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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.
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The Neuroscience of Goals and Behavior Change:
Lessons Learned for Consulting Psychology
Elliot T. Berkman
Department of Psychology
Center for Translational Neuroscience
University of Oregon
Berkman Consultants, LLC
Abstract word count: 169
Main text word count: 7633
Figures: 2
Tables: 1
Address correspondence to:
Elliot T. Berkman
Department of Psychology
1227 University of Oregon
Eugene, OR 97403-1227
Ph: 541-346-4909
In press at Consulting Psychology Journal
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.
Keywords: Goal Striving; Behavior change; Habit formation; Neuroplasticity; Motivation
Setting goals is easy; achieving them is hard. Why? This question has long stumped
humanity and will certainly not be answered in this article. A full explanation of why it is
hard to accomplish a goal or change old habits may never be possible. However, all
hope is not lost. Research at the interface of neuroscience and psychology has made
significant strides in uncovering the machinery behind goal pursuit. This knowledge, in
turn, provides clues about the various ways that behavior change can go wrong and
how to improve it. In this article, I present a brain-based framework for understanding
how goal pursuit works and how to facilitate behavior change. Along the way, I highlight
specific and practical lessons learned that are relevant to the science and practice of
consulting psychology.
Goals and the Four Types of Behavior
What do I mean by goals? Colloquially, a goal is any desired outcome that wouldn’t
otherwise happen without some kind of intervention. In other words, a goal is a detour
from the path of least resistance. Formally, a goal is a desired future state (an end)
coupled with a set of antecedent acts that promote the attainment of that end state
(means; see Kruglanski, Shah, Fishbach, Friedman, Chun, & Sleeth-Keppler, 2002 for a
summary). I present the informal definition first because it captures something that is
missing from the formal one: a sense of what people actually mean by the word “goals”
and how we use them. Technically, according to the formal definition, going out with
friends to celebrate someone’s birthday is goal; it is an imagined end state and one
must deploy various means to make it happen. But most people wouldn’t think of
planning to go to a party later tonight as a goal. In practice, we set goals in cases where
we need to do something that hasn’t happened yet and isn’t likely to happen on its own.
The difference between the two definitions of goals highlights an important aspect of
goals and the way it is often overlooked. Goals are usually things we want but have
difficulty achieving even when we know they are achievable. Otherwise, we wouldn’t
need a goal in the first place. That sense of struggle is also captured in the term
behavior change, which I use interchangeably with goal pursuit here. It’s not engaging
in behavior, per se, but rather new behavior that is hard. To pursue what most people
call a goal involves doing something different than what has been done before. For
example, a primary incentive underlying achievement motivation (i.e., the need for
achievement) is to demonstrate one’s capability to perform well on a new or challenging
task (McClelland, 1985).
To understand why new behavior is so hard, it’s useful to think about two dimensions
that give rise to behaviors. The first dimension captures the skills, capacities, and
knowledge required to engage in a behavior. This includes mapping out the steps to
take and having the skill to execute an action, as well as related cognitive processes
such as attentional focus, inhibitory control, and working memory capacity. Because it
reflects the means used to achieve a goal, I refer to the first dimension as the way. The
second dimension captures the desire for and importance of a behavior. This includes
wanting to achieve a goal and prioritizing it over other goals, as well as related
motivational processes such as volition, intention, and the nature and strength of the
drive for achievement. Because it relates to the motivation to engage in a behavior, I
refer to the second dimension as the will.
As shown in Figure 1, these two dimensions give rise to four broad types of action.
Complex-Routine behavior, in the top-left quadrant, requires some level of skill or
knowledge but little motivation. Habitual behaviors reside in this quadrant: they can be
quite complex yet are often triggered by external cues without motivation. For example,
many drivers have piloted their car somewhere familiar, such as a child’s school,
without thinking and despite an intention to go elsewhere. Indeed, a hallmark of habitual
behavior is engaging in it even (or especially) in the absence of a conscious goal to do
so (Wood & Neal, 2007). Simple-Routine behavior, in the bottom-left quadrant, requires
little skill and motivation. For example, walking, eating, and other behaviors related to
primary rewards reside in this quadrant. These behaviors are so easy and effortless that
we hardly think of them as goals at all. Because they are located in the same place on
the horizontal axis and on different places on the vertical axis, the key difference
between the first two types of behaviors is the level of skill they require. Simple-Novel
behavior, in the bottom-right quadrant, requires high motivation but low skill to
accomplish. Simple but new (and at times unpleasant) tasks such as changing a diaper
belong in this quadrant. The most interesting kind of behavior is in the fourth quadrant:
Complex-Novel behavior that requires high skill and high motivation. The goals that
people care about most reside there.
Figure 1. Behavior can be divided into four broad categories defined by the level of motivation they
demand (horizontal axis) and the level of skill or ability they require (vertical axis). Behavior change
typically involves moving from left-to-right, from bottom-to-top, or both. Moving from left-to-right increases
the motivational demand (why) of an action, whereas moving from bottom-to-top increases the skill level
(how). It is useful to identify the vector of change required during goal pursuit and to target motivational
(horizontal) and cognitive (vertical) processes as necessary.
Differences between adjacent quadrants within this space are instructive. The key
distinction between a rote, unpleasant task (bottom-right) and a complex, hard one (top-
right) is skill- and knowledge-oriented. Changing one diaper doesn’t take much ability,
but building a machine to do the task for you would require decades of schooling. Both
require high levels of motivation. The lesson is that moving up and down in this space is
a matter of skill-building. In contrast, the distinction between a complex task that
happens easily (top-left) and one that requires effort (top-right) is motivational. Driving to
your child’s school is easy because you’ve done it so many times that it has become a
matter of habit. In contrast, driving for the first time in a new country relies on the same
skillset but feels much harder because it forces you to focus and apply the driving and
navigation skills you already have. As you do it more it becomes easier, of course, but
you can still do it on the first attempt as long as you try hard enough. Moving from left to
right in this space, therefore, is a matter of effort more than one of skill or knowledge.
Once a person possesses the capacity and knowledge to accomplish a difficult task, the
missing piece is motivation.
Lessons learned for consulting psychology
In light of this framework, the first step to facilitating behavior change is to diagnose the
source of the difficulty. Consultants and coaches can do foundational work with their
clients early in the behavior change process to pinpoint the nature of the behavior
change and identify how the new behavior is different from old patterns. The first step to
helping a client with behavior change can involve answering these questions:
Does the client already have the skills required for the new task?
Is the barrier to change a lack of a way or a lack of a will?
Is the person trying to move up, to the right, or both on the axes in Figure 1?
Once the most relevant dimension of change is identified, the second step is to drill
down to learn more about the specific nature of the motivation or skills/capacities that
will be the target. For example, consider the questions:
If motivation, is the client lacking motivation to approach a desirable outcome or
to avoid an undesirable one (e.g., Berkman & Lieberman, 2010)?
If motivation, is the client generally unmotivated, or highly motivated to a different
goal besides than the behavior change goal?
If skills, are they related to interpersonal abilities (e.g., empathy and perspective
taking) or executive functioning (e.g., inhibition and attentional control)?
If skills, is it possible that the client already possesses the skills but is stuck in a
closed mindset and overly focused on one aspect of the behavior, such that a
broadening of perspective might open new avenues for progress using other
The relevant neuroscience will be quite different depending on the answer to these
questions. In the following sections, I summarize the neuroscientific literatures on the
will and the way with an emphasis on practical lessons for consulting psychology.
The neuroscience of the “way”: Executive function and cognitive control
Research on “the way” of goals and behavior change has mostly focused on constructs
such as attention, working memory, inhibitory control, and planning – collectively known
as executive function. A great deal of knowledge has been gained from neuroscientific
studies about executive function, mostly about the neural systems and circuits that
implement executive function (sometimes referred to as the task-positive network; Fox
et al., 2005), and also about how disruptions to those circuits can cause alternately
specific or broad impairment depending on the precise location and nature of the
damage (Alvarez & Emory, 2006; Stuss & Knight, 2012). Recent work has even begun
to explore the bidirectional relationship between central and peripheral nervous system
functioning in the context of goals, such as how activation of the sympathetic nervous
system and hypothalamic-pituitary-adrenal axis during stress can influence executive
function (Roos et al., 2017). Together, imaging and lesion studies have illuminated
many of the mechanistic elements and processes involved in complex goal pursuit
(Stuss, 2011). This information, in turn, contains some important lessons for consulting
psychology about the capabilities and limits of executive function that are directly
relevant to goals.
Despite substantial progress in knowledge of how executive function operate at the
level of the brain, there is only sparse neuroscience research about how executive
function might be improved. What little research there is suggests that executive
function is more fixed than malleable by intervention, but there are some hints that
targeted improvement might be possible. In this section, I review recent neuroscientific
studies on executive function with respect to three questions that are pertinent to goals
and behavior change: What is the nature of executive function? Is executive function a
limited resource? And can executive function be improved with practice?
What is the nature of executive function?
Executive function refers to a suite of higher-level cognitive skills and capacities that
generally promote successful human functioning. Attention, task switching, working
memory, and inhibitory control are usually described as executive functions, though
there is debate about the precise definition of the term (Banich, 2009). Executive
function involves some degree of updating information, shifting focus between targets or
mental sets, and inhibiting irrelevant or distracting information (Miyake, Friedman,
Emerson, Witzki, Howerter, & Wager, 2000). Rather than enter that debate, I will
describe broad features of executive function that are shared across most definitions.
These features are useful for providing clarity and context for the subsequent questions
regarding the limits and improvability of executive functions.
Figure 2. Regions implicated in the will and the way. Left: Lateral view featuring the lateral prefrontal
cortex (LPFC) and the ventrolateral prefrontal cortex (VLPFC), premotor cortex (pMC) and motor cortex
(MC), and the temporalparietal junction (TPJ) and supramarginal gyrus (SMG). Top Right: Medial view
featuring the dorsal anterior cingulate cortex (dACC) and ventral striatum (vS), and the dorsomedial
(dmPFC), medial (mPFC) and ventromedial (vmPFC) aspects of the prefrontal cortex. Bottom Right:
Coronal view featuring the ventral (vS) and dorsolateral (dlS) aspects of the striatum.
