View
5
Download
0
Category
Preview:
Citation preview
1
An Interdisciplinary Lens on Consciousness:
The Consciousness Continuum and How to (Not) Study It in the Brain and the Gut
HILKE PLASSMANN, INSEAD
MILICA MORMANN, School of Business, University of Miami, USA
Author Note
Hilke Plassmann (e-mail: hilke.plassmann@insead.edu, phone: +33 160724313) is an Associate
Professor of Marketing at INSEAD, Boulevard de Constance, 77300 Fontainebleau, France.
Milica Mormann (e-mail: mmormann@bus.miami.edu, phone: +1 3052844937) is a Research
Assistant Professor at the University of Miami School of Business, 514 Jenkins Building, Coral
Gables, FL 33124.
This paper is an invited comment forthcoming in the April issue of the Journal of Consumer
Research
2
Abstract
We welcome Williams and Poehlman’s (forthcoming) effort to better conceptualize
consciousness in consumer research. In this comment, our goal is to complement their ideas
based on models and methods from cognitive and consumer neuroscience. We extend their
suggestions in two important ways. First, we offer an extended framework based on a taxonomy
of consciousness from visual neuroscience that suggests a continuum rather than a dichotomy
between unconscious and conscious processes. This continuum is determined by the role of
perception and attention and the communication between different functional systems in the
brain. We then clarify and make suggestions about how different methods from the neurobiology
toolbox can be used (or not) as measures of mediating and moderating variables underlying
consciousness in consumer behavior.
Keywords: visual consciousness, attention, neuroscience, brain imaging, gut bacteria, functional
and structural brain connectivity
3
We applaud Williams and Poehlman’s (WP) efforts to conceptualize the role of consciousness in
consumer research more clearly (Williams and Poehlman forthcoming). The authors propose a
novel perspective on consciousness, provide a clearer definition (i.e., consciousness =
awareness), and call for more careful evidence when proposing a conscious causation for
consumer behavior. Although we are generally sympathetic to WP’s conclusions and believe that
their work is of great value to consumer researchers, we see two points for improvement:
(1) The consciousness continuum: A conceptual extension of WP’s new framework based
on neuroscientific models of (visual) consciousness that is also in line with Baumeister et al.’s
call for a better integration of conscious and unconscious influences and processes in consumer
research (Baumeister et al. forthcoming).
(2) How to (not) study the consciousness continuum in the brain and the gut: In light of
issues measuring conscious and unconscious influences and processes pointed out by Sweldens,
Tuk, and Huetter, we provide a clarification and extension of how the neurobiology toolkit may
(or may not) provide additional insights to the consciousness debate in consumer research
(Sweldens et al. forthcoming).
THE CONSCIOUSNESS CONTINUUM: AN EXTENDED TAXONOMY FROM
NEUROSCIENCE
WP’s improved framework suggests a simple dichotomy of conscious versus unconscious
influences on several key mental operations (i.e., control, deliberation, intention, and effort).
This dichotomy, and their novel definition of consciousness = awareness, is based on their
systematic examination of the field’s key journals and terms related to conscious processes.
4
However, to be able to decide when and how we should study conscious versus unconscious
processes in consumer behavior, we need to better understand what “unconscious” means in this
context and how these terms are related. To provide a comprehensive overview of how
consciousness is studied and why we should focus more on unconscious processes, a parallel
search for the terms “unconscious,” “nonconscious,” “subconscious,” “preconscious,” and
“subliminal” is needed. We thus performed such a search, and propose an augmented framework
that provides further clarifications on the definition and nature of conscious and unconscious
influences and processes.
We followed the same methodology as WP and examined papers published in the same
four top marketing and decision-making journals, during the same time range, using the same
databases. We identified 189 papers that include the word “unconscious,” 127 articles that
include “nonconscious,” 38 articles that include “subconscious,” 22 articles that use
“preconscious,” and 71 articles that mention “subliminal,” for a total of 447 articles that refer to
processes other than conscious processes (compared to 220 articles WP identified that use
consciousness terms).
Importantly, our literature search shows that various terms are being used to refer to what
WP call “unconscious processes.” WP do not discuss these differences and the multitude of
processes that fit under their term “unconscious.” We suggest that more conceptual work is
needed to clarify what these terms mean and propose the seminal framework by Dehaene et al.
from visual neuroscience as a starting point (Dehaene et al. 2006; see figure 1).
Dehaene et al.’s framework is based on the global neuronal workspace hypothesis (Baars
2002; Dehaene and Naccache 2001; Koch and Tsuchiya 2007) proposing that, simply put, the
level of consciousness of a visual stimulus depends on the level of communication or
5
connectivity between the brain’s primary visual system (see figure 1 in orange) and other
functional subnetworks within the visual system, extending to object and spatial recognition as
well as memory systems via short-range communications (see figure 1 in green) and longer-
range communications with higher cognitive systems important for executive functions,
including cognitive control and valuation (see figure 1 in blue). Importantly, it suggests that there
is no clear dichotomy between unconscious and conscious processes but that they occur on a
continuum. On a neural level this continuum depends on the level of communication between
sensory systems (here visual input in the primary visual system; an extension to other senses is
possible) and other associative brain systems (e.g., the motor system or the brain’s valuation
system). On a conceptual level this continuum depends on whether a stimulus is perceptible and
how attention is deployed (see figure 1).
