ArticlePDF Available

AIM: An Integrative Model of Goal Pursuit



Content may be subject to copyright.
An Integrative Model
of Goal Pursuit
by Elliot T. Berkman
and David Rock
© NeuroLe adership Ins titute 2014 For Permis sions, email jo urnal@neurol eadership.o rg
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:
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 aliates 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 September 2014.
Elliot T. Berkman University of Oregon, Department of Psychology
The NeuroLeadership Institute
Corresponding author:
Department of Psychology
1227 University of Oregon
Eugene, OR USA 97403-1227
David Rock The NeuroLeadership Institute
© NeuroLe adership Ins titute 2014 For Permis sions, email jo urnal@neurol eadership.o rg
Eective goal pursuit is integral to organizational success. Because of this, a large number of models
have been developed that describe the process of goal pursuit in whole or part. However, these models
often have little overlap with one another, making it unclear how they relate to each other, and they do
not incorporate emerging evidence from neuroscience about “brain-friendly” modes of goal pursuit.
We solve these two problems by proposing the AIM framework of goals, a neurally-informed model
which divides the goal pursuit process into three parts—Antecedents, Integration, and Maintenance.
This framework organizes existing models by describing where in the overall goal pursuit process they
fit (e.g., the SMART model is about goal setting, which is an antecedent), and thus has the distinct
advantage of being able to integrate across existing models in a meaningful way. Because it is based in
neuroscience, the framework can also serve as a bridge between the neuroscience and organizational/
leadership fields over which relevant knowledge about brain functioning can be imported into the
study of goal pursuit for organizations. In this paper, we briefly review popular models of goals and
describe where they fit within the AIM framework, describe each step of AIM and the corresponding
current neuroscience knowledge, and then discuss how the AIM framework can set an agenda for
future organizational and neuroscience research in this area. This paper is written to be equally relevant
to and useful for those pursuing their own goals as well as those facilitating goal pursuit in others.
© NeuroLe adership Ins titute 2014 For Permis sions, email jo urnal@neurol eadership.o rg
AIM: An Integrative Model of Goal Pursuit
by Elliot T. Berkman and David Rock
Goals are a critical part of organizational life. In a sense,
organizations are giant collections of interconnected
goals. There are goals set for tangible issues such as
revenues, profits, market share, or new products, which
are reported out in public companies, with significant
impact on share price movements based on whether or
not these goals are achieved. There are also intangible
goals around consumer sentiment, service levels, or
employee engagement. Organizations set a wide range
of goals across a variety of metrics, from multi-year goals
to annual goals, from goals for a quarter to goals for a
month, week, or even for the day. People at nearly every
level in organizations are managed against these goals
in systems that determine their career prospects and
compensation. Leaders play a critical dual role in goal
pursuit in organizational contexts, both facilitating goal
pursuit in others and providing examples of positive goal
progress as they strive toward their own goals in view of
others in the organization. With so much goal setting
going on, one might think that organizations would be
passionately following the research on eective goal
setting and pursuit and using that research to tweak
their organizational strategy for goal achievement, the
same way a technology company might closely follow
developments in the use of silicon. However, this does not
appear to be the case.
Most organizational goal setting processes are based
on ideas that are decades old, with little updating from
new findings from psychology or neuroscience. If your
organization promotes “SMART” goals, you might be
interested to know this idea was published in the 1980’s
(Doran, 1981), and the science of goal setting has advanced
substantially since that time. Thus, the main purpose of
this paper is to review recent scientific developments,
particularly in neuroscience, as they relate to popular
models of goals. As part of this review, we will present
an overarching framework for goals that accommodates
both the existing models and the new evidence.
There is one peculiar feature of goals that makes them
simultaneously dicult to pursue and to study: Integration.
Success at a goal is caused by the integration of a
collection of small victories, carefully orchestrated, across
both physical and mental time and space. Before we can
ask how to be successful at goal pursuit, whether the goal
is increased sales or improved personal confidence, we
must first ask what a goal even is. Is the goal the small
steps, the desired endpoint, or the path that took you
there? Thinking about goal pursuit in this way introduces a
useful metaphor for goal pursuit—the road trip. Just like a
goal, a road trip is not defined merely by its destination or
its pit stops or even the roads travelled en route; just like a
goal, a road trip is an emergent gestalt that is greater than
any one or all of the parts. It follows, then, that to study
goals with the ultimate purpose of understanding and
improving how humans pursue goals, one must consider
the entire “road trip” in part and in whole, and particularly
how those come together in synthesis. Here, we propose
a new framework for thinking about goals that recognizes
their inherently multi-component and integrative nature.
This framework provides a means to (a) organize existing
models of goals (e.g., SMART) and point out connections
between them, (b) to highlight places where neuroscience
© NeuroLe adership Ins titute 2014 For Permis sions, email jo urnal@neurol eadership.o rg
can provide insight into goal models, and (c) to guide
future research and leadership on goals. We developed
the AIM framework to be equally relevant to and useful for
those pursuing their own goals as well as those facilitating
goal pursuit in others.
In the first part of this paper, we briefly review current
models of goal pursuit (see Moskowitz & Grant, 2009
for a more comprehensive guide), explaining how each
model fits into our new overarching framework. Next,
we describe the three components of the framework,
illustrating each with empirical research from psychology
and neuroscience. Finally, we conclude with a discussion
of future directions for the science of goals that are
unlocked using our framework and important open
Existing Goal Pursuit Models
A proper review of existing models of goal pursuit could fill a
book—and has several times over (e.g., Aarts & Elliot, 2012;
Locke & Latham, 1990; Moskowitz & Grant, 2009). Rather
than to reiterate what has already been written about goal
pursuit models, our purpose in this section is to explain
how these existing models can be encompassed within a
larger framework that also includes recent neuroscientific
developments and why any individual model has limited
power to explain success or failure in goal pursuit. Thus,
the word “integrate” in this paper serves a dual purpose,
referring both to the integration of various components
of goal pursuit themselves, and the integration of current
goal models, into a broad and unified framework. We
begin by mapping a few existing models onto the road
trip framework which maps onto the three phases of a
road trip. Though the list of models reviewed here is by
no means exhaustive, we believe it is representative of
current ideas in leadership and psychology and serves to
illustrate the usefulness of our framework for organizing
current and future thinking in this area.