Executive function has three characteristic features: it is effortful, operates consciously,
and engaged in service of novel goals as opposed to rote or overlearned ones (e.g.,
Miyake & Friedman, 2012). Effortful means that they feel hard and must be completed
serially. In fact, emerging evidence suggests that one function of the dorsal anterior
cingulate cortex (dACC; Figure 2), among several others, is to efficiently allocate
cognitive resources by tracking the amount of mental work a task will require (Shenhav,
Cohen, & Botvinick, 2016). For example, activity in the dACC scales with the upcoming
demand for control and also the potential payoff of that control (Kouneiher, Charron, &
Koechlin, 2009). It appears that the brain has dedicated regions not only to executing
control but also allocating that control to various tasks.
Table 1. Functional neuroanatomy of key networks
Primary regions
Major functions
Summary citation
Dorsal anterior cingulate (dACC),
anterior insula, subgenual ACC
Interoceptive awareness,
emotional distress, pain
Menon & Uddin, 2010
Lateral prefrontal cortex (lPFC),
parietal cortex, dACC,
temporalparietal junction (TPJ)
Attentional control, working
memory, task switching
Niendam, Laird, Ray,
Dean, Glahn, &
Carter, 2012
Medial prefrontal cortex (mPFC),
medial temporal lobes, posterior
cingulate cortex (PCC)
Task negative network, mind
wandering, self-processing
Greicius, Supekar,
Menon, & Dougherty,
Ventrolateral prefrontal cortex
(vlPFC), dorsolateral prefrontal
cortex (dlPFC), lPFC
Cognitive reappraisal, self-
distancing, emotional
Berkman &
Lieberman, 2009
mPFC, PCC, TPJ, middle
temporal lobe
Self-related cognition,
introspection, self-
consciousness, self-
Northoff, Heinzel, de
Greck, Bermpohl,
Dobrowolny, &
Panksepp, 2006
Ventromedial prefrontal cortex
(vmPFC), orbitofrontal cortex
(OFC), ventral striatum (vS)
Valuation/evaluation, reward
anticipation, reward learning,
affective significance
Bartra, McGuire, &
Kable, 2013
Executive function is conscious, which means that it occurs within awareness and
requires conscious attention. People know when they are engaging in executive
function because it becomes the center of attention in a given moment. A classic
example of executive function is mental math, such as multiplying 13 by 17. In contrast
to things such as breathing or adding 1+1, you know when it happens because it
occupies all of your attention, and it is generally voluntary. The steps involved in solving
that problem recruit a host of executive functions surrounding attention: focusing
attention on the appropriate column, swapping information in and out of attention, and
restricting attention to the desired part of the operation to the exclusion of others. These
short-term memory and attentional processes are supported by complex interactions
among lateral prefrontal and parietal cortices including aspects of all three frontal gyri,
the superior frontal sulcus and precentral gyrus, and the supramarginal gyrus and
temporalparietal junction (Figure 2; Nee, Brown, Askren, Berman, Demiralp, Krawitz, &
Jonides, 2012). The role of these regions is not just to maintain information, but also to
disengage attention from irrelevant or previously-relevant information as appropriate to
the task (Shipstead, Harrison, & Engle, 2016). The importance of redirecting attention
underscores the limited-capacity nature of working memory and executive function more
generally. Extensive cognitive processes and neural resources are dedicated to gating
which information enjoys the focus of attention and which must be ignored. In this way,
executive function generally, and attention specifically, play a key role in how open or
closed we are to new ideas and perspectives during goal setting and goal striving.
In addition to feeling effortful and occupying conscious attention, a third characteristic
property of executive function is that it specializes in novel tasks. It enables humans to
do things that we’ve never done before. In fact, the basic role of the entire prefrontal
cortex has been described broadly as coordinating behavior to achieve novel goals
(Miller & Cohen, 2001). The ability of our prefrontal cortex to plan and execute novel
behaviors is one of the defining characteristics of humans and one that sets us apart
from nearly all other animals. However, this ability is not unlimited. In light of the limited
capacity of attention and working memory, the prefrontal cortex has a second function
that is nearly as critical: to learn to automate novel behaviors to the point that they no
longer take up precious space in consciousness. Research on this process of habit
formation shows that as a particular behavior in a particular behavior is repeatedly
rewarded, the systems that control it shift from the dorsomedial to the ventral and
dorsolateral aspects of the striatum (Figure 2; Yin, Mulcare, Kilario, Clouse, Holloway,
Davis, et al., 2009). This shift is in part supported by the differential connectivity in these
parts of the striatum, with the dorsomedial more strongly connected to the prefrontal
and parietal cortices (involved in attention and working memory) and the other two parts
of the striatum more strongly connected to the sensory and motor cortices (Liljeholm &
O’Doherty, 2012). That the process of routinizing behavior has a robust pathway
embedded within some of the oldest structures in the brain speaks to the evolutionary
importance of offloading effortful mental activities from the cortex as early and efficiently
as possible. Thus, these regions are key for habit formation.
Is executive function a limited resource?
The answer to this question is both yes and no. Many readers will be familiar with the
concept of ego depletion, or the idea that the “active self” that implements executive
functions draws upon a finite resource that exhausts over time with repeated use, not
unlike a fuel tank (Baumeister, Bratlavsky, Muraven, & Tice, 1998). Though there are
literally hundreds of published studies showing the effect (Hagger, Wood, Stiff, &
Chatzisarants, 2010), it is likely that many of those studies are false positives or
unreliable (Hagger, Chatzisarantis, Alberts, Anggono, Batailler, Birt, et al., 2016). A
large, highly powered, preregistered study recently failed to replicate the ego depletion
effect (Lurquin, Michaelson, Barker, Gustavson, von Bastian, Carruth, et al., 2016), and
a meta-analysis uncovered evidence of publication bias in the ego depletion field such
that studies finding the effect are much more likely to appear in print than those that do
not (Carter & McCullough, 2014).
On a deeper level, there is strong counter-evidence to the basic ego depletion effect, for
example that taking a short break, watching a fun film clip, or even smoking a cigarette
can reverse the effect (see Inzlicht & Berkman, 2015 for a summary). Active-self
processes such as executive function are unlikely to draw upon a limited physiological
resource if simple psychological manipulations can replenish it. Even more suggestive,
there is strong physiological evidence that the neuronal processes involved in executive
function demand no more energy than simpler functions or even than the brain at rest
(see Kurzban, 2010, for a review). There is simply no special physiological resource for
executive function to deplete. The bottom line is that people get tired when they work
hard – which is nothing new – but that, contrary to popular belief about ego depletion,
that sense of fatigue is mostly psychological and can be short circuited by a short rest
and a variety of positive experiences.
But what about the experience of depletion? Everyone has the intuition that some
mental activities – certainly including executive function – feel hard and seem to drain
our energy. The answer may be found by adjusting our understanding what exactly the
limited resource is. The original formulation of ego depletion hypothesized a
physiological resource, likely centered in the brain. That prediction is no longer tenable
given the data. Newer models focus on the contributions of psychological and
motivational factors to depletion instead beyond strictly physiological ones. For
example, a shift in priorities from effortful, obligation-based, and prevention-focused
“have-to” goals to enjoyable, desire-based, promotion-focused “want-to” goals could
explain the decline in performance on tough cognitive tasks (Inzlicht, Schmeichel, &
Macrae, 2014); perhaps the “resource” is prioritization. Another possibility is that
depletion results from an interaction between psychological processes, such as
perceptions of upcoming task demands and available resources, and physiological
factors including the peripheral nervous system, hormones, and afferent inputs (Evans,
Boggero, & Segerstrom, 2016).
A psychological model that fits particularly well with the characterization of executive
function above focuses on its opportunity cost (Kurzban, Duckworth, Kable, & Myers,
2013). Because we can only focus our executive function capacity on one task at a
time, then any time we engage in one executive function task we are likely forgoing
others. The cost of what we’re giving up is reflected in the sense of effort that comes
along with executive function. The feeling of depletion, therefore, reflects the tipping
point when the cost of putting off alternative tasks begins to outweigh the benefit of
continuing on the current course of action (Berkman, Kahn, & Livingston, 2016).
The evidence at this point indicates that executive function is limited in terms of
bandwidth – how much can be done or stored or attended to in a given moment – but
not in terms of duration in the ego depletion sense. That limit stems directly from the
properties of the executive function system: the facts that only a small amount of
information can be consciously accessible and operated upon in a given moment
(Unsworth, Fukuda, Awh, & Vogel, 2015), and that we actively track the processing
costs of potential cognitive operations with respect to ongoing goals (Westbrook &
Braver, 2015). For precisely this reason, executive function was likened by the
mathematician and philosopher Alfred North Whitehead to cavalry in an army,
“Operations of thought are like cavalry charges in a battle – they are strictly limited in
number, they require fresh horses, and must only be made at decisive moments.” (pp.
61; Whitehead, 1911).
Can executive function be improved with practice?
There is naturally great interest in the question of whether executive function can be
improved, expanded, or strengthened with practice given its bandwidth limitations.
Study of this kind of “brain training” is an active research area and a controversial one.
Some researchers make claims about the ability to improve executive function with
training (Jaeggi, Buschkuehl, Jonides, & Shah, 2011), though these claims have been
tempered by compelling counter-evidence (Redick, Shipstead, Harrison, Hicks, Fried,
Hambrick, et al., 2013). A fair characterization of the research to date is that people can
certainly improve on a given executive function task with practice, but there is no
evidence that practice generalizes to other, even closely related tasks, and task-specific
improvements are unlikely to endure over time (Berkman, 2016).