Insert Figure 1 about here
On one end of the continuum are subliminal processes that are characterized by very little
activation in the brain’s primary visual cortex and are below the level of perceptibility.1 A typical
example of a study within this category is that of subliminal primes (Fitzsimons, Chartrand, and
Fitzsimons 2008).
Next on the continuum, consistent with Dehaene et al. (2006), are a set of processes that
are above the level of perceptibility and are likely a result of intense activation in the complete
visual system, allowing perception by extending to object and spatial recognition and also
memory systems, yet go mostly unnoticed because attention is engaged elsewhere. Thus,
1 Dehaene et al. (2006) describe two different sets of subliminal processes: those that are unattended and those that are attended and can receive various depth of processing and can reach semantic level. For the sake of simplicity for consumer research applications we summarize them here in one category.
6
communication to higher cognitive systems is blocked. For example, a consumer may see a piece
of chocolate cake and be aware of his goal to eat healthfully, but his lack of attention to that
goal—and thus the lack of communication with higher cognitive processes linked to self-
control—makes the healthy goal stay in the preconscious domain. This is an example of the set
of processes that WP refer to as “unconscious.” However, we argue that the term is confusing
because unconscious processes can also include subliminal processes. We suggest that the term
“preconscious” (Dehaene et al. 2006) is a better descriptor of the processes WP label as
“unconscious.” Importantly, we propose that this term is a more accurate descriptor of the
underlying processes and that it should be used in the consumer research literature instead of a
plethora of other terms (i.e., nonconscious, unconscious, subconscious; see the above literature
search).
Finally, on the other end of the continuum are conscious processes that are both above the
level of perceptibility and currently attended to. They are characterized by amplification of brain
activity and its spreading to other, higher-level brain areas in the prefrontal cortex important for
decision making and its control (Dehaene et al. 2006). To stay with the above example, attention
is the key process that moves consumers’ goals (i.e., to eat healthfully) from the “preconscious”
to the “conscious” realm. This means that we need to examine how various mental operations
(e.g., control, deliberation, intention, effort, etc.) are affected by where along the conscious
continuum they fall. This is consistent with WP, but instead of suggesting that all primary
consumer operations have both conscious and unconscious aspects, we suggest that these
operations fall at the various points of the consciousness continuum at various times during the
decision-making process.2
2 Our framework based on visual neuroscience models of visual consciousness is similar in its nature to some other models of information processing in the consumer research literature. For example, Greenwald and Leavitt (1984)
7
The above discussion emphasizes the fact that attention plays a key role in moving
mental processes along the consciousness continuum, yet attention receives little space in WP
and is in general under-researched in consumer psychology (Milosavljevic and Cerf 2008). For
example, WP state that there is a dissociation between consumers’ awareness of mental
processes and their ability to control these processes. Specifically, they explain one’s failure to
adhere to the healthful eating goal as a self-control conflict (e.g., staying healthy vs. eating a
cookie). These types of explanation should give more weight to the role of attention; that is, it is
the consumer’s lack of attention to the health goal—and as a result the lack of communication
with higher cognitive processes in the prefrontal cortex that are linked to self-control—that keeps
the health goal in the preconscious domain.
More research is needed to examine the role of attention within the consciousness
continuum—that is, to better understand the influences and processes that consumers are aware
of but that are not receiving attentional spotlight (i.e., the processes that stay in the preconscious
domain). And more specifically, we need to understand when and how key mental operations
move from one domain to the other in the course of consumer behavior.
In addition to defining consciousness and unconsciousness not as a dichotomy but rather
as a continuum, we also agree with Sweldens et al.’s (forthcoming) request that consumer
researchers need to be more specific about which parts of the decision-making processes they are
studying. In other words, it is crucial for consumer researchers to be clear about the process level
they are investigating—that is, whether they are predicting that an external or internal influence
is conscious (or subliminal or preconscious) or that the evaluation process itself is conscious (or
describe four levels of audience involvement in advertising; Raghubir (2008) lays out a visual information processing model. Our hope is to provide a starting point to reconcile differences in terminology and definitions across such models as well as to provide a more general consumer consciousness framework that should be relevant across various areas of consumer research, including but not limited to the examples mentioned above.
8
subliminal or preconscious). We suggest for this purpose that the adaptation of the lens model, as
done by Newell and Shanks (2014), is an excellent starting point to clarify various loci of
conscious influences.
