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Self-Determination, Self-Regulation, and the Brain: Autonomy Improves Performance by Enhancing Neuroaffective Responsiveness to Self-Regulation Failure


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The importance of autonomous motivation in improving self-regulation has been a focal topic of motivation research for almost 3 decades. Despite this extensive research, however, there has not yet been a mechanistic account of how autonomous motivation works to boost self-regulatory functioning. To address this issue, we examined the role of autonomy in 2 basic self-regulation tasks while recording a neural signal of self-regulation failure (i.e., the error-related negativity; ERN). Based on the notion that autonomy improves self-regulation, we anticipated that autonomous motivation would enhance neuroaffective responsiveness to self-regulatory failure and thus improve performance relative to controlled motivation. In Study 1 (N = 43), we found that trait autonomy was positively associated with self-regulatory performance and that this effect was mediated by increased brain-based sensitivity to self-regulation failure, as demonstrated by a larger ERN. Study 2 (N = 55) replicated and extended this pattern using an experimental manipulation of autonomy; when autonomous motivation was contextually supported, task performance increased relative to those for whom autonomy was undermined and those in a neutral condition. In addition, this effect was mediated by both increased perceptions of autonomy and larger ERN amplitudes. These findings offer deeper insight into the links among motivational orientation, brain-based performance monitoring, and self-regulation. (PsycINFO Database Record (c) 2012 APA, all rights reserved).
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Journal of Personality and Social Psychology
Self-Determination, Self-Regulation, and the Brain:
Autonomy Improves Performance by Enhancing
Neuroaffective Responsiveness to Self-Regulation Failure
Lisa Legault and Michael Inzlicht
Online First Publication, October 29, 2012. doi: 10.1037/a0030426
Legault, L., & Inzlicht, M. (2012, October 29). Self-Determination, Self-Regulation, and the
Brain: Autonomy Improves Performance by Enhancing Neuroaffective Responsiveness to
Self-Regulation Failure. Journal of Personality and Social Psychology. Advance online
publication. doi: 10.1037/a0030426
Self-Determination, Self-Regulation, and the Brain: Autonomy Improves
Performance by Enhancing Neuroaffective Responsiveness to
Self-Regulation Failure
Lisa Legault and Michael Inzlicht
University of Toronto
The importance of autonomous motivation in improving self-regulation has been a focal topic of
motivation research for almost 3 decades. Despite this extensive research, however, there has not yet been
a mechanistic account of how autonomous motivation works to boost self-regulatory functioning. To
address this issue, we examined the role of autonomy in 2 basic self-regulation tasks while recording a
neural signal of self-regulation failure (i.e., the error-related negativity; ERN). Based on the notion that
autonomy improves self-regulation, we anticipated that autonomous motivation would enhance neuroaf-
fective responsiveness to self-regulatory failure and thus improve performance relative to controlled
motivation. In Study 1 (N43), we found that trait autonomy was positively associated with
self-regulatory performance and that this effect was mediated by increased brain-based sensitivity to
self-regulation failure, as demonstrated by a larger ERN. Study 2 (N55) replicated and extended this
pattern using an experimental manipulation of autonomy; when autonomous motivation was contextually
supported, task performance increased relative to those for whom autonomy was undermined and those
in a neutral condition. In addition, this effect was mediated by both increased perceptions of autonomy
and larger ERN amplitudes. These findings offer deeper insight into the links among motivational
orientation, brain-based performance monitoring, and self-regulation.
Keywords: autonomy, motivation, self-regulation, error-related negativity, performance monitoring
Human autonomy plays a pivotal role in self-regulation and
performance. Whatever the behavioral domain, feelings of engage-
ment, diligence, and vitality are higher when the motivation un-
derlying a goal or behavior is autonomous or self-endorsed rather
than pressured or controlled. As a result, goal-related performance
tends to be better. Researchers attribute the effect of autonomy on
goal-regulation to the fact that autonomy represents volition and
cohesion in action. In other words, feelings of choice, interest,
deep personal relevance, and internal causality underlie the expe-
rience of autonomous behavior, which energizes and sustains
This explanation, however, does not address the precise mech-
anism responsible for the self-regulatory or goal-related benefits of
autonomy. Although many studies in social, personality, and mo-
tivational psychology have noted that autonomy is critical to good
self-regulation, little is known about why, exactly, autonomy leads
to better self-regulation. Therefore, we seek a deeper understand-
ing of the effect of autonomy on self-regulatory performance. By
inspecting the neural mechanisms that link autonomy to self-
regulatory performance, we hope to illustrate why autonomous
motivation is more effective and efficient than other forms of
motivation. More specifically, we assess error-related brain activ-
ity in order to test whether autonomy improves performance by
promoting receptivity to instances when self-regulation should be
Self-Regulation and Self-Determination Theory
The ability to control and restrain automatic impulses and habits
in the service of goal attainment is the oft-cited crux of self-
regulation (cf. self-control; Barkley, 1997;Miyake et al., 2000),
but it also refers more generally to the many processes individuals
use to manage behavior, thoughts, and emotions (Fujita, 2011). It
is, therefore, an extremely important executive function and, in-
deed, one of the defining features of human behavior (Baumeister,
Heatherton, & Tice, 1994;Damasio, 1994). Moreover, the failure
of self-regulation is one of the central problems of the human
condition. For instance, deficits in self-regulation are found in a
large number of psychological disorders including attention-defi-
cit/hyperactivity disorder [ADHD], antisocial personality disorder,
borderline personality disorder, addiction, eating disorders, and
impulse control disorders (Barkley, 1997;Pierce & Cheney, 2004).
In contrast, good self-regulators—those who can aptly manage the
circumstances and impulses that obstruct goal attainment—are
happier, healthier, and more productive (e.g., Tangney, Baumeis-
ter, & Boone, 2004).
Lisa Legault and Michael Inzlicht, Psychology Department, University
of Toronto, Toronto, Ontario, Canada.
This research was supported by a postdoctoral fellowship awarded to
Lisa Legault from the Social Sciences and Humanities Research Council of
Canada, as well as by grants from the Canada Foundation for Innovation
and the Ontario Ministry of Research and Innovation awarded to Michael
Correspondence concerning this article should be addressed to Lisa Legault,
who is now at the Department of Psychology, Clarkson University, 8 Clarkson
Avenue, Potsdam, NY 13699-5825. E-mail:
Journal of Personality and Social Psychology © 2012 American Psychological Association
2012, Vol. 103, No. 12, 000 0022-3514/12/$12.00 DOI: 10.1037/a0030426
Given the importance of self-regulation to adjustment and per-
formance, it becomes important to understand the ways in which it
can be enhanced. As it turns out, cultivating the right kind of
motivation can help increase self-regulatory capacity and success.
For example, if an individual is attempting to regulate his or her
diet or trying to quit smoking, the reasons she or he draws upon to
substantiate his or her self-regulatory effort can influence whether
goal-regulation is successful or not (Muraven, Gagné, & Rosman,
2008). Indeed, 3 decades of research in self-determination theory
(SDT) indicate that when motivation underlying regulatory efforts
is autonomous and self-driven, rather than externally controlled,
goal achievement is more likely (Deci & Ryan, 1985b,2002,
The Role of Autonomy in Self-Regulatory
Self-determination theory suggests that autonomous motivation
is an effective means to self-regulation because it is initiated and
guided by choices that cohere with one’s needs, values, and
self-selected aspirations. Similarly, autonomously functioning in-
dividuals seek out choices and self-direction, feeling generally free
of interpersonal coercion. For instance, an autonomously moti-
vated high school student might complete his or her homework
every night after school because she or he finds the work to be
enjoyable and interesting (i.e., intrinsic motivation) or because she
or he believes it is an important aspect of learning (i.e., personally
endorsed value). In order to encourage autonomy, autonomy-
supportive environments offer choice, enhance interest, and sup-
port inner motivational resources. In contrast, individuals with a
controlled motivational orientation look to external prompts and
controls to determine their behavior and experience their environ-
ment as restrictive. Accordingly, controlling environments use
pressure, threat, and contingent regard to extract prescribed think-
ing and behavior, which undermines autonomy and promotes
controlled motivation. To continue with the academic example, a
controlled student might complete his or her homework because
his or her parents require him or her to do so, or solely for the
purpose of obtaining a desired grade.
Of central relevance to the current study is the finding that
autonomy improves self-regulatory performance. Thus, because
autonomous motivation is self-concordant, reflecting what a per-
son truly desires, values, or finds interesting, it is associated with
better self-regulatory success, compared to controlled forms of
motivation. In the academic domain, for instance, autonomously
motivated students study harder, pay more attention in class, ask
more questions, and get better grades (Guay, Ratelle, & Chanel,
2008;Reeve, Bolt, & Cai, 1999;Sheldon & Elliot, 1998;Valler-
and, Fortier, & Guay, 1997;Vansteenkiste, Simons, Lens, Shel-
don, & Deci, 2004). They also show enhanced cognitive flexibil-
ity, conceptual understanding, and active information processing
(Grolnick & Ryan, 1987). In the health regulation domain, auton-
omous motivation leads to superior self-regulation in weight loss
and weight loss maintenance (Teixeira et al., 2010;Williams,
Grow, Freedman, Ryan, & Deci, 1996), as well as in smoking
cessation (Williams et al., 2006;Williams, Niemiec, Patrick, Ryan,
& Deci, 2009), alcohol abstinence (Ryan, Plant, & O’Malley,
1995), and diabetes management (Williams, Patrick, et al., 2009).
In the domain of environmental behavior, autonomous motivation
toward the environment has been shown to predict greater success
in the achievement of personal environmental goals over the
course of a 7-day assessment period (Osbaldiston & Sheldon,
2003). Autonomy also appears to play an important role in long-
term persistence in sports (Pelletier, Fortier, Vallerand, & Brière,
2001), as well as persistence and problem solving on cognitive
tasks (Moller, Deci, & Ryan, 2006).
Additional work suggests that when the social context supports
autonomy by offering choices and promoting interest, autonomous
motivation increases, and cognitive control is thus enhanced, in-
cluding improved thought suppression and delay of gratification
(Muraven et al., 2008), as well as increased concentration (Bernier,
Carlson, & Whipple, 2010;Muraven et al., 2008) and superior
inhibition of implicit biases (Legault, Green-Demers, Grant, &
Chung, 2007;Legault, Gutsell, & Inzlicht, 2011). These studies are
important in demonstrating that autonomous motivation (and the
support thereof) is likely to boost both cognitive and behavioral
Despite the abundance of work indicating that autonomy im-
proves self-regulation and goal-related performance, a basic and
mechanistic understanding of this effect is currently absent. That
is, we do not really understand why autonomy improves perfor-
mance on tasks and behaviors requiring self-regulation. Until now,
the “mechanisms” used to explain the relative effectiveness of
autonomous regulation over controlled regulation suggest that
autonomy fosters relatively more energy (Muraven et al., 2008;
Ryan & Deci, 2008) and automaticity (Legault, Green-Demers, &
Eadie, 2009) during self-regulatory pursuits. However, these ex-
planations do not delineate a clear process through which auton-
omy exerts its benefits. That is, they do not provide an explanation
of how autonomy affects the processing of information and the
monitoring and correcting of behavior in the service of optimal
performance. Like previous motivation researchers, we propose
that autonomy fosters superior behavioral performance through
better cognitive control. However, we go a step further by propos-
ing a mechanism for this effect. Specifically, we suggest that
autonomy bolsters self-regulation by amplifying brain-based sen-
sitivity to self-regulation failure.
Autonomy and Performance:
The Mediating Role of Error Sensitivity
Because autonomy facilitates goal directed behavior and pro-
tects against self-regulatory depletion (Moller et al., 2006;Mu-
raven, 2008; see Inzlicht & Schmeichel, in press), it follows that it
might also promote more adaptive responses to self-regulatory
errors and failures—responses that might in fact enhance further
performance and goal pursuit. Autonomy promotes behavioral
persistence in a wide variety of contexts that require continuous
self-regulation over extended periods of time (e.g., studying, diet-
ing, exercising, quitting smoking, etc.). This constant self-
regulatory effort and attention makes failure (and its accompany-
ing distress) unavoidable. Considering the high probability of
self-regulatory failures, errors, and setbacks in any domain requir-
ing cognitive, affective, and behavioral control, it becomes clear
that adaptive responding and behavioral adjustment following such
failures is important in minimizing further errors and in predicting
the success of future performance (Rabbitt & Rodgers, 1977).
