Content uploaded by Sebastiano Massaro
Author content
All content in this area was uploaded by Sebastiano Massaro on May 12, 2016
Content may be subject to copyright.
10
NEUROSCIENTIFIC METHODS FOR
STRATEGIC MANAGEMENT
Sebastiano Massaro1
In the past decade, social disciplines have looked with increasing interest at
neuroscience. From anthropology (Adenzato and Garbarini, 2006) to law (Greene
and Cohen, 2004; Jones and Shen, 2012), from politics (Connolly, 2002) to
sociology (Franks, 2010), the integration of neuroscientific aspects into social
studies has become a phenomenon of considerable interest. Business scholarship
has not been immune from this trend with contributions crossing leadership
(Ghadiri, Habermacher, and Peters, 2012), marketing (Ar iely and Ber ns, 2010; Lee,
Broderick, and Chamberlain, 2007) and strategy (Powell, 2011), among others.
Accordingly, universities have created dedicated centers of research; journals and
conferences have started to offer substantial space to the role of neuroscience in
management; and several researchers have developed international partnerships
aiming to extend these cutting-edge approaches.2
Although some scholars have questioned the appropriateness and viability of
neuroscience to effectively advance social analyses and business research (e.g.,
Bennett, Hacker, and Bennett, 2003; Gul and Pesendorfer, 2008; Lindebaum and
Zundel, 2013), the intensification and effects of this type of studies cannot be
denied. Indeed, knowing more on how our brain works can help to advance our
understanding of human cognition, emotions, behavior, and decision making, both
inside and outside organizations.
In this work, Iargue that to better appreciate how neuroscience can inform
research in management, understanding the rationale of relevant neuroscience
methods, is afundamental step currently missing in the scholarship.3Only through
this knowledge,will management scholars fully appreciatethe potential of the related
research and possibly incorporatefurther these instrumentsinto their exploratory
equipment.
By mainly concentrating on brain-imaging methods, this chapter provides an
opening review, for both those researchers who are new to neuroscience,and those
multidisciplinary-oriented scholars who are seeking to refresh their knowledge on
the topic. Following an introductory excursus on the early applications of these
methods to management studies, I will explain how they can be classified, and
supply a core description of relevant techniques. Moreover, I will offer some
evidence related to management scholarship, and in particular to strategy, one of
the fields most attentive to neuroscience (e.g., Powell, 2011; Powell, Lovallo, and
Fox, 2011). Finally, the chapter will pinpointcritical considerations for
management research related to employing neuroscience approaches.
The partnership between neuroscience and management
The idea of coupling descriptions of human behaviors and the brain has actively
engaged researchers for centuries (for ahistory of neuroscience see, e.g., Finger
[2001]).Ye t, the experimental partnership betweencognitive neuroscience and social
disciplines –nowadays often called social cognitive and affective neuroscience –has
acquired wider resonance only in the past couple of decades, thanks to the
emergence of functional neuroimagingof behavior (Raichle, 2003; 2009a), the
combination of neuroimaging and behavioral research approaches.This researcharea
generally refersto the use of technologies measuring hemodynamic,electromagnetic,
or biophysical properties and changes in the brain and in the nervous system,
following an experimental behavioral manipulation, task or stimulus, to provide
visual metrics(e.g.graph, image, scan) of the underlying brain regions and neural
functions.The resulting outcomes enable inferences about the relationshipsbetween
neural substratesand behavioral or mental processes associated.4
One of the first techniques used to measure brain activity, electroencephalography
(EEG), emerged in the 1930s when Berger (1929) demonstrated that electrical
activity from the brain could be measured by placing conducting material on the
scalp and amplifying the consequential signal. After Dawson (1951) developed a
method of signal averaging, management research suggested EEG’s use to
investigate the neural basis of performance decrements in the workplace (Scott,
1966).The successive, essentially misleading, idea that the left hemisphere of the
human brain would control only logic, analytical ability, sequential perception, and
language, while the right hemispherespatial and simultaneous perception,
imagination, and intuition (for a review see, e.g., Gazzaniga and LeDoux [1978])
further inspired management inquiries. Mintzberg (1976) addressed the challenge
of coupling neuroscience information with management research when he
imprecisely claimed that “right-brain is holistic and the left-brain logical,”
suggesting differences in the brain hemispheres were compelling for business
studies, training, and practice. Afterwards, such claims offered room to a body of
management neuromythology supported by a rather lay dissemination (Hines,
1987).Those outcomes allowed, for instance, to argue that executives tend to use
more right brain processing than analysts, and vice versa (Doktor, 1978), and
contributed to setting the basis for the development of frameworks seeking to
enhance managers’ analytical and intuitive skills (Robey andTaggart, 1982).
254 Massaro
Although EEG offered preliminary management-related findings obtained by
examining brain activity, it is with the advent of tomographic techniques that the
actual imaging takeover began.5In 1973, Godfrey Hounsfield (1973) introduced a
breakthrough technique: X-ray Computed Tomography (CT). It had immediate
impact; not only did it revolutionize medical clinical practice, facilitating screening
and diagnosis, but it also provoked behavioral scientists to consider new ways of
imaging the brain (Garvey and Hanlon, 2002; Raichle, 2009b; Rogers, 2003).
Subsequently, another type of tomography,Positron Emission Tomography (PET),
enabled creating autoradiographs of brain functions (Tilyou, 1991).This ushered in
the beginning of the “hemodynamic era”for functional brain-imaging:by injecting
a radioactive pharmaceutical in a subject, it was possible to quickly measure blood
flow changes and associate them with measurements of brain function (Phelps and
Mazziotta, 1985). PET also made possible“experimentalizing” a strategy of cognitive
subtraction with functional neuroimaging (Donders, 1969; Petersen et al., 1988),
which, although often questioned (Friston et al., 1996; Sartori and Umiltà, 2000),
has represented a pillar for numerous studies.Cognitive subtraction mostly relies on
assumptions of linearity and pure insertion: an elicited mental component evokes an
“extra” physiological activation that is the same regardless of preexisting mental and
physiological contexts (Price and Friston, 1997).This suggests that functional
imaging of behavioral processes can then be derived by subtraction of a control task
from an experimental assignment, so that differences in brain activity can be
attributed to selected mental components (Friston et al., 1996). Due to its logistics
(i.e. requirement for local presence of a particle accelerator) and concerns for
participants’ health (i.e., use of radioactive material) PET has not arisen as the
technique of choice for most of management inquiries. However, it has been
employed to identify neural substrates of phenomena such as planning (Dagher et
al., 1999), and risk-avoiding and ambiguity-avoiding behaviors (Smith et al., 2002).
More recently,another neuroimagingtechnique has offered theability to apprise
where activity is occurring in thebrain while we areperforming experimental
behavioral tasksor we areatrest. This is functional Magnetic Resonance Imaging
(fMRI), which grounds on nuclear magnetic resonance physics (Bloch, 1946;
Lauterbur, 1973; Purcell, To rrey,and Pound, 1946).The revolution in neuroscience
arrived in 1992, when researchers associatedMagnetic Resonance Imagingwith
brain activity-relatedchanges in blood oxygenation.The signal arising fromthe
unique combination of brainphysiology andnuclear physics became known as the
Blood Oxygen Level Dependent(BOLD)signal (Ogawa,Lee, and Tank, 1990).6There
rapidly followed severalevidences of BOLD signal changesin humans during
“brain activation,” giving officialbirth to fMRI (Bandettini et al., 1992; Kwong et
al., 1992; Ogawa et al., 1992). fMRI has dominated functional brain imagingof
behavior research ever since, and has been the neuroimagingtechniquebringing
thegreatest promises to management research.Forinstance, some encouraging
contributions have been those applied to strategic games (e.g., Sanfey et al., 2003)
and those investigating the neural underpinnings associated with strategic insight
and intuition (e.g., Vo l zand vonCramon, 2006).
Neuroscientific methods 255
Classifications: Between resolution and functionality
To fully appreciate how neuroscience techniques can attempt to “open the black
box of the brain,” it is important to classify them under a systemic outlook,
according to their distinctive characteristics. Experimental methodsin
neuroscience have traditionally been organized according to a matrixed perspective
that considers and emphasizes the distinct spatial and temporal resolutions of each
technique (Churchland and Sejnowski, 1988), as shown in Figure 10.1a.7
The concept of resolution is an essential prerequisite for understanding the
essence of each neuroimaging procedure. Simply speaking, it allows providing
answers for questions such as “how good is a brain scan image?”The response is
commonly disentangled into concepts of spatial or temporal resolution, which are
respectively the abilities to discriminate between two points in space and time
(Menon et al., 1998). A high spatial resolution determines a sharp image, while a
low one gives a“pixely” appearance to the image; for example,when two spatially
close (i.e., a few millimeters) anatomical structures are distinguishable in an image,
this has a higher resolution than one when they are not discernable. Spatial
resolution depends on the properties of the system that creates the images, such as
gradient strength and digitalizing rate (Bandettini, 2002), being therefore limited
by hardware and acquisition protocols. Severaltechniques provide spatial
information of the human brain with high resolution, including fMRI and PET.
However, understandingthe neuroscience of mental processesrequires
information not only on the spatial localization of brain activities, but also on their
temporal evolution. Analyses with a temporal resolution of milliseconds can be
conducted by electroencephalographic (EEG) and magnetoencephalographic
(MEG) methods, which are based on the electric or magnetic activity caused by
movements of ions inside and outside cellular membranes (e.g., Kristeva et al.,
1979).These methods provide almost real-time information on brain activity; yet,
EEG has lower spatial localization and resolution.
The importance of understandingthe properties of eachmethod is
fundamental. Limited resolutions bound practical applications of the techniques.
Moreover, each technique allows adifferent examination of the neural functions
specifically on the basis of its intrinsic characteristics. Somemight believe the
results obtained exploiting different resolutions of several techniques couldjust
converge in an overall explanation of the neural processes.This claim however is
inaccurate. Severalother influencesdetermine an experimental outcome, such as
whether the process is recording physiological brain activity, or instead
interfering withit,or stimulating the brain to change abehavioral response.
Therefore, theappropriate methods and levels at which to examine brain
function largely depend on the researchquestion being addressed (Stewart and
Walsh, 2006).
Organizational scholars have drawn from this resolution-based classification,
arguing its ease in depicting the relative advantages and disadvantages of each
method: as seen in Figure 10.1b, this categorization has helped delineating the
256 Massaro
FIGURE 10.1 Spatial and temporal resolutions of neuroscience techniques
Notes: The vertical axes show the spatial extent of the techniques; the horizontal axes represent the
time intervals over which information can be collected with each technique.Recordings from
the central nervous system are often limited in resolution by the properties of nervous tissue
and of the specific method.
Sources: (a) Churchland and Sejnowski, 1988; (b) Senior, Lee, and Butler, 2011.
(a)
(b)
3
Brain
Map
'E
Col
umn
0
E
'"
,!!.
