and
89
graphically represent, the mental process under investigation, or they represent a
statistical evidence of its associated brain activity.
It has been pointed out by Beaulieu (2000) and Galison (1997) that these
different perspectives on modeling/imaging are often grounded in different
disciplinary traditions (respectively in their underlying epistemic practices and
standards). In fact, such a disciplinary pattern is also discernible in the context of
fMR-imaging. For many neuroscientists coming from a clinical-radiological or
medical background, the brain maps have the status of “vera icons” - true images
because they are based on models that closely conform with, and thus validly
visualizes, objective reality. In contrast, those neuroscientists trained as psychologists
or behavioral scientists usually treat the brain images as statistical maps whose
quantifiable content, and not their visual qualities, make them so valuable and
veracious.
However, this argument for the incommensurability of those disciplinary
(modeling) practices somewhat blends out how much epistemological and ontological
ground both camps still have in common: for once, each side considers the existence
of an objective reality as beyond doubt, and second, they believe that their models and
methods -although contested in detail, but not in principle - enable them to analyze
and gain valid knowledge about this reality. These assumptions mirror the classical
realist position in science laid out in the theoretical chapter, for whom exists only one,
objective reality “out there” that can be accessed with the right set of procedures.
According to such a view, disciplinary controversies regarding the accuracy and
validity of different models are part of the scientific progress, but in the end, the right
one will prevail due to its better match with reality. Such an argumentation thus tends
90
to downplay the confusions around the images as a problem that sooner or later will
be settled by the true nature of things anyway.
Instead of conceiving the reality-model-relationship in such a hierarchical
order (i.e. reality determines the model), and explaining the predominance of one
model over the other on behalf of its greater “realism”, a constructivist view offers an
alternative understanding on the neuroscientific modeling efforts and their
controversial results. From this position, a model does not necessarily relate to a
reality out there; instead, model and reality mutually constitute each other (Schinzel
2001)39. As such, both the model and the reality to which it corresponds must be seen
as the result of a construction- (or, to be more precise, a translation-) process. As has
been explained in the theoretical chapter, constructivism argues that both knowledge
forms (i.e. brain images) and ontological orderings (i.e. the reality in the brain they
refer to) are the result of a heterogeneous process of association and transformation
which generates these outcomes, and depending on how you decide to structure this
translation process, it will produce different outcomes -real “images of the mind” or
statistical “brain activation maps”- and different realities - one in which emotions in
the brain can be easy “ seen” , and another one where their existence be demonstrated
through statistical calculation.
This view helps to integrate much of the findings about the imaging process
that I presented in this chapter. We saw that in each of its stages, from the first signal
detection through image construction and analysis to the final inscription, there were
different options and opinions about what and how to translate. Depending on the
choices made in these situations, different aspects of the phenomenon under
39 This also means that different models refer to different realities, which makes it pointless to evaluate them in their degree of “realism”, or believe in a progress from less to more realistic models.
91
observation (i.e. activations of the emotional brain) would get translated, and different
outcomes would be produced. Moreover, it seems as if the choices are condensed into
two disciplinary distinct translation sequences that produce inscriptions (and objects)
with a quite different epistemological and ontological character – images of the mind,
or maps of the brain.40 In this context, the analysis of the translation chain offered an
important insight into the controversial nature of the brain images: instead of
advocating one kind of image over the other, it illustrated how both of them are
grounded in different inscription practices that each offer good reasons for their
legitimate existence. Both images are created in a different fashion, and both refer to
two different kinds of objects - and as long as their respective translation chains and
networks of association are unbroken, it is difficult to counter their respective claims
to truth.
40 This split might be best illustrated by the different disciplinary publications (journals for clinicians on the one side, and those for neurocognitive researchers on the other) which accept either the one or other kind of brain image as worthy of being published.
92
6. What are emotions?
In the previous chapters, we have followed the genesis of the emotional brain
from the formation of its theoretical bases via the production of experimental
evidence to its final representation as an MR-image. In the process, the nature of
emotions dramatically changed - in the beginning, they were a mysterious, mental
process of obscure origins; in the end, they could be precisely located by an image of
the brain. Yet, as I have tried to illustrate, some questions remain about the
epistemological and ontological consequences of the complex (discursive,
experimental, and representational) transformations that emotions have been
subjected to in the course of their neuroscientific exploration: Do the current concepts
of emotion adequately reflect the subjective experiences and feelings that are part of
our emotional life? How can we relate such concepts to the emotional states
experimentally generated in the laboratory? What emotional phenomena are the
referents of a brain activation map?
