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Neuroleadershipin2014

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NeuroLeadershipJOURNAL

VOLUME FIVE | JANUARY 2015

NEUROLEADERSHIP IN 2014 by Al H. RinglebDavid Rock andChris Ancona

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The NeuroLeadership Journal is for non-commercial research and education use only. Other uses, including reproduction

and distribution, or selling or licensing copies, or posting to personal, institutional or third-party websites are prohibited.

In most cases authors are permitted to post a version of the article to their personal website or institutional repository.

Authors requiring further information regarding the NeuroLeadership Journal’s archiving and management policies are

encouraged to send inquiries to: journal@neuroleadership.com

The views, opinions, conjectures, and conclusions provided by the authors of the articles in the NeuroLeadership Journal

may not express the positions taken by the NeuroLeadership Journal, the NeuroLeadership Institute, the Institute’s Board

of Advisors, or the various constituencies with which the Institute works or otherwise affiliates or cooperates. It is a

basic tenant of both the NeuroLeadership Institute and the NeuroLeadership Journal to encourage and stimulate creative

thought and discourse in the emerging field of NeuroLeadership.

NeuroLeadership Journal (ISSN 2203-613X) Volume Five published in January 2015.

AUTHORSAl H. Ringleb Director CIMBA/University of Iowa

Co-founder, NeuroLeadership Institute

Corresponding author: ringleb@cimba.it

David Rock Director, NeuroLeadership Institute, New York City

Co-founder, NeuroLeadership Institute: Co-Editor, NeuroLeadership Journal

david@neuroleadership.com

Chris Ancona Senior Research Associate, NeuroLeadership Labs

Faculty, CIMBA

ancona@cimba.it

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Every year seems to bring greater interest in the human brain. Recently, both Europe and the United

States announced major brain-mapping projects, a significant indication of the growing appreciation

of the benefits likely to flow from accelerating its study. Both projects share the intention to “fill the gap

in our knowledge of brain activity at the circuit level [and] ... provide a bridge that will enable recording

and manipulating the activity of circuits, networks, and possibly eventually whole brains with single-

neuron precision” (Alivisatos et al., 2013; Kupferschmidt, 2013). Research scientists continue to expand

the field of knowledge, providing practitioners with a growing array of insights and applications for use

in assisting their clients and colleagues. In working to both disseminate neuroscience findings applicable

to the effective practice of leadership, and support practitioners in their personal and leadership

development efforts, the NeuroLeadership Institute’s Executive Certificate in Applied Neuroleadership

Program, Institute membership, Summit attendance, requests for Institute and Summit materials, and

participation in local chapters worldwide are following a similar growth pattern.

As in past issues of the NeuroLeadership Journal, the intent of this article is to assist practitioners in

reflecting upon recent neuroscience and social psychology research and thinking trends relevant to

NeuroLeadership. First, in continuing past traditions, we will begin by taking a look at some of the most

interesting trends and issues that seem most likely to affect NeuroLeadership’s future and direction.

Then, as in the past, we will categorize the research based on the four domains set out in the initial

Journal (Ringleb & Rock, 2008): 1) Decision Making and Problem Solving, 2) Emotion Regulation,

3) Collaborating With and Influencing Others, and 4) Facilitating Change. In selecting research for

inclusion, the following basic criteria were followed to the extent possible: Significance to the field

of NeuroLeadership, likelihood of significantly expanding or creating research linkages between

neuroscience and the practices of leadership and leadership development, impacts on current thinking

as driven by social science research, and, perhaps most importantly, relevance to the interests of

practitioners in this growing field.

NeuroLeadershipJOURNAL

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Trends and Issues

A number of recent trends and issues have developed that

have had or will likely have an impact on NeuroLeadership

going forward. First, with a growing number of consultants

and practitioners using neuroscience explanations

in working with their clients, increased attention is

understandably being placed upon the credibility of the

science, more specifically the research methods and

technology from which many of the important findings

are being derived. Secondly, seemingly in anticipation of

the affirmation of the technology’s veracity, practitioners

are making a slow but discernible move away from the

mere “descriptive” use of neuroscience (for example, using

neuroscience explanations for behavior in addition to or

instead of social science explanations) to more “prescriptive”

uses (for example, using neuroscience findings to enhance

intervention strategies intent on changing behavior). We

are beginning to see growing interest and curiosity in “big

data” as more sophisticated practitioners collect, process,

and analyze neurobiofeedback data from the various

measuring devices that are becoming more readily

accessible and usable by practitioners and their clients.

Finally, in their admirable effort to make raw science

more accessible and understandable to lay audiences,

both practitioners and science writers are developing

conceptual language (for example, SCARF® [Rock, 2008])

that ends up influencing both the scientists as well as the

lay audience. Following this trend, there has been a recent

resurgence of dual process theory in the name of “System

1 and System 2” thinking.

Credibility of Research Methods

Given the impressive volume of research output over

the last decade, it takes a sincerely reflective moment

to realize that one of the primary neuroscience research

tools, fMRI, is a relatively new technology. Importantly,

that technology has enjoyed continuous improvement

over its relatively short lifespan, a trend that is almost

certain to continue well into the future. As would be

expected from any such device that places its primary

attention on a proxy of what it is purporting to measure

(here, blood flow as a proxy for neural activity), much

has been written on the pros and cons of brain imaging

and its interpretation. Still, even a dead salmon showing

neural activity (Bennet et al., 2009; Bennet, Wolford, &

Miller, 2009) and other such claims (e.g., Ioannidis, 2005)

have not been sufficient to discourage either the research

or practitioner communities. At the NeuroLeadership

Institute, we view this challenge to the credibility of brain

imaging research as a positive event, as closer scrutiny and

accountability will almost certainly lead to better research,

better experimental design, and more dependable results

(e.g., Fanelli, 2013).

Concern with this issue was brought to the practitioner

forefront by Professors Satel and Lilienfeld (2013) and Dr.

Robert Burton (2013), who in both of their books offer a

different perspective on neuroscience research. These

inquiries come at a time when the “health of the scientific

enterprise” is a growing worldwide concern (e.g., Fang

et al., 2013; Mobley, et al., 2013; Labbe & Labbe, 2013).

Both books also particularly malign the over-reliance

NEUROLEADERSHIP IN 2014by Al H. Rengleb

David Rock and

Chris Ancona

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on fMRI data and their interpretation, particularly to the

general public (but see Illes et al., 2010; Racine et al.,

2005). At the NeuroLeadership Labs, we follow very strict

conventions in building our system—focusing on research

from the most reliable labs, being mindful of participant

pools (e.g., 20-year-old, female, left-handed, psychology

students who are involved as part of their course grade or

are being paid to participate vs. an executive group, etc.),

being cautious about relying on “averages” at the expense

of individual differences, and avoiding a narrow focus at

the expense of more expansive explanations (often due

to failure to look at the whole brain reaction rather than

a specific part), just to name a few. The best labs are well

aware of these concerns and are taking the necessary

steps to assure the integrity of their results (e.g., Lieberman

& Cunningham, 2009; Button et. al., 2013). Further, there

is considerable concern in the literature about the under-

reporting of negative results (Fanelli, 2012), overly flexible

experiment design (Simmons et al., 2011), over reliance on

economic games among strangers, and pressures toward

publication at the expense of truthfulness (Nosek et al.,

2012).

An additional concern, and one that is not well addressed

in either work (Satel & Lilienfeld, 2013; Burton, 2013) or in

the literature, is that neuroscientists all too often rely on

decades old social science research paradigms in setting

up participant pools—and fail to control for variables (e.g.,

individual differences in self-regulation or regulatory

focus or social awareness) that neuroscience and its

supportive technology now allow us to measure and

assess with a considerable degree of accuracy. Neither

book meaningfully addresses this notion in their criticism

of neuroscience’s attempts to understand human

behavior. Importantly, at this stage in the development of

NeuroLeadership as a field, we both need and encourage

credible critics to make sure the protocols of the scientific

method are being observed (e.g., Ochsner & Lieberman,

2007; Berkman, et al., 2014).

From Description to Prescription: The Use and

Availability of Big Data

NeuroLeadership as a discipline initially focused on

bringing “hard science” to the social science field of

leadership. Neuroscience research provided behavioral

insights and explanations based on scientific data,

simultaneously confirming, expanding, and not too

infrequently contradicting social science data, which

had developed in large measure based on observation of

behavior in controlled environments (Berg & Lune, 2011;

Leary, 2011). For example, data reflective of emotions

were largely gathered on the basis of self-reported data

and from observations of others in reaction to a defined

behavioral stimulus. Neuroscience allowed us to “see” the

emotion, by observing how an individual marshaled brain

resources to react to emotional stimuli (Carter & Shieh,

2009). In this sense, neuroscience provided leadership

scholars with a “hard-science” or “descriptive” explanation

of behavior (Rock & Schwartz, 2006).

In addition to the technological advances making such

brain imaging possible, technology of a far more personal

nature is also being developed and marketed (Swan,

2012). This technology allows curious individuals to self-

track a wide variety of biological, physical, and behavioral

information—technology in the form of wearable sensors,

mobile apps, and software interfaces (Wilson, 2012). With

neuroscience research having provoked them to seek

out brain-based descriptions of their behavior and that of

their clients in many cases, the curious are now looking

for neuroscience to provide more “prescriptive” solutions

to behavioral issues: “I have the data; now, what can I do

with it?” (Forbes & Grafman, 2013).

Neuroscience research provided behavioral insights and explanations based on scientific data, simultaneously confirming, expanding, and not too infrequently contradicting social science data...

