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From: Kristen E. Lyons and Philip David Zelazo, Monitoring, Metacognition, and
Executive Function: Elucidating the Role of Self-Reflection in the Development of
Self-Regulation. In Janette Benson, editors: Advances in Child Development and
Behavior, Vol. 40, Burlington: Academic Press, 2011, pp. 379-412.
© Copyright 2011 Elsevier Inc.
MONITORING, METACOGNITION, AND
EXECUTIVE FUNCTION: ELUCIDATING
THE ROLE OF SELF-REFLECTION IN
THE DEVELOPMENT OF SELF-REGULATION
Kristen E. Lyons and Philip David Zelazo
INSTITUTE OF CHILD DEVELOPMENT, UNIVERSITY OF MINNESOTA,
MINNEAPOLIS, MINNESOTA, USA
II. FOUR LITERATURES INVESTIGATING THE DEVELOPMENT
A. EXECUTIVE FUNCTION
B. ERROR MONITORING
D. UNCERTAINTY MONITORING
III. INTEGRATING DISPARATE LITERATURES
IV. THE ROLE OF SELF-REFLECTION IN THE DEVELOPMENT
A. SOURCES OF AGE-RELATED IMPROVEMENTS IN SELF-REFLECTIVE
B. SOURCES OF AGE-RELATED IMPROVEMENTS IN IMPLEMENTING
V. DYNAMIC INTERACTIONS BETWEEN AUTOMATIC AND
CONTROLLED PROCESSES IN SELF-REGULATION
While an abundance of research has investigated the development of the
automatic and controlled processes through which individuals control
their thoughts, emotions, and actions, less research has emphasized the
role of the self in self-regulation. This chapter synthesizes four literatures
that have examined the mechanisms through which the individual acts in a
managerial role, evaluating the current status of the system and initiating
regulatory actions as necessary. Taken together, these literatures
Advances in Child Development and Behavior Copyright ©2011 Elsevier Inc. All rights reserved.
Janette B. Benson : Editor
Author's personal copy
(on executive function, error monitoring, metacognition, and uncertainty
monitoring) suggest that self-reﬂection plays a critical role in self-regulation,
and that developmental improvements in self-reﬂection (via increasing
levels of conscious awareness and enhanced calibration of monitoring sys-
tems) may serve as driving forces underlying developmental improvement
(and temperamental individual differences) in children’s ability to control
their thoughts and actions.
The development of self-regulation has long been a focus of developmen-
tal research. From early observations by Piaget and Vygotsky through con-
temporary behavioral and neuroscientiﬁc investigations, this line of inquiry
provides insight into the genetic, neurophysiological, and behavioral
mechanisms underlying the willful control of thought, emotion, and action.
Self-regulation frees children from reactive, stimulus-driven patterns of
responding, allowing them to plan for their future, maintain optimal levels
of emotional reactivity, and act in a manner that is consistent with their
higher order goals or standards for behaving. As such, this core capacity
has critical implications for emotional well-being, social competence, and
academic and professional success (Blair, 2002; Blair & Diamond, 2008;
Eisenberg, Valiente, & Eggum, 2010; Mofﬁtt et al., 2011).
Accordingly, an extensive body of research has investigated the myriad of
automatic and controlled processes that underlie the development of self-
regulation (Kochanska, Coy, & Murray, 2001; Schneider & Lockl, 2008;
Zelazo, Carlson, & Kesek, 2008). Overall, this line of research has revealed
that self-regulation begins to emerge in the ﬁrst few months of life, as chil-
dren develop basic motor skills allowing them to engage in rudimentary
forms of self-soothing (e.g., shifting their gaze from aversive stimuli, seeking
proximity to their caregivers; Buss & Goldsmith, 1998; Rothbart, Ziaie, &
O’Boyle, 1992). Dramatic improvements in self-regulation occur during
early childhood as children become increasingly able to control their
thoughts (e.g., Paz-Alonso, Ghetti, Matlen, Anderson, & Bunge, 2009),
emotions (e.g., Lewis & Stieben, 2004), and actions (e.g., Zelazo, Müller,
Frye, & Marcovitch, 2003). However, self-regulation continues to mature
until well into adolescence (Best & Miller, 2010; Lewis, Lamm, Segalowitz,
Stieben, & Zelazo, 2006; Olson & Luciana, 2008) and probably beyond (e.g.,
Weintraub et al., 2011). In short, an abundance of research has revealed that
clear age-related improvements in the ability to purposefully adjust one’s
thoughts and behavior are observed in the ﬁrst two decades of life.
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To date, relatively little attention has been directed toward the self
aspect of self-regulation, including the self-reﬂective processes that allow
children to evaluate their current (internal and contextual) status and ini-
tiate corrective adjustments to their ongoing activity. Just as regulatory
agencies cannot enforce laws without actually monitoring whether com-
panies are adhering to them, human self-regulatory systems cannot ade-
quately control their behavior without some degree of awareness of the
ongoing operations of the system.
How do children develop the ability to monitor and modify their ongo-
ing thoughts, emotions, and actions? The aim of this chapter is to provide
an integrative review of four diverse literatures (on executive function
(EF), metacognition, uncertainty monitoring, and error monitoring) that
are beginning to converge on this question and that collectively provide
some promising answers to it.
Before proceeding, we note that self-regulation encompasses a broad
range of automatic and controlled processes (Lewis & Todd, 2007). Our
primary focus will be the development of the latter. However, age-related
changes and individual differences in automatic processes likely interact
with more controlled operations to yield self-regulation, as discussed in
later sections of this chapter. We also note that the terms self-monitoring
and self-regulation are used here as functional constructs. That is to say,
these and related terms (see Table I for a list of key constructs) are
intended to describe the processes that make it possible for individuals
to track and adjust their behavior (e.g., Zelazo et al., 2008), and not as
the operations of a homunculus or neural module.