In spite of their huge efforts, the social brain scientists are thus still in doubt
about the true nature of their object of inquiry - as one of their main protagonists puts
it, “there is little agreement about what emotions are.” (Le Doux 2002:1066).
What, then, is an emotion? With regard to that question, a constructivist view
might offer an alternative understanding of the neuroscientific efforts and their
controversial results, and maybe also some clues where to look for an answer.
For a start, the constructivist perspective reminds us not to blindly accept
scientific accounts of the nature of emotion as uncontestable facts, and instead advises
93
to look at the actual practices of knowledge making when assessing such truth claims.
Abiding by this precept, my analysis followed the heterogeneous, local and complex
processes through which knowledge claims about the emotional brain were
constructed. Most importantly, I found out that they are the result of long lines of
transformation, the validity of which is not grounded in compelling logic, but in the
strategic arrangement of messy experimental activities, technical potentialities, and -at
times- controversial assumptions. As such, their epistemological status -although
based on some well-grounded arguments- is certainly not immune to debate and
change, but dependent on the stability and continuity of these transformations. If they
work properly, however, these heterogeneous and fragile origins of a knowledge
claim are often pushed into the background, “black boxed”, and glossed over by an
idealist (or better: realist) view in which the finding is the outcome of conclusive logic
reasoning, uncontestable empirical evidence, and its congruence with the natural
world (i.e. the classical scientific method). From such a perspective, brain activation
maps appear as accurate representations of our human nature and any ontological
reordering of the body-mind dualism effectuated by these new facts would be seen as
just being in correspondence with, and having their cause in the -now finally
unveiled- order of things.
The analytical value of the constructivist approach, then, is that its insights
into the black box of scientific practice enable us to go beyond this idealistic gloss of
scientific reasoning and thus distinguish the causes from the effects of neuroscientific
knowledge production – that is, to recognize that the biological basis of the human
condition and its the emotional brain is the outcome and consequence, not the origin
of the experimental phenomena inscribed onto a brainmap. From such a position, it
94
becomes possible to analyze the biological nature of emotions not as given in the
order of things, but instead as constructed by the different scientific practices that
determine how to reason about them, how do experimentally examine them, and how
to depict and represent them.
My own empirical findings should therefore be seen as a case in point for the
ontological effects of such practices. If anything, the examination of the different -
discursive, experimental, and representational – practices illustrated the vital role they
played in the formation of new knowledge about the (neural) nature of emotions:
In the first part of the analysis, we saw how the discourse of the emotional
brain reconfigured emotions from phenomena of the mind into phenomena of the
brain: They too were no longer taken to be the outcome of some vague and subjective
mental processes, but became “naturalized” as the results of some physiological
operations embedded in and carried out by the brain. As such, emotions could now
enter the neuroscientist’s laboratory. Here, as the second part pointed out,
experimental practices effectuated another ontological reconfiguration: In order to
generate and measure emotions in an experimental situation, they had to be redefined
once more - this time in an operational manner, that is, by the tasks that were designed
to make them appear during an experiment. Based on this transformation of emotional
states into experimental tasks, the experimental subject and his/her brain could now
be stimulated, and the resulting neural effects could be physically traced with the
neuroimaging instrumentation. The last part of my analyses then looked closer at the
ontological changes that took place under the MR-scanner: Here, the emotions were
first converted into a data matrix that contains the BOLD-signals of the brain, which -
if minds, brains, tasks and machines have been coordinated properly- correspond to
95
the affective stimulus imposed on the subject whose brain was in the scanner. After
that, the result of this translation -the emotion as a mathematical-numerical object-
was subjected to further mathematical transformation to identify and extrapolate
events of statistical significance. In a final step, the numbers were then rendered into a
photo-realistic diagram on which any significant emotional brain activation would be
visually discernible.
