SOCIETY, ORGANIZATIONS

AND THE BRAIN: BUILDING

TOWARDS A UNIFIED

COGNITIVE NEUROSCIENCE

PERSPECTIVE

EDITED BY : Carl Senior, Nick Lee and Sven Braeutigam

PUBLISHED IN : Frontiers in Human Neuroscience

1 June 2015 | Society, Organizations and the BrainFrontiers in Human Neuroscience

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ISSN 1664-8714

ISBN 978-2-88919-580-0

DOI 10.3389/978-2-88919-580-0

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2 June 2015 | Society, Organizations and the BrainFrontiers in Human Neuroscience

This e-book brings together scholars in both the neurosciences and organizational sciences

who have adopted various approaches to study the cognitive mechanisms mediating the

social behavior that we see within organizations. Such an approach has been termed by

ourselves, and others, as ‘organisational cognitive neuroscience’. In recent years there has

been a veritable increase in studies that have explored the cognitive mechanisms driving such

behaviours, and much progress has been made in understanding the neural underpinnings of

processes such as ﬁnancial exchange, risk awareness and even leadership. However, while these

studies are informative and add to our understanding of human cognition they fall short of

providing evidence-based recommendations for practice. Speciﬁcally, we address the broader

issue of how the neuroscientiﬁc study of such core social behaviors can be used to improve

the very way that we work. To address these gaps in our understanding the chapters in this

book serve as a platform that allows scholars in both the neurosciences and the organizational

sciences to highlight the work that spans across these two ﬁelds.

The consolidation of these two ﬁelds also serves to highlight the utility of a uniﬁed and

singular organizational cognitive neuroscience. This is a fundamentally important outcome of

the book as the application of neuroscience to address economically relevant behaviours has

seen a variety of ﬁelds evolve in their own right, such as neuromarketing, neuroeconomics

and so forth. The use of neuro-scientiﬁc technologies,in particular fMRI, has indeed led

to a bewildering and somewhat suffocating proliferation of new approaches, however, the

speed of such developments demands that we must proceed carefully with such ventures or

risk some fundamental mistakes. The book that you now hold will consolidates these new

neuroscience based approaches and in doing so highlight the importance of this approach

in helping us to understand human social behaviour in general. Taken together the chapters

provide a framework for scholars within the neurosciences who wish to explore the further

the opportunities that the study of organisational behaviour may provide.

Citation: Senior, C., Lee, N., Braeutigam, S., eds. (2015). Society, Organizations and the Brain:

Building Towards a Uniﬁed Cognitive Neuroscience Perspective. Lausanne: Frontiers Media.

doi: 10.3389/978-2-88919-580-0

SOCIETY, ORGANIZATIONS AND THE BRAIN:

BUILDING TOWARDS A UNIFIED COGNITIVE

NEUROSCIENCE PERSPECTIVE

Topic Editors:

Carl Senior, Aston University, UK

Nick Lee, Loughbourgh University, UK

Sven Braeutigam, Oxford University, UK

3 June 2015 | Society, Organizations and the BrainFrontiers in Human Neuroscience

Table of Contents

05 Society, organizations and the brain: building toward a unified cognitive

neuroscience perspective

Carl Senior, Nick Lee and Sven Braeutigam

09 The relationship between self-report of depression and media usage

Martin Block, Daniel B. Stern, Kalyan Raman, Sang Lee, Jim Carey, Ashlee A.

Humphreys, Frank Mulhern, Bobby Calder, Don Schultz, Charles N. Rudick, Anne J.

Blood and Hans C. Breiter

19 Antagonistic neural networks underlying differentiated leadership roles

Richard E. Boyatzis, Kylie Rochford and Anthony I. Jack

34 Redefining neuromarketing as an integrated science of influence

Hans C. Breiter, Martin Block, Anne J. Blood, Bobby Calder, Laura Chamberlain, Nick

Lee, Sherri Livengood, Frank J. Mulhern, Kalyan Raman, Don Schultz, Daniel B.

Stern, Vijay Viswanathan and Fengqing (Zoe) Zhang

41 Age-related striatal BOLD changes without changes in behavioral loss aversion

Vijay Viswanathan, Sang Lee, Jodi M. Gilman, Byoung Woo Kim, Nick Lee, Laura

Chamberlain, Sherri L. Livengood, Kalyan Raman, Myung Joo Lee, Jake Kuster,

Daniel B. Stern, Bobby Calder, Frank J. Mulhern, Anne J. Blood and Hans C. Breiter

53 On the interpretation of synchronization in EEG hyperscanning studies: a

cautionary note

Adrian P. Burgess

70 Operationalizing interdisciplinary research—a model of co-production in

organizational cognitive neuroscience

Michael J. R. Butler

73 Dehumanization in organizational settings: some scientific and ethical

considerations

Kalina Christoff

78 Cognitive requirements of competing neuro-behavioral decision systems: some

implications of temporal horizon for managerial behavior in organizations

Gordon R. Foxall

95 The marketing firm and consumer choice: implications of bilateral contingency

for levels of analysis in organizational neuroscience

Gordon R. Foxall

109 Near-infrared spectroscopy (NIRS) as a new tool for neuroeconomic research

Isabella M. Kopton and Peter Kenning

122 Ideology in organizational cognitive neuroscience studies and other misleading

claims

Dirk Lindebaum

4 June 2015 | Society, Organizations and the BrainFrontiers in Human Neuroscience

125 The evolution of leader–follower reciprocity: the theory of service-for-prestige

Michael E. Price and Mark Van Vugt

142 Recommendations for sex/gender neuroimaging research: key principles and

implications for research design, analysis, and interpretation

Gina Rippon, Rebecca Jordan-Young, Anelis Kaiser and Cordelia Fine

155 Using evolutionary theory to enhance the brain imaging paradigm

Gad Saad and Gil Greengross

158 A sociogenomic perspective on neuroscience in organizational behavior

Seth M. Spain and P. D. Harms

173 A face for all seasons: searching for context-specific leadership traits and

discovering a general preference for perceived health

Brian R. Spisak, Nancy M. Blaker, Carmen E. Lefevre, Fhionna R. Moore and Kleis

F. B. Krebbers

182 A case for neuroscience in mathematics education

Ana Susac and Sven Braeutigam

185 The role of attachment styles in regulating the effects of dopamine on the

behavior of salespersons

Willem Verbeke, Richard P. Bagozzi and Wouter E. van den Berg

198 A comment on the service-for-prestige theory of leadership

Christopher R. von Rueden

200 Interdisciplinary research is the key

David A. Waldman

203 Consumer neuroscience to inform consumers—physiological methods to

identify attitude formation related to over-consumption and environmental

damage

Peter Walla, Monika Koller and Julia L. Meier

EDITORIAL

published: 19 May 2015

doi: 10.3389/fnhum.2015.00289

Frontiers in Human Neuroscience | www.frontiersin.org May 2015 | Volume 9 | Article 289

Edited and reviewed by:

Srikantan S. Nagarajan,

University of California, San Francisco,

USA

*Correspondence:

Carl Senior,

c.senior@aston.ac.uk;

Nick Lee,

n.lee@lboro.ac.uk;

Sven Braeutigam,

sven.braeutigam@psych.ox.ac.uk

Received: 25 March 2015

Accepted: 04 May 2015

Published: 19 May 2015

Citation:

Senior C, Lee N and Braeutigam S

(2015) Society, organizations and the

brain: building toward a uniﬁed

cognitive neuroscience perspective.

Front. Hum. Neurosci. 9:289.

doi: 10.3389/fnhum.2015.00289

Society, organizations and the brain:

building toward a uniﬁed cognitive

neuroscience perspective

Carl Senior

, Nick Lee

and Sven Braeutigam

School of Life & Health Sciences, Aston University, Birmingham, UK,

School of Business and Economics, Loughborough

University, Loughborough, UK,

Oxford Centre for Human Brain Activity, Oxford University, Oxford, UK

Keywords: organizational cognitive neuroscience, functional brain imaging, neuromarketing, neuroeconomics

The Oxford English Dictionary contains the following entry for the word “postal” as:

• adjective relating to or carried out by post.

– PHRASES go postal US informal go mad, especially from stress. With reference to cases in

which postal employees have run amok and shot colleagues.

Even a superﬁcial knowledge of recent events may lead to the conclusion that the contemporary

organization is perhaps not an easy thing to manage in a way that guarantees both economic

and social prosperity. As such, it seems to be part of the modern human condition to be at least

somewhat unhappy, stressed, or otherwise negatively impacted by either organizational life itself,

or the impact of organizations on today’s society. Fortunately, however, worst-case scenarios—as

implied by the OED above—are very rare.

It does not come as a surprise then, that researchers have expended considerable eﬀorts on

exploring and understanding the formation, management, and ethical sustentation of organizations

of all kinds and sizes, from bleeding-edge venture enterprises operating in break-ne ck markets to

perhaps non-competitive, non-proﬁt charities. Drawing from an interest in the negative eﬀects

workplaces can have on individuals, some of us published a clarion call, raising questions about

how a better understanding of our biological systems could inform an understanding of the social

behavior that we manifest within organizations (Butler and Senior, 2007a,b). The critical question

here is how the organization and the individual interact and inﬂuence each other, given that it that

organizations are designed as they are by t he very same species which will work in them, and equally

important how cognitive neuroscience in particular can help to unravel such mechanisms.

Scholars have indeed begun to explore the neuroscience of organizational behavior. These eﬀorts

go under the names of Organizational Neuroscience and Organizational Cognitive Neuroscience,

terms that refer to cross-disciplinary perspectives on organizational research, which take as their

foci of study the cognitive mechanisms that drive human behaviors in response to organizational

manifestations (Senior et al., 2008, 2011; Becker et al., 2011; Lee et al., 2012a). Such approaches seem

to have some merit in the study of the eﬀects of organizational life on human beings, and also on

how one can mitigate the more deleterious eﬀects that appear inherent to such contexts. However,

even with such rich empirical intercourse there remains an opportunity to examine further the

current state of the art research endeavors that span the biological and organizational domains to

inform our understanding of the type of social behavior that most of us will carry out most days for

most of our lives.

The articles contained within this research topic do just that, and go beyond merely explicating

further the possible mechanisms that drive “social behavior that occurs within organizational

manifestations” (Senior et al., 2011, p. 2) but ensure that such an understanding actually informs

Senior et al. Building toward a uniﬁed cognitive neuroscience

our knowledge of a socially rele vant and species speciﬁc social

behavior. In the call for papers we chose not to restrict the

nature of articles, but to ensure that all submissions could inform

our wider understanding social behavior in this applied context

(Waldman, 2013). The resulting submissions can be loosely

grouped into four main clusters–(a) general management, (b)

leadership, (c) neuromarketing science, and (d) papers that have

made speciﬁc recommendations for subsequent work.

To fully realize the potential for the impact of these articles,

it is important to ﬁrst reﬂect upon the industrial revolution and

how it showed that complex products could most proﬁtably be

made by breaking them up into small spe cialized, repetitive t asks.

As far back as the early 20th Century, with the emergence of

“scientiﬁc management” (e.g., Taylor, 1911), and the principles of

Fordism, the place of humans in this workﬂow was also treated

as a mechanistic process, to be designed in such a way as to

maximize eﬃciency and minimize defects. In such a context,

one could be forgiven for wondering whether working in such

organizations was what humans were ideally suited to. Even so, it

is undeniable that humans are the only species to have organized

itself into abstract organizations (i.e., not solely related to survival

or socialization), suggesting that perhaps something about this

ability does confer a collective advantage, if not an individual

one. In such a context, one would be forgiven for fearing that

the application of cognitive neuroscientiﬁc technology to helping

us understand more about our behaviors within the workplace

may drive the onset of what might become a neo-scientiﬁc

management; one that sees the data from workers as merely a

mechanism to maximize eﬃciency and minimize defects. Yet the

articles contained in this research topic show that this is far from

the case and, rather than driving biological reductionism, the

articles collectively demonstrate the signiﬁcant impact that such

approaches can bring to helping us understand human behavior.