The core issue in executive function training is transfer, or whether the improvements
on a training task generalize to other tasks. In some theories such as the Strength
Model, on which the ego depletion hypothesis is based, executive function is a common
resource that is shared across many discrete capacities (e.g., working memory and self-
control), so expanding that common resource should improve a range of executive
abilities (Muraven, 2010). However, counter-evidence to ego depletion specifically and
the Strength Model generally have raised the question about whether a common
underlying resource even exists (Inzlicht et al., 2014). A recent meta-analysis of studies
attempting to train one form of executive function, self-control, revealed a negligible
transfer effect (Inzlicht & Berkman, 2015). Additionally, at least two highly-powered
studies have failed to find generalizable training effects on executive function despite
showing practice effects on the training task (Miles, Sheeran, Baird, Macdonald, Webb,
& Harris, in press; Redick et al., 2013).
What is happening? Neuroscientific investigations provide some clues. A series of
training studies on inhibitory control, an executive function involving the prevention of
ongoing or prepotent behavior, found that performance on an inhibitory control task
improves with practice and does not transfer to other tasks. Interestingly, to the degree
that performance on the training task improved, activity in the lateral prefrontal regions
and dACC that is associated with successful inhibitory control shifted earlier in time,
peaking in anticipation of the need for control (Beauchamp, Kahn, & Berkman, 2016;
Berkman, Kahn, & Merchant, 2014). This effect can be characterized as a reactive-to-
proactive shift in the neural activation involved in inhibitory control, and is akin to gently
applying a car’s brakes when a light turns yellow instead of slamming on the brakes
only upon a red light.
The observed shift in brain activity from later to earlier in time fits well with the general
characteristics of executive function described earlier. Inhibitory control feels hard and
occupies attention, so it is beneficial to the individual to automate the operation when
possible. With enough practice and exposure, the habit learning system discovers
regularities in the environment that allow the need for inhibitory control to be anticipated
using contextual cues. Just as the frequent association of a yellow light with a red light
teaches experienced drivers to automatically move their foot to the brake when seeing a
yellow, so too do participants in inhibitory control training studies learn the specific task
cues that anticipate the need for control. This cue-learning effect in training occurs
automatically (Lenartowicz, Verbruggen, Logan, & Poldrack, 2011), suggesting that
performance improvements during inhibitory control training studies are a result of the
transfer of at least some effortful behavior to the habit system. Habits increase
efficiency during goal striving.
This habit learning process also explains the lack of transfer to new tasks. The
advantages of executive function are mirrored in the limitations of the habit learning
system. Specifically, while executive function evolved to deal with novel challenges,
habit learning evolved for routine ones. Habits create efficiency by shrinking the range
of responses in a situation down to one behavior. By function, they forestall new and
creative behaviors in that situation. Habitual behaviors are triggered by specific
contextual cues, which is why habits do not require vigilant and costly monitoring; that
work is offloaded to more efficient stimulus-response mappings. The tradeoff is that
habitual behaviors are necessarily tied to a particular context. If the cues that had been
associated with a response change, then the habitual response will no longer emerge.
For example, the ease of slowing on a yellow would be lost if the cue that preceded a
red light suddenly became blue instead. In the case of executive function, training
doesn’t transfer to new contexts (or tasks) because the cues are different. The brain
treats the tests of transfer as novel tasks, which is exactly what executive function
evolved to deal with in the first place.
Lessons learned from neuroscience about “the way”
The neuroscience literature on executive function offers some practical if not entirely
hopeful advice about the “way” of behavior change. The first lesson is that executive
function feels hard for a reason. It is a serial process, so the sense of effort that
accompanies executive function is a signal that working on a difficult task necessarily
means losing out on other opportunities. In other words, effort reflects an opportunity
cost. In this view, effort also signals one’s internal priorities; the more important the
alternatives are, the harder a focal task will feel. The inverse is also true: a given task
will feel relatively easy when it is more important to a person than the alternative
choices. Consultants and coaches can work with clients to reflect on their priorities and
make them explicit, which can explain why some goals feel harder than others.
The mental processes related to the “way” operate sequentially, not in parallel.
Executive functions can only be performed one at a time, so the most important ones
should come first even if executive processing will not exhaust over time with use.
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. Licensing clients to
reserve work time specifically for new tasks can help.
Our executive function abilities evolved to help us deal with novel challenges. So, the
precious resource of executive function should be brought to bear on any and all
aspects of behavior change, such as goal setting, that benefit from openness to new
ideas, broadened attention, and a wide survey of possibilities. In contrast, habit
formation evolved to create efficiency by rigidly attaching one behavior to one cue.
Habits can be formed to aid in other aspects of behavior change, such as goal striving,
that benefit from a narrower focus and relatively consistent, fixed behaviors in a given
Finally, there is not much evidence that executive function can be improved broadly by
focused interventions (e.g., Lumosity; Redick et al., 2013; Shute, Ventura, & Ke, 2015),
and some compelling counter-evidence. However, complex mental operations can
become routinized by leveraging the habit learning system (Foerde, Knowlton, &
Poldrack, 2006). Habit learning is facilitated when the new behavior is consistently
preceded by specific cues and then rewarded. This procedure can be particularly useful
for behavior change if the new behavior will occur repeatedly in similar contexts.
Research is underway to test whether a highly variable set of cues used in training can
broaden the range of contexts to which training effects generalize. Nonetheless, some
executive functions such as working memory may simply be fixed capacities for
neuroarchitectural reasons (Zhange & Luck, 2008). Rather than attempting to improve
executive function generally, consultants and coaches should help their clients focus on
improving specifically the skillsets relevant to the goal or new behavior. These will
improve with practice and, with some proper motivation, become habitual in time.
The neuroscience of the “will”: Motivation, Reward, and Subjective Value
The question of what motivates behavior, in a general sense, runs at least back to the
Greeks, with Plato’s famous analogy of the charioteer and his horses, through William
James and Abraham Maslow, and continues to this day. In contrast, the question of
what motivates behavior change has received considerably less attention.
Psychologists have developed taxonomies of different “stages of change” to capture
individual variability in readiness to engage in sustained behavior change
(Transtheoretical Model; Prochaska, DiClemente, & Norcross, 1992), and of different
types of behaviors within a person to capture relatively self-motivated, “intrinsic” versus
more externally-motivated, “extrinsic” types of goals (Self-Determination Theory; Deci &
Ryan, 2000). Much of this work is descriptive rather than prescriptive – it says what
motivation is but does not indicate how to increase it. A person can be confidently
described as in the precontemplation stage, but there is not much evidence-backed
knowledge about moving him or her to the contemplation stage; likewise, some
behaviors are clearly extrinsically motivated, though there is a lack of prescriptive
advice about how one can transform them into intrinsically motivated ones.
As it did with studies on the “way,” neuroimaging research provides some clues about
how to increase motivation to change a specific behavior. In this section, I review
neuroscientific insights into the “way” of behavior change surrounding three questions
that are relevant to consulting psychology. Which brain systems are involved in
motivational processes? How do those systems interact with other networks in the
brain? And what does neuroscience indicate about motivating behavior change?
How and where is motivation represented in the brain?
Motivation is conceptualized here as the strength of the desire to attain a particular
outcome, irrespective of how pleasant or unpleasant the experience of actually attaining
it is. This distinction between the motivational component of a reward – “wanting” – and
the hedonic component of consuming it – “liking” – is maintained with remarkable
evolutionary consistency in the brains of both humans and animals (Berridge &
Robinson, 2003). I focus here on the “wanting” side because of its direct bearing on
behavior and behavior change. Wanting a reward is closely tied with activity of
mesolimbic dopaminergic neurons, particularly within the ventral striatum and
ventromedial prefrontal cortex (Berridge, 2006; Figure 2), which is sometimes also
called the orbitofrontal cortex (Wallis, 2007). Of course, there are many other regions
and interactions involved in reward learning, but I focus on these because they are the
best characterized in terms of human functional neuroanatomy to date.
The dopaminergic reward system has been conserved evolutionarily because it plays a
critical role in the reinforcement learning cycle. When a particular behavior in a given
context it is rewarded, that behavior and context are paired and tagged with reward
value for later repetition (Rescorla & Wagner, 1972). Reinforcement learning is why
behaviors that are rewarded are likely to be repeated in the future. (This is also why the
dopamine system is implicated in addictive behavior.) The amount of cumulative,
learned reward value of a behavior is its expected value, sometimes referred to as
subjective value (Rangel & Hare, 2010). In short, subjective value represents the
amount of reward that an actor expects to receive for a given action, largely based on
past learning. This learning cycle is one of the key impediments to behavior change: old
behavior has been rewarded and new behavior has not. A protein called brain-derived
neurotrophic factor (BDNF) is important for maintaining new behaviors after engaging in
them initially because of its critical role in memory consolidation (Bekinschtein et al.,
2008). As described in the following sections, the key to launching this reward learning
and consolidation cycle is finding ways to increase the subjective value of new behavior.
A notable feature of activity in the ventromedial prefrontal cortex is that it represents the
subjective values of diverse types of actions, presumably to facilitate “apples to
oranges” decisions between qualitatively different behaviors (Levy & Glimcher, 2011).
For example, activity in the ventromedial prefrontal cortex tracks the value of approach
appetitive and avoiding aversive stimuli (Tom, Fox, Trepel, & Poldrack, 2007), and also
the subjective value of a range of stimulus types, including food, money, gains for the
self and others, charitable decisions, and emotional and utilitarian benefits of moral
actions (Hare, Camerer, Knoepfle, O’Doherty, & Rangel, 2010; Hutcherson, Montaser-
Kouhsari, Woodward, & Rangel, 2015; Lebreton, Jorge, Michel, Thirion, & Pessiglione,
2009; Zaki, Lopez, & Mitchell, 2014). These findings converge on the idea that the
ventromedial prefrontal cortex plays a central role in tracking the subjective value of
different kinds of actions during choice, which strongly implicates that region in
motivational processing during behavior change.
How do motivation regions interact with other brain systems?