Before the Trip Begins: Goal Setting
Many popular models of goals focus mainly on the
first step in the process—goal setting. In the road trip
metaphor, goal setting involves planning the route,
packing the gear, and making sure the vehicle is up to
snu to make the journey. Models of goal setting provide
insight about the structure of the goal as a mental object—
how it should be defined, what it should contain, and so
forth. Examples include SMART goals, which are Specific,
Measurable, Attainable, Realistic, and Time-bound (Doran,
1981; Locke & Latham, 2006), and the GROW model of
coaching, which includes Goal setting, Reality checking,
development of Options, and What-when-whom
questions that specify the conditions of action (Gallwey,
2000, attributed to John Whitmore). Each of these
models provides excellent guidance about what a goal
should be in a cognitive or informational sense. At the goal
formulation stage, they address what kinds of information
a useful goal should contain (e.g., dates, specific actions)
and what kinds of information should be associated with it
(e.g., alternative options, outcomes). This approach is the
predominant model in how organizations educate their
employees to set goals across both day-to-day as well as
annual performance management systems. Goal setting,
according to these models, is about the mental work
you do to map out and prepare for your journey before
it begins.
The greatest
strength of goal-
setting models
is their focus on
the cognitive/
aspects about goals,
but that upside
comes at the cost
of neglecting the
emotional and
motivational parts
of goals.
As important as the information provided by goal setting
models is, no one—not even the progenitors of those
models—claims that there is nothing more to goal pursuit
than goal setting. The greatest strength of goal-setting
models is their focus on the cognitive/informational
aspects about goals, but that upside comes at the cost
of neglecting the emotional and motivational parts of
goals. Neuroscience in particular can contribute to these
models because of the increasingly detailed picture it
paints of motivation. Another limitation attached to the
cognitive focus of goal setting models is their silence on
what might be called “human factors” in goal setting. The
SMART model is ideal for teaching a robot how to set a
goal; just input the right parameters and set it o to go.
But humans are another case because we need more
© NeuroLe adership Ins titute 2014 For Permis sions, email jo urnal@neurol eadership.o rg
than just information; humans are sensitive to how goals
are framed and the subtleties of how a given goal relates
to other aspects of our personalities. Though the SMART
model has been updated to some degree to reflect some
of these concerns (for example, by adding “Appealing” to
the list of A terms to acknowledge the power of attractive
marketing in goal setting), it still provides guidance on only
one aspect of goal pursuit. Goal setting is the beginning,
not the end, of the goal pursuit process. We’ve only just hit
the road. Despite this, goal setting is sometimes the end
of the road for how organizations think about goal pursuit.
Hitting the Road: Goal Striving
In contrast to models of goal setting reviewed above,
other models engage mostly with the actual process of
taking action toward the goal, which we’ll refer to as goal
striving. Following the road trip metaphor, goal striving
involves navigating the route to the destination, managing
roadblocks, and deciding when to stay on the road and
when to take a pit stop. This aspect of goals is where
theoretical models from psychology shine, often under
the rubric of self-regulation, including self-discrepancy
theory (Higgins, 1987), action control theory (Carver
& Scheier, 1980), and goal systems theory (Kruglanski,
Shah, Fishbach, Friedman, Chun, & Sleeth-Keppler, 2002).
Each of these theories oers a model and, sometimes,
practical guidance about how to get from the start to the
finish of a known (i.e., already set) goal. They engage with
topics such as the perceived distance from the goal, the
role of emotion in guiding action, and how multiple goals
compete and cooperate when pursued simultaneously.
Unlike goal-setting theories, they posit an explicit role for
motivation and describe where it comes from and how it
can be enhanced.
In the road trip metaphor, these models of goal striving
tell you which roads to take, when to accelerate or brake,
and which maps are the best. This is exactly the kind of
information you need when you’re behind the steering
wheel. However, when you’re behind the steering wheel,
you also tend to lose sight of the overall journey because
you’re focused on the goal immediately ahead. Some
of these models, notably Carver and Scheier’s (1980)
action control theory, address this scope-of-perspective
problem by introducing the idea of a goal hierarchy, or an
organization of goals ranging from tangible and near-term
on one end, and abstract and long-term on the other.
Recent advances in neuroscience have unveiled how
the brain processes goal hierarchies, which in turn have
yielded important insights into more eective integration
across their levels. Other research suggests a critical
role for self-processing during goal striving, which has
expanded the existing psychological theories in new and
unexpected ways with direct implications for improving
goal striving. Still, like those of goal setting, models of goal
striving on their own do not account for how goals lead
to lasting change. For that, we turn to another topic: Goal
Cruise Control: Goal Maintenance
Getting onto the highway and headed in the right
direction can be hard. Goal striving is cognitively
eortful, in the sense that it requires precious and limited
resources such as attention, working memory, and self-
regulation. Over the long haul, having to rely upon those
powerful yet finite capacities for goal striving is a road
that leads only to failure; an achievable goal is one that
can be sustained using less eortful, more “automatic”
processes such as habit. Psychologists have studied
habit for over a hundred years (James, 1890) and have
made some important discoveries—particularly about
the critical role of reward (Berridge & Robinson, 2003)
and learning (Shirin & Schneider, 1977)—and new data
from neuroscience have sharpened those insights even
further. Also, and particularly relevant to organizational
settings, social psychologists have recently taken up the
question of whether and how the social context can
support goal maintenance. This is particularly important
given how deeply social most work has become, where
teams of people now need to collaborate more than ever
to achieve many organizational goals. The results from
these studies underscore the powerful eect of the social
environment on habit formation and goal maintenance,
and as such demand to be included in any overarching
framework of goal pursuit.
Goal setting is the
beginning, not the
end, of the goal
pursuit process.
Taken to ge th er, the facts that existing models of goals
focus on only one of the three phases of goal pursuit
described above and insights from neuroscience have not
yet been integrated into most of the models indicate that it
is time to change the way we think about the goal-pursuit
process. We need a model that integrates previous work
into a unified framework and that accounts for research in
neuroscience that is illuminating previously dark corners
of the scientific study of goals at an accelerating pace.