In a novel approach to addressing a signiﬁcant question, Block

et al. (2014) carried out a large-scale interrogation of an existing

database on media behavior and found a signiﬁcant relationship

between media usage e.g., Internet, television and other social

media, and self-reports of depression. Christoﬀ (2014) takes

exploration of the relationship between organizational settings

and mental-health a stage further, and argues that a discourse

exploring the role emotions play in organizational decision-

making is needed. In light of the fact that in modern

organizations, so many of us place such heavy emphasis on such

media outlets when enacting our working roles, considering the

possible eﬀect that they may have on mental health ensures that

we consider the welfare of the individual workers of paramount

importance (see also Senior and Lee, 2013 for further discussion

here).

Taken together, the work by Spain and Harms (2014) and

Verbeke et al. (2014), converges on a greater understanding of

individual behavior within an organization at a genetic level.

This socio-economic approach is t hen examined further with the

submission by Foxall (2014a), who suggests a model for eﬀective

managerial behavior; that is, the function of competitive neural

systems. The notion of dual systems operating in competition to

drive eﬀective managerial behavior was examined further with

work by Boyatzis et al. (2014) who carried out an fMRI study

identifying ant

agonistic neural systems responsible for diﬀerent

types of leadership behaviors.

Such work continues to inform our understanding of how

social cognitive neuros cience (Ochsner and Lieberman, 2001) can

adv

ance organizational research—a project essentially started by

our e arlier work (e.g., Lee et al., 2012a). In particular, and possibly

as the result of serendipitous collaboration, neuroscientiﬁc

measuring tools such as functional magnetic resonance imaging

(fMRI) and magnetoencephalography (MEG) have been applied

to a number of organizational research questions (e.g., McClure

et al., 2004; Braeutigam, 2005; Deppe et al., 2005). Such

approaches have given the world terms as “neuroeconomics”

(Braeutigam, 2005) and “neuromarketing” (Breiter et al., 2015b),

and have inspired some considerable controversy in the scientiﬁc

press (e.g., Nature Neuroscience July 2004). Such debate is

healthy and as is shown by Butler (2014), Lindebaum (2014),

and Waldman (2013) helps to drive consolidation of theory and

clariﬁcation of approaches.

This, then, is the foundation of Organizational Cognitive

Neuroscience (OCN), which as an approach brings together

diversity in research approaches that use neuroscientiﬁc theories

and methods to examine organizational research issues (Senior

et al., 2011; Lee et al., 2012a). Indeed, the beneﬁts of an OCN

approach are exempliﬁed by Foxall (2014b),Walla et al. (2014)

and Breiter et al. (2015a), who each describe how the study of

exchanges within a market scenario can provide insig hts more

general human behavior, which in turn would lead to a more

“integrated science of inﬂuence” (Breiter et al., 2015b p. 1). These

scholars highlight both theoretical and methodological advances

within mainstream cognitive neurosciences and the implications

for a greater understanding of human behavior when market

exchanges are speciﬁcally investigated. Such methodological

advances are explored further with work by Kopton and Kenning

(2014) and Burgess (2013) who, among other things, develop

novel statistical approaches for the analysis of hyper scanning

data—which looks likely to be a crucial technique in exploring

the sort of interactions so central to organizational life.

That said, such work clearly shows that theoretical

advancement is not dependent on simply grafting advanced

measurement tools (such as fMRI) on to existing theories, as

implied by many early uses terms such as “neuromarketing”

(and here we recognize that Breiter et al. (2015b) clearly deﬁne a

more scientiﬁcally-rigorous useage of neuromarketing). Instead,

the OCN approach explicitly recognizes that it is the interaction

between cognitive neuroscience and organizational research as

distinct ﬁelds of research which is critical—incorporating not

just new methods, but also new theoretical explanations. In such

a way, the ﬁeld can lead to advances in both its parent disciplines

(Lee et al., 2012b).

We have previously conceptualized OCN as an approach that

considers human behavior made in response to organizational

manifestations (e.g., products, advertisements) as a set of

theoretical layers, e ach building upon the last to add more

context-speciﬁc theory (Lee et al., 2012b). At the most abstract

level, the behavior of individuals and groups at the intersection

between t he organization and the human is considered. Yet

such behavior is a subset of human social behavior in general.

Frontiers in Human Neuroscience | www.frontiersin.org 2 May 2015 | Volume 9 | Article 289

Senior et al. Building toward a uniﬁed cognitive neuroscience

Therefore it is an additional layer of theory that can be added

upon social psychology. In turn, social psychology is founded

on theories of cognitive psychology, which also impact directly

on many of our responses to organizational manifestations such

as advertisements and products. At an even more basic level,

are the lower-level brain systems and structures that drive such

cognitions, analysis here could be termed the neural level of

analysis. To facilitate investigations across the various layers

of analysis that are diagnostic of the organizational cognitive

neuroscience approach, Rippon et al. (2014) provide a set of

recommendations that could be adopted when studying the

eﬀects of gender on particular task.

The organizational, social, and neural levels that are described

above have been the focus of existing OCN theory (e.g., Lee

and Chamberlain, 2007). Yet, at a more fundamental level one

can also describe the adaptive forces t hat have shaped our brain

physiology in an evolutionary advantageous manner (Saad and

Greengross, 2014). Knowledge of the evolutionary adaptations

that may mediate our behavior at the social and ultimately

organizational level is essential to complete the explanation

of why we behave in the way we do, and also critical in

understanding the potential negative (and positive) inﬂuence of

organizational life on human beings.

To move back to the example of “scientiﬁc management”

previously alluded to; an understanding of whether the ability

to focus on repetitive small tasks may have conferred an

evolutionary advantage in the past (which therefore would have

led to a predilection for this ability in humans) may then

lead to greater understanding of whether scientiﬁc management

principles are likely to be beneﬁcial to employees. Importantly,

this is quite apart from the logical principles of the approach,

which may indeed suggest that it may be the most eﬃcient

manner with which to produce a complex product with

minimum defe cts. Indeed, the key social processes (wit hin

organizations) that humans have a predilection toward are

discussed subsequently.

Such an idea has been developed further with t he work

by Saad and Greengross (2014), who go so far as to say

that an understanding of evolutionary theory is of paramount

importance when using cognitive neuroscientiﬁc technology to

explore organizationally-relevant behaviors. However, it is with

the work by Spisak et al. (2014) and Price and Van Vugt (2014)

where the importance of studying adaptive behaviors and the role

that they may play in facilitating eﬀective organizational is made

crystal clear (See also von Rueden, 2014). Developing this further,

Susac and Braeutigam (2014) describe how an understanding

of the neural substrates underpinning mathematical cognition

may in fact facilitate the ability for mathematical re asoning—

which itself has implic ations for the subsequent design of eﬀective

education.

Here it is clear t hat it is not possible to fully understand a

given organizationally-relevant behavior by ignoring the various

interweaved layers of theory introduced above, Focusing on the

neural level—without taking into account the more fundamental

evolutionary level, or e ven the more abstract organizational

and social levels—is likely to result in important explanatory

contextual factors being overlooked. OCN explicitly recognizes

the symbiotic relationship between the layers of theory and in

doing so develops more rigorous testable hypotheses, and ties this

to advances in research methods that can more accurately test

these hypotheses. The studies noted above develop existing OCN

theory (e.g., Butler and Senior, 2007a) to show in more depth the

evolutionary processes that may impact on our organizationally-

relevant actions. The focus here is on how the neural and

evolutionary levels interact, and the question of whether such

adaptations actually can inﬂuence our behavior within, and our

response to, organizations and their manifestations.

As noted above, organizations that are designed around

the social processes that humans have a predilection for are

likely to operate more eﬃciently. Yet we should not consider

the application of neuroscience to understanding organizational

behavior as a means merely to make such organizations more

eﬃcient. In spite of the working environment being constantly

in ﬂux, the central concept of organizational behavior has and

will always remain the same. Most of us are likely to spend a

major proportion of our lives in a work-related environment.

One may argue thus that organizational cognitive neuroscience

is an approach by which to understand the cognitive signature of

our own species-speciﬁc social behavior.

We would like to dedicate this research topic to the many

reviewers who considered the submitted papers in such a timely

fashion–without them this collection would not have happened.

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Conﬂict of Interest Statement: The authors declare that the research was

conducted in the absence of any commercial or ﬁnancial relationships that could

be construed as a potential conﬂict of interest.

distributed under the terms of the Creative Commons Attribution License (CC BY).

The use, distribution or reproduction in other forums is permitted, provided the

original author(s) or licensor are credited and that the original publication in this

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Frontiers in Human Neuroscience | www.frontiersin.org 4 May 2015 | Volume 9 | Article 289

ORIGINAL RESEARCH ARTICLE

published: 12 September 2014

doi: 10.3389/fnhum.2014.00712

The relationship between self-report of depression and

media usage

Martin Block

1,2

†

,DanielB.Stern

2,3†

, Kalyan Raman

1,2‡

, Sang Lee

2,4‡

,JimCarey

1,2‡

Ashlee A. Humphreys

1,2‡

, Frank Mulhern

1,2‡

, Bobby Calder

2,5‡

, Don Schultz

1,2‡

, Charles N. Rudick

2,6†

Anne J. Blood

2,4,7†

and Hans C. Breiter

2,3,4,7†

Medill Integrated Marketing Communications, Northwestern University, Evanston, IL, USA

Applied Neuromarketing Consortium, Medill, Kellogg, and Feinberg Schools, Northwestern University, Evanston, IL, USA

Department of Psychiatry and Behavioral Science, Warren Wright Adolescent Center, Northwestern University Feinberg School of Medicine, Chicago, IL, USA

Laboratory of Neuroimaging and Genetics, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA

Department of Marketing, Kellogg School of Management, Northwestern University, Evanston, IL, USA

Department of Urology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA

Mood and Motor Control Laboratory, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA

Edited by:

Sven Braeutigam, University of

Oxford, UK

Reviewed by:

Christian Lambert, St. George’s

University of London, UK

Jessica Clare Scaife, Oxford

Univeristy, UK

*Correspondence:

Martin Block, Northwestern

University, Medill Integrated

Marketing Communications, MTC

3-123, 1845 Sheridan Road,

Evanston, IL 60208, USA

e-mail: mp-block@northwestern.edu

†,‡

Authors made equal contribu-

tions, corresponding to First (

†)or

Second (

‡) authorship.

Depression is a debilitating condition that adversely affects many aspects of a person’s life

and general health. Earlier work has supported the idea that there may be a relationship

between the use of certain media and depression. In this study, we tested if self-report of

depression (SRD), which is not a clinically based diagnosis, was associated with increased

internet, television, and social media usage by using data collected in the Media Behavior

and Inﬂuence Study (MBIS) database (N = 19,776 subjects). We further assessed the

relationship of demographic variables to this association. These analyses found that SRD

rates were in the range of published rates of clinically diagnosed major depression. It

found that those who tended to use more media also tended to be more depressed,

and that segmentation of SRD subjects was weighted toward internet and television

usage, which was not the case with non-SRD subjects, who were segmented along

social media use. This study found that those who have suffered either economic or

physical life setbacks are orders of magnitude more likely to be depressed, even without

disproportionately high levels of media use. However, among those that have suffered

major life setbacks, high media users—particularly television watchers—were even more

likely to report experiencing depression, which suggests that these effects were not just

due to individuals having more time for media consumption. These ﬁndings provide an

example of how Big Data can be used for medical and mental health research, helping

to elucidate issues not traditionally tested in the ﬁelds of psychiatry or experimental

psychology.