II. Four Literatures Investigating the Development
The question of how children acquire the ability to purposefully control
their thoughts and actions has received considerable attention in recent
years, in part due to the well-documented importance of self-regulation
for daily functioning and long-term interpersonal, academic, and profes-
sional success (Blair, 2002; Blair & Diamond, 2008; Eisenberg et al.,
2010; Manly, Hawkins, Evans, Woldt, & Robertson, 2002; Mofﬁtt et al.,
2011). Given that self-regulation involves a highly complex array of pro-
cesses, different researchers have focused on different aspects of self-reg-
ulation and approached the topic in different ways, generating at least four
disparate research literatures on EF, error detection, metacognition, and
uncertainty monitoring. While each approach offers distinct methodological
advantages and unique insight into the development of self-regulation, they
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have progressed relatively independently of one another. However, even a
cursory review of these literatures reveals important ways in which the core
processes under investigation may be fundamentally intertwined. In what
follows, we survey these literatures and emphasize how developmental
changes in each may be critically dependent on age-related increases in self-
reﬂection. Taken together, these literatures provide a more comprehensive
characterization of the development of self-reﬂection and its myriad con-
sequences for behavior.
A. EXECUTIVE FUNCTION
EF entails the conscious control of thoughts and actions (Zelazo et al.,
2008) and refers to the more controlled, top-down processes in self-regu-
lation that depend importantly on neural circuits involving prefrontal
Key construct Deﬁnition
Self-regulation The broad range of automatic and controlled processes through which
thoughts, emotions, and actions are adjusted
Self-control The deliberate adjustment of one’s cognitive, emotional, or
The capacity to evaluate one’s ongoing thoughts, emotions, and actions
Executive function The deliberate adjustment of one’s cognitive, emotional, or behavioral
responses, often conceptualized as the cognitive processes of
ﬂexibility (task switching), inhibitory control, and working memory
Error monitoring Tracking one’s performance on a task and noticing when one has
committed an error, often assessed using ERP
Metacognition Awareness and control of one’s cognitive activity
Reﬂecting on one’s ongoing cognitive activity
Top-down control of cognitive activity based on metacognitive
Evaluating how certain one feels about the likely accuracy of one’s
Interoception Subjective awareness of one’s physiological, emotional, and cognitive
An indirect means of monitoring, such that individuals are implicitly
aware of lower-order cognitive operations, which gives rise to
subjective feelings of knowing (although they cannot explicitly
reﬂect on the source of the subjective feelings)
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cortex (PFC). Although it has widespread consequences for daily func-
tioning (e.g., maintaining focus on one’s current task, inhibiting oneself
from becoming distracted, ﬂexibly shifting one’s attention during multi-
tasking), EF is typically assessed using neuropsychological tests of pre-
frontal cortical function that have been modiﬁed for use with children
(e.g., go/no-go, backward digit span, ﬂanker; Fernandez-Duque, Baird,
& Posner, 2000). Often, these tasks require children to overcome the ten-
dency to respond reﬂexively, impulsively, or by habit (Davidson, Amso,
Anderson, & Diamond, 2006; Shimamura, 2000) and instead purposefully
select the appropriate course of action.
EF emerges relatively early, as indicated by infants’passing of the A-not-B
task toward the end of the ﬁrst year of life. In this task, infants are repeatedly
shown an object being hidden in a certain location (location A) where they
repeatedly retrieve the object. Next, the object is conspicuously hidden in a
different location (location B). Whereas 8- to 10-month-old infants typically
search for the object in location A, older infants typically search for the object
in location B, indicating that they were able to inhibit their habitual, prepotent
response to search for the object where it was previously retrieved
(Marcovitch & Zelazo, 2009). According to one view, this achievement is crit-
ically dependent upon infants’emerging ability to mentally represent
elements of the task at hand by reﬂecting upon the contents of lower levels
of processing. This psychological distancing allows infants to decouple them-
selves from the stimulus–response pattern of searching (in location A),
permitting them to selectively direct their searching to location B
(Marcovitch & Zelazo, 2009).
EF continues to improve throughout childhood and adolescence as
individuals become able to achieve increasingly higher levels of reﬂections
of their ongoing mental activity. Developmental improvements in EF
accuracy are particularly dramatic during early childhood, when rapid
increases are observed in the highest level of consciousness that children
can experience (Zelazo, Gao, & Todd, 2007). During middle childhood,
as children gain metacognitive awareness of the effects of response speed
on performance accuracy (namely that faster responses are more likely to
be incorrect), a trade-off between speed and accuracy begins to emerge
(Best, Miller, & Jones, 2009; Davidson et al., 2006) along with overall
improvements in reaction times on EF tasks. Performance on many EF
tasks does not reach adult levels until well into adolescence, coinciding
with the rather protracted structural and functional maturation of PFC,
which supports EF (Barnea-Goraly et al., 2005; Blakemore & Choudhury,
2006; Gogtay et al., 2004).