In sum, the different discursive, experimental, and representational practices
did far more that simply mediate the object of inquiry - they re-constructed it on each
step anew, and every single instantiation can plausibly claim to be called “emotion”:
the emotion-concepts that are mused about in neuroscientific texts, the emotion-
phenomena that are generated in the confines of the laboratory, or the emotion-
activations that are inscribed in brain maps. Thus, instead of conceiving emotions as a
singular entity, we should understand them as different, multiple objects brought into
being by these different scientific practices.
Coming back to the point we started with -“what is an emotion?”- we see that
this question somewhat misses the point, at least if we cannot presuppose the
existence of a natural, unique order of things which would provide us with an
conclusive definitive answer. Instead, one should better ask how the different objects
that go under the name “emotion” are related to each other, and how they all can be
used to produce a common ontological ordering effect - obviously, if emotions are not
a single entity but multiple objects, they do not necessary converge, but might as well
clash, contradict or ignore each other. It thus needs much more efforts to re-arrange
and realign them in such a manner that they act as such an unitary object with clear
ontological characteristics.
96
For the time being, the social brain scientists have yet to coordinate their
translations and practices more effectively before emotions could be treated as such a
single ontological entity - as has been seen exemplarily in the case of the image-
versus-number debates, different representational practices still generate diverging
emotions which mutually challenge each other’s existence. The question of how to
integrate the many emotions into a single object is thus a challenge that the social
brain scientists have to meet, and a topic that begs closer analytical scrutiny.
Most likely, the answers will be found in the practical activities of scientific
work, and in particular in the many ways and strategies with which they engineer new
heterogeneous associations and sociomaterial orderings between the multiplicity of
emotions. Tying these things together in order to create a universal object thus has to
be seen as another example of the ontological politics embedded in scientific
practices. It truly is a deeply political matter - by defining what emotions are, we not
only define ourselves, but also the reality these emotions will be part of. As such, the
further exploration of these ontological practices and their consequences should be of
prime academic interest not only to the STS-community, but also beyond.
97
7. References
7.1. List of Interviewees
Dr. Silke Anders, Institute of Medical Psychology and Behavioral Neurobiology University of Tuebingen
Falk Eippert, Institute of Medical Psychology and Behavioral Neurobiology, University of Tuebingen
Dr. Hans Henning, Institute of Medical Psychology and Behavioral Neurobiology, University of Tuebingen
Dr. Tilo Kircher, Department of Psychiatry, University of Tuebingen
Dr. Martin Lotze, Institute of Medical Psychology and Behavioral Neurobiology, University of Tuebingen
Dr. Henrik Walter, Department of Psychiatry, University of Ulm
Dr. Niklas Weiskopf, Institute of Medical Psychology and Behavioral Neurobiology, University of Tuebingen
Dr. Barbara Wild, Department of Psychiatry, University of Tuebingen
98
7.2 . Bibliography
Adolphs, Ralph (2003): Cognitive Neuroscience of Human Social Behaviour, in: Nature Reviews Neuroscience Vol 4, No 3
Beaulieu, Anne (2000): The Space inside the Skull. Digital Representations, Brain Mapping and Cognitive Neuroscience in the Decade of the Brain, Amsterdam.
Beaulieu, Anne (2002): Images Are Not the (Only) Truth: Brain Mapping, Visual Knowledge and Iconoclasm, in: Science, Technology and Human Values, Vol 27, 53-86.
Bechtel, William (2001): Epistemic Issues in Procuring Evidence about the Brain, in: Bechtel, William et. al. (Eds): Philosophy and the Neurosciences. A Reader. (55-81). Oxford, UK: Blackwell Publishers
Bloor, David (1976): Knowledge and Social Imagery. London: Routeledge & Kegan Paul.
Burri, Regula (2001): Doing Images: Zur soziotechnischen Fabrikation visueller Erkenntnisin der Medizin, in: Heintz, Bettina / Huber, Jörg (Hg): Mit dem Auge denken. Strategien der Sichtbarmachung in wissenschaftlichen und virtuellen Welten (277-304), Zürich/Wien.