Keywords: depression, big data, marketing communications, media use

INTRODUCTION

Depression is known to affect many kinds of human behavior,

and is quite common. As of 2005, the lifetime prevalence of

major depressive disorder in the US population was reported to be

16.5% (Kessler et al., 2005a), with 6.7% prevalence in a 12-month

period, 30.4% of which were severe (or 2.0% of the U.S. popula-

tion) (Kessler et al., 2005b). Given the prevalence of depression,

there is interest from a neuromarketing perspective in how it

may be related to patterns of media consumption. Such issues

are of fundamental concern for mechanisms of behavior change

research and psychology (e.g., Morgenstern et al., 2013).

There is a developing literature e valuating the relationship

between v arious types of media use and psychiatric conditions.

For instance, one study found a high positive correlation between

internet addiction and depression among university students

(Orsal et al., 2012). Another study found that adults with major

depressive disorder spent excessive amounts of leisure time on

the computer, while those with dysthymia, panic disorder, and

agoraphobia spent more time watching television than the con-

trol group or those with other disorders (de Wit et al., 2011).

However, results have not always been consistent, particularly in

thedomainofsocialmediause.Arecentpaperfailedtoﬁnd

any association between social network use and depression in

older adolescents (Jelenchick et al., 2013), while other studies have

found positive associations between Facebook use and depression

in high school students (Pantic et al., 2012), and Facebook use

and a lack of subjective well-being in young adults (Kross et al.,

2013). Given the heterogeneity across previous studies, and the

rapid evolution of media formats over the past decade, we used a

large consumer database (>19,000 subjects) to assess the relation-

ship between self-reported depression (SRD) and media usage,

taking into account demographic information which may impact

the incidence of SRD such as employment status and disability.

We used SRD since major depression cannot be diagnosed with

Frontiers in Human Neuroscience www.frontiersin.org September 2014 | Volume 8 | Article 712

HUMAN NEUROSCIENCE

Block et al. The relationship between self-report of depression and media usage

big data surveys, and compared the rate of SRD to published

incidence data on the diagnosis of major depression.

This study differed from previous studies in the following

ways. (1) The sample size of the dataset was substantially larger

than any previous study e v aluating the relationship between

media use and depression. (2) We evaluated the link between

depression and multiple domains of media use, whereas most

previous studies have focused primarily on single domains. For

example, recent work with a smaller database has suggested there

is an increase in digital media usage in “depressed” adolescents

(Primack et al., 2009), but this study did not investigate its rela-

tionship to different subcomponents of media, such as social

media, internet, and television.

Our analysis started with descriptive and bivariate statisti-

cal analyses. These were followed by omnibus approaches to

assess general effects given the number of variables describing

media usage: (a) Chi-squared Automatic Interaction Detection or

CHAID tree analysis (Kass, 1980; Biggs et al., 1991)(aformof

recursive partitioning; Zhang and Singer, 1999) and (b) discrim-

inant analysis.

MATERIALS AND METHODS

DATA ACQUISITION

The dataset was derived from the Media Behavior and Inﬂuence

Study (MBIS), a syndicated online study of American adult (i.e.,

>18 years of age) consumers, conducted twice yearly since 2002

by BIGinsight of Columbus, Ohio. The current wave of 19,776

participants was completed in December, 2012. Using a dou-

ble opt-in methodology, each MBIS study was balanced to meet

demographic criteria established by the US census. MBIS data

has been used by a variety of well-known, commercial market-

ing organizations. Variables of interest included depression by

gender, age, employment status, marital status, race and ethnic-

ity, income, education, measures of isolation, and internet, TV

and social media use. These variables were selected because they

have been variables of interest in previous depression studies,

andhavebeenshowntohavepredictivevalue(e.g.,Catalano and

Dooley, 1977; Wilkowska-Chmielewska et al., 2013). Media usage

for internet, television, and social media are based on yes/no

responses to several day-parts of variable hour durations for

a typical weekday (see Supplementary Materials). These blocks

of time were shorter for typical waking hours and longer for

overnight and weekend periods. Block length was used to weight

media usage probability dur ing the calculation of total hours of

consumption (i.e., divided by the number of hours in each block

of time). Average hour exposure probabilities were calculated for

a 24 h period, and minutes per day were estimated by multi-

plying the result by 1440. Internet, TV, and social media usage

were hence composite variables created as probabilities of num-

ber of minutes daily usage, derived from data indicating w hether

or not subjects used the respective services in seven discrete

variable-hour blocks.

DATA ANALYSIS

Three types of analysis were performed with this data. First,

we performed a descriptive statistical analysis, inclusive of cor-

relations between depression and media consumption variables

to facilitate interpretation of the subsequent analyses. Second,

weusedtheresultstoinformatypeofrecursivepartitioning

(Morgan and Sonquist, 1963; Friedman, 1977; Breiman et al.,

1984; Gordon and Olshen, 1984; Quinlan, 1986; Mingers, 1989),

namely CHAID tree analysis (Kass, 1980; Biggs et al., 1991).

Third, we performed a multivariate discriminate analysis. Given

the descriptive statistical analyses were standard, these are not fur-

ther discussed herein. In all analyses below excepting the CHAID

analysis, fewer than 50 total comparisons were made; to correct

for multiple comparisons we used a Bonferroni correction for 50

comparisons, requiring a p < 0.001 to be considered a signiﬁcant

result.

CHAID tree analysis

We performed two recursive partitioning analyses, one focused

on SRD and the second on a variable not of interest, namely non-

SRD, to act as a control for SRD results. Our working hypothesis

was that the control analysis of non-SRD subjects would not repli-

cate or provide an opponent (i.e., completely non-overlapping)

set of nodes to the analysis of SRD subjects.

Construction of statistical CHAID trees (SPSS tree) evaluated

the interaction among a number of predictor variables of SRD,

and separately non-SRD. Typically, such schemes are deﬁned in

terms of demographic variables such as age and gender; however

we have also included occupation, education, marital status and

media use. Splitting criteria included minimum parent node size

of 100 and child node size of 50, and a p-value threshold of 0.05.

These splitting criteria were used for both CHAID analyses.

Discriminant analyses

Discriminant analysis was used to conduct a multivariate analy-

sis of variance for the hy pothesis that people who self-reported

having depression would differ signiﬁcantly from non-SRD sub-

jects on a linear combination of eleven variables: income, internet

usage, TV usage, social media usage, education, age, living in top

10 metropolitan area (MSA), gender, having children, employ-

ment status, and disability. The discriminant analysis was run

using SPSS defaults, resulting in the canonical linear discriminant

analysis. Depression was the binary dependent variable entered

in the “group” dialog. The discriminating variables were entered

together (i.e., not stepwise) in the variables subcommand. The

discriminating variables income, internet usage, TV usage, social

media usage, and education, all took on continuous values in the

range from 0 to 1. “Living in top 10 MSA,” gender, employment

status and disability were binary categorical variables while hav-

ing children was ordinal. Overall, the data were complete with no

missing values (i.e., every subject had every data point).

RESULTS

DESCRIPTIVE AND CORRELATION ANALYSES

Geographic and temporal patterns

The MBIS study shows little to no geographic pattern for SRD

(Figure 1).

The data does show that SRD among all adults in the USA (18

and over) has grown from 11.2% in 2009 to 12.1% in December,

2012, with a linear trend (r

= 0.246) (Figure 2). It is interest-

ing that the r ate rises to 15.2% in June 2012 (similar to rates in

Frontiers in Human Neuroscience www.frontiersin.org September 2014 | Volume 8 | Article 712

Block et al. The relationship between self-report of depression and media usage

FIGURE 1 | Rates of Self-Reported Depression by State, December

2012. This infographic characterizes rates of self-reported depression by

state, with darker states showing greater rates of depression. The image

demonstrates few patterns in depression by geography, with perhaps the

exception that state with large metropolitan areas tend to show somewhat

less depression.

the 2005 MBIS data where the depressive rate was reported to be

14.9%), then drops to 12.1% in December 2012, which is con-

sistent with a previous study that used the emotive content of

tweets to show a similar annual pattern of decreased depression

over Christmas (Dodds et al., 2011).

Depression demographics

Gender. RatesofSRDinthecurrentstudywavewerenearlyiden-

tical by gender as shown in Tab le 1, with males slightly lower at

11.8%, compared to females at 12.3%.

Age and marital status. Bivariate analysis suggested an inverse

linear association of SRD with age, which is consistent with pre-

viously reported studies (Henderson et al., 1998). Individuals who

were married were also different than those who were unmar-

ried as shown in Ta ble 1, with married respondents representing

a large portion of the sample (42.5%), and reporting a lower

SRD rate of 9.5%. The highest rate of SRD was from those in

same sex unions, at 22.2%. Those living with an unmarried part-

ner, divorced or separated, or single (never married) reported

rates between 14.1% and 15.5%, while those that were widowed

reported rates (12.4%) nearly the same as the overall average.

Race and ethnicity. Tab le 1 showed lower rates among Hispanics

(10.9%), and lower yet among African-Americans (8.7%) and

Asians (7.9%) as compared to Multi-ethnic individuals and

Caucasians. SRD was highest among Caucasians (13.6%), who

represented more than half (58.4%) of the sample studied.

Income and education. Both income and education (Ta b le 1),

also demonstrated a strong inverse linear association with SRD,

similar to age (statistics not provided given omnibus analyses to

follow). Non-high school graduates self-reported a 21.7% depres-

sion rate compared to those with post college study or degree at

8.8%. The overall average income was $62,800, with those report-

ing depression indicating an average of $49,000. Occupation

levels showed similar effects, as shown in Tab le 1, with those dis-

abled (unable to work) reporting a 42.7% depression rate. Other

high reporting categories included the unemployed at 18.8%,

and students at 13.0%. The lowest category was professional and

management at 8.2%.

Health and lifestyle characteristics. SRD was also related to

the reporting of other health conditions as shown in Tabl e 1 .

Generally, those reporting depression were likely to say they

had other health-related conditions, such as anxiety (54.8%).

Other conditions more prevalent in SRC subjects included:

back pain (42.7%), overweight (37.6%), acid reﬂux (30.5%),

headaches/migraines (29.6%), insomnia/difﬁculty sleeping

(27.5%) (Tabl e 1 ).

Isolation. Residents of states with large urban areas and those

living in the top 10 metropolitan statistical areas (MSAs), have

lower rates of SRD. The top 10 MSAs include Los Angeles, New

York, Chicago, San Francisco, Philadelphia, Washington, Boston,

Detroit, Phoenix and Houston. This suggests that residents of

rural areas tend to report higher rates of depression.

Media use. Overall there were low but signiﬁcant positive lin-

ear correlations between SRD and media consumption. In these

descriptive analyses, the three most consumed media were televi-

sion, on average 129 min per day per adult (18+), the internet,

on average 143 min per day, and social media, on average 83 min

perday.Thebivariateassociation(r)betweenSRDandtelevision

consumption was 0.089, surﬁng the internet was 0.089 and social

media was 0.063 (all p < 0.001).

Media usage quintiles, a method commonly used in the media

industry, were created using the composite media usage variables

described above, and showed higher rates of depression among

the most active users of media. Figure 3 shows that for the h igh-

est 20% of telev ision users (quintile 5, 289 min per day) the SRD

rate was 16.9%. The SRD rate among the highest internet users

(327 min per day) was also 16.9%. SRD was slightly lower among

the highest social media users (279 min per day) at 15.4%. The

patterns among all three media categories were the same: higher

consumption of any form of media was associated with higher

rates of reported depression.

It should be noted that there was some co-linearity between the

three media categories. The correlation of television and internet

consumption was moderate at 0.495, slightly higher for inter-

net and social media at 0.510, but lower for television and social

media at 0.247. All of these correlations were signiﬁcant (p <

0.001), raising the possibility of simultaneous consumption.