One way to approach the deeper issue of where motivation originates is to examine the
connectivity of its neural systems. In the same way that it is adaptive to humans and
informative to scientists that sensory and motor regions in the brain are adjacent and
highly interconnected, the regions involved in motivation are themselves intertwined
with several other brain networks. Those interrelations contain insights about how
motivation operates and how it might be increased in the service of behavior change.
As Self-Determination Theory suggests, autonomously choosing to engage in a
behavior (relative to being forced) increases performance on that behavior because
autonomy is an intrinsic motive. At the neural level, autonomy also prevents a reduction
in reward system activity in the face of negative feedback, particularly in the
ventromedial prefrontal cortex (Murayama, Matsumoto, Izuma, Sugiura, Ryan, Deci, et
al., 2013). Interestingly, the ventromedial prefrontal cortex has also been found to be
active in studies of self-processing and particularly of self-affirmation, such as
considering one’s core personal values (Cascio, O’Donnell, Tinney, Lieberman, Taylor,
Strecher, et al., 2016). Brain activation related to self-affirmation during health
messaging has even been shown to predict the eventual degree of health behavior
change that would follow (Falk, O’Donnell, Cascio, Tinney, Kang, Lieberman, et al.,
2015). Finally, a meta-analysis using the Neurosynth study database (Yarkoni,
Poldrack, Nichols, Van Essen, & Wager, 2011) found that the ventromedial prefrontal
cortex was one of the largest regions of overlap between 812 studies on identity (“self”
and “self-referential” terms in the database) and 324 subjective value and reward
(“value” term in the database). The meta-analysis contained several regions along the
medial cortical wall including the ventromedial prefrontal cortex, the posterior cingulate
cortex, and the mid-cingulate. The ventromedial prefrontal cortex was the single largest
cluster to be consistently associated with both identity and value.
The overlap between intrinsic goals, core values, and subjective value has several
implications for consulting psychology. First, identity (e.g., self-concept) and subjective
value are closely functionally connected to one another. This is not a surprise given the
extensive evidence from social psychology and other fields that people have
disproportionate positive regard for themselves (and behaviors related to the self)
compared to others (Greenwald, 1980; Pelham & Swann, 1989). We want, and perhaps
need, to see our selves as good (Rosenberg, 1979). Second, the value derived from
identity and other self-related processes may have a special status compared to other
sources of value (e.g., monetary) because of the high degree of overlap in the neural
systems and conceptual representation of identity and value. It may even be that
identity and value are inseparable, leading one researcher to hypothesize that the
defining function of the self is to organize and prioritize the world by assigning it
motivational significance (Northoff & Hayes, 2011). By this definition, the self-concept is
exactly the set of places, things, and actions in the world that hold value.
It is important to note that the valuation process subserved by the vmPFC reflects not
only positive value, but negative value as well. For example, just as social affiliation
holds positive value, the threat of social rejection can be highly negative in value. The
experience of social rejection invokes similar brain networks as physical pain
(Lieberman & Eisenberger, 2015). Beyond its unpleasantness, this experience can
enhance defensiveness and facilitate a stress response that detracts from other
ongoing goals because it narrows attentional focus on the social threat (Muscatell et al.,
The ventromedial prefrontal cortex and related dopaminergic motivational structures
also interact with cognitive networks, including those related to executive function
(Botvinick & Braver, 2015). The ventromedial prefrontal cortex appears to be a point of
convergence where the motivational value of various options in a choice are integrated,
notably including both effortful actions that require cognitive control and also easier,
more hedonic ones (Bartra, McGuire, & Kable, 2013). For example, the dorsolateral
prefrontal cortex is functionally connected with the ventromedial prefrontal cortex when
higher-order goals such as health concerns or social factors are made salient (Hare et
al., 2010; Hutcherson, Plassman, Gross, & Rangel, 2012). There is also evidence that
the value of potential actions are reflected in the ventromedial prefrontal cortex before
any specific action plans is selected (Wunderlich, Rangel, & O’Doherty, 2010), but that
value signals provide input to downstream brain regions that are responsible for
selecting and implementing behavior (Hare, Schultz, Camerer, O’Doherty, & Rangel,
2011). Taken together, then, the emergent view from the neuroscience literature is that
the ventromedial prefrontal cortex receives a variety of value signals relevant to
decisions about behavior, and its activation reflects a dynamic value integration process
that subsequently biases behavior toward higher-valued actions. A promising route to
increasing motivation, then, is identifying the value inputs to a new behavior (i.e., the
reasons why the behavior is or is not valued) and learning ways to modulate them. I
address this possibility in the next section.
How can motivation be increased?
The neurally-informed model described above suggests that motivation is guided by an
integration of the value of features of the behavioral options. Behavior change can be
accomplished by amplifying the value of the new (goal-related) behavior, reducing the
value of old (goal-counter or goal-unrelated) behaviors, or some combination of the two.
A clear example of the effectiveness of the first approach is contingency management
treatment for substance use disorders (Bigelow & Silverman, 1999), in which the value
of drug abstinence is increased with monetary incentives. A meta-analysis found this
approach to have an effect size d = 0.42 on treatment for alcohol, tobacco, and illicit
drugs, which was larger than therapy (d = 0.25) and outpatient treatment (d = 0.37), and
comparable to methadone treatment for opiate use (Prendergast, Podus, Finney,
Greenwell, & Roll, 2006). Similarly, “precommitting” to buy more healthy foods at the
risk of losing financial incentives is more effective than having the incentives alone
(Schwartz, Mochon, Wyper, Maroba, Patel, & Ariely, 2014). Monetary incentives also
increase persistence at exercise (Cabanac, 1986), endurance on a cold-pressor task
(Baker & Kirsch, 1991), and performance on a difficult cognitive task (Boksem, Meijman,
& Lorist, 2006). Simple monetary payments are an effective way to motivate behavior
“Money walks,” as the saying goes, but its scarcity makes it a less than ideal option for
many goal pursuit contexts. Above, I noted the deep connections between identity and
motivation. Other researchers have, too, and are now beginning to deploy identity
interventions to increase motivation. For example, one study leveraged the fact that
most people consider willpower to be a desirable trait (Magen & Gross, 2007). The
participants in that study completed an executive function task twice, and in between
were randomly assigned to reconstrue the task itself as a measure of their own
willpower or not. Performance improved from the first to the second run only among
participants whose perceptions of the task were changed from non-diagnostic to
diagnostic of willpower. Similarly, noting that identity is somewhat susceptible to
cognitive shifts such as framing, construal, or priming effects, other researchers used a
simple “noun-verb” manipulation to increase motivation for behavior change,
presumably through a subtle shift in the extent to which the new behavior is construed
as identity-relevant. For example, phrasing questions about voting intentions in terms of
identity (noun: “being a voter”) instead of an action (verb: “voting”) increased voting
intentions and actual turnout in statewide elections (Bryan, Walton, Rogers, & Dweck,
2011). In another study, participants were less likely to cheat by claiming money they
were not entitled to if that behavior was described as a (negative) identity (noun: “being
a cheater”) instead of an action (verb: “cheating”; Bryan, Adams, & Monin, 2013). Each
of these results is consistent with the idea that identity can influence motivation,
presumably by highlighting the subjective value of desired (e.g., “voter”, “willpower”) or
undesired (e.g., “cheater”) identity. This path is a promising future direction for
motivation interventions because it is low-cost, modest in scope, and easily scalable to
a broad range of populations and types of desired identities.
Finally, merely highlighting certain attributes of a behavior can alter the value placed on
that behavior. After all, our attentional bandwidth is fairly narrow, so not all relevant
properties will be equally salient at all times. For example, people’s motivation to act on
a choice option increases as attention is allocated to it (Krajbich, Armel, & Rangel,
2010). In another study (Hare et al., 2011), participants were presented with health-
versus-taste decisions with or without reminders about health. As expected, health
reminders increased the likelihood of healthy choices. Tellingly, the healthiness rating of
the foods (assessed earlier, and separate from the tastiness) was strongly correlated
with activity in the ventromedial prefrontal cortex at the moment of decision, which in
turn predicted the food choice. In contrast, when unhealthy foods were selected, the
earlier tastiness ratings were correlated with ventromedial prefrontal cortex activity
during choice. The results of these studies are broadly consistent with psychological
framing effects (e.g., gain vs. loss frame; Kahneman & Tversky, 1984), whereby altering
the relative salience of the features of a decision can dramatically change it. Though
they are most often applied to decision-making, the neuroscientific evidence presented
here suggests that motivation may also be susceptible to framing effects.
In light of the present framework, I focused on ways to increase motivation that are
grounded in valuation. But there are other ways to increase motivation from
complementary lines of research that nonetheless may be connected to subjective
value. For example, Higgins has argued that people experience “value from fit” when
their regulatory style (promotion versus prevention focus) matches the particular means
through which goals are pursued (Higgins, Idson, Freitas, Spiegel, & Molden, 2003). A
similar “matching” effect on motivation has been observed with achievement motivation
and performance goals: people high in achievement motivation experience greater
intrinsic motivation when provided with performance (vs. mastery) goals, whereas
people low in achievement motivation experience greater intrinsic motivation with
mastery (vs. performance) goals (Elliot & Harackiewicz, 1994). A plausible cause of
these kinds of “matching” effects, which can be tested in future research, is that there is
subjective value in experiencing fit between one’s dispositional tendencies and the
nature of the goal at hand.
Lessons learned from neuroscience about “the will”
Neuroscientific investigations of motivation have established the major brain systems for
motivation and identified ways that those systems interact with other parts of the brain.
This knowledge, in turn, contains clues about how motivation works and how to
increase it on the psychological level. Two are particularly relevant to consulting
The first lesson surrounds the extent to which motivation is tied to the past. The neural
mechanisms of reinforcement learning are some of the most basic and ancient parts of
our brains. For good reason, we evolved to be highly sensitive to learn where we
receive rewards and to work hard to recreate the situations that brought them about.
Attempting to change behavior in a systematic way by engaging in new behaviors,
which have never been reinforced, often means working against this powerful system.