We hope that future researchers will use and further develop the consciousness
continuum framework suggested here and clearly identify where the processes they are studying
fit within this continuum. Doing so will not only ease knowledge building and allow researchers
to clearly communicate which individual processes they are studying but will also allow them to
begin studying how some of these processes may interact (Laran 2016).
HOW THE TOOLKIT FROM NEUROBIOLOGY MAY HELP (OR NOT)
WP suggest adding various neurobiological markers to the toolkit of consumer
researchers to enable them to better understand the role of consciousness in consumer behavior.
This call seems intuitive because neurobiological measures may allow inferences without
reliance on (potentially consciously biased) self-reports, and in general, we are supportive of this
intuition (Plassmann and Karmarkar 2015; Plassmann, Ramsøy, and Milosavljevic 2012).
However, we also strongly encourage researchers to apply a multi-method approach that
combines self-reports with methodological approaches from neurobiology, being aware of their
strengths and weaknesses and appropriateness for which consumer research questions
(Plassmann et al. 2015).
This is in line with Sweldens et al.’s (forthcoming) call for multiple operationalization of
awareness and Baumeister et al.’s (forthcoming) comment on the importance of self-reports for
consumer research. We also agree with both of their requests that as a field we need to agree on
9
standardized criteria to establish consciousness, such as those proposed by Newell and Shanks
(2014)—reliability, relevance, immediacy, and sensitivity—and believe that neurobiological
tools could contribute to act on these criteria in important ways, as outlined below. However,
contrary to what WP suggest, not every neural pathway needs to be “low level” in the sense of
whether it correlates with conscious, preconscious, or subliminal processes as outlined above.
Thus, we find WP’s description of how neurobiological markers could be useful misleading at
times.
Next, we would like to clarify how neurobiological markers could indeed shed new light
on the consciousness debate in consumer research. More specifically, we suggest integrating
neurobiological markers for the study of consciousness in two ways: (1) as real-time measures of
mediating processes and (2) as more trait-like individual difference measures.
Real-Time Neurophysiological Measures as Mediators of Consumer Behavior
This section is based on the notion that most neurophysiological measures, such as brain
imaging, eye-tracking, and psychophysiological measures, allow for the measurement of activity
not only during actual marketing-relevant behavior (e.g., attention, memory, affect, choice) but
also in the periods directly preceding and following such behaviors (for more detailed
description of the neuroscience toolkit see, e.g., Kable 2011; Plassmann and Karmarkar 2015). In
these periods, important information processes are taking place that are crucial to better
understanding of consumer behavior. These measurement characteristics of neurobiological
markers are extremely powerful, considering Newell and Shanks’ (2014) proposition that
consciousness measurements should be temporally near the outcome (immediacy) and elicited by
10
the same cue as the outcome (sensitivity).3 These characteristics are also important because they
shed light on the information processes and how they interact, which is important when
attempting to understand psychological mechanisms (subliminal, preconscious, and conscious
ones) and their choice architecture (Weber and Johnson 2009).
An interesting recent development has been the approach of testing whether
neurophysiological measures formally mediate behavior or self-reports from consumers. This
approach was first used with brain imaging data to test brain mediators of pain placebo effects by
Tor Wager’s lab (Atlas et al. 2010; Atlas et al. 2014). For this purpose, his group developed a
freely available single- and multi-level mediation toolbox (http://wagerlab.colorado.edu/tools).
A similar approach has recently been used by consumer researchers. For example,
Mormann et al. (2016) examined how consumers form reference points and use eye-tracking as a
measure of mediating processes—that is, attention to various past prices. While the existing
literature focuses on the stream of previous prices (Briesch et al. 1997; Weaver and Frederick
2012; Winer 1986), Mormann et al. showed that how much consumers use specific past prices in
forming their reference points depends on the amount of attention these past prices receive, as
measured via eye-tracking (Mormann et al. 2016). They then showed that attention to high prices
can shift the reference point up and that attention to relatively low prices can shift it down.
Other examples include studies from Plassmann’s group that used this approach to
understand, for example, how valence (as measured by facial affective muscle response) and
arousal (as measured by skin conductance response) in response to an incidental affective cue,
such as a surprise monetary gain, mediates an increase in subsequent preferences for unrelated
objects, such as wines or pictures (Ling, Shiv, and Plassmann forthcoming). A last example is
3 Needless to say, depending on the experimental setup, neurophysiological measures also act on reliability (as they are based on physiological changes they are tentatively less biased, specifically considering the standard of using repeated measures designs) and relevance (as a function of using appropriate control conditions).
11
the study of brain mediators assessed with functional magnetic resonance imaging (fMRI) to
investigate why hefty price tags increase consumers’ enjoyment of wines (Plassmann et al.
2008). Brain mediators of such “placebo effects of pricing” include neural signatures of wanting,
liking, and cognitive regulation (Schmidt et al. 2016).