CHAID TREE ANALYSIS

The analyses reported above were limited to bivariate correla-

tions. To better understand how multiple variables for media

consumption and other demographics/activities related to SRD,

a multivariate segmentation scheme was employed based on

recursive partitioning (Morgan and Sonquist, 1963; Friedman,

1977; Breiman et al., 1984; Gordon and Olshen, 1984; Quinlan,

1986; Mingers, 1989). The ﬁrst CHAID tree (Kass, 1980; Biggs

et al., 1991)(Figure 4) shows the interaction among the predictor

Frontiers in Human Neuroscience www.frontiersin.org September 2014 | Volume 8 | Article 712

Block et al. The relationship between self-report of depression and media usage

FIGURE 2 | Trends in MBIS Rates of Self-Reported Depression in

Adults (18+), June 2009–December 2012. This chart reports the rate

of self-reported depression every 6 months, beginning in June 2009

and ending in December 2012, ﬁtted with a linear trend line. All data

was collected the same way by BIGinsight of Ohio as part of the

MBIS study.

variables on the rate of SRD (the target variable). The second

CHAID tree (Figure 5) shows the interaction among the predic-

tor variables and those who did not self-report being depressed.

The ﬁrst analysis on depressed individuals generated 22 terminal

nodes, while the second on non-depressed subjects generated 21.

The trees (Figures 4, 5) were pruned to include only 8 and 10

terminal nodes where the depression rate was 15% or more and

the non-depression rate was 87% or higher, respectively. The tree

nodes showed the variable used to create the node, the depression

rate, and the percent of all adults that the node represented. In

Figure 4, those that were unemployed, for example, were 6.0% of

the sample and reported a depression rate of 18.8%. Note that

media-related nodes were shown in white and other variables

shown in blue/gray.

In the analysis of SRD subjects, the CHAID tree segments

(Figure 4) that were the basis for understanding the relation-

ship of depression to media and other variables were as follows.

In general, factors such as disability, unemployment and lower

incomes were associated with higher rates of SRD. Media con-

sumption tended to signiﬁcantly leverage the rate attributable to

these characteristics. Six nodes of interest are brieﬂy described.

The node with the highest depression rate (47.3%) was being dis-

abled(1)andinthetoptwoTVconsumptionquintiles.Thiswas

compared to being disabled and in the bottom three TV quin-

tiles w ith a somewhat lower depression rate (35.2%). The next

highest depression node (30.7%) consisted of (2) those who were

unemployed, in the top internet quintile, and had less than a col-

lege education. Those in a professional or managerial occupation

that made $30,000 or less, and were in the highest TV quintile

(3), reported a depression rate of 26.9%. (4) Female students or

homemakers older than 34, reported a 20.0% depression rate.

(5) Those in other occupations, including workers, sales, military

and retired, that make less than $42,500 and were in the high-

est social media quintile, reported a depression rate of 19.1%.

(6) Male students or homemakers in the highest two internet

quintiles reported a depression rate of 17.4%.

In the analysis of non-depressed individuals (non-SRD), the

CHAID tree segments (Figure 5)thatbestexplainedtherelation-

ships between media use, demographic variables, and non-SRD,

described ten nodes. The node with the highest non-depression

rate (96.2%) was being professional (1) with a salary of $150,000

and more in the lowest social media quintiles. This was com-

pared to being professional (2) with a salary of $150,000 and

more in the highest social media quintile (89.1%). The next

highest non-depression node (3) consisted of those in other occu-

pations, including workers, sales, military and retired, making

$100,000 and more (94.3%). This was contrasted with (4) those in

other occupations making $50,000 to $100,000 and in the lowest

social media quintiles (91.4%), and (5) those in other occupa-

tions making $50,000 to $100,000 and in the highest social media

quintiles (87.7%). Those who were professional making $75,000

to $100,000 and in the lowest social media quintile had a non-

depression rate of 94.2% (6), whereas those who were professional

making $75,000 to $100,000 and in the highest social media quin-

tile had a non-depression rate of 89.1% (7). Professionals making

$35,000 to $75,000 and male gender (8) were higher (91.9%) than

those professionals making $35,000 to $75,000 and female gender

(9).Lastly,studentswhoweremaleinthelowestweb-radioquin-

tile (10) had a non-depression rate of 90.8%. In general, being a

student or employed with a high income were most closely associ-

ated with not being depressed, particularly when combined with

varying levels of social media use.

Frontiers in Human Neuroscience www.frontiersin.org September 2014 | Volume 8 | Article 712

Block et al. The relationship between self-report of depression and media usage

Table 1 | Depression by demographics, December 2012.

% All adults Adults with Index

depression

Age (average years)* 45.443.896.3

Male 48.311.897.5

Female 51.712.3 101.7

Income (000)* 62.849.078.0

Have children 29.130.2103.9

Live in top 10 MSA 24.710.082.6

Married 42.59.578.5

Living with unmarried partner 7.215.5128.1

Divorced or separated 10.215.4128.3

Widowed 3.012.4102.5

Single, never married 25.714.1116.5

Same sex union 0.522.2183.5

Have not graduated high

school

1.521.7179.3

Graduated high school 16.813.1108.3

Technical school or vocational

training

5.71

8114.0

1–3 years of college (did not

graduate)

20.215.1124.8

Associates or professional

degree

8.913.2109.1

Bachelor’s degree 22.59.477.7

Post college study or degree 13.58.872.7

Business Owner 4.211.796.7

Professional/managerial 25.58.267.8

Salesperson 3.611.595.0

Factory worker/laborer/driver 3.39.679.3

Clerical or service worker 9.511.998.3

Homemaker 3.614.7121.5

Student, high school or

college

8.413.0107.4

Military 0.711.695.9

Retired 13.710.88

Unemployed 5.518.8155.4

Disabled (unable to work) 2.042.7 352.9

Obsessive-compulsive

disorder (OCD)

2.19.8 458.1

Anxiety 12.954.8 425.8

Dyslexia 0.82.6 334.5

Fibromyalgia 2.37.4 327.5

Insomnia/difﬁculty sleeping 8.427.5 325.8

Restless leg syndrome(RLS) 4.111.6 279.8

Irritable Bowel Syndrome

(IBS)/crohn’s disease

2.46.3 262.6

Chronic bronchitis/COPD 2.97.3 252.5

Sleep apnea 6.616.4 248.2

Heartburn/indigestion 9.922.7 230.3

Headaches/migraines 14.029.6211.5

Back pain 21.542.71

8.4

Acid reﬂux 15.830.5192.9

Heart disease 3.25.9185.9

Hearing impairment 4.38.0184.6

(Continued)

Table 1 | Continued

% All adults Adults with Index

depression

Overweight 21.237.6177.1

Arthritis 15.527.3176.4

Asthma 9.717.0174.8

Vision impairment 15.024.8165.2

Enlarged prostate/Benign

Prostatic Hyperplasia (BPH)

2.23.6162.6

Diabetes 9.315.0162.1

Osteoporosis 2.54.1161.6

High cholesterol 18.829.3155.9

Black 18.08.771.9

Asian 3.07.965.3

Multi 0.816.9139.7

Native 0.415.5128.1

White 58.413.6112.4

Other 0.59.981.8

Hispanic 18.91

990.1

The three columns of numeric values represent (i) the percentage of all adults

in the survey with the given attribute (excepting income and age, which lists

the survey mean), (ii) the percentage of subjects having the given attribute that

self-reported depression, and (iii) the index of the given attribute as it relates to

depression, which is calculated as the percentage of those depressed, divided

by the total number depressed multiplied by 100. Rows on the left of the table

are clustered around demographics, relationship status, education, and work

identiﬁcation. Rows on the right are clustered by illnesses separate from depres-

sion, and with race/ethnicity (Note: These do not follow NIH deﬁnitions of race

and ethnicity).

It is important to note that the CHAID analysis with non-

SRD did not replicate the analysis with SRD. Furthermore, there

was a segmentation observed between these analyses which was

distinct, in that the types of media use that segmented the SRD

subjects was not the same as that which segmented the non-SRD

subjects. The terminal nodes of the two analyses were different

along dimensions of occupation, income, and media use.

DISCRIMINANT ANALYSIS

The results of the discriminant analysis revealed that, other

than disability and income, the three single best predictors of

depression in this model were increased use of television, the

internet, and social media (Tabl e 2 ). The overall Chi-squared

test of the discriminant model was signiﬁcant (Wilk’s λ = 0.945,

Chi-square = 922.117, df = 9, Canonical correlation = 0.235,

p < 0.001). The st ructure matrix demonstrated the weights of the

discriminating variables, an indication of their importance for

predicting depression: being disabled (0.760), income (−0.519),

internet consumption (0.399), television consumption (0.368),

social media use (0.278), level of education ( −0.255), being

unemployed (0.223), age (−0.170), top 10 MSA (−0.142), female

gender (0.062), and having children (0.010).

DISCUSSION

The primary ﬁnding of this study is that those who tend to

use more media in general, also tend to have more self-reports

Frontiers in Human Neuroscience www.frontiersin.org September 2014 | Volume 8 | Article 712

Block et al. The relationship between self-report of depression and media usage

FIGURE 3 | MBIS Rates of Depression by Media Use Quintiles,

December 2012. This chart demonstrates the percent of subjects with

depression in each media quintile. Quintiles were determined by ordering

subjects based on estimated minutes of a given media consumed; the ﬁrst

1/5 used the least of a given media and comprised the 0–20% quintile, the

second ﬁfth used more than the ﬁrst 1/5 (but less than the third 1/5) and

comprised the 21–40% quintile, and so on. Quintiles were computed for

each type of media use of interest and graphed side by side. The graph

depicts a clear trend associating increased media usage with increased

rates of depression.

of depression. We found a current incidence of SRD at 12.1%

which is slightly less than reports of lifetime clinical depression

and more than the 12 month incidence of diagnoses of major

depression. However, the picture is far more nuanced than sim-

ple description of descriptive statistics and bivariate correlations

between media use and depression. For instance, the CHAID tree

analysis with SRD subjects (along with the discriminant anal-

ysis) shows that those who have suffered either economic or

physical life setbacks are orders of magnitude more likely to be

depressed, even without disproportionately high levels of media

use (37.2%). However, among those that have suffered major life

setbacks, hig h media users—particularly television watchers—

were even more likely to report experiencing depression (47.3%

in the highest two quintiles, as compared with 35.2% in the

lower three quintiles), which suggests that these effects were not

just due to individuals having more time for media consump-

tion. These effects were not observed with the control analysis

in non-SRD subjects. That the economically disadvantaged are

signiﬁcantly more likely to experience depression is well sup-

ported by research in social psychology, which suggests that

lower-income groups feel a sense of disempowerment (Henry,

2005; Stephens et al., 2007). The lack of ﬁnancial and temporal

resources they experience can lead to feelings of a lack of control

over one’s life and an inability to act efﬁcaciously in the world,

which is thought to be a basic human need. Support ing this inter-

pretation, the CHAID analysis of the non-SRD subjects showed

that high earners that use less social media tend to be signiﬁcantly

less depressed.

Life challenges may not be the only experiences related to

depression. As noted with our descriptive statistical analysis, per-

sistent environmental factors such as isolation can also contribute

to the prevalence of a psychological experience. Generally, isola-

tion is a known correlate of depression symptomology, and our

data suggest that residents of rural areas tend to report higher

rates of depression. Within the context of isolation, one can dis-

tinguish between physical and non-physical isolation; and within

non-physical isolation one can look at social and emotional iso-

lation. These various subclasses of isolation ﬁnd ample support

in the literature. Weiss (1973) ﬁrst distinguished the two types of

non-physical isolation—social and emotional—which have sub-

sequently been empirically shown as distinct (DiTommaso and

Spinner, 1997). Although conceptually distinct, the various typ es

of isolation interact. Physical isolation has been shown to affect

social and emotional isolation, especially for the elderly (Dugan

and Kivett, 1994) and adolescents (Brage et al., 1993). We mea-

sured social isolation through the proxy of living alone and

physical isolation through the proxy of place of residence, ﬁnd-

ing that both correlate to rates of self-reported depression. For

instance, those living in more populated cities (top 10 MSAs) tend

to report lower rates of depression.