Big data has arrived in an almost unimaginable scale

(Davenport, 2014). According to the research firm IDC,

more than three zettabytes (1 zettabyte = 1 million

terabytes) were created or replicated in 2012. Some

60% of U.S. adults are currently tracking their weight,

diet, or exercise routine, while 33% are monitoring other

functions such as blood sugar, blood pressure, or sleep

patterns. There are more than 40,000 smart phone health

applications available to assist them (Swan, 2013). The

Quantified Self website lists over 500 tools available for

personal measurement as of November 2014. Individual

projects and experiments are becoming an interesting

data management challenge for big data science in the

areas of data collection, integration, and analyses. In the

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long-term, big data solutions are needed to implement

the vision of a systematic and continuous approach to

automated, unobtrusive data collection from multiple

sources to be processed into a stream of behavioral

insights and coaching intervention strategies in real time.

At the NeuroLeadership Labs we are collecting and

integrating data from a wide variety of psychometric,

neurobiofeedback, and other sources. Data streams

from diverse applications (for example, Fitbit®,

Zephyrsuperscript, and others) are being uploaded and

aggregated with similar data from other sources. As with

other big data projects, the challenge is in extracting

signal from noise (Davenport & Kim, 2013). While much

of the neurobiofeedback data may be seen currently as

having little value and easily discarded, this is a new era

of big data science, meaning that caution often leads us

to store it for further investigation and validation as we

develop both a better understanding of the data and the

necessary algorithms to make the data more usable. Our

wearable sensors and mobile apps are allowing for greater

objective data collection, assisting us in addressing issues

with self-reported data (such as traditional psychometric

instruments).

Dual-Process Theory and System 1 Versus System 2

Circuitry

In disseminating neuroscience research and the practical

connections that often flow from it, the NeuroLeadership

Institute has been instrumental in popularizing theories

and concepts not only for practitioners but academics as

well. One important example is the concept of SCARF®

(Rock, 2008), which grew out of the NeuroLeadership

Institute’s own research and has made its way into a variety

of useful applications. In another recent example, dual-

process theory (Shiffer, 1998)—most frequently referred to

as “System 1 versus System 2 thinking” (Stanovich & West,

2000)—has made its way into popular blogs, newspaper

articles, and books. Both science writers (e.g., Konnikova,

2013) and scientists writing for lay audiences (e.g.,

Kahneman, 2011; Goleman, 2013) have found its usage

beneficial in explaining connections between human

behavior and neuroscience research findings. The social

neuroscience community propose that understanding the

relationship between System 1 and System 2 thinking is

part of the “second phase” of social neuroscience, “which

focuses less on where things are happening in the brain

and more on how regions of the brain form networks

that interact to engender a psychological process [and] is

poised to have a big impact on existing theories in social

psychology” (Forbes & Grafman, 2013).

Dual-process theory asserts the fundamental notion that

we have two basic brain circuitries—one that is automatic,

unconscious, and fast; the other, controlled, conscious,

and slow (Evans & Frankish, 2009). According to dual

process theorists, the “fast” system is an evolutionarily old

system that is associative, automatic, unconscious, and

operates in parallel (System 1 Circuitry), while the “slow”

system is a more recent, distinctively human system that

is rule-based, controlled, conscious, and operates in serial

(System 2 Circuitry) (Lieberman, 2007; Lieberman et al.,

2002). Scientists have proposed a variety of labels for the

two circuitries, including implicit vs. explicit, automatic

vs. controlled, default process vs. inhibitory, bottom-up

vs. top-down, and evolutionarily old vs. evolutionarily

new, among others (Evans, 2008). Here, we use the more

neutral System 1 vs. System 2 designation.

System 2 Circuitry allocates the brain’s attention to the

effortful mental activities that demand it and is associated

with mental concentration and focus. Recent work has

highlighted how such demanding mental activities are

crucial for the navigation of and survival in the complex

social environment of humans, and it has been proposed

that the development of System 2 neural circuitry is

directly related to these “social brain” needs (Dunbar,

2014; Lieberman, 2013), including self-regulation, impulse

control, and willpower. As Lieberman says, “We are wired

to be social” (Lieberman, 2013).

Are there decisions for which the brain uses information in an optimal way and others for which its processing is suboptimal?

Now that we have highlighted recent trends and issues in

the field of NeuroLeadership, we will summarize some of

the latest research relevant to each of the four domains

of NeuroLeadership, beginning with Decision Making and

Problem Solving.

Decision Making and Problem Solving

In our 2010 review, the Decision Making and Problem

Solving domain was broadened to encompass the

neural bases of the processes and procedures a leader

uses to produce results (Ringleb, Rock, & Conser,

2010). This delineation closely accords with the Do (or

Doing) component of the widely accepted Know-Be-

Do leadership model (Hesselbein & Shinseki, 2004).

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This change has also provided a more consistent

representation of what this domain means to the fields of

both NeuroLeadership and traditional leadership, defined,

at a minimum, as groups making decisions and solving

problems in the Doing sense. This broader definition has

also served to distinguish NeuroLeadership from the fields

of neuroeconomics and neuromarketing, both of which

are more focused on how an individual makes decisions.

Differing Brain Algorithms

Social scientists have long understood that decisions are

often the result of complex interactions between several

factors, making it difficult to determine each individual

factor’s contribution to the final decision. In breaking down

the brain’s decision-making circuitry, neuroscientists face

a similar problem, as decisions involve many neurons. A

research team from the University of Tübingen and the

Max Planck Institute has shown how the contribution of

individual neurons in the decision-making process can

be reconstructed despite interdependencies between

the neurons (Haefner et. al, 2013). Using simulated data

from a realistic population model, the research team was

able to build and test an interesting theoretical model. The

model provided a means to control for communications

between neurons and for more precise measurement of

the neurons providing information involved in the brain’s

decision process. The research team intends to use the

models to determine whether a few neurons carry a lot of

decision related information or whether the information

contained in a larger number of neurons gets combined.

In this way, the model provides a means for addressing

a more fundamental question: Are there decisions for

which the brain uses information in an optimal way and

others for which its processing is suboptimal?

With regard to the brain’s decision-making process

from a macro perspective, virtually all decision-making

processes consider alternatives. Singer et al. (2013)

implanted electrodes in the hippocampus of the brains

of rats and then observed rats in a maze “playing out

memories” to help them decide which way to turn. The

research team observed that when the rats paused before

an upcoming choice, the hippocampus, which is critical

for memory processing, was more active at times and

less active at others. When more active, the animal was

more likely to go to the right place, arguably because it

did a better job of recalling memories of places it could

go next—implicating the importance of the hippocampus

in decision making. Combined with other research, the

study suggests that when the brain does a better job of

thinking about alternatives, it makes better decisions. That

is, decision-making is improved by the use of System

2 thinking, providing support for the use of express

process as a means to overcome System 1 thinking errors

(Kahneman, 2011).

In an interesting study of first responders, individuals who

prefer to combine quick, intuitive decisions with analysis

were found to make the best decisions in a crisis situation

(Bakken, 2013; see also, Dane et al., 2012). Intuitive decision-

making was defined as the ability to make decisions based

on previous experience; analytical decision-making was

defined as making use of systematic processes, taking time

to review the details in compliance with formal guidelines

and requirements (for the novice and the inexperienced,

the analytical approach was found to be a necessary tool

in decision-making). Utilizing some 800 participants in a

computer-based simulation incorporating a variety of crisis

scenarios, Bakken found that “those who normally prefer

combining intuitive decisions with analysis made the best

decisions in the crisis situations.” The author concluded

that while experience is valued, organizations should be

encouraged to use express process (analytical decision-

making) in combination with their intuitive thinking. That

is, in effect by “slowing down” the brain’s tendency to

jump to alternatives (System 1 thinking), express processes

provide System 2 thinking with the opportunity to engage.

Princeton University researchers addressed a fundamental

question among neuroscientists about whether bad

decisions result from noise in the external environment—

or sensory input—or because the brain makes mistakes

when compiling that information (Brunton et. al, 2013).

There is little doubt that many decisions are based on noisy

and unreliable evidence (Gold & Shadlen, 2007). Brunton

and colleagues, however, separated sensory inputs from

internal mental processes to show that the former can be

noisy while the latter is remarkably reliable. The research

subjects—four college-age volunteers and 19 laboratory

rats—listened to streams of randomly timed clicks coming

into both the left ear and the right ear. After listening to a

stream, the subjects had to choose the side from which

more clicks originated. The rats had been trained to

turn their noses in the direction from which more clicks

originated. While the test subjects chose the correct

side most often, they did occasionally make errors. By

comparing various patterns of clicks with the volunteers’

responses, researchers found that all of the errors arose

when two clicks overlapped, and not from any observable

noise in the brain system that compiled the clicks. The

researchers found that errors are mostly driven by the

inability to accurately encode sensory information—

express processing slows the brain down to allow System

2 to engage, and to avoid sensory input errors coming

from biases, stereotypes, and preconceived ideas.

Brain Circuitry for Social Decisions

Most of the work on decision-making in the academic

business community focuses on non-social decisions,

despite the fact that arguably the majority of our decisions

are social (Lieberman, 2013), particularly those involving

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leadership issues. The cognitive processes associated

with theory of mind, or mentalizing, provide important

mental inputs into the decisions we make about others

and about what others are deciding about us (Olsson &

Ochsner, 2008).

Although many areas of the human brain are devoted to

social tasks, a brain imaging study conducted by researchers

at the Duke Center for Interdisciplinary Decision Science

found that one small region carries information only for

decisions during social interactions (Carter et. al., 2012).