Successful self-regulation, in other words, requires that people
notice, orient, and react to errors when they occur, so that they can
learn from them and thus minimize future instances of them
(Holroyd & Coles, 2002). Therefore, we propose that autonomy
enhances self-regulatory performance because it encourages recep-
tivity to self-regulation failures.
Why should autonomy promote such responsiveness to errors?
Two fundamental components of autonomous functioning are the
acceptance of negative affect (Reeve, 2009) and nondefensiveness
to threatening self-relevant information (Hodgins & Knee, 2002;
Hodgins et al., 2010). As such, we theorize that errors, failures,
and negative feedback should be attended to in a receptive and
responsive manner when the motivation underlying behavior is
autonomous. Indeed, past research has shown that when people are
autonomously motivated, they are less defensive and ego-
protective and tend to openly acknowledge negative affect, criti-
cism, and personal shortcomings (Hodgins & Liebeskind, 2003;
Weinstein et al., 2011). Controlled motivation, on the other hand,
is associated with increased defensiveness and denial in response
to threatening self-relevant information (Hodgins et al., 2010). In
line with these findings, we suggest that autonomy increases
vigilance in performance monitoring by promoting awareness of
error-related distress. Because personal errors are not affect-
neutral events (quite the contrary, they alert us that goal attainment
is in jeopardy) and because autonomy promotes deep and mindful
engagement in action, it follows that feelings of autonomy should
increase attention and emotional reactivity to those moments when
self-regulation efforts have failed. This “caring about” one’s mis-
takes is a key adaptation to the environment that allows people to
slow down, recalibrate their behavior, and ultimately improve their
performance. Here, we examine whether autonomy enhances per-
formance by augmenting reactivity to self-regulation failures at the
level of the brain.
The ERN: A Distress Signal of Self-Regulation Failure
Self-regulation involves a cognitive and affective system that is
supported by specific brain areas and that facilitates optimal per-
formance through its ability to plan, think flexibly and abstractly,
acquire rules, attend selectively, initiate appropriate behavior, and
inhibit inappropriate behavior. Based on cybernetic feedback-loop
theories of self-regulation (e.g., Wiener, 1948), psychological and
neuroscientific models of self-regulation suggest that two comple-
mentary systems are necessary to perform these various functions.
In social and personality psychology, for instance, Carver and
Scheier (1981) have described a “test” process that continually
compares current behavior with ideal criteria, which then signals
the “operating” process to elicit change toward a desired end.
Similarly, Wegner (1994) has discussed the dual-action of a mon-
itoring process, which scans for lapses in self-control, and an
operating process, which acts to rectify any self-control failure.
Although both monitoring and operating systems are important,
the monitoring system is especially critical because it determines
when self-control needs to be initiated. One of the best known
neural correlates of self-control in general, and the monitoring
system in particular, is the error-related negativity (ERN; Falken-
stein, Hohnsbein, & Hoormann, 1991;Gehring, Goss, Coles, &
Meyer, 1993).
The ERN is an event related potential (ERP) that is character-
ized by a pronounced negative deflection on electroencephalogra-
phy (EEG) that occurs within 100 ms of making an error on a task
and is thought to be generated by the anterior cingulate cortex
(ACC; Dehaene, Posner, & Tucker, 1994). Holroyd and Coles
(2002) have suggested that the ERN reflects an error detection
system that monitors performance and detects incongruity between
intended and actual responses (see also Yeung, Botvinick, &
Cohen, 2004). This process is implemented in the ACC (Kerns et
al., 2004)—a brain structure that connects to both limbic and
prefrontal regions of the brain and is attuned to response conflict,
negative affect, errors, uncertainty, and psychological pain (Bush,
Luu, & Poser, 2000;Ridderinkhof, Ulsperger, Crone, & Nieuwen-
huis, 2004;Shackman et al., 2011).
Another view of the ERN suggests that, rather than simply
reflecting attention to errors or discrepancies between desired and
actual responses, the ERN is in fact linked to motivational and
affective responses to such errors (Bartholow, Henry, Lust, Saults,
& Wood, 2012;Hajcak & Foti, 2008;Hajcak, McDonald, &
Simons, 2003;Inzlicht & Al-Khindi, 2012;Luu, Collins, &
Tucker, 2000). Indeed, it has been suggested that the ERN might
partially reflect a “distress signal” when performance is worse than
expected (Bartholow et al., 2005, p. 41). This perspective asserts
that ERN magnitude is associated with the value placed on errors
and that increased motivation will amplify the ERN (Weinberg,
Riesel, & Hajcak, 2012). Extending this recent motivational view,
we suggest that the quality—and not just the quantity— of moti-
vation matters when it comes to enhancing the ERN. That is,
autonomous motivation is expected to be related to the degree to
which performance is monitored and improved. Because feelings
of autonomy promote acknowledgment of negative affect (rather
than denial or suppression), as well as the integration of mistakes
and personal faults (e.g., Weinstein et al., 2011), autonomous
motivation should predict sensitivity to errors in performance. To
the extent that the ERN reflects such an affective response to
errors, autonomous motivation should enhance the ERN.
The Role of Autonomy in the ERN
Although some recent research has examined patterns of brain
activation associated with autonomous and controlled motivation
in general (see Murayama, Matsumoto, Izuma, & Matsumoto,
2010; as well as Lee & Reeve, 2012), knowledge of the brain-
mediated mechanisms through which autonomy influences self-
regulation processes is limited. Only two correlational studies (in
the domains of prejudice regulation and education) have implied a
link between autonomous motivation and the ERN. Specifically, it
has been suggested that those who display more personal reasons
for inhibiting prejudice demonstrate increased ERN amplitude
when failing to suppress bias relative to those with more external
reasons (Amodio, Devine, & Harmon-Jones, 2008). Additionally,
intrinsic academic motivation among 3rd to 5th graders has been
correlated with larger ERNs (Fisher, Marshall, & Nanayakkara,
2009). Although these studies suggest that autonomy is related to
increased neural responding to self-regulation errors, the link be-
tween general autonomy and brain-mediated self-regulation is
unknown. More importantly, we are not aware of any studies that
have examined how experimental changes in autonomy might
impact the brain bases of self-regulation on performance tasks.
Therefore, to fill this gap and provide a more complete picture of
the link between autonomy and performance, we assess how
brain-implemented performance monitoring relates to trait differ-
ences in autonomy as well as how it may be affected by experi-
mental manipulations of autonomy. By increasing our understand-
ing of the neurophysiological processes that mediate autonomy’s
impact on self-regulation, we can join recent work (i.e., Lee &
Reeve, 2012;Murayama et al., 2010) in shedding much-needed
light on the neural bases of self-determination and offer additional
validation of the real, far-reaching difference between autonomous
and controlled motivation.
Overview of Studies
Our goal was to understand how autonomous motivation en-
hances performance-based self-regulation. To do so, we assessed
performance monitoring in the anterior cingulate cortex during two
tasks requiring self-regulation. In Study 1, we examined associa-
tions among trait autonomy, self-regulatory performance, and neu-
roaffective responses to self-regulation failure (i.e., the ERN). In
Study 2, we assessed the impact of state manipulations of auton-
omy on self-regulation and the ERN. We expected that both trait
autonomy and state-induced autonomy would increase perfor-
mance on the self-regulation task (i.e., by reducing the number of
performance errors) and that this effect would be mediated by the
heightened neuroaffective responding to those errors, that is, larger
Study 1
Participants and procedure. In exchange for course credit,
43 participants (28 women) from the University of Toronto Scar-
borough were invited to complete a computer task while brain
activity was recorded. Participants’ age ranged from 18 to 30
(M19.3 years; SD 1.97). Before electrophysiological record-
ing, participants were asked to complete a trait measure of moti-
vational orientation.
Trait motivational orientation. Individual differences in mo-
tivational orientation were assessed using the General Causality
Orientation Scale (GCOS; Deci & Ryan, 1985a). The GCOS
consists of 12 vignettes describing interpersonal scenarios, fol-
lowed by a list of responses that represent either an autonomous
(12 items), controlled (12 items), or impersonal/helpless orienta-
tion (i.e., an absence of motivation; 12 items). These dimensions
are thought to represent relatively enduring aspects of personality.
Items reflecting an autonomous orientation illustrate a preference
for interest-enhancing and optimally challenging situations and a
tendency to interpret social contexts as autonomy-supportive
rather than controlling or imposing. In contrast, the controlled
orientation assesses the extent to which a person is oriented toward
being controlled by rewards, deadlines, structures, ego-
involvements, and the directives of others. The impersonal orien-
tation taps personal ineffectiveness and a general lack of motiva-
tion. Such individuals are likely to believe that attaining desired
outcomes is beyond their control and that achievement is largely a
matter of luck or fate. An example item from the GCOS asks
respondents to rate on a 7-point scale “the most important consid-
eration when embarking on a new career.” The autonomous ori-
entation item states, “How interested I am in that kind of work”;
the controlled orientation item states, “Whether there are good
possibilities for advancement”; and the impersonal orientation
item states, “Whether I could do the work without getting in over
my head.” Internal consistency was ␣⫽.77 for the autonomous
motivation subscale; ␣⫽.72 for the controlled motivation sub-
scale, and ␣⫽.74 for the impersonal (i.e., no motivation)
The go/no-go task. After completing the GCOS, participants
performed the Go/No-Go task, which served as the main behav-
ioral indicator of self-regulatory performance. Stimuli consisted of
the letter M(the “Go” stimulus) and the letter W(the “No-Go”
stimulus). Participants were required to press a button when the
“Go” stimulus appeared and to refrain from pressing the same
button when the “No-Go” stimulus appeared. Each trial consisted
of a fixation cross (“”) presented for 500 ms, followed by either
a “Go” or “No-Go” stimulus for 100 ms. The maximum time
allowed for a response was 500 ms, and the intertrial interval was
50 ms. Participants first completed a practice block and then
completed six experimental blocks, each consisting of 40 “Go”
trials and 20 “No-Go” trials (presented randomly). The perfor-
mance score was based on errors of commission (going on a
No-Go trial) rather than the incongruency effect because there is
no latency-based response for correct No-Go trials (that is, the
correct response is no response), and thus it is not possible to
generate incongruency effects for the Go/No-Go task.
we stressed accuracy rather than speed by encouraging participants
to respond as accurately as possible. It should also be noted that
this task indeed required self-control. That is, because “Go”
was the dominant trial type, “going” (i.e., pushing the button)
became the dominant response. Thus, “No-Go” trials required
self-regulation since participants had to suppress or inhibit their
prepotent response to press the “Go” button.
Finally, a primary behavioral indicator of posterror adjustment,
namely, posterror slowing (PES), was calculated. PES refers to the
prolonged reaction time (RT) on trials following an error com-
pared to RTs following correct trials (Rabbitt, 1966) and is thought
to reflect the recruitment of executive control resources in the
service of correcting performance. PES was computed as the
difference in reaction time for correct responses following correct
trials versus correct responses following incorrect trials.
Neurophysiological recording. Continuous EEG during the
Go/No-Go task was recorded using a stretch Lycra cap embedded
with 32 tin electrodes (Electro-Cap International, Eaton, Ohio).
Recordings were digitized at 512 Hz using Advanced Source
Analysis (ASA) acquisition software (Advanced Neuro Technol-
ogy B.V., Enschede, the Netherlands) with average-ear reference
No-go errors (i.e., “going” when instructed not to) were used as the
basis for both ERN and performance analyses, rather than go errors (i.e.,
“not going” when instructed to “go”), as it is not possible or appropriate to
generate an ERN for go errors. Go errors are errors of omission and thus
they do not produce a button-press response. As we have discussed, ERNs
must be locked to a response. For this same reason, ERP response nega-
tivity in response to correct trials (see Figure 1) was also locked to correct
go trials rather than correct no-go trials. For the sake of completeness,
however, we include associations between motivation and go errors here.