In addition to the current state of depression, the data we

analyzed reveals that SRD has been in a state of ﬂux over the

past decade. At the beginning of this time frame, the rates we

observed were low compared to 2005 MBIS data where the

depressive r ate was reported to be 14.9%; a ﬁgure consistent with

a co-occurring 2005 study wherein a lifetime prevalence rate of

16.5% for major depressive disorder was reported (Kessler et al.,

2005a,b). Interestingly, the 2005 MBIS data and the Kessler et al.

(2005a,b) data show remarkable concordance despite differences

in inclusion criteria (exclusion of non-English speakers in the

Kessler et al., 2005a,b studies), the use of a structured clinical

interview vs. self-report data, and overall subject demographics.

This ﬂux in reported incidence of depression over the past decade

is further supported by the MBIS data showing self-reported

depression has been on the rise in adults (18+) over the last 4

years in the United States.

It is worth considering the demographics of individuals (e.g.,

gender) reporting SRD in the context of a ﬂux in depression rates

over time. As recently as 5 years ago, females were more likely to

report being depressed (i.e., SRD). However, in the most recent

MBIS study, the data shows SRD to be similarly associated with

both genders, with males reporting only a slightly lower rate of

depression. This is different than the rates reported by Primack

et al. (2009) where females were shown to be signiﬁcantly more

likely to be depressed, as was also observed in the December 2005

MBIS data. In comparison to prior big data reports, there appears

to be a narrowing in the gap of reported depression in females and

males, which could potentially reﬂect a change in the likelihood of

genders to self-report. One factor that has remained constant was

that depression is inversely related to age, with those younger than

24 reporting the highest rate, and older mar ried persons reporting

the lowest.

There are several important limitations to this study that are

worth mentioning. First, the data used was self-reported depres-

sion, which does not necessarily reﬂect whether the subject has

ever received a clinical diagnosis of depression. The subjective

phenotypes of those who have a clinical diagnosis of major

depression versus those that self-report depression could skew

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Block et al. The relationship between self-report of depression and media usage

FIGURE 4 | Pruned CHAID Tree Characterizing SRD, December 2012. The

pruned CHAID tree shows groups of subjects wherein rates of depression

were greater than 15%. These nodes represent only a subset of all nodes

generated by the CHAID tree. Of particular interest for this paper are nodes

that are white (instead of blue); these nodes have been highlighted because

they are partially deﬁned by the presence of a media use quintile.

the data in a number of different ways. For instance, it has been

observed that those who have been diagnosed with depression are

sometimes reticent to share their diagnosis. Alternatively, there is

a multiplicity of reasons to think that subjects without depression

may report being depressed. The balance of these considerations

leaves uncertainty in the true sample parameters, although the

percentage of subjects with SRD in this study was quite similar

to rates of depression found in previous studies.

Second, the variables computed for amount of television,

internet, and social media use are not direct measures. These

variables are composite variables computed from self-reports

of whether or not subjects used those various media during

discrete variable-hour-length blocks. This can introduce inter-

subject variability along a number of dimensions. For instance,

some subjects may report “yes” for one of the intervals based

on an hour’s worth of use, while others may respond the same

based on several hours’ worth of use. The probabilities computed

represent just that, a probability of time spent using a given media

relative to other subjects.

Third, the analyses done cannot speak to a causal relation-

ship between media consumption and depression, or to any

directionality between the observed associations. We think the

likeliest explanation is that these two variables form a com-

plex bi-directional relationship with autocatalytic properties. An

alternative explanation is that depression and increased media

use are a byproduct of a third confounding factor. It should also

be noted that the direction of causality between depression and

media use could also vary across individuals (i.e., whether media

usage helps to ameliorate depression or whether it contributes

to it). Whatever the exact relationship between depression and

increased media use, it is clear that the two are closely associated.

Fourth, it is important to acknowledge the potential con-

founds of concurrent medical illness on assessing associations

with SRD. In the literature on major depression, hypotheses

have been raised that depression in association with a med-

ical illness does not necessarily reﬂect the same structural

and functional circuitry alterations seen in depression with

strong familial heritability (e.g., see Cloninger, 2002; Breiter

Frontiers in Human Neuroscience www.frontiersin.org September 2014 | Volume 8 | Article 712

Block et al. The relationship between self-report of depression and media usage

FIGURE 5 | Pruned CHAID Tree Characterizing Non-SRD, December 2012.

The pruned CHAID tree shows groups of subjects wherein rates of

non-depression were greater than 87%. These nodes represent only a subset

of all nodes generated by the CHAID tree. Of particular interest for this paper

are nodes that are white (instead of blue); these nodes have been highlighted

because they are partially deﬁned by the presence of a media use quintile.

and Gasic, 2004; Breiter et al., 2006). There is a strong pos-

sibility of biological subtypes in depression (e.g., see Blood

et al., 2010), meaning depression comorbid with other ill-

nesses may reﬂect a directionality with media that is distinct

from other putative depressive subtypes. Depression in associ-

ation with another medical (e.g., se vere coronary artery dis-

ease) or psychiatric condition (e.g., OCD, generalized anxiet y,

or body image disorders) may have a complex directional rela-

tionship with these other conditions, and there is published

evidence that TV viewing itself is associated with anxiety and

body image issues (e.g., see Thompson and Heinberg, 2002;

de Wit et al., 2011), potentially leading to the self-reported

depression. These issues also relate to the potential for drug

and alcohol to confound effects with SRD; this data set did

not contain such information, so future work is needed to

assess the relationship of drug and alcohol effects on SRD and

media use.

This information can help to form hypotheses to test in future

studies of relevance to psychology. One such hypothesis could

relate to the directionality of the relationship between SRD and

media, to determine if any media use acts as feedback to exacer-

bate symptoms. Another hypothesis might attempt to relate the

relationship to existing social psychological constructs such as the

“empty self ” hypothesis. Cushman (1990) developed the “empty

self” hypothesis to describe those who feel depressed and may be

likely to engage in impulsive or excessive consumption behavior

in order to “ﬁll up” a perceived deﬁciency in the self (see also

Ahuvia, 2005). In the context of media usage, the “empty self”

might be expected to show increased consumption of media asso-

ciated with increased SRD; such behavior might be indicative of

a subtype of major depression. A third possibility, is a hypothesis

that increased media use by SRD subjects acts as indicator of the

illness, much like a biomarker. Such hypotheses point to further

opportunities for use of big data with psychological research.

Frontiers in Human Neuroscience www.frontiersin.org September 2014 | Volume 8 | Article 712

Block et al. The relationship between self-report of depression and media usage

Table 2 | Structure matrix of discriminant analysis predicting

depression, December 2012.

Canonical correlation 0.235 Chi-square 922.117

Wilk’s lambda 0.945 signiﬁcance <0.001

Disabled 0.760

Income −0.519

Internet usage 0.399

TV usage 0.368

Social media usage 0.278

Education −0.255

Unemployed 0.223

Age −0.170

Living in top 10 MSA −0.142

Female 0.062

Having children 0.010

This table reports the structure matrix of the discriminant analysis. The nine pre-

dictive variables used in the discriminant analysis are reported in the left column,

while a measure of that variable’s predictive importance in the discriminant func-

tion is listed on the right. A variable’s importance is determined by its magnitude,

while its relationship to depression is determined by its valence. Negative num-

bers describe an inverse relationship: for example, higher income is predictive

of less depression.

In summary, the data reveal that there is a consistent pat-

tern of results that link self-reported depression with increased

media use, even when taking into account other variables, such

as disability and unemployment. This media use was focused

more on internet use and TV exposure, for those making self-

reports of depression. The rate of SRD was between two standard

indices used in published reports of clinically diagnosed major

depression, namely the lifetime prevalence, and recent 12 month

incidence of major depression. These observations suggest the

current ﬁndings with big data may have relevance to the literature

focused on the clinical diagnosis of depression.

SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found

online at: http://www.frontiersin.org/HumanNeuroscience/10.

3389/fnhum.2014.00712/abstract

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ducted in the absence of any commercial or ﬁnancial relationships that could be

construed as a potential conﬂict of interest.

Received: 17 April 2014; accepted: 26 August 2014; published online: 12 September

2014.

Citation: Block M, Stern DB, Raman K, Lee S, Carey J, Humphreys AA, Mulhern F,

Calder B, Schultz D, Rudick CN, Blood AJ and Breiter HC (2014) The relationship

between self-report of depression and media usage. Front. Hum. Neurosci. 8:712. doi:

10.3389/fnhum.2014.00712

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Frontiers in Human Neuroscience www.frontiersin.org September 2014 | Volume 8 | Article 712

REVIEW ARTICLE

published: 04 March 2014

doi: 10.3389/fnhum.2014.00114

Antagonistic neural networks underlying differentiated

leadership roles

Richard E. Boyatzis

1,2

*, Kylie Rochford

and Anthony I. Jack

Department of Cognitive Science, Case Western Reserve University, Cleveland, OH, USA

Department of Organizational Behavior, Case Western Reserve University, Cleveland, OH, USA

Edited by:

Carl Senior, Aston University, UK

Reviewed by:

Carl Senior, Aston University, UK

Sven Braeutigam, Oxford Centre for

Human Brain Activity, University of

Oxford, UK

*Correspondence:

Richard E. Boyatzis, Department of

Organizational Behavior, Case

Western Reserve University, 10900

Euclid Avenue, Cleveland, OH 44106,

USA

e-mail: richard.boyatzis@case.edu

The emergence of two distinct leadership roles, the task leader and the socio-emotional

leader, has been documented in the leadership literature since the 1950s. Recent research

in neuroscience suggests that the division between task-oriented and socio-emotional-

oriented roles derives from a fundamental feature of our neurobiology: an antagonistic

relationship between two large-scale cortical networks – the t ask-positive network (TPN)

and the default mode network (DMN). Neural activity in TPN tends to inhibit activity in

the DMN, and vice versa. The TPN is important for problem solving, focusing of attention,

making decisions, and control of action. The DMN plays a central role in emotional self-

awareness, social cognition, and ethical decision making. It is also strongly linked to

creativity and openness to new ideas. Because activation of the TPN tends to suppress

activity in the DMN, an over-emphasis on task-oriented leadership may prove deleterious to

social and emotional aspects of leadership. Similarly, an overemphasis on the DMN would

result in difﬁculty focusing attention, making decisions, and solving known problems. In

this paper, we will review major streams of theory and research on leadership roles in the

context of recent ﬁndings from neuroscience and psychology. We conclude by suggesting

that emerging research challenges the assumption that role differentiation is both natural

and necessary, in particular when openness to new ideas, people, emotions, and ethical

concerns are important to success.

Keywords: leadership, roles, neural networks and behavior, DMN,TPN, anti-correlated networks, opposing domains

hypothesis

INTRODUCTION

The emergence of two distinct leadership roles, the task leader and

the socio-emotional leader, has been documented in the leadership

literature since the 1950s (Bales, 1958). The separation of these

roles has been seen as pragmatic in applications and is accepted

as a ﬁnding in behavioral research. However, in practical applica-

tions, it leaves an organization with a challenge that can detract

from leadership development, succession planning, and organi-

zational ﬂexibility. In this article, we will show that the division

between these two types of leadership roles lies far deeper than has

traditionally been thought. Recent research in neuroscience sug-

gests that the division between task-oriented and socio-emotional

leadership roles derives from a fundamental feature of our neu-

robiology: an antagonistic relationship between two large-scale

cortical networks that is present in every individual.

Neural activity in the task-positive network (TPN) tends to

inhibit activity in the default mode network (DMN; Raichle et al.,

2001; Fransson, 2005; Raichle and Snyder, 2007; Buckner et al.,

2008), and vice versa (Uddin et al., 2009; Jack et al., 2012). The

TPN is activated during a broad range of non-social tasks (Fox

et al., 2005; Buckner et al., 2008; Uddin et al., 2009; Andrews-

Hanna, 2012), and is thought to be important for problem solving,

focusing of attention, making decisions, and control of action –

in other words for getting things done. However, activation of the

TPN also has a deleterious effect on other cognitive functions that

are essential to leadership: it suppresses activity in the DMN.