Neurobiological Markers of Individual Differences in Consumer Behavior
WP suggest that trait-like neurobiological markers such as prenatal testosterone levels
and increased levels of specific gut bacteria could serve as relevant individual differences
affecting consumer behavior that are not consciously accessible by the consumer. We strongly
agree that there is potential merit in investigating individual differences on the basis of
neurobiological markers. We also believe more generally that predictions of behavior that are
further from the context where the data were collected, in terms of stimuli or time, could be of
even more practical use in answering marketing questions, such that they might help to identify
different types of consumers (Kable and Levy 2015; Venkatraman et al. 2012).
In what follows we point to more recently established markers that also tie into the idea
that consciousness depends on the level of communication or connectivity in the brain (as
conceptualized in figure 1) and extend this notion with first findings on the communication
between the gut and the brain. We focus specifically on four kinds of measures: measures of gray
matter volume, measures of structural connectivity, measures of functional resting-state
connectivity of the so-called “default-mode network,” and new findings about gut bacteria–brain
communication.
Measures of Gray Matter Volume. Differences in brain structures, such as gray matter
volume, can be linked to individual differences in brain function, personality, and behavior, in
12
both health and disease. The basic assumption underlying such approaches is that regional gray
matter volume, as measured by structural magnetic resonance imaging (MRI), corresponds to the
regional volume and wiring of nerve cell layers in the brain. This approach has recently been
applied to predict individual differences relevant for consumer behavior (Gilaie-Dotan et al.
2014; Plassmann and Weber 2015). For example, Plassmann and Weber (2015) applied this
approach to explore brain regions that showed a variation in gray matter volume that predicted
individual differences in how much high prices and indulgent product claims enhanced
consumers’ enjoyment of wines and milkshakes. They then used the neurobiological individual
difference marker to inform follow-up behavioral experiments that shed more light on the
personality traits linked to the functioning of these brain regions. Their findings showed that
consumers high in reward-seeking, low in somatosensory awareness, and high in need for
cognition were more responsive to the effects of marketing.
Measures of Structural Connectivity. If consciousness is a function of connectivity
between brain regions, then individual differences in how the brain is wired—as assessed by a
technique called diffusion tensor imaging (DTI)—could be a relevant neurobiological marker of
individual differences in consciousness of consumer behavior. Simply put, DTI measures the
range and strength of white matter connectivity (Johansen-Berg and Rushworth 2009; Mori and
Zhang 2006). Several studies have applied this technique to the study of consumer decision-
making and found that the structural connectivity in the brain’s valuation system can, for
example, be linked to delay discounting (for a review, see Kable and Levy 2015). As an
example, van den Bos et al. (2014) found that the extent of delay discounting was negatively
correlated with the strength of connection between a region involved in self-control (the
dorsolateral prefrontal cortex) and a region involved in reward prediction and motivation (the
13
striatum) and positively correlated with the strength of tracts from the regions involved in the
coding of emotional intensity (the amygdala) to the same region involved in reward prediction
and motivation (the striatum) (van den Bos et al. 2014). These findings give first evidence for the
idea that weakened structural communication between the neural signatures of self-control and
reward-related regions and stronger communication between neural signatures of emotional
intensity and reward-related structures may increase preference for immediate rewards.
Measures of Resting-State Functional Connectivity. In addition to the two types of
anatomical markers described above, functional neural connectivity at rest can also be predictive
of individual personality traits and cognitive abilities potentially outside the realm of
consciousness. Brain activity at rest has been more popularly referred to as the brain’s so-called
“default-mode network” (Biswal et al. 1995, Buckner, Andrews-Hanna, and Schacter 2008;
Raichle et al. 2001; Shulman et al. 1997). The default-mode network (DMN) describes the
brain’s ongoing or intrinsic activity when participants are asked to do nothing—and thus in the
absence of tasks that require deliberative processing. Its neural signatures overlap with those of
mind-wandering, thinking about the self and others, remembering the past, and planning for the
future (Raichle 2015). It also has overlaps with the brain’s valuation system, which is important
for consumer decision-making (Bartra, McGuire, and Kable 2013).
Interestingly for consciousness researchers, general features of the DMN have also been
identified in animals—including monkeys, cats, rats, and mice—that clearly differ with respect
to their (conscious) mental states. Further, the DMN has been shown to be negatively correlated
with the brain’s attention networks (Broyd et al. 2009; Fox et al. 2005). Finally, patterns of
resting-state functional connectivity are also present under anesthesia (Greicius et al. 2008) and
during the early stages of sleep (Fukunaga et al. 2006; Larson-Prior et al. 2009). These
14
observations make it unlikely that the DMN represents primarily the result of unconstrained,
conscious cognition (Raichle 2015).
For example, first research related to consumer behavior found evidence for a negative
correlation between delay discounting and the functional connectivity among regions that have
been implicated in self-control and reward-related structures important for the choice process (Li
et al. 2013). These findings support the notion that weakened communication between the neural
signatures of self-control and reward-related regions may increase preference for immediate
rewards.