Clore, Gerald L / Ortony, Andrew (2000): Cognition in Emotion: Always, Sometimes, of Never?, in: Lane, Richard D. / Nadel, Lynn (Eds): Cognitive Neuroscience of Emotion (24-56), New York: Oxford University Press
Collins, H.M: Science Studies and Machine Intelligence, in: Jasanoff, Sheila, Markle, Gerald E., Petersen, James C., Pinch, Trevor: Handbook of Science and Technology Studies (286-311), London: Sage Publications
Damasio, Antonio R. (1999): The Feeling of What Happens: Body and Emotion in the Making of Consciousness. New York: Hartcourt Brace
Damasio, Antonio R (2000): A second Chance for Emotion, in: Lane, Richard D. / Nadel, Lynn (Eds): Cognitive Neuroscience of Emotion (12-23), New York: Oxford University Press
Damasio, Antonio R (2002): Wie das Gehirn Geist erzeugt , in Spektrum der Wissenschaft Digest No 3, 6-11.
Duden, Barbara (1991): Der Frauenleib als öffentlicher Ort. Vom Mißbrauch des Begriffs Leben, Hamburg/Zürich.
Fleck, Ludwik (1935): Entstehung und Entwicklung einer wissenschaftlichen Tatsache. Frankfurt a.M.: Suhrkamp
99
Foucault, Michel (1977): Der Wille zum Wissen, Sexualität und Wahrheit I, Frankfurt a. M.
Foucault, Michel (1975): The Birth of the Clinic. An Archeology of Medical Perception. New York: Vintage Books
Galison, Peter (1997): Image and Logic: A Material Culture of Microphysics. Chicago: University of Chicago Press
Gazzaniga, Michael S, Ivry, Richard B., and Magnun, George R (2002): Cognitive Neuroscience: The Biology of the Mind, New York: Norton
Gieryn, Thomas F. (1999): Cultural Boundaries of Science. Credibility on the Line, Chicago: The University of Chicago Press
Gugerli, David (1999): Soziotechnische Evidenzen. Der „pictioral turn“ als Chance für die Geschichtswissenschaft, in: Traverse: Wissenschaft, die Bilder schafft, Nr. 3, 131-159.
Hagner, Michael (1996): Der Geist bei der Arbeit. Überlegungen zur visuellen Repräsentation cerebraler Prozesse, in: Borck, Corneluis (Hg): Anatomien medizinischen Wissens (259-286), Frankfurt a.M.
Hagner, Michael (2001): Kopfgeburten. Über Hirn- und Menschenbilder, in: Neue Züricher Zeitung Nr. 167, 75.
Hall, Stephen T. (2000): Mapping the next Millenium, New York.
Henning, Jürgen (2002): Chancen und Probleme bildgebender Verfahren fuer die Neurologie, in: Schinzel, Britta (Hg): Interdisziplinäre Informatik: Neue Möglichkeiten und Probleme für die Darstellung und Integration komplexer Strukuren in verschiedenen Feldern der Neurologie (67-86), Freiburg.
Heintz, Bettina / Huber, Jörg (2001): Der verführerische Blick. Formen und Folgen wissenschaftlicher Visualisierungsstrategien, in: Heintz, Bettina / Huber, Jörg (Hg): Mit dem Auge denken. Strategien der Sichtbarmachung in wissenschaftlichen und virtuellen Welten (9-42), Zürich/Wien.
Kandel, Eric R. (2000): The Brain and Behavior, in: Kandel, Eric, Schwartz, James and Jessell, M. Thomas: Principles of Neural Science, (5-17), New York: McGraw-Hill
Kuhn, Thomas S. (1976): Die Struktur wissenschaftlicher Revolutionen, 2. Auflage, Frankfurt a. M.
Kosik, Kenneth S. (2003): Beyond Phrenology, at Last, in: Nature Reviews Neuroscience Vol 4, No 3
100
Lane, Richard D. / Nadel, Lynn (2000): Epiloque, in: Lane, Richard D. / Nadel, Lynn (Eds): Cognitive Neuroscience of Emotion (407-410), New York: Oxford University Press
Latour, Bruno (2001): What is Iconoclash?, in: Latour, Bruno/ Weibel, Peter (Eds). Iconoclash: Beyond the Image wars in Science, Religion and Art, (14-37). Karlsruhe, ZKM
Latour, Bruno (1999): Pandora’s Hope: Essays on the Reality of Science Studies. Cambridge, MA: Harvard University Press.
Latour, Bruno (1993): We have never been modern. Cambridge, MA: Harvard University Press.
Latour, Bruno (1988):The Pasteurization of France. Cambridge, MA: Harvard University Press.