The DMN plays a central role in emotional self-awareness

(Ochsner et al., 2005; Schilbach et al., 2008), social cognition

(Schilbach et al., 2008; Jack et al., 2012; Mars et al., 2012), and

ethical decision making (Koenigs et al., 2007; Bzdok et al., 2012;

Jack et al., in press). It is also strongly linked to creativity and

insightful problem solving (Subramaniam et al., 2009; Takeuchi

et al., 2011). The antagonistic relationship between the TPN

and DMN creates a fundamental neural constraint on cogni-

tion that is highly relevant to the different roles and capabilities

that effective leaders must astutely juggle and deploy. An impor-

tant consequence of this constraint is that an over-emphasis on

task-oriented leadership can prove deleterious to an organiza-

tion: in particular when openness to new ideas, people, emotions,

and ethical concerns are important to success. On the other

hand, the over emphasis on relationship oriented leadership may

prove deleterious to focus and the execution of clearly deﬁned

goals.

We are not the ﬁrst to note the striking correspondence between

the functions of brain networks and leadership. In a recent book,

social neuroscientist Matthew Lieberman distinguishes between

the social brain and the business brain and doesnote the antagonis-

tic relationship between the TPN and the DMN (Lieberman, 2013,

p. 257). In this paper, we extend Lieberman’s preliminary work by

providing a more nuanced account of the antagonistic relationship

between the TPN and the DMN and their corresponding task and

relationship leadership roles. We then extend Lieberman’s work by

Frontiers in Human Neuroscience www.frontiersin.org March 2014

Volume 8

Article 114

Boyatzis et al. Leadership roles and opposing domains

theorizing strategies that will allow leaders to effectively navigate

this fundamental cognitive constraint.

OPPOSING NEURAL DOMAINS

When discussing the functional anatomy of the brain, it is

important to appreciate that the literature is less clear than is

often acknowledged. The cognitive characterization of the ten-

sion between the DMN and the TPN that guides this inquiry is

different in important respects from the view that is frequently

stated as accepted and uncontroversial (including in the context

of leadership, e.g., Waytz and Mason, 2013). Nonetheless, as we

will brieﬂy review, the characterization we offer is better sup-

ported by the scientiﬁc evidence. A number of inconsistencies

in the literature facilitate misunderstanding and over-conﬁdence:

ﬁrst, anatomical labels are not always used consistently and some-

times fail to distinguish areas which careful evidence reveals have

quite distinct functional roles (Kubit and Jack, 2013). Second,

researchers working with different types of cognitive tasks often

form quite different views about the primary functional role

of a region or a set of regions (i.e., a network). In particular,

it is now well acknowledged that researchers have not always

been careful to critically evaluate the evidence supporting their

inferences about the functional role of brain areas, and some-

times fail to consider evidence about function that derives from

a broader view of the literature (Henson, 2006; Poldrack, 2011).

Third, networks can be deﬁned according to a number of different

criteria.

The issue of how networks can be deﬁned is important to clar-

ify for our purposes. Networks of regions are most often deﬁned

(and g iven a label denoting a provisional functional role) either

because: (1) they are frequently found to be activated by a class

of cognitive tasks (Corbetta et al., 1998; Duncan and Owen, 2000;

Van Overwalle, 2009); or (2) the regions demonstrate strong posi-

tive resting state connectivity w ith each othe r – a ﬁnding which is

commonly interpreted as indicating a deg ree of functional coher-

ence amongst the regions in the network (Fox et al., 2005; Cohen

et al., 2008; Vincent et al., 2008; Van Dijk et al., 2010; Yeo et al.,

2011). These two criteria are thought to be complementary and

broadly consistent with each other, although they do not always

yield identical results (Laird et al., 2013).

A third quite different principle that can be used to deﬁne a

network is through the tendency of a set of regions to be deac-

tivated (i.e., less active than when the participant is at rest) by

a class of cognitive tasks (Shulman et al., 1997b). This is how

the DMN was originally deﬁned (Raichle et al., 2001). Regions

involved in the DMN have also be deﬁned: (a) on the basis of pos-

itive resting connectivity between regions in the network, and (b)

on the basis of negative correlation (“anti-correlation”) with other

regions as revealed by resting functional connectivity. These three

ways of deﬁning the DMN (deactivation, positive correlation, and

anti-correlation) are broadly consistent and regarded as comple-

mentary (Fox et al., 2005; Raichle and Snyder, 2007; Buckner et al.,

2008; Uddin et al., 2009; Andrews-Hanna, 2012).

The deﬁnition we take as primary for both the DMN and the

TPN is their anti-correlation. This follows the original deﬁnition

of these networks (Fox et al., 2005). An illustration of the two

networks, deﬁned in this way, can be seen in Figure 1. Figure 1

also illustrates a recent state-of-the-art division of the entire cor-

tex into seven networks based on functional coupling between

regions, i.e., positive resting connectivity (Yeo et al., 2011). The

mapping between Yeo et al.’s (2011) positive connectivity maps

and the anti-correlated networks is illustrated in greater detail in

Figures 2 and 3, for the DMN and TPN, respectively. This reveals

the need for some revision of early characterizations of the anti-

correlated networks. The broad characterization of the DMN has

remained quite stable, and is reasonably consistent using positive

and negative correlation criteria (Figure 2). However, this is not

the case for the TPN (Figure 3). It was originally thought (Fox

et al., 2005) that the DMN was anti-correlated primarily with the

dorsal attention network (Corbetta et al., 1998). However, sub-

sequent work using more data-driven methods (Fox et al., 2005;

Chang and Glover, 2009; Chai et al., 2012; Jack et al., 2012) has

revealed that the TPN overlaps par ts of both the dorsal atten-

tion network and the fronto-parietal control network (Vincent

et al., 2008

). Figur

e 3 also illust

rates clear overlap between the

TPN and the resting state network which Yeo et al. (2011) identify

with the ventral attention network (Fox et al., 2006). The ven-

tral attention network is recruited during a var iety of demanding

attention tasks, however, its precise functional characterization

is still subject to debate. It has been characterized as involved

primarily in the reorienting of attention, however, more recent

analysis suggests that the network as identiﬁed by Yeo et al. (2011)

can be better characterized as being involved in detecting and

responding to task-relevant stimuli (Kubit and Jack, 2013). Hence,

particularly for the TPN, it is important to note there is inconsis-

tency between the criteria for deﬁning networks. It appears that

DMN is in tension with a set of regions (i.e., the TPN) that lie

within a number of different cortical networks that can be dis-

tinguished and deﬁned by distinct proﬁles of positive functional

coupling.

THE DEFAULT MODE NETWORK

Until quite recently, the function of the DMN was regarded as

largely mysterious. In a comprehensive review, Buckner et al.

(2008) noted that “a unique challenge for understanding the func-

tions of the brain’s default network is that the system is most

active in passive settings and during tasks that direct attention

away from external stimuli.” While these and other researchers

acknowledged the difﬁculty in deﬁnitively identifying a function

for the DMN, nonetheless a consensus view began to emerge: that

the tension between the TPN and DMN could be best explained by

a distinction between two kinds of attention, namely externally vs.

internally directed cognitive processing. T his view was encouraged

by some inﬂuential early work (Gusnard et al., 2001), by the mis-

taken view that the DMN was anti-correlated with regions whose

primary function was external attention (Fox et al., 2005), and by

the paucity of evidence that any externally oriented task could pro-

duce activation of the DMN above resting le vels (but see Iacoboni

et al., 2004).

It is clear that the DMN is reliably engaged by some tasks that

involve attention to internal stimuli. Notable examples include:

autobiographical recall and prospection (imagining future events;

Buckner and Carroll, 2007; Spreng et al., 2009; Spreng and Grady,

2010), self-related processing (Mitchell et al., 2006), and emotion

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FIGURE 1

Different criteria for deﬁning brain networks. Functional

networks shown on the left hemisphere of the PALS atlas (top – lateral

view above, bottom – medial view). Left panels show anti-correlated

networks derived from resting state data. The TPN is shown in blue, the

DMN in orange/yellow. Darker colors correspond to anti-correlated

networks using a global (whole brain) regressor, brighter colors without a

global regressor (i.e., most strongly anticorrelated areas). Maps derived

from data and methods described in Jack et al. (2012). Right panels show

the seven network stable solution derived by Yeo et al. (2011) on the basis

of positive correlations in resting state data. Area shown in black

corresponds to a cross-section through sub-cortical structures, not

mapped by Yeo et al. (2011).

FIGURE 2

The default mode network (DMN). Strong overlap in the DMN

representation based on anti-correlations and positive correlations in resting

state data. Left panels show just the DMN derived from anti-correlations in

orange/yellow. Right panels show networks derived by Yeo et al. (2011) with

substantial overlap. Borders of anti-correlated regions are carried over from

the left panels. Key to Yeo et al. (2011) networks is shown in the middle of the

ﬁgure. Labels denote best current understanding of the primary functions of

key parts of the DMN.

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FIGURE 3

The task-positive network (TPN). The TPN represent ation based

on anti-correlations corresponds to multiple networks deﬁned by positive

correlations. Left panels show just the TPN derived from anti-correlations in

blue. Right panels show networks derived by Yeo et al. (2011) with subst antial

overlap. Borders of anti-correlated regions are carried over from the left

panels. Key to Yeo et al. (2011) networks is shown in the middle of the ﬁgure.

Labels denote best current understanding of the primary functions of key

parts of the TPN.

self-regulation (Ochsner et al., 2005). However, the characteriza-

tion of the DMN as being primarily involved in internal processing

glosses over other highly reliable ﬁndings which associate DMN

activation with externally oriented tasks. These types of externally

oriented tasks fall into two broad categories, which correspond to

a fractionation of the DMN which can be observed both through

meta-analysis of activation ﬁndings (Andrews-Hanna et al., 2010)

and by resting connectivity analysis (Uddin et al., 2009). More

dorsal parts of the midline structures of the DMN, medial parietal

and dorsal medial prefrontal cortex (dmPFC) are reliably associ-

ated with thinking about the mental states of others, including

both their emotional and cognitive (e.g., belief) states (Amodio

and Frith, 2006; Schilbach et al., 2008; Van Overwalle, 2009).

In contrast, ventral medial prefrontal cortex (vmPFC) is associ-

ated with representing the value of external objects (Grabenhorst

and Rolls, 2011). Moral decision making tasks, which are again

predominantly external in focus, are associated with activity in

both dorsal and ventral parts of the DMN (Greene et al., 2004;

Bzdok et al., 2012; Koenigs et al., 2012; Jack, in press). Since these

ﬁndings appear to be inconsistent with the commonly held view

that the tension between the TPN and DMN reﬂects a tension

between external and internal attention, we will temporarily set

aside that view and return to it in a later section. Instead, the

characterization of the DMN we offer here will be guided by

a broad view of tasks that are positively associated with DMN

engagement.

The DMN may be seen as having two primary circuits. The

ﬁrst is the dorsal portions of the midline structures and the right

temporo-parietal junction (rTPJ), which are most clearly asso-

ciated w ith mentalizing, that is, thinking about our own and

other’s mental states (Ochsner et al., 2005; Amodio and Frith,

2006; Schilbach et al., 2008; Van Overwalle, 2009). It is impor-

tant to note that the anatomy of this mentalizing circuit is distinct

from other regions that make distinct contributions to social

cognition. These include: a set of regions primarily involved in

perceptual processing of social stimuli such as faces and bodies

(Wiggett et al., 2009) and the mirror neuron network, which is

involved in both executing and observing actions, and thought to

underlie our ability to mimic the actions of others. In contrast,

the mentalizing system is thought to be involved not in emo-

tional contagion, but in the cognitive representation of emotion

(Lindquist et al., 2012).