The study put human subjects through a functional MRI

brain scan while playing a simplified game of poker against

a computer and human opponents. Using computer

algorithms to sort out what amount of information each

area of the brain was processing, the team found that

only one brain region—the temporal-parietal junction, or

TPJ—carried information unique to decisions against the

human opponent.

In general, the scientists found that participants paid more

attention to their human opponent than their computer

opponent, consistent with the notion, “We are wired to

be social” (Lieberman, 2013). For example, while brain

signals in the TPJ told the researchers whether the subject

would soon bluff against a human opponent, signals in

the TPJ did not predict the subject’s decisions against

a computer. This and other studies (e.g., Fletcher et. al,

1995) show us there are fundamental neural differences

between decisions in social and non-social situations.

There is reason to believe that social information may

cause our brain to play by different rules than non-social

information. Those “rules” are likely to be influenced by

individual differences, a conclusion important to both

scientists and business leaders in understanding what

causes a particular individual to approach a decision in a

social or a non-social manner.

Individual differences in empathy are one factor that

can affect social decision-making. In a period of social

transition, of particular importance are the brain’s social

decision-making processes in identifying competitors

and collaborators—those people who are most relevant

for our future behavior. Our interactions with others are

critically influenced by empathy—the ability to understand

and identify with another’s emotions. Research shows

that empathy among college students is in serious

decline, particularly since 2000 (Konrath et. al, 2011),

a trend we have also observed at the NeuroLeadership

Labs in both psychosomatic instrument measures

and neurobiofeedback measures in experiential

environments. The Duke study discussed above (Carter et

al., 2012) implies that the brain’s circuitry for mentalizing,

perspective taking, and empathy, which includes the TPJ,

was designed for face-to-face moments.

Assessing Decision-Making and Problem-Solving

Abilities

In making the natural move from using neuroscience

to describe behavior to using its research findings

to bring about long-term, sustainable behavioral

change, understanding the dimensions upon which

the “prescriptive” activity can be based becomes

increasingly important, particularly to practitioners. At the

NeuroLeadership Labs, we are working with biofeedback

providers to develop a wireless, unobtrusive device

to measure skin conductance. A prototype has been

successfully tested for use in showing the emotional

differences between group decisions undertaken with

and without express process. Preliminary data suggests

that the use of explicit (as opposed to relying on individual,

idiosyncratic) critical thinking processes significantly

reduces skin conductance responses, an indication

of stress and arousal, in group settings. The intent is to

provide participants with data in making the argument

for the use of express process as a means of moderating

organizational emotion and, in the words of Daniel

Goleman, avoiding organizational ADD (Goleman, 2013).

...the brain regions responsible for making decisions continue to be active even when the conscious brain is distracted...

Many decisions must be made under stress, and many

decisions by themselves elicit a stress reaction. The

neuroscience literature draws an important distinction

between those decisions where (1) stress impacts the

outcome (e.g., decisions regarding risk avoidance,

strategy use, or the reliance on higher-level brain systems)

and (2) stress activates the “fight-or-flight” response

(Starcke & Brand, 2012). The former is most characterized

by analytical, System 2 brain circuitries, while the latter

is characterized by automatic, heuristic-based, System 1

circuitries.

Van den Bos et al. (2009) found that participants showing

higher levels of the stress hormone cortisol after the Trier

Social Stress Test performed less well on a decision making

task. Furthermore, men did more poorly in managing the

stress than did women. The scientists concluded that

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the “results of this study suggest that … men in jobs at

high-risk positions who are high-cortisol responders are

at risk when taking decisions under conditions of acute

achievement stress, while women may be less at risk

under such conditions.” While this remains consistent

with prior, more general investigations of decision making

under stress (Raymond & O’Brien, 2009), Creswell et al.

(2013) shows us further that the brain regions responsible

for making decisions continue to be active even when the

conscious brain is distracted with a different, non-stressful

task. This non-stressful distraction can even result in

improved decision making.

Following this line of research, Santos-Ruiz et al. (2012)

examined whether differences in decision-making skills

impacted stress levels as measured by cortisol levels.

Following a research protocol similar to Van den Bos

et al. (2009), but using only female participants, they

found cortisol levels to be significantly higher in those

participants with poor decision-making skills. A survey of

600 board directors provides further support from the

“wild,” showing that women are more likely to consider

the rights of others and to take a cooperative approach

to decision making, arguably translating into better

performance for their companies (Bart & McQueen, 2013).

With regard to workplace stress, research is showing

that stress detrimentally impacts decision-making and

problem-solving abilities, and that gender as well as

baseline cortisol levels may be important factors in how

well individuals can use System 2 processes to manage

stress.

Understanding the Impact of System 1 Thinking Errors

Daniel Kahneman has had a considerable impact on

NeuroLeadership by raising awareness of the importance

of understanding the consequence of System 1 thinking

errors, particularly in the form of biases, stereotypes, and

preconceived notions. In fact, at the 2013 NeuroLeadership

Summits, Dr. Matt Lieberman dedicated an entire main

session to the topic, and an article, “Breaking Bias,”

has been published in the NeuroLeadership Journal

(Lieberman, Rock, & Cox, 2014). Perhaps influenced

by some of Kahneman’s biases, proposed solutions to

these System 1 thinking errors have been the subject

of considerable debate. At the NeuroLeadership Labs,

we come down on the side of Kahneman based on the

efficiency gains we have observed in both executive and

university students from the use of express process in

group settings. Our sense is that those efficiency gains

are largely attributable to lowered group emotions (e.g.,

managing SCARF® threats) and directing the brain to use

the appropriate issue resolution brain circuitry. This is an

example of System 2 engaging to overcome System 1

errors and cognitive bias, and is stressed in Lieberman et

al. (2014) as the engagement of cognitive control neural

circuitry to challenge our automatic, biased behaviors and

responses.

In an interesting study affirming Kahneman, Reyna et al.

(2014) found that U.S. intelligence agents may be more

prone to irrational inconsistencies in decision making than

college students and post-college adults. The study found

that intelligence agents both exhibited larger biases on 30

gain-loss framing decisions, and were also more confident

in those decisions. Thirty-six agents were recruited for

the study from an anonymous federal agency and were

presented with several “framed” scenarios much like those

made famous by Kahneman’s research. Participants who

had graduated college seemed to occupy a middle ground

between college students and the intelligence agents,

suggesting that people with more advanced reasoning

skills are also more likely to show reasoning biases.

...U.S. intelligence agents may be more prone to irrational inconsistencies in decision making than college students...

In a very creative study, De Martino et al. (2013) found that

a trader’s Theory of Mind (ToM) bias (i.e., the tendency

to infer others’ intentions) explains how financial bubbles

are created. The research offers the first insight into the

processes in the brain that underpin financial decisions

and behavior leading to the formation of market bubbles.

The key difference between non-bubble markets and

bubble markets is that in non-bubble markets, the value

of a share is determined only by the fundamental value of

the asset; in bubble markets, profitable trading depends

on accurately judging the intentions of other players in

the market. An increase in value representation during a

bubble market was a consequence of the fact that traders

use inferences about the intentions and mental states

of other agents to update their value representation.

The researchers used fMRI to map participants’ brain

activity as they traded within the experimental market.

They found that the formation of bubbles was linked to

increased activity in the vmPFC, the part of the brain that

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processes value judgments. The study showed how the

same ToM brain mechanisms that have been extremely

advantageous in our evolutionary history could result in

maladaptive behaviors when interacting with complex

modern institutions like financial markets.

Decision Making, Emotions, and Emotion Regulation

Decision research in economics, business, psychology,

and neuroscience readily accept emotion’s significant

role in decision-making. In making risk-based decisions,

we normally deliberate the pros and cons of our

choices, taking into consideration past experiences in

similar situations before making a final decision. As we

have discussed, research demonstrates that cognitive

stress, such as stressful distraction, can influence this

balanced, logical approach to decision making (Raymond

& O’Brien, 2009). Yip & Cote (2012) show that such

stressful distractions do affect our decisions, but not if

the decision maker has high emotional intelligence. This

study shows that understanding the source and relevance

of emotional influences and how much sway they have

over our decision making can affect our willingness to

take risks. Participants with lower levels of emotional

understanding allowed anxiety unrelated to decisions they

were making influence their decisions; those with higher

emotional intelligence did not. In a second experiment,

the researchers observed that individuals with lower levels

of emotional intelligence could also block unrelated

emotions from influencing their decisions about risk,

simply by being made self-aware that their anxiety was not

related to the decisions at hand. Self-awareness was the

key to emotional control.

Decision Making and the Healthy Mind Platter

In addition to the impact of stress, factors such as sleep,

exercise, diet, and mindfulness can also impact decision-

making efficacy. Recall that Singer et. al (2013) identified

the hippocampus as being important in assessing

alternatives in the decision-making process in the brain.

Coplan et al. (2014) have found that being overweight

appears to be related to reduced levels of a molecule

that reflects brain cell health in the hippocampus. Using

magnetic resonance spectroscopy, a non-invasive

magnetic resonance imaging (MRI) application, the

research team visualized the molecule N-acetyl-aspartate

(NAA); NAA is associated with brain cell health. Overweight

study participants exhibited lower levels of NAA in the

hippocampus than normal weight subjects. The effect

was independent of age, sex, and psychiatric diagnoses.

By providing individuals with instant access to work-related

information and communications outside of the office,

smartphones have become a ubiquitous technology.