Over the last several years, we have developed the AIM
model, a neurally-informed and integrative model of goal
setting, striving, and maintenance, to fulfill exactly this
© NeuroLe adership Ins titute 2014 For Permis sions, email jo urnal@neurol eadership.o rg
An Integrative Framework for Goal Pursuit:
The AIM Model
Our model organizes existing theories into a unified
framework and moves beyond them by importing
knowledge from neuroscience to each phase of goal
pursuit. The model is called AIM, which stands for
Antecedents, Integration, and Maintenance (Figure 1).
The AIM framework reflects the three phases of goal
pursuit reviewed above: Goal setting is an antecedent,
goal striving is where integration happens, and habit
formation is important for maintenance. The AIM model
is innovative and significant for two main reasons: First,
it integrates neuroscience evidence into each phase of
goal pursuit, and thus uses brain function as one way of
unifying across the dierent phases of goal pursuit. This is
particularly important right now, given the current dearth
of neuroscience research on goal pursuit, because the AIM
model will serve as a tool that researchers and practitioners
can use to map existing neuroscience knowledge onto
current and future models of goal pursuit. Second, the
role of motivation is imbued throughout the model. At
each phase of the goal-pursuit road trip, motivation plays
a central role, sitting in the front seat, helping the driver
navigate the road for the entire journey. We view AIM
as a way of organizing knowledge about goals (e.g., the
SMART model for goal setting, reward learning theories
of habit for goal maintenance) into a unified framework,
which in turn enables scientists and practitioners to
easily identify areas where insights from neuroscience
are relevant and also where further research is needed.
In the following sections, we describe how new research
from neuroscience and psychology build upon existing
models at each stage, and what that means for eective
goal pursuit.
Antecedents: Essential Luggage for Any Road Trip
We noted above that existing models of goal setting focus
on the cognitive aspects of goals such as what pieces of
information they should specify and be linked with. Those
models are great as far as they go, but are “cold” in that they
do not contain any emotional or motivational elements.
The “hot” parts are what make a goal exciting and which
sustain our focus through the rough patches and for the
long haul. Approach-and-avoidance motivation (Gray,
1970) has long been considered one of the most powerful
ways to heat up the motivational temperature of goal. The
general idea is that there are two systems for motivation:
One that is sensitive to reward (the approach system) and
another that is sensitive to punishment (the avoidance
system). Though we all have both of these systems, and
indeed need each to survive, there are dierences from
person to person in the relative strength of one system
compared to the other. One person might be more of an
“approach” person, motivated by the desire for reward,
success, or gain, whereas another might be more of an
“avoidance” person, motivated by the fear of punishment,
failure, or loss. As you might expect, setting a goal that is
matched to a person’s trait level of motivation (i.e., more
Figure 1. The AIM model of goal pursuit.
© NeuroLe adership Ins titute 2014 For Permis sions, email jo urnal@neurol eadership.o rg
approach or more avoidance) increases the likelihood of
that goal being successful. For example, people who are
approach-motivated are more likely to floss after seeing
messages that flossing promotes good breath, whereas
people who are avoidance-motivated are more likely to
floss after seeing messages that not flossing causes bad
breath (Mann, Sherman, & Updegra, 2004). Identifying
a person’s trait level of motivation (e.g., using an existing
measure; Carver & White, 1994) and framing the goal to
match it is a strong and evidence-based way to increase
motivation. While not a hard and fast rule, jobs involving
potential gains such as sales will tend to be filled by
those who identify as approach-motivated, whereas jobs
involving mitigating losses, such as legal or compliance
jobs, may be filled by those who identify as avoidance-
The neuroscience behind approach-avoidance
motivation provides even more clues about how to
leverage motivation to enhance goal setting. One of the
earliest findings in motivation neuroscience is that there
is a large hemispheric asymmetry in the prefrontal cortex
(PFC) between approach- and avoidance-motivational
states: Individuals who are approach-motivated (either as
an enduring trait or as a temporary state) show greater
left (than right) PFC activation, whereas individuals who
are avoidance-motivated (again, either as a trait or state)
show greater right (than left) PFC activation (Coan &
Allen, 2004; Sutton & Davidson, 1997). What is fascinating
is that this neuromarker of motivation tracks the goal
value of an action regardless of the intrinsic value of
the action (Berkman & Lieberman, 2010); approaching
creates left-lateralized PFC activation even if what is being
approached is unpleasant, and avoiding creates right-
lateralized PFC activation even if what is being avoided
is otherwise tempting. Put another way, PFC asymmetry
supports goal actions even, and perhaps especially, when
they work against the path of least resistance. The fact
that PFC asymmetry is stable over time within a given
person indicates that people have a preferred direction of
travel along that path, so setting goals so they flow in the
right direction for an individual can help motivate the goal
to stay on track for the long journey.
The notion of tailoring goals to be consistent with trait
motivation is one way to make that goal more self-
relevant, or linking the goal to a person’s enduring sense
of who they are. Neuroscience has recently uncovered an
interesting overlap between the brain systems involved
in thinking about oneself and particularly about one’s
goals (Cunningham, Johnsen, & Waggoner, 2011) and
value (Hare, Camerer, & Rangel, 2009). One region in
particular—the ventromedial PFC, or vmPFC—is active
when contemplating the value of something (a purchase
or decision) and also when thinking about one’s own
traits, preferences, and identity (Kelley, Macrae, Wyland,
Caglar, Inati, & Heatherton, 2002). It is not hard to make
the logical leap from there to the prediction that, at least
to the brain, the self is rewarding. Indeed, theoretical
perspectives from neuroscience are already beginning to
make this case (e.g., Schmitz & Johnson, 2007). Another
way of thinking about this is that goals that have achieved
the status of being highly self-relevant will be rewarding
intrinsically because of their close connection to the self.
For instance, a person who identifies strongly as being
budget-conscious may be able to overcome the desire
to spend money on fun but not on unnecessary oce
equipment, because the temptation is counterweighed
by the reward of reinforcing his or her identity.