=
layer
- 1
iii
Neuron
-2
Dendrite
-3
Q)
(ii
Synapse
Systems
Brain
Region
-4
Column
"'
g
Q)
[ Layer
en
Neuron
Dendrite
Synapse
-3
-2
-1 0
Millisecond
Multiunit
recordings
Second Minute Hour
Time (log seconds)
fMRI
Single ce
ll
recordings
Millisecond Second Minute Hour
Time (log scale)
5
Day
PET
Day
6
• Neuro
: psychology
Cognitive
n uropsychiatry
Year Lifetime
Millisecond
Millisecond
Millisecond
Millisecond
Millisecond
Millisecond
Millisecond
Millisecond
Millisecond
Millisecond
Millisecond
Millisecond
Millisecond
Millisecond
Millisecond
Millisecond
Millisecond
Millisecond
Millisecond
Millisecond
Millisecond
Millisecond
preliminaryexperimental boundaries of organizational neuroscience (Becker,
Cropanzano, and Sanfey, 2011; Senior, Lee, and Butler, 2011).
Kable(2011) has suggested acomplementary framework to organize
neuroscience techniques when applied to social sciences, on the ground of their
underlying testing rationale. Association tests are those experimental methods that
implicate a manipulation of a psychological state or behavior, the simultaneous
measurements of the neural activity, and the following analysis of the correlation
between the two.These include classic fMRI, PET, EEG, and MEG approaches.
Necessity tests are instead those that involve a disruption of the neural activity and
aim to show how this manipulation impairs a specific mental function. Sufficiency
tests are those enhancing a neural activity and seeking to establish that this process
results in a specific behavior or mental state.Necessity and sufficiency tests, such as
lesion studies, neuropharmacological or Transcranial Magnetic Stimulation (TMS)
experiments, are able to directly probe the causality between neural and mental
states.
Overall, it is quite straightforward to argue that knowledge of these classifi-
cations represents an important apparatus for scholars who both seek to understand
the technicalities, and also inquire what different kinds of evidence they should
gather to allow the most appropriate inferences about brain functions.
The techniques
The main neuroimaging techniques examined in this chapter include functional
Magnetic Resonance Imaging (fMRI), Positron Emission Tomography (PET), and
electroencephalography (EEG). These methods generally allow identifying brain
areas displaying increased activity, in comparison to controls,while the subjects are
performing specific tasks.
Functional Magnetic Resonance Imaging (fMRI)
Functional Magnetic Resonance Imaging (fMRI)is probably the most known and
widely appliedneuroscience methodologyin business research (Dimoka et al., 2012).
To understand the foundation of fMRI it is first necessary to appreciate the
underlying principles of Magnetic Resonance Imaging (MRI). MRI exploits the fact
that protons (atomic hydrogen nuclei) of our body in the presence of an external
magneticfield behave likecompass needles, aligning in parallel to that field (Le
Bihan, 1996). Simply put, after electromagnetic pulses are applied to these protons
(and then switchedoff), theyemit detectable and characteristic radio signals, allowing
acomputerto reconstruct images of the inner organs (for areview, see Brown and
Semelka [2010]).Imposing the magnetic field and pulses and acquiring the resulting
signals requires specific equipment,consisting of an MRI magnet, a system of coils
and signal amplifiersystems (Figure 10.2). An MRI scanner is acylindrical tube
whose core is constituted by avery powerful electro-magnet (Chapman, 2006); a
typical magnet, well-suited for fMRI research, has field strength of 3Te s l as (T).8
258 Massaro
Functional MRI uses these principles to detect the magnetic signal from
hydrogen nuclei in water (H2O). It relies on differences in magnetic properties
between venous (oxygen-poor) and arterial (oxygen-rich) blood, which allow
revealing the changes in blood oxygenation and flow that occur in response to
neural activity, the so-named neurovascular coupling (Logothetis et al., 2001;
Logothetis and Wandell, 2004). When a brain area is more active it requires more
oxygen, and as a consequence blood flow increases to the active area (Fox and
Raichle, 1986; Uludag et al., 2004).By using the BOLD signal (Ogawa et al., 1990)
fMRI allows researchers to examine activation maps showing which parts of the
brain are involved in a particular mental process (Bandettini et al., 1992; Ogawa et
al., 1992; Kwong et al., 1992).
Nonetheless, the extent, dynamics, and underlying mechanisms of neurovascular
coupling are not yet fully understood (Attwell and Iadecola, 2002; Magistretti and
Pellerin, 1999) and the BOLD signal depends on several parameters, so its
biophysical link with neuronal activation is not yet entirely straightforward
(Malonek et al., 1997). Moreover, the fact that fMRI experiments elicit a BOLD
signal does not indicate that subjects necessarily had psychological events associated
with that part of the brain (Poldrack, 2006).
Neuroscientific methods 259
FIGURE 10.2 Main components of an MRI scanner
Source: http://www.themesotheliomalibrary.com
Caring
Caring
Caring
Caring
Caring
Caring
Caring
Caring
Caring
Caring
These concerns have lead to some research issues for fMRI research, which
however can rely on high spatial (typically 3 millimeters) and high temporal (about
2 seconds) resolution (Song, Huettel, and McCharty, 2006).9For instance, the
spatial localization of the BOLD signal can be distant from the actual site of neural
activity, because the signal source includes various vascular networks sized from
capillaries to large draining veins, and the physiological delay necessary for the
mechanisms triggering the vascular response to work limits the temporal resolution
of the technique (Le Bihan et al., 2006). Research is constantly improving
resolution parameters, yet these may be ultimately limited by our physiology. For
example, the brain vascular supply is not regulated on the scale of individual
neurons and might then be restricted to 0.5-1.5 mm (Menon and Kim, 1999).
Nonetheless, recent work has suggested that water diffusion MRI (for a review,
see Beaulieu [2002]), could among other methods, overcome some of these limits:
changes in the magnitude of diffusion of water molecules within cerebral tissue
during neuronal activation would likely reflect transient changes in the
microstructure of the neurons, which can then be imagined (Le Bihan, 2003).
Although this suggestion has been challenged (Yacoub et al., 2008), captur ing such
effects would have a remarkable consequence on neuroimaging applications in
behavioral reserach, since they would be directly linked to neuronal events in
contrast to blood flow effects, which are secondary.
Despite these and other concerns, fMRI has been extensively recruited in social
sciences (e.g. Camerer, 2003; Crockett et al., 2008; Damasio, 1994; Glimcher and
Rustichini, 2004). Not surprisingly, it has also been the technique of choice to
investigate several strategic management paradigms.
For example, fMRI studies have significantly contributed to investigating the
neural basis for cooperation. Cooperation, the willful contribution of personal
effort to the completion of interdependent tasks, including jobs, has been a
mainstay in the management literature (e.g., Barnard, 1938; March and Simon,
1958; the whole special issue of the Academy of Management Journal 38(1) [1995]).
McCabe and colleagues (2001) employed fMRI in two-person reciprocity games
in which participants were facing both human and computer counterparts.They
found that cooperation with humans was highly correlated with increased
activation of brain regions responsible for joint attention and mutual gains, and
decreased activation of regions associated with immediate reward gratification.This
study prompted further exploration of other aspects key for management research,
such as the role of fairness and trust in the workplace. For instance, equity theory
(Adams, 1963; 1965) suggests that perceptions of fairness are job-related motiva-
tional grounds that can influence responses of job performers. Research has argued
that fair treatment has positive effects on individual employee attitudes (e.g.,
satisfaction and commitment) and individual behaviors (e.g., absenteeism and
citizenship behavior) (Colquitt et al., 2001; Moorman, 1991), while unfair
treatment conveys opposite behaviors and attitudes (Cohen-Charash and Mueller,
2007). Research has measured the neural responses and identified key correlates
underlying social exchanges and sense of fairness in volunteers playing different
260 Massaro
strategic games, such as the prisoner dilemma, the ultimatum game, or the
reciprocal trust game. For instance, King-Casas and colleagues (2005) found that
reciprocity expressed by one social actor strongly predicts trust expressed by his or
her partner, a behavioral finding mirrored by an increased activation in the dorsal
striatum as compared to control conditions.
In recent years, functional magnetic resonance imaging studies have begun to
covered other strategic management paradigms, embracing topics spanning from
exploration and exploitation (Daw et al., 2006) to escalation of commitment. For
example, research has widely established how the inability to plan ahead often
results in escalation of commitment, myopia of learning, or unnecessary risk taking
(Levinthal and March,1993). Escalation of commitment is that situation whenever
a manager, or any decision maker, keeps committing considerable resources to a
course of action in the hope of achieving a positive outcome, but instead
experiences disappointing results (Staw, 1981; Brockner, 1992).Campbell-
Meiklejohn and colleagues (2008) highlighted neural correlates of this complex
behavior: in comparison to control conditions, decisions not to escalate were
associated with increased activity in the anterior cingulate, left anterior insula,
posterior cingulate, and parietal cortices, but decreased activity in the ventro-
medial prefrontal cortex. Decisions to escalate were associated with a decrease of
activity in the anterior cingulate, right anterior insula, and inferior frontal gyrus,
but there was no increase in activity in comparison with the control condition,
which instead suggested increased activity in the ventro-medial prefrontal cortex.
The burst of wide applicability in presenting such imaging research outcomes
has perhaps raised the bitterest criticisms around fMRI results, despite scholars
having highlighted actual limitations of the methodology (Logothetis, 2008;
Poldrack, 2012). As seen above, spatial resolution barriers would not allow to map
the intimate nature of individual neurons (i.e., in a voxel there are about 5.5
millions neurons) and directly distinguish between functional activities relevant to
the task, irrelevant to the task, and noise.The averaging of imaging, which often
leads to ignoring differences between individuals, random effect analysis, and
statistical issues are other arguments often brought up to underlie problems of
research reproducibility (Vul et al., 2009; the whole Perspectives on Psychological
Science issue 4(3), [2009] is a must read, dedicated to the issue of correlations in
psychological research using fMRI). Recent investigations have started to address
these concerns and suggest that increased reproducibility can be achieved through
the combined results from multicenter fMRI studies (Stöcker et al., 2005), the
development of neuroimaging databases, the use of consistent protocols (Liu et al.,
2004), similar machineries, homogeneous sampling, and multiple comparisons
correction methods (Poldrack et al., 2011).
In any case,these considerations per se should not prevent management scholars
from exploring the use of this methodology, since the debate is a current challenge
accompanying the daily routine of every neuroimaging scientist. For one, an
analysis of the rise of brain imaging methods from a socio-historical point of view
(Beaulieu, 2000), has revealed that neuroscientists have a love-hate relationship
Neuroscientific methods 261
with their images: these are useful for blending data and convenient for communi-
cating results to a large audience; however, they hold incredible exposure to the
most disparate criticisms.