Autonomy was negatively associated with making go errors, r(42) ⫽⫺.38,
p.01, whereas neither controlled nor impersonal orientations were
associated with go errors.
and forehead ground. EEG was corrected for vertical electroocu-
logram artifacts (Gratton, Coles, & Donchin, 1983) and digitally
filtered offline between 0.1 and 15 Hz (fast Fourier transform
implemented, zero phase-shift Butterworth filter). Epochs were
defined as 200 ms prior to and 800 ms subsequent to response.
The EEG signal was baseline-corrected by subtracting the average
voltage during the 200 ms time period prior to the response.
Artifacts were automatically detected with –75 V and 75 V
thresholds. Data for these epochs were averaged within partici-
pants independently for correct and incorrect trials and then grand-
averaged within the respective conditions. The ERN was defined
as the minimum peak deflection occurring between 50 ms prere-
sponse and 150 ms postresponse at the frontocentral midline
electrode (FCz). ERNs were based on no fewer than six artifact-
free error trials (Olvet & Hajcak, 2009b).
Results and Discussion
We hypothesized that trait autonomy would be positively related
to both task performance (i.e., self-regulatory effectiveness) and
brain-based error monitoring. Thus, as autonomous motivation
increased, we anticipated that ERN amplitude would increase and
errors would decrease. In addition, we anticipated that error mon-
itoring would mediate the association between autonomy and
performance. Finally, we expected that neither controlled nor
impersonal motivation would be associated with error monitoring
or performance, thereby providing evidence that it is the quality—
and not simply the quantity— of motivation that really matters.
Autonomous motivation and self-regulation. The pattern of
correlations (see Table 1) suggested that as autonomous motiva-
tion increased, so did performance (as measured by number of
errors of commission), r(42) ⫽⫺.35, p.05, and ERN ampli-
tude, r(42) ⫽⫺.38, p.01 (see Figure 1 for a scatterplot of
autonomy and the ERN). In turn, the ERN was correlated with
number of performance errors, r(42) .34, p.05. An exami-
nation of partial correlations ensured that, even after controlling
for the possible effect of error rate, a significant association be-
tween autonomy and the ERN remained, r(42) ⫽⫺.30, p.05.
Moreover, we assessed correlations between the correct related
negativity (CRN; the neurophysiological response to making a
correct response) and the remaining variables. Importantly, this
pattern of results revealed that the CRN was unrelated to perfor-
mance and not significantly related to autonomy. This finding
suggests that effects of the ERN, as a function of autonomy, are
specifically related to error processing rather than performance
monitoring, more generally. To further underscore this point, we
assessed correlations between the ERN difference score (i.e.,
ERN–CRN) and related variables. This “difference wave” ap-
proach is important because it allows us to cancel out processes
common to all performance monitoring and to specifically isolate
our variable of interest, error processing (Luck, 2005). The pattern
of associations for the ERN remained intact (see Table 1). Figure
2illustrates ERN and CRN as a function of high and low trait
Dipole source localization confirmed that the ERNs were gen-
erated in an area consistent with the ACC. That is, coordinates of
the preauricular-nasion (in millimeters) were x0.1, y0.1, z
60.0; dipole strength was 65.48 nAm, and this source accounted
for 84.1% of the variance of the signal.
We next examined the process via which autonomous motiva-
tional orientation predicted improvements in self-regulatory per-
formance. The mediating effect of the ERN on the link between
autonomy and performance was ascertained using the bootstrap
method outlined by Preacher and Hayes (2004,2008). Again, we
used the ERN–CRN difference score in this analysis, to hone in on
error processing (Luck, 2005). Results are presented in Figure 3.
First, as noted above, an analysis of behavioral performance re-
vealed that autonomy was negatively related to number of errors
on the Go/No-Go, t(42) ⫽⫺2.41, p.05. Second, as autonomy
increased, so did ERN difference scores, t(42) ⫽⫺2.27, p.05,
with more autonomy associated with higher (more negative) ERN
difference scores. Third, after controlling for autonomy, the ERN
difference score significantly predicted task performance, t(42)
2.34, p.05. To ascertain the indirect effect, percentile-based
bootstrap confidence intervals (CI) and bootstrap estimates of
standard errors were generated based on 5,000 bootstrap samples.
The indirect effect was reliable, M⫽⫺.18 (SE .09), 95%
bootstrap CI ⫽⫺.40 to .03, suggesting that the ERN mediates
the link between autonomy and performance on the self-regulation
Inspection of behavioral posterror adaptations revealed that,
although autonomy was weakly associated with posterror slowing
(␤⫽.18), this link was not significant (p.25). Given that past
research has failed to find group differences in posterror slowing
on the Go/No-Go task (e.g., Inzlicht & Al-Khindi, 2012), this
Table 1
Descriptive Statistics and Pearson Correlations for Main Variables in Study 1
MSDCommission errors Omission errors ERN CRN ERN–CRN PES
Autonomy (trait) 5.41 0.66 .35
.12 .35
Commission errors 14.47 10.61 .53
.02 .43
Omission errors 3.64 4.51 .28
.12 .40
ERN 6.66 4.57 .50
CRN 3.36 2.28 .07 .20
ERN–CRN 3.31 3.87 .09
PES 63.74 50.41
Note. Theoretical range for trait autonomy is 1–7. The ERN, CRN, and ERN-CRN are scored negatively, as they represent negative-going waveforms;
thus, more negative scores represent greater ERP amplitude. ERN error-related negativity; CRN correct related negativity; ERP event-related
potential; PES posterror slowing.
result is not entirely surprising (we revisit this issue in Study 2
using a different task and a direct induction of autonomy). See
Table 1 for descriptive statistics for posterror slowing.
Controlled motivation and self-regulation. We also exam-
ined associations between controlled motivational orientation, on
one hand, and performance and error monitoring, on the other. The
assessment of this relationship is important because previous work
has suggested that general motivational magnitude is associated
with the ERN (e.g., Weinberg, et al., 2012). However, we propose
that it is the quality—rather than simply the quantity— of motiva-
tion that boosts self-regulation by enhancing error detection. In
support of our expectation, controlled motivation was not signif-
icantly associated with performance errors, r(42) .12 (ns), nor
the ERN, r(42) ⫽⫺.17 (ns). Interestingly, although it did not quite
reach significance, the association between impersonal orientation
(i.e., those showing a lack of motivation) and the ERN showed the
opposite pattern compared to the two types of motivation, r(42)
.23, p.16, suggesting that as motivation diminishes, the ERN
does as well. Like controlled motivation, impersonal orientation
was not significantly related to performance, r(42) .10 (ns).
In sum, these findings suggest that autonomous motivation is
significantly related to error processing, which serves to increase
self-regulation. In contrast, the association between controlled
motivation and error processing was negligible, suggesting that
controlled motivation is not a sufficient predictor of the ERN.
Rather, the source of motivation (i.e., autonomous vs. controlled)
is important in understanding the neural mechanism of self-
regulation. These results lend support to the idea that the quality of
motivation (and not just the quantity) is a significant factor in
signaling when self-regulation has failed (but not necessarily when
it has succeeded!).
Study 2
Study 2 sought to extend Study 1 in various ways. First, rather
than measure trait autonomy, we used an experimental induction of
autonomy to test the causal impact of autonomous motivation on
self-regulatory performance. According to self-determination the-
ory, autonomous motivation is multifaceted and can be enhanced
in various ways. That is, the following situations are said to be
autonomy-supportive: the provision of choice; the promotion of
interest and intrinsic motivation, the support of inner motivational
resources, the provision of optimal challenge, and the administra-
tion of informational feedback and structure. In contrast, control-
ling environments override autonomy and instead induce motiva-
tion by emphasizing external demands. In line with this reasoning,
we either supported autonomy toward a task (i.e., by enhancing
choice and interest, which are key means through which to target
autonomy) or exerted external control over completion of the task.
Second, we used a different measure of self-regulatory capacity,
the Stroop task, to verify the robustness of the effect. Indeed, it
may be argued that the Go/No-Go and the Stroop assess different
dimensions of self-regulation. That is, because the Go/No-Go
requires the ability to keep changes in task instruction online (i.e.,
“No-Go” vs. “Go”), it taps into the switching component of
self-regulation. The Stroop, on the other hand, is a canonical test
of inhibitory control. Compared to the Go/No-Go, which estab-
lishes prepotent responding by presenting twice as many Go trials
Figure 1. Scatterplot of autonomy and the ERN (Study 1). ERN error-related negativity.
as No-Go trials (thus, “going” becomes the dominant response
because it is more frequent), the Stroop entails a more deeply
entrenched prepotent response tendency (i.e., reading) that may be
more difficult to override. In other words, for the Go/No-Go,
inhibitory control involves the stopping of a habitualized motor
response. Conversely, the Stroop assesses inhibition of the prepo-
tent response to read the visual presentation of words. Because
reading is automatic and well-learned, the dominant response is to
read the text presented rather than to respond to other physical
characteristics of the text (e.g., naming the color of the ink with
which the word is written). Thus, although both tasks represent
self-regulatory ability, they are somewhat different in the degree to
which they assess inhibition and switching capacities. Finally, in
addition to assessing the mediating effect of the ERN in the
association between autonomy-support and performance, we also
examined the intervening role of intrinsic task motivation and
perceived task value—two key features of autonomous motivation.
Participants and procedure. Fifty-five undergraduates (29
at the University of Toronto Scarborough participated for
either course credit or $15.00 compensation. Participants’ age
ranged from 18 to 30 years (M19.6 years; SD 2.03).
Participants were invited to complete a study titled “Brain Games,”
wherein they performed a computer task while EEG was recorded.
In order to manipulate autonomous and controlled motivation,
participants were randomly assigned to one of three conditions
(i.e., autonomy-supportive, controlling, or neutral). Our experi-
mental manipulation was based on the notion that the enhancement
of choice and interest increases autonomous motivation, whereas
the administration of pressure and requirement thwarts it (Deci &
Ryan, 2000,2008;Moller et al., 2006).
One reason why slightly smaller sample sizes tend to have sufficient
power to detect motivational and affective effects on the ERN (e.g.,
Amodio et al., 2008;Fisher et al., 2009;Gonsalkorale, Sherman, Allen,
Klauer, & Amodio, 2011) is because the ERN is a highly reliable and stable
measure (Olvet & Hajcak, 2009a,2009b;Weinberg & Hajcak, 2011).
Figure 2. Differences in the ERN as a function of trait autonomy (median split). ERN error-related
Figure 3. The mediating role of error monitoring in the link between
autonomy and performance (Study 1). Unstandardized regression coeffi-
cients are presented. ERN is operationalized as the ERN–CRN difference
score. ERN error-related negativity; CRN correct related negativity;
ns not significant. * p.05.
Participants in the two experimental conditions (i.e., the
autonomy-supportive and controlling conditions) read a descrip-
tive list of four computer tasks (i.e., “brain games”) These games
included (a) The Mental Distraction Game; (b) A Game of Accu-
racy; c) Ignore Your Impulses, and (d) Cognitive Response La-
tency Test. Unbeknownst to participants, each selection described
the same task (i.e., the Stroop task) in a unique way. Participants
in the autonomy-supportive condition (n18) were instructed to
select the game they most wanted to perform and were then
directed toward the task (which was always the Stroop task). It is
worth noting that we attempted to tap into choice (directly) and
interest (indirectly) in the autonomy induction because both are
key experiences in autonomous motivation. In the controlled con-
dition, participants (n18) saw the list of choices but were
pressured by the experimenter (who was unaware of the nature and
purpose of the study) to perform task d(i.e., the Cognitive Re-
sponse Latency Test). In the neutral condition (n19), partici-
pants went directly to the Stroop task, without being presented
with a list of choices. After completion of the Stroop, various
motivational variables were assessed.