However, the benefits of smartphone use for work at

home may be offset by the inability of users to fully recover

from work activities while away from the office, especially

at night. Employees often use smartphones for work

within an hour of going to bed, and many sleep within

reach of their smartphones (Perlow, 2012). From a sample

of 82 mid- to high-level managers, and relying on the ego

depletion theory literature (Baumeister et al., 1998), Lanaj

et al. (2014) found that late-night smartphone usage for

work may interfere with sleep, leaving users depleted in

the morning and thus more subject to the influence of

distractions (Baumeister & Vohs, 2007).

Collaborating With and Influencing Others

In our most recent review of research in the important

domain of Collaborating with and Influencing Others

(Ringleb, Rock, & Ancona, 2012), we developed a theme

around what we at the NeuroLeadership Labs refer to

internally as the “Social Brain Theory of Leadership.”

Derived in part through the work of Matt Lieberman (2013)

and insights from research by Todd Heatherton (2011), the

theory espouses that the human brain was obligated to

adapt to a complex social environment to survive, and so

evolved dedicated neural mechanisms acutely sensitive to

social context—particularly to any signal (real or perceived)

that our social inclusion was somehow at risk. The

neural drivers for controlling oneself to be a good group

member imply a need for dedicated neural circuitries to

enhance social awareness (mentalizing, theory of mind,

mirror neurons), threat and reward detection (social pain,

SCARF®), self-awareness, and self-regulation. To the

brain, survival means acceptance by the social group,

with the consistent underperformance of any one of

those component circuitries leading to social exclusion

and “death.” Moderated by self-regulation (reviewed in

the Facilitating Change domain discussion below), the

adaptive behavioral challenges of the social environment

include adherence to group values and beliefs and

managing SCARF® threats. To this end, self-protection is

a fundamental brain goal. In that specific regard, in this

domain, we focus on social awareness and threat and

reward detection circuitries.

Social Transition, Mental Complexity, and Wisdom

Our thinking at the NeuroLeadership Labs has been greatly

assisted by insights gleaned from our ever-expanding

database and the assistance of the neuroscience and

social psychology research communities at the Summits.

Much of the social group adaptation to which Heatherton

refers would be applicable to anyone making a transition

to a new social group, whether that individual was intent

on being a follower or a leader. A leader’s responsibility is

not only to adapt to the new social group (a follower’s core

responsibility), but also to adapt or mold the social group

so that it can thrive and be more successful. The ability

of an individual to make such adaptations is a function of

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something we have begun to call “mental complexity.”

Within our core development system, mental complexity

measures an individual’s ability to effectively adapt to and

manage social connections. The higher an individual’s

level of mental complexity, the more readily an individual

can move purposefully in, between, and among social

groups. It is a complex function of social, cognitive, and

affective variables led by self-awareness, social awareness,

and self-regulatory abilities. As discussed in the Decision

Making domain, for a leader, mental complexity involves

being able to differentiate individuals within the social

group on the basis of the relevancy of their behavior. The

failure to distinguish relevancy from irrelevancy—between

more- and less-important social group members—may

lead to following deceptive advice or conforming with

the unproductive, unconstructive, or unhealthy attitudes

or opinions of individuals less likely to move the group

forward. Internally at the NeuroLeadership Labs, we see

this interpersonal neurobiological ability as defining an

individual’s Wisdom, a concept discussed in a similar

context at the NeuroLeadership Summits in 2013.

To the brain, survival means acceptance by the social group...

We have observed that challenges to mental complexity

are most experienced or observed during periods

of “social transition”—changes in current, important

social connections. That is, issues of significance to the

Collaborating With and Influencing Others domain are

most prominent during periods of social transition. In our

experience, the author who best explains both the what

of this social transition notion and, importantly, the how

of going about addressing it is Dan Siegel, a frequent

contributor to the NeuroLeadership Summits. Obvious

examples from a business leader’s perspective would

include taking a new job with managerial responsibilities

or being promoted into the same. Seemingly less

obvious examples, but equally as challenging in many

cases, would include a change in team membership—

consider the inclusion of a new team member who then

brings to bear both a different perspective and a strong

personality—or a change in organizational leadership,

both of which would involve important adaptations for

affected leaders and followers. Dr. Siegel artfully exposes

the depth and complexity of social transitions as he shows

us the growth in mental complexity demanded of all

members of a primary social group as a member goes

through adolescence—something to which we can all

relate (Siegel, 2013). In fact, most of the research in this

important area centers on adolescence-to-adulthood

social transitions (e.g., Allen et al., 2013; Sheridan, et al.,

2013; Olsson, et al., 2013).

Returning to the concept of Wisdom in this specific regard,

with human traits and attributes assumed to be normally

distributed, individual differences in Wisdom imply that

individual reactions to social transitions will range in

the extremes from being relatively painless to being

agonizingly painful. While science may be able to observe

this phenomena of Wisdom descriptively on average—as

in “individuals performing this task light up these circuitries

in their brain”—prescriptively, as coaches and consultants

we need to understand not only what circuitries are

involved, but how we can assist in strengthening those

pathways, particularly in those underperforming in such

social transitions. In addition, by controlling for behavioral

characteristics such as self-regulation, we may find it

possible to predict where an individual may fall on the

continuum and better focus those intervention strategies

(we will turn to this notion in the Emotion Regulation

domain).

To get a sense of this from a slightly different perspective,

recall the claim made by Malcolm Gladwell (2008) that it

takes 10,000 hours of practice to become an expert at

something—a pianist, an athlete, or a video gamer. Dan

Goleman (2013) asserts further that 10,000 hours of

focused attention towards a goal will lead to a measure of

expertise, making it clear that the “expertise” so achieved

may be proficiency at doing something poorly. With more

direct relevance to the Collaborating with and Influencing

Others domain, Lieberman (2013) adds that if we make

even modest assumptions about how much time we

spend on social thoughts, our brains will put in 10,000

hours well before we turn 10—more than enough to

become experts in the enormously complex realm of our

primary social group. In bringing this thinking together,

the strengths individuals carry from their previous social

group will likely be the consequence of “10,000 hours” of

practice in social survival in that social group. With regard

to mental complexity, while those practiced strengths

may have been fundamental to survival in the individual’s

previous social group, they may not be applicable or

function in the same way in the social group to which

the individual is transitioning (Izuma, 2013). In making

an effort to adapt to the new social group (meeting the

human need for social conformity [Shestakova et al., 2013;

Trautmann-Lengsfeld & Herrman, 2013]), social anxiety

often arises from the misinterpretation of the cues that

had previously activated those strengths to a beneficial

effect in the former social group. This consequential

social anxiety in the new social group is something we

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refer to at the NeuroLeadership Labs as an Emotive

System 1 Thinking Error (as opposed to a Cognitive System

1 Thinking Error which arises from the brain’s tendency

to jump to conclusions based on a heuristic arising from

a bias, stereotype, or preconceived idea [Kahneman,

2011]). The notion is similar to that espoused by another

Summit contributor, Jeffrey Schwartz, who refers to it as

a “deceptive brain message” (Schwartz & Gladding, 2011).

In the majority of cases, it is not the failure of the leader to

use his or her strengths, but rather the consequence of their

automatic, unconscious System 1 thinking circuitries using

those strengths inappropriately when confronting social

stimuli which they have had relatively little experience in

interpreting. The brain’s System 1 will deploy an adaptive

habit that was effective in the prior social group in response

to the perceived or real stimulus. However, in the new

social environment, the habit is having an unproductive,

unconstructive, or unhealthy result, bringing about social

anxiety and stress (Morrison & Heimberg, 2013; Mobini

et al., 2013; Duval et al., 2013; Prater et al., 2013), or its

adverse effects are more pronounced or consequential

in this social group relative to the other. With the solution

to stress being to take on more stress (Doehrmann et al.,

2013), individuals would benefit by being encouraged and

supported to practice in those areas where they have less

experience to build resiliency and bring about the social

conformity that allows them to be successful—to build

mental complexity, to build Wisdom.

Descriptive Developments from Neuroscience

In defining Emotive System 1 thinking errors, SCARF®

provides us with a definable set of social stimuli. Perhaps

best illustrating the importance of SCARF®’s Status

component is the fact that it has been examined and

investigated by a wide variety of disciplines, including

anthropology, psychology, sociology, organizational

behavior, and social neuroscience. To grasp the notion

in context of the “social brain” from the perspective of

neuroscience, let’s consider one element of social status:

Social rank, which refers generally to an individual’s social

standing as either dominant or subordinate in a social

group (Sapolsky, 2004).

Organizational hierarchies such as those found in

employment settings, for example, serve to create natural

dominants (managers) and subordinates (team members).

With perceived social rank impacting the individual as

much as actual social rank (Adler et al., 2000), social rank

reflects the ability to attract the attention, admiration,

and investment of others. When this goal is thwarted or

unattainable, hopelessness and depression often ensue

(Gilbert, 2005). In this sense, SCARF® stimuli are clearly

in abundance and well researched: Status (Bartram, 2013),

Certainty (Fergas, 2013), Autonomy (Anand et al., 2012;

Trougakos et al., 2014), Relatedness (Gillet et al., 2012),

and Fairness (Crockett et al., 2013; Corradi-Dell’Acqua et

al., 2013).

Neuroscience and Social Rank.

In moving beyond the behavior observation experiments

of the social sciences, brain imaging technology enables

the study of social rank in relation to brain function and

structure. The brain imaging evidence provides support

for the role of limbic, prefrontal, and striatal pathways

in human social-rank processing (Beasley et al., 2012).

These findings suggest that social-hierarchy stability and

perceived rank differentially impact the neural activation

of relative status processing (Rushmore, et al., 2012).