Research with adults indicates that distinct aspects of EF are supported
by distinct regions of PFC. Speciﬁcally, the capacity to inhibit oneself from
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responding impulsively and the capacity to adjust one’s behavior so that
the correct course of action is selected (e.g., on conﬂict tasks such as the
Stroop color–word task or the ﬂanker task; Gerstdat, Hong, & Diamond,
1994; Rueda et al., 2004) have distinct neural substrates (Garavan, Ross,
Murphy, Roche, & Stein, 2002). These ﬁndings are relevant for the devel-
opment of self-regulation in two ways. First, given that distinct regions of
PFC mature at different rates (e.g., Bunge & Zelazo, 2006; Gogtay et al.,
2004), these ﬁnding suggest that subdomains of EF may follow distinct
developmental trajectories. This notion is well supported by the literature
(Best et al., 2009; Huizinga, Dolan, & van der Molen, 2006): Whereas inhib-
itory control and performance on conﬂict tasks (requiring ﬂexible shifting of
attention to relevant task dimensions) undergo substantial developmental
improvement during early childhood (Gerstdat et al., 1994; Jacques &
Zelazo, 2001; Rueda et al., 2004), selective attention, working memory,
problem solving, and planning ability continue to improve steadily through-
out childhood and adolescence (Anderson, Anderson, Northam, Jacobs, &
Catroppa, 2001; Best et al., 2009; Huizinga et al., 2006).
Second, these ﬁndings underscore two critical roles of the “executive”
in executive functioning: ﬁrst, to evaluate one’s ongoing performance in
order to reign in responding when contextual cues indicate that it is neces-
sary to do so; and second, to reﬂectively adjudicate among response
alternatives to select the appropriate course of action. Such distinctions
are helpful in understanding how different aspects of EF develop
(Garavan et al., 2002). While there is some debate concerning the under-
lying structure of EF (i.e., whether it is best characterized as a unitary or
modular function; e.g., Best et al., 2009), it is generally agreed that EF
includes at least three core skills: working memory, inhibitory control,
and cognitive ﬂexibility (e.g., Hughes, 1998; Miyake, Friedman, Emerson,
Witzki, & Howerter, 2000).
1. Working Memory
Working memory, individuals’capacity to maintain and manipulate infor-
mation in short-term memory, steadily improves throughout childhood and
adolescence (Siegel & Ryan, 1989). With age, the maximum number of items
that individuals can retain (i.e., working memory capacity) steadily increases
(e.g., Klingberg, Forssberg, & Westenberg, 2002), as does their ability to
mentally manipulate information (e.g., Kwon, Reiss, & Menon, 2002). One’s
ability to maintain and manipulate information likely has critical con-
sequences for self-regulation, as appropriate regulation requires that one
reason about two or more potential response alternatives in accordance with
the parameters of the current context and knowledge about the capabilities
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and limits of the system. As such, age-related improvements in the ability to
reﬂect upon and reason about relevant features of task performance likely
are an important factor underlying age-related improvements in self-regula-
tion (e.g., Zelazo, 2004).
2. Inhibitory Control
Inhibitory control entails the capacity to willfully withhold or suppress a
thought or action (Best et al., 2009; Paz-Alonso et al., 2009; Williams,
Ponesse, Schachar, Logan, & Tannock, 1999). This capacity is often
assessed by examining children’s ability to refrain from producing a pre-
potent response (e.g., on a go/no-go task; Thorell, Lindqvist, Nutley,
Bohlin, & Klingberg, 2009), although it is sometimes also assessed in more
emotional contexts (e.g., by examining children’s ability to refrain from
peeking at a present or their ability to suppress their disappointment in
receiving an undesirable gift; e.g., Carlson & Wang, 2007). In both types
of task, successful inhibition requires that children maintain awareness
of their ongoing performance and consciously suppress responses that
Inhibitory control develops throughout childhood and adolescence
(Best & Miller, 2010), but improvements in this capacity are particularly
dramatic during early childhood (Carlson, 2005; Carlson, Mandell, &
Williams, 2004). During this period, children also become increasingly
able to delay gratiﬁcation, electing to forgo smaller immediate rewards
in order to receive larger rewards in the future. Whereas elementary
school-aged children typically make the advantageous selection,
preschoolers are less consistent in their choices, frequently succumbing
to their desire for immediate gratiﬁcation over the long-term beneﬁts of
waiting (Mischel, 1974; Thompson, Barresi, & Moore, 1997).
Developmental improvement in the ability to delay gratiﬁcation
appears to be dependent upon children’s ability to mentally connect their
present and future selves (Lemmon & Moore, 2007). Although children
display mirror self-recognition around 2 years of age (as indicated by pass-
ing the mirror test; Amsterdam, 1972), and some aspects of self awareness
likely emerge gradually before that (Rochat, 2004), it is not until around
age 4 that children seem to appreciate that who they are in the present
moment is the same individual who experienced past events and who will
experience future events (Moore & Lemmon, 2001; Povinelli, Landau, &
Perilloux, 1996). Thus, younger children may fail delay of gratiﬁcation
tasks because they simply fail to appreciate that their actions will affect
their future (so for them, choosing an immediate reward may represent
the logical decision). As children develop the ability to represent their
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both present and future selves and to reason about the consequences of
current actions on the future self, children more consistently make the
decision that is more advantageous over the long run.
In addition to dramatic developmental differences in inhibitory control,
there are substantial individual differences in inhibitory control that are rela-
tively stable throughout early childhood, as indicated by performance on
behavioral measures and by parental reports of aspects of temperament
related to self-regulation (e.g., Carlson et al., 2004; Kochanska, Murray, &
Harlan, 2000). Moreover, it has long been known that children vary signiﬁ-
cantly in their performance on delay of gratiﬁcation tasks (with individual
differences at age 4 predicting academic and social success into adolescence;
e.g., Shoda, Mischel, & Peake, 1990). The presence of systematic differences
in individuals’ability (or propensity) to inhibit inappropriate thoughts or
actions raises intriguing questions about whether individuals differ in their
ability to access the contentsof their mental activity and/or whether they differ
in the quality of the to-be-accessed material (i.e., whether they are actually
receiving a stronger “signal”to be read). Of course these alternatives are
not exclusive, and the source of differences in inhibitory control may vary.