Stroop task. The main behavioral measure was performance
on a color-naming Stroop task (MacLeod, 1991). This task, which
lasted about 20 min, consisted of color words (i.e., red, green, blue,
and yellow), each of which was presented in a color that either
matched (congruent) or did not match (incongruent) the written
meaning of the word. Self-regulation is required on incongruent
trials in order to correctly identify the color of the ink with which
the word is printed and to inhibit prepotent responding to the
semantic meaning of the word. Responses were measured by
having participants press a corresponding color button on a re-
sponse box. In each trial, a fixation cross (“”) appeared for 500
ms, followed by the stimulus word presented for 200 ms. Partic-
ipants were given 800 ms in which to respond. The task contained
10 blocks, each consisting of 32 congruent trials (i.e., the baseline
response, e.g., the word “yellow” in yellow ink) and 16 incongru-
ent trials (e.g., the word “yellow” in blue ink). An incongruency
effect was calculated by subtracting reaction times on correct
congruent trials from reaction times on correct incongruent trials.
Performance was calculated by tallying the number of errors on
incongruent trials (i.e., the self-regulation trials). We used incon-
gruent errors as the main measure of performance to remain
consistent with Study 1 and because we emphasized accuracy on
the task, rather than speed. As in Study 1, posterror slowing was
also calculated.
Neurophysiological recording. EEG during the Stroop task
was recorded and processed according to the technique outlined in
Study 1.
Self-determined task motivation. Self-determined motivation
toward the Stroop task was measured using the Intrinsic Motiva-
tion Inventory (McAuley, Duncan, & Tammen, 1989;Ryan,
1982). That is, we measured three key constituents of autonomous
motivation toward the Stroop task, including perceived choice,
task interest/enjoyment, and task value/usefulness. Perceived
choice assessed the extent to which participants felt as though they
freely chose to do the task (four items; e.g., “I felt like it was my
choice to do this task”; ␣⫽.75). The interest/enjoyment scale
reflected intrinsic motivation toward the task (four items; e.g., “I
enjoyed doing this computer task very much”; ␣⫽.89), and the
value/usefulness dimension tapped the extent to which the value of
the task had been internalized, or had come to be seen as having
some importance (four items; e.g., “I think that doing this activity
is useful for improved concentration”; ␣⫽.76). Correlations
among these three dimensions ranged from .53 to .75.
General task motivation. Apart from measuring self-
determined motivation, we also assessed other dimensions of mo-
tivation toward the Stroop task, in order to verify that any exper-
imental effects could be attributed specifically to changes in
autonomous motivation rather than changes in motivation more
generally. Moreover, we wanted to ensure that the controlling task
instructions did not undermine participants’ motivation to com-
plete the task. This check is important because, in order to test
whether the ERN can be attributed to motivational quality rather
than quantity, we need to verify that both autonomous and con-
trolled motivational inductions increased motivation overall. Thus,
although we expected that those in the autonomy-supportive condi-
tion would be more autonomously motivated, we nonetheless antici-
pated that both motivation groups would be generally motivated to
complete the task. To ascertain dimensions of motivation that are
relatively less proximal to autonomy than those tapped by the self-
determined task motivation measure (above), we assessed task con-
fidence (three items; e.g., “I feel like I would do well on this task in
the future”), and effort exerted (two items; e.g., “I tried to do well on
this task”).
Results and Discussion
We hypothesized that motivational quality, rather than quantity,
would predict the degree to which self-regulation failure would be
detected and performance enhanced. Specifically, we anticipated
that the support of autonomous motivation toward the task would
produce better performance and a larger ERN relative to the
controlling and neutral conditions. We also expected that the
causal relationship between autonomy-support and performance
would be reliably mediated by neural responsiveness to self-
regulation errors.
Correlations. Correlations and descriptive statistics for each
variable are presented in Table 2. As in Study 1, inspection of
correlations provided preliminary support for our hypotheses. Au-
tonomy (coded: autonomy-supportive 1; neutral 0; control-
ling ⫽⫺1) was negatively related to performance errors, r(54)
.38, p.01, and associated with more negative ERN ampli-
tudes, r(54) ⫽⫺.43, p.01. In turn, ERN amplitude (negatively
scored) was associated with a reduction in performance errors,
r(54) .45, p.01. Correlational analyses also revealed that the
CRN was unrelated to autonomy and not significantly related to
performance. Furthermore, autonomy and performance remained
associated with the ERN–CRN difference score. These data de-
scribe a pattern of associations that is specific to error processing
and not responses in general.
Self-determined task motivation. We wanted to verify that
that autonomy-supported participants did indeed show increases in
self-determined (i.e., autonomous) motivation. An analysis of the
self-reported self-determination data supported the causal role of
the autonomy manipulation in increasing perceived self-
determination toward the task (i.e., feelings of choice, interest, and
task value). That is, those in the autonomy-supportive condition
experienced greater perceived task choice (M4.38; SD 0.69)
than those in the controlling (M3.62; SD 0.62) and neutral
(M3.91; SD 0.60) conditions, F(2, 52) 7.38, p.001,
2.21. Autonomy-supported participants also reported more
interest in the task (M4.25; SD 1.54), and placed greater
value on the task (M4.95; SD 1.32), compared to controlled
3.30; SD 1.35; M
3.57; SD 1.03) and
neutral participants (M
3.16; SD 0.96; M
SD 1.07), F
(2, 52) 4.23, p.05, p
(2, 52) 8.22, p.001, p
2.22. These data indicate that
the autonomy manipulation exerted its intended effect on various
dimensions of perceived autonomy. There were no statistically
meaningful differences in perceived self-determination among
neutral and controlled participants—although controlled partici-
pants felt they had marginally less choice than those not shown the
list of task choices (i.e., neutral participants), F(1, 35) 2.63, p
.11, p
General task motivation. We also wanted to confirm that,
although the autonomy supported group displayed the greatest
autonomous task motivation (as shown in the previous paragraph),
both motivation groups experienced general motivation toward the
task. This check was important to ensure that subsequent effects on
the ERN could be attributable to the quality of motivation rather
than the quantity. Thus, we analyzed additional dimensions of
motivation to ensure that the controlling task instructions did not
reduce participants’ self-reported level of motivation toward the
Stroop task. Supporting the notion that controlled participants
showed a general desire to complete the task, results suggested that
both controlled (M4.86; SD 1.52) and autonomy-supported
participants (M4.63; SD 1.34) demonstrated greater confi-
dence in their ability to complete the task than did neutral partic-
ipants (M3.68; SD 1.34), F(1, 35) 6.85, p.05, p
(controlled vs. neutral); F(1, 35) 4.76, p.05, p
(autonomous vs. neutral). Indeed, task confidence was similar for
autonomy-supported and controlled participants (F1). This
suggests that our manipulation of autonomy and control increased
general task motivation relative to the neutral group. A similar
pattern emerged for effort exerted on the task, such that controlled
(M4.89; SD 0.82) and autonomy-supported (M4.87;
SD 1.23) participants exerted the same amount of effort on the
task (F1), which was greater than the effort reported by those
in the neutral condition (M3.92; SD 0.85), F(1, 35) 13.67,
p.001, p
2.26 (controlled vs. neutral); F(1, 35) 7.57, p
.01, p
2.17 (autonomous v. neutral). These results suggest that
the motivational manipulation targeted autonomous motivation
specifically and did not exert an undermining effect on general
motivation toward the task for those in the controlled condition, as
indexed by task confidence and effort.
Performance. Behavioral data were assessed using a one-way
between-subjects analysis of variance (ANOVA). In line with our
expectations, the experimental manipulation exerted an effect on
the primary performance variable (i.e., incongruent errors, or
Stroop errors), F(2, 52) 4.36, p.05, p
2.15. That is, those
in the autonomy-supportive condition made fewer Stroop errors
(M15.47; SD 10.67) than those in the neutral condition
(M27.16; SD 17.16) and those in the controlled condit-
ion (M30.61; SD 18.42). Planned contrasts suggested that the
support of autonomy reduced Stroop errors relative to those in the
neutral group, F(1, 35) 7.63, p.01, p
2.18. Autonomy-
supported participants also committed substantially fewer Stroop
errors than controlled participants, F(1, 34) 6.45, p.05, p
.17. The neutral and controlled participants, however, showed no
differences in Stroop errors (F1). In addition, there were no
meaningful group differences in the incongruency reaction time
effect, F1.
We also assessed performance effects for errors on congruent
Stroop trials. We analyzed these separately because they are less
central to self-control processes but also because the number of
congruent and incongruent trials was not equal. Again, the moti-
vational induction demonstrated an overall effect on congruent
errors, F(2, 52) 5.45, p.01, p
2.16. Specifically, those
whose autonomy was supported in the Stroop task made fewer
congruent errors (M12.47; SD 9.16) than those in the neutral
(M19.75; SD 14.84) and controlled conditions (M33.83;
This lack of effect is not entirely surprising. That is, the incongruency
effect is an ambiguous indicator of performance because it may actually
reflect greater deliberation during incongruent trials, which can sometimes
be associated with better control rather than worse control. Because error
rate is a clear indicator of performance, we were principally concerned with
errors and not in the incongruency effect. Moreover, we instructed partic-
ipants to be accurate rather than speedy, thus underscoring the need to
focus on error effects.
Table 2
Descriptive Statistics and Pearson Correlations for Main Variables in Study 2
Autonomy (condition) .41
.04 .43
.02 .36
Self-determination 3.61 0.98 .30
.18 .46
.00 .38
Incongruent errors 24.98 17.09 .73
.00 .45
.16 .30
Congruent errors 22.24 16.77 .27
.08 .39
Incongruency effect 77.56 59.57 .02 .20 .08 .00
ERN 5.33 3.38 .08 .86
CRN 1.03 2.08 .58
ERN–CRN 4.30 4.04 .47
PES 40.18 95.83
Note. Theoretical range for self-determination scores is 1–7. The ERN, CRN, and ERN–CRN are scored negatively, as they represent negative-going
waveforms; thus, more negative scores represent greater ERP amplitude. Self-det. self-determination; ERN error-related negativity; CRN correct
related negativity; ERP event-related potential; PES posterror slowing.
SD 32.52). Contrasts revealed that autonomy support reduced
errors relative to both the neutral, F(1, 35) 3.30, p.08, p
.09 (marginal effect), and controlled conditions, F(1, 34) 6.91,
p.05, p
2.17. In addition, controlled participants made
marginally more errors than neutral participants, F(1, 35) 2.78,
p.10, p
In sum, these findings reveal that enhancing autonomy in rela-
tion to the task exerted a positive effect on task performance. In
contrast, increasing external control showed no performance im-
provement on Stroop errors (as compared to doing nothing to
influence task engagement) and actually hindered performance on
congruent errors.
Behavioral adjustment. As in Study 1, we assessed behav-
ioral adaptation following errors by calculating posterror slowing
(PES) in reaction time (i.e., posterror RT minus postcorrect RT).