Thus, wise advice for clients that is grounded in the neuroscience of motivation and
reinforcement learning is to start behavior change with modest goals and reward even
the smallest steps toward them. New behaviors emerge slowly because they are usually
working against the power of prior reinforcement. Consultants and coaches can help
clients anticipate and understand the difficulty of behavior change by explaining the
neuroscience of reinforcement learning. Being cognizant of the challenges of behavior
change can prevent frustration on both sides.
The second lesson is to leverage the intrinsic connections between the motivation
system and other parts of the brain, particularly self and identity. The elaborated web of
memories, beliefs, values, objects, and relationships that comprise our sense of self is
paralleled perhaps only by executive function in its distinctiveness to humans. And it
may offer a pathway to behavior change and goal achievement that is just as potent. A
behavior will hold greater subjective value to the degree that it is related to one’s core
values and sense of self. Identity-linked goals are more likely to be successful than
identity-irrelevant or identity-counter ones. Consultants and coaches can be particularly
helpful to clients in this arena by helping them discover core aspects of their self-
concepts and the ways those aspects are linked to the behavior change at hand. And
remember that identity is not a fixed construct, but rather is susceptible to framing,
reconstrual, and other kinds of subtle influences. 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.
Pursing goals and changing behavior is hard. Neuroscience will never change that fact,
but it can provide some brain-level explanations for the difficulty as well as some new
insights about how to mitigate it. This article reviewed the neuroscientific literatures on
the “way” of goal pursuit – the set of cognitive skills, capacities, and abilities collectively
known as executive function – and the “will” – the motivational factors that propel
behavior. Although parts of the “way” are limited by constraints that may be difficult to
change, the “will” can be influenced by incentives both within the person and without.
Though neuroscientific investigations into long-term behavior change are only just
starting to emerge they have already begun to contribute to the body of practical
scientific knowledge about goals. The science and practice of consulting psychology will
benefit directly from this research in the coming years.
Alvarez, J. A., & Emory, E. (2006). Executive function and the frontal lobes: A meta-
analytic review. Neuropsychology Review, 16(1), 17–42.
Baker, S. L., & Kirsch, I. (1991). Cognitive mediators of pain perception and tolerance.
Journal of Personality and Social Psychology, 61(3), 504–510.
Banich, M. T. (2009). Executive function: The search for an integrated account. Current
Directions in Psychological Science, 18(2), 89–94.
Bartra, O., McGuire, J. T., & Kable, J. W. (2013). The valuation system: A coordinate-
based meta-analysis of BOLD fMRI experiments examining neural correlates of
subjective value. NeuroImage, 76, 412427.
Baumeister, R. F., Bratslavsky, E., Muraven, M., & Tice, D. M. (1998). Ego depletion: Is
the active self a limited resource? Journal of Personality and Social Psychology,
74(5), 1252–1265.
Beauchamp, K. G., Kahn, L. E., & Berkman, E. T. (2016). Does inhibitory control
training transfer?: behavioral and neural effects on an untrained emotion regulation
task. Social Cognitive and Affective Neuroscience, 11(9), 1374–1382.
Bekinschtein, P., Cammarota, M., Katche, C., Slipczuk, L., Rossato, J. I., Goldin, A., et
al. (2008). BDNF is essential to promote persistence of long-term memory storage.
Proceedings of the National Academy of Sciences, 105(7), 2711–2716.
Berkman, E. T. (2016). Self-regulation training. In K. D. Vohs & R. F. Baumeister (Eds.),
Handbook of Self-Regulation (3rd ed., pp. 440–457). New York: Guilford Press.
Berkman, E. T., & Rock, D. (2014). AIM: An integrative model of goal pursuit.
NeuroLeadership Journal, 5, 111.
Berkman, E. T., Kahn, L. E., & Livingston, J. L. (2016). Valuation as a mechanism of
self-control and ego depletion. In Self-Regulation and Ego Control (pp. 255–279).
New York: Elsevier.
Berkman, E. T., Kahn, L. E., & Merchant, J. S. (2014). Training-induced changes in
inhibitory control network activity. The Journal of Neuroscience, 34(1), 149–157.
Berkman, E. T., & Lieberman, M. D. (2009). Using neuroscience to broaden emotion
regulation: Theoretical and methodological considerations. Social and Personality
Psychology Compass, 3(4), 475–493.
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. (2006). The debate over dopamine’s role in reward: The case for
incentive salience. Psychopharmacology, 191(3), 391–431.
Berridge, K. C., & Robinson, T. E. (2003). Parsing reward. Trends in Neurosciences,
26(9), 507–513.
Bigelow, G. E., & Silverman, K. (1999). Theoretical and empirical foundations of
contingency management treatments for drug abuse. In S. T. Higgins & K. Silverman
(Eds.), Motivating Behavior Change Among Illicit-Drug Abusers: Research on
Contingency Management Interventions (pp. 15–31). Washington, DC: American
Psychological Association.
Boksem, M. A. S., Meijman, T. F., & Lorist, M. M. (2006). Mental fatigue, motivation and
action monitoring. Biological Psychology, 72(2), 123–132.
Botvinick, M., & Braver, T. (2015). Motivation and cognitive control: From behavior to
neural mechanism. Annual Review of Psychology, 66(1), 83–113.
Bryan, C. J., Adams, G. S., & Monin, B. (2013). When cheating would make you a
cheater: Implicating the self prevents unethical behavior. Journal of Experimental
Psychology: General, 142(4), 1001–1005.
Bryan, C. J., Walton, G. M., Rogers, T., & Dweck, C. S. (2011). Motivating voter turnout
by invoking the self. Proceedings of the National Academy of Sciences, 108(31),
Cabanac, M. (1986). Money versus pain: Experimental study of a conflict in humans.
Journal of the Experimental Analysis of Behavior, 46(1), 37–44.
Carter, E. C., & McCullough, M. E. (2014). Publication bias and the limited strength
model of self-control: Has the evidence for ego depletion been overestimated?
Frontiers in Psychology, 5(1), 1–11.
Cascio, C. N., O'Donnell, M. B., Tinney, F. J., Lieberman, M. D., Taylor, S. E., Strecher,
V. J., & Falk, E. B. (2016). Self-affirmation activates brain systems associated with
self-related processing and reward and is reinforced by future orientation. Social
Cognitive and Affective Neuroscience, 11(4), 621–629.
Deci, E. L., & Ryan, R. M. (2000). The "what" and “why” of goal pursuits: Human needs
and the self-determination of behavior. Psychological Inquiry, 11(4), 227–268.
Elliot, A. J., & Harackiewicz, J. M. (1994). Goal setting, achievement orientation, and
intrinsic motivation: A mediational analysis. Journal of Personality and Social
Psychology, 66(5), 968–980.
Evans, D. R., Boggero, I. A., & Segerstrom, S. C. (2016). The nature of self-regulatory
fatigue and “ego depletion”: Lessons from physical fatigue. Personality and Social
Psychology Review, 20(4), 291–310.
Falk, E. B., O'Donnell, M. B., Cascio, C. N., Tinney, F., Kang, Y., Lieberman, M. D., et
al. (2015). Self-affirmation alters the brain's response to health messages and
subsequent behavior change. Proceedings of the National Academy of Sciences,
112(7), 201500247–7.
Foerde, K., Knowlton, B. J., & Poldrack, R. A. (2006). Modulation of competing memory
systems by distraction. Proceedings of the National Academy of Sciences, 103(31),
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),
Greenwald, A. G. (1980). The totalitarian ego: Fabrication and revision of personal
history. American Psychologist, 35(7), 603–618.
Greicius, M. D., Supekar, K., Menon, V., & Dougherty, R. F. (2008). Resting-State
Functional Connectivity Reflects Structural Connectivity in the Default Mode Network.
Cerebral Cortex, 19(1), 72–78.
Hagger, M. S., & Chatzisarantis, N. (2016). A multi-lab pre-registered replication of the
ego-depletion effect. Perspectives on Psychological Science, 11(4), 546-573.
Hagger, M. S., Wood, C., Stiff, C., & Chatzisarantis, N. L. D. (2010). Ego depletion and
the strength model of self-control: A meta-analysis. Psychological Bulletin, 136(4),
Hare, T. A., Camerer, C. F., Knoepfle, D. T., O'Doherty, J. P., & Rangel, A. (2010).
Value computations in ventral medial prefrontal cortex during charitable decision
making incorporate input from regions involved in social cognition. The Journal of
Neuroscience, 30(2), 583–590.
Hare, T. A., Schultz, W., Camerer, C. F., O'Doherty, J. P., & Rangel, A. (2011).
Transformation of stimulus value signals into motor commands during simple choice.
Proceedings of the National Academy of Sciences, 108(44), 18120–18125.
Higgins, E. T., Chen Idson, L., Freitas, A. L., Spiegel, S., & Molden, D. C. (2003).
Transfer of value from fit. Journal of Personality and Social Psychology, 84(6), 1140–
Hutcherson, C. A., Montaser-Kouhsari, L., Woodward, J., & Rangel, A. (2015).
Emotional and utilitarian appraisals of moral dilemmas are encoded in separate
areas and integrated in ventromedial prefrontal cortex. The Journal of Neuroscience,
35(36), 12593–12605.
Hutcherson, C. A., Plassmann, H., Gross, J. J., & Rangel, A. (2012). Cognitive
regulation during decision making shifts behavioral control between ventromedial and
dorsolateral prefrontal value systems. The Journal of Neuroscience, 32(39), 13543–
Inzlicht, M., & Berkman, E. (2015). Six questions for the resource model of control (and
some answers). Social and Personality Psychology Compass, 9(10), 511–524.
Inzlicht, M., Schmeichel, B. J., & Macrae, C. N. (2014). Why self-control seems (but
may not be) limited. Trends in Cognitive Sciences, 18(3), 127–133.
Jaeggi, S. M., Buschkuehl, M., Jonides, J., & Shah, P. (2011). Short- and long-term
benefits of cognitive training. Proceedings of the National Academy of Sciences,
108(5), 10081–10086.
