Head, Heart, and Gut in Decision Making: Development of a Multiple Brain Preference Questionnaire

Abstract

There is a growing body of literature that supports the idea that decision making involves not only cognition, but also emotion and intuition. However, following extant “dual-process” decision theories, the emotional and intuitive aspects of decision making have predominantly been considered as one “experiential” entity. The purpose of this article is to review the neurological evidence for a three-factor model of head, heart, and gut aspects of embodied cognition in decision making and to report on a study carried out to design and validate a psychometric instrument that measures decision-making preferences across three separable interoceptive components, representing the complex, functional, and adaptive neural networks (or “brains”) of head (analytical/cognitive), heart (emotional/affective), and gut (intuition). Development and validation of the Multiple Brain Preference Questionnaire (MBPQ) instrument was carried out in three phases. Translational validity was assessed using content and face validity. Construct validity was undertaken via exploratory factor analysis of the results from the use of the instrument with 301 subjects from a global sampling, and reliability tests were performed using internal consistency and test–retest analysis. Results confirmed extraction of three factors (head, heart, and gut) was appropriate and reliability analysis showed the MBPQ to be both valid and reliable. Applications of the tool to coaching and leadership are suggested.

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https://doi.org/10.1177/2158244019837439
SAGE Open
January-March 2019: 1 –17
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DOI: 10.1177/2158244019837439
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Original Research
Introduction
There is now a robust body of research into the nature of
decision making and in particular into the roles of cognition,
emotion, and intuition in human decision making. This
research spans more than three decades (e.g., see Bohm &
Brun, 2008; Burke & Miller, 1999; Lerner, Li, Valdesolo, &
Kassam, 2015; Schwarz, 2000; Sinclair, 2014). In earlier
research, decision theorists suggested there were two domi-
nant systems humans use in decision making: the “analytic
system” and the “experiential system” (Gutnik, Forogh
Hakimzada, Yoskowitz, & Patel, 2006). Evans and Stanovich
(2013) discuss two major channels for decision making
within the “dual-process/dual-system” decision theories.
These two theories both assert that human information pro-
cessing is accomplished in two different, but complementary
ways (“analytically” or “intuitively”) through two substan-
tially different and differently evolved types of thinking.
System 1 is both fast and intuitive and System 2 is much
slower and more deliberate in function. System 2, the ana-
lytic system, is slower and involves conscious, deliberate
cognitive processes and logical, reason-oriented thinking.
In contrast, System 1, the faster experiential system, uses
emotion-related associations, intuitions, and “gut instincts”
when making decisions (Bechara, Damasio, Tranel, &
Damasio, 1997).
This decision model also fits well with work emerging
over the last decade from the fields of embodied cognition
and interoceptive awareness. Most notably this includes
Damasio’s somatic marker theory (Bechara & Damasio,
2005; Damasio, Tranel, & Damasio, 1991), Thayer’s neuro-
visceral integration model (Park & Thayer, 2014; Thayer &
Lane, 2000, 2009), Craig’s findings on the neurobiological
basis of interoceptive awareness (IA; Craig, 2002, 2009,
2014), and Critchley’s work on heart-based viscerosensory
signaling (Critchley, 2015; Critchley, Wiens, Rotshtein,
837439SGOXXX10.1177/2158244019837439SAGE OpenSoosalu et al.
research-article20192019
1mBIT International, Loch Sport, Victoria, Australia
2mBraining4Success, Auckland, New Zealand
3Unitec Institute of Technology, Auckland, New Zealand
Corresponding Author:
Grant Soosalu, mBIT International, PO Box 168, Loch Sport, Victoria
3851, Australia.
Email: grant@soosalu.com
Head, Heart, and Gut in Decision
Making: Development of a Multiple
Brain Preference Questionnaire
Grant Soosalu1, Suzanne Henwood2, and Arun Deo3
Abstract
There is a growing body of literature that supports the idea that decision making involves not only cognition, but also
emotion and intuition. However, following extant “dual-process” decision theories, the emotional and intuitive aspects of
decision making have predominantly been considered as one “experiential” entity. The purpose of this article is to review the
neurological evidence for a three-factor model of head, heart, and gut aspects of embodied cognition in decision making and
to report on a study carried out to design and validate a psychometric instrument that measures decision-making preferences
across three separable interoceptive components, representing the complex, functional, and adaptive neural networks (or
“brains”) of head (analytical/cognitive), heart (emotional/affective), and gut (intuition). Development and validation of the
Multiple Brain Preference Questionnaire (MBPQ) instrument was carried out in three phases. Translational validity was
assessed using content and face validity. Construct validity was undertaken via exploratory factor analysis of the results
from the use of the instrument with 301 subjects from a global sampling, and reliability tests were performed using internal
consistency and test–retest analysis. Results confirmed extraction of three factors (head, heart, and gut) was appropriate
and reliability analysis showed the MBPQ to be both valid and reliable. Applications of the tool to coaching and leadership
are suggested.
Keywords
decision making, intuition, interoception, embodied cognition, leadership, coaching