Descriptive statistics and correlations with other variables can be
found in Table 2. A one-way ANOVA with PES as the dependent
variable revealed an overall effect of motivational condition on
PES, F(2, 52) 4.62, p.01, p
2.14. Specifically, those whose
autonomy was supported in the task showed greater PES (M
91.08; SD 136.09) compared to those in the neutral condition
(M24.89; SD 35.21), F(1, 35) 4.21, p.05, p
2.11, and
compared to those who were motivated with controlling tactics
(M7.95; SD 71.23), F(1, 34) 6.02, p.02, p
2.14 (in
fact, controlled participants showed nonsignificant posterror slow-
ing). PES did not differ significantly between the neutral and
controlled groups (F1). These data indicate that the support of
autonomy during the Stroop task caused participants to recalibrate
their behavior following errors relative to correct trials. This slow-
ing effect underscores the notion that autonomy promotes process-
ing of errors and response conflict, which promotes behavioral
The ERN. A 3 (Condition: autonomy-supportive vs. neutral
vs. controlling) 2 (Response: error vs. correct) mixed-factor
ANOVA with peak minimum amplitude as the dependent variable
was performed. A significant main effect of response revealed that
the waveform following error trials (i.e., the ERN) was signifi-
cantly greater in magnitude (M⫽⫺5.33; SD 3.93) than the
waveform following correct trials (M⫽⫺1.03; SD 2.08), F(1,
52) 67.33, p.001, p
2.56. In addition, there was a
significant main effect of group, F(2, 52) 5.14, p.01, p
.17, indicating that overall the waveform was smaller in the con-
trolling (M⫽⫺2.54; SE 0.41) and neutral (M⫽⫺2.68; SE
0.40) conditions, relative to the autonomy-supportive condition (M
⫽⫺4.22; SE 0.41). However, it is important to note that this
main effect was qualified by a significant interaction between
condition and response type, F(2, 52) 4.09, p.05, p
That is, although the three conditions showed similar amplitudes on
correct trials (i.e., the CRN; F1), the effect of condition on the
ERN was significant, F(2, 52) 7.71, p.001, p
2.23 (see Figure
4for an illustration). To be specific, those in the autonomy-supportive
condition displayed a larger ERN (M⫽⫺7.58; SD 4.02) than those
in the neutral condition (M⫽⫺4.57; SD 2.43), F(1, 35) 7.63,
p.01, p
2.18. Autonomy-supported participants also displayed a
significantly larger ERN than those in the controlling condition (M
Figure 4. Differences in the ERN as a function of autonomy support. ERN error-related negativity.
SD 2.24), F(1, 34) 11.59, p.01, p
2.26. These results
indicate that all three groups displayed comparable neural responses
to successful regulation; it was instead their responses to self-
regulation failure that differed. There was no significant ERN differ-
ence between the neutral and controlling conditions (F1), which
suggests that instilling pressure/control produces no more error mon-
itoring than doing nothing to boost incentive. Finally, we verified that,
although error rate was significantly associated with the ERN, F(1,
51) 6.22, p.05, p
2.11, condition remained a significant
predictor of ERN amplitude even after controlling for error rate, F(2,
51) 4.08, p.05, p
Dipole source localization confirmed that the ERNs were gen-
erated in an area around the ACC (preauricular-nasion coordinates,
in millimeters, were x.15, y0.0, z50.0). Dipole strength
was 66.33 nAm, and this source accounted for 91.8% of the
variance of the signal. When using only 32 electrode channels,
source localization in EEG is not as precise as certain other
neuroscientific methods. Nonetheless, the localization found here
is consistent with previous source localization (Pizzagalli, Peccor-
ralo, Davidson, & Cohen, 2006) and magnetoencephalographic
(Miltner et al., 2003) and intracerebral findings (Brázdil, Roman,
Daniel, & Rektor, 2005).
In sum, these results suggest that motivation does indeed wield
an influence on the ERN, but only when that motivation is auton-
omous. We can conclude that the presence of incentives or moti-
vational salience in itself does not increase performance monitor-
ing (as suggested by previous research; e.g., Weinberg et al., in
press) but that effective task engagement is enhanced when moti-
vation is experienced as volitional and self-driven.
Autonomy and performance: The mediating roles of per-
ceived self-determination and error monitoring. Finally, we
tested the mediating effect of both perceived self-determination
and the ERN on the link between motivational condition and
self-regulation performance (please see Figure 5). We chose a
different mediation strategy from Study 1 because we wanted to
examine two mediators rather than one; we also wanted to assess
the link between the mediators. Therefore, a test of multiple
mediation was performed using the SPSS modeling macro proce-
dure, MEDTHREE, outlined by Hayes, Preacher, and Myers
(2011). This multiple mediation procedure offered the advantage
of testing two mediators simultaneously rather than separately, in
order to determine the overall effect of both mediators, as well as
to obtain a clearer picture of the unique effects of each mediator.
The total, direct, and indirect effects of condition on performance
were estimated using a set of ordinary least squares regressions. To
ascertain indirect effects, percentile-based bootstrap confidence
intervals and bootstrap estimates of standard errors were generated
based on 5,000 bootstrap samples.
A condition variable was calculated (autonomy-supportive 1;
neutral 0; controlling ⫽⫺1), which predicted perceived self-
determination (i.e., the summation of choice, interest, and value),
t(54) 3.04, p.01, as well as ERN amplitude (i.e., after
subtracting the CRN), t(54) ⫽⫺2.52, p.05. Perceived self-
determination also uniquely predicted ERN amplitude, t(54)
2.46, p.05, and ERN amplitude, in turn, uniquely predicted
performance, t(54) 2.26, p.05. Using the bootstrap method,
the total effect of all variables (i.e., condition, self-determination,
and the ERN) on performance was significant (estimate ⫽⫺4.02),
with a 95% bootstrap confidence interval (CI) of 8.70 to 0.93
(SE 1.98). In addition, the unique indirect effect of the ERN on
the link between condition and performance was significant, esti-
mate ⫽⫺2.61, 95% bootstrap CI ⫽⫺6.10 to .26 (SE 1.50).
This suggests that the ERN mediates the link between condition
and performance. Furthermore, although the unique indirect effect
of self-determination on the path between condition and perfor-
mance was not significant, it did exert a significant mediating
effect on the link between condition and the ERN, estimate
.95, 95% bootstrap CI ⫽⫺2.62 to 0.042 (SE .68). Thus, the
combined effect of both mediators on performance was reliable.
These findings support our hypothesis and suggest that the manip-
ulation of autonomy increases performance on the Stroop through
heightened perceptions of autonomy and enhanced neural signals
of self-regulation errors.
General Discussion
This is the first article to illustrate a clear mechanism through
which autonomous motivation increases self-regulatory perfor-
mance. Using both personality and situational indicators of moti-
vational orientation, we examined the role of autonomy in pro-
moting performance on self-regulation tasks and, in addition,
assessed the neural mechanism involved in this effect. Data from
Study 1 suggest that trait-level autonomous motivational orienta-
tion is positively associated with self-regulatory performance.
Study 2 lends causal support to Study 1 by establishing that the
support of autonomous motivation (i.e., by enhancing choice and
interest in the context of a self-regulation task) also increases
self-regulatory performance. In both studies, the effect of auton-
omy on performance was significantly mediated by brain-based
error monitoring. Moreover, in Study 2, the effect of autonomy-
support on error monitoring was indirectly explained by increased
perceptions of task choice, task interest, and task value, which are
constituent features of autonomous motivation. Thus, when par-
ticipants’ autonomous task motivation was supported, ensuing
feelings of autonomy and task importance were positively associ-
ated ERN magnitude, which was positively related to performance.
Complementing the finding that autonomous motivation pro-
motes error-sensitivity at the neural level, Study 2 also demon-
strated that autonomy-supported individuals displayed greater re-
sponse slowing after errors, compared to neutral and controlled
participants. In other words, whereas those in the controlled group
Figure 5. The mediating roles of self-determined task motivation and
error monitoring in the link between autonomy support and performance
(Study 2). Unstandardized path coefficients are presented. ERN is opera-
tionalized as the ERN–CRN difference score. ERN error-related nega-
tivity; CRN correct related negativity; ns not significant.
showed similar reaction times on trials immediately following
their correct and incorrect responses, those in the autonomous
motivation group took slightly longer after errors, suggesting in-
creased error processing and correction (although their reaction
times were not slower overall).
The Quality of Motivation Is Key in
Self-Regulatory Success
With this research, we wish to emphasize the importance of
motivational quality in behavior and brain processes. All of the
research on motivation and the ERN, for instance, has focused on
the quantity of motivation, noting that the ERN increases along
with the level of motivational engagement (for a review, see
Weinberg et al., 2012). While motivational magnitude is certainly
important in any self-regulatory activity, we suggest that the rea-
sons underlying behavior are equally, if not more, important. That
is, in the current work, autonomous motivation was related to the
ERN, whereas controlled motivation was not (Study 1). Similarly,
in Study 2, both autonomy-supported and controlled participants
were motivated to complete the Stroop task (more so than the
neutral group), but those who were autonomously motivated per-
formed better and showed greater sensitivity to self-regulation
error, at both neural and behavioral levels. Thus, we gave partic-
ipants the option either to choose the task they most wanted to
complete or to complete the task that we, the experimenters,
wanted them to complete. Although it may seem that giving people
the chance to choose the task they want (and are presumably most
interested in) should elicit an increase in motivation, our data
suggest that autonomy-supported participants were not necessarily
more motivated toward the Stroop than controlled participants
(pleasing the experimenter is indeed strong motivation) but rather
more autonomously motivated. Accordingly, those who chose an
interesting task displayed more intrinsic motivation and self-
determination than did participants who completed the task at the
experimenter’s request. However, both motivation conditions
demonstrated more confidence and more effort in the task com-
pared to the neutral condition. In other words, both motivation
groups exhibited a drive to complete the task, but they differed in
the quality of this drive. Indeed, SDT suggests that engaging in
that which is concordant with one’s desires and goals is a more
productive form of motivation. More external forms of motivation
may appear to be equally as strong, at least on the surface or in the
short term (e.g., the desire for wealth or fame), but to the extent
that they are not autonomous, they are less likely to produce
self-regulatory benefits or positive effects on well-being (Kasser,
Kanner, Cohn, & Ryan, 2007;Kasser & Ryan, 1996;Ryan & Deci,
Thus, our findings offer a novel explanation for the self-
regulatory benefits of autonomy. Whereas the mechanisms via
which autonomy produces its deep behavioral engagement and
success have previously been underresearched, we go beyond past
research in self-determination theory by uncovering a basic neural
mechanism underlying the effectiveness of autonomous self-
regulation. In line with past work suggesting that the ERN is a
primary signal for self-regulation failure (e.g., Inzlicht & Gutsell,
2007), we propose that the experience of autonomy heightens
responsiveness to this signal. This finding may help to account for
the effectiveness of autonomous self-regulation observed across
numerous behavioral domains, including food and exercise regu-
lation, smoking cessation, medication management, and the regu-
lation of academic and work behavior.
Autonomy and Self-Regulation Failure:
The Importance of Being Aware of Negative
Affect and Threat
Despite the prominence of the cognitive interpretations of the
ERN in particular and self-regulation in general, recent research
suggests that the ERN is associated with affect—particularly neg-
ative affect (Inzlicht & Al-Khindi, 2012). Thus, in addition to
reflecting conflict detection, the ERN (and perhaps, by extension,
self-regulation) may represent an affective (Bartholow et al., 2012;
Hajcak, MacDonald, & Simons, 2004) and defensive/motivational
(e.g., Hajcak & Foti, 2008;Hajcak et al., 2003;Luu et al., 2000)
response to that conflict (see Schmeichel & Inzlicht, in press). In
other words, gaps in performance are met with negative affect and
We suggest that, in order to better understand this recent view of
the ERN, it may indeed be helpful to turn to the role played by
autonomy. The link between autonomy and increased reactivity
might, at first, seem counterintuitive. This is because, at a dispo-
sitional level, autonomous individuals tend to display more posi-
tive (rather than negative) affect, as well as less (rather than more)
psychological defensiveness in comparison to controlled individ-
uals. Specifically, autonomous individuals tend to perceive infor-
mation, individuals, and experiences openly and accurately, with-
out distortion (Hodgins, 2008;Hodgins & Knee, 2002), and having
an autonomous motivational style is thought to predict flexibility
and approach in relation to novel and challenging experiences,
rather than denial or defensiveness (Lakey, Kernis, Heppner, &
Lance, 2008). Nonetheless, it is important to consider the adaptive
and dynamic role played by both negative affect and threat reac-
tivity in signaling when performance goals have not been met.
Indeed, these regulatory signals appear to be stronger when auton-
omy is high.