Kahneman, D., & Tversky, A. (1984). Values, choices and frames. American
Psychologist, 39(4), 341–350.
Kouneiher, F., Charron, S., & Koechlin, E. (2009). Motivation and cognitive control in the
human prefrontal cortex. Nature Neuroscience.
Krajbich, I., Armel, C., & Rangel, A. (2010). Visual fixations and the computation and
comparison of value in simple choice. Nature Neuroscience, 13(10), 1292–1298.
Kruglanski, A. W., Shah, J. Y., Fishbach, A., Friedman, R., Chun, W. Y., & Sleeth-
Keppler, D. (2002). A theory of goal systems. Advances in Experimental Social
Psychology, 34(1), 331–378.
Kurzban, R. (2010). Does the brain consume additional glucose during self-control
tasks? Evolutionary Psychology, 8(2), 244–259.
Kurzban, R., Duckworth, A., Kable, J. W., & Myers, J. (2013). An opportunity cost model
of subjective effort and task performance. The Behavioral and Brain Sciences,
36(06), 661–679.
Lebreton, M., Jorge, S., Michel, V., Thirion, B., & Pessiglione, M. (2009). An automatic
valuation system in the human brain: Evidence from functional neuroimaging.
Neuron, 64(3), 431–439.
Lenartowicz, A., Verbruggen, F., Logan, G. D., & Poldrack, R. A. (2011). Inhibition-
related activation in the right inferior frontal gyrus in the absence of inhibitory cues.
Journal of Cognitive Neuroscience, 23(11), 3388-3399.
Levy, D. J., & Glimcher, P. W. (2011). Comparing apples and oranges: Using reward-
specific and reward-general subjective value representation in the brain. The Journal
of Neuroscience, 31(41), 14693–14707.
Lieberman, M. D., & Eisenberger, N. I. (2015). The dorsal anterior cingulate cortex is
selective for pain: Results from large-scale reverse inference. Proceedings of the
National Academy of Sciences, 112(49), 15250–15255.
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.
Lurquin, J. H., Michaelson, L. E., Barker, J. E., Gustavson, D. E., Bastian, von, C. C.,
Carruth, N. P., & Miyake, A. (2016). No evidence of the ego-depletion effect across
task characteristics and individual differences: A pre-registered study. PLoS ONE,
11(2), e0147770–20.
Magen, E., & Gross, J. J. (2007). Harnessing the need for immediate gratification:
Cognitive reconstrual modulates the reward value of temptations. Emotion, 7(2),
McClelland, D. C. (1985). Human Motivation. Glenview, IL: Scott, Foresman and
Menon, V., & Uddin, L. Q. (2010). Saliency, switching, attention and control: a network
model of insula function. Brain Structure and Function, 214(5-6), 655667.
Miles, E., Sheeran, P., Baird, H., Macdonald, I., Webb, T. L., & Harris, P. R. (in press).
Does self-control improve with practice? Evidence from a six-week training program.
Journal of Experimental Psychology: General, 118.
Miller, E. K., & Cohen, J. D. (2001). An integrative theory of prefrontal cortex function.
Annual Review of Neuroscience, 24, 167202.
Miyake, A., Friedman, N. P., Emerson, M. J., Witzki, A. H., Howerter, A., & Wager, T. D.
(2000). The unity and diversity of executive functions and their contributions to
complex “frontal lobe” tasks: A latent variable analysis. Cognitive Psychology, 41(1),
Muraven, M. (2010). Building self-control strength: Practicing self-control leads to
improved self-control performance. Journal of Experimental Social Psychology,
46(2), 465–468.
Murayama, K., Matsumoto, M., Izuma, K., Sugiura, A., Ryan, R. M., Deci, E. L., &
Matsumoto, K. (2013). How self-determined choice facilitates performance: A key
role of the ventromedial prefrontal cortex. Cerebral Cortex, 25(5), 1241–1251.
Muscatell, K. A., Dedovic, K., Slavich, G. M., Jarcho, M. R., Breen, E. C., Bower, J. E.,
et al. (2016). Neural mechanisms linking social status and inflammatory responses to
social stress. Social Cognitive and Affective Neuroscience, 11(6), 915–922.
Nee, D. E., Brown, J. W., Askren, M. K., Berman, M. G., Demiralp, E., Krawitz, A., &
Jonides, J. (2012). A meta-analysis of executive components of working memory.
Cerebral Cortex.
Niendam, T. A., Laird, A. R., Ray, K. L., Dean, Y. M., Glahn, D. C., & Carter, C. S.
(2012). Meta-analytic evidence for a superordinate cognitive control network
subserving diverse executive functions. Cognitive, Affective, and Behavioral
Neuroscience, 12(2), 241–268.
Northoff, G., & Hayes, D. J. (2011). Is our self nothing but reward? Biological
Psychiatry, 69(11), 1019–1025.
Northoff, G., Heinzel, A., de Greck, M., Bermpohl, F., Dobrowolny, H., & Panksepp, J.
(2006). Self-referential processing in our brain—A meta-analysis of imaging studies
on the self. NeuroImage, 31(1), 440–457.
Pelham, B. W., & Swann, W. B. (1989). From self-conceptions to self-worth: On the
sources and structure of global self-esteem. Journal of Personality and Social
Psychology, 57(4), 672–680.
Prendergast, M., Podus, D., Finney, J., Greenwell, L., & Roll, J. (2006). Contingency
management for treatment of substance use disorders: a meta-analysis. Addiction,
101(11), 1546–1560.
Prochaska, J. O., DiClemente, C. C., & Norcross, J. C. (1992). In search of how people
change: Applications to addictive behaviors. The American Psychologist, 47(9),
Rangel, A., & Hare, T. (2010). Neural computations associated with goal-directed
choice. Current Opinion in Neurobiology, 20(2), 262–270.
Redick, T. S., Shipstead, Z., Harrison, T. L., Hicks, K. L., Fried, D. E., Hambrick, D. Z.,
et al. (2013). No evidence of intelligence improvement after working memory training:
A randomized, placebo-controlled study. Journal of Experimental Psychology:
General, 142(2), 359-379.
Rescorla, R. A., & Wagner, A. R. (1972). A theory of Pavlovian conditioning: Variations
in the effectiveness of reinforcement and nonreinforcement. Classical Conditioning II
Current Research and Theory, 2, 6499.
Roos, L.E., Knight, E.L., Beauchamp, K.G., Berkman, E.T., Faraday, K., Hyslop, K., and
Fisher, P.A. (2017). Acute stress impairs inhibitory control based on individual
differences in parasympathetic nervous system activity. Biological Psychology, 125,
Rosenberg, M. (1979). Conceiving the Self. New York: Basic Books.
Schwartz, J., Mochon, D., Wyper, L., Maroba, J., Patel, D., & Ariely, D. (2014). Healthier
by precommitment. Psychological Science, 25(2), 538–546.
Shenhav, A., Cohen, J. D., & Botvinick, M. M. (2016). Dorsal anterior cingulate cortex
and the value of control. Nature Neuroscience, 19(10), 1286–1291.
Shipstead, Z., Harrison, T. L., & Engle, R. W. (2016). Working memory capacity and
fluid intelligence: Maintenance and disengagement. Perspectives on Psychological
Science, 11(6), 771–799.
Shute, V. J., Ventura, M., & Ke, F. (2015). The power of play: The effects of Portal 2
and Lumosity on cognitive and noncognitive skills. Computers & Education, 80, 58-
Stuss, D. T. (2011). Functions of the frontal lobes: Relation to executive functions.
Journal of the International Neuropsychological Society, 17(05), 759–765.
Stuss, D. T., & Knight, R. T. (2012). Principles of Frontal Lobe Function (2nd ed.). New
York: Oxford University Press.
Tom, S. M., Fox, C. R., Trepel, C., & Poldrack, R. A. (2007). The neural basis of loss
aversion in decision-making under risk. Science, 315(5811), 515–518.
Unsworth, N., Fukuda, K., Awh, E., & Vogel, E. K. (2015). Working memory delay
activity predicts individual differences in cognitive abilities. Journal of Cognitive
Neuroscience, 27(5), 853–865.
Wallis, J. D. (2007). Orbitofrontal cortex and its contribution to decision-making. Annual
Review of Neuroscience, 30(1), 31–56.
Westbrook, A., & Braver, T. S. (2015). Cognitive effort: A neuroeconomic approach.
Cognitive, Affective, and Behavioral Neuroscience, 15(2), 395–415.
Whitehead, A. N. (1911). An Introduction to Mathematics. New York: Holt.
Wood, W., & Neal, D. T. (2007). A new look at habits and the habit-goal interface.
Psychological Review, 114(4), 843–863.
Wunderlich, K., Rangel, A., & O'Doherty, J. P. (2010). Economic choices can be made
using only stimulus values. Proceedings of the National Academy of Sciences,
107(34), 15005–15010.
Yarkoni, T., Poldrack, R. A., Nichols, T. E., Van Essen, D. C., & Wager, T. D. (2011).
Large-scale automated synthesis of human functional neuroimaging data. Nature
Methods, 8(8), 665–670.
Yin, H. H., Mulcare, S. P., Hilário, M. R. F., Clouse, E., Holloway, T., Davis, M. I., et al.
(2009). Dynamic reorganization of striatal circuits during the acquisition and
consolidation of a skill. Nature Neuroscience, 12(3), 333–341.
Zaki, J., Lopez, G., & Mitchell, J. P. (2014). Activity in ventromedial prefrontal cortex co-
varies with revealed social preferences: Evidence for person-invariant value. Social
Cognitive and Affective Neuroscience, 9(4), 464–469.
Zhang, W., & Luck, S. J. (2008). Discrete fixed-resolution representations in visual
working memory. Nature, 453(7192), 233–235.
This work was supported by grants AG048840, CA175241, and DA035763 from the National
Institutes of Health to ETB, as well as support from the Bezos Family Foundation and the
Center for the Developing Child at Harvard University.