2 SAGE Open
Ohman, & Dolan, 2004). These models and theories and the
research supporting them all suggest that human cognition
and decision making are strongly influenced by, or actively
involved with, deep somatic and embodied re-representation
and interoceptive processing. For example, Damasio’s
“somatic marker” hypothesis (Damasio, 1994, 1999) states
that meta-representation of bodily states constitutes a set of
emotional feelings, accessible to consciousness and provid-
ing the “gut-feeling” and “heart intelligence” that guides our
decision processes.
According to Burr (2017), the older traditional cognitivist
account of analytical decision-making “views choice behav-
iour as a serial process of deliberation and commitment,
which is separate from perception and action” (p. 1).
However, as Burr points out, recent work in embodied deci-
sion making has shown that this account is incompatible with
emerging neurophysiological data. For example, current
work on embodied decision making (Cisek & Pastor-Bernier,
2014; Lepora & Pezzulo, 2015) indicates that decision mak-
ing is inextricably intertwined with sensorimotor control
such that there is a blurring of the boundaries between per-
ception, action, and cognition, involving reciprocal commu-
nication between affective and sensorimotor neural regions.
Burr also highlights that Barrett and Bar (2009) have con-
vincingly argued that neural activity in perception is reflec-
tive of ongoing integration of sensory information from
exteroceptive cues, with interoceptive information from the
body and that this supports the view that when it comes to
decisions, the involved perceptual states are “intrinsically
infused with affective value,” such that the affective or emo-
tional salience is deeply intertwined with its perception. This
indicates that far from involving only head–brain based cog-
nitive or logical (System 2) processes, decision making is
intrinsically and deeply entwined with emotional and intero-
ceptive bodily sensorimotor (System 1) experiences.
Interestingly, this notion that decision making involves
deep aspects of somatic re-representation and embodied “cog-
nition” leads to an important insight. Given our interoceptive
processing and embodied cognition emerges up from embod-
ied neural circuits into the deep limbic structures and eventu-
ally the frontal lobes of our cranial brain (Critchley, 2009;
Critchley & Harrison, 2013), then this neuroceptive process-
ing must deeply involve our system of autonomic afferents
(Craig, 2014; Critchley, 2009; Porges, 2001, 2011). And this
embodied autonomic and affective processing has two major
key neural systems communicating to it and interacting with it
within the body: the intrinsic cardiac neural plexus (Armour,
2007) and the enteric neural plexus (Gershon, 1999).
In colloquial terms, humans often ascribe intuitive and
informational roles to heart and gut regions of the body. We
talk about “gut instincts,” “gut feelings,” “messages from the
heart,” and “heart intuitions” (Soosalu & Oka, 2012a). Given
that we have two separable and complex neural plexuses in
these regions, it may not be surprising then that the impor-
tance of the heart and gut in human processes such as
decision making are being validated by a growing list of
studies both in the lab and in real-world scenarios.
The Intrinsic Cardiac Network
The heart contains a complex, functional and adaptive intrin-
sic neural network (Armour, 2007). Intracardiac neurons are
concentrated in multiple heart ganglia, and the structure of
the interactions between neurons, both within intracardiac
ganglia and also between individual ganglia, provide the
basis for the complex nervous network of the heart (both
anatomically and functionally) and has been labeled by
researchers in the new field of neurocardiology as a func-
tional “brain” (Ardell, 2004; Brack, 2014; Kukanova &
Mravec, 2006; D. Randall, 2000; C. Randall, Wurster,
Randall, & Xi-Moy, 1996).
Dr. J. Andrew Armour (1991), a pioneer in this field, has
undertaken extensive research and introduced the concept of
the intrinsic cardiac network as a functional “heart brain.”
His work demonstrated a complex intrinsic nervous system
in the heart, that is deemed sufficiently sophisticated to qual-
ify as a “little brain” in its own right (Armour, 2007). The
complexity of the neural circuitry in the heart allows inde-
pendent action, separate from the cranial brain. Armour
(1991) has demonstrated the ability of the heart to learn inde-
pendently, it has its own memories, and it can feel and sense
information. This information from the heart is sent to the
brain through a variety of different afferents, including auto-
nomic afferents. These afferent nerves enter the brain at the
medulla, and from there are dispersed to the higher centers of
the brain, where they may have a variety of influences
including in the context of perception, decision making, and
other cognitive processes (Armour, 2004; Thayer, 2007). In
Thayer’s (2007) work on neurovisceral integration, he has
shown how the heart influences neural structures in the
head–brain deeply involved in cognitive, affective, and auto-
nomic regulation.
The Enteric Neural Plexus
The enteric neural plexus consists of approximately 500 mil-
lion neurons (Cognigni, Bailey, & Miguel-Aliaga, 2011) and
is said to be of a similar size and complexity to that of a cat’s
head-brain (Mosley, 2012; Watzke, 2010). The network of
enteric neural tissue is spread across the organs of the gastro-
intestinal tract, from oral cavity and esophagus to anus. Dr.
Michael Gershon (1999) in his groundbreaking work in the
field of neurogastroenterology has described the enteric ner-
vous system as “the second brain.” Gershon’s work, how-
ever, follows as a rediscovery, since Byron Robinson, MD,
an American medical physician and anatomist working over
100 years ago, published in 1907 a book titled The Abdominal
and Pelvic Brain, in which he described a complex nervous
system or “brain” that he had discovered in the region of the
gut (Robinson, 1907).

Soosalu et al. 3
The enteric brain has been shown to be able to control the
gut independently of the cranial brain (Gershon, 1999;
Goldsteon, Hofstra, & Burns, 2013). Virtually every aspect
of gut activity is under the regulatory influence of this inde-
pendent enteric nervous system (Holzer, 2017; Holzer,
Schicho, Holzer-Petsche, & Lippe, 2001). There is also
growing evidence that the enteric brain deeply influences
head-based affective information processing (Berntson,
Sarter, & Cacioppo, 2003; Holzer, 2017). As Mayer (2011)
points out in his paper titled “Gut Feelings: The Emerging
Biology of Gut-Brain Communication,”
Recent neurobiological insights into this gut–brain crosstalk
have revealed a complex, bidirectional communication system
that not only ensures the proper maintenance of gastrointestinal
homeostasis and digestion but is likely to have multiple effects
on affect, motivation and higher cognitive functions, including
intuitive decision making. (p. 453)
Head, Heart, and Gut in Decision Making
Thus we see that both of these gut and heart neural systems
evince complex processing, learning and appear to be
involved in higher order human functioning. That these
“brains” or complex, adaptive and functional neural systems
are involved in decision making is being uncovered by a
growing body of fascinating research. For example, a num-
ber of researchers have found that enhanced cardiac percep-
tion is associated with benefits in decision making (e.g., see:
Dunn et al., 2010; Werner, Jung, Duschek, & Schandry,
2009).
As Dunn et al. (2010) state,
These findings identify both the generation and the perception
of bodily responses as pivotal sources of variability in emotion
experience and intuition, and offer strong supporting evidence
for bodily feedback theories, suggesting that cognitive-affective
processing does in significant part relate to “following the
heart.” (p. 1835)
In terms of gut-based functioning, Klarer et al. (2014)
examined anxiety and fear learning and decision behaviors in
rats that had their gut vagal afferent nerves severed. They
found that once the gut vagal neural pathways that subserve
“gut feel” had been disconnected, the rats, as compared to
sham controls, were no longer able to respond with normal
innate anxiety decision-behaviors to fearful stimuli and that
fear learning and conditioning was concomitantly affected.
As they suggest, “The innate response to fear appears to be
influenced significantly by signals sent from the stomach to
the brain” (Meyer, 2014, p. 1) and “These data add weight to
theories emphasizing an important role of afferent visceral
signals in the regulation of emotional behavior” (Klarer
et al., 2014, p. 7067).
That similar processes operate in humans is suggested by
Mayer (2011) in his examination of the emerging biology of
gut–brain communication and the gut–brain interface. As
Mayer points out, “ . . . the popular statement that somebody
has made a decision based on their gut feelings may have an
actual neurobiological basis related to brain–gut interactions,
and to interoceptive memories related to such interactions”
(p. 463).
Also supporting this notion that there are three separable
domains in decision making of head (rational/logic), heart
(emotions), and gut (intuitions) is the work of Sadler and
Zeidler (2005), who examined patterns of informal reasoning
and moral decision making and demonstrated evidence for
individual patterns of rationalistic, emotive, and intuitive
styles. They found that while some subjects employed all
three decision styles, many subjects utilized individual pat-
terns or combinations of these three styles of reasoning.
In the field of leadership decision-making, there is also a
growing awareness of the importance of the separable
domains of head, heart, and gut (Brack, 2011; Genovese,
2016). For example, Dotlich, Cairo, and Rhinesmith (2006)
found that in complex business decision environments, the
use of head, heart, and gut in decision styles lead to wiser and
more effective decisions. As they point out, “Complex times
require complete leaders . . . leaders capable of using their
head, their heart, and their guts as situations demand” (p. 1).
And backing this up, Heifetz and Linsky (2004) in their work
on adaptive leadership claim that
Solutions to technical problems lie in the head and solving them
requires intellect and logic. Solutions to adaptive problems lie in
the stomach and the heart and rely on changing people’s beliefs,
habits, ways of working or ways of life. (p. 35)
Finally, as Markic (2009) points out in her examination of
“Rationality and Emotions in Decision Making,”
Decision making is traditionally viewed as a rational process
where reason calculates the best way to achieve the goal.
Investigations from different areas of cognitive science have
shown that human decisions and actions are much more
influenced by intuition and emotional responses than it was
previously thought. (p. 54)
Showing that there is a burgeoning awareness in the litera-
ture that logic, emotion, and intuition are all involved in the
process of decision making.
Individual Differences
Given that current research findings suggest that within the
body there are three key neural systems, or “brains,” involved
in decision making, one in the head, one in the heart, and
another in the gut, it would not be surprising then that indi-
vidual differences, competencies, and preferences might
show up in how people use these neural systems in decision
making. Indeed, emotions involving the heart and instincts/
feelings involving the gut have evolved over time because of