Thus, rather than suggesting a link with negative emotionality or
trait-level defensiveness, our findings suggest that autonomously
motivated behavior produces a stronger affective reaction when
performance is not optimal— due to the high level of engagement
and investment experienced. We therefore suggest that autonomy
predicts better and more accurate awareness and acceptance of
negative affect and threat, which results in improved spontaneous
coping with such negative affect and threat, including dynamic
adjustments to performance that can improve self-regulation (see
Teper & Inzlicht, in press). Such adaptive strategies might include
vigilance to threat and acknowledgment of negative affect. This
improved self-regulation may help to explain why, in the long run,
negative affect and psychological defensiveness are relatively low
among autonomously motivated individuals.
Furthering the notion that autonomy may promote affective and
motivational “tending” to errors, recent research suggests that
autonomy, more than control, permits the acknowledgment of
negative affect and personal faults (Weinstein et al., 2011), and
increases openness to negative feedback (Hodgins & Liebeskind,
2003;Hodgins et al., 2010). Indeed, autonomous individuals are
inclined to respond to failure in a mastery-oriented fashion by
accepting responsibility and focusing on self-improvement (Koes-
tner & Zuckerman, 1994). Conversely, controlled individuals tend
to deny or rationalize failure (Hodgins, 2008;Hodgins, Yacko, &
Gottlieb, 2006;Lakey et al., 2008). Moreover, a criterion of
autonomy-supportive contexts is that they acknowledge that
errors—and their accompanying distress—are a natural part of the
self-regulation/goal-seeking process and that, as such, they ought
to be embraced for the information, accuracy, and authenticity that
they provide (Reeve, 2009). It is possible, then, that autonomy
should lead to preparedness and to attention to errors and other
signals of self-regulation failure and that the ERN may underlie
such awareness and openness to challenge and threat.
In sum, there are several reasons why autonomy might be
negatively related to psychological defensiveness but positively
related to “defensive” reactivity to threat and error. As a further
point of clarification, there is some degree of semantic confusion
between the term “defensive motivation” in ERN research (e.g.,
Hajcak & Foti, 2008;Weinberg et al., 2012) and the term “defen-
siveness” in the self-determination literature. The first is repre-
sented at a neuroaffective level within milliseconds of making a
response, whereas the other is an overt behavioral manifestation of
threatened self-esteem. More importantly, the ERN represents
defensive motivation to the extent that it signifies a reactive,
brain-level “gasp” at threat or error. This startle-based view of
defensive responding is akin to preparedness or responsiveness to
the situation at hand, which is qualitatively different from cogni-
tive or psychological defensiveness—a process designed to protect
self-esteem by ignoring and transforming the (often harsh) reality
of a given situation. Indeed, our findings indicate that the interre-
lation among motivational orientation, negative affect, and threat
awareness is more nuanced than previously suggested and that
negative affect awareness and defensive reactivity (at the neural
level rather than the behavioral level) may be adaptive responses
germane to autonomy. Taken with other related findings (e.g.,
Legault, Al-Khindi, & Inzlicht, in press;Teper & Inzlicht, in
press), the current research suggests that autonomy increases
awareness of negative affect and threat, which serves the purpose
of monitoring for discrepancies between actual and ideal behavior,
and in doing so, prompts the increases in attention and perception,
as well as the readiness for action, which is required for optimal
self-regulation. This self-regulatory alertness helps to explain why
autonomously motivated behavior is prone to succeed in meeting
its regulatory objective. The continued investigation of the role of
threat awareness and the resolution of negative affect in mediating
the link between autonomy and various life outcomes—such as
performance, decision making, goal and life satisfaction, and well-
being—may prove to be a very fruitful avenue for future research.
Boundaries of the Current Study
By mapping the neuroaffective process through which autono-
mous motivation bolsters performance on self-regulation tasks,
this research joins recent efforts to understand the self-determined
brain (see also Lee & Reeve, 2012;Murayama et al., 2010). It is
important to note, however, that our findings may be limited to
explaining how autonomous motivation affects performance on
tasks where errors and self-regulation failures are likely. Undoubt-
edly, there are other key neurological mechanisms that mediate the
effects of autonomous motivation more generally—mechanisms
that extend beyond error monitoring and self-regulatory perfor-
mance. In particular, previous functional magnetic resonance im-
aging (fMRI) research has linked feelings of choice (a component
of autonomy) to general increases in ACC activity (e.g., Walton,
Devlin, & Rushworth, 2004). This finding coheres with our more
specific event-related finding that autonomy activates the error
responsiveness function of the ACC. More general brain differ-
ences in motivational orientation have also been noted. For in-
stance, Lee and Reeve (2012) have recently suggested (again using
fMRI) that feeling autonomously motivated, as opposed to feeling
controlled, is related to increased activity in the anterior insular
cortex—a brain region associated with feelings of agency. In
addition, recent work has demonstrated that activity in the anterior
striatum and prefrontal cortex is reduced when intrinsic motivation
is undermined (Murayama et al., 2010). As we begin to understand
the neurophysiology of human autonomy, it would be prudent for
future research to consider how these broader patterns of (de)ac-
tivation are related to more specific processes (as those described
Although previous work has indicated that autonomously moti-
vated individuals show improved self-regulation relative to con-
trolled individuals (e.g., Muraven, Gagné, & Rosman, 2007;Pel-
letier et al., 2001;Teixeira et al., 2010;Williams et al., 2009a,
2009b), we take this finding further by revealing (a) the causality
of this effect and (b) its underlying neural implementation. Thus,
whereas previous studies have attested that autonomy boosts self-
regulatory ability, the precise cognitive and neuroaffective mech-
anisms remained relatively unexplored. Specifically, although past
research has suggested that autonomy improves self-regulation by
generating more “energy” and “vitality” available to the self
(Moller et al., 2006;Muraven, 2008;Muraven et al., 2008), it is
not precisely or mechanistically clear what energy and vitality
mean or how they are represented. Past work on “process,” in other
words, has relied on metaphors and less on actual information
processing mechanisms. Here, we offer something more specific
by demonstrating that autonomous motivation enhances basic self-
regulation processes by increasing attention and emotional reac-
tions to performance errors. Because controlled motivation does
not elicit this neuroaffective effect, we suggest that the analysis of
brain differences in the quality rather than the quantity of motiva-
tion is an important consideration. Indeed, cognitive models of
self-regulation might be expanded by examining how autonomy
further improves the error monitoring process.
In addition, by pointing to its neural underpinnings, we offer
a contribution to self-determination research by clearly linking
autonomy to the well-delineated performance monitoring func-
tion of the anterior cingulate cortex: autonomy increases error-
related processing and distress in the service of enhancing
self-regulation. Rather than reducing mind to brain function, the
naturalization of autonomy instead lends additional evidence of
the real, far-reaching difference between feeling autonomous
and feeling controlled. Our findings underscore the importance
of noncoercion in action and suggest that self-determination has
a neural basis that plays a critical role in cognitive control and
optimal performance.
Amodio, D. M., Devine, P. G., & Harmon-Jones, E. (2008). Individual
differences in the regulation of intergroup bias: The role of conflict
monitoring and neural signals for control. Journal of Personality and
Social Psychology, 94, 60 –74. doi:10.1037/0022-3514.94.1.60
Barkley, R. A. (1997). ADHD and the nature of self-control. New York,
NY: Guilford Press.
Bartholow, B. D., Henry, E. A., Lust, S. A., Saults, J. S., & Wood, P. K.
(2012). Alcohol effects on performance monitoring and adjustment:
Affect modulation and impairment of evaluative cognitive control. Jour-
nal of Abnormal Psychology, 121, 173–186. doi:10.1037/a0023664
Bartholow, B. D., Pearson, M. A., Dickter, C. L., Fabiani, M., Gratton, G.,
& Sher, K. H. (2005). Strategic control and medial frontal negativity:
Beyond errors and response conflict. Psychophysiology, 42, 33– 42.
Baumeister, R. F., Heatherton, T. F., & Tice, D. (1994). Losing control:
How and why people fail at self-regulation. San Diego, CA: Academic
Bernier, A., Carlson, S. M., & Whipple, N. (2010). From external regula-
tion to self-regulation: Early parenting precursors of young children’s
executive functioning. Child Development, 81, 326 –339. doi:10.1111/j
Brázdil, M., Roman, R., Daniel, P., & Rektor, I. (2005). Intracerebral
error-related negativity in a simple go/no-go task. Journal of Psycho-
physiology, 19, 244 –255. doi:10.1027/0269-8803.19.4.244
Bush, G., Luu, P., & Posner, M. I. (2000). Cognitive and emotional
influences in anterior cingulate cortex. Trends in Cognitive Sciences, 4,
215–222. doi:10.1016/S1364-6613(00)01483-2
Carver, C. S., & Scheier, M. F. (1981). The self-attention-induced feedback
loop and social facilitation. Journal of Experimental Social Psychology,
17, 545–568. doi:10.1016/0022-1031(81)90039-1
Damasio, A. R. (1994). Descartes’ error. New York, NY: Penguin Putnam.
Deci, E. L., & Ryan, R. M. (1985a). The General Causality Orientations
Scale: Self-determination in personality. Journal of Research in Person-
ality, 19, 109 –134. doi:10.1016/0092-6566(85)90023-6
Deci, E. L., & Ryan, R. M. (1985b). Intrinsic motivation and self-
determination in human behavior. New York, NY: Plenum Press.
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, 227–268.
Deci, E. L., & Ryan, R. M. (2002). Handbook of self-determination theory
research. Rochester, NY: University of Rochester Press.
Deci, E. L., & Ryan, R. M. (2008). Facilitating optimal motivation and
psychological well-being across life’s domains. Canadian Psychology,
49, 14 –23. doi:10.1037/0708-5591.49.1.14
Dehaene, S., Posner, M. I., & Tucker, D. M. (1994). Localization of a
neural system for error detection and compensation. Psychological Sci-
ence, 5, 303–305. doi:10.1111/j.1467-9280.1994.tb00630.x
Falkenstein, M., Hohnsbein, J., & Hoormann, J. (1991). Effects of errors in
choice reaction tasks on the ERP under focused and divided attention. In
C. H. M. Brunia, W. K. Gailard, & A. Kok (Eds.), Psychophysiological
brain research (Vol. 1, pp. 192–195). Tilburg, the Netherlands: Tilburg
University Press.
Fisher, K. R., Marshall, P. J., & Nanayakkara, A. R. (2009). Motivational
orientation, error monitoring, and academic performance in middle
childhood: a behavioral and electrophysiological investigation. Mind,
Brain, and Education, 3, 56 – 63.
Fujita, K. (2011). On conceptualizing self-control as more than the effortful
inhibition of impulses. Personality and Social Psychology Review, 15,
352–366. doi:10.1177/1088868311411165
Gehring, W. J., Goss, B., Coles, M. G., & Meyer, D. E. (1993). A neural
system for error detection and compensation. Psychological Science, 4,
385–390. doi:10.1111/j.1467-9280.1993.tb00586.x
Gonsalkorale, K., Sherman, J. W., Allen, T. J., Klauer, K. C., & Amodio,
D. M. (2011). Accounting for successful control of implicit racial bias:
The roles of association activation, response monitoring, and overcom-
ing bias. Personality and Social Psychology Bulletin, 37, 1534 –1545.
Gratton, G., Coles, M. G., & Donchin, E. (1983). A new method for
off-line removal of ocular artifact. Electroencephalography & Clinical
Neuropsychology, 55, 468 – 484. doi:10.1016/0013-4694(83)90135-9
Grolnick, W. S., & Ryan, R. M. (1987). Autonomy in children’s learning:
An experimental and individual difference investigation. Journal of
Personality and Social Psychology, 52, 890 – 898. doi:10.1037/0022-
Guay, F., Ratelle, C. F., & Chanel, J. (2008). Optimal learning in optimal
contexts: The role of self-determination in education. Canadian Psy-
chology, 49, 233–240. doi:10.1037/a0012758
Hajcak, G., & Foti, D. (2008). Errors are aversive: Defensive motivation
and the error- related negativity. Psychological Science, 19, 103–108.
Hajcak, G., McDonald, N., & Simons, R. (2003). Anxiety and error-related
brain activity. Biological Psychology, 64, 77–90. doi:10.1016/S0301-
Hajcak, G., McDonald, N., & Simons, R. (2004). Error-related psycho-
physiology and negative affect. Brain and Cognition, 56, 189 –197.