Positron Emission Tomography (PET)
Positron Emission Tomography (PET) was one of the first techniques used to
exploit the links between neural activity and metabolism to study brain functions
(Phelps and Mazziotta, 1985; Raichle and Snyder, 2007). It is an analytical nuclear
imaging technique, able to provide high spatial resolution images of functional
processes occurring in the brain, and it has traditionally been used to make in vivo
measurements of the anatomical distribution and rates of specific biochemical
reactions (Gulyas et al., 2002).The term nuclear signifies that the technique relies
on radioactively labeled molecules (tracers). Similar to MRI, PET requires
dedicated instrumentation, which includes a ring of detectors located around the
patient’s head (Turkington, 2001) (Figure 10.3).
In a typical PET experiment, a short-lived radioisotope of a biologically
relevant element (carbon, nitrogen, oxygen, fluorine) is produced locally using a
low-energy particle accelerator (i.e., a cyclotron). It is then synthetically bound to
a biomolecule, usually glucose or oxygen, or to a drug, to form a physiological
radiotracer able to emit positrons (positively charged particles of the mass of an
electron).This radiotracer is then injected intravenously into the subject, so that it
can bind to a specific receptor or enter into specific metabolic pathways. During
the natural process of radioactive decay the positron is emitted and travels for a
short distance within the brain, then collides with an electron.This impact
produces two coincidental rays (gamma rays), which can be measured by the
262 Massaro
FIGURE 10.3 Examples of PET detectors
Source: Humm, Rosenfeld, and Del Guerra, 2003
Caring
Caring
Caring
Caring Caring Caring
detectors around the subject’s head (Ter-Pogossian and Herscovitch, 1985). When
two opposite detectors on the ring simultaneously recognize a gamma ray, a
computerized system records this as a coincidence event.The computer records all
of the coincidence events that occur during the imaging period and then
reconstructs cross-sectional images. Bi- and three-dimensional images are often
accomplished with the aid of an X-ray CT scan performed on the subject during
the same session, in the same machine (Pelizzari et al., 1989). Since the tracer
accumulates in the brain in direct proportion to the blood flow, the greater the
flow, the greater the radioactive count rate.Thus, the distribution and intensity of
the uptake of the positron-emitting radiotracer indicates the underlying neural
activity, and the regional cerebral blood flow (rCBF) works as the dependent variable
(Raichle, 1979; Raichle, Martin, and Herscovitch, 1983).
PET presents several disadvantages in comparison to fMRI.Above all,it involves
the use of ionizing radiations, which have potential harmful effects on the research
subjects. Moreover, it affords relatively poorer spatial (4 millimeters) and temporal
(30–40 seconds) resolutions and generally involves one to two measurements per
subject, with each measurement reflecting neural activity averaged over one minute
(Kato, Taniwaki, and Kuwabara, 2000).
Nonetheless, PET has provided intriguing insights on topics central to strategic
management. One of the most productive lines of research developed around the
concept of planning. Planning has been a growing topic in strategy inquiries since
the 1950s (Payne,1957), and has been identified as a variable able to both impact
firm performance and have a role in strategic decision making (Ansoff, 1991;
Armstrong, 1982; Mintzberg, 1994). Strategic planning decisions emerge from
complex interactions among individuals with subjective interests and perception;
understanding the respective neural correlates can inform further on both planning
processes and theories. Several neuroimaging studies have independently addressed
the issue. Associating PET studies with the Tower of London (TOL) task – an
adaptation of the To w er of Hanoi (Anzai and Simon, 1979), which consists of
moving colored balls within a limited number of moves in order to achieve a given
goal configuration – researchers shed a light on the anatomic and physiological
correlates of planning processes. Longer planning times and fewer moves to
complete a problem are associated with significantly higher regional cerebral blood
flow in the left prefrontal cortex, whereas execution time is negatively correlated
with both left and right prefrontal rCBF (Baker et al., 1996; Dagher et al., 1999).
With the preponderant emergence of fMRI, more widely accessible and
cheaper, the use of PET in social sciences has seemingly plateaued. Nevertheless,
PET may still hold a relevant role for management scholarships.This technique
measures blood flow in absolute terms (while fMRI measures changes in blood
oxygenation), permitting therefore a more precise comparison between subjects,
sessions, and brain regions (Minoshima et al., 1994).Therefore, there is considerable
reliability for research investigating associations within subjects across different
tasks.
Moreover, PET has the unique ability to measure cerebral metabolism, hence
Neuroscientific methods 263
associate differences in molecular synthesis with difference in behavior (Phelps and
Mazziotta, 1985). For instance, in the striatum, differences in the synthesis of
dopamine, a molecule frequently implicated in impulsive behaviors, has been
associated withdifferences in reversal learning (Cools et al., 2009).This
phenomenon is connected to decisions made under emotional situations and
conflicts (Fellows and Farah, 2003; Kovalchik and Allman, 2005), which are
circumstances often experienced across several management levels (Huy, 2002).
Electroencephalography (EEG)
While measuring regional cerebral flow can provide a detailed anatomical mapping
of active brain areas, the time resolution of the related methods is generally too
slow to reveal the rapid flux of neuronal communication. Conversely, surface
recordings of the electric fields emanating from active populations of neurons offer
a higher degree of temporal resolution (on the order of milliseconds), but yield a
less complete picture of anatomical sources.This method, called electroen-
cephalography (EEG), is the oldest non-invasive method to measure brain activity
(Nunez, 1995).
The existence of electrical currents in the brain was discovered by Richard
Caton (1875), and the first electroencephalographic experiment was performed in
1929 by Hans Berger (1929), in which he discovered the alpha rhythm, waves with
a uniform rhythm typical of a subject awake in a quiet resting state (Adrian and
Matthews, 1934). Since this pioneering discovery, researchers have conducted
thousands of experiments, leading to advances in both the recording systems and
the understanding of brain functions (Freeman and Quiroga, 2013). Nowadays, it
is acknowledged that our brain produces several types of brainwaves with different
frequencies, and each of them is associated with particular mental states. As an
example, beta waves have a frequency of 15–38 Hz and are characteristic of
individuals who are fully awake and aler t (Nunez, 1995).
EEG employs advanced signal processing methods to infer data about the brain
through the scalp and skull (Niedermeyer and Lopes da Silva, 1995). It develops
around the concept that neurons are excitable cells, which transmit information
through electrical or chemical signals via dedicated structures called synapses.
Populations of neurons are connected into networks and communicate with each
other repeatedly by sending electrical impulses.The technique specifically
measures the resulting electrical currents that flow underneath the scalp while
short extensions of certain cortical neurons (the dendrites of pyramidal neurons)
are excited (Atwood and Mackay, 1989). When the brain processes an event,
thousands of these cells are activated at the same time, causing a fluctuation in
voltage.In order to measure these signals a cap with several electrodes is placed on
the subject’s head.By quantifying the differences between the electrodes,the flow
and strength of the electric field can be inferred (Tyner et al., 1989). Since the
signals that reach the scalp are very small (usually in the range of 1 to 100 µV) they
are then amplified and converted into a digital form (Luck, 2005). However,
264 Massaro
because EEG detects electrical signals at the scalp, it can only measure activity
coming from the cortex, making almost impossible evaluating direct activation in
deeper lying structures (Bronzino, 1995). Moreover, EEG has a very high temporal
resolution, hence allowing for very fast measurement within milliseconds.
However, the low spatial resolution makes it challenging to precisely localize the
source of the signal.
To be able to extract the correct electrical signal associated with a behavioral
experimental task and distinguish it from the background noise, a functional EEG
study requires multiple averaged measurements. By averaging signals, researchers
can indicate that a certain task causes a specific activation of the measured brain
region at a specific time stamp. Simply put, the resulting response is called event-
related potential (ERP) (Squires et al., 1976).
The preparation of a standard EEG setup takes a relatively long time (Lebedev
and Nicolelis, 2006); however, an EEG assessment can be accomplished while
people are seated and engaged in everyday activities, including conversations
associated with the type of task the experiment involves. Being relatively
inexpensive, non-invasive,and non-harmful for the participants of a study,EEG has
been one of the most applied techniques in management studies.
Already in the 1980s, Robey and Ta ggart (1981; 1982) sought to establish a
linkage between measures of managerial styles and brain activity recorded with
EEG. Drawing on the insights on hemispheric dominance and on the initial claims
of Doktor (1978) they argued relationships between cerebral dominance and scales
able to assess distinct strategic leadership types. More recently, Waldman and
colleagues (2011) focused on inspirational management and its association with
electrical brain activity, by recording subjects’ beta waves in terms of coherence. In
this way they were able to measure the coordinated activity between multiple parts
of the brain when the subjects were presented a visual task on activities related to
inspirational leadership (Figure 10.4).They showed that coherence in the right
frontal areas of the brain could offer the basis for social visionary communication,
which helps to build followers’ perceptions of charismatic leaders. Although this
research pipeline has ambitious potential, with some authors foreseeing the
eventuality to train managers to “replicate such brain patterns,” its results shall be
understood with caution (as reported in Dvorak and Badal [2007]).
EEG has been employed to understand several other management constructs,
such as punishment behavior.This topic has received increasing attention from
management researchers (Simons, 1991), as it is associated with important variables
such as power, reward, cooperation, and fairness. For instance, when managers are
considered to have punished others unfairly, they not only impair their own
reputations, but also risk eliciting negative attitudes and counterproductive
behaviors,weakening the perceived legitimacy of their authority (Ball, Treviño,and
Sims, 1994). Knoch and colleagues (2010) disclosed that the right lateral prefrontal
cortex may play a central role in punishment behavior: subjects with an active PFC
region seem most likely to punish an unfair proposal, even though the action has
disadvantages for themselves,and vice versa.
Neuroscientific methods 265
Lesion studies, VLSM, TMS, and MEG
It is central to highlight that while the techniques examined so far can identify
brain activation, they cannot independently determine which of these areas are
indispensable for performing the experimental task.This information can instead
be provided by neuropsychological studies.
Among them, lesion studies are the oldest approaches to the study of mental
functions. Already in 1861, Paul Broca (1861) suggested a relation between
language and the brain’s left hemisphere, setting the basis for localizing human
brain function by studying the correlation between a behavioral disorder and the
site of a brain injury.This approach has been the milestone for a long tradition of
neuropsychological research, grounding therationale of cognitivedissociation
266 Massaro
FIGURE 10.4 Spectral analysis of right front coherence in leadership research
Notes: The gradient shows the levels of coherence on 3 right frontal electrode locations,including
areas at 0% (indicated by the minus signs), areas at 100% (indicated by plus signs), and in
between. Dark regions with a + represent areas with high degrees of coherence (75% or
higher); dark reg ions with minus signs characterize areas with low coherence (25% or lower).
The numeric values indicate the summed averaged coherence scores for such brain regions in
different leaders.