Figure Captions
Figure 1. Behavior can be divided into four broad categories defined by the level of
motivation they demand (horizontal axis) and the level of skill or ability they require
(vertical axis). Behavior change typically involves moving from left-to-right, from bottom-
to-top, or both. Moving from left-to-right increases the motivational demand (why) of an
action, whereas moving from bottom-to-top increases the skill level (how). It is useful to
identify the vector of change required during goal pursuit and to target motivational
(horizontal) and cognitive (vertical) processes as necessary.
Figure 2. Regions implicated in the will and the way. Left: Lateral view featuring the
lateral prefrontal cortex (LPFC) and the ventrolateral prefrontal cortex (VLPFC),
premotor cortex (pMC) and motor cortex (MC), and the temporalparietal junction (TPJ)
and supramarginal gyrus (SMG). Top Right: Medial view featuring the dorsal anterior
cingulate cortex (dACC) and ventral striatum (vS), and the dorsomedial (dmPFC),
medial (mPFC) and ventromedial (vmPFC) aspects of the prefrontal cortex. Bottom
Right: Coronal view featuring the ventral (vS) and dorsolateral (dlS) aspects of the
... Future-oriented thinking is a broad construct, which characterizes the various cognitive capabilities employed to generate future states of thought and project oneself into a variety of hypothetical scenarios in the future (Atance and O'Neill, 2005;Szpunar et al., 2014). The ability to mentally project oneself into a desired future state-whether that be a mental simulation as a keynote speaker or achieving a high distinction on a university assignment is argued to be an important function in our capacities as human beings to set goals and guide behavior (Stanescu and Iorga, 2015;Andre et al., 2018;Berkman, 2018). ...
... Temporal distance is important when considering the likelihood of goal self-congruence, which refers to the degree to which one actively works toward achieving a goal, which is consistent with the view that one holds of themselves (Peetz et al., 2009;Ernst et al., 2018). It is argued that short-term goals are often easier to establish than long-term goals, as shortterm goals require less cognitive input, such as implementation and planning facilitated by the prefrontal cortex (Berkman, 2018), are more concrete (i.e., grounded in the present), and are considerable drivers of self-regulatory behavior, such that they contain more instant motivational properties (i.e., gratification and reward) that can be accessed earlier (Bulley et al., 2016). ...
... A unique aspect of pursuing a degree in academia, whether that be an undergraduate, postgraduate diploma or finishing high school is recognized in delays of reward or gratification (also known as delayed gratification). Being recognized as a key contributor to sustained effort, Berkman (2018) suggests that reward and recognition are not typically realized in academia for years due to the typical length of university degrees. In Australia, this can be upwards of four years for an undergraduate degree (Study Australia, 2022). ...
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Future-oriented thought is a broad construct that characterize the ability to generate mental representations of the future and project oneself into a variety of hypothetical states. It is well established that the degree to which one is focused more on the past, present, or future has a variety of implications on psychological functioning. This study focuses on the relationship between future-oriented thought and academic performance of students. To bridge this gap, we conducted the first systematic review investigating the benefit of future-oriented thought on promoting positive outcomes in academic settings. Our systematic review comprised 21 studies ( k = 21). Results identified a clear relationship between future-oriented thought and positive outcomes in academic settings. Furthermore, our systematic review reveals important relationships between future-oriented thought and academic engagement, as well as future-oriented thought and academic performance. Our findings suggest that those who are more future-oriented demonstrate higher levels of academic engagement compared to those who were less future-oriented. Our findings suggest that probing and guiding students toward a future goal may increase their academic engagement and performance.
... Such behavioral changes include health-related changes, such as getting more exercise or eating healthier, spending more time with family, or spending time more effectively. However, changing behavior is difficult and assistance is, therefore, very welcome [2]. Technology such as AI is increasingly used to provide such assistance, since, unlike human coaches, it is relatively cheap, and always available to help [24]. ...
... These further changes are drawn from the actions which the desired change is agnostic about 3 , which we name "repair options", and, in fact, we only consider minimal repair options 4 (denoted MRO). 2 Although people might in practice wish non-achievable things, e.g. biking and driving to work at the same time, a system trying to help them achieve conflicting things would not be very useful. ...
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Changing one’s behavior is difficult, so many people look towards technology for help. However, most current behavior change support systems are inflexible in that they support one type of behavior change and do not reason about how that behavior is embedded in larger behavior patterns. To allow users to flexibly decide what they desire to change, a system needs to represent and reason about that desire. Moreover, we argue that reasoning about the context of a behavior could improve an agent’s support. Therefore, we propose a formal framework for a reasoning agent to represent and reason about the personal behavioral context of desired user changes. This framework models an individual’s possible and current behavior, their desire for change, as well as other relevant changes that a system could use to support a desired change. In a user survey we show that people feel these other relevant changes would be useful in more flexibly supporting their desired change in behavior. This work provides a foundation for more flexible personalized behavior change support.
... The activated RAS guides our brain through the exhausting process of attention, improving our filter of reality, by filtering the inputs we receive and considering what is important to achieve our goals (Garcia-Rill et al., 2013). Including the values in the vision offers the necessary sense of purpose that act as fuel, involving the neural substrates of motivational behaviour in the brain, which keeps the individuals moving ahead with determination and strong will (Berkman, 2018). ...
... The mission is how the vision is accomplished through daily operations, and includes the more operational values of the Triaxial model, offering a sense of purpose fulfilment every day and, hence, an important sense of realisation, undeniably necessary, in daily routines and activities. To execute the mission in an effective way, individuals must use the neural pathways of the executive control of their brains, learning, acquiring new capabilities, and attaining behavioural change to become closer to their future vision every single day (Berkman, 2018). ...
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In this paper, we present an original framework that applies the latest research in neuroscience and psychology to enhance the concept of resilience. We borrow and focus on the intersection of neurobiology and cognitive behavioural therapies in proposing an innovative angle to build resilience.
... С другой стороны, при таком большом количестве определений целей, необходимым становится выделение ключевых их характеристик. Конструкция цели была по-разному определена в терминах познания (Locke, 2000;Locke & Latham, 1990;Fishbach & Ferguson, 2007), поведения (Bargh et al., 2001;Elliot, 2005), аффекта (Pervin, 1983;Ferguson & Bargh, 2004), направленности личности (Васильев, 2016), нейробиологии (Berkman & Rock, 2014;Berkman, 2018). ...
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Введение. В статье представлен обзор категории «цель» с психологической точки зрения, раскрывается состояние исследования целей в настоящее время. Обобщены результаты порядка 100 литературных источников, включая эмпирические исследования целей. Предпринимается попытка собрать определения цели и обобщить их в единый конструкт. Показаны структура и свойства целей, отношения между средствами и целями, иерархия целей и подцелей, взаимосвязь между целями и планами, процессы постановки и достижения целей. Новизна работы заключается в уточнении категории «цель» с учетом имеющихся на данный момент научных знаний, конструировании психологического феномена «цель» на основе ключевых характеристик, используемых в литературе, выявлены дефициты в сфере изучения целей. Теоретическое обоснование. Теоретические наработки психологии в данной области используются во всех сферах деятельности – в образовании, на производстве, в спорте, системе здравоохранения и быту. Внимание уделяется конфликту целей и распределению ресурсов между несколькими целями, модели T.O.T.E., модели фаз действия «Рубикон», теории разворота Аптера, концепции психологической дистанции до цели, теме целенаправленного поведения с кибернетических позиций. Результаты. Автор дает исторический экскурс, касающийся категории цель конца XIX–XX вв., также отражены результаты исследований современного времени. На основании проведенного анализа установлено, что категория «цель» в XX в. бурно развивается и становится одним из звеньев мотивационной сферы человека. Обсуждение результатов. Категория «цель» в психологии является объектом исследования различных психологических школ и направлений и имеет ключевое значение в прогнозировании поведения, занимает одно из центральных мест в психологии личности. На данном этапе развития психологической науки недостаточно изучены вопросы динамики системы целей в различной экологической среде и во времени, характер связей в системе целей раскрыт в виде иерархических схем, без учета весов самих связей.
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Client feedback research is a new but encouraging area that recognizes the importance of engaging patients in offered treatments and the relevance of the relationship between therapist and client. This study aimed to explore clients' experiences of goal-oriented work using Personal Projects Analysis (PPA). PPA was applied to 5 participants of a psychodrama group after their consent and in agreement with the ethics and deontology research university committee. Their progress was evaluated with Clinical Outcomes in Routine Evaluation Outcome Measure (CORE-OM; 4 moments) and subjective well-being measures. Findings show how personal projects may be informative about clients' difficulties and change processes. All outcomes on CORE-OM went below clinical cut-off points, and all these changes are reliable and clinically significant. PPA offers a consistent way to implement the goals approach in a psychotherapeutic context successfully. Nevertheless, some adjustments need to be implemented in the goal-oriented work using PPA.
Background: Error reporting and speaking up are mechanisms to reduce the incidence of healthcare errors. However, organizational policies don't always align with individuals' perceptions and beliefs to promote these mechanisms. When this misalignment produces fear, moral courage, which is taking action regardless of personal consequences, becomes necessary. Teaching moral courage in pre-licensure education may set a foundation for individuals to speak up in post-licensure careers. Aim: To explore health professionals' perceptions of healthcare reporting and organizational culture to inform pre-licensure education on how to promote moral courage. Methods: Thematic analysis of four semi-structured focus groups with fourteen health professions educators followed by in-depth, semi-structured individual interviews. Findings: Organizational factors, characteristics that an individual must possess to enact moral courage and priority methods to guide moral courage were identified. Conclusions: This study outlines the need for leadership education in moral courage and offers educational interventions to promote reporting and aid in developing moral courage academic guidelines to improve healthcare error reporting and speaking up behaviors.