4 SAGE Open
their adaptive functions in both genotypic and phenotypic
survival (Haselton & Ketelaar, 2006; Ketelaar, 2004). We
also know that the enteric nervous system evolved first
before the intrinsic cardiac network and before the encepha-
lization of the head-brain (Bishopric, 2005; Mayer, 2011;
Porges, 2001). So it would not be surprising therefore if
head, heart, and gut neural intelligences have come to be
used for differing aspects of decision making and that thereby
different people might have differing propensities and pref-
erences in their use of embodied cognitive functions.
Cardiovascular system research, looking at interoception
(Critchley et al., 2004; Katkin, 1985; Pollatos, Herbert,
Matthias, & Schandry, 2007; Pollatos, Kirsch, & Schandry,
2005) and the gastrointestinal system (Herbert & Pollatos.,
2012; Stephan et al., 2003), demonstrates that there are a
range of important interindividual differences in “interocep-
tive awareness” (IA) and interoceptive sensitivity. As Herbert
and Pollatos (2012) indicate, individual degrees of IA can be
conceptualized as a trait-like sensitivity toward one’s vis-
ceral signals. With, for example, a greater sensitivity to how
an individual emotionally responds being related to cardiac
awareness, which can be developed through a range of
embodied learning processes. In addition, Wiens,
Mezzacappa, and Katkin (2000) reported that individuals
with heightened IA (as quantified objectively from perfor-
mance in a heartbeat detection task) report more intense
emotional experiences. So it would not be surprising then
that such individuals might give more attention or salience to
heart-based affective signals during decision making.
From a gut perspective, Riezzo, Porcelli, Guerra, and
Giorgio (1996) found that gastric electrical activity as mea-
sured by electrogastrography (EGG) was a useful indicator of
psychophysiological stress created by activities such as arith-
metic tasks and Stroop color–word tests, and that wide inter-
individual variability was observed during the stress period.
Thus people may have marked individual differences in
their awareness of and focus on head versus heart versus gut
aspects of decision making. Supporting this idea, Fetterman
and Robinson (2013) explored the different ways individuals
metaphorically perceived or located the self in either head or
heart. In a paper reporting seven studies, Fetterman and
Robinson (2013) demonstrated that those individuals
described as head-locators perceived themselves to be ratio-
nal, logical, and interpersonally cold, whereas heart-locators
described themselves as emotional, feminine, and interper-
sonally warm. Head-locators showed more accuracy in gen-
eral knowledge assessments and obtained higher grade
results. Conversely, heart-locators favored emotional rather
than rational considerations within the context of moral deci-
sion making. Adam, Obodaru, and Galinsky (2015) also
examined head versus heart-locators and found strong indi-
vidual differences among men versus women and in
American versus Indian cohorts. These findings show strong
support for individual differences in head versus heart pref-
erence in decision-making style.
Epstein, Pacini, Denes-Raj, and Heier (1996) and Epstein
(1990) in their work on cognitive-experiential self-theory
(CEST) and the associated Rational-Experiential Inventory
(REI) also showed that people differ in their reliance on the
experiential/intuitive system versus the rational/cognitive
system. CEST is a dual-process model of perception and
cognition that posits that people operate using two separate
systems for information processing: analytical-rational and
intuitive-experiential. Norris and Epstein (2011), more
recently, identified intuitive-experiential system: intuition,
emotionality, and imagination as three reliable subfactors,
and we can see that these three facets nicely mirror the
aspects of head (imagination), heart (emotion), and gut (intu-
ition) that Soosalu and Oka (2012a, 2012b) have highlighted
as key functions in decision making of the three brains. The
research using the REI has also found strong individual dif-
ferences in preference for these particular decision styles and
that this preference is often associated with a number of
meaningful life outcomes (Shiloh, Salton, & Sharabi, 2002;
Sladek, Bond, & Phillips, 2010).
Intuition and the Conflation of Heart and Gut
One of the key challenges in the existing decision-making
research literature is the conflation or mixing of heart and
gut into the “intuitive” domain. Researchers often appear to
lump heart, gut, and (general) intuitive labels into their
questionnaire instruments. This is not surprising given the
focus in decision-making research on the dual-factor theory
of System 1 (intuitive/experiential) and System 2 (cogni-
tive/rational).
However, if it is true that embodied cognition utilized in
decision making involves separable interoceptive compo-
nents from the key neural plexuses of the cardiac and enteric
regions, then it would be useful for greater theoretical and
empirical specificity for the field of decision-making
research to begin examining head, heart, AND gut prefer-
ences in decision-making mode or style.
To show that heart and gut are often conflated together in
studies on intuitive versus cognitive decision making, let us
examine some representative research. For example, in a
series of studies examining differences in decision modes
(intuitive vs. analytical), Weber and Lindemann (2008) used
questions such as,
How likely would you be to make this decision based on your
immediate feelings or gut reaction to the situation? (p. 199)
Thus showing that (heart-based) feelings and gut reactions
have been conflated or mixed into the one question.
Interestingly from an individual differences perspective, the
results of their research showed that while many respondents
could be influenced into using either the intuitive or analyti-
cal modes based on domain and situational compatibility,
nevertheless nearly one third of subjects exhibited a chronic