Source:Waldman, Balthazard, and Peterson, 2011
Caring
Caring Caring
Caring
Caring
Caring
Caring Caring
Caring Caring
Caring
Caring
(Caramazza, 1986; Shallice, 1988). Single dissociation occurs when damage in a
brain region causes a disruption in one specific mental function but not in another,
allowing inference that those functions are independent of each other (Kolb and
Whishaw, 2009). Alternatively, double dissociation is conceivable when a subject
with brain damage shows poor performance on one task and good performance
on another task, while other patients show the opposite performance.This allows
inference that the two related mental processes function independently of each
other. Several researchers have criticized the logic or some of its applications,
arguing, for example, that double dissociations do not necessarily imply a difference
in processing mechanisms between tasks (Bullinaria and Chater, 1995; Chater and
Ganis, 1991).
Despitethese criticisms and the advancements provided by modern
neuroimaging techniques, managementresearch can still gather important
investigative elements from lesion studies (Rorden and Karnath, 2004). One
approach, for example, is that of grouping subjects according to the lesions’
locations and comparing the performance of each group.This can be exemplified
in attention studies. Attention is a topic of great interest in strategic management,
since it fosters questions not only in problem solving (Bower, 1986; Newell and
Simon, 1972), but also in aspects such as strategic issue diagnosis (Dutton, Fahey,
and Narayanan,1983), and organizational mindfulness (Levinthal and Rerup, 2006;
Weick and Sutcliffe, 2006). In parallel, neuroscience studies on attention have
proposed the existence of three systems of attention: orienting, alerting, and
executive control (Posner et al., 2007). Research has compared their efficiencies
between differently brain-damaged subjects (frontal, temporal, and parietal lesions)
and healthy controls using the Attention Network Te st (ANT) (Raz and Buhle,
2006). A reduced efficiency of the executive network was found in patients with
frontal lobe and parietal lobe injuries, patients with parietal lobe injuries showed a
deficit in the orienting network, and analysis of lateralization indicated the right
hemisphere superiority to the alerting system.
Subjects with brain damages, as those recruited for that research, can easily be
enrolled from different sources (i.e., ischemic, tumor removal, degenerative diseases
patients). However, aside from the fairly obvious ethical concerns, there are some
practical implications in conducting large-scale lesions studies in management,
since they require dedicated infrastructure and personnel. Moreover, a major
drawback of this approach is that brain damage is not under easy experimental
controls (Brett, Johnsrude, and Owen, 2002).This ongoing uncertainty means that
it is difficult to control for phenomena such as brain reorganization, different
severity of lesion, and more generally individual differences.
A combination of imaging and lesion studies could prove useful to overcome
these difficulties and advance management investigations (Shallice, 2003). Voxel-
based Lesion-Symptom Mapping (VLSM) is a relatively recent method for analyzing
the relationships between behavioral deficits in a neurological population and
lesion sites associated with those deficits.The major advantage of VLSM over
classic lesion studies is that it allows researchers to examine such data without
Neuroscientific methods 267
articulating behavioral and lesion sites’ boundaries (e.g., parietal patients vs. frontal
patients) (Bates et al., 2003). For instance, Driscoll and colleagues (2012) have
elucidated the neural bases of self-reported emotional empathy comparing a group
of Vietnam combat veterans who had traumatic brain injuries with a group of
non-brain-injured veterans, by using VLSM on computed tomographic scans.
Empathy is essential for managing relations in organizations, and research has
suggested that the ability to understand others’ emotions enables a manager to
foster strategic management (Nonaka and Toyama, 2007).
Another method alternative to classical lesion studies is offered by Transcranial
Magnetic Stimulation (TMS). However, differently from classic lesion studies, this
approach does not involve permanent brain damage.Transcranial magnetic
stimulation is a non-invasive technique that electromagnetically induces very brief
electrical current pulses through a coil placed above the brain area to be stimulated;
this produces weak electrical currents in the underlying neurons (Walsh and
Cowey, 2000).TMS thus holds a unique role in understanding how the brain
works,because it can be used to disengage a brain area for a minimal time,allowing
scientists to understand its functional role (Pascual-Leone, Bartes-Faz, and Keenan,
1999).TMS has a temporal resolution of milliseconds, while the spatial resolution
depends on the coil and the target area, which can be located thanks to a navigator
device (Stewart andWa lsh, 2006).
Although promising, the neurophysiological effects of TMS are not fully
understood, often leading to difficulties in interpretation of the results; it may have
excitatory, as well as inhibitory effects on brain regions, and differences in TMS
stimulation parameters can influence experimental results (Rorden and Karnath,
2004). Moreover, current TMS systems are able to directly disrupt regions only
near the scalp, usually evoke slight changes in behavior, and may induce epileptic
seizures if applied at high intensities (Sack and Linden, 2003).These limitations
make the technique not yet ideal for investigating long-term and social effects, such
as those characterizing organizations. Thus, one of the most promising research
directions is to explore multimodal imaging modalities, the combined use of two or
more experimental techniques able to complement each other (e.g., TMS and
fMRI) (Siebner and Rothwell, 2003; Babiloni et al., 2004).
Another neuroimaging method that might receive increasing resonance in
management studies is magnetoencephalograpy (MEG). It uses signals emerging from
the scalp and measures fluctuations in the magnetic field as a result of changes in
neural activity (Hämäläinen et al., 1993). Since the fields have strength of only
50–500fT (about 100 million times weaker than the Earth’s magnetic field), MEG
instrumentation requires the use of special devices placed on the subject’s head
(superconducting SQUID-based magnetometers), and a magnetically shielded
room (Vrba and Robinson, 2002). Magnetoencephalography is specular to TMS:
while MEG detects magnetic fields generated by neural currents, TMS induces
currents in the brain via magnetic fields. Moreover, MEG provides elevated
temporal resolution and, due to poor signal degradation, results in high spatial
discrimination of neural contributions (Pascual-Marqui, Michel, and Lehmann,
268 Massaro
1994). Finally, it allows for absolute measures, which are not dependent on the
choice of a reference, introducing new opportunities to further investigation of
strategic management topics.
Other neuroscience methods in management research
While this workhaslargely concentrated on neuroimaging technologies, it is neces-
sary to mention that several other neuroscientific approaches (e.g., those measuring
autonomic parameters, neurogenetics, and neuropharmacologytechniques) can
provide important information for management and strategyinquiries.
For instance, a method with the potential to offer novel insights in strategic
management research is that of eye-tracking. Eye-movement data usually consist of
eye fixations, when the gaze position is relatively still so that the foveae remain
directed at a particular point in space and information is extracted from the
stimulus (Pieters, 2008).The rationale of this method is then aimed at determining
the spatial point at which a viewer’s foveae are directed and the extent of time they
remain focused there. For instance, eye-tracking has been employed in the
evaluation of facial perception, which is an important antecedent to successful
social and business communication, since human social inferences are derived
largely from viewing facial expression (Schulte-Mecklenbeck, Kühberger, and
Ranyard,2011). Similarly, it can be employed to provide insights in the processing
of risky decisions (Glöckner and Herbold, 2011).
Research in strategy could also be further supported by neurogenetics experi-
mental procedures,investigating the basis of cognition,sociality,and behavior.Such
method has already been applied to business disciplines, for instance to entrepre-
neurship (Nicolau and Shane, 2010).This type of studies generally relies on
comparisons between twins or examines allelic differences, suggesting that genetic
variances translate into functional differences. Neurogenetics may be particularly
useful for strategy research by linking polymorphisms of selected genes affecting
neurotransmitter systems, or by employing genome-wide approaches to investigate
mental functions and behavioral phenotypes. For instance, research on exploration
and exploitation has shown that basal ganglia support learning to exploit decisions
that have yielded positive outcomes in the past, while the prefrontal cortex is
associated to strategic exploratory decisions when the magnitude of potential
outcomes is unknown. Distinct genetic processes sustain these differences: genes
controlling striatal dopamine function (DARPP-32 and DRD2) are associated with
exploitation, while a gene controlling prefrontal dopamine function (COMT) is
associated with “directed exploration” (Frank et al., 2009).
Although the sampling collecting procedures for these studies are quite simple
(a saliva or blood sample is usually sufficient), these analyses require advanced
expertise and facilities. Moreover, due to the intimate nature of the approach,
research findings are at high risk of producing serious ethical concerns or stigma-
tization (i.e.,associating specific polymorphisms to supposed deviant attitudes or to
targeted populations) (Illes and Racine, 2005).
Neuroscientific methods 269
Finally, neuropharmacological studies rely on the rationale that specific compounds
excite or inhibit particular neurotransmitter actions (neurotransmitter loading or
depletion), thereby influencing a subject’s behavior. Also in this case, these
approaches hold important concerns, in particular in relation to cognitive
enhancement (Bostrom and Sandberg, 2009). Examples of this methodological
approach are studies,which employ neuropeptides such as oxytocin and vasopressin
(Heinrichs, von Dawans, and Domes, 2009). For example, research has proposed a
role for oxytocin in modulating trust, thus influencing cooperative relations.
Administration of intranasal oxytocin increased the amount of money that a social
actor was ready to offer to a “trustee,” who could return either a smaller or larger
sum back to the person (Kosfeld et al., 2005). However, oxytocin did not increase
monetary distributions when the feedback was determined by a random draw,
indicating that these results are specific to the social interaction between the two
actors. In support to this research, imaging studies revealed that oxytocin decreased
amygdala activity, independently from the experimental scenario, providing further
insights into the neural mechanisms by which this neuropeptide regulates
cooperation (Petrovic et al., 2008).
Ethics, hype, and hope
Despite its complexity and technicalities, neuroscience research has engaged the
interest and curiosity of several audiences,including non-expert scholars (Frazzetto
and Anker, 2009). Since the 1990s we have seen the rise of a neuroculture (Rolls,
2012), with “neuro” concepts increasingly assimilated in the social sciences,
including management research.
In response to this phenomenon, some scholars have argued that managerial,
organizational, and strategy frameworks involve dynamic systems, multilevel
analyses, depend on environment,interaction with people, tasks, and structures, and
these paradigms cannot be fully appreciated with neuroscientific methods currently
available (Powell, 2011). Others have associated neuroimaging research with
phreonological cults (Dobbs, 2005; Simpson, 2005; Uttal, 2001), pointing out
methods, such as fMRI, inform about the location of neural activities, yet offer a
very plastic snapshot of the complex mental and behavioral processes occurring in
the brain (Coltheart, 2006; Page, 2006). On the other hand, researchers have
responded that functional neuroimaging allows for broader and more complex
explanations, and have proposed connectivist and network frameworks (Cowell,
Huber, and Cottrell, 2009; Rogers et al., 2007; Rubinov and Sporns, 2010).
Moreover, neuroimaging methods were primarily conceived for clinical
applications, and only later have been applied to behavioral and management
inquiries. What could happen if some incidental pathological abnormality emerges
during a management study? What if a non-clinical researcher thinks there is an
abnormality, which is instead just an ordinary physiological variant, and worries the
subject unreasonably?