Hayes, A. F., Preacher, K. J., & Myers, T. A. (2011). Mediation and the
estimation of indirect effects in political communication research. In
E. P. Bucy & R. Lance Holbert (Eds.), Sourcebook for political com-
munication research: Methods, measures, and analytical techniques (pp.
434 465). New York, NY: Routledge.
Hodgins, H. S. (2008). Motivation, threshold for threat, and quieting the
ego. In H. A. Wayment & J. J. Bauer (Eds.), Transcending self-interest:
Psychological explorations of the quiet ego (pp. 117–124). Washington,
DC: American Psychological Association. doi:10.1037/11771-011
Hodgins, H. S., & Knee, R. C. (2002). The integrating self and conscious
experience. In E. L. Deci & R. M. Ryan (Eds.), Handbook of self-
determination research (pp. 87–100). Rochester, NY: The University of
Rochester Press.
Hodgins, H. S., & Liebeskind, E. (2003). Apology versus defense: Ante-
cedents and consequences. Journal of Experimental Social Psychology,
39, 297–316. doi:10.1016/S0022-1031(03)00024-6
Hodgins, H. S., Weibust, K. S., Weinstein, N., Shiffman, S., Miller, A.,
Coombs, G., & Adair, K. C. (2010). The cost of self-protection: Threat
response and performance as a function of autonomous and controlled
motivations. Personality and Social Psychology Bulletin, 36, 1101–
1114. doi:10.1177/0146167210375618
Hodgins, H. S., Yacko, H. A., & Gottlieb, E. (2006). Autonomy and
nondefensiveness. Motivation and Emotion, 30, 283–293. doi:10.1007/
Holroyd, C. B., & Coles, M. G. H. (2002). The neural basis of human error
processing: Reinforcement learning, dopamine, and the error-related
negativity. Psychological Review, 109, 679 –709. doi:10.1037/0033-
Inzlicht, M., & Al-Khindi, T. (2012). ERN and the placebo: A misattribu-
tion approach to studying the arousal properties of the error-related
negativity. Journal of Experimental Psychology: General. Advance on-
line publication. doi:10.1037/a0027586
Inzlicht, M., & Gutsell, J. N. (2007). Running on empty: Neural signals for
self-control failure. Psychological Science, 18, 933–937. doi:10.1111/j
Inzlicht, M., & Schmeichel, B. J. (2012). What is ego depletion? Toward
a mechanistic revision of the resource model of self-control. Perspec-
tives on Psychological Science, 7, 450 – 463. doi:10.1177/
Kasser, T., Kanner, A., Cohn, S., & Ryan, R. M. (2007). Psychology and
American corporate capitalism: Further reflections and future directions.
Psychological Inquiry, 18, 60 –71. doi:10.1080/10478400701459103
Kasser, T., & Ryan, R. M. (1996). Further examining the American dream:
Differential correlates of intrinsic and extrinsic goals. Personality and
Social Psychology Bulletin, 22, 280 –287. doi:10.1177/
Kerns, J. G., Cohen, J. D., MacDonald, A. W., III, Cho, R. Y., Stenger,
V. A., & Carter, C. S. (2004). Anterior cingulate, conflict monitoring,
and adjustments in control. Science, 303, 1023–1026. doi:10.1126/
Koestner, R., & Zuckerman, M. (1994). Causality orientations, failure, and
achievement. Journal of Personality, 62, 321–346. doi:10.1111/j.1467-
Lakey, C. E., Kernis, M. H., Heppner, W. L., & Lance, C. E. (2008).
Individual differences in authenticity and mindfulness as predictors of
verbal defensiveness. Journal of Research in Personality, 42, 230 –238.
Lee, W., & Reeve, J. (2012). Self-determined, but not non-self-determined,
motivation predicts activations in the anterior insular cortex: An fMRI
study of personal agency. Social Cognitive and Affective Neuroscience.
Advance online publication. doi:10.1093/scan/nss029
Legault, L., Al-Khindi, T., & Inzlicht, M. (in press). Preserving integrity in
the face of performance threat: Self-affirmation enhances neurophysio-
logical responsiveness to errors. Psychological Science.doi:10.1177/
Legault, L., Green-Demers, I., & Eadie, A. L. (2009). When internalization
leads to automatization: The role of self- determination in automatic
stereotype suppression and implicit prejudice regulation. Motivation and
Emotion, 33, 10 –24. doi:10.1007/s11031-008-9110-4
Legault, L., Green-Demers, I., Grant, P., & Chung, J. (2007). On the
self-regulation of implicit and explicit prejudice: A self-determination
perspective. Personality and Social Psychology Bulletin, 33, 732–749.
Legault, L., Gutsell, J. N., & Inzlicht, M. (2011). Ironic effects of anti-
prejudice messages: How motivational interventions can reduce (but
also increase) prejudice. Psychological Science, 22, 1472–1477. doi:
Luck, S. J. (2005). An introduction to the event-related potential technique.
Cambridge, MA: MIT Press.
Luu, P., Collins, P., & Tucker, D. M. (2000). Mood, personality, and
self-monitoring: Negative affect and emotionality in relation to frontal
lobe mechanisms of error monitoring. Journal of Experimental Psychol-
ogy: General, 129, 43– 60. doi:10.1037/0096-3445.129.1.43
MacLeod, C. M. (1991). Half a century of research on the Stroop effect: An
integrative review. Psychological Bulletin, 109, 163–203. doi:10.1037/
McAuley, E., Duncan, T., & Tammen, V. V. (1989). Psychometric prop-
erties of the Intrinsic Motivation Inventory in a competitive sport setting:
A confirmatory factor analysis. Research Quarterly for Exercise and
Sport, 60, 48 –58.
Miltner, W. H. R., Lemke, U., Weiss, T., Holroyd, C., Scheffers, M., &
Coles, M. (2003). Implementation of error-processing in the human
anterior cingulate cortex: A source analysis of the magnetic equivalent
of the error-related negativity. Biological Psychology, 64, 157–166.
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, 49 –100. doi:10.1006/cogp.1999
Moller, A. C., Deci, E. L., & Ryan, R. M. (2006). Choice and ego-
depletion: The moderating role of autonomy. Personality and Social
Psychology Bulletin, 32, 1024 –1036. doi:10.1177/0146167206288008
Muraven, M. (2008). Autonomous self-control is less depleting. Journal of
Research in Personality, 42, 763–770. doi:10.1016/j.jrp.2007.08.002
Muraven, M., Gagné, M., & Rosman, H. (2007). Lack of autonomy and
self-control: Performance contingent rewards lead to greater depletion.
Motivation and Emotion, 31, 322–330. doi:10.1007/s11031-007-9073-x
Muraven, M., Gagné, M., & Rosman, H. (2008). Helpful self-control:
Autonomy support, vitality, and depletion. Journal of Experimental
Social Psychology, 44, 573–585. doi:10.1016/j.jesp.2007.10.008
Murayama, K., Matsumoto, M., Izuma, K., & Matsumoto, K. (2010).
Neural basis of the undermining effect of monetary reward on intrinsic
motivation. PNAS: Proceedings of the National Academy of Sciences of
the United States of America, 107, 20911–20916. doi:10.1073/pnas
Olvet, D. M., & Hajcak, G. (2009a). Reliability of error-related brain
activity. Brain Research, 1284, 89 –99. doi:10.1016/j.brainres.2009.05
Olvet, D. M., & Hajcak, G. (2009b). The stability of error-related brain
activity with increasing trials. Psychophysiology, 46, 957–961. doi:
Osbaldiston, R., & Sheldon, K. M. (2003). Promoting internalized moti-
vation for environmentally responsible behavior: A prospective study of
environmental goals. Journal of Environmental Psychology, 23, 349 –
357. doi:10.1016/S0272-4944(03)00035-5
Pelletier, L. G., Fortier, M. S., Vallerand, R. J., & Brière, N. M. (2001).
Associations among perceived autonomy support, forms of self-
regulation, and persistence: A prospective study. Motivation and Emo-
tion, 25, 279 –306. doi:10.1023/A:1014805132406
Pierce, W. D., & Cheney, C. D. (2004). Behavior analysis & learning (3rd
ed.). Mahwah, NJ: Erlbaum.
Pizzagalli, D. A., Peccorralo, L. A., Davidson, R. J., & Cohen, J. D. (2006).
Resting anterior cingulate activity and abnormal responses to errors in
subjects with elevated depressive symptoms: A 128-channel EEG study.
Human Brain Mapping, 27, 185–201. doi:10.1002/hbm.20172
Preacher, K. J., & Hayes, A. F. (2004). SPSS and SAS procedures for
estimating indirect effects in simple mediation models. Behavior Re-
search Methods, Instruments & Computers, 36, 717–731. doi:10.3758/
Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling
strategies for assessing and comparing indirect effects in multiple me-
diator models. Behavior Research Methods, 40, 879 – 891. doi:10.3758/
Rabbitt, P. M. A. (1966). Error and error correction in choice-response
tasks. Journal of Experimental Psychology, 71, 264 –272. doi:10.1037/
Rabbitt, P. M. A., & Rodgers, B. (1977). What does a man do after he
makes an error? An analysis of response programming. The Quarterly
Journal of Experimental Psychology, 29, 727–743. doi:10.1080/
Reeve, J. (2009). Understanding motivation and emotion (5th ed.). Hobo-
ken, NJ: Wiley.
Reeve, J., Bolt, E., & Cai, Y. (1999). Autonomy-supportive teachers: How
they teach and motivate students. Journal of Educational Psychology,
91, 537–548. doi:10.1037/0022-0663.91.3.537
Ridderinkhof, K. R., Ulsperger, M., Crone, E. E., & Nieuwenhuis, S.
(2004). The role of the medial frontal cortex in cognitive control.
Science, 306, 443– 447. doi:10.1126/science.1100301
Ryan, R. M. (1982). Control and information in the intrapersonal sphere:
An extension of cognitive evaluation theory. Journal of Personality and
Social Psychology, 43, 450 – 461. doi:10.1037/0022-3514.43.3.450
Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the
facilitation of intrinsic motivation, social development, and well-being.
American Psychologist, 55, 68 –78. doi:10.1037/0003-066X.55.1.68
Ryan, R. M., & Deci, E. L. (2008). From ego-depletion to vitality: Theory
and findings concerning the facilitation of energy available to the self.
Social and Personality Psychology Compass, 2, 702–717. doi:10.1111/
Ryan, R. M., Plant, R. W., & O’Malley, S. (1995). Initial motivations for
alcohol treatment: Relations with patient characteristics, treatment in-
volvement, and drop out. Addictive Behaviors, 20, 279 –297. doi:
Schmeichel, B. J., & Inzlicht, M. (in press). Incidental and integral effects
of emotions on self-control. In M. D. Robinson, E. R. Watkins, & E.
Harmon-Jones (Eds.), Guilford handbook of cognition and emotion.
New York, NY: Guilford Press.
Shackman, A. J., Salomons, T. V., Slagter, H. A., Fox, A. S., Winter, J. J.,
& Davidson, R. J. (2011). The integration of negative affect, pain, and
cognitive control in the cingulate cortex. Nature Reviews Neuroscience,
12, 154 –167. doi:10.1038/nrn2994
Sheldon, K. M., & Elliot, A. J. (1998). Not all personal goals are “per-
sonal”: Comparing autonomous and controlling goals on effort and
attainment. Personality and Social Psychology Bulletin, 24, 546 –557.
Tangney, J. P., Baumeister, R. F., & Boone, A. L. (2004). High self-control
predicts good adjustment, less pathology, better grades, and interper-
sonal success. Journal of Personality, 72, 271–324. doi:10.1111/j.0022-
Teixeira, P. J., Silva, M. N., Coutinho, S. R., Palmeira, A. L., Mata, J.,
Vieira, P. N.,...Sardinha, L. B. (2010). Mediators of weight loss and
weight loss maintenance in middle-aged women. Obesity, 18, 725–735.