Measures of Gut Bacteria–Brain Connectivity. Another new development is our
improved understanding of the role that the gut (the “second brain”) plays in influencing
consumer behavior—most likely taking place beyond the realm of consciousness. One recent
discovery is the “new organ” in our gut: the trillions of bacteria that colonize our intestines. A lot
of research has been dedicated to how various psychological decision theories, formal models,
and neural correlates can explain and predict what we eat and how we regulate food intake (e.g.,
what, when, and why we prefer the healthy apple over an immediately rewarding, yet unhealthy,
piece of chocolate cake). What about the other direction: Does what we have been eating affect
who we are and how we make decisions? Recent evidence suggests that what we eat (Cotillard et
al. 2013), combined with various factors in our environment (Cryan and Dinan 2012), shapes our
gut bacteria. Different gut bacteria profiles influence gut–brain communication (for extensive
reviews see Collins, Surette, and Bercik 2012; Mayer 2011), which in turn affects both our
emotional responses, such as mood and stress, and how we decide (Mayer 2016; Mueller et al.
2012; Simpson et al. 2005). First work in this area indicates that an increased gut bacteria (gene)
richness can be linked to better health (e.g., lower body fat) and psychological well-being.
15
Although this research is exciting and potentially has an important impact on consumer
behavior, it is still in its infancy. Most of the studies have been conducted in rodents, and
mechanisms in humans remain essentially unclear. Although first dietary interventions as
described by WP indeed may increase gut bacteria diversity in humans for a short period of time
(gut bacteria is mostly stable during adulthood and variable only during early childhood and late
adulthood), the impact of gut bacteria diversity on questions related to consumer behavior is still
unknown. For example, the study by Tillisch et al. (2013) referred to by WP did indeed show in
a very small sample of participants that a probiotic diet might increase emotional responding.
However, the authors did not find an impact of the intervention on gut bacteria, and hence the
effects could be due to a variety of other factors, including a placebo response to the dietary
intervention (Tillisch et al. 2013). This is why consumer researchers at this stage cannot make
reverse inferences on gut–brain links using dietary interventions but need to measure gut
bacteria using fecal samples to make any scientifically grounded claims.
The same is essentially true for hormonal influences. For example, findings that prenatal
levels of testosterone matter for consumer behavior are also inconclusive. A recent large-scale
testosterone intervention study found an impact of testosterone on several processes related to
consumer behavior, such as cognitive reflection (Nave et al. 2016) and status signaling (Nave et
al. forthcoming), yet prenatal testosterone levels did not moderate these effects.
Though still early in development, taken together the neurobiological markers outlined in
this comment could prove to be very useful for the goals of consumer researchers and other
behavioral scientists interested in consciousness research.
RECOMMENDATIONS AND CONCLUSION
16
We greatly appreciate WP’s efforts to more clearly conceptualize the role of
consciousness in consumer research. Their paper undoubtedly makes an important contribution
to consumer research. However, we believe that their ideas about the conceptualization and the
measurement of consciousness could be refined and extended based on models and methods
informed by the study of consciousness in cognitive and consumer neuroscience. More
specifically, in this comment, we first suggested an extended framework based on a taxonomy of
consciousness from vision neuroscience that suggests a continuum rather than a dichotomy
between unconscious and conscious processes, determined by the role of attention and the
communication between different functional systems in the brain. Importantly, we proposed that
“preconscious” influences and processes should be integrated in concepts of consumer
researchers (rather than other currently used terms, such as “nonconscious,” “unconscious,” and
“subconscious”). We then clarified and made suggestions about how different tools from the
neurobiology toolbox could be used (or not) as measures of mediating and moderating variables
underlying consciousness in consumer behavior.
Finally, we applaud WP’s recommendation that marketing Ph.D. students should receive
training in cognitive neuroscience. However, acquiring some basic knowledge through classes is
not as good as years of research. That is why we suggest that in addition to acquiring some basic
levels of understanding through classes, those interested in using the theories and methods of
cognitive neuroscience should seek collaborations with experts in that area. For example,
consumer researchers interested in consciousness can seek collaborations with cognitive
neuroscientists and microbiologists to understand gut–brain–behavior links relevant for
consumer research. Collaborative, interdisciplinary research is often cited as a goal of
17
departments, schools, universities, and journals, and a mix of coursework and research
collaborations may prove to be a winning strategy in achieving that goal.
18
FIGURES
Figure 1: The consciousness continuum, adapted from Dehaene et al. (2006). Three types of consciousness states are schematically shown, defined by whether a stimulus is perceptible in the primary visual cortex and whether awareness and attention are present. Colors illustrate location of strongest activation (primary visual cortex = orange, other parts of visual cortex extending to temporal and parietal cortex = green, parieto-frontal and pre-frontal cortex = blue), and small arrows illustrate communication between them. Large arrows schematically illustrate whether attention is paid to the stimulus.