These ethical issues are not insignificant (Grossman and Bernat, 2004). An
270 Massaro
unexpected finding may turn the naive desire of a volunteer to have a picture of
his or her brain into a major incident with severe consequences impacting both
health and everyday life (Kirschen, Jaworska, and Illes,2006). And if it is unethical
not to provide result interpretations, detecting pathological abnormalities is a
relatively frequent event, especially with functional neuroimaging systems
(Katzman, Dagher, and Patronas, 1999).Therefore,in order to minimize the impact
of incidental findings, research protocols should include informed consent and
adhere to detailed guidelines (Illes et al., 2004) and research outputs should be
examined and reported by qualified personnel able to flag minor normal variants
as well as pathologies (Illes et al., 2004).
Despite these vibrant considerations and debates, it is possible to claim that
learning about the brain can help to understand further people’s behaviors in firms
and organizations; thus neuroscience methods can add to the understanding of
management and strategy frameworks’ elements on the basic neural process
involved.To this end, knowledge on the techniques presented in this chapter
represents a key instrument to acquire new awareness about those paradigms, and
to ascertain the basis for a durable and doable association between neuroscience
and management research. Nevertheless, researchers should not only understand
and recognize both these tools’ potentials and limitations, but also be careful about
getting into the hype of including a“brain-talk” or neuroimage with any and every
research output. For instance, there is growing evidence that an untrained audience
too often trusts catchy neuroscience claims blindly (Racine, Bar-Ilan, and Illes,
2005;Weisberg et al., 2008). Once research results are publicized, especially when
linked to personality or social constructs, non-experts often relate with lay
interpretations of these outcomes. Although this phenomenon should not be
confused with the merits of sound research (Beck, 2010), it is also true that the way
in which some findings are presented tends to be vigorously loaded (Racine et al.,
2010). Extensively incorporating brain region labels and scans, perhaps supported
by amateurish statistics or imprecise anatomical knowledge, may become just
rhetoric, if not supported by clear experimental and scientific agendas and precise
methodological disclosure (Illes, 2006; McCabe and Castel, 2008;Weisberg et al.,
2008). Similarly, management scholars shall rethink the epistemological urge of
outlining new “neuro” disciplines (Bennett, Hacker, and Bennett, 2003; Legrenzi
and Umiltà, 2011). For one, here I have provided an introductory review on how
experimental neuroscience for management and strategy must necessarily be
considered as a set of instruments, suggesting that also uprising “neuroman-
agement” discussions must not disengage from the fuller understanding of the
underlying neuroscience.
The hope in and the competitive advantage of neuroscience and management
is thus an integrated framework, established through systematic understanding of
neuroscientific methods, multidirectional communication, and planned collabo-
rations between scholars as the most appropriate means to achieve a fuller
knowledge on human strategic behavior.
Neuroscientific methods 271
Notes
1I would like to thank Sigal Barsade,James Ber ry, Giambattista Dagnino,Mar tin Kilduff,
and Simcha Jong for their useful suggestions.
2Examples of these evidences include e.g. the Zhejiang University’s Neuromanagement
Laboratory; dedicated sessions at the Academy of Management Meetings; the Open
Research Area NESSHI (www.n e sshi.eu/) and the Human Brain
(https://www.humanbrainproject.eu/) projects.
3An all-inclusive analysis of the neuroscience methods would have to be book-length to
cover eachof the techniques presented in thischapter.Afew examplesof
neuroscience-specific texts able to exhaustively address these topics, which however do
not touch management paradigms, are: Cabeza and Kingstone, 2001; Senior, Russell,
and Gazzaniga, 2006; To g a and Mazziotta, 2002.
4The notion of functional neuroimaging of behavior employed in this work seeks to
highlight the differences with the use of these techniques in clinical practice (i.e.,
clinical functional neuroimaging). I will interchangeably use the terms mental and
behavioral to broadly encompass cognitive, emotional,and affective processes. Readers
must note that there is direct inference when the investigator infers something about
the role of particular brain regions in cognitive function. Reverse inference, which is
instead not recommended, occurs when the investigator infers the engagement of
particular cognitive functions based on activation in particular brain regions (Poldrack
2006).
5Tomographic techniques are those methods that allow imaging a body by sections
through a penetrating wave. They allow imaging of a slice through, rather than a
projection of a three-dimensional structure (Natterer and Ritman,2002).
6Technically a T2* relaxation time.
7This work does not review classic electrophysiological techniques (e.g., single- and
multi-unit recordings, patch clamp; for more information on these methods, see
Bretschneider and de Weille, 2006), and methods that currently have received marginal
applications in the strategic management scholarship (e.g., Magnetic Resonance
Spectroscopy [MRS]). Moreover, the work will solely cover neuroscience methods in
humans, hence excluding those applications carried out in primates (for more
information on this, see Murray and Baxter, 2006).
83 Teslas are roughly 60 thousand times greater than the Earth’s magnetic field.
9MRI and fMRI are also characterized by high contrast resolution, which is the ability to
distinguish the differences between two arbitrarily similar but not identical tissues,such
as white and grey matter (Bushberg et al., 2002).
References
AA.VV. (2009) Perspectives on Psychological Science, 4(3).
AA.VV. (1995) Academy of Management Journal, 38(1).
Adams, J. S. (1963) “Toward an understanding of inequity”, Journal of Abnormal Social
Psychology, 67(5): 422–436.
Adams, J. S. (1965) “Inequity in social exchange,” in L. Berkowitz (ed.) Advances in
Experimental Social Psychology, 267–269, New York: Academic Press.
Adenzato, M. and Garbarini, F. (2006) “The As if in cognitive science, neuroscience and
anthropology: A journey among robots, blacksmiths and neurons”, Theory & Psychology,
16(6): 747-759.
Adrian, E. D. and Matthews, B. H. (1934) “The Berger rhythm: Potential changes from the
occipital lobes in man,” Brain, 57(4): 355-385.
Ansoff, H. I. (1991) “Critique of Henry Mintzberg”sThe design school: reconsidering the
basic premises of strategic management,” Strategic Management Journal, 12(6): 449–461.
272 Massaro
Anzai,Y. and Simon, H. A. (1979) “The theory of learning by doing,” Psycholgical Review,
86(2): 124–140.
Ariely,D. and Ber ns, G. S. (2010)“Neuromarketing: The hope and hype of neuroimaging in
business,” Nature Reviews Neuroscience, 11(4): 284–292.
Armstrong, J. S. (1982) “The value of for mal planning for strategic decisions: Review of
empirical research,” Strategic Management Journal, 3(3): 197–211.
Attwell, D. and Iadecola, C. (2002) “The neural basis of functional brain imaging signals,”
TRENDS in Neurosciences, 25(12): 621–625.
Atwood, H. L. and MacKay,W. A. (1989) Essentials of Neurophysiology, New York: Decker.
Babiloni, F., Mattia, D., Babiloni, C., Astolfi, L., Salinari, S., Basilisco, A., and Cincotti, F.
(2004) “Multimodal integration of EEG, MEG and fMRI data for the solution of the
neuroimage puzzle,” Magnetic Resonance Imaging, 22(10): 1471–1476.
Baker, S. C., Rogers, R. D., Owen, A. M., Frith, C. D.,Dolan, R. J., Frackowiak, R. S. J.,and
Robbins, T. W. (1996) “Neural systems engaged by planning: A PET study of the Tower
of London task,” Neuropsychologia, 34(6): 515–526.
Ball, G. A., Trevino, L. K. and Sims, H. P. (1994) “Just and unjust punishment: Influences on
subordinate performance and citizenship,” Academy of Management Journal, 37(2):
299–322.
Bandettini, P. A. (2002) “fMRI: The spatial, temporal, and interpretive limits of functional
MRI,” in Davis, K., Charney, D., Coyle, J. and Nemeroff (eds), Neuropsychopharmacology:
The Fifth Generation of Progress, 343–356, Philadelphia: Lippincott Williams and Wilkins.
Bandettini, P. A. (2009) “What’s new in neuroimaging methods?,” Annals of the New York
Academy of Sciences, 1156(1): 260–293.
Bandettini, P. A.,Wong, E. C., Hinks, R. S., Tikofsky, R. S. and Hyde, J. S. (1992) “Time
course EPI of human brain function during task activation,” Magnetic Resonance in
Medicine, 25(2): 390–397.
Barnard, C. I. (1938) The Functions of the Executive, Cambridge, MA: Harvard University
Press.
Bates, E.,Wilson, S. M., Saygin,A. P., Dick, F.,Sereno, M. I., Knight, R.T., and Dronkers, N.
F. (2003) “Vox e l-based lesion–symptom mapping,” Nature Neuroscience, 6(5): 448–450.
Beaulieu, A. (2000) “The Space inside the skull: Dig ital representations, brain. mapping and
cognitive neuroscience in the decade of the brain,” Ph.D. Dissertation, Science and
Technology Dynamics, University of Amsterdam, the Netherlands.
Beaulieu, C. (2002) “The basis of anisotropic water diffusion in the nervous system: A
technical review,” NMR in Biomedicine, 15(7–8): 435–455.
Beck, D. M. (2010)“The appeal of the brain in the popular press,”Perspectives on Psychological
Science, 5(6): 762–766.
Becker,W. J., Cropanzano R., and Sanfey A. G. (2011) “Organizational neuroscience: Taking
organizational theory inside the neural black box,” Journal of Management, 37(4): 933–961.
Bennett, M. R., Hacker, P. M. S., and Bennett, M. R. (2003) Philosophical Foundations of
Neuroscience, Oxford: Blackwell.
Berger, H. (1929) “Uber das Elektrenkephalogramm des Menschen,” Arch. Psychiatrie Nerv.,
87: 527–570.
Bloch, F. (1946) “Nuclear induction,” Physical Review, 70(7–8): 460.
Bostrom, N. and Sandberg, A. (2009) “Cognitive enhancement: Methods, ethics, regulatory
challenges,” Science and Engineering Ethics, 15(3): 311–341.
Bower, J. L. (1986) Managing the Resource Allocation Process,Vol. 3, Cambridge, MA: Harvard
Business Press.
Bretschneider, F. and De Weille, J. R. (2006) Introduction to Electrophysiological Methods and
Neuroscientific methods 273
Instrumentation, London: Elsevier.
Brett, M., Johnsrude, I. S., and Owen,A. M. (2002) “The problem of functional localization
in the human brain,” Nature Reviews Neuroscience, 3(3): 243–249.
Broca,P. (1861)“Perte de la parole,ramollissement chronique et destruction partielle du lobe
antérieur gauche du cerveau,” Bulletin Society Anthropology, 2: 235–238.
Brockner, J. (1992) “The escalation of commitment to a failing course of action: Toward
theoretical progress,” Academy of Management Review, 17(1): 39–61.
Bronzino, J. D. (ed.) (1995) The Biomedical Engineering Handbook,Vol. 1, Boca Raton, FL:
CRC Press.