3. Cognitive Flexibility
Although inhibitory control can be isolated in laboratory tasks, in the real
world, inhibitory control demands are often intertwined with demands for
shifting one’s attention or response set to coincide with an updated context
(e.g., Bunge & Zelazo, 2006). Similar to inhibitory control, this capacity
undergoes substantial developmental improvement during early childhood.
For example, on the dimensional change card sort task, children are pres-
ented with cards varying in two dimensions (e.g., color and shape). Children
are ﬁrst asked to sort the cards by one dimension (e.g., color) and then to sort
the cards by the other dimension (e.g., shape). While most 4-year-olds typi-
cally succeed in sorting by the second dimension, the majority of 3-year-olds
usually fail to do so, persisting in sorting by the ﬁrst dimension (despite being
able to articulate the rules of the current task; Zelazo, Frye, & Rapus, 1996).
This failure appears to arise from young children’s inability to consider the
two dimensions in contradistinction (i.e., in the color game, you should sort
by color with red here and blue there, BUT in the shape game you should
sort by shape, with rabbits here and boats there), arising from limitations
in the degree of iterative self-reﬂection that they can engage in. As children
become increasingly capable of integrating mental representations of lower-
order cognitive operations, they are increasingly able to willfully and ﬂexibly
shift their responses, overcoming their perseverative tendencies (Zelazo
et al., 2003).
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The preschool years are marked by substantial age-related
improvements in cognitive ﬂexibility, as indexed by children’s perfor-
mance on conﬂict EF tasks (in which children must inhibit a prepotent
response and instead engage in a less automatized response; e.g., Carlson
&Moses, 2001). For example, performance on Stroop-like tasks (in which
the correct response conﬂicts with one’s natural tendency, e.g., to say
“day”to a picture of a moon and to say “night”to a picture of a sun)
improves signiﬁcantly between early and middle childhood (Gerstdat
et al., 1994), as does performance on Simon Says (Strommen, 1973)and
ﬂanker tasks (in which children must indicate which direction a central
arrow points, while the surrounding arrows may point in similar or oppo-
site directions; e.g., Rueda et al., 2004). As in the A-not-B task (e.g., Dia-
mond & Doar, 1989; Marcovitch & Zelazo, 1999), ﬂexible responding
requires that one reﬂectively choose the appropriate response, rather than
reﬂexively responding with the prepotent response.
The results of event-related potential (ERP) studies suggest that suc-
cessful performance on such tasks involves two phases: conﬂict detection
and conﬂict resolution (i.e., selecting the appropriate response; e.g.,
Rueda et al., 2004). Children’sERP responses on both of these phases
are slower than adults’, suggesting that developmental improvement in
ﬂexible responding in the face of conﬂict may result from improvements
in children’s ability to detect conﬂict and their ability to resolve conﬂict
(by selecting the appropriate response; Rueda et al., 2004).
In sum, research on EF indicates that children’s ability to control their
thoughts and actions improves substantially during early childhood and
continues to improve gradually into adolescence. One approach to these
improvements suggests that they arise from age-related increases in
children’s capacity to consciously reﬂect upon their lower-order mental
processes, affording them with the psychological distance to evaluate their
ongoing performance, adjudicate among response alternatives, and ﬂexi-
bly adapt their behavior to changing contexts.
B. ERROR MONITORING
Originating from research in cognitive neuroscience, the error monitor-
ing approach to the study of self-regulation focuses on individuals’ability
to monitor their performance on a task. Speciﬁcally, this approach focuses
on whether individuals detect when they have committed an error and
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whether they adjust their behavior accordingly in order to improve the
accuracy of their performance on subsequent trials (e.g., Ridderinkhoff,
van den Wildenberg, Segalowitz, & Carter, 2004).
An abundance of research with adults has implicated the medial PFC
generally, and the anterior cingulate speciﬁcally, as critical areas
supporting performance monitoring, response selection, and performance
adjustment (e.g., Ridderinkhoff, van den Wildenberg, et al., 2004). The
basis of performance monitoring is a topic of debate, with some con-
tending that performance monitoring is achieved via conﬂict monitoring
(i.e., detecting when the appropriate response is not clearly apparent,
and thus, one should proceed with caution; e.g., Carter & van Veen,
2007) and others contending that performance monitoring is achieved
via outcome monitoring (i.e., evaluating whether outcomes are worse than
expected; e.g., if one has made an erroneous response, missed a response
deadline, or received negative feedback; Holroyd & Coles, 2002). How-
ever, it is generally accepted that the function of performance monitoring
operations is to alert the cognitive control system when increased control
is required to achieve acceptable performance levels (e.g., McGuire &
Botvinick, 2010; Ridderinkhof, Ullsperger, Crone, & Nieuwenhuis, 2004).
The neural hallmark of the error monitoring system is a frontally gener-
ated negative deﬂection in the ERP waveform, termed the error-related
negativity or ERN, that is initiated at the onset of an erroneous response
and peaks about 100 ms post-response. This is sometimes followed by a
positive deﬂection (PE) that peaks at about 500 ms post-response.