The notion of
tailoring goals to
be consistent with
trait motivation is
one way to make
that goal more
self-relevant, or
linking the goal to
a person’s enduring
sense of who they
One final “hot” element that has been missing from models
of goal setting to date is stickiness,” or how to set goals
that will always be at the front of your mind and on the tip
of your tongue. Stickiness is important because people are
busy and have only limited attentional resources—a goal
that does not stick firmly in mind can easily be washed
away in the tidal wave of other priorities and distractions.
So what is the best way to leverage goal setting to make
goals sticky? In a word: Tangibility. Goals should be
related to concrete objects and manifest actions as much
as possible. Though our brains are capable of abstract
thought (which will be relevant in the following section),
that kind of thinking requires eort and concentration,
and is not our default way of thinking. Neuroscience
has supported this idea by providing evidence that new
concepts (e.g., goals) that are linked closely to action are
more easily learned, recalled, and acted upon compared
© NeuroLe adership Ins titute 2014 For Permis sions, email jo urnal@neurol eadership.o rg
to concepts that are not linked to action, primarily because
tangible goals activate associated motor and object
identification regions in the brain (Jirak, Menz, Buccino,
Borghi, & Binkofski, 2010; Kuhn, Keizer, Rombouts, &
Hommel, 2011; McNamara, Buccino, Menz, Glascher,
Wolbers, Baumgartner, et al., 2008). In the words of the
psychologist Susan Fiske (paraphrasing the inimitable
William James), “Thinking is for doing” (Fiske, 1992). The
lesson for goal setting is to craft your thinking to resemble
doing as much as possible.
Goals should be
related to concrete
objects and manifest
actions as much as
Integration: When the Rubber Meets the Road
Setting SMART and motivation-savvy goals is only the first
step. The next phase of goal pursuit is striving to attain
those goals in a process we call integration. The critical
part of integration is to maintain cohesion between the
near-term, concrete actions of goal striving and the long-
term, abstract objectives of the goal. A useful tool to think
about integration is the idea of a goal hierarchy, where
smaller, concrete actions are embedded within larger,
abstract goals (Carver & Scheier, 1980). Arranging a goal
in this way gives it structure and, because of that, can be
incredibly helpful when roadblocks crop up.
For example, suppose I have the goal of increasing my
productivity by 10% this quarter. I can locate that goal
within a hierarchy by identifying the higher-order goals
above it and the lower-order goals below it by asking
two critical questions: “Why” and “how,” respectively.
Why do I want to increase productivity by 10%? Because I
want to be a good employee (a higher-order goal). How
can I increase productivity by 10%? By working an extra
hour each day (a lower-order goal). I can dig further up
or down by repeating this process: How can I work an
extra hour each day? By starting 30 minutes sooner and
staying an extra 30 minutes later. Why do I want to be a
good employee? Because I want to feel like a competent
person. Try engaging in this process for one of your goals
and see what happens when you elaborate on your goal
by embedding it in a hierarchy.
Several useful properties emerge from these hierarchies.
Foremost, notice how motivation lives at the higher levels.
We are motivated by the “why” of goals and their broader
implications, usually boiling down to either achievement/
competency or aliation/belongingness (McClelland,
1987). These kinds of motivations can also be viewed
through the lens of the SCARF model (Rock, 2008). For
example, a feeling of achievement (Status), competence
(Autonomy), or belongingness (Relatedness) all activate
the primary reward network of the brain, which means
they impart an intensely rewarding experience similar to
physical pleasure. In short, the “why” of goals may be
deeply intrinsically rewarding, especially when this “why”
connects to social needs and motives.
Conversely, the “how” of goals contain the details of their
implementation but are otherwise devoid of inherent
meaning. The implication of this is critical: Success at a
goal requires both a will and a way, both the why and the
how. Also, note how each higher-order “why” goal can be
achieved through many dierent “how” goals. There are
many dierent ways to be a competent person, to be a
good employee, and to increase productivity. This point
highlights another critical feature of the goal hierarchy,
which is that flexible and fluid movement up and down
within it (using why and how questions) is absolutely
essential. If at first you don’t succeed, try again—by moving
up the hierarchy asking “why,” generating a new plan by
asking “how,” and then implementing that new course of
action. Moving up and down the hierarchy is like taking
an alternative route when your original course is blocked,
one that still gets you to where you ultimately want to be.
Success at a goal
requires both a will
and a way, both the
why and the how.
Neuroimaging studies of goal hierarchies have revealed
a stunning insight into goal pursuit. The brain systems
for thinking about “why” and “how” are entirely separate
(Spunt, Falk, & Lieberman, 2010), and may in fact be
mutually inhibitory (Fox, Snyder, Vincent, Corbetta, Van
Essen, & Raichle, 2005; Sprengler, von Cramon, & Brass,
2009). “Why” thinking engages networks for intention
and mental state reasoning, whereas “how” thinking
engages in networks for action preparation and object
identification. This finding echoes the conclusion from
the psychological literature that both “how” and “why”
thinking are required for successful goals, but it goes
beyond what was previously known in suggesting that
they cannot both be activated simultaneously. More
specifically, they cannot be activated within the same
person at the same time. One major implication is that
© NeuroLe adership Ins titute 2014 For Permis sions, email jo urnal@neurol eadership.o rg
another person’s perspective (e.g., a leader or coach) can
be helpful in maintaining both kinds of thinking for a given
goal. At the least, one critical skill for goal pursuit in the
long run is the ability to switch adaptively between “why”
and “how” modes of thinking to enable flexible movement
throughout a goal hierarchy.