The second of the primary DMN circuits is the more ven-

tral portions of the midline structures, which are associated w ith

self-related processing, autobiographical memory and prospec-

tion, cognitive representation of emotion, representation of

value/reward, emotion self-regulation, and autonomic process-

ing. We endorse the characterization of these processes offered

byarecentreview(Roy et al., 2012), which concludes: “The

vmPFC is not necessary for affective responses per se, but is

critical when affective responses are shaped by conceptual infor-

mation about speciﬁc outcomes. The vmPFC thus functions as

a hub that links concepts with brainstem systems capable of

coordinating organism-wide emotional behavior, a process we

describe in terms of the generation of affective meaning.” The

severe consequence of poor function in this circuit has long

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been known through the highly inﬂuential work of Damasio

(1994) looking at patients with vmPFC damage. These patients

exhibit severely impaired social, moral, and decision making

behavior, cannot hold down a job, and tend to be shunned

by family members, even though they often have high IQs

(Anderson et al., 1999). More recent work with moral decision

making tasks indicates that they also tend to favor a course of

action, which appears to promote the best over all outcome, even

when that involves denying individual rights and directly harming

others. In other words, they are more utilitarian in their thinking

(Koenigs et al., 2007).

With regard to leadership roles, the DMN is the basis for rela-

tional roles in which the leader makes sense of their own and

other people’s emotions and helps to construct a sense of purpose

or vision for the group. Given that both subsystems of the DMN

are typically deactivated by tasks that activate the TPN, it is trou-

bling to imagine the potential consequences of placing too strong

an emphasis on adopting leadership roles that recruit the TPN.

THE TASK-POSITIVE NETWORK

The broadly deﬁned functions of the TPN are agreed in the lit-

erature, although some debate persists about the best way to

characterize the function of the ﬁner grained functionally cou-

pled networks which it overlaps (Kubit and Jack, 2013). The

TPN is activated, and the DMN deactivated, by wide variety

of non-social tasks including those involving focused attention,

working memory, language, logical reasoning, mathematical rea-

soning, and causal/mechanical reasoning (Shulman et al., 1997a,b;

Duncan and Owen, 2000; Fox et al., 2005; Owen et al., 2005; Van

Overwalle, 2011). While the TPN includes some regions of the

brain associated with social processes such as the mirror neu-

ron network, it is distinct from the mentalizing network of the

DMN both in its location and in the types of tasks that activate

it (Van Overwalle and Baetens, 2009; Van Overwalle, 2011; Jack

et al., 2012). The TPN includes parts of the dorsal attention sys-

tem (Fox et al., 2005), the fronto-parietal control network (Vincent

et al., 2008), and the ventral attention network (Fox et al., 2006;

Kubit and Jack, 2013). Using more relaxed criteria, it can also

be seen to overlap the somatomotor network (Figure 3). These

networks are broadly associated with focus on, and execution of,

well-deﬁned tasks that are non-social in nature. Jack (in press)

deﬁnes the TPN as “analytical–empirical–critical reasoning, such

as mechanical reasoning.” Given our current understanding of

the TPN, leadership roles associated with this network are those

focused on ﬁnancial planning, metrics, forecasting, problem solv-

ing, as well as strategic social engagement for the purpose of task

achievement.

THE OPPOSING DOMAINS HYPOTHESIS

As discussed, it is generally accepted in the cognitive neuroscience

literature that the DMN and the TPN are anti-correlated (Greicius

et al., 2003; Fox e t al., 2005; Fransson, 2005; Golland et al., 2007;

Tian et al., 2007; Buckner et al., 2008; Jack et al., 2013b). It is also

broadly agreed that a variety of cognitively demanding non-social

tasks tend to activate the TPN and deactivate the DMN. There has

been less agreement about how best to cognitively characterize the

tension between these networks.

The opposing domains hypothesis predicts that activation of

the DMN or the TPN is a result of the type of thinking a per-

son engages in to order to complete a given task, regardless of

whether the task is externally or internally oriented (e.g., relates

to perceived stimuli vs. to information recalled from memory).

Personality factors play a role in the deployment of these net-

works, particularly for tasks where the most productive strategy is

unclear or ambiguous. Nonetheless, it is thought that all neurotyp-

ical individuals are capable of ﬂexibly deploying these networks,

hence tasks which have a clear affordance for one type of pro-

cessing over the other will tend to engage the relevant network.

When a person engages in a cognitively demanding non-social

task, they will tend to deﬁne their role as having an analytic focus.

In this circumstance, both the opposing domains hypothesis and

other accounts predict the TPN will tend to be activated and the

DMN deactivated. When a person engages in a task that involves

mentalizing and/or thinking about affective meaning, and as a

result, deﬁnes their role as social and/or relational, the oppos-

ing domains hypothesis predicts the DMN will be activated and

the TPN deactivated. This prediction, which entails that an indi-

vidual’s analytic abilities are suppressed when they are empath-

ically engaged with people, is unique to the opposing domains

account.

Jack et al. (2012) used functional magnetic resonance imaging

(fMRI) to record brain activity when participants were engaged

in social vs. mechanical/analytic tasks. The social tasks required

participants to answer questions about the beliefs and attitudes of

the characters in emotionally and morally laden text passages or

video clips. The mechanical tasks required participants to com-

plete science puzzles, presented either as written passages, or as

video clips taken from the Video Encyclopedia of Physics. A rest

condition was included, in which participants were only asked to

stare at a ﬁxation point, for the purpose of establishing a resting

baseline against which both activations and deactivations could be

observed.

The ﬁndings showed that the neural activation during the

social tasks, speciﬁcally the activation of the rTPJ, medial pari-

etal/posterior cingulate and the medial prefrontal cortex, was

accompanied by the deactivation of the neural networks respon-

sible for mechanical reasoning, speciﬁcally, the superior frontal

sulcus, lateral prefrontal cortex, and the intraparietal sulcus. Con-

trols were put in place for task and perceptual demands to rule out

the alternate hypothesis that the TPN vs. DMN dichotomy can be

best accounted for by internal vs. external attention.

Jack et al. (2012) conclude that the anti-correlation between

the TPN and DMN “reﬂects a powerful human tendency to dif-

ferentiate between conscious persons and inanimate objects in

both our attitudes and modes of interaction” (p. 396). Further

work (Jack et al., 2013b) looking at humanizing vs. objectifying

(where people tend to be viewed as objects) narratives about peo-

ple provides additional support for the view that these networks

are not driven by the surface characteristics of the stimuli, but

rather reﬂect ﬂexible cognitive stances which can be deployed

depending on the attitudes and role adopted by the individual.

The implications of these ﬁndings for leadership and organiza-

tional behavior are vast. The dualit y of task and relationship;

inanimate and animate; and social and non-social can be found in

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the personalit y, motivation, group dynamics, socialization, con-

ﬂict, trust, decision making, mindfulness, and moral reasoning

literatures.

A further wrinkle to this account is important to note. While

it is very well documented that the TPN and DMN tend to be

antagonistic, both at rest and during the performance of tasks, the

dichotomy between these networks is not absolute. They can be

simultaneously activated and work in cooperation with each other

(Fornito et al., 2012; Meyer et al., 2012). Both theory and observa-

tion indicates that this occurs when participants are engaged in a

type of social reasoning that is highly instrumental, and lacking in

genuine empathy. For instance, this pattern is seen more in indi-

viduals who are prone to Machiavellian thinking (Bagozzi et al.,

2013). It is also the neural signature that is seen when people are

animalistically dehumanized, rather than objectiﬁed (Jack et al.,

2013b).

This recruitment of the TPN during social cognition is likely

due to a person thinking critically, strategically, or mechanically

about people. Hence, it does not appear to be the case that we

can be both genuinely empathetic and analytic at the same time.

Instead, our view is that when these networks work co-operatively

with each other (Fornito et al., 2012), they realize a different type

of cognitive processing from either genuine empathy or pure ana-

lytic reasoning. When we engage the analytic network alongside

the DMN, this corresponds to a mode of social interaction that

is alienating to others. This is reﬂected in everyday language.

When we say that people are being “manipulative” or “calculat-

ing,” the metaphor may make sense because they are using TPN

brain regions involved in ﬁne motor control and/or mathemat-

ical reasoning to operate on social representations (Meyer et al.,

2012). Although this cognitive mode involves a sense of social

or emotional distance, it is clearly useful to think “strategically”

or “politically” in this way on o ccasion. This is reﬂected in the

observation that strategic social reasoning plays a crucial role in

leadership research. For example, Boyatzis and Goleman (2007)

make the distinction between emotional intelligence (EI) and

social intelligence (SI). EI is deﬁned as the ability of a person

to recognize, understand, and use emotional information about

oneself or others (Boyatzis, 2006). We expect these competencies

will tend to recruit both the DMN and the TPN in tandem. By con-

trast, SI competencies are non-task focused, for example, acting

with compassion, and would therefore primarily recruit the DMN

(Boyatzis and Goleman, 2007). Interestingly, Boyatzis et al. (2006)

predicted that SI competencies are linked to the parasympathetic

nervous system, while focusing on tasks and instrumental use of

EI is linked to the sympathetic nervous system. This would suggest

that EI competencieswould relate tothe TPN.Research is currently

underway to explore what overlap exists between the divisions of

the autonomic nervous system and the TPN and DMN. While the

evidence is far from conclusive, early ﬁndings based on recent

meta-analyses (see, for example, Beissner et al., 2013) indicate

that the TPN is more closely linked to the sympathetic ner-

vous system while the DMN is closer to the parasympathetic

nervous system. The labels in Figures 2 and 3 indicate regions

which show the closest correspondence between the DMN and

TPN and regions implicated in autonomic nervous function

(Eisenberger and Cole, 2012).

OPPOSING ROLES OF LEADERSHIP

LEADERSHIP ROLES

A role is a constellation of behavior and expectations enacted in a

social situation. Mead (1934) claimed that roles were embedded in

the situation and therefore were constantly emerging and evolving.

As a sociologist and helping to formulate the interactionist theory

of roles, Mead (1934) emphasized the expectations of others in

the forming and reforming of roles. In this way, roles are distinct

from style. Leadership style describes a dispositional comportment

of a person as a leader that can vary by situation and demand.

Leadership style contributes to role enactment as Biddle (1986)

explained from a functionalist theory of roles.

In leadership, a role provides a guide to one’s behavior and

helps new role occupants become socialized into how they could

or should act in a way consistent with the norms and culture of

an organization. A leadership role of a person emerges in response

to the expectations of others around the leader, especially the fol-

lowers, and the leader’s own style/s, competencies, and values

(Boyatzis, 1982). This process of role emergence as a result of the

interaction between expectations and behavior has been explained

by cognitive role theory (LeMay, 1999).

There appears to be two distinct components of the enactment

of leadership roles that parallel the distinction between the TPN

and the DMN: task achievement and relationship development.

We will place the evolution of this duality of leadership roles in

historical context.

FROM TAYLOR TO BASS APPROACHES TO LEADERSHIP ROLES

The centrality of tasks to a leader’s role was evident in Cowley’s

(1928) deﬁnition of a leader: “an individual who is moving in a

particular direction and who succeeds in inducing others to follow

him...A leader then, is a person who is going somewhere, who has

a motive, who has a program.” (p. 145–146). In short, without a

task, leaders do not exist.

Fredrick Taylor’s scientiﬁc management movement empha-

sized the task aspect of leadership. Followers were viewed as

“machines” to be used efﬁciently by the leader. In other words,

by treating humans as inanimate objects subject to analyti-

cal analysis, the role of a leader in the scientiﬁc management

era was largely reliant on recruitment of the TPN rather than

the DMN (Jack et al., 2013b) and therefore discouraged the

leader from fostering more meaningful social–emotional con-

nections with followers. The introduction of the “human” side

of leadership in the late 1930s gave rise to a multitude of

leadership frameworks that reﬂected the scientiﬁc management

(task) – human relations (relationship) duality. These frameworks

include the autocratic (task)-democratic (relationship) contin-

uum (Tannenbaum and Schmidt, 1958), the Ohio State Leader-

ship Studies on structure (task) and relationship (Shartle, 1950;

Fleishman, 1953; Halpin and Winer, 1957), and the Michigan

Leadership Studies (Katz et al., 1950) of a production orientation

and an employee orientation.