Teper, R., & Inzlicht, M. (in press). Meditation, mindfulness, and executive
control: The importance of emotional acceptance and brain-based per-
formance monitoring. Social Cognitive Affective Neuroscience.doi:
Vallerand, R. J., Fortier, M. S., & Guay, F. (1997). Self-determination and
persistence in a real-life setting: Toward a motivational model of high
school dropout. Journal of Personality and Social Psychology, 72,
1161–1176. doi:10.1037/0022-3514.72.5.1161
Vansteenkiste, M., Simons, J., Lens, W., Sheldon, K. M., & Deci, E. L.
(2004). Motivating learning, performance, and persistence: The syner-
gistic role of intrinsic goals and autonomy-support. Journal of Person-
ality and Social Psychology, 87, 246 –260. doi:10.1037/0022-3514.87.2
Walton, M. E., Devlin, J. T., & Rushworth, M. F. S. (2004). Interactions
between decision making and feedback monitoring within prefrontal
cortex. Nature Neuroscience, 7, 1259 –1265.
Wegner, D. M. (1994). Ironic processes of mental control. Psychological
Review, 101, 34 –52. doi:10.1037/0033-295X.101.1.34
Weinberg, A., & Hajcak, G. (2011). Longer term test–retest reliability of
error-related brain activity. Psychophysiology, 48, 1420 –1425. doi:
Weinberg, A., Riesel, A., & Hajcak, G. (2012). Integrating multiple per-
spectives on error-related brain activity: The ERN as a neural indicator
of trait defensive reactivity. Motivation and Emotion, 36, 84 –100.
Weinstein, N., Deci, E. L., & Ryan, R. M. (2011). Motivational determi-
nants of integrating positive and negative past identities. Journal of
Personality and Social Psychology, 100, 527–544. doi:10.1037/
Wiener, N. (1948). Cybernetics: Control and communication in the animal
and the machine. Cambridge, Mass: MIT Press.
Williams, G. C., Grow, V. M., Freedman, Z. R., Ryan, R. M., & Deci, E. L.
(1996). Motivational predictors of weight loss and weight-loss mainte-
nance. Journal of Personality and Social Psychology, 70, 115–126.
Williams, G. C., McGregor, H. A., Sharp, D., Levesque, C. S., Kouides,
R. W., Ryan, R. M., & Deci, E. L. (2006). Testing a self-determination
theory intervention for motivating tobacco cessation: Supporting auton-
omy and competence in a clinical trial. Health Psychology, 25, 91–101.
Williams, G. C., Niemiec, C. P., Patrick, H., Ryan, R. M., & Deci, E. L.
(2009). The importance of supporting autonomy and perceived compe-
tence in facilitating long- term tobacco abstinence. Annals of Behavioral
Medicine, 37, 315–324. doi:10.1007/s12160-009-9090-y
Williams, G. C., Patrick, H., Niemiec, C. P., Williams, L. K., Devine, G.,
Lafata, J. E.,...Pladevall, M. (2009). Reducing the health risks of
diabetes: How self-determination theory may help improve medication
adherence and quality of life. The Diabetes Educator, 35, 484 – 492.
Yeung, N., Botvinick, M. M., & Cohen, J. D. (2004). The neural basis of
error-detection: Conflict monitoring and the error-related negativity.
Psychological Review, 111, 931–959. doi:10.1037/0033-295X.111.4
Received March 1, 2012
Revision received August 1, 2012
Accepted September 13, 2012
... Second, monitoring also has a central role in allowing individuals and teams to identify when their regulatory action has been ineffective and might need to be adjusted. The first and second points go together as those who have their needs supported are more likely to pay attention to (negative) feedback, and rather than be threatened by it, use that information as a way of taking corrective action (Legault and Inzlicht, 2013;Reeve and Lee, 2019). The model presented in this paper offers direction for monitoring efforts, providing team members with explicit reference points (e.g., perceptions of basic needs, perceptions of social environment) against which they can judge whether changes need to be implemented in order to optimize well-being and subsequent team dynamics. ...
Why do some individuals thrive during the process of new venture creation, while others experience the dark side of the venturing process? This paper contributes to a growing literature on entrepreneurial well-being by connecting it to the literature on entrepreneurial teams. We begin with two persistent findings from the entrepreneurship literature: first, that individual well-being is critical to venturing outcomes; and second, that entrepreneurship is most often a team sport. Building on self-determination theory and recent research on extreme teams, we argue that the satisfaction and thwarting of an entrepreneur’s basic needs is, fundamentally, socially situated—and, consequently, team dynamics are a critical, but relatively undertheorized, input to individual well-being in entrepreneurial settings. We further argue that an individual’s regulatory strategies shape the relationship between entrepreneurial team dynamics and individual well-being. Our work contributes to an emerging literature on the well-being of entrepreneurs, illuminating the critical role team dynamics and individual coping strategies play in the well-being of entrepreneurs.
Self‐regulation often involves foregoing short‐term pleasures and impulses in favor of long‐term goals, such as pursuing a particular career, raising a family, or maintaining good health to promote longevity. Like many other fields in psychology, the study of self‐regulation has experienced some growing pains in the wake of the replication crisis, with previously held theories called into question, including seemingly intuitive phenomena such as ego depletion. Despite these challenges, there is burgeoning interest in characterizing people's experiences of self‐regulation success and failure in real world settings. In this review, I argue that utilizing tools and approaches from neuroscience will yield valuable insights into how self‐regulatory processes are engaged in daily life, which in turn will refine and advance self‐regulation models and theorizing, as well as generate new hypotheses. I also unpack some conceptual and practical considerations when combining neuroscience methods with real‐world assessment of behaviors, such as ecological momentary assessment. With these issues and points for consideration taken together, I hope this review will help pave a fruitful path forward for the field with implications for how we might become better self‐regulators.
This experiment tested whether personal task choice can shield against implicit affective influences on sympathetically mediated cardiovascular response, reflecting effort. Participants were N = 121 healthy university students who completed a moderately difficult memory task with integrated briefly flashed and masked fear vs. anger primes. Half of the participants believed they could choose between an attention and a memory task, while the other half was automatically assigned to the task. Replicating previous research, we expected an influence of the affect primes on effort when the task was externally assigned. By contrast, when participants were given a task choice, we predicted strong action shielding and thus a low implicit affect effect on resource mobilization. As expected, participants in the assigned task condition showed stronger cardiac pre-ejection period reactivity when exposed to fear primes than when processing anger primes. Importantly, this affect prime effect disappeared when participants could ostensibly choose the task. These findings add to other recent evidence for action shielding by personal task choice and importantly extend this effect to implicit affective influences on cardiac reactivity during task performance.
It has been demonstrated in previous studies that prolonged mental effort exertion evokes mental fatigue and leads to impairments in task performance. In the current investigation, we aimed to test the hypothesis that mental fatigue depends on motivational processes and can be influenced by task valuation. In two studies, we experimentally manipulated the value of the task by financial rewards (Study 1) and the sense of autonomy (Study 2). Contrary to our predictions, those manipulations did not influence the main dependent variables. We also introduced additional rewards after prolonged effort exertion. In line with our expectations, the results showed that mental fatigue increases with time spent on effortful tasks. Importantly, however, mental fatigue decreases when the value of the task rises. This effect is accompanied by stronger effort engagement and improvement in task performance. The findings support the motivational theories of mental effort and fatigue, showing that mental fatigue might serve as a signal of diminishing value of the ongoing task.
The motive domains of control and truth align with conceptions of God's omnipotence and omniscience, but the link between control-versus truth-dominant motives and God concepts remains unclear. A convergence account predicts a positive association between these variables; a divergence account predicts an inverse association. Furthermore, the causal direction of an association under either account is unknown. To test between these competing accounts, Study 1 investigated whether people with God concepts dominant in control versus truth report motives that are also control- (vs. truth-) dominant. To investigate causality, Study 2 manipulated God concepts and measured motive dominance; Study 3 manipulated motives and measured God concepts. Study 4 replicated Study 1 with methodological enhancements. Collectively, results supported the convergence account, indicating that God concepts influence motive predominance. By integrating motivation and religious cognition research, this work elucidates the relations between beliefs about God's attributes and personal motives.
The present study investigated the moderating role of peer attachment style in the relationship between mood and creativity. An experiment was conducted with a sample of 267 undergraduate students (Mage = 19.85, range = 17-24 years). First, participants' peer attachment style was measured, following which positive, neutral, or negative mood was induced; subsequently, two creative tasks were conducted. A MANOVA revealed significant interactions between peer attachment and mood. Specifically, for secure participants, creativity was significantly higher in the positive mood state compared to the neutral and negative mood states; for insecure participants, the effect of positive mood was not pronounced. Moreover, negative mood exerted a significant beneficial effect on the originality dimension for participants with an anxious-ambivalent peer attachment style; they showed higher creativity in the negative mood state than in the neutral or positive mood states. In general, peer attachment style moderated the relationship between mood and creativity; specifically, positive mood was beneficial to creativity among secure persons, and negative mood was beneficial to creativity among anxious-ambivalent persons.
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Understanding the relationship between personality traits and e-learning autonomy can help to provide tailored e-learning environments and student support. Although the related literature is rich regarding learner autonomy and personality traits, there is a dearth of research exploring the predictive relationship. This research investigates the relationship between personality traits and e-learning autonomy of distance education students. Data was collected using the Big 5 Personality Traits and e-Learning Autonomy scales from 3435 distance education students of Anadolu University’s Open Education Faculty. Results show that male students have more e-learning autonomy than female students, while female students have higher scores of personality traits except for agreeableness. The e-learning autonomy of distance education students had positive correlations with four personality traits except for neuroticism. Furthermore, extraversion, agreeableness, conscientiousness, and openness were significant predictors of e-learning autonomy behaviors of distance education students.
Anterior cingulate cortex (ACC) is a part of the brain's limbic system. Classically, this region has been related to affect, on the basis of lesion studies in humans and in animals. In the late 1980s, neuroimaging research indicated that ACC was active in many studies of cognition. The findings from EEG studies of a focal area of negativity in scalp electrodes following an error response led to the idea that ACC might be the brain's error detection and correction device. In this article, these various findings are reviewed in relation to the idea that ACC is a part of a circuit involved in a form of attention that serves to regulate both cognitive and emotional processing. Neuroimaging studies showing that separate areas of ACC are involved in cognition and emotion are discussed and related to results showing that the error negativity is influenced by affect and motivation. In addition, the development of the emotional and cognitive roles of ACC are discussed, and how the success of this regulation in controlling responses might be correlated with cingulate size. Finally, some theories are considered about how the different subdivisions of ACC might interact with other cortical structures as a part of the circuits involved in the regulation of mental and emotional activity.
A theory of ironic processes of mental control is proposed to account for the intentional and counterintentional effects that result from efforts at self-control of mental states. The theory holds that an attempt to control the mind introduces 2 processes: (a) an operating process that promotes the intended change by searching for mental contents consistent with the intended state and (b) a monitoring process that tests whether the operating process is needed by searching for mental contents inconsistent with the intended state. The operating process requires greater cognitive capacity and normally has more pronounced cognitive effects than the monitoring process, and the 2 working together thus promote whatever degree of mental control is enjoyed. Under conditions that reduce capacity, however, the monitoring process may supersede the operating process and thus enhance the person's sensitivity to mental contents that are the ironic opposite of those that are intended.
The authors present a unified account of 2 neural systems concerned with the development and expression of adaptive behaviors: a mesencephalic dopamine system for reinforcement learning and a "generic" error-processing system associated with the anterior cingulate cortex. The existence of the error-processing system has been inferred from the error-related negativity (ERN), a component. of the event-related brain potential elicited when human participants commit errors in reaction-time tasks. The authors propose that the ERN is generated when a negative reinforcement learning signal is conveyed to the anterior cingulate cortex via the mesencephalic dopamine system and that this signal is used by the anterior cingulate cortex to modify performance on the task at hand. They provide support for this proposal using both computational modeling and psychophysiological experimentation.