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L’objectif principal de ce projet était d’appliquer les connaissances en neuroéducation sur les deux réseaux cérébraux, le réseau de saillance (SN) et le réseau de mode par défaut (DMN) et leur importance dans le processus d’apprentissage pour activer et maintenir l’attention et la motivation des étudiants. Suite à l'explication de comment notre cerveau fonctionne lors de l'apprentissage d'une langue seconde, les commentaires que j’ai reçus de la part des étudiants suggèrent qu'ils sont effectivement motivés à participer à mes cours. Cependant, comme il n’y a pas de rétroaction concernant l’attention, je ne peux pas en tirer des conclusions quantitatives. Une grande limite de ce projet est le questionnaire, qui était trop ouvert et n’abordait pas l’aspect de l’attention. Une autre limite de ce projet est que je ne peux pas comparer la motivation et l’attention au début et à la fin du projet. En conséquence, je ne peux pas dire si la forte motivation est effectivement due à mes méthodes d’enseignement ou à l'explication de comment le cerveau fonctionne. Enfin, le temps du projet était trop court pour tirer des conclusions significatives sur le succès des techniques d’enseignement, c’est pourquoi j’ai l’intention de poursuivre ce projet jusqu’à la fin de cette année scolaire et de répéter le questionnaire avec des questions plus précises les années à venir.
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Identifying environmental influences on inhibitory control (IC) may help promote positive behavioral and social adjustment. Although chronic stress is known to predict lower IC, the immediate effects of acute stress are unknown. The parasympathetic nervous system (PNS) may be a mechanism of the stress-IC link, given its psychophysiological regulatory role and connections to prefrontal brain regions critical to IC. We used a focused assessment of IC (the stop-signal task) to test whether an acute social stressor (the Trier Social Stress Test) affected participants’ pre- to post-IC performance (n = 58), compared to a control manipulation (n = 31). High frequency heart-rate variability was used as an index of PNS activity in response to the manipulation. Results indicated that stress impaired IC performance, blocking the practice effects observed in control participants. We also investigated the associations between PNS activity and IC; higher resting PNS activity predicted better pre-manipulation IC, and greater PNS stressor reactivity protected against the negative effects of stress on IC. Together, these results are the first to document the immediate effects of acute stress on IC and a phenotypic marker (PNS reactivity to stressors) of susceptibility to stress-induced IC impairment. This study suggests a new way to identify situations in which individuals are likely to exhibit IC vulnerability and related consequences such as impulsivity and risk taking behavior. Targeting PNS regulation may represent a novel target for IC-focused interventions.
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Working memory capacity and fluid intelligence have been demonstrated to be strongly correlated traits. Typically, high working memory capacity is believed to facilitate reasoning through accurate maintenance of relevant information. In this article, we present a proposal reframing this issue, such that tests of working memory capacity and fluid intelligence are seen as measuring complementary processes that facilitate complex cognition. Respectively, these are the ability to maintain access to critical information and the ability to disengage from or block outdated information. In the realm of problem solving, high working memory capacity allows a person to represent and maintain a problem accurately and stably, so that hypothesis testing can be conducted. However, as hypotheses are disproven or become untenable, disengaging from outdated problem solving attempts becomes important so that new hypotheses can be generated and tested. From this perspective, the strong correlation between working memory capacity and fluid intelligence is due not to one ability having a causal influence on the other but to separate attention-demanding mental functions that can be contrary to one another but are organized around top-down processing goals.
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This chapter presents a model of self-control that explains many phenomena related to self-control including ego depletion. We propose that valuation-the process of integrating multiple sources of subjective value for a given choice option-is a mechanism of self-control. The conflict between a goal and an impulse or another goal is resolved by comparing the cumulative subjective value of the choice options. A given choice option can have an arbitrary number of value sources, and these sources can shift over time depending on situational or intrapersonal constraints. We review the behavioral economics literature on three anomalies in valuation that are directly relevant to self-control (endowment, delay discounting, and diminishing marginal utility) and explain how the properties of the valuation system explain the ego-depletion effect. We close by discussing ways to improve self-control through interventions that target its sources of value, notably including identity.
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Recent studies have documented that self-determined choice does indeed enhance performance. However, the precise neural mechanisms underlying this effect are not well understood. We examined the neural correlates of the facilitative effects of self-determined choice using functional magnetic resonance imaging (fMRI). Participants played a game-like task involving a stopwatch with either a stopwatch they selected (self-determined-choice condition) or one they were assigned without choice (forced-choice condition). Our results showed that self-determined choice enhanced performance on the stopwatch task, despite the fact that the choices were clearly irrelevant to task difficulty. Neuroimaging results showed that failure feedback, compared with success feedback, elicited a drop in the vmPFC activation in the forced-choice condition, but not in the self-determined-choice condition, indicating that negative reward value associated with the failure feedback vanished in the self-determined-choice condition. Moreover, the vmPFC resilience to failure in the self-determined-choice condition was significantly correlated with the increased performance. Striatal responses to failure and success feedback were not modulated by the choice condition, indicating the dissociation between the vmPFC and striatal activation pattern. These findings suggest that the vmPFC plays a unique and critical role in the facilitative effects of self-determined choice on performance.
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Inhibitory control (IC) is a critical neurocognitive skill for successfully navigating challenges across domains. Several studies have attempted to use training to improve neurocognitive skills such as IC, but few have found that training generalizes to performance on non-trained tasks. We used functional magnetic resonance imaging (fMRI) to investigate the effect of IC training on a related but untrained emotion regulation (ER) task with the goal of clarifying how training alters brain function and why its effects typically do not transfer across tasks. We suggest hypotheses for training-related changes in activation relevant to transfer effects: the strength model and several plausible alternatives (shifting priorities, stimulus-response automaticity, scaffolding). Sixty participants completed three weeks of IC training and underwent fMRI scanning before and after. The training produced pre- to post-training changes in neural activation during the ER task in the absence of behavioral changes. Specifically, individuals in the training group demonstrated reduced activation during ER in the left inferior frontal gyrus and supramarginal gyrus, key regions in the IC neural network. This result is less consistent with the strength model and more consistent with a motivational account. Implications for future work aiming to further pinpoint mechanisms of training transfer are discussed.
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Good self-control has been linked to adaptive outcomes such as better health, cohesive personal relationships, success in the workplace and at school, and less susceptibility to crime and addictions. In contrast, self-control failure is linked to maladaptive outcomes. Understanding the mechanisms by which self-control predicts behavior may assist in promoting better regulation and outcomes. A popular approach to understanding self-control is the strength or resource depletion model. Self-control is conceptualized as a limited resource that becomes depleted after a period of exertion resulting in self-control failure. The model has typically been tested using a sequential-task experimental paradigm, in which people completing an initial self-control task have reduced self-control capacity and poorer performance on a subsequent task, a state known as ego depletion. Although a meta-analysis of ego-depletion experiments found a medium-sized effect, subsequent meta-analyses have questioned the size and existence of the effect and identified instances of possible bias. The analyses served as a catalyst for the current Registered Replication Report of the ego-depletion effect. Multiple laboratories (k = 23, total N = 2,141) conducted replications of a standardized ego-depletion protocol based on a sequential-task paradigm by Sripada et al. Meta-analysis of the studies revealed that the size of the ego-depletion effect was small with 95% confidence intervals (CIs) that encompassed zero (d = 0.04, 95% CI [−0.07, 0.15]. We discuss implications of the findings for the ego-depletion effect and the resource depletion model of self-control.
Debates over the function(s) of dorsal anterior cingulate cortex (dACC) have persisted for decades. So too have demonstrations of the region's association with cognitive control. Researchers have struggled to account for this association and, simultaneously, dACC's involvement in phenomena related to evaluation and motivation. We describe a recent integrative theory that achieves this goal. It proposes that dACC serves to specify the currently optimal allocation of control by determining the overall expected value of control (EVC), thereby licensing the associated cognitive effort. The EVC theory accounts for dACC's sensitivity to a wide array of experimental variables, and their relationship to subsequent control adjustments. Finally, we contrast our theory with a recent theory proposing a primary role for dACC in foraging-like decisions. We describe why the EVC theory offers a more comprehensive and coherent account of dACC function, including dACC's particular involvement in decisions regarding foraging or otherwise altering one's behavior.
Numerous experiments have recently sought to identify neural signals associated with the subjective value (SV) of choice alternatives. Theoretically, SV assessment is an intermediate computational step during decision making, in which alternatives are placed on a common scale to facilitate value-maximizing choice. Here we present a quantitative, coordinate-based meta-analysis of 206 published fMRI studies investigating neural correlates of SV. Our results identify two general patterns of SV-correlated brain responses. In one set of regions, both positive and negative effects of SV on BOLD are reported at above-chance rates across the literature. Areas exhibiting this pattern include anterior insula, dorsomedialprefrontal cortex, dorsal and posterior striatum, and thalamus. The mixture of positive and negative effects potentially reflects an underlying U-shaped function, indicative of signal related to arousal or salience. In a second set of areas, including ventromedial prefrontal cortex and anterior ventral striatum, positive effects predominate. Positive effects in the latter regions are seen both when a decision is confronted and when an outcome is delivered, as well as for both monetary and primary rewards. These regions appear to constitute a “valuation system,” carrying a domain-general SV signal and potentially contributing to value-based decision making.
Can self-control be improved through practice? Several studies have found that repeated practice of tasks involving self-control improves performance on other tasks relevant to self-control. However, in many of these studies, improvements after training could be attributable to methodological factors (e.g., passive control conditions). Moreover, the extent to which the effects of training transfer to real-life settings is not yet clear. In the present research, participants (N 174) completed a 6-week training program of either cognitive or behavioral self-control tasks. We then tested the effects of practice on a range of measures of self-control, including lab-based and real-world tasks. Training was compared with both active and no-contact control conditions. Despite high levels of adherence to the training tasks, there was no effect of training on any measure of self-control. Trained participants did not, for example, show reduced ego depletion effects, become better at overcoming their habits, or report exerting more self-control in everyday life. Moderation analyses found no evidence that training was effective only among particular groups of participants. Bayesian analyses suggested that the data was more consistent with a null effect of training on self-control than with previous estimates of the effect of practice. The implication is that training self-control through repeated practice does not result in generalized improvements in self-control.