Soosalu et al. 5
disposition to operate in an affect-based (intuitive) or a cal-
culation-based (analytic) mode, showing that individual dif-
ferences in decision mode preference can be strong and
enduring.
Betsch (2008) also examined chronic preferences for intu-
ition and deliberation in decision making. In her study she
developed what she called the “Preference for Intuition and
Deliberation Scale (PID).” This scale grouped questions
such as the following:
My feelings play an important role in my decisions.
When it comes to trusting people, I can usually rely on my gut
feelings. (p. 246)
And grouped such questions into the single “intuition” (or
affective-decision) category, once again mixing and conflat-
ing emotional/affective (heart) with gut (visceral) signals.
Importantly, however, she found, “People differ in the way
they rely on their heads or their hearts. Even though virtually
everybody is able to feel and to think, people follow their
strategy preferences if they have the chance to” (p. 243). In
an earlier series of studies, Betsch (2004) asked people
directly which strategy they would rely on in different situa-
tions (those requiring intuition or deliberation to different
degrees). She found that, beyond the situational requirement,
a subject’s preferred strategy significantly explained vari-
ance in strategy selection (Betsch, 2004, Study 3), which led
people who favored intuition to choose intuition more fre-
quently than deliberation across all scenarios.
A further example of the conflation of heart and gut
interoception in decision research is that of the work of
Katkin, Wiens, and Ohman (2001). They examined the
development of “gut feelings” in subjects presented with fear
inducing stimuli through behavioral conditioning; however,
they used heartbeat detection as a measure of visceral or gut
feeling sensitivity.
In examining decision making in nursing practice, Hams
(2000) also looked at intuition as “gut feeling.” However, she
then conflated gut instinct with heart-based intuiting, stating
that
For me [the nurse] it’s a physical sensation. I have two kinds of
knowing. I have the knowing that comes from my head that is
subject to conscious awareness. And I have the knowing that, for
me, comes out of my heart which is where I feel it. (p. 311)
Unfortunately, this mixed focus on head, heart, and gut and
the undifferentiated lumping of heart and gut into the appella-
tion “intuition” has lead to a number of challenges in the study
of individual difference in decision making. Indeed, Appelt,
Milch, Handgraaf, and Weber (2011), in their development of
a Decision-Making Individual Differences Inventory lamented
that “Individual differences in decision making are a topic of
long-standing interest, but often yield inconsistent and
contradictory results” (p. 252). One possible reason for such
inconsistency in the examination of individual differences is
that researchers have tended to contrast decision-making style
as either cognitive or intuitive, and have conflated intuitive
style with differing focus on heart interoceptive–based intu-
itions versus gut-feel intuitions. Indeed in numerous studies
we see that authors talk about studying intuitive decision mak-
ing by examining “gut feel” and then use heart interoception
monitoring as the experimental measure, thus conflating heart
and gut embodiment aspects of interoceptive intuition. In con-
trast, intuition can be divided into at least three domains of
head (based on conscious reasoning or unconscious cognitive
heuristics, for example, Gigerenzer & Gaissmaier, 2011;
Kahneman, 2011), heart (cardiac interoception), and gut
(enteric/visceral interoception).
That the field of decision-making research is only now
beginning to become aware of the difference in types of intu-
itive signaling is shown by a very recent study. Sadler-Smith
(2016) examined the linguistic structure of human resource
practitioners’ experience of intuition. He found that intu-
itions emerge into consciousness as “bodily awareness” and
“cognitive awareness” and that bodily awareness comprised
two first-order concepts of “gut reactions” and “feelings.”
Such a categorization of intuition specifically into differing
elements of cognitive, feeling, and gut reaction is currently
relatively rare and a commendable addition to the field of
decision-making research. For as Pollatos (2015) in examin-
ing cardiac versus gut-based IA and sensitivity points out,
these bodily signals represent distinct and separate processes
and should therefore not be conflated.
Head, Heart, and Gut Preference in Decision
Making
To support the examination of and research focus on head,
heart, and gut domains in decision making, in the present
study, we developed and validated a psychometric instru-
ment that explores multiple brain (head, heart, and gut) pref-
erences in decision making. While it is expected that people
will exhibit individual differences in their preference for
head, heart, and gut decision-making patterns, existing
research suggests that these neural systems are intercon-
nected and interdependent (Mayer, 2011; Thayer & Lane,
2009). The Multiple Brain Preference Questionnaire (MBPQ)
instrument explores individual patterns or preferences for
head (analytical/cognitive), heart (emotional/affective), and
gut (intuition) based decision-making styles, which accumu-
latively create an individuals’ holistic and integrated response
in decision making.
Method
The MBPQ was developed using a systematic process as
articulated by Parsian and Dunning (2009). This process
included the following:

6 SAGE Open
 Translational validity: content validity and face validity.
 Construct validity: factor analysis.
 Reliability tests: internal consistency (Cronbach’s
alpha) and test–retest.
Initial Questionnaire Development
Academic subject matter experts (SMEs) on questionnaire
design and statistical analysis were approached to guide
and support the questionnaire construction. Two subject
experts were consulted regarding the conceptual frame-
work and the pertinent literature to assist in the develop-
ment of the initial cohort of items for the questionnaire.
This process occurred over a total of three iterations to
ensure that the scope and breadth of the field was fully and
authentically represented and resulted in an initial cohort of
54 questionnaire items.
Content and Face Validity (Translational Validity)
Content validity was conducted to demonstrate whether the
questionnaire content was appropriate and relevant. Ten
SMEs from the field of multiple Brain Integration Techniques
(mBIT) Coaching (sampled purposively from the global data-
base of mBIT professionals to signify the leading edge and
early innovators in this field) then completed a questionnaire
utilizing Survey Monkey to assess content and face validity,
including a 4-point Likert-type scale for appropriateness,
coverage, and relevancy of each question (from 1 = not rel-
evant, 2 = somewhat relevant, 3 = relevant, to 4 = very rel-
evant). A 4-point scale was utilized, as opposed to a 5-point
scale with a neutral or undecided option, to ensure that SMEs
were obliged to judge the appropriateness and relevance of
the questions. In addition, the SMEs assessed appearance,
readability, clarity of language, usability formatting and style.
Content validity index (CVI) analysis was undertaken to
assess the validity of the questions in the survey in line with
Lynn (1996).
A face validity check was then undertaken with a further
25 SMEs from a range of cultural backgrounds, recruited by
asking for volunteers via the mBIT Coach global Facebook
network. Face validity “evaluates the appearance of the
questionnaire in terms of feasibility, readability, consistency
of style and formatting, and the clarity of the language used”
(Parsian & Dunning, 2009, p. 3). A simple face validity eval-
uation questionnaire was distributed via Survey Monkey to
the above (again using a Likert-type scale of 1 = strongly
disagree, 2 = disagree, 3 = agree, 4 = strongly agree).
Coding and thematic analysis was undertaken on the qualita-
tive feedback in each section.
Construct Validity
Construct validity indicates the degree to which each item is
perceived to be relevant to the theoretical construct (DeVon
et al., 2007, from Parsian & Dunning, 2009).
A total of 301 subjects (60 male, 241 female, ages ranging
from 19 to 85 years old) were recruited voluntarily from
Unitec, a tertiary education provider in New Zealand, cover-
ing a range of professional disciplines, as well as from
Facebook to a global population. Unitec subjects were
recruited from the intranet and posters—requesting volun-
teers to reply by a prescribed date. Additional volunteers
were recruited via open Facebook invitation. New Zealand
subjects constituted 48.5% of respondents, and the remain-
der were spread across 20 countries. The question order was
randomized to minimize any potential systematic bias due to
the ordering of the questions. To ensure that participants
were answering the questions in the context of decision mak-
ing, they were requested at the top of the questionnaire to
reflect on the questions in the practice and context of deci-
sion making.
At the end of the questionnaire, participants were asked to
self-assess their brain preference and offer comment as to
which brain(s) they preferred to utilize in decision making.
This was requested to provide another layer of validity check
and to assess self-awareness of brain preferences.
Factor Analysis
Factor analysis was performed to explore the relationship
between variables and determine construct validity. Factor
analysis is a statistical method commonly used for investi-
gating variable relationships for complex concepts and used
by researchers in developing and evaluating test or scales
(Barholomew, Steele, Galbraith, & Moustaki, 2008). In this
process, each factor is interpreted according to the items hav-
ing a high association with it, summarizing the items into a
smaller number of factors. Related items that define part of a
construct are usually grouped together and unrelated items
are deleted.
In the context of factor analysis, there are two commonly
related techniques that can be utilized (Barholomew et al.,
2008). In the principal components analysis (PCA) approach,
the original variables are transformed and grouped into a
smaller set of variables that have very strong linear correla-
tions or combinations. The variance in all the variables is
then examined. In the standard factor analysis (SFA)
approach, the factors are estimated using a mathematical
model. So the only variance that is analyzed is the shared
variance instead of the total variance.
For this research article, it was decided that the PCA
method was suitably reliable and appropriate and this was
used to perform exploratory factor analysis on the question-
naire results.
Reliability Tests: Internal Consistency (Cronbach’s
Alpha) and Test–Retest
The MBPQ was tested for reliability to assess how consis-
tently the questionnaire measures the 54 items. Thirty-two