Brown,M. A. and Semelka, R. C. (2011) MRI: Basic Principles andApplications, Hoboken, NJ:
Wiley.
Bullinaria, J. A. and Chater, N. (1995) “Connectionist modeling: Implications for cognitive
neuropsychology,” Language and Cognitive Processes, 10(3–4): 227–264.
Bushberg, J.T., Seibert, J. A., Leidholdt, Jr, E. M., and Boone, J. M. (2002) The Essential
Physics of Medical Imaging, Philadelphia, PA: Lippincott Williams & Wilkins.
Cabeza, R. and Kingstone, A. (eds) (2001) Handbook of Functional Neuroimaging of Cognition,
Cambrdige, MA: MIT Press.
Camerer, C. F. (2003) Behavioral Game Theory: Experiments in Strategic Interaction, Princeton,
NJ: Princeton University Press.
Campbell-Meiklejohn, D. K.,Woolrich, M. W., Passingham, R. E., and Rogers, R. D. (2008)
“Knowing when to stop: The brain mechanisms of chasing losses,” Biological Psychiatry,
63(3): 293–300.
Caramazza, A. (1986) “On drawing inferences about the structure of normal cognitive
systems from the analysis of patterns of impaired performance: The case for single-
patient studies,” Brain and Cognition, 5(1): 41–66.
Caton, R. (1875) “Electrical currents of the brain,” Journal of Nervous and Mental Disease, 2(4):
610.
Chapman, B. L. (2006) “Gradients: The heart of the MRI machine,” Current Medical Imaging
Reviews, 2(1): 131–138.
Chater, N. and Ganis, G. (1991) “Double dissociation and isolable cognitive processes,”
Behaviour, 5(9): 668–672.
Churchland, P. S. and Sejnowski, T. J. (1988) “Perspectives on cognitive neuroscience,”
Science, 242(4879): 741–745.
Cohen-Charash, Y. and Mueller, J. S. (2007) “Does perceived unfairness exacerbate or
mitigate interpersonal counterproductive work behaviors related to envy?,” Journal of
Applied Psychology, 92(3): 666–680.
Colquitt, J. A., Conlon, D. E.,Wesson, M. J., Porter, O. L. H., and Ng, K.Y. (2001) “Justice at
the millennium: A meta-analytic review of 25 years of organizational justice research,”
Journal of Applied Psychology, 86(3): 425–445.
Coltheart, M. (2006) “What has functional neuroimaging told us about the mind (so far)?,”
Cortex, 42: 323–331.
Connolly, W. E. (2002) Neuropolitics: Thinking, Culture, Speed, Minneaoplis, MN: University
of Minnesota Press.
Cools, R., Frank, M. J., Gibbs, S. E., Miyakawa, A., Jagust, W., and D’Esposito, M. (2009)
“Striatal dopamine predicts outcome-specific reversal learning and its sensitivity to
dopaminergic drug administration,” The Journal of Neuroscience, 29(5): 1538–1543.
Cowell, R. A., Huber, D. E., and Cottrell, G. W. (2009) “Virtual brain reading: A connec-
tionist approach to understanding fMRI,” Paper presented at the 31st Annual Meeting of
the Cognitive Science Society.
274 Massaro
Crockett, M. J., Clark, L., Tabibnia, G., Lieberman, M. D., and Robbins, T. W. (2008)
“Serotonin modulates behavioral reactions to unfairness,” Science, 320(5884): 1739–1739.
Cushman, F. and Greene, J. D. (2012) “Finding faults: How moral dilemmas illuminate
cognitive structure,” Social Neuroscience, 7(3): 269–279.
Dagher,A., Owen,A. M., Boecker, H., and Brooks, D. J. (1999) “Mapping the network for
planning: A correlational PET activation study with the Tower of London task,” Brain,
122(10): 1973–1987.
Damasio, A. R. (1994) Descartes’ Error: Emotion, Reason, and the Human Brain, London:
Penguin Books.
Daw, N. D., O’Doherty, J. P, Seymour, B., Dayan, P., and Dolan, R. J. (2006) “Cortical
substrates for exploratory decisions in humans,”Nature, 441, 876–879.
Dawson, D. G. (1951) “A summation technique for detecting small signals in a large irregular
background,” Journal of Physiology, 494: 251–326.
Dimoka, A., Banker, R. D., Benbasat, I., Davis, F. D. , Dennis, A. R., Gefen, D., and Weber, B.
(2012) “On the use of neuropyhsiological tools in IS research: Developing a research
agenda for NeuroIS,” MIS Quarterly, 36(3): 679–702.
Dobbs, D. (2005) “Fact or phrenology?,” Scientific Amer ican Mind, April 1–8: 24–31.
Doktor, R. (1978) “Problem solving styles of executives and management scientists,” TIMS
Studies in the Management Sciences, 8(2): 123–134.
Donders, F. C. (1969) “On the speed of mental processes,” Acta Psychologica, 30: 412–431.
Driscoll, D. M., Dal Monte, O., Solomon, J., Krueger, F. and Grafman, J. (2012) “Empathic
deficits in combat veterans with traumatic brain injury: A voxel-based lesion-symptom
mapping study,” Cognitive and Behavioral Neurology, 25(4): 160–166.
Dutton, J. E., Fahey, L. and Narayanan,V. K. (1983) “Toward understanding strategic issue
diagnosis,” Strategic Management Journal, 4(4): 307–323.
Dvorak, P. and Badal, J. (2007) “This is your brain on the job,” The Wall Street Journal,
September 20. Retrieved at: http://online.wsj.com/news/articles/
SB119024585835733168
Fellows,L. K. and Farah, M. J. (2003) “Ventromedial frontal cortex mediates affective shifting
in humans: Evidence from a reversal learning paradigm,” Brain, 126(8): 1830–1837.
Finger, S. (2001) Origins of Neuroscience: A History of Explorations into Brain Function, New
York: Oxford University Press.
Fox, P.T. and Raichle, M. E. (1986) “Focal physiological uncoupling of cerebral blood flow
and oxidative metabolism during somatosensory stimulation in human subjects,”
Proceedings of the National Academy of Sciences, 83(4): 1140–1144.
Frank, M. J., Doll, B. B., Oas-Terpstra, J. and Moreno, F. (2009) “Prefrontal and striatal
dopaminergic genes predict individual differences in exploration and exploitation,”
Nature Neuroscience, 12(8): 1062–1068.
Franks, D. D. (2010) Neurosociology: The Nexus Between Neuroscience and Social Psychology. New
York: Springer.
Frazzetto,G. andAnker, S. (2009) “Neuroculture,” Nature Reviews Neuroscience,10(11): 815–821.
Freeman,W. J. and Quiroga, R. Q.(2013) Imaging Brain Function with EEG: AdvancedTemporal
and Spatial Analysis of Electroencephalographic Signals, New York: Springer.
Friston, K. J. (2005) “Models of brain function in neuroimaging,” Annual Review of
Psychology, 56: 57–87.
Friston, K. J., Price, C. J., Fletcher, P., Moore, C., Frackowiak,R. S. J., and Dolan, R. J. (1996)
“The trouble with cognitive subtraction,” Neuroimage, 4(2): 97–104.
Garvey, C. J. and Hanlon, R. (2002) “Computed tomography in clinical practice,” BMJ:
British Medical Journal, 324(7345): 1077.
Gazzaniga, M. and LeDoux, J. (1978) The Integrated Mind, New York: Plenum.
Neuroscientific methods 275
Ghadiri, A., Habermacher, A., and Peters, T. (2012) “Neuroleadership: The backdrop,” in
Neuroleadership: A Journey Through the Brain for Business Leaders, 1–15, Berlin: Springer.
Glimcher, P. W. and Rustichini, A. (2004) “Neuroeconomics: The consilience of brain and
decision,” Science, 306(5695): 447–452.
Glöckner,A. and Herbold, A. K. (2011) “An eye tracking study on information processing
in risky decisions: Evidence for compensatory strategies based on automatic processes,”
Journal of Behavioral Decision Making, 24(1): 71–98.
Greene, J.and Cohen, J. (2004) “For the law, neuroscience changes nothing and everything,”
Philosophical Tra nsactions of the Royal Society, 359(1451): 1775–1785.
Grossman, R. I. and Bernat, J. L. (2004) “Incidental research imaging findings Pandora’s
costly box,” Neurology, 62(6): 849–850.
Gul, F. and Pesendorfer, W. (2008) “The case for mindless economics,” in Caplin, A. and
Schotter, A. (eds), The Foundations of Positive and Normative Economics, 3–39, Oxford:
Oxford University Press.
Gulyas, B., Halldin, C., Sandell, J., Karlsson, P., Sóvágó, J., Kárpáti, E., and Farde, L. (2002)
“PET studies on the brain uptake and regional distribution of [11C] vinpocetine in
human subjects,” Acta Neurologica Scandinavica, 106(6): 325–332.
Hämäläinen, M., Hari, R., Ilmoniemi, R. J., Knuutila, J., and Lounasmaa, O. V. (1993)
“Magnetoencephalography: Theory, instrumentation, and applications to noninvasive
studies of the working human brain,” Reviews of Modern Physics, 65(2): 413.
Heinrichs, M., von Dawans, B., and Domes, G. (2009) “Oxytocin, vasopressin, and human
social behavior,” Frontiers in Neuroendocrinology, 30(4): 548–557.
Hines, T. (1987) “Left brain/right brain mythology and implications for management and
training,” Academy of Management Review, 12: 600–606.
Hounsfield, G. N. (1973) “Computerized transverse axial scanning (tomography): Part 1.
Description of system,” British Journal of Radiology, 46(552): 1016–1022.
Humm, J. L., Rosenfeld, A. and Del Guerra, A. (2003) “From PET detectors to PET
scanners,” European Journal of Nuclear Medicine and Molecular Imaging, 30(11): 1574–1597.
Huy, Q. N. (2002) “Emotional balancing of organizational continuity and radical change:
The contribution of middle managers,”Administrative Science Quarterly, 47(1): 31–69.
Illes, J. (ed.) (2006) Neuroethics: Defining the Issues in Theory, Practice and Policy, New York:
Oxford University Press.
Illes, J. and Racine, E. (2005) “Imaging or imagining? A neuroethics challenge informed by
genetics,” The Amer ican Journal of Bioethics, 5(2): 5–18.
Illes, J., Moser, M. A., McCormick, J. B., Racine, E., Blakeslee, S., Caplan, A., and Weiss, S.
(2009) “Neurotalk: improving the communication of neuroscience research,” Nature
Reviews Neuroscience, 11(1): 61–69.
Illes, J., Rosen,A. C., Huang, L., Goldstein, R. A., Raffin, T. A., Swan, G., and Atlas, S. W.
(2004) “Ethical consideration of incidental findings on adult brain MRI in research,”
Neurology, 62(6): 888–890.