19
REFERENCES
Atlas, Lauren Y., Niall Bolger, Martin A. Lindquist, and Tor D. Wager (2010), “Brain Mediators
of Predictive Cue Effects on Perceived Pain,” Journal of Neuroscience, 30 (39), 12964–
77.
Atlas, Lauren Y., Martin A. Lindquist, Niall Bolger, and Tor D. Wager (2014), “Brain Mediators
of the Effects of Noxious Heat on Pain,” Pain, 155 (8), 1632–48.
Baars, Bernard J. (2002), “The Conscious Access Hypothesis: Origins and Recent Evidence,”
Trends in Cognitive Sciences, 6 (1), 47–52.
Bartra, Oscar, Joseph T. McGuire, and Joseph W. Kable (2013), “The Valuation System: A
Coordinate-Based Meta-Analysis of BOLD fMRI Experiments Examining Neural
Correlates of Subjective Value,” NeuroImage, 76, 412–27.
Baumeister, Roy. F., Cory J. Clark, Jonghan Kim, and Stephan Lau (forthcoming), “Consumers
(and Consumer Researchers) Need Conscious Thinking in Addition to Unconscious
Processes: A Call of an Integrative Model,” Journal of Consumer Research.
Biswal, Bharat, F. Zerrin Yetkin, Victor M. Haughton, and James S. Hyde (1995), “Functional
Connectivity in the Motor Cortex of Resting Human Brain Using Echo-Planar MRI,”
Magnetic Resonance in Medicine, 34 (4), 537–41.
Briesch, Richard A., Lakshman Krishnamurthi, Tridib Mazumdar, and Sevilimedu P. Raj (1997),
“A Comparative Analysis of Reference Price Models,” Journal of Consumer Research,
24 (2), 202–14.
Broyd, Samantha J., Charmaine Demanuele, Stefan Debener, Suzannah K. Helps, Christopher J.
James, and Edmund J. S. Sonuga-Barke (2009), “Default-Mode Brain Dysfunction in
20
Mental Disorders: A Systematic Review,” Neuroscience & Biobehavioral Reviews, 33 (3),
279–96.
Buckner, Randy L., Jessica R. Andrews-Hanna, and Daniel L. Schacter (2008), “The Brain’s
Default Network: Anatomy, Function, and Relevance to Disease,” Annals of the New
York Academy of Sciences, 1124, 1–38.
Collins, Stephen M., Michael Surette, and Premysl Bercik (2012), “The Interplay Between the
Intestinal Microbiota and the Brain,” Nature Reviews Microbiology, 10 (11), 735–42.
Cotillard, Aurélie, Sean P. Kennedy, Ling Chun Kong, Edi Prifti, Nicolas Pons, Emmanuelle Le
Chatelier, Mathieu Almeida, Benoit Quinquis, Florence Levenez, Nathalie Galleron,
Sophie Gougis, Salwa Rizkalla, Jean-Michel Batto, Pierre Renault, ANR MicroObes
consortium, Joel Doré, Jean-Daniel Zucker, Karine Clément, Stanislav Dusko Ehrlich,
Hervé Blottière, Marion Leclerc, Catherine Juste, Tomas de Wouters, Patricia Lepage,
and Charlene Fouqueray (2013), “Dietary Intervention Impact on Gut Microbial Gene
Richness,” Nature, 500 (7464), 585–8.
Cryan, John F. and Timothy G. Dinan (2012), “Mind-Altering Microorganisms: The Impact of
the Gut Microbiota on Brain and Behaviour,” Nature Reviews Neuroscience, 13 (10),
701–12.
Dehaene, Stanislas, Jean-Pierre Changeux, Lionel Naccache, Jérôme Sackur, and Claire Sergent
(2006), “Conscious, Preconscious, and Subliminal Processing: A Testable Taxonomy,”
Trends in Cognitive Sciences, 10 (5), 204–11.
Dehaene, Stanislas and Lionel Naccache (2001), “Towards a Cognitive Neuroscience of
Consciousness: Basic Evidence and a Workspace Framework,” Cognition, 79 (1–2), 1–37.
21
Fitzsimons, Gráinne, Tanya L. Chartrand, and Gavan J. Fitzsimons (2008), “Automatic Effects
of Brand Exposure on Motivated Behavior: How Apple Makes You ‘Think Different,’”
Journal of Consumer Research, 35 (1), 21–35.
Fox, Michael D., Abraham Z. Snyder, Justin L. Vincent, Maurizio Corbetta, David C. Van Essen,
and Marcus E. Raichle (2005), “The Human Brain Is Intrinsically Organized Into
Dynamic, Anticorrelated Functional Networks,” Proceedings of the National Academy of
Sciences of the United States of America, 102 (27), 9673–8.