Whereas the ERN appears to be automatically generated and observed
for all errors, the amplitude of the PE predicts subsequent response
slowing and seems speciﬁcally associated with errors that an individual
consciously recognizes (Nieuwenhuis, Ridderinkhof, Blom, Band, &
Kok, 2001). It has been suggested that the PE may be indicative of
individuals’evaluation of the error (as its time course and topography is
similar to the P3; Overbeek, Nieuwenhuis, & Ridderinkhof, 2005).
The ERN and PE have been functionally localized to the anterior cin-
gulate cortex (ACC), a region that is ideally positioned to serve as an
interface between monitoring and control operations, as its dorsal region
has projections to areas involved in response selection and cognitive con-
trol, including the dorsolateral PFC and motor regions, and its
rostral–ventral region has projections to limbic structures (which may
result in negative affective reactions in response to erroneous responses;
Wiersema, van der Meere, & Roeyers, 2007,cf. the somatic marker
hypothesis; Damasio, 1996). Consistent with such a division, the ERN
has been localized to the dorsal region of ACC, while the PE has been
localized to the rostral/ventral region (Herrmann, Rommler, Ehlis,
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Heidrich, & Fallgatter, 2004; Van Boxtel, Van der Molen, & Jennings,
2005; van Veen & Carter, 2002).
Although the ERN was initially noted as occurring just before the com-
mission of an error (e.g., on a go/no-go task), it has since been shown that
the ERN also appears following negative feedback and after late
responses on tasks with a response deadlines (Holroyd & Coles, 2002).
The ERN is also observed on correct responses when trials involve a high
degree of conﬂict (e.g., on incongruent trials on a ﬂanker task). Moreover,
the amplitude of the ERN correlates with the magnitude of the error (e.g.,
if one’s response was similar versus very different from the correct
response) and error value (e.g., if one will receive a small or large penalty
for the mistake; Ridderinkhoff Ullsperger et al., 2004). Thus, this system
appears to be quite adept at evaluating the quality of one’s performance
using a variety of indicators of performance acceptability.
Developmental research indicates that ERN amplitude increases with age
from at least around age 8 years through early adulthood, while PE amplitude
remains relatively age invariant (e.g., Davies, Segalowitz, & Gavin, 2004;
Wiersema et al., 2007). Further, although children’s reaction times are gener-
ally slower than adults’, post-error slowing (i.e., the tendency to respond more
slowly following trials on which one has committed an error, as compared to
following trials on which one responded correctly) is relatively age invariant
between childhood and adulthood (Wiersema et al., 2007; but see also Hogan,
Vargha-Khadem, Kirkham, & Baldeweg 2005). Hence, error monitoring (i.e.,
ERN amplitude) increases with age, but self-correction in the face of errone-
ous responding remains relatively constant across age.
This curious collection of developmental ﬁndings surrounding the ERN
has generated speculation concerning error monitoring systems and their
neural generators. One account posits that the ERN and PE may arise
from parallel error monitoring systems supported by distinct regions (with
those generating the PE maturing earlier in development than those
generating the ERN; Wiersema et al., 2007). From this perspective,
individuals monitor their performance at a relatively implicit level
(generating the ERN) as well as at more explicit level (generating the
PE). Given the neural connectivity between ACC and PFC
(Ridderinkhoff et al., 2004), implicit monitoring operations (of conﬂict
or outcomes) could automatically signal to prefrontal structures that
enhanced cognitive control is required. This would result in enhanced per-
formance accuracy on subsequent trials, without the need for explicit
reﬂection. That is, the system may be aware of its performance, without
the individual having access to this knowledge about the system. Con-
scious error monitoring (signiﬁed by the PE) may offer an alternative
route to self-regulation, with explicit awareness of inadequate
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performance yielding additional reasoning-based adjustments (e.g., the
implementation of strategies for increased performance accuracy).
Alternatively, this collection of ﬁndings may be observed because the
ERN is generated as a result of neural connectivity between PFC and
ACC, rather than being generated simply from the ACC, as is commonly
believed (Luu, Tucker, Derryberry, Reed, & Poulsen, 2003). Support for
this notion comes from the ﬁndings that patients with ACC lesions are
aware of errors but fail to produce an ERN (Stemmer, Segalowitz,
Witzke, & Schönle, 2004) and adolescents with white-matter lesions in
PFC exhibit reduced ERN amplitudes (Gehring & Knight, 2000). This
theory would also account for the ﬁnding that ERN amplitude is
associated with post-error slowing in adult populations (Ridderinkhoff
et al., 2004). From this perspective, error detection may be decoupled
from error correction, especially in young children. With increasing age,
however, the two systems may become functionally intertwined, such that
in adults, error correction more reliably follows error detection.
In addition to striking developmental differences, marked individual
differences in ERN have been noted. Higher degrees of socialization in chil-
dren (e.g., increased understanding of manners and norms for behavior) are
associated with increased ERN amplitude (Santesso, Segalowitz, & Schmidt,
2005). Increased ERN amplitude is also observed in children with anxiety
disorders compared to age-matched controls. However, no differences in
PE amplitude are observed between these groups (Ladouceur, Dahl,
Birmaher, Axelson, & Ryan, 2006; McDermott et al., 2009). ERN amplitude
is also negatively correlated with risk taking and sensation seeking in adoles-
cent males (Santesso & Segalowitz, 2009). Experimentally increasing the
stakes for erroneous responses yields increases in ERN amplitude.
Individuals high in conscientiousness are not affected by this manipulation,
however, presumably because they already exhibit high-amplitude ERNs,
even for low-consequence errors (Pailing & Segalowitz, 2004). Thus, while
children become generally more capable of monitoring their performance
on a task with increasing age, there may also be endophenotypic differences
in the degree to which individuals are neurophysiologically sensitive to
errors. Similar to inhibitory control, these differences may arise as a result
of differences in the strength of the neural signal or in individuals’ability
or propensity to reﬂect upon that signal.