ATTEMPTING TO INTEGRATE TASK AND RELATIONSHIP ROLES FROM

THE 1950s TO THE 1970s

When symbolic interaction theory transferred from social to orga-

nizational psychology, leadership theory changed to require both

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relationship building and task attainment. This development par-

allels the duality of the neural opposing domains: relational

leadership competencies are facilitated by the DMN, while the

task competencies are facilitated by the TPN. Bales (1958) was

instrumental in this theoretical shift in the leadership literature

with his claim that a task-oriented group requires two types of

leaders to maximize effectiveness; one leader to attend to the

task functions of the group (inst rumental leader) and another

to attend to the emotional needs of the group (expressive leader).

The suggestion that two leaders are required in a group stood

in stark contrast to the idea that all leadership roles could be

carried out by one individual. Given that we now know that

the neural networks underlying these two roles are antagonis-

tic, having two leaders in a group presents what initially appears

to be a reasonable strategy for relieving the task–relationship

tension.

By the 1960s, a consensus began to form that building relation-

ships with followers was a fundamental component of leadership

and a critical ingredient in task achievement. Leadership theorists

become interested in developing strategies that would enable lead-

ers to integrate these two fundamental leadership roles. The early

Ohio State Leadership Studies (Fleishman, 1953) conﬁrmed the

conceptual distinction between task-oriented and relationship-

oriented leadership and developed a measurement tool for each.

Following from this work, task-oriented leadership has been iden-

tiﬁed as being concerned with production (Blake and Mouton,

1964), goal achievement (Cartwright and Zander, 1960; Bowers

and Seashore, 1966), labor allocation, enforcement of sanctions

(McGregor, 1960), and creating a context and structure for fol-

lowers (Shartle, 1950; Fleishman, 1953; Halpin and Winer, 1957).

At the individual level, task-oriented leaders were said to have a

high need for achievement (McClelland, 1961; Wofford, 1970), to

be achievement oriented (Indvik, 1986), and to be cold and aloof

to signal their preference for psychological distance from follow-

ers (Blau and Scott, 1962). Interestingly, these descriptions are not

only consistent with the role of the TPN discussed earlier, but also

allude to the suppression of the DMN – in particular, they ﬁt well

with our observation that the socially distancing effect of dehu-

manizing is associated with a shift from DMN to TPN engagement

(Jack et al., 2013b).

Conversely, relationship-oriented leaders were described as

being focused on follower well-being (Hemphill, 1950), con-

cerned with developing and maintaining relationships (Hersey

and Blanchard, 1969), to be more democratic as opposed to

autocratic (Bass, 1990), and as placing value on friendships,

open communication, and mutual trust. These psychological fea-

tures ﬁt a proﬁle of DMN activation and TPN deactivation –

the neural signature associated with thinking about the expe-

riences of others (Jack et al., 2012) and humanizing (Jack et al.,

2013b). Individuals who are humanized experience positive emo-

tions, whereas those who are dehumanized experience negative

emotions (Bastian and Haslam, 2011). Correspondingly, research

has shown that relationship-oriented leaders are associated with

higher job satisfaction and lower turnover in organizations (Bass,

1990; Yukl, 2006).

Yukl (1999) provides the most recent taxonomy of task and rela-

tion oriented leader roles. He also distinguished a third dimension

in his taxonomy, labeled “change orientation” which includes a

mix of task and relationship oriented roles. However, in critiquing

other’s work, Yukl (1999, 2008) notes the limitations of the task–

relationships distinction, speciﬁcally, the inconsistent empirical

results and the lack of evidence that it is indeed possible to be

high in both task and relationship competencies and that being

so is related to leadership effectiveness (Yukl, 1999). Research con-

ducted since adds support toYukl’s critique; speciﬁcally, Kaiserand

Kaplan (unpublished, as cited in Bass and Bass, 2008 ) found that

in a sample of managers from a consulting ﬁrm, 46% were highly

task oriented, 19% were highly relationship oriented, and only 6%

showed ﬂexibility between the two dimensions. The remaining

29% were low in both task and relationship orientation.

The distinction between task-oriented roles vs. relationship-

oriented roles presented in Yukl’s taxonomy appears to closely

parallel the distinction made between social and mechanical tasks

used to test the opposing domains hypothesis (Jack et al., 2012).

Speciﬁcally, the task-oriented tasks are, by and large, mechanical

and analytical in nature and would therefore activate the leader’s

TPN, while the relationship oriented tasks are generally social in

nature and would therefore activate the leader’s DMN.

The anti-correlation between these two networks suggests that

when a leader is focused on a task-oriented role, their ability

and desire to attend to the relationship needs of their followers

is diminished. This is not to say that leaders do not have the ability

to be both highly analytical and to build effective relationships. In

the absence of any task, it is known that the human brain naturally

cycles between activation of the TPN and activation of the DMN,

activating each several times in a period of a minute (Fox et al.,

2005). Hence, it is clearly possible to switch between networks and

possibly roles.

However, the evidence from neuroscience does suggest that

leaders

cannot sim

ultaneously attend to these distinct roles, hence

conﬂicts are likely to arise if leaders get “stuck” in one role,

which decreases their ability to switch between the two networks,

resulting in the prolonged suppression of one of the networks

and associated roles. This trade-off has been noted by a num-

ber of scholars. Most recently Yukl (2008) notes that “efforts to

improve one performance determinant may have an adverse effect

on another performance determinant...when leaders are preoccu-

pied with responding to external threats (task), there is less time for

people-oriented concerns such as being supportive and developing

member skills” (pp. 711–714). The opposing domains hypothesis

provides an alternative explanation for why this trade-off occurs.

Rather than, or at least in addition to, a simple lack of time

and overall cognitive resources, the opposing domains hypoth-

esis suggests that the suppression of the opposing neural network

further exacerbates the “adverse effect on another performance

determinant.”

The antagonistic relationship between the TPN and DMN

may also help us to understand why the type of role a leader

adopts moderates the relationship between leadership role (task

vs. relationship), task performance and leadership effectiveness

respectively. Speciﬁcally, Burke (1965) found that a group’s

performance of a coding task was completed more effectively

under a production-oriented (task) leader while the shared

decision-making (task) was carried out more effectively under a

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Boyatzis et al. Leadership roles and opposing domains

relationship-oriented leader. The opposing domains hypothesis

offers two explanations for this ﬁnding – the ﬁrst related to the

ability of the followers to perform the task, the second related to

the leadership role that the followers’ perceived to be effective.

First, followers performed more effectively on the analyti-

cal task when the leadership role matched the neural network

activated as a result of the analytical nature of the task (the

TPN). When a relationship-oriented leadership role was used,

the leader’s behavior and focus on relationships may have acti-

vated the DMN in followers, which we now know to be associated

with lapses in attention and performance errors (McKiernan et al.,

2003; Mason et al., 2007; Fassbender et al., 2009; Pyka et al., 2009),

hence the lower performance on the analytical task. Conversely,

the shared decision task, which required interpersonal interaction

and therefore would have activated the DMN, was performed more

effectively under a relationship oriented leader. A relationship-

oriented leader in this task would have allowed for the follower’s

DMN to be dominant, which is the network required to perform

this task effectively. A task-oriented leader would have activated

the follower’s TPN, which would suppress the DMN resulting in

a reduction of human and ethical insight (Jack et al., 2013b, in

press) and consequently lower task performance.

Second, when completing the coding tasks the followers were

predominately engaging their TPN, hence the task-oriented lead-

ership role may have been perceived by followers to be more

effective because it was more psychologically resonant. Similarly

the relationship-oriented leadership role may have been preferred

by followers engaged in the moral decision task because of the

resonant emphasis on DMN engagement for both the task and the

leadership role.

Further evidence of the interaction between task type and

follower preference for a task vs. relationship-oriented leader is

offered by Weed et al. (1976). This study found that the only type

of task that was a signiﬁcant moderator between leadership role

and task performance was the difﬁcult–ambiguous task. While this

study was focused on the difﬁculty and ambiguity of the task rather

than the mechanical or social nature of the task, the authors note

that the difﬁcult–ambiguous task was the only task that involved

ethical/moral problems and therefore “the signiﬁcant ﬁnding s on

this task may be partially attributable to the types of skills required

to deal with these problems” (p. 65).

Finally, Hersey and Blanchard (1969) developed the Tri-

Dimensional Leadership Model that sought to link task and

relationship oriented leadership roles to follower perceptions of

leadership effectiveness. The Tri-Dimensional Leadership Model

suggests that leaders who are highly task oriented will be per-

ceived as “effective” because they know what they want and are

able to impose this to accomplish a task without causing resent-

ment. A highly task-oriented leader will be perceived as ineffective

when followers’ perceive the leader has no conﬁdence in others, is

unpleasant, or only shows interest in short-run output. In other

words, a highly task-oriented leader will only be seen as effec-

tive if they are aware of, and attend to, relationships – rather

than dehumanizing their followers – while accomplishing the

task.

Similarly, a leader with a high relationship-orientation will be

seen as effective by followers when they perceive the leader has

implicit trust in them and is primarily concerned with develop-

ing their talents – behaviors associated with humanizing and the

DMN. They will be seen as ineffective when followers perceive

the leader to be passive and showing little care about the task at

hand – behaviors associated sustained activity of the DMN that

suppresses the TPN (Mason et al., 2007; Jack et al., 2012). These

ﬁndings extend the hypotheses derived from the Burke (1965)

study with the suggestion that regardless of a leader’s preferred

role (task-oriented or relationship oriented), the leader must be

able to switch ﬂuidly between the opposing neural networks (the

TPN and the DMN) in order to be perceived as effective by their

followers. Given this, it seems that rather than identifying the

individual difference variables and situational variables that are

associated with each st yle in isolation, it may be more important

to identify variables that correspond to the ability of a leader to

switch between the TPN and the DMN in order to maximize leader

effectiveness.

DEALING WITH THE DIALECTICAL TENSION

The previous section has shown that the distinction between task

and relationship roles is evident throughout the leadership liter-

ature. In this section, we explore potential strategies to resolve,

or at least minimize the consequences of the tension that leaders

face in developing their roles to attend to task requirements and

attending to relationships as a result of the antagonism between

the TPN and the DMN.

NEURAL DISPOSITION: MATCHING THE LEADER TO THE SITUATION

As with hormonal disposition, there is some evidence to suggest

that humans have a natural disposition toward either analytical–

mechanical reasoning and therefore the TPN or social–relational

reasoning and therefore the DMN (Jack et al., 2012). While neural

disposition is a relatively nascent area, hormonal dispositions are

well documented in the literature (see Insel, 1997; Schulkin, 1999).

For example, people with higher unconscious power motives are

known to have higher resting levels of epinephrine secretion, while

people with a higher unconscious Need for Achievement appear

to have higher resting levels of vasopressin secretion (Boyatzis and

Sala, 2004; Boyatzis et al., 2006). At the behav ioral level, neural

and hormonal dispositions may underlie certain personality char-

acteristics, learning styles, and perhaps preferred leadership roles

(Boyatzis and Sala, 2004).

Considering the opposing domains model, one way to address

the tension between the task and relationship leadership roles

could be to match a leader’s predisposition toward either task

or relationship to the type of task they perceive as their primary

role and responsibility. In many ways, Bales’ (1958) suggestion of

the need for two leaders was an attempt to do this. The utility of

matching a leader’s natural inclination toward either a task or rela-

tionship role to the situation has found some empirical support

in the literature. For example, Slatter (1955) found that when task

demands are high, being liked does not contribute to leadership

effectiveness and social or relational skills are not highly valued,

whereas in therapy groups, sensitiv ity training groups, and social

clubs socio-emotional skills were important. However, the practi-

cal results from dividing the leadership roles have been less than

ideal.

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