Mills et al. (2014) provide insights into how the social

brain develops structurally across adolescence before

stabilizing in the early twenties. On the basis of data

drawn from 288 participants and 857 scans, the scientists

examined the social brain—medial prefrontal cortex

(mPFC), temporoparietal junction (TPJ), posterior superior

temporal sulcus (pSTS), and anterior temporal cortex

(ATC). While they were able to show that gray matter

volume and cortical thickness in the mPFC, TPJ, and pSTS

decreased from childhood into the early 20s, and that the

ATC increased in gray matter volume until adolescence

and in cortical thickness until early adulthood, the analysis

did not correlate brain structure with social-cognitive

skill. In other words, we can observe the brain function

and structural development, but cannot yet account for

individual differences in social cognitive skill levels.

Neuroscience and the Mirror Neuron and Mentalizing

Systems.

The notions of Mental Complexity and Wisdom reflect

the idea that social information processing abilities differ

from person-to-person. In this sense, social cognition

involves the ability to understand the behaviors and

actions of others, an ability identified as being important

in a leader’s social transition. Two neurocognitive systems

have been identified as being associated with such social

cognitive abilities: the mirror neuron and mentalizing

systems. Research has demonstrated that the mirror

neuron system is activated during both the execution and

the observation of motor actions; the mentalizing system

is activated when an individual infers another person’s

mental state (Van Overwalle & Baetens, 2009). While

together they encompass the social brain areas to which

Mills et al. (2014) refers above, the action orientation of the

mirror neuron system embraces additional brain regions,

including the ventral premotor cortex (vPMC) and dorsal

premotor cortex (dPMC).

Spunt and Lieberman (2013) provide valuable insights into

the structure and function of these two important brain

systems. Using fMRI, 19 participants were confronted

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with 24 silent videos as stimuli with the intent to test

the automaticity of social-cognitive processing. In a

clever research design, the participants were asked to

observe the actions of the actor in a video according to

one of four pre-specified goals—observe, what, why, or

how—while remembering an easy or difficult telephone

number (cognitive load). Note that a standard test for

automaticity (for System 1 circuitry) is whether a brain

function or activation can be distracted (in which case, it

is more likely to be System 2 circuitry), here being tested

by changing cognitive load. The scientists observed that

brain activation in areas within the mirror neuron system

was unaffected by load regardless of the participant’s

designated goal; that is, they were not distractible, and

thus more likely to be a System 1 circuitry component.

Conversely, they observed that brain activation in areas

within the mentalizing system was affected by load only

when participants were prompted to attribute observed

actions to a motive (the “why” goal), but not when actions

were understood in terms of their implementation (the

“how” goal); the cognitive demands of the need to process

causality necessary for the “why” calculation activated

additional mentalizing system resources, suggesting a

System 2 affiliation. These results support a dual-process

model, whereby the mirror neuron system supports

relatively automatic behavior identification (affiliation with

System 1 circuitry) and the mentalizing system supports

relatively controlled components of social causal

attribution (affiliation with System 2 circuitry).

Prescriptive Developments and Concerns

An integral component of a leader’s successful social

transition is the Mental Complexity to differentiate

between individuals within a social group on the basis

of the relevancy of their behavior. As the study by Spunt

and Lieberman (2013) suggests, this ability is influenced

by the leader’s System 2 mentalizing system. With the

understanding that Mental Complexity is likely to follow a

normal distribution like other human traits and attributes,

individual differences in this ability undoubtedly impact

the likelihood of success. The Spunt and Lieberman

(2013) study describes the brain’s function and structure

in this regard, and by the nature of academic science

leaves the prescriptive aspects of this important finding

to coaches and consultants. As we discussed above, the

interpretation of the “why,” or social causality, of another’s

actions or behaviors are likely to be influenced by a

leader’s experience in interpreting those social cues in

prior social groups. To the extent those interpretations or

misinterpretations lead to unproductive or unconstructive

results, the individual is likely going to suffer dysfunctional

social anxiety in an effort to adapt or conform to the

new social group. Both our observations here at the

NeuroLeadership Labs and the neuroscience research

indicates that coaching intervention strategies focused

on enhancing self-regulatory ability, thereby increasing

the vigilance of System 2 circuitry, and self-awareness

through brain-based computer exercises and mindfulness

practice will deliver positive results; we will return to this

topic in the Facilitating Change domain.

Effective leaders harness and direct the power of emotion...

Emotion Regulation

The past decade has witnessed remarkable growth both

in understanding and in applications of affective science.

Of particular interest here is emotion regulation. As

we have discussed, transitions to and maintenance of

leadership is heavily influenced by an individual’s level

of Mental Complexity—the ability to perceive, identify,

understand, and successfully manage both his or her

emotions and the emotions of others (Olsson et al., 2013).

Effective leaders harness and direct the power of emotion

to build trust and improve follower satisfaction, morale,

and motivation, and thus enhance overall organizational

effectiveness (Riggio & Reichard, 2008). In prior reviews,

we considered the beneficial contributions of mindfulness

(Farb et al., 2007), meditation (Tang et al., 2007), labeling

(Lieberman et al., 2007), and reappraisal (Ray et al., 2005)

to emotion regulation, and the consequential elevation

of emotion regulation’s visibility in the leadership and

business academic research literature (e.g., Gooty et. al.,

2010; Rajah et al., 2011). Given its conceptual relevance

to leadership development and intervention strategies,

it is not surprising that emotion regulation has garnered

considerable interest within the practitioner community.

The focus in NeuroLeadership more generally, and in

this review more specifically, is in understanding the

functionality of the emotion regulation process with the

intent to provide guidance as to how those development

and intervention resources might be allocated most

efficiently.

Current Research

In recent years, scientists investigating emotion regulation

have continued to fill the gaps in our understanding of

the applications and limitations of the emotion regulation

strategies the field has thus far identified. From the

practitioner standpoint, while there are many different

strategies an individual could deploy to regulate emotions

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(Parkinson & Totterdell, 1999), it may be more important

to understand which emotion-regulation strategies are

most likely to deliver effective emotion regulation. In this

sense, an “effective” emotion regulation strategy would,

at a minimum, encompass an understanding of which

strategy is most effective in what context. Further, and

something we will come back to below, efficiency would

also require consideration of the individual, given how

individuals differ in their adjustment to stressful life events,

with some exhibiting impaired functioning while others

exhibit impressive resilience (Olsson et al., 2013).

...individuals who engage in cognitive reappraisal tend to suffer less from anxiety than those who ignore, hide, or repress their feelings.

Studies Considering Strategies

By definition, emotion regulation involves an individual’s

use of behavioral and cognitive strategies to change the

duration and intensity of an emotion (Gross & Thompson,

2007). Llewellyn et al. (2013) found those individuals who

engage in cognitive reappraisal tend to suffer less from

anxiety than those who ignore, hide, or repress their

feelings. The study involved 179 healthy men and women

who were asked how they managed their emotions and

how anxious they felt in various situations. Jamieson et al.

(2013) showed that stress levels could be better managed

by simply encouraging people to reframe the meaning

of the signs of stress from a forthcoming stressful task

as being natural and helpful. The study involved 67 men

and women who were subjected to the Trier Social Stress

Test under varying conditions. The study suggests that

while some individuals may generally find such calming

techniques as deep breathing helpful when confronting

stress, such techniques are not likely to be as beneficial

in those situations requiring peak performance (such as a

job interview or a speaking engagement); using cognitive

reappraisal and reframing how we think about stress

appears to be the better strategy. Townsend et al. (2013)

found that individuals may be able to cope with stress

by sharing their feelings with someone who is having a

similar emotional reaction to the same situation. The study

involved 52 participants who were paired up and asked

to give a speech while being video-recorded. Prior to the

speech, participant pairs were encouraged to discuss with

each other how they were feeling about making their

speeches. Each participant’s level of the stress-related

hormone cortisol was measured before, during, and after

their speeches.

Studies Considering Context

Troy et al. (2013) show us that cognitive reappraisal may

actually be harmful when it comes to stressors that

are under our control. For someone facing a stressful

situation over which they have little control, the ability to

use reappraisal should be extremely helpful; the ability

to change emotions may be something over which the

individual can exert some degree of control in order to

cope. Conversely, for an individual experiencing stress

at work because of poor performance, for example,

reappraisal might not be so adaptive. The study suggests

that reframing the situation to make it seem less negative

may make that person less inclined to attempt to change

the underlying situation. The participants were required to

have experienced a stressful life event in the eight weeks

preceding study recruitment. The study involved assessing

both changes in self-reported data on sadness and data

gathered on cognitive reappraisal ability quantified in

a lab setting by measuring change in skin conductance

activity while watching a series of videos clips. In contrast

to prior studies, the results bring into question the

breadth of cognitive reappraisal’s adaptability, finding

that the degree to which emotion regulation is adaptive

depends on the type of stress. Specifically, high cognitive-

reappraisal ability was associated with less depression

and increased well-being in the context of uncontrollable

stress. When stress was relatively controllable, however,

higher cognitive-reappraisal ability was associated with

decreased psychological health.