A final insight into goal integration comes from the study
of the self. We wrote above that “why” thinking engages
brain systems that are otherwise involved in mental state
attribution. “Why” is about intentions, and one of the
central regions for thinking about intentions—the medial
PFC—is also central to thinking about the self (and directly
adjacent to the vmPFC described above; Amodio &
Frith, 2006). There is perfect convergence here between
neuroscience data and psychological theory: The self, writ
large, including one’s identity, preferences, and long-term
aspirations, is the ultimate answer to every “why” question
(Carver & Scheier, 1980). The motivation for any action,
when viewed from high enough in the goal hierarchy, is
to move closer to an ideal version of oneself. We pursue
goals, fundamentally, to live up to standards that we and
others set for ourselves, to become dierent and better
people (Higgins, 1987). It is for this reason that striving
to attain goals—aside from attaining them—is related to
overall well-being (Sheldon & Elliot, 1999). Drawing from
all of this, we suggest that the idea of “self-concordance”,
or the degree to which a goal is seen as fulfilling core
values of an individual, will impart to that goal sustaining
motivation because actions toward that goal (i.e., the
lower-level “hows”) will always be integrated with the
ultimate “why”—the self.
Maintenance: Cruise Control and Staying the Course
The final leg of the goal pursuit journey is maintaining
the behavior change that was earned during goal striving.
Prevailing knowledge on how to do that mostly involves
habit and automaticity: Repeat something enough times,
and reward it consistently, and it will become routine
and, importantly, less eortful. Consider learning to drive.
When you first learn, you need to consciously think about
how fast to turn the steering wheel, how hard to step on
the pedals, and how to sequence those actions to get the
car where you want it to go. Through this process, some
actions are reinforced because they move the car in the
desired direction and others are not, and after a while
you can drive eectively while singing along to the radio
or chatting with your passengers without thinking about
driving at all.
Goals can become automated in this way, too, and some
elegant neuroscience has specified how. A tiny part of the
brain’s reward system, the striatum, is involved in building
associations between actions and rewards (Liljeholm
& O’Doherty, 2012). As those associations are built,
activation within that region migrates from the anterior
(front) to the posterior (back) aspects of the striatum,
eectively handing o control of actions from a goal-
directed action system to more habit-based action system
(Jankowski, Scheef, Huppe, & Boecker, 2009; Tricomi,
Balleine, & O’Doherty, 2009). Critically, the habit-based
action system is triggered by learned cues more than by
rewards, so one of the key lessons for goal maintenance
is to be deliberate about which cues are paired with your
goal as you work toward it, then use those cues to launch
the habit system into action during goal maintenance. deliberate
about which cues
are paired with
your goal as you
work toward it,
then use those
cues to launch the
habit system into
action during goal
Research from our group has further elucidated the brain
changes to accompany the transition from eortful to
automatic goal striving. We wanted to test the boundaries
of reward learning to see if they would extend to even
highly eortful parts of goal pursuit such as self-control.
To do this, we had our participants practice one kind of
self-control (response inhibition) for three weeks, and we
measured the change in their brain activity from before to
after. What we found was fascinating because it painted
a new picture of how the brain learns to automate self-
control. Instead of merely getting stronger with practice,
the brain activity associated with self-control shifted earlier
in time to peak slightly before self-control was actually
required (Berkman, Kahn, & Merchant, 2014). This result is
in line with a new, neurally-informed model of how self-
control works called dual mechanisms of control (Braver,
2012), which describes the eects of practice or expertise
in terms of shifts in time from later, reactive control to
earlier, proactive control. The key advantage of proactive
control derives from the fact that it’s far easier and more
eective to engage self-control ahead of time rather than
wait until it’s absolutely necessary, much in the same way
© NeuroLe adership Ins titute 2014 For Permis sions, email jo urnal@neurol eadership.o rg
that it’s easier to stop your car by slowing down at the
yellow light rather than slam on the brakes when the light
turns red. The lesson for goal maintenance is to learn
how to detect cues or situations that are the equivalent
of yellow lights for goal pursuit that signal when to slow
down, pull over, or turn on to a new road. This is especially
important in organizational life, when people will often be
cognitively depleted from lack of sleep, chronic stress, or
simply dealing with too many distractions. In this instance,
self-regulation after the fact may well fail due to limited
resources for regulating.’s far easier and
more eective to
engage self-control
ahead of time
rather than wait
until it’s absolutely
From a broader perspective, the insights from
neuroscience about goal maintenance are encapsulated
in the idea that goal maintenance is highly sensitive
to the context. What do we mean by context?” In our
inclusive definition, context includes not just the physical
world with its cues and nudges for action, but also the
intra-psychic milieu of one’s own habits of thought
and, critically, the interpersonal social environment. The
social world provides one of the most important and
unexplored forces that maintain or derail goals for the
simple reason that other people are powerful, perhaps the
most powerful, contextual influences on us (Lieberman,
2013). Preliminary research has begun to explore exactly
how, for example by illustrating how “instrumental others
can help us achieve our personal goals when we draw
closer to them (Fitzsimons & Shah, 2008), and that even
thinking about becoming closer to an instrumental other
can make us feel like we’re making progress toward our
goals (Slotter & Gardner, 2011). An elegant study using
electroencephalography found that closely watching
others—but not strangers—make a mistake on a learning
task creates the same neural signature in our brains
as would making that mistake ourselves (Kang, Hirsh,
& Chasteen, 2010). The lesson here is simple: Seek out
and engage with people who will help with your goals.
The social environment you build can help you set better
goals, learn more from your mistakes and those of others,
and sustain your eorts on the long journey toward your
Conclusions and Future Directions
We have described the AIM framework of goal pursuit. The
framework organizes current thinking about goals into
antecedent, integration, and maintenance phases, and
leverages new knowledge from neuroscience to form a
deeper and more comprehensive understanding of goals.
The model also emphasizes the overlooked importance
of trait motivation in goal setting, self-processes in goal
striving, and automaticity and social context in goal
maintenance. The goals of the AIM framework are twofold:
to provide an integrated account of the entire goal pursuit
process that recognizes its heterogeneous phases and the
various processes that are relevant to each, and to import
emerging insights gained from the study of the human
brain to the study of human goal pursuit. As such, we view
the AIM framework as merely the beginning of the work
that needs to be done in this area.