Jones, O. D. and Shen,F. X.(2012) “Law and neuroscience in the United States,” in Spranger,
T. M. (ed.) International Neurolaw: A Comparative Analysis, 349–380, Berlin: Springer.
Kable, J. W. (2011) “The cognitive neuroscience toolkit for the neuroeconomist,” Journal of
Neuroscience, Psychology, and Economics, 4(2): 63–84.
Kato, M., Taniwaki, T. and Kuwabara,Y. (2000) “The advantages and limitations of brain
function analyses by PET,” Clinical Neurology, 40(12): 1274.
Katzman, G. L., Dagher, A. P., and Patronas, N. J. (1999) “Incidental findings on brain
magnetic resonance imaging from 1000 asymptomatic volunteers,” JAMA: the Journal of
the American Medical Association, 282(1): 36–39.
King-Casas, B., To mlin, D., Anen, C., Camerer, C. F., Quartz, S. R., and Montague, P. R.
276 Massaro
(2005) “Getting to know you: Reputation and trust in a two-person economic
exchange,” Science, 308(5718): 78–83.
Kirschen, M. P. ,Jaworska, A., and Illes, J. (2006) “Subjects’ expectations in neuroimaging
research,” Journal of Magnetic Resonance Imaging, 23(2): 205–209.
Knoch, D., Gianotti, L. R., Baumgartner, T. and Fehr, E. (2010) “A neural marker of costly
punishment behavior,” Psychological Science, 21(3): 337–342.
Kolb, B. and Whishaw, I. Q. (2009) Fundamentals of Human Neuropsychology, New York:
Macmillan.
Kosfeld, M., Heinrichs, M., Zak, P. J., Fischbacher, U., and Fehr, E. (2005) “Oxytocin
increases trust in humans,” Nature, 435(7042): 673–676.
Kovalchik, S. and Allman, J. (2006) “Measur ing reversal learning: Introducing the Variable
Iowa Gambling Task in a study of young and old normals,” Cognition & Emotion, 20(5):
714–728.
Kristeva, R., Keller, E., Deecke, L., and Kornhuber, H. H. (1979) “Cerebral potentials
preceding unilateral and simultaneous bilateral finger movements,” Electroencephalography
and Clinical Neurophysiology, 47(2): 229–238.
Kwong, K. K., Belliveau,J. W.,Chesler, D. A., Goldberg, I. E.,Weisskoff, R. M., Poncelet,B.
P. , and Tu rner,R. (1992)“Dynamic magnetic resonance imaging of human brain activity
during primary sensory stimulation,” Proceedings of the National Academy of Sciences, 89(12):
5675–5679.
Lauterbur, P. C. (1973) “Image formation by induced local interactions: examples employing
nuclear magnetic resonance,” Nature, 242(5394): 190–191.
Le Bihan, D. (1996) “Functional MRI of the brain principles, applications and limitations,”
Journal of Neuroradiology, 23(1): 1–5.
Le Bihan, D. (2003) “Looking into the functional architecture of the brain with diffusion
MRI,” Nature Reviews Neuroscience, 4(6): 469–480.
Le Bihan, D., Urayama, S. I.,Aso, T., Hanakawa, T., and Fukuyama, H. (2006) “Direct and
fast detection of neuronal activation in the human brain with diffusion MRI,” Proceedings
of the National Academy of Sciences, 103(21): 8263–8268.
Lebedev, M. A. and Nicolelis, M. A. (2006) “Brain–machine interfaces: Past, present and
future,” TRENDS in Neurosciences, 29(9): 536–546.
Lee, N., Broderick, A. J., and Chamberlain, L. (2007) “What is ‘Neuromarketing?’ A
discussion and agenda for future research,” International Journal Of Psychophysiology, 63(2):
199–204.
Legrenzi, P. and Umiltà, C. (2011) Neuromania: On the Limits of Brain Science, New York:
Oxford University Press.
Levinthal, D. A. and March, J. G. (1993) “The myopia of learning,” Strategic Management
Journal, 14(S2): 95–112.
Levinthal, D. and Rerup, C. (2006) “Crossing an apparent chasm: Bridging mindful and less-
mindful perspectives on organizational learning,” Organization Science, 17(4): 502–513.
Lindebaum, D. and Zundel, M. (2013) “Not quite a revolution: Scrutinizing organizational
neuroscience in leadership studies,” Human Relations, 66(6): 857–877.
Liu, J. Z., Zhang, L., Brown, R. W., and Yue, G. H. (2004) “Reproducibility of fMRI at 1.5
T in a strictly controlled motor task,” Magnetic Resonance in Medicine, 52(4): 751–760.
Logothetis, N. K. (2008) “What we can do and what we cannot do with MRI,” Nature,
453(7197): 869–878.
Logothetis, N. K. and Wandell, B. A. (2004) “Interpreting the BOLD signal,” Annual Review
of Physiology, 66: 735–769.
Logothetis, N. K., Pauls, J., Augath,M., Tr i nath, T., and Oeltermann, A. (2001)
“Neurophysiological investigation of the basis of the fMRI signal,” Nature, 412(6843):
Neuroscientific methods 277
150–157.
Luck, S. J. (2005) “Ten simple rules for designing ERP exper iments,” in T.C. Handy (ed.),
Event-Related Potentials: A Methods Handbook, 16–32, Cambridge, MA: MIT Press.
Magistretti, P. J., Pellerin, L., Rothman, D. L., and Shulman, R. G. (1999) “Energy on
demand,” Science, 283(5401): 496–497.
Malonek, D., Dirnagl, U., Lindauer, U., Yamada, K., Kanno, I., and Grinvald, A. (1997)
“Vascular imprints of neuronal activity: Relationships between the dynamics of cortical
blood flow, oxygenation, and volume changes following sensory stimulation,” Proceedings
of the National Academy of Sciences, 94(26): 14826–14831.
March, J. G. and Simon, H. A. (1958) Organizations, Oxford: Wiley.
McCabe, D. P. and Castel, A. D. (2008) “Seeing is believing: The effect of brain images on
judgments of scientific reasoning,” Cognition, 107(1): 343–352.
McCabe, K., Houser, D., Ryan, L., Smith,V., and Trouard, T. (2001) “A functional imaging
study of cooperation in two-person reciprocal exchange,” Proceedings of the National
Academy of Sciences, 98(20): 11832–11835.
Menon, R. S. and Kim, S. G. (1999) “Spatial and temporal limits in cognitive neuroimaging
with fMRI,” TRENDS in Cognitive Sciences, 3(6): 207–216.
Menon, R. S., Gati, J. S., Goodyear, B. G., Luknowsky, D. C. and Thomas, C. G. (1998)
“Spatial and temporal resolution of functional magnetic resonance imaging,” Biochemistry
and Cell Biology, 76(2-3): 560–571.
Minoshima, S., Koeppe,R. A., Frey, K. A. and Kuhl, D. E. (1994) “Anatomic standardization:
linear scaling and nonlinear warping of functional brain images,” Journal of Nuclear
Medicine, 35(9): 1528–1537.
Mintzberg, H, (1976) “Planning on the left and managing on the right,” Harvard Business
Review, 54(4): 49–58.
Mintzberg, H. (1994) “The f all and rise of strategic planning,”Harvard Business Review, 72(1):
107–114.
Moorman, R. H. (1991) “Relationship between organizational justice and organizational
citizenship behaviors. Do fairness perception influence employee citizenship?,” Journal of
Applied Psychology, 76(6): 845–855.
Murray, E. A. and Baxter, M. G. (2006) “Cognitive neuroscience and nonhuman primates:
Lesion studies,” in Senior, C. E., Russell, T. E., and Gazzaniga, M. S. (eds), Methods in
Mind, 43–69. Cambrdige, MA: MIT Press.
Natterer, F. andRitman, E. L. (2002) “Past and future directions in x ray computed
tomography (CT),”International Journal of Imaging Systems and Technology,12(4):
175–187.
Newell,A. and Simon, H. A. (1972) Human Problem Solving,Vo l. 14, Englewood Cliffs, NJ:
Prentice-Hall.
Nicolaou, N. and Shane, S. (2009) “Born entrepreneurs? The genetic foundations of
entrepreneurship,” Journal of BusinessVe ntur ing, 23: 1–22.
Niedermeyer, E. and Lopes da Silva, F.H. (eds) (1999) Electroencephalography: Basic Principles,
Clinical Applications, and Related Fields, 4th edn, Baltimore: Williams &Wilkins.
Nonaka, I. and Toyama, R. (2007) “Strategic management as distributed practical wisdom
(phronesis),” Industrial and Corporate Change, 16(3): 371–394.
Nunez, P. L. (1995) Neocortical Dynamics and Human EEG Rhythms, New York: Oxford
University Press.
Ogawa, S., Lee, T. M., Kay,A. R., and Tank, D. W. (1990) “Brain magnetic resonance imaging
with contrast dependent on blood oxygenation,” Proceedings of the National Academy of
Sciences, 87(24): 9868–9872.
Ogawa, S., Tank, D. W., Menon, R., Eller mann, J. M., Kim, S. G., Merkle, H., and Ugurbil,
278 Massaro
K. (1992) “Intrinsic signal changes accompanying sensory stimulation: functional brain
mapping with magnetic resonance imaging,” Proceedings of the National Academy of Sciences,
89(13): 5951–5955.
Page, M. (2006) “What can’t functional neuroimaging tell the cognitive psychologist?,”
Cortex, 42(3): 428–443.
Pascual-Leone, A.,Walsh,V., and Rothwell, J. (2000) “Transcranial magnetic stimulation in
cognitive neuroscience–virtual lesion, chronometry,and functional connectivity,” Current
Opinion in Neurobiology, 10(2): 232–237.
Pascual-Marqui, R. D., Michel, C. M., and Lehmann, D. (1994) “Low resolution electro-
magnetic tomography: A new method for localizing electrical activity in the brain,”
International Journal of Psychophysiology, 18(1): 49–65.
Payne, B. (1957) “Steps in long-range planning,” Harvard Business Review, 35(2): 95–101.
Pelizzari, C. A., Chen, G.T., Spelbring, D. R.,Weichselbaum, R. R., and Chen, C.T. (1989)
“Accurate three-dimensional registration of CT, PET, and/or MR images of the brain,”
Journal of Computer AssistedTo mography, 13(1): 20–26.
Petersen, S. E., Fox, P.T., Posner, M. I., Mintun, M., and Raichle, M. E. (1988) “Positron
emission tomographic studies of the cortical anatomy of single- word processing,”
Nature, 331(6157): 585–589.
Petrovic, P., Kalisch, R., Singer, T., and Dolan, R. J. (2008) “Oxytocin attenuates affective
evaluations of conditioned faces and amygdala activity,” The Journal of Neuroscience,
28(26): 6607–6615.
Phelps, M. E. and Mazziotta, J. C. (1985) “Positron emission tomography: human brain
function and biochemistry,” Science, 228(4701): 799–809.