Emotion Regulation and Intervention Strategies

Increasingly, emotion regulation is becoming an integral

component of an effective leadership development

intervention strategy. Research from a variety of fields is

contributing to our understanding of this fundamental

control process. In this sense, emotion regulation is seen

as belonging to a larger family of processes whereby an

individual, and, more specific to our interests, a leader

exerts control over his or her own behavior in adapting

to the social group. Indeed, in that regard, modern

emotion-regulation research has drawn considerable

inspiration from theories of human self-regulation and

cognitive control (e.g., Carver & Scheier, 1999; Rueda et

al., 2005; Diamond, 2013; Bridgett et al., 2013). Across a

wide variety of fields, self-regulation has been identified

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as a contributor to adaptive and adverse outcomes

in children, adolescents, and adults, affecting such

emotion-influenced behaviors as coping skills, social

competence, interpersonal relations, and self-esteem,

as well as impulsivity, self-control, self-discipline, mind

wandering, and time management, among a significant

list of other behaviors (Bridgett et al., 2013; Smallwood,

2013). In fact, recent research argues persuasively that the

right ventrolateral prefrontal cortex (rVLPFC) is the neural

region commonly recruited across many different forms

of both self-control (Cohen et al., 2013) and emotion

regulation (Goldin et al., 2013). However, there is also

remarkable variability in individual adjustments to stress

(Ong et al., 2006). Research has identified a number of

factors in explaining these individual differences, including

neurobiological, genetic, cognitive, and psychosocial

factors (Lau & Eley, 2008; Morris, Ciesla, & Garber, 2008;

Southwick, Vythilingam, & Charney, 2005), among

others. At the NeuroLeadership Labs, we believe that

accounting for individual differences is important not only

in intervention strategies, but also in the research.

At the NeuroLeadership Labs, we believe that accounting for individual differences is important not only in intervention strategies, but also in the research.

From the standpoint of a development intervention,

we have observed that successful emotion regulation

strategies have as a precursor an individual’s self-regulatory

ability. It has long been our belief—a belief supported

increasingly by our data and research (e.g., Niles et al.,

2013)—that individual differences in self-regulatory ability

predict the success of an emotion regulation deployment.

With habitual reactions to social stimuli being largely

governed by the brain’s System 1 circuitry, the ability to

modulate those reactions is dependent upon the relative

vigilance of an individual’s System 2 circuitry (Oschsner,

2013). In large measure, the conceptualization of this

observation has been unnecessarily encumbered by the

fact that the term “self-regulation” has been presented

to the practitioner community under a variety of names

including self-control, self-discipline, effortful control, ego

strength, inhibitory control, and willpower (Duckworth

et al., 2011), not to mention that those concepts may

mean the same thing in some disciplines but have

very distinctive meanings in others. This encumbrance

becomes particularly evident when making a comparison

between self-control and impulsiveness (Hamilton et al.,

2014). As reflected in the research literature, the issue

is further compounded by the fact that the various

disciplines studying self-regulation “largely lead separate

lives” (Hofmann et al, 2012). We encountered much of

the same issue in considering research on emotion in a

previous review (Ringleb et al., 2012).

In this light, we sought an interdisciplinary compromise

with the intent to provide a working definition to assist in

building intervention strategies. Baumeister et al. (1998)

stated, “[S]ome internal resource is used by the self to

make decisions, respond actively, and exert self-control.”

In this sense, we see self-regulation as this “internal

resource,” and as including self-control, self-discipline,

emotional regulation, and other control processes,

as well as active responding and decision making. We

recognize that the psychology and neuroscience of

explicit emotion regulation have been fruitfully studied

for over two decades, yielding much understanding

of the neural mechanisms of emotions and behavioral

control (Ochsner & Gross, 2005; Gross & Thompson,

2007; Ochsner & Gross, 2008; Ochsner et al., 2012). More

specifically, recent research argues persuasively that the

right ventrolateral prefrontal cortex (rVLPFC) is the neural

region commonly recruited across many different forms

of both self-control (Cohen et al., 2013) and emotion

regulation (Goldin et al., 2013).

In working with our various client groups on emotion-

regulation issues, we began to see the Gross (2001)

distinction along the lines of emotion-regulatory

processes (situation selection, situation modification,

attentional deployment, cognitive change, and response

modulation) as benefiting from the additional designation

as being reactive, proactive, or some mix of the two

(Braver, 2012). The distinction becomes important when

the relative vigilance of an individual’s System 2 circuitry

significantly influences the outcome, as would be

expected in the emotion regulation strategies of attention

deployment, cognitive change, and response modulation

(DeStono et al., 2013). In other words, individuals

exhibiting higher levels of self-regulation (and thus the

ability to more readily engage System 2 circuitry) are more

likely to successfully deploy emotion regulation strategies

with a reactive component (holding as a constant factors

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such as experience, and thus habitual responses; that is,

an individual’s prior social group experience may have

“hardwired” in a functional System 1 response).

To get a sense of this, let’s consider the workplace

environment. Clearly, a wide variety of factors in addition

to emotion regulation and self-control influence work

safety (Kotze & Steyn, 2013; Hogan & Foster, 2013; Lawton

& Parker, 1998). In a study to examine whether System

2 thinking (“controlled cognition”) together with System

1 thinking (“automatic cognition”) could better predict

safety behaviors in the workplace, Xu et al. (2014) found

that individual differences in inhibition can shift the relative

weight of System 2 and System 1 cognitive processes in

predicting employees’ safety behaviors in the workplace.

That is, instead of developing an experiment to test

worker responses to a defined stimulus, the research

team first controlled for individual self-regulatory ability

(“inhibitory control”) as measured by a computerized

Stroop task. Safety behaviors were measured through an

assessment of safety compliance and participation (Neal,

et al., 2000). The System 2 cognitive process (“controlled

cognitive process”) regarding safety was measured using

responses of the safety attitude questionnaire (Henning

et al., 2009). The System 1 cognitive process (“automatic”)

was measured by a computerized Implicit Association

Test (IAT) task, which provides an indirect measure of the

strength of automatic associations of two categories (i.e.,

safety vs. risk) with two attributes (positive vs. negative)

(Greenwald et al., 1998). The results indicated that the

relative weight of System 1 (“automatic”) and System 2

(“controlled”) cognitive processes in predicting safety

behaviors depended on individual differences in self-

regulatory (“inhibition”) ability. Employees with low self-

regulatory ability were more influenced by System 1

cognitive processes; employees with high self-regulatory

ability were more likely to be guided by System 2 cognitive

processes. Clearly, both System 1 and System 2 cognitive

processes influence worker behavior, but through

different pathways. System 1 cognitive processes affect

behavior through an impulsive and spontaneous process,

largely driven by habit. System 2 cognitive processes

were seen to drive behavior through a deliberative and

reflective process, in which automatic, habitual impulses

are inhibited, and the employee’s behavior is guided by

conscious thought and analysis.

With specific regard to the efficient allocation of

development and intervention resources, given the

measurable impact of individual differences in self-

regulation behavior, the Xu et al. (2014) study suggests that

intervention strategies may be more effective for differing

subgroups of employees. Those employees with higher

self-regulatory abilities may benefit more from traditional

interventions focused on information-based techniques

and courses. Employees with lower self-regulatory ability

may benefit from interventions that attempt to strengthen

self-regulation, either through a mindfulness program

(Teper et al., 2013) or targeted computer-based brain

exercises (Schweizer et al., 2013; Onraedt et al., 2014),

which we will discuss in the next domain, Facilitating

Change.

...our System 1 Circuitry is responsible for...listening to and speaking our first language, recognizing faces, using general problem-solving techniques, and engaging in basic social relations...

At the NeuroLeadership Labs, we draw an important

distinction between technical and adaptive solutions to

leadership challenges (Heifetz, 1998). The distinction is

illustrated through an example of an individual in transition

from a social group where avoidance of conflict is the

survival skill, to another where he or she finds it necessary

to confront conflict. In the interest of finding a means of

managing the unproductive emotional anxiety that arises

in such conflict-laden encounters, the technical solution is

to send the individual to a course on conflict management.

Typically, such a technical solution is easy to identify,

those in need of it are generally receptive, and it can be

implemented quickly (Heifetz & Laurie, 1997; Heifetz &

Linsky, 2002). System 2 Circuitry will undoubtedly allocate

the brain’s attention to the effortful mental activities the

course will demand of it. But will the course knowledge

solve the individual’s emotional anxiety issue? Will having

the technical knowledge behind conflict management

provide the individual with the necessary skills to manage

conflict productively? Will an individual armed with an

understanding of an emotion regulation strategy be able

to control their emotions and those of others effectively?

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In that regard, recall that our System 1 Circuitry is

responsible for delivering our habitual reactions, good

or bad, to environmental stimuli, largely with the intent

of self-protection. As such, it operates automatically,

efficiently, often unconsciously, and cannot be turned

off. It is associated with survival-based information that

learners acquire easily and unconsciously. Not surprisingly,

such information is largely social and includes listening

to and speaking our first language, recognizing faces,

using general problem-solving techniques, and engaging

in basic social relations, all of which are acquired readily

outside of educational contexts. In the interest of both

adapting to and then molding the social group, a leader

transitioning from one social group to another is likely

to require changes in values, beliefs, roles, relationships,

and approaches to work to be successful—an adaptive

solution that is likely to be difficult to identify, multifaceted,

and thus easy to deny (and largely operating automatically

and unconsciously). In addition, adaptive solutions are

influenced more significantly by individual differences

(e.g., self-regulatory ability), which will manifest themselves

in varying degrees of resistance to acknowledging the

adaptive challenge as well as to the personal discovery

that may be an integral part of the adaptive process.

Understanding those individual differences (e.g., Gross

& John, 2003; English & John, 2013) will significantly

influence both the development and intervention

strategies resources allocated to addressing the issue and

the consequential success of those efforts.