The AIM model also suggests some exciting opportunities
for research on the horizon. We’ll hint at a few here, and
encourage the reader to think creatively about the AIM
model and how it might be approached in new ways using
neuroscience. First, consider the antecedents to a journey
into unknown territory. One important planning step is
to imagine what potential hazards might be on the road
ahead and to plan for them to the extent possible. The
psychological name for that plan is an implementation
intention, or a preconceived if-then statement that pairs
a particular eventuality with a specific action to deal with
it (Gollwitzer, 1999). Neuroscience has only just begun to
reveal how that kind of future thinking works and why it
is valuable (Peters & Buchel, 2010), and implementation
intentions have never been applied systematically to goal
maintenance. Second, we highlighted the importance
of maintaining integration between higher-level “why”
motives and lower-level “how” actions, but also noted
that the brain networks that implement “how” and why”
thinking may be mutually antagonistic. However, other
recent evidence has revealed a surprising amount of
neuroplasticity in adulthood as a function of “brain training”
interventions (Bryck & Fisher, 2012). Would it be possible
to develop the ability to maintain both “how” and “why”
thoughts simultaneously? We know which neural circuits
to target, and the upside of improving that ability would be
immense. And finally, some of the research we described
hinted that even high-level functions such as self-control
could become automated under the right conditions. Can
other complex capacities, ones that usually feel “eortful
or mentally taxing, become routinized as well? What
about the entire goal pursuit process? There are inklings in
the literature that this might be the case (Custers & Aarts,
2010), but the full extent of the power of habit learning for
sophisticated behaviors is unknown.
© NeuroLe adership Ins titute 2014 For Permis sions, email jo urnal@neurol eadership.o rg
There are far more questions than answers, but we don’t
want to leave you with a sense that nothing has been
accomplished. Science has already yielded considerable
knowledge about goals that proers invaluable wisdom
and makes models such as the AIM model possible in
the first place. We hope that AIM will serve to sharpen
that knowledge and make it even more relevant, even as
it continues to develop. It remains to be seen whether
approaches fitting within the AIM framework can address
previously intractable problems. Indeed, one of the
central purposes of AIM is to provide ways to organize
new insights about goal pursuit and identify how those
insights connect (or don’t) to the current understanding.
Our goal is to help organizations sharpen the eects of
one of the most central tools in business by updating their
knowledge about goal setting with fresh insights from
psychology and neuroscience in a coherent and hopefully
“sticky” form. The journey has already been worthwhile,
and it has only just begun.
© NeuroLe adership Ins titute 2014 For Permis sions, email jo urnal@neurol eadership.o rg
Aarts, H., & Elliot, A. J. (2012).
Goal-Directed Behavior.
W. Kruglanski & J. P. Forgas, Eds.). New York: Psychology
Amodio, D. M., & Frith, C. D. (2006). Meeting of minds:
the medial frontal cortex and social cognition.
Reviews Neuroscience, 7
(4), 268 –277.
Berkman, E. T., Kahn, L. E., & Merchant, J. S. (in press).
Training-induced changes in inhibitory control network
The Journal of Neuroscience.
Berkman, E. T., & Lieberman, M. D. (2010). Approaching
the bad and avoiding the good: Lateral prefrontal cortical
asymmetry distinguishes between action and valence.
Journal of Cognitive Neuroscience, 22
(9), 1970–1979.
Berridge, K. C., & Robinson, T. E. (2003). Parsing reward.
Tre nds i n N eu ros cie nce s, 26
(9), 507–513.
Braver, T. S. (2012). The variable nature of cognitive
control: a dual mechanisms framework.
Trends in
Cognitive Sciences, 16
(2), 105–112.
Bryck, R. L., & Fisher, P. A. (2012). Training the brain:
Practical applications of neural plasticity from the
intersection of cognitive neuroscience, developmental
psychology, and prevention science.
The American
Psychologist, 67
(2), 87–100.
Carver, C., & Scheier, M. (1980).
On the Self-Regulation of
New York, NY: Cambridge University Press.
Carver, C., & White, T. L. (1994). Behavioral inhibition,
behavioral activation, and aective responses to
impending reward and punishment: The BIS/BAS scales.
J Pers Soc Psychol, 67
(2), 319–333.
Coan, J. A., & Allen, J. J. B. (2004). Frontal EEG
asymmetry as a moderator and mediator of emotion.
Biological psychology, 67
(1-2), 7–49.
Cunningham, W. A., Johnsen, I. R., & Waggoner, A.
S. (2011). Orbitofrontal cortex provides cross-modal
valuation of self-generated stimuli.
Social Cognitive and
Aective Neuroscience, 6
(3), 286-293.
Custers, R., & Aarts, H. (2010). The unconscious will:
How the pursuit of goals operates outside of conscious
Science, 329
(5987), 47–50.
Doran, G. T. (1981). There’s a S.M.A.R.T. way to write
management’s goals and objectives.
Review, 70
(11), 35–36.
Fiske, S. T. (1992). Thinking is for doing: Portraits of social
cognition from daguerreotype to laserphoto.
Journal of
Personality and Social Psychology, 63
(6), 877–889.
Fitzsimons, G. M., & Shah, J. Y. (2008). How goal
instrumentality shapes relationship evaluations.
of Personality and Social Psychology, 95
(2), 319–337.
Fox, M. D., Snyder, A. Z., Vincent, J. L., Corbetta, M., Van
Essen, D. C., & Raichle, M. E. (2005). The human brain
is intrinsically organized into dynamic, anticorrelated
functional networks.
Proceedings of the National
Academy of Sciences, 102
(27), 9673–9678.
Gallwey, T. (2000).
The Inner Game of Work.
New York:
Random House.
Gollwitzer, P. M. (1999). Implementation intentions:
Strong eects of simple plans.
American Psychologist, 54
(7), 493–503.
Gray, J. A. (1970). The psychophysiological basis of
Behaviour Research and
Therapy, 8
(3), 249–266.
Hare, T. A., Camerer, C. F., & Rangel, A. (2009). Self-
control in decision-making involves modulation of the
vmPFC valuation system.
Science, 324
(5927), 646–648.
Higgins, E. T. (1987). Self-discrepancy: A theory relating
self and aect.
Psychological Review, 94
(3), 319–340.
James, W. (1890).
New York: Henry Holt & Co.
Jankowski, J., Scheef, L., Hüppe, C., & Boecker, H. (2009).
Distinct striatal regions for planning and executing novel
and automated movement sequences.