Fukunaga, Masaki, Silvina G. Horovitz, Peter van Gelderen, Jacco A. de Zwart, J. Martijn
Jansma, Vasiliki N. Ikonomidou, Renxin Chu, Roel H. R. Deckers, David A. Leopold,
and Jeff H. Duyn (2006), “Large-Amplitude, Spatially Correlated Fluctuations in Bold
fMRI Signals During Extended Rest and Early Sleep Stages,” Magnetic Resonance
Imaging, 24 (8), 979–92.
Gilaie-Dotan, Sharon, Agnieszka Tymula, Nicole Cooper, Joseph W. Kable, Paul W. Glimcher,
and Ifat Levy (2014), “Neuroanatomy Predicts Individual Risk Attitudes,” Journal of
Neuroscience, 34 (37), 12394–401.
Greenwald, Anthony G. and Clark Leavitt (1984), “Audience Involvement in Advertising: Four
Levels,” Journal of Consumer Research, 11 (1), 581–92.
Greicius, Michael D., Vesa Kiviniemi, Osmo Tervonen, Vilho Vainionpää, Seppo Alahuhta,
Allan L. Reiss, and Vinod Menon (2008), “Persistent Default-Mode Network
Connectivity During Light Sedation,” Human Brain Mapping, 29 (7), 839–47.
Johansen-Berg, Heidi and Matthew F. S. Rushworth (2009), “Using Diffusion Imaging to Study
Human Connectional Anatomy,” Annual Review of Neuroscience, 32, 75–94.
22
Kable, Joseph W. (2011), “The Cognitive Neuroscience Toolkit for the Neuroeconomist: A
Functional Overview,” Journal of Neuroscience, Psychology, and Economics, 4 (2), 63–
84.
Kable, Joseph W. and Ifat Levy (2015), “Neural Markers of Individual Differences in Decision-
Making,” Current Opinion in Behavioral Sciences, 5, 100–07.
Koch, Christof and Naotsugu Tsuchiya (2007), “Attention and Consciousness: Two Distinct
Brain Processes,” Trends in Cognitive Sciences, 11 (1), 16–22.
Larson-Prior, Linda J., John M. Zempel, Tracy S. Nolan, Fred W. Prior, Abraham Z. Snyder, and
Marcus E. Raichle (2009), “Cortical Network Functional Connectivity in the Descent to
Sleep,” Proceedings of the National Academy of Sciences of the United States of America,
106 (11), 4489–94.
Laran, Juliano (2016), “Consumer Goal Pursuit,” Current Opinion in Psychology, 10, 22–6.
Li, Nan, Ning Ma, Ying Liu, Xiao-Song He, De-Lin Sun, Xian-Ming Fu, Xiaochu Zhang, Shihui
Han, and Da-Ren Zhang (2013), “Resting-State Functional Connectivity Predicts
Impulsivity in Economic Decision-Making,” Journal of Neuroscience, 33 (11), 4886–95.
Ling, Aiqing, Baba Shiv, and Hilke Plassmann (forthcoming), “Modulation of Judgments by
Incidental Affect: The Dynamic Integration of Affect and Its Temporal Sustainability,”
Advances in Consumer Research.
Mayer, Emeran A. (2011), “Gut Feelings: The Emerging Biology of Gut-Brain Communication,”
Nature Reviews Neuroscience, 12 (8), 453–66.
Mayer, Emeran A. (2016), The Mind-Gut Connection: How the Hidden Conversation Within Our
Bodies Impacts Our Mood, Our Choices, and Our Overall Health, New York:
HarperCollins.
23
Milosavljevic, Milica and Moran Cerf (2008), “First Attention Then Intention: Insights From
Computational Neuroscience of Vision,” International Journal of Advertising, 27 (3),
381–98.
Mori, Susumu and Jiangyang Zhang (2006), “Principles of Diffusion Tensor Imaging and Its
Applications to Basic Neuroscience Research,” Neuron, 51 (5), 527–39.
Mormann, Milica, Luke Nowlan, Uzma Kahn, and Joseph Johnson (2016), “(Emotional)
Reference Point Formation,” working paper, University of Miami, Coral Gables, FL
33146.
Mueller, Kristen, Caroline Ash, Elizabeth Pennisi, and Orla Smith (2012), “The Gut Microbiota:
Introduction,” Science, 336 (6086), 1245.
Nave, Gideon, Amos Nadler, David Dubois, David Zava, Colin Camerer, and Hilke Plassmann
(forthcoming), “Single Dose Testosterone Administration Increases Men’s Preference for
Status Goods,” Advances in Consumer Research.
Nave, Gideon, Amos Nadler, David Zava, and Colin Camerer (2016), “Single Dose Testosterone
Impairs Cognitive Reflection in Men,” working paper, California Institute of Technology.
Newell, Ben R. and David R. Shanks (2014), “Unconscious Influences on Decision Making: A
Critical Review,” Behavioral and Brain Sciences, 37 (1), 1–19.