Facilitating Change

As in our previous annual surveys, here we again focus

on individual change efforts. In large part, this reflects

the type of research most likely to be undertaken by

the neuroscience research community. Although

within the business academic community there is

growing interest in “organization neuroscience” and the

contributions to the understanding of social processes it

can make in organizations (Becker et al., 2011; Lee et al,

2012; Cropanzano & Becker, 2013), the field is still in its

relative infancy. In its absence, some of the knowledge

gap on change is being filled by interesting works from

the practitioner community; consider, for example,

such work as that by McFarland and Goldsworthy

(2013). Dr. McFarland is a long-time contributor to the

NeuroLeadership Summits. Based on our observations at

the NeuroLeadership Labs, we are hopeful that some of

that research will come from investigating the increased

use of express rational process (as we discussed in the

Decision Making and Problem Solving domain), particularly

as it relates to the ability of express process to modulate

unproductive organizational emotions, and in developing

a better understanding of the demands of social transitions

on both followers and particularly leaders. With regard to

the latter, we are also of the opinion that researchers in this

area would benefit from following research by Dan Siegel

on interpersonal neurobiology (e.g., Siegel, 2012). At the

moment, however, the field seems to be preoccupied

with whether neuroscience will either replace existing

research or add to it (Lee et al., 2012; Edwards, 2013).

From the academic management community, we are

beginning to see the use of neuroscience technologies

and methods in reexaminations of existing organization

theories and concepts (Waldman et al., 2013). Taken

from any perspective, there is little question that the area

offers meaningful interdisciplinary research opportunities

(Waldman, 2013; Butler, 2013).

Change Interventions: The Use of Mindfulness and

Computer-Based Brain Exercises

At the NeuroLeadership Labs, preliminary data suggests

that almost any development intervention benefits

from both mindfulness practice and computer-based

brain training to enhance self-regulation. In concert,

mindfulness and enhanced self-regulation ability also serve

to influence an individual’s self-awareness. Interestingly,

with regard to the latter, virtually every mass-media book

discussing the brain and behavior prescriptively asserts

self-awareness (in one form or another) as a “solution” to

the particular malady it addresses—something with which

we are in general agreement.

In setting the stage for mindfulness and computer-based

brain exercises, the principal “barrier” to individual change

is not infrequently the individual’s brain and its evolutionary

penchant for self-protection. Goals set through an

individual’s rational, conscious, System 2 thinking are

unconsciously undermined by the brain’s System 1 goals,

making us “immune to change” (Kegan & Lahey, 2009).

That is, individuals often pursue other actions or activities

that are inconsistent with their System 2 goals. Those

inconsistent actions and activities are often undertaken

in response to powerful social anxieties that arise as the

individual works to conform to the social norms of group

to which he or she is transitioning. The vast majority of

those fears, worries, anxieties, or concerns have their

basis in reactions to SCARF® threats. For example, in the

face of a System 2 goal to be a more effective delegator,

the individual’s fear of losing control manifests itself as a

System 1 self-preservation goal to “not lose control,” an

unconscious, automatic System 1 goal that makes it very

difficult for the individual to achieve their System 2 goal.

A developmental intervention would focus on examining

the assumption the individual is making that is driving

such SCARF® fears (often it is assumed that he or she will

be rejected [e.g., Olsson et al., 2013]) and then testing the

assumption experientially in much the way a person would

conduct a self-tracking or “quantified-self” experiment

(e.g., Kim, 2014; Lupton, 2013). It is quite commonly

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revealed that the fear has its basis in the misinterpretation

of cues in the social-transition process and often involves

a particular behavior in the new social group that was not

sufficiently practiced in the prior group because it was not

needed or accepted for conformity. While the primary

intervention strategy would be to assist the individual in

managing the SCARF® concern and to build emotional

resilience around the skill by practicing it, we have found

that the intervention can be beneficially augmented with

mindfulness practice and computer-based brain training.

Computer-Based Brain Exercises

Despite early concerns about the efficacy of computer-

based brain exercises (e.g., Owen et al., 2010), they

continue to show promise. For the most part, the

improved efficacy seem to be due to improved exercise

quality, enhanced attention to the brain circuitry to which

an exercise is directed with regard to the deficiency it is

intended to address, clearer delineation of what brain

structures and functions can be targeted, and defined

measurement and assessment of results. At the most

general level of inquiry, research has focused on video

games; increasingly, research attention is shifting to

focused interventions.

Research on Video Game Training and Brain Structure

and Function.

Several recent experimental studies support the idea

that training increases various components of brain

(executive) functioning (e.g., Strobach et al., 2012).

Anguera et al. (2013) reported that older adults without

video game experience show enhanced cognitive control

after training when compared to both active and passive

control groups. In terms of neurophysiology, action video

game training appears to engage neural structures and

circuits that mediate executive functions. EEG studies

have shown associations between improved performance

in executive-function tasks and increases in both frontal-

alpha (Maclin et al., 2011; Mathewson et al., 2012) and

midline-frontal theta power (Anguera et al., 2013) after

video game training.

In addition to post-training changes in brain function,

there is preliminary evidence that training may also lead to

structural brain changes. Kuhn at al. (2013) demonstrated

that video gaming causes increases in brain regions

responsible for spatial orientation, memory formation,

and strategic planning, as well as fine motor skills. The

study compared a control group to a video-gaming

training group that trained for two months for at least

30  minutes per day. Specifically, the scientists found

significant gray-matter increases in right hippocampal

formation (HC), right dorsolateral prefrontal cortex

(DLPFC), and bilateral cerebellum in the training group. In

a study of 152 14-year-olds, Kuhn et al. (2014) used MRI to

estimate cortical thickness. Cortical thickness across the

whole cortical surface was correlated with self-reported

hours per week of video gaming. The scientists observed

a positive association between cortical thickness and

video gaming duration in the left dorsolateral prefrontal

cortex (DLPFC) and left frontal eye fields (FEFs). No

regions showed cortical thinning in association with

video-gaming frequency. DLPFC is the core correlate of

executive control and strategic planning, which in turn

are essential cognitive domains for successful video

gaming. The FEFs are a key region involved in visuo-motor

integration important for programming and execution of

eye movements and allocation of visuo-spatial attention.

Still, while noting that behavioral and neurophysiological

evidence tentatively support the efficacy of training, a

number of researchers are calling for additional data from

evidence-based practices, particularly as it relates to the

magnitude and specificity of these effects. Others are

suggesting the importance of accounting for individual

differences (Basak et al., 2011). Still others are asserting

that observed improvements in individual performance

is task specific and as such are not transferable to other

tasks (Melby-Lervag & Hulme, 2013).

...video gaming causes increases in brain regions responsible for spatial orientation, memory formation, and strategic planning, as well as fine motor skills.

Research on Targeted Brain Exercise Interventions.

There is growing body of research documenting

functional and structural changes in the brain resulting

from specific interventions and targeted training regimes

(e.g., Salminen et al., 2012; Enriquez-Geppert et al., 2013).

Working memory training remains controversial (Melby-

Lervag et al., 2013; von Bastian et al., 2013; Jaeggi, et al.,

2008), particularly (and not surprisingly) when it “targets”

intelligence or working-memory improvement (Redick

et al., 2013; Chooi et al., 2012). Consistent with basic

neuroscience research in demonstrating increases in

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specific sectors of prefrontal activation and decreases in

amygdala activation, research is beginning to document

the effect of explicit interventions designed to decrease

stress and promote pro-social behavior and well-being on

brain structure and function. For example, in Gordon et

al. (2013), some 2,752 users of the computer-based brain-

training program MyBrainSolutions completed a validated

assessment battery of cognitive and emotional states

before and after training. Users of the MyBrainSolutions

training program have access to 22 brain-training

exercises, each designed to train skills within one of

four key domains—thinking, emotion, feeling, and self-

regulation. The intent is to both recommend exercises

to users that will best target weaker brain circuitry and

then to enable tracking of improvements in performance

in those circuitries over time. Using statistical analysis to

measure the relationships between brain games played

and improvement in assessment scores, the scientists

were able to show that the most significant benefits

were found for those games training positivity to improve

anxiety and stress. In addition, training in self-regulation

was found to be beneficial in terms of improved memory,

attention, and executive function, as well as in reducing

anxiety and stress. Significantly, self-regulation training

was found to have beneficial consequences across all the

other cognitive and emotional domains. Note that this is

the same training program we have fully incorporated into

our system for testing at the NeuroLeadership Labs.

At this stage of development of computer-based brain

exercises, such interventions appear to enjoy greater

efficacy when combined with other tools, although it

makes identifying the brain exercise’s actual contribution

to an individual’s performance improvement more

challenging. Such interventions have been developed

to promote pro-social behavior, such as emotion

regulation, for incorporation in school curricula with the

intent to target the development of more positive social

and emotional behavior growth in K-12 school children.

Through a recent meta-analysis of 213 programs involving

more than 270,000 school children, Durlack et al. (2011)

found that participants in social-emotional learning

programs demonstrated significant gains in social and

emotional skills and performed better on standardized

measures of academic achievement, suggesting gains in

self-regulation.

In moving away from behavior to task development, recall

that we discussed earlier that a number of studies have

suggested that brain exercises may be task-specific (e.g.,

Melby-Lervag & Hulme, 2013). In an interesting study that

enlisted the involvement of the University of California-

Riverside baseball team, Deveau et al. (2014) combined

multiple perceptual-learning approaches to determine

if improvements gained from an integrated, perceptual

learning-based training program would transfer to the

playing field. The scientists assigned 19 baseball players

to complete 30 25-minute sessions of a computer-based

vision-training exercise they had developed, while another

18 team members received no training. Trained players

showed improved vision after training, had decreased

strike-outs, and created more runs. At a minimum,

these results demonstrate the transferability of benefits

from a vision-training program based on perceptual

learning principles. They provide encouragement for the

development of targeted interventions versus relying on

“spillover” benefits from video gaming in general.