NeuroImage, 44
(4), 13691379.
Jirak, D., Menz, M. M., Buccino, G., Borghi, A. M., &
Binkofski, F. (2010). Grasping language—A short story
on embodiment.
Consciousness and Cognition, 19
Kang, S. K., Hirsh, J. B., & Chasteen, A. L. (2010). Your
mistakes are mine: Self-other overlap predicts neural
response to observed errors.
Journal of Experimental
Social Psychology, 46
(1), 229–232.
Kelley, W. M., Macrae, C. N., Wyland, C. L., Caglar, S., Inati,
S., & Heatherton, T. F. (2002). Finding the self? An event-
related fMRI study.
Journal of Cognitive Neuroscience, 14
(5), 785–794.
Kruglanski, A. W., Shah, J. Y., Fishbach, A., Friedman, R.,
Chun, W. Y., & Sleeth-Keppler, D. (2002). A theory of goal
Advances in Experimental Social Psychology, 34
(1), 331–378.
Kühn, S., Keizer, A. W., Rombouts, S. A., & Hommel, B.
(2011). The functional and neural mechanism of action
preparation: Roles of EBA and FFA in voluntary action
Journal of Cognitive Neuroscience, 23
Lieberman, M. D. (2013).
Social: Why Our Brains Are
Wired to Connect.
New York: Crown Publishers.
© NeuroLe adership Ins titute 2014 For Permis sions, email jo urnal@neurol eadership.o rg
Liljeholm, M., & O’Doherty, J. P. (2012). Contributions of
the striatum to learning, motivation, and performance:
An associative account.
Trends in Cognitive Sciences, 16
(9), 467–475.
Locke, E. A ., & Latham, G. P. (1990).
A Theory of Goal
Setting and Task Performance.
Englewood Clis, NJ:
Prentice Hall.
Locke, E. A ., & Latham, G. P. (2006). New directions in
goal-setting theory.
Current Directions in Psychological
Science, 15
(5), 265–268.
Mann, T., Sherman, D., & Updegra, J. (2004).
Dispositional motivations and message framing: a test of
the congruency hypothesis in college students.
Psychology, 23
(3), 330–334.
McClelland, D. C. (1987).
Human Motivation.
New York:
Cambridge University Press.
McNamara, A., Buccino, G., Menz, M. M., Gläscher,
J., Wolbers, T., Baumgärtner, A., & Binkofski, F. (2008).
Neural dynamics of learning sound—action associations.
(12), e3845.
Moskowitz, G. B., & Grant, H. (2009).
The Psychology of
New York: The Guilford Press.
Peters, J., & Buchel, C. (2010). Episodic future thinking
reduces reward delay discounting through an
enhancement of prefrontal-mediotemporal interactions.
Neuron, 66
(1), 138–148.
Rock, D. (2008). SCARF: A brain-based model
for collaborating with and influencing others.
Neuroleadership Journal, 1,
Schmitz, T. W., & Johnson, S. C. (2007). Relevance to
self: A brief review and framework of neural systems
underlying appraisal.
Neuroscience and Biobehavioral
Reviews, 31
(4), 585–596.
Sheldon, K. M., & Elliot, A. J. (1999). Goal striving, need
satisfaction, and longitudinal well-being: The self-
concordance model.
Journal of Personality and Social
Psychology, 76
(3), 482–497.
Shirin, R. M., & Schneider, W. (1977). Controlled and
automatic human information processing: II. Perceptual
learning, automatic attending, and a general theory.
Psychological Review, 84
(2), 127–190.
Slotter, E. B., & Gardner, W. L. (2011). Can you help me
become the “me” I want to be? The role of goal pursuit in
friendship formation.
Self and Identity, 10
(2), 231–247.
Spengler, S., Cramon, von, D. Y., & Brass, M. (2009).
Control of shared representations relies on key processes
involved in mental state attribution.
Human Brain
Mapping, 30
(11), 3704–3718.
Spunt, R. P., Falk, E. B., & Lieberman, M. D. (2010).
Dissociable neural systems support retrieval of how and
why action knowledge.
Psychological Science, 21
1593–1598. w
Sutton, S., & Davidson, R. (1997). Prefrontal brain
asymmetry: A biological substrate of the behavioral
approach and inhibition systems.
Psychological Science,
(3), 204–210.
Tricomi, E., Balleine, B., & O’Doherty, J. (2009). A specific
role for posterior dorsolateral striatum in human habit
The European Journal of Neuroscience.
... Based on the portrait of executive function drawn here, the factors that influence the capacity for executive function most directly are other concurrent cognitive operations and the relative importance of the task compared to other possibilities. Together, this suggests that it is optimal to carve out dedicated, distraction-free time to work on important novel tasks and challenges (Berkman & Rock, 2014). Our cognitive bandwidth is precious and operates most efficiently in (mental) solitude. ...
... To some extent, motivation can be gained by finding ways to think about goals that makes their connection to important parts of one's identity salient. Sometimes it is easier for other people to make these connections than for us because they have more distance from them (Berkman & Rock, 2014); coaches can be particularly helpful in this regard. Paying people works, too, but connecting goals to the self-concept in various ways may be a more sustainable and accessible approach to increasing motivation. ...
Full-text available
The ways that people set, pursue, and eventually succeed or fail in accomplishing their goals are central issues for consulting psychology. Goals and behavior change have long been the subject of empirical investigation in psychology, and have been adopted with enthusiasm by the cognitive and social neurosciences in the last few decades. Though relatively new, neuroscientific discoveries have substantially furthered the scientific understanding of goals and behavior change. This article reviews the emerging brain science on goals and behavior change, with particular emphasis on its relevance to consulting psychology. I begin by articulating a framework that parses behavior change into two dimensions, one motivational (the will) and the other cognitive (the way). A notable feature of complex behaviors is that they typically require both. Accordingly, I review neuroscience studies on cognitive factors, such as executive function, and motivational factors, such as reward learning and self-relevance, that contribute to goal attainment. Each section concludes with a summary of the practical lessons learned from neuroscience that are relevant to consulting psychology.