Soosalu et al. 7
mBIT Coaches were asked to volunteer to undertake the
test–retest reliability examination. Coaches were offered the
opportunity to remain anonymous by using an alias (and
were clearly instructed to use the same alias on both occur-
rences to ensure the two questionnaires could be used in the
test–retest).
Results
Content and Face Validity (Translational Validity)
CVI scores ranged from 9 to 10, with a cutoff of 0.87 (DeVon
et al., 2007). All question items were valid and therefore
retained.
Face validity results showed that on average 95.2% of
SMEs agreed that the wording of the items were clear and
understandable to the target audience, and 84.7% of SMEs
agreed that the layout and style would be acceptable for the
target audience. Face validity was also analyzed using a
validity ratio similar to that suggested by Rungtusanatham
(1998). Under this regime, 52 of the 54 items strongly met
the required criteria. For the two items that did not, SME
feedback was used to adjust the wording to make the items
clear and understandable to meet SME requirements.
In addition, based on SME face validity feedback, the fol-
lowing minor changes were made to the questionnaire:
1. Two minor typographical errors were fixed.
2. The wording of three questions was modified to
ensure a clear distinction between factors.
3. Further clarity was offered in the instructions for
completion of the questionnaire.
Factor Analysis
Assessing the suitability of data for factor analysis. There are
two key issues to be considered in assessing the suitability
for factor analysis: sample size and the strength of the rela-
tionships among the variables. To enable factor analysis to
be reliable, a large sample size is required. While there are
different opinions around the number of participants required,
many researchers recommend a minimum of five to 10 par-
ticipants per variable or at least 300 cases or subjects in total
(Tabachnick & Fidell, 2012). In this study, we had 301 par-
ticipants complete the MBPQ and so this meets the first
criterion.
In considering the strength of the relationships among the
variables, the standard is that the items need to have a bivari-
ate correlation of at least 0.3 or greater for larger sample
sizes (MacCallum, Wideman, Zhang, & Hong, 1999).
Statistical measures that can be used to inform the appropri-
ateness of the relationships include the following:
1. Kaiser–Meyer–Olkin (KMO)—This measure ranges
from 0 to 1. A value of 0 indicates that the sum of
partial correlations is large in comparison to the sum
of correlations, and indicates diffusion in the pattern
of correlation, and that factor analysis is inappropri-
ate (Parsian & Dunning, 2009). It is recommended to
accept values ≥0.5. Values between 0.5 and 0.7 are
described as mediocre, 0.7 and 0.8 as good, 0.8 and
0.9 as great, ≥0.9 is superb, and 1 as perfect (Kaiser,
1974). In this study, there were 54 variables and on
average this yielded five cases per variable. The
KMO measure of sampling adequacy was 0.77. This
was above the minimum value of 0.5 and fell in
Kaiser’s good category.
2. Bartlett’s test of sphericity—This uses a p value. If
the p value ≤0.05, the Bartlett’s test is significant and
≤0.01 the Bartlett’s test is very highly significant and
it passes the suitability test. In this study, the p value
for Bartlett’s test of sphericity was 0.00. This value
was considered to be very highly significant and
passed the suitability test.
All the conditions met the suitability criteria and enabled fac-
tor analysis to be undertaken.
Factor or component extraction. This determines the smallest
number of items that can be best used to represent the inter-
relationships among all items. It is typically up to the
researcher to determine the number of factors considered
best to describe the underlying relationship; however, two
conflicting needs must be balanced. The first of these is to
find a simple solution with as few factors as possible while
second ensuring as complete a picture is obtained in explain-
ing as much variance in the original data as possible.
Two main criteria are used to determine the number of
factors that should be retained, and some statisticians use a
third criterion of parallel analysis (Field, 2013):
1. Kaiser’s criterion—select those factors that have
eigenvalue ≥1. The eigenvalues represent the amount
of the total variance explained by that factor. In this
study, 10 factors had eigenvalues ≥1. These 10 fac-
tors explained a cumulative variance of 61.83%. This
is certainly above 50% and falls in the mediocre
range. Field (2013) considers a total variance of 50%
or more to be reasonable.
2. The scree test—this depicts the descending variances
that account for the factors extracted in graph form.
This involves plotting each of these eigenvalues on
each of the items and inspecting the plot to find a
point where the shape of the curve starts to change
direction (points of inflection) and becomes horizon-
tal. The factors which come before the point where
eigenvalues begin to drop can be retained. In this
study, the points of inflection occurred at both four
and six factors. Therefore, the analysis could justify
retaining either three or five factors. Given the large

8 SAGE Open
sample size in the study and that there was consis-
tency between Kaiser’s criterion and the scree plot, it
was deemed reasonable to extract three factors. This
certainly fits with the underlying theoretical model.
3. Parallel analysis—used as a quality-control check.
The size of the eigenvalues that have been collected
from data is compared with the eigenvalues obtained
from a randomly generated dataset of the same size.
Only the eigenvalues that exceed the randomly gen-
erated eigenvalues are retained. This approach is
more accurate than the scree test or Kaiser’s crite-
rion. In this study, Monte Carlo PCA for parallel
analysis was used to randomly generate eigenvalues
of a random dataset. For the first three factors, the
eigenvalues exceeded the randomly generated eigen-
values. This further confirms that the first three fac-
tors should be retained.
In this study, three different methods were used to determine
the number of factors to be retained and these three compari-
sons agreed with one another very well. Thus, a three factor
solution with Oblimin rotation was deemed to be most statis-
tically and conceptually appropriate.
Factor rotation and interpretation. To undertake the most
appropriate interpretation, the loading values were carefully
examined using the guidelines for practical significance
published in Parsian and Dunning (2009). A factor loading of
±0.3 indicates that the item is of minimal significance, ±0.4
indicates it is more important, ±0.5 indicates it is significant,
and beyond indicates highly significant.
Steven’s (2002) guideline of statistical significance for
interpreting factor loadings, which is based on sample size,
suggests the following statistically acceptable loadings. For
50 participants the loading cutoff is 0.72, for 100 participants
0.51, and for 200 to 300 participants 0.29 to 0.38. The sam-
ple size used in the validation process was 301: As a result,
all item loadings above the range 0.29 to 0.38 were retained,
leaving 22 items from the original 54. The final PCA for the
three-factor solution with 22 items accounted for 35.47% of
the total variance, while a five-factor solution accounted for
44.87% of the total variance. The factor loadings of the final
PCA with their factorial weights for three factors are shown
in Table 1.
Reliability Tests
Internal consistency reliability. To explore how well items fit
together conceptually, and to examine the inter-item corre-
lations, internal consistency is undertaken (Parsian & Dun-
ning, 2009). This was analyzed using Cronbach’s alpha. The
Cronbach’s alpha for 21 out of 22 questionnaire items
ranged from .7 to 1 with only one statement having a value
of .3. The overall average Cronbach’s alpha was .86,
Table 1. The Factor Loadings of the Final PCA With Their Factorial Weights.
Items Factor 1 Factor 2 Factor 3
When undertaking a task I tend to use a thought-through structured approach 0.667
I am very head based in my decisions 0.588
I prefer to think things through to make meaning of them 0.647
When undertaking a task I don’t use a logical head-based approach 0.674
I am a very logical person 0.625
I do not let my head lead when I make decisions 0.567
I am a very creative (imaginative, inventive and/or innovative, etc.) person 0.850
I trust my gut reactions when making decisions 0.528
I am self-motivated and get moving easily 0.613
I do not have strong gut intuitions/instincts 0.410
I am an action oriented person who gets things done 0.489
I often lack energy and motivation 0.580
I am a gutsy courageous person 0.647
I look after and protect myself 0.489
I have a very strong core/gut sense of self 0.735
I am quite warm hearted 0.673
I feel what is important to me in my heart 0.458
I am very compassionate (kind, empathic, loving, etc.) 0.639
I like to connect deeply with people 0.649
My heart feelings are not very important to me 0.483
I do not like to get too close to people 0.507
I always follow my heart 0.319
Note. PCA = principal components analysis.