In short, the error monitoring literature offers a neural account for how
individuals are able to adaptively control their behavior by monitoring
their ongoing performance and adjusting their attentional control and
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behavioral responses, based on the results of monitoring evaluations. Age-
related improvements in self-regulation are generally thought to arise
from age differences in children’s ability to monitor their performance
accuracy, consistent with the emphasis on self-reﬂection noted in the liter-
ature on EF. However, we cannot rule out the possibility that
improvements in self-regulation might also result from age-improvements
in children’s ability to translate the results of their monitoring evaluations
into effective self-corrective adjustments.
A third literature investigating the development of self-regulation is
metacognition, or the study of how individuals monitor their ongoing
mental activity and use the results of these evaluations to guide their
subsequent cognitive or behavioral responses (Flavell, 1979; Lyons &
Ghetti, 2010; Nelson & Narens, 1990). The notion that individuals have
the capacity to evaluate their ongoing mental activity (i.e., introspection)
has a long history in psychology (as noted by James, 1890, a characteristic
quality of the mature, adult mind is that it knows what it knows).
Piaget proposed that introspective awareness of one’sthoughts is an
aspect of cognitive development that appears to emerge around age 7
(Fox & Riconscente, 2008). Notably, this is approximately the same age
that Flavell and colleagues established as the age at which children
become aware of their thoughts in stream of consciousness (Flavell,
Green, & Flavell, 1995, 2000). Similarly, Vygotsky noted that children’s
awareness of their cognitions, including attention and memory, follow a
rather protracted time course (Fox & Riconscente, 2008), consistent with
the results of later research investigating children’s explicit understanding
of aspects of memory functioning. For example, children gradually come
to understand that to forget something, one must have already
remembered it, and that some types of material (such as lists organized
around a theme) are easier to remember than others (such as lists of ran-
dom items) (Kreutzer, Leonard, & Flavell, 1975).
Contemporary conceptualizations of metacognition typically distinguish
between two levels of function. The ﬁrst is the cognitive level, where basic
cognitive functions (e.g., memory, attention, learning) occur. The second
is the metacognitive level which monitors the operations occurring at the
cognitive level (i.e., metacognitive monitoring) and controls the oper-
ations occurring at the cognitive level in a top-down manner (i.e.,
metacognitive control; Nelson & Narens, 1990; Pasquali, Timmermans,
&Cleermans, 2010; Plude, Nelson, & Scholnick, 1998). Research on
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metacognition thus generally focuses on the extent to which children
maintain ongoing explicit awareness of their cognitive activities and the
role of explicit awareness of ongoing cognitive activity in guiding behav-
ioral adjustments (in contrast to the more implicit or mixed levels of mon-
itoring that are emphasized in the EF and error monitoring literatures).
1. Metacogntive Monitoring
Typically, metacognitive research involves collecting introspective
reports from individuals, for example, asking individuals to report how
certain they are about the accuracy of their response on a particular trial
or how well they have learned the items on a list of words. Metacognitive
insight is assessed by examining the degree to which participants’intro-
spective judgments correspond with their performance on the task (i.e.,
response accuracy or reaction time).
Metacognitive monitoring may take many forms depending upon the
task at hand and the individuals’progress on a task (Lyons & Ghetti,
2010). For example, a student who is studying for an exam might evaluate
how well she has mastered different aspects of the test material (i.e., a
judgment of learning;Metcalfe, 2009; e.g., “How well have I learned this
material?”) to decide how to allot her remaining study time. Alternatively,
a person might experience a tip of the tongue phenomenon when trying to
remember the name of a restaurant to recommend to a friend (i.e., a
feeling-of-knowing that one possesses a given piece of knowledge
although it is not immediately recallable; Hart, 1965; e.g., “I know that
I know this information, but I cannot bring it to mind.”). Or, a witness
to a crime might be asked how certain she is that the individual on trial
was the perpetrator that she saw commit a robbery (i.e., a conﬁdence judg-
ment;Ghetti, Lyons, Lazzarin, & Cornoldi, 2008; e.g., “How sure am I that
my memory is correct?”).
There is some debate concerning the source of metacognitive
judgments, namely whether they are achieved via direct access to the
actual contents of mental activity (e.g., whether conﬁdence judgments
about learning or response accuracy stem from reﬂective evaluations of
one’s knowledge) or whether metacognitive judgments correspond to
inferences based on heuristic cues concerning qualities of the cognitive
experience (Koriat, 2000; Scott & Dienes, 2010). Agrowing body of
research supports the latter point of view. For example, there is evidence
that judgments of learning are based on how ﬂuently one processes the
material being learned (Benjamin, Bjork, & Schwartz, 1998; Koriat &
Ma’ayan, 2005; Koriat, Ma’ayan, & Nussinson, 2006; Matvey, Dunlosky,
& Guttentag, 2001). Indeed, adults’feeling-of-knowing judgments appear
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to be driven by the familiarity of the question being asked, rather than the
amount of information that is actually retrieved (e.g., Schwartz, 2002).