... Given these dynamics, when should Polaris be deployed? Polaris plays an integral role in every phase of goal pursuit: the antecedents phase, the integration phase, and the maintenance phase (Berkman & Rock, 2014). The antecedents phase involves determining which goals to pursue and linking this goal to a person's enduring sense of who they are. ...
In this chapter, we pay attention to the Seventh Enabler: Embarking. Identification of potential risks associated with implementing the change is an important element of this enabler. We offer exercises to systematically identify forces against the change and those people or things that are conducive to the implementation of the change. In this chapter, the resistance to change receives attention, as well as formulating a detailed communication strategy. Several tools offer the opportunity to systematically plan the when, what, how of executing the change process, for specific team members. When team members deliver actions, aligned to strategy on specific agreed upon dates, real progress towards implementation is evident.
While the previous enabler paid attention to formulating the change plans when embarking on the change, this chapter Eighth Enabler focuses on the actual execution of these change plans and in particular, centring on entrenching the new way of doing in the culture of the organisation. We emphasise the cascading of the change strategy lower down in the organisation, requiring diverse, cross-functional teams to indeed execute relevant actions. This chapter highlights the forming of a habit as well as how to change habits on the individual level. Change leaders must take notice of this chapter, because they must be role models to others due to their own ability to change habits. The insight into how difficult it is and how much dedication and attention are required to change habits also assists change leaders in having empathy with others who are struggling to truly change behaviour.
Full-text available
The theory outlined in the present chapter adopts a cognitive approach to motivation. In the pages that follow we describe a research program premised on the notion that the cognitive treatment affords conceptual and methodological advantages enabling new insights into problems of motivated action, self-regulation and self-control. We begin by placing our work in the broader historical context of social psychological theorizing about motivation and cognition. We then present our theoretical notions and trace their implications for a variety of psychological issues including activity-experience, goal-commitment, choice, and substitution. The gist of the chapter that follows describes our empirical research concerning a broad range of phenomena informed by the goal-systemic analysis. Motivation Versus Cognition, or Motivation as Cognition Motivation versus cognition: the “separatist program. ” Social psychological theories have often treated motivation as separate from cognition, and have often approached it in a somewhat static manner. The separatism of the “motivation versus cognition ” approach was manifest in several major formulations and debates. Thus, for example, the dissonance versus self-perception debate (Bem, 1972) pitted against each other motivational (i.e., dissonance) versus cognitive (i.e., self-perception) explanations of attitude change phenomena. A similar subsequent controversy pertained to the question of whether a motivational explanation of biased causal attributions in terms of ego-defensive tendencies (cf. Kelley, 1972) is valid, given the alternative possibility of a purely cognitive explanation (Miller & Ross, 1975). The separatism of the “motivation versus cognition ” approach assigned distinct functions to motivational and cognitive variables. This is apparent in major social psychological notions of persuasion, judgment or impression formation. For instance, in the popular dual-mode theories of
Full-text available
When people encounter problems in translating their goals into action (e.g., failing to get started, becoming distracted, or falling into bad habits), they may strategically call on automatic processes in an attempt to secure goal attainment. This can be achieved by plans in the form of implementation intentions that link anticipated critical situations to goal-directed responses ("Whenever situation x arises, I will initiate the goal-directed response y!"). Implementation intentions delegate the control of goal-directed responses to anticipated situational cues, which (when actually encountered) elicit these responses automatically. A program of research demonstrates that implementation intentions further the attainment of goals, and it reveals the underlying processes. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Gray (1981, 1982) holds that 2 general motivational systems underlie behavior and affect: a behavioral inhibition system (BIS) and a behavioral activation system (BAS). Self-report scales to assess dispositional BIS and BAS sensitivities were created. Scale development (Study 1) and convergent and discriminant validity in the form of correlations with alternative measures are reported (Study 2). In Study 3, a situation in which Ss anticipated a punishment was created. Controlling for initial nervousness, Ss high in BIS sensitivity (assessed earlier) were more nervous than those low. In Study 4, a situation in which Ss anticipated a reward was created. Controlling for initial happiness, Ss high in BAS sensitivity (Reward Responsiveness and Drive scales) were happier than those low. In each case the new scales predicted better than an alternative measure. Discussion is focused on conceptual implications.
Resting anterior brain electrical activity, self-report measures of Behavioral Approach and Inhibition System (BAS and BIS) strength, and general levels of positive and negative affect (PA and NA) were collected from 46 unselected undergraduates on two separate occasions Electroencephalogram (EEG) measures of prefrontal asymmetry and the self-report measures showed excellent internal consistency reliability and adequate test-retest stability Aggregate measures across the two assessments were computed for all indices Subjects with greater relative left prefrontal activation reported higher levels of BAS strength, whereas those with greater relative right prefrontal activation reported higher levels of BIS strength Prefrontal EEG asymmetry accounted for more than 25% of the variance in the self-report measure of relative BAS-BIS strength Prefrontal EEG, however, was not significantly correlated with PA or NA, or the relative strength of PA versus NA Posterior asymmetry was unrelated to the self-report measures
Individuals' relationships often provide pathways to self-change. One pathway involves individuals using others to help them pursue important, self-relevant goals. Past research has demonstrated that individuals prefer existing friends who can help them pursue important goals. The current research expands upon these findings in two studies by demonstrating the role that goal pursuit plays in friendship formation. The current work demonstrates that individuals prefer, largely without their own awareness, to approach potential friends that can help them achieve their goals over those who cannot. The findings also demonstrated that simply expressing the preference for potential friends who could help them achieve their goals predicted individuals feeling that they were subjectively closer to their desired self than they had been previously.
ABSTRACT Goal-setting theory is summarized regarding the effectiveness of specific, difficult goals; the relationship of goals to affect; the mediators of goal effects; the relation of goals to self-efficacy; the moderators of goal effects; and the generality of goal effects across people, tasks, countries, time spans, experimental designs, goal sources (i.e., self-set, set jointly with others, or assigned), and dependent variables. Recent studies concerned with goal choice and the factors that influence it, the function of learning goals, the effect of goal framing, goals and affect (well-being), group goal setting, goals and traits, macro-level goal setting, and conscious versus subconscious goals are described. Suggestions are given for future research.