Soosalu et al. 9
indicating a good correlation and that the items and the
questionnaire are consistently reliable. A Cronbach’s alpha
value ≥0.9 is considered as excellent correlation, a value
between .8 and .9 as good, and between .7 and .8 as accept-
able. Field (2013) suggests .7 as the cutoff point.
Test–retest. Thirty-two respondents completed the question-
naire as part of test–retest reliability process over 2 weeks.
The test and retest reliability was measured by correlating
the scores of the test and retest on a question-by-question
basis. This correlation is known as the test–retest reliability
coefficient or the intra-class correlation coefficient (ICC).
The ICC ranges from a value of 0 to 1 with 1 being a perfect
correlation between the test and the retest. A coefficient cor-
relation of 0 indicates that the respondents’ scores at test
were completely unrelated to their scores at retest; therefore,
the test is not reliable. An ICC value >0.9 indicates test–
retest reliability is excellent, >0.8 as optimal, and >0.7 as
typical.
The ICCs for the 22 statements were statistically signifi-
cant (p ≤ .05) and ranged from .7 to 1. One statement had a
nonsignificant ICC value of .34. Fifty percent of the state-
ments’ test–retest reliability was excellent, 18% optimal,
27% typical, and 5% below typical range. The overall aver-
age ICC value was 0.87, which fell in the optimal range.
Self-Awareness of Brain Preferences
Of the 301 participants in this study, only 266 provided their
indication of which brains they believed they preferred to use
in decision making. Four subjects did not answer the ques-
tion regarding this, and 31 indicated they were unsure about
what their preference was.
Of the 266 valid responses, matching was performed on
the Head–Heart–Gut scores from the three factors against the
indicated Head–Heart–Gut stated preference. Only 124
(47%) subjects had a match between the Head–Heart–Gut
preference scores from the instrument versus their indicated
conscious belief preference. Including those who responded
with “Unsure,” this equated to a total of 52% of all respon-
dents were not able to accurately assess their measured pref-
erence using conscious belief or guessing.
Brain Preferences Results
The question scores for the three factors were summed and a
weighted percentage score computed to produce head, heart,
and gut scores that could range from 0 to 100. Of the 301
participants, 27% scored with head as the highest score, 44%
with heart as highest, and 29% with gut as highest, the
remainder were matched on head, heart, and gut scores. This
indicated that heart and gut preferences in decision making
were higher in this sample than that of head preference, and
also showed that heart and gut were relatively similar in pref-
erence, with heart being somewhat more preferred by a larger
number of subjects. For those with head as the highest score,
39% had a head score that was at least 10 points higher than
either heart or gut scores. For those with heart as the highest,
50% had a heart score that was at least 10 points higher than
the other brains’ scores. And for gut as the highest, 31% had
a score that was at least 10 points higher than the rest. Also in
this sample, only a minimal 4% had balanced scores where
head, heart, and gut were equal or within five points of each
other, and only 14% had balanced scores within 10 points of
each other for head, heart, and gut scores.
Gender and Age Differences
Analysis of head, heart, and gut scores found that the mean
head scores of males and females differed significantly
(p = .01) from each other with males having higher mean
head scores (mean difference = 4.87, t statistics = 2.591).
Heart scores of males and females also were significantly
different (p = .005) from each other with females having on
average a higher mean heart score (mean difference = 5.47,
t statistics = 2.809). Mean gut scores were not significantly
different between genders (p = .005).
By age group, the mean head scores were not significantly
different except for the difference between age groups 41 to
50 and 51 to 60 (p = .03) with the 41 to 50 group having a
higher mean head score than the 51 to 60 age group (mean
difference = 5.46). The mean scores for both heart (p =
.468) and gut (p = .36) were similar across all age groups,
showing no significant difference. The majority (68%) of
subjects fell into the age groups of 41 to 60, with only 12%
of subjects older than 60, 4% younger than 30%, and 15% in
the 31- to 40-year bracket.
Discussion
In this study, we developed and validated a psychometric
instrument that explores multiple brain (head, heart, and gut)
preferences in decision making. Factor analysis found three
reliable factors that correlated with head (Factor 1—
accounted for 14.6% of total variance and included seven
items), gut (Factor 2—accounted for 13.2% of total variance
and included eight items), and heart (Factor 3—accounted
for 7.6% of total variance and included seven items). Item
questions such as “I am very head based in my decisions”
and “I am a very logical person” obviously explored prefer-
ences for cognitive head-based thinking. Items such as “I
feel what is important to me in my heart” and “I always fol-
low my heart” focused on heart-based affective emoting.
And items such as “I trust my gut reactions when making
decisions” and “I do not have strong gut intuitions/instincts”
examined gut-based intuitions and processing.
While the three-factor solution only accounted for 35.47%
of the total variance, we chose it because it supported the
theoretical model of embodied cognition of head, heart, and
gut neural intelligences and kept the instrument scoring

10 SAGE Open
relatively simple and congruent with the theoretical model. It
also aligned with the findings of Norris and Epstein (2011).
However, it is interesting to consider the five-factor solution
to our results which accounted for 44.87% of the total vari-
ance. With this solution, we obtained two subfactors for
head, two subfactors for gut, and one for heart. The gut sub-
factors, for example, divided into two classes of gut ques-
tions, one around the theme of motility and gutsy action, the
other around issues of protection and self-preservation. In
their examination of the prime functions of the three brains,
Soosalu and Oka (2012a) suggested that each of the brains
had three key prime functions of
HEAD BRAIN PRIME FUNCTIONS
Cognitive perCeption—cognition, perception, pattern
recognition, etc.
thinking—reasoning, abstraction, analysis, synthesis, meta-
cognition etc.
Making meaning—semantic processing, languaging, narrative,
metaphor, etc.
HEART BRAIN PRIME FUNCTIONS
emoting—emotional processing (e.g., anger, grief, hatred, joy,
happiness etc.)
values—processing what’s important to you and your priorities
(and its relationship to the emotional strength of your aspirations,
dreams, desires, etc.)
relational affeCt—your felt connection with others (e.g.,
feelings of love/hate/indifference, compassion/uncaring, like/
dislike, etc.)
GUT BRAIN PRIME FUNCTIONS
Core identity—a deep and visceral sense of core self, and
determining at the deepest levels what is “self” versus “not-self”
self-preservation—protection of self, safety, boundaries,
hungers and aversions
mobilization—motility, impulse for action, gutsy courage and
the will to act
The emergence of subfactors of self-preservation and mobi-
lization for the gut factor items provides some support for
Soosalu and Oka’s contention and would be a fruitful area
for further research.
Separating Heart From Gut in Embodied Intuition
Nearly a decade ago, Bohm and Brun (2008) in summarizing
the state of decision research in a special issue of the journal
Judgment and Decision Making made the following impor-
tant claim that
In sum, decision research has seen a proliferation of approaches that
look beyond rational, deliberate, and purely cognitive processes in
decision making and investigate intuitive and emotional judgments
in this area. This seemed like a good point in time to reflect the state
of this emerging field in a special issue that addresses the question
of how intuition and affect are related to each other and how they
shape risk perception and decision making. (p. 1)
In doing so they brought attention to the fact that intu-
ition and affect/emotion are not the same thing. As we
have discussed here, embodied cognition involves embod-
ied feelings and ways of knowing that involve heart and
gut neural signals. Gut feelings and reactions are not the
same as heart emotions and heart-based experiencing, and
the research in this study has shown there are separable
factors for preference in head, heart, and gut. Thus deci-
sion making involves not just head-based cognitions, but
separate aspects of heart (affective) and gut (intuitive)
processing, and in exploring the separable factors of these
for decision making we have highlighted a focus that car-
ries forward the ideas of Böhm and Brun and created and
validated an instrument that can be used to begin to
explore how intuition and affect might both differ and
relate to each other and to do so from a neurologically
informed stance.
There is little doubt that the heart and gut are involved in
the embodied aspects of decision making. As Mayer (2011)
points out in his seminal review of “Gut Feelings: The
Emerging Biology of Gut-Brain Communication,” there is a
close connection between the gut and the brain and this inter-
action plays an important part in feeling states and intuitive
decision making. And in terms of the heart, the following
quote from Gomes Silva (2014, p. 97) nicely summarizes the
emergent view:
Recent scientific research suggests that consciousness emerges
from the brain and body acting together. A growing body of
evidence suggests that the heart plays a particular significant
role in this process. Far more than a simple pump, the heart, now
is recognized by scientists, as a highly complex system with its
own functional “brain” (McCraty, 2005; McCraty, Atkinson,
Tomasino, & Bradley, 2009).
Indeed, as Sinclair (2010) points out, the view that intuitive/
experiential aspects of decision making include an affective
component is becoming more widespread.
Thus we suggest that it is time that the field of decision
making moves from a two-factor (cognitive and experiential/
intuitive) model to a neurologically informed three-factor
model of head, heart, and gut.
Age and Gender Differences
The extant literature suggests there are robust age and gender
differences between cognitive, intuitive, and emotional deci-
sion making. Fetterman and Robinson (2013), for example,
explored whether people locate their sense of self in the heart
or head, and found gender differences such that 64% of
women chose the heart, whereas only 43% of men chose the
heart. Norris and Epstein (2011) using the REI found that
men assessed themselves as more rational whereas women
assessed themselves as more experiential, intuitive, and
emotional.