Pieters, R. (2008) “A review of eye-tracking research in marketing,”in Naresh, K. M. (ed.),
Review of Marketing Research,Vol. 4, 123–214, Bingley, UK: Emerald Group Publishing
Limited.
Poldrack, R. A. (2006) “Can cognitive processes be inferred from neuroimaging data?,”
TRENDS in Cognitive Sciences, 10(2): 59–63.
Poldrack, R. A. (2012) “The future of fMRI in cognitive neuroscience,” Neuroimage, 62(2):
1216–1220.
Poldrack, R. A., Kittur, A., Kalar, D., Miller, E., Seppa, C., Gil,Y., and Bilder, R. M. (2011)
“The cognitive atlas: To w ard a knowledge foundation for cognitive neuroscience,”
Frontiers in Neuroinformatics, 5: 5–17.
Powell, T. C. (2011) “Neurostrategy,” Strategic Management Journal, 32(13): 1484–1499.
Powell, T. C., Lovallo, D., and Fox, C. R. (2011) “Behavioral strategy,” Strategic Management
Journal, 32(13): 1369–1386.
Price, C. J. and Friston, K. J. (1997) “Cognitive conjunction: A new approach to brain
activation experiments,” Neuroimage, 5(4): 261–270.
Purcell, E. M., To rrey, H. C., and Pound, R. V. (1946) “Resonance absorption by nuclear
magnetic moments in a solid,” Physical Review, 69(1–2): 37.
Racine, E., Bar-Ilan, O., and Illes, J. (2005) “fMRI in the public eye,” Nature Reviews
Neuroscience, 6(2): 159–164.
Racine, E.,Waldman, S., Rosenberg, J., and Illes, J. (2010) “Contemporary neuroscience in
the media,” Social Science & Medicine, 71(4): 725–733.
Raichle, M. E. (1979) “Quantitative in vivo autoradiography with positron emission
tomography,” Brain Research Reviews, 1(1): 47–68.
Raichle, M. E.(2003) “Functional brain imaging and human brain function,” The Journal of
Neuroscience, 23(10): 3959–3962.
Raichle, M. E. (2009a) “A paradigm shift in functional brain imaging,” The Journal of
Neuroscience, 29(41): 12729–12734.
Neuroscientific methods 279
Raichle, M. E. (2009b) “A brief history of human brain mapping,” TRENDS in
Neurosciences, 32(2): 118–126.
Raichle, M. E.and Snyder,A. Z. (2007) “A default mode of brain function: A brief history
of an evolving idea,” Neuroimage, 37(4): 1083–1090.
Raichle, M. E., Martin,W. R., Herscovitch, P., Mintun, M. A., and Markham, J. (1983) “Brain
blood flow measured with intravenous H2(15)O. II. Implementation and validation,”
Journal of Nuclear Medicine, 24(9): 790–798.
Raz, A. and Buhle, J. (2006) “Typologies of attentional networks,” Nature Reviews
Neuroscience, 7(5): 367–379.
Robey, D. and Ta ggart, W. (1981) “Measur ing managers’ minds: The assessment of style in
human information processing,” Academy of Management Review, 6(3): 375–383.
Robey, D. and Taggart, W. (1982) “Human information processing in information and
decision support systems,” MIS Quarterly, 6(2): 61–73.
Rogers,B.P.,Morgan,V. L.,Newton,A.T., and Gore,J. C. (2007) “Assessing functional connec-
tivity in the human brain by fMRI,” Magnetic Resonance Imaging,25(10): 1347–1357.
Rogers, L. F. (2003) “Helical CT: The revolution in imaging,” American Journal of
Roentgenology, 180(4): 883–883.
Rolls, E.T. (2012) Neuroculture: On the Implications of Brain Science, New York: Oxford
University Press.
Rorden, C. and Karnath, H. O. (2004)“Using human brain lesions to infer function: A relic
from a past era in the fMRI age?,” Nature Reviews Neuroscience, 5(10): 812–819.
Rubinov, M. and Sporns, O. (2010) “Complex network measures of brain connectivity:Uses
and inter pretations,” Neuroimage, 52(3): 1059–1069.
Sack, A.T. and Linden, D. E. (2003) “Combining transcranial magnetic stimulation and
functional imaging in cognitive brain research: Possibilities and limitations,” Brain
Research Reviews, 43(1): 41–56.
Sanfey, A. G., Rilling, J. K.,Aronson, J. A., Nystrom, L. E., and Cohen, J. D. (2003) “The
neural basis of economic decision-making in the ultimatum game,” Science, 300(5626):
1755–1758.
Sartori, G. and Umiltà, C. (2000) “How to avoid the fallacies of cognitive subtraction in
brain imaging,” Brain and Language, 74(2): 191–212.
Schulte-Mecklenbeck, M., Kühberger,A., and Ranyard, R. (2011) “The role of process data
in the development and testing of process models of judgment and decision making,”
Judgment and Decision Making, 6(8): 733–739.
Scott Jr.,W. E. (1966) “Activation theory and task design,” Organizational Behavior and Human
Performance, 1(1): 3-30.
Senior, C. E., Russell, T. E., and Gazzaniga, M. S. (2006) Methods in Mind, Cambr idge, MA:
MIT Press.
Senior, C., Lee, N., and Butler, M. (2011) “PERSPECTIVE – Organizational Cognitive
Neuroscience,” Organization Science, 22(3): 804–815.
Shallice, T. (1988) From Neuropsychology to Mental Structure, New York: Cambridge University
Press.
Shallice, T. (2003) “Functional imaging and neuropsychology findings: How can they be
linked?,” Neuroimage, 20(S1): 46–54.
Siebner, H. and Rothwell, J. (2003) “Transcranial magnetic stimulation: New insights into
representational cortical plasticity,” Experimental Brain Research, 148(1): 1–16.
Simons, R.(1991) “Strategic orientation and top management attention to control systems,”
Strategic Management Journal, 12(1): 49–62.
Simpson, D. (2005) “Phrenology and the neurosciences: Contributions of FJ Gall and JG
Spurzheim,” ANZ Journal of Surgery, 75(6): 475–482.
280 Massaro
Smith, K., Dickhaut, J., McCabe, K., and Pardo, J. V. (2002) “Neuronal substrates for choice
under ambiguity, risk, gains, and losses,” Management Science, 48(6): 711–718.
Song, A. W., Huettel, S. A., and McCarthy, G. (2006) “Functional neuroimaging: Basic
principles of functional MRI,” in Cabeza, R. and Kingstone, A. (eds) Handbook of
Functional Neuroimaging of Cognition, 21–52, Cambridge, MA: MIT Press.
Squires, K. C.,Wickens, C., Squires, N. K., and Donchin, E. (1976) “The effect of stimulus
sequence on the waveform of the cortical event-related potential,” Science, 193(4258):
1142–1146.
Staw, B. M. (1981) “The escalation of commitment to a course of action,” Academy of
Management Review, 6(4): 577–587.
Stewart, L. and Walsh,V. (2006) “Transcranial magnetic stimulation in human cognition,” in
Senior, C. E., Russell, T. E., and Gazzaniga, M. S. (eds), Methods in Mind, 1–27,
Cambridge, MA: MIT Press.
Stöcker, T., Schneider, F., Klein, M., Habel, U., Kellermann, T., Zilles, K., and Shah, N. J.
(2005) “Automated quality assurance routines for fMRI data applied to a multicenter
study,” Human Brain Mapping, 25(2): 237–246.
Tang,Y. Y. and Posner, M. I. (2009).“Attention training and attention state training,” Trends
in Cognitive Sciences, 13(5): 222–227.
Taggart, W., Robey, D., and Kroeck, K. (1985) “Managerial decision styles and cerebral
dominance: An empirical study,” Journal of Management Studies, 22(2): 175–192.
Ter-Pogossian, M. M. and Herscovitch, P. (1985) “Radioactive oxygen-15 in the study of
cerebral blood flow, blood volume, and oxygen metabolism,” Seminars in Nuclear Medicine,
15(4): 377–394.
Tilyou, S. M. (1991) “The evolution of positron emission tomography,” Journal of Nuclear
Medicine, 32(4): 15N–26N.
To g a, A. W. and Mazziotta, J. C. (eds) (2002) Brain Mapping: The Methods,Vol. 1, San Diego:
Elsevier.
Turkington, T.G. (2001) “Introduction to PET instrumentation,” Journal of Nuc lear Medicine
Technology, 29(1): 4–11.
Ty ner, F. S., Knott, J. R.,and Mayer Jr,W. B. (1989) Fundamentals of EEG Technology: Clinical
Correlates, Philadelphia: Lippincott Williams and Wilkins.
Uludag, K., Dubowitz, D. J.,Yoder, E. J., Restom, K., Liu, T. T., and Buxton, R. B. (2004)
“Coupling of cerebral blood flow and oxygen consumption dur ing physiological
activation and deactivation measured with fMRI,” Neuroimage, 23(1): 148–155.
Uttal,W. R.(2001) The New Phrenology: The Limits of Localizing Cognitive Processes in the Brain.
Cambridge, MA: MIT Press.
Vol z, K. G. and von Cramon, D. Y. (2006)“What neuroscience can tellabout intuitive processes
in the context of perceptual discove ry,” Journal of Cognitive Neuroscience, 18(12):2077–2087.
Vrba, J. and Robinson, S. E. (2002) “SQUID sensor array configurations for magnetoen-
cephalography applications,” Superconductor Science and Technology, 15(9): R51.
Vul, E., Harris, C., Winkielman, P., and Pashler, H. (2009) “Puzzlingly high correlations in
fMRI studies of emotion, personality, and social cognition,” Perspectives on Psychological
Science, 4(3): 274–290.
Waldman, D. A., Balthazard, P. A., and Peterson, S. J. (2011) “Leadership and neuroscience:
Can we revolutionize the way that inspirational leaders are identified and developed?,”
Academy of Management Perspectives, 25(1): 60–74.
Wal sh, V. and Cowey, A. (2000) “Transcranial magnetic stimulation and cognitive
neuroscience,” Nature Reviews Neuroscience, 1(1): 73–80.
Weick, K. E. and Sutcliffe, K. M. (2006) “Mindfulness and the quality of organizational
attention,” Organization Science, 17(4): 514–524.
Neuroscientific methods 281
Weisberg, D. S., Keil, F. C., Goodstein, J., Rawson, E., and Gray, J. R. (2008) “The seductive
allure of neuroscience explanations,” Journal of Cognitive Neuroscience, 20(3): 470–477.
Willingham, D.T. and Dunn, E. W. (2003) “What neuroimaging and brain localization can
do, cannot do and should not do for social psychology,” Journal of Personality and Social
Psychology, 85(4): 662.
Yacoub, E., Uludag, K., Ugurbil, K., and Harel, N. (2008) “Decreases in ADC observed in
tissue areas during activation in the cat visual cortex at 9.4 T using high diffusion sensiti-
zation,” Magnetic Resonance Imaging, 26(7): 889–896.
282 Massaro