Developmental research indicates that, like adults, children base their
metacognitive judgments on heuristic cues (e.g., latency to retrieve a
response), although the magnitude of their reliance on such cues may
increase with age (Koriat & Ackerman, 2010,see also Lockl & Schneider,
2002). Hence, metacognitive judgments may be characterized as “intuitive
feelings”(Price & Norman, 2008) arising from subjective features of the
decision-making experience. Following William James, some researchers
have speculated that while these ﬂeeting metacognitive experiences are
clearly consciously accessible, they represent a fringe consciousness, as
the individual is implicitly aware of what they know, which gives rise to
the subjective feelings, of which they are aware. However, the individual
is not consciously aware of the knowledge that generates these feelings
(Norman, Price, & Duf, 2010). From this perspective, metacognitive
judgments may actually reﬂect content knowledge even if the judgments
are arrived at indirectly. Consistent with this proposal, experimental stud-
ies using implicit learning paradigms suggest that differences in fringe
experiences, such as hunches about the correct response, do not simply
arise from differences in heuristic cues (e.g., familiarity). Rather, they
appear to be inﬂuenced by individuals’content knowledge, even though
individuals cannot consciously reﬂect upon this content (e.g., Dienes,
Altmann, Kwan, & Goode, 1995). Moreover, experiences that arise in
fringe consciousness may lead to efforts by the individual to bring the
source of these feelings into full conscious awareness, resulting in a more
analytical, rather than intuitive, form of self-reﬂection (Norman et al.,
2010). Thus, while debate concerning the source of metacognitive
judgments is typically framed in terms of an either-or question, monitoring
of ongoing cognitive performance likely arises from evaluations at multi-
ple levels of conscious access, similar to EF and error monitoring.
Neuroimaging research with adults has begun to investigate the neural
substrates of interoception, or individuals’subjective awareness of their
physiological, cognitive, or emotional states (e.g., Craig, 2002; Khalsa,
Rudrauf,Feinstein, & Tranel, 2009). This work has implicated the insula
and ACC as critical structures supporting conscious awareness of these
states. Individual differences in the size of the ACC are correlated with
introspective accuracy (Fleming, Weil, Nagy, Dolan, & Rees, 2010), and
ACC and insula activity are correlated with the strength of feeling-of-
knowing judgments (Craig, 2009). Thus, like error monitoring, inter-
oception appears to be supported by structures in medial PFC, including
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What neural mechanisms might underlie interoception and
metacognitive monitoring more generally? Computational modeling
suggests that by evaluating the coherence of a neural signal in response
to a query, such as a question about whether an item on a memory test
is old or new, neural systems may be able to “know when they know”
(Pasquali et al., 2010). One can easily see how such a mechanism may
underlie the ﬁndings that ﬂuency and familiarity are associated with high
degrees of conﬁdence, feelings-of-knowing, and judgments of learning.
Alternatively, Craig (2002, 2009) has argued that by integrating neural
signals from all of the inputs that the body receives at a given moment,
including physiological sensations (e.g., hunger, satiation, pain), emotional
experiences (e.g., empathic concern), motor and proprioceptive
experiences (e.g., body movement), motivational sensations (e.g., reward
signals), as well as environmental input (e.g., cues concerning social or
physical risk), the insula generates a “meta-representation of the ‘global
emotional moment’” (Craig, 2009, p. 67), giving rise to a personal sense
of agency and subjective sense of knowing oneself.
Developmental research indicates that metacognitive monitoring
follows a protracted developmental time course (see Schneider & Lockl,
2002 for a review). The capacity to monitor one’s cognitive operations
appears to emerge during the preschool years (Lyons & Ghetti, 2010).
Between the ages of 3 and 5 years, children begin to be able to provide
feeling-of-knowing judgments that predict their subsequent memory per-
formance (Cultice, Somerville, & Wellman, 1983) and to show conscious
awareness of comprehension failures (e.g., comprehension monitoring;
e.g., Revelle, Wellman, & Karabenick, 1985). During this period, children
also begin to be able to provide crude verbal reports of their mental activ-
ity. For example, preschoolers as young as 4 years refer to mental imagery
when describing how they make their decisions on a mental rotation task
(Estes, 1998). However, it is not until the elementary school years that
children become adept at describing the contents of their thoughts in
stream of consciousness (Flavell et al., 1995, 2000).
Throughout childhood and into early adolescence, the accuracy of
metacognitive judgments improves substantially (e.g., Roebers, 2002;
Schneider & Lockl, 2008), with children’s metacognitive reports becoming
increasingly concordant with their actual performance. This developmen-
tal improvement likely results from a number of factors, including age-
related reductions in the inﬂuence of wishful thinking on metacognitive
reports (Schneider, 1998) and age-related increases in children’s content
knowledge that provide a more accurate foundation for assessing the qual-
ity of their performance (Kruger & Dunning, 1999). The accuracy of
metacognitive judgments also likely improves as a result of age-related
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improvements in individuals’ability to psychologically distance them-
selves from their ongoing mental activity, affording them with a broader
perspective of their cognitive activity and its likely outcomes (Zelazo,
2004; Zelazo et al., 2007).
2. Metacognitive Control
Of course, metacognitive insight is only useful to the extent that it can
be used to inform the control of behavior. Accordingly, much research
has investigated the mechanisms through which individuals adjust their
responses based on the self-insight gleaned via metacognitive monitoring
(Pasquali et al., 2010).