Soosalu et al. 11
Sladek et al. (2010) also found both significant age and
gender differences in rational versus experiential (emotional
and intuitive) thinking and decision making. Their results
suggested that there was a convergence of the rational and
experiential systems in adulthood, but they highlighted that
the timing may be different for women and men. In later
adulthood, the relationship appeared to diverge again. Mikels
et al. (2010) examined health decisions in older versus
younger adults and demonstrated that younger adults per-
formed better with a focus on information, whereas in a con-
trol group older adults performed better in the emotion-focus
condition. This is in accord with the work of Satchell,
Akehurst, Morris, and Nee (2017) who found that experien-
tial gut instinct about whether a stranger poses a threat is as
good when people are 80 as when they are 18. Older people
were as good as young adults at knowing intuitively when
someone was potentially aggressive.
Finally, according to Sinclair, Ashkanasy, and Chattopa-
dhyay (2010),
female decision makers appear to rely more heavily on intuition
because they can access it more easily through their heightened
awareness of emotions. Affective orientation mediates the effect
of gender on intuition because its activation is much stronger in
female decision makers, possibly for neurobiological and social
reasons. (p. 393)
In the current study we found that indeed women had
higher mean heart scores than men, and men displayed
higher mean head scores compared with women. This con-
cords with previous studies on gender differences in decision
making and lends additional support. However, there was no
significant difference between genders in gut scores. Thus
our findings bring a more nuanced insight into how the
“experiential” aspect of two-process theories (rational vs.
experiential) separates out into heart (emotive) versus gut
(intuitive) factors of the three-factor model. It appears from
the current study that while women are more likely to prefer
heart-based emotional decision making, when it comes to
gut-based intuitive focus, they are not necessarily likely to
differ from males in this regard. This would be a fruitful area
for future studies to examine.
In terms of age differences, the current study found that
those in middle age group (41-50 years) had significantly
higher head scores than the 51 to 60 age group, but no differ-
ences were found across age for heart or gut or head for the
other age groups. However, the majority of the cohort in this
study were in the middle age range, and only 4% were under
30 years of age and 12% above 60 years, so the paucity of
data for these outer age ranges may have influenced the
results. While this study was unable to validate previous
research findings such as those of Mikels et al. (2010), the
present findings do lend tentative support, however, to the
findings of Satchell et al. (2017), who found that older adults
were just as good as younger adults at gut-level intuitive
decision making.
State and Context
One of the limitations of the current study is that we did not
control for the affective state of the participants. In addition,
while we attempted to control for decision-making context
by explicitly asking subjects at the top of the questionnaire
wording to answer the item questions with respect to deci-
sion making, nevertheless we did not contextualize this to
specific decision-making domains such as work, finance,
family, relationship, and so on. This may be important
because there is some evidence that a person’s domain-gen-
eral decision style (intuitive vs. deliberative) does not neces-
sarily generalize across decision domains (Pachura & Spaar,
2015). On the contrary, de Vries, Holland, and Witteman
(2008) in examining intuitive versus deliberative decision-
strategy preferences found that decision-style preferences
can be strong and enduring across decision-style context.
Given the lack of clarity in the literature on the importance of
domain and context specificity, this would be a fruitful area
for further research using the instrument developed in the
current study.
In our test–retest study there were a small number of
items that had below typical or optimal results and one state-
ment that had a nonsignificant result. In informal post hoc
discussion with a small number of the test–retest subjects, in
particular, those whose responses to the lower reliability
items appeared to show mistakes in how they answered the
negatively worded questions between test and retest, the sub-
jects expressed that as the retest occurred in the week leading
up to the Christmas period, they were feeling quite stressed
and mood altered and this may have influenced the accuracy
of their responses.
There is a growing body of research evidence that sug-
gests that stress and emotional state can impact decision
strategy. For example, moderate degrees of positive mood
were found to facilitate intuition (Elsbach & Barr, 1999),
whereas negative mood appeared to block it (Sinclair, 2010).
Mather and Lighthall (2012) explored decisions made under
stress and found that subjects processed information differ-
ently in this condition. Furman, Waugh, Bhattacharjee,
Thompson, and Gotlib (2013) alternately found that depres-
sion can impact the ability to use interoceptive feedback to
inform decision making, and Avery et al. (2014) found that
depression is associated with abnormal interoceptive repre-
sentation in the insular cortex, the area responsible for repre-
senting embodied heart, gut, and autonomic signals. Finally,
George and Dane (2016), in a review of the literature, found
that both incidental mood and discrete emotion “play a mul-
titude of nuanced roles in decision-making.”
Therefore, future studies would benefit from examining
and controlling affective state before exploring head, heart,