Importantly, with increasing evidence that videos

games and computer-based brain exercises affect

brain function and structure, scientists are calling for

increased collaboration between the gaming industry

and brain scientists to design new, more focused games/

exercises that train the brain. The intent is to encourage

collaboration in developing exercises producing

positive effects on behavior, such as decreasing anxiety,

sharpening attention, and improving empathy (Davidson

& McEwen, 2012; Bavelier & Davidson, 2013). In addition,

a number of scientists are investigating the potential for

future interventions based on a variety of brain-based

technologies, either by themselves or in combination with

computer-based brain exercises (Zotev et al., 2014; Ruiz

et al., 2014). It is our sense that, to some degree, these

exercises may serve to provide emotion-tagged artificial

experiences that the individual’s prior social experiences

were unable to provide.

Mindfulness Practice

Our understanding of the importance of mindfulness

practice continues to grow, with recent research

demonstrating its positive effect on decision making

(Hafenbrack et al., 2013), creativity (Colzato et al., 2012),

emotion regulation (Teper et al., 2013; Creswell et al.,

2014), self-regulation (Tang et al., 2014), and general

mental and physical health (e.g., Creswell, in press).

From the beginning, Drs. Tang and Creswell have been

important contributors at the NeuroLeadership Summits

on this topic and its importance to the effective practice of

leadership. Collectively, these findings raise the possibility

that mindfulness interventions produce specific plasticity-

related alterations in brain function and structure. Of

particular concern to an individual—particularly if the

individual is a leader—successfully navigating a social

transition means efficiently and effectively reading and

comprehending social cues (Olsson et al., 2013). To this

end, supported interventions that enhance empathetic

accuracy and related neural activity are likely to prove

beneficial. It is not surprising then that mindfulness

practices emphasizing the cultivation of positive affect,

such as compassion and kindness, have received increased

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empirical attention. In fact, Hofmann et al. (2011) conclude

that mindfulness interventions oriented toward enhancing

the positive emotions of compassion and kindness do

increase positive effect and decrease negative effect. More

recently, two additional studies provide further support for

this notion, one promoting the benefits of a secularized,

analytical compassion-meditation program, cognitive-

based compassion training (Mascaro et al., 2013), and

the other a compassion-mindfulness practice led by an

experienced instructor (Klimecki et al., 2013).

In addition to better understanding their own emotions

and the emotions of others through mindfulness practice,

leaders need the self-discipline to generate results. Tang

et al. (2014) showed us that mindfulness meditation is

effective in improving brain function related to executive

attention. Although the study focused on the use of the

mindful meditation technique Integrated Body-Mind

Training (IBMT)—a powerful technique introduced here by

Dr. Tang several years ago—several forms of mindfulness

have been shown to enhance self-regulation (Davidson &

McEwen, 2012).

...mindfulness interventions oriented toward enhancing the positive emotions of compassion and kindness do increase positive effect and decrease negative effect.

Changing the Business School Paradigm

At the NeuroLeadership Labs, we have long prescribed to

the knowing (Knowledge)-doing (Rational Process)-being

(Behavior) model of leadership and personal development

(Snook et al., 2011; Hesselbein & Shinseki, 2004). We

associate IQ with knowing, RQ (Rational Quotient, a term

coined jointly by the NeuroLeadership Labs and Kepner

Tregoe) with doing, and EQ (Emotional Intelligence

Quotient) with behavior. Within relevant ranges, we have

consistently found that while RQ and EQ are positively

correlated, they are not correlated with IQ. Students are

often brought to the realization of the veracity of this

statistical relationship through the comment: “Smart

people make dumb decisions, and vice versa,” something

with which most of us would likely agree. We then add

the following additional comment: “An individual may be

hired on the basis of their IQ, but will be fired or promoted

on the basis of their RQ and EQ.” With regard to business

education, it is this latter comment that is of increasing

concern.

A number of influential works have highlighted both the

blessings (Gray et al., 2013; Yang et al., 2013) and the

curses (Rosen, et al., 2012; Turkle, 2012) of social media

and the internet. Of particular concern here is an insightful

study by Konrath et al. (2011) showing a marked decline in

empathy amongst young people, especially after the year

2000. Although research has shown a decline in social

awareness in both the long-term (Kesebir & Kesebir, 2012)

and the near-term (Twenge et al., 2013), the degree and

extent of the latter has been attributed to social media

and the Internet. According to Turkle (2012), the extensive

use of social media has led to a decline in face-to-face

interactions amongst young people. Neuroscience and

social psychology are both in support of the notion

that brain circuitries supporting social interactions

depend heavily on those face-to-face interactions for

their development (e.g., Bickart et al., 2011, 2012; Kanai

et al., 2012; Meshi et al., 2013; Stanley & Adolphs, 2013).

Others have noted a corresponding decline in attention,

focus, and self-control (Wilcox et al., 2013). As a general

summary statement of the discussions that we have had

in the four fundamental domains of NeuroLeadership in

this survey, those are important attributes for individuals,

and particularly leaders, as they make the social transitions

that we expect will be an integral part of their careers. How

concerned should business education be about these

trends in RQ and EQ, and what can they do about them?

Two hundred years ago, university libraries were small, the

curriculum was fixed, and, as a consequence, students

were required to read little more than the assigned

textbooks (Shiflett, 1994). In this sense, “knowledge” was

in short supply, and the professor played a key role in

student development. Conversely, interpersonal skill-

development opportunities were in abundance, as the

primary source of entertainment was personal interaction.

Moving ahead 200 years, we find just the opposite to

be true; “knowledge” is now in great abundance, while

technology has had a significant impact on the quality

and quantity of social interactive experiences necessary

for the neural growth of those important social circuitries.

Yet, little has changed in the way we educate young

people in today’s business schools (Bennis & O’Toole,

2005; Ghemawat, 2011).

According to McClelland (1973), once an individual is in

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a given job, “specific competencies like self-discipline,

empathy, and persuasion [are] far stronger forces in success

than a person’s ranking in academics.” McClelland makes a

strong argument for competency testing over intelligence

testing. The anecdotal evidence from the business

community is equally compelling. For example, Mr. Laszlo

Bock, the senior vice president of people operations for

Google, is widely quoted as saying, “G.P.A.’s are worthless

as a criteria for hiring, and test scores are worthless [...]

We found that they don’t predict anything” (Friedman,

2014). Goleman (2013) quotes an unnamed former head

of a major bank as saying, “I was hiring the best and the

brightest, but I was still seeing a bell-shaped curve for

success and wondering why.” Unfortunately, McClelland’s

article has been controversial among many academics,

some of which “could not grasp that doing well in their

classes had little to do with how their students would

perform once in a job” (Goleman, 2013). In one of the few

tests of this seemingly basic notion, Eisenberg et al. (2013)

found that students in a traditional academic course in

cross-cultural management showed “pronounced” effects

on the cognitive aspects of the topic, but no significant

effects on behavioral aspects. Mendenhall et al. (2013) offer

the possibility to develop relevant competencies through

the use of cognitive behavior therapy in the classroom.

While limited in this case to cultural competencies, why

not open the approach to competencies in general?

With dire predictions of the majority of business courses

to be offered online in the near future (Clark, 2014), the

corresponding declined in face-to-face interactions

suggest that behavioral shortcomings are arguably only

going to get worse. Although research has touched on

social capital concepts of trust, collective action, and

communication (Lu et al., 2013), relatively little research

has been done showing behavioral-development benefits

that can be attributed to online courses. However, as

Bavelier and Davidson (2013) suggest, with regard to

computer-based brain exercises that assist in behavioral

interventions involving social perceptions and empathy,

this is a developmental linkage that business schools

should not ignore. To meet the needs of the business

community, business education needs to move beyond

the confines of the knowledge-predominated classroom

and embrace development responsibilities in the areas of

process (doing) and behavior (being).

Conclusion

In bringing together and reflecting upon the breadth and

depth of the latest research in neuroscience, it is evident

that significant progress has been made in not only

furthering the definition of this field of NeuroLeadership,

but also in outlining its developmental responsibilities

going forward. The research continues to clarify thinking,

motivate creativity, inspire learning, enhance productivity,

and promote well-being—having the impact we all

anticipated just eight short years ago when the discipline

was first considered. As we have said before: Much work

has been done; much is yet to begin. As quickly as we

discover answers, we just as quickly uncover more

intriguing questions.

...technology has had a significant impact on the quality and quantity of social interactive experiences necessary for the neural growth of those important social circuitries.

Still, with the growing recognition of emotion’s

indispensable role in personal and leadership

development, it is becoming increasingly evident that

social psychologists, neuroscientists, OB and leadership

theorists, and leadership practitioners need to be working

together more closely to break down terminology barriers

where they are needlessly inhibiting advancements in

new thinking and applications. We need to embrace

the seemingly endlessly evolving technology and begin

developing intervention tools and techniques for use in

both our classrooms and our workshops. A functional

co-mingling of concepts ranging from definitions to

terminology to functioning models amongst these

disciplines will serve to focus the usefulness of those

tools and have the beneficial effect of accelerating “time-

to-market” for working practitioners. With what we see

in the research pipeline and what we anticipate, there

is an enormous body of interesting and compelling

work forthcoming. In light of this anticipation and our

expectations, perhaps the best advice we can give may

well be: “Now may be a good time to take a neuroscientist

to lunch.” Except this time, we might suggest considering

bringing along a video gamer.

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