Latour, Bruno (1987): Science in Action: How to follow Scientists and Engineers through Society. Cambridge, MA: Harvard University Press.
Law, John (1994) Organizing Modernity. Oxford, UK: Blackwell Publishers,
Le Doux, Joseph (1996): The Emotional Brain. New York: Simon and Schuster
Le Doux, Joseph (2002): Milestones in Cognitive Neuroscience. An Interview with Joseph Le Doux, Ph.D, in: Gazzaniga, Michael S, Ivry, Richaed B., and Magnun, George R (Eds): Cognitive Neuroscience: The Biology of the Mind (542-543), New York: Norton
Logothetis, N.K., Pauls, J, Augath M., Trinath, T. and Oeltermann, A. (2001): Neurophysiological Investigation of the Basis of the FMRI Signal, in: Nature, No 412, 105 –107.
Lynch, Michael /Woolgar Steve (1990): Representation in Scientific Practice. Cambridge: MIT Press
Lynch, Michael (1991): Laboratory Space and the Technological Complex: An investigation of Topical Contextures, in: Science in Context Vol 4, 51-78.
Mol, Annemarie (2002): The Body multiple. Ontology in Medical Practice. Durham and London: Duke University Press.
Nikolow, Sybilla / Bluma, Lars (2002): Bilder zwischen Öffentlichkeit und wissenschaftlicher Praxis. Neue Perspektiven für die Geschichte der Medizin, Naturwissenschaften und Technik, in: NTM Vol. 10, 201-208.
Ogawa, S., Tank, D.W., Menon, R., Ellerman, J.M., Kim, S., Merkle, H., and Ugurbil, K. (1992): Intrinsic Signal Changes Accompagning Sensory Stimulation: Functional Brain Mapping with MRI. Proceedings of the National Academy of Science, USA, Vol. 89, 5951-5.
101
Orland, Barbara (1999): Babys in der Röhre. Wie die Pädiatrie in den 1980er-Jahren die Normalisierung der Magnetresonanztechnik unterstütze, in: Gugerli, David / Orland, Barbara (Hg): Ganz normale Bilder. Historische Beiträge zur visuellen Herstellung von Selbstverständlichkeit (227-251), Zürich.
Papanicolaou, Andrew C. (1998): Fundamentals of Functional Brain Imaging: A Guide to the Methods and Their Applications to Psychology and Behavioral Neurosciences. Lisse: Swets & Zeitlinger B.V.
Petersen, S.E, Fox, P.T., Posner, M.I., Mintun, M, and Raichle, M.E.(1988): Positron Emitter Tomographic Studies of the Cortical Anatomy of Single-Word Processing. Nature Vol. 331, 855-8
Pickering, Andrew (1995): The Mangle of Practice: Time, Agency and Science. Chicago: University of Chicago Press
Popper, Karl (1959): The Logic of Scientific Discovery. New York: Harper
Rheinberger, Jörg.(1997): Towards a history of epistemic things: Synthesizing Proteins in the Test Tube. Stanford: Stanford University Press
Roepstorff, Andreas (2002): Transforming Subjects into Objectivity – an “Ethnography of Knowledge” in a Brain Imaging Laboratory, in: Folk, Vol 44, 145-170.
Schinzel, Britta (2002): Einleitung, in: Schinzel, Britta (Hg): Interdisziplinäre Informatik: Neue Möglichkeiten und Probleme für die Darstellung und Integration komplexer Strukuren in verschiedenen Feldern der Neurologie (6-13), Freiburg
Schinzel, Britta (2002): Entwicklung eines informationssystems fuer die Neurowissenschaften, in: Schinzel, Britta (Hg): Interdisziplinäre Informatik: Neue Möglichkeiten und Probleme für die darstellung und Integration komplexer Strukuren in verschiedenen Feldern der Neurologie (33-49) Freiburg.
Shapin, Steven / Schaeffer, Simon (1985): Leviathan and the Air-Pump. Hobbes, Boyle and the Experimental Life. Princeton: Princeton University Press.
Singer, Wolf / Metzinger, Thomas (2002): Hirnforschung mit Folgen - Ein Frontalangriff auf unser Selbstverständnis und unsere Menschenwürde, in: Gehirn&Geist No 4/2002, 32-35
102