12 SAGE Open
and gut decision preferences to see just how much individu-
als’ preferences are influenced by state and context.
Making Wise Leadership Decisions
In our study we found that 27% of participants scored with
head as the highest score, 44% with heart as highest, and
29% with gut as highest, showing that there were more peo-
ple with a heart or gut (i.e., experiential vs. rational) prefer-
ence. In contrast, Fetterman and Robinson (2013) explored
whether people located their sense of self in the heart or head
(forced choice), and found that 52% rated themselves as
heart-based and 48% as head-based. It may be that in forcing
a choice between head and heart, some of those who were
actually gut-based in their preference may have chosen head
over heart as their gut-based reaction to this question (as gut
and heart are quite different affective domains). If this were
true, then our findings would align with those of Fetterman
and Robinson.
As indicated above in the section on gender differences,
we had a much larger cohort of women than men in our study
(60 male, 241 female) and there is evidence to suggest that
women are much more emotional and experiential compared
with men in their decision styles. This could account for our
larger percentage of heart preferrers. In any case, our results
showed very small percentages (only 4%) expressed a rela-
tively balanced preference for head, heart, and gut together.
Yet there are suggestions from both the leadership decision-
making literature and psychiatric literature that effective and
wise decisions require a balance and use of all three decision
styles (Coget, 2011; Coget & Keller, 2010; Dotlich et al.,
2006; Fenton-O’Creevy, Soane, Nicholson, & Willman,
2011) or a balance between rational and experiential/intui-
tive styles (Dane & Pratt, 2007; Freeman, Evans, & Lister,
2012; Hogarth, 2002; Shapiro & Spence, 1997).
Indeed, Zhong (2011) found that a focus on head-based
rational, deliberative decision making alone increased uneth-
ical behaviors, increased deception, and decreased altruism
especially when it overshadowed implicit, intuitive influ-
ences on moral judgments and decisions. And this was
backed up by the work of Feinberg, Willer, Antonenko, and
John (2012) who found that people with a preference for
cognitive reappraisal have diminished emotional intensity
and make more deliberative moral judgments, leading them
to find moral dilemmas less immoral than people with a pref-
erence for emotional judgment. Supporting this, Soosalu and
Oka (2012a, 2012b) in action research found that wise and
effective leadership decision making requires a balanced mix
of head, heart, and gut reasoning, appraisal and decision
making.
Given the findings in the current study, that only a small
percentage of subjects displayed balanced preference for
head, heart, and gut decision-strategy, one important applica-
tion of the MBPQ instrument developed in this study may be
to utilize it to coach people to become more balanced in their
use and preference of all three factors. Also, a fruitful area
for future research would be to examine those with a bal-
anced versus unbalanced preference for the three factors with
respect to decision effectiveness and quality.
Conscious Awareness of Strategy
Research has shown that the majority of people exhibit deci-
sion-making bias blindspots and are consciously unaware of
their own judgmental biases and preferences (Scopelliti
et al., 2015). In addition, people can exhibit low levels of
meta-awareness with respect to IA as shown by studies such
as those by Azevedo, Agliotti, and Lenggenhager (2016),
who found that while subjects had above chance recognition
of their own heart cardiodynamics, their metacognitive
awareness of interoceptive signals was exceedingly poor.
Leach and Weick (2018) also found that people’s beliefs in
their intuitions were not reflective of actual performance,
and Sobyra (2010) in her study of the accuracy of self-
reported intuitive and analytic ability reported that self-
reported rationality was significantly correlated with
cognitive performance, but that participants struggled with
accurately reporting their intuitive ability. Finally, Norris and
Epstein (2011) examined self versus other ratings of rational
versus experiential thinking styles and found that people
overestimated their rational thinking and underestimated
their experiential thinking, while McNulty, Olson, Metlzer,
and Shaffer (2013) found that newlywed’s automatic atti-
tudes measured using an associated priming task, and not
their conscious ones, predicted changes in marital satisfac-
tion over a 4-year period.
In alignment with this, the current study found that 52%
of respondents were not able to accurately predict their head,
heart, gut decision-preference. This means that over half of
those in our study believe they are using a decision-prefer-
ence that does not conform with what the instrument found
as their likely preference. As discussed above, if it is impor-
tant for wise decision-making that people utilize all of their
conscious and intuitive intelligences (head, heart, and gut) in
balance, then it could be important for people to learn what
their actual underlying preferences are and learn ways to
rebalance their decision-strategies.
Research has shown that people can be trained to shift
their decision-strategies and overcome their implicit prefer-
ences and biases; however, it takes a combination of aware-
ness, learning, and support (Devine, Forscher, Austin, &
Cox, 2012). Also, there is growing evidence that personal
beliefs play an important role in emotional processing and
may modulate interoception and the perception of emotional
cues (Paulus, 2011; Ring, Brener, Knapp, & Mailloux, 2015).
Therefore, people’s beliefs about the importance of cogni-
tion, intuition, or emotion in decision making may setup self-
fulfilling loops that lead them to value and utilize interoceptive
cues over head-based cognitive rational processing or vice-
versa in their decision-making strategies. Thus coaching and

Soosalu et al. 13
training using the current instrument may be a powerful and
useful application that can support more accurate awareness
of metacognitive processes, a shift in personal beliefs about
decision preference and the learning of wiser decision-mak-
ing strategies.
Conclusion and Summary
As we have seen in this article, emerging sources of evi-
dence from the fields of neurocardiology and neurogastro-
enterology are showing that both the heart and gut regions
have complex, adaptive and functional neural networks or
what the researchers in these fields are calling “brains.”
There is also a substantive body of work that strongly sug-
gests that cognition and decision making is embodied and
involves neuroception and interoception from embodied
neural systems. Work on the embodiment of metaphor also
supports that people imbue a sense of self in areas such as
the head, heart, or gut, and that this focus on these regions
influences decision making. In the work presented in the
current study, based on the notion that there are three key
areas of embodied neural intelligence—head, heart, and
gut—we have developed and validated a questionnaire
instrument (MBPQ) for examining individual’s prefer-
ences for head, heart, and gut focus in decision making. It
is of course worth reiterating here that the head, heart, and
gut are within one complex adaptive human body or sys-
tem that behaves in response to the integrated decision
made. The benefit of specifying the source of the decision-
making activity to head, heart, or gut enables skilled pro-
fessionals to work specifically with people to change
decision making with even finer distinction: to give an
even deeper awareness and understanding of how deci-
sions are being made so that people can reflect on and
evaluate that for wisdom and effectiveness, and make spe-
cific changes if desired at an individual head, heart, and
gut aspects of the whole.
In summary, we’d like to conclude with a wonderful quote
from Loewenstein:
With all its cleverness, however, decision theory is somewhat
crippled emotionally, and thus detached from the emotional and
visceral richness of life.
(George Loewenstein, 1996, p. 289; in Shiv & Fedorikhin,
1999)
And as we have attempted to do in this current research, we
hope that by bringing a focus onto the three separable (and
neurologically based) aspects of head, heart, and gut in deci-
sion making, and providing a psychometrically validated
tool for examining these, we can begin, in the field of deci-
sion making, to honor the importance of all of the multiple
embodied neural components of how humans make wise
decisions.
Acknowledgments
The authors wish to thank Nasrin Parmisian for her early sugges-
tions on the construction of the Questionnaire, and all of the subject
matter expert participants for their assistance with the development
and validation of the instrument. We also deeply thank all those
individuals who participated in this project. Gratitude is also given
to Unitec for their support of this research.
Ethical Statement
This research was conducted in accord with American Psychological
Association (APA) ethical principles and was approved by the
Unitec Research Ethics Committee (UREC)—Ethics application
number: 2013-1073.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect
to the research, authorship, and/or publication of this article.
Funding
The author(s) received no financial support for the research, author-
ship, and/or publication of this article.
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Author Biographies
Grant Soosalu is an independent researcher with interests in coach-
ing, leadership and human intuitive decision making. He graduated
from Melbourne University (BSc Hons) and Monash University
(MAppSc). His research focuses on embodied cognition and leader-
ship decision making.
Suzanne Henwood is a health care professional by background,
with 25+ years in development, education and research including 9
years as an associate professor, focusing on professional develop-
ment, commnuication and leadership. She is currently director and
lead coach and trainer at mBraining4Success.
Arun Deo is a biostatistician at Unitec Institute of Technology,
Auckland. His areas of expertise include statistical analysis, organ-
isational strategic performance, and business intelligence. His main
area of interest is in tertiary education. Arun graduated (BSc) from
University of the South Pacific and has an MSc from the same insti-
tution, specializing in mathematics and statistics.