Plassmann, Hilke and Uma R. Karmarkar (2015), “Consumer Neuroscience,” in The Cambridge
Handbook of Consumer Psychology, ed. Michael I. Norton, Derek D. Rucker, and Cait
Lamberton, Cambridge, UK: Cambridge University Press, 122–51.
Plassmann, Hilke, John O’Doherty, Baba Shiv, and Antonio Rangel (2008), “Marketing Actions
Can Modulate Neural Representations of Experienced Pleasantness,” Proceedings of the
National Academy of Sciences of the United States of America, 105 (3), 1050–4.
24
Plassmann, Hilke, Thomas Zoëga Ramsøy, and Milica Milosavljevic (2012), “Branding the
Brain: A Critical Review and Outlook,” Journal of Consumer Psychology, 22 (1), 18–36.
Plassmann, Hilke, Vinod Venkatraman, Scott Huettel, and Carolyn Yoon (2015), “Consumer
Neuroscience: Applications, Challenges, and Possible Solutions,” Journal of Marketing
Research, 52 (4), 427–35.
Plassmann, Hilke and Bernd Weber (2015), “Individual Differences in Marketing Placebo
Effects: Evidence From Brain Imaging and Behavioral Experiments,” Journal of
Marketing Research, 52 (4), 493–510.
Raghubir, Priya (2008), “Are Visual Perceptual Biases Hard-Wired,” in Visual Marketing: From
Attention to Action, ed. Michel Wedel and Rik Pieters, New York: Lawrence Erlbaum
Associates, 143–65.
Raichle, Marcus E. (2015), “The Brain’s Default Mode Network,” Annual Review of
Neuroscience, 38, 433–47.
Raichle, Marcus E., Ann Mary MacLeod, Abraham Z. Snyder, William J. Powers, Debra A.
Gusnard, and Gordon L. Shulman (2001), “A Default Mode of Brain Function,”
Proceedings of the National Academy of Sciences of the United States of America, 98 (2),
676–82.
Schmidt, Liane, Claus Kullen, Bernd Weber, and Hilke Plassmann (2016), “How Context Alters
Value: Brain Mediators of Price Information on Experienced Value Recruit Different
Parts of the Brain’s Valuation System and Are Moderated by Task-Independent
Sensitivity of the Ventral Striatum,” INSEAD Working Paper Series, 77300
Fontainebleau, France.
25
Shulman, Gordon L., Julie A. Fiez, Maurizio Corbetta, Randy L. Buckner, Francis M. Miezin,
Marcus E. Raichle, and Steven E. Petersen (1997), “Common Blood Flow Changes
Across Visual Tasks: II. Decreases in Cerebral Cortex,” Journal of Cognitive
Neuroscience, 9 (5), 648–63.
Simpson, Stephen, Caroline Ash, Elizabeth Pennisi, and John Travis (2005), “The Gut: Inside
Out,” Science, 307 (5717), 1895.
Sweldens, Steven, Mirjam A. Tuk, and Mandy Huetter (forthcoming), “How to Study
Consciousness in Consumer Research,” Journal of Consumer Research.
Tillisch, Kirsten, Jennifer Labus, Lisa Kilpatrick, Zhiguo Jiang, Jean Stains, Bahar Ebrat, Denis
Guyonnet, Sophie Legrain-Raspaud, Beatrice Trotin, Bruce Naliboff, and Emeran A.
Mayer (2013), “Consumption of Fermented Milk Product With Probiotic Modulates
Brain Activity,” Gastroenterology, 144 (7), 1394–401, 401 e1–4.
van den Bos, Wouter, Christian A. Rodriguez, Julie B. Schweitzer, and Samuel M. McClure
(2014), “Connectivity Strength of Dissociable Striatal Tracts Predict Individual
Differences in Temporal Discounting,” Journal of Neuroscience, 34 (31), 10298–310.
Venkatraman, Vinod, John A. Clithero, Gavan J. Fitzsimons, and Scott A. Huettel (2012), “New
Scanner Data for Brand Marketers: How Neuroscience Can Help Better Understand
Differences in Brand Preferences,” Journal of Consumer Psychology, 22 (1), 143–53.
Weaver, Ray and Shane Frederick (2012), “A Reference Price Theory of the Endowment Effect,”
Journal of Marketing Research, 49 (5), 696–707.
Weber, Elke U. and Eric J. Johnson (2009), “Mindful Judgment and Decision Making,” Annual
Review of Psychology, 60, 53–85.
26
Williams, Lawrence E. and Andrew Poehlman (forthcoming), “Conceptualizing Consciousness
in Consumer Research,” Journal of Consumer Research.
Winer, Russell S. (1986), “A Reference Price Model of Brand Choice for Frequently Purchased
Products,” Journal of Consumer Research, 13 (2), 250–56.
Recommended