Like monitoring, metacognitive control may take many forms, depending
upon the task at hand and the resources available to the individual. Even
very young children engage in rudimentary forms of metacognitive control,
for example, by seeking information from knowledgeable adults (e.g., Cho-
uinard, 2007; Koenig & Harris, 2005) or by spending additional time playing
with toys when one cannot quickly identify their causal properties (e.g.,
Schulz & Bonawitz, 2007). With age, children become increasingly able to
engage in more advanced forms of metacognitive control, such as slowing
down their response times to avoid committing errors (Davidson et al.,
2006), refraining from answering questions that they are likely to answer
incorrectly (e.g., Koriat, Goldsmith, Schneider, & Nakash-Dura, 2001), or
providing more general rather than more speciﬁc answers to questions,
increasing the likelihood of providing the correct answer (Goldsmith,
Koriat, & Weinberg-Eliezer, 2002). Older children and adults may also
selectively allocate more attention to more difﬁcult or relevant tasks (e.g.,
Miller, 1990) or strategically allot their remaining study time to maximize
learning (e.g., Metcalfe, 2009).
While these behaviors differ signiﬁcantly superﬁcially, at their core, they
represent means by which individuals may strategically adjust their behavior
in order to achieve higher levels of performance accuracy. Models of meta-
cognition posit that when monitoring operations indicate that individuals’
current cognitive or behavioral activity is insufﬁcient for achieving one’s
aims, control operations are engaged to ensure that goals are achieved more
effectively or efﬁciently (e.g., Nelson & Narens, 1990). For example,
research with adults indicates that judgments of learning guide the allocation
of study time, with individuals discontinuing the study of already mastered
material and focusing their attention on the material that is most likely to
be learned in the remaining time period, such as the material that is the most
well learned but not yet mastered (Metcalfe, 2009).
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However, it has been noted that under some conditions, control can
occur in the absence of conscious monitoring (Moulin, Perfect, & Fitch,
2002). Indeed, in daily life, it is often the case that individuals seem to
automatically regulate their actions with little conscious deliberation.
Recall, however, that fringe consciousness affords individuals with implicit
awareness of their cognition, and hence the ability to selectively respond
in a manner consistent with their intuitive assessment of their ongoing
cognitive operations (Norman et al., 2010). In this manner, as in error cor-
rection, metacognitive monitoring operations might guide metacognitive
control at a relatively implicit level.
Metacognitive control develops gradually over the course of childhood.
During the preschool years, children begin to evince rudimentary control
strategies (e.g., beginning to selectively seek information from reliable
sources (Koenig & Harris, 2005)and withholding incorrect responses on
memory tests (Balcomb & Gerken, 2008)). With increasing age, children
become more adept at selectively directing their attention and study time
to the more advantageous to-be-studied materials (Metcalfe, 2009; Miller,
1990), as well as becoming increasingly well calibrated in their ability
selectively to withhold responses that are unlikely to be correct (Balcomb
& Gerken, 2008; Koriat et al., 2001).
Although little research has directly investigated the relation between
monitoring and control operations in children, it has been hypothesized
that one reason children may evince poor metacognitive control
skills is that they are challenged in translating their monitoring
evaluations into appropriate adjustments in behavior (Metcalfe, 2009;
Schneider & Lockl, 2002). In addition, age-related improvements in
metacognitive control likely arise as a function of age-related increases
in metacognitive insight as children become increasingly equipped with
the necessary self-insight to facilitate accurate and appropriate
adjustments in behavior.
The metacognitive approach to the study of self-control emphasizes
the role of self-reﬂection on one’s current mental activity as a motivator
for the initiation of appropriate performance adjustments. From this
perspective, age-related improvements in individuals’ability accurately
assess their current cognitive performance and, to adaptively alter
their subsequent cognitive or behavioral responses in accordance
with these evaluations, play a critical role in the development of
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D. UNCERTAINTY MONITORING
A spin-off from the metacognitive literature with connections to
research on risk-taking and the development of reasoning, research on
uncertainty monitoring concerns individuals’capacity to evaluate their
subjective sense of certainty about the likely accuracy of a response or
decision (Lyons & Ghetti, in press; Roebers, 2002). Uncertainty monitor-
ing is a core aspect of metacognition (Ghetti, Qin, & Goodman, 2002;
Howie & Roebers, 2007), but given that uncertainty is such a pervasive
feature of human existence, and because the topic has generated such a
large literature with its own paradigms and theoretical frameworks (e.g.,
Berenbaum, Bredemeier, & Thompson, 2008; Hsu, Bhatt, Adolphs,
Tranel, & Camerer, 2005; Osman, 2010), we consider this topic separately.
Uncertainty monitoring has been investigated under a number of
rubrics, including research on infants’developing ability to detect
differences in the statistical regularity of events in the environment (e.g.,
Xu & Garcia, 2008)and children’s developing ability to evaluate the
conditions under which various degrees of certainty may reasonably be
inferred (e.g., Deak & Narasimham, 2003; Fay & Klahr, 1996; Pillow &
Anderson, 2006). Here, we focus on research about the development of
subjective uncertainty monitoring from a perspective that emphasizes
self-reﬂection (e.g., Fleming et al., 2010). Stemming from the eyewitness
memory literature, the main approach used in this line of research is to
examine whether individuals’conﬁdence judgments differ systematically
as a function of the accuracy of their responses (e.g., Bornstein &
Zickafoose, 1999). Although research on eyewitness memory tends to
underscore dissociations between conﬁdence and accuracy, mainstream
cognitive research with adults consistently ﬁnds that the two indices are
correlated with one another, with individuals reporting higher conﬁdence
in their accurate responses than their inaccurate responses (e.g.,
Robinson, Johnson, & Herndon, 1997). Thus, like judgments of learning
or feelings of knowing, conﬁdence judgments appear to provide a rea-
sonably accurate representation of the individuals’knowledge, although
these judgments may be arrived at indirectly via monitoring of ﬂuency
or familiarity (Robinson & Johnson, 1996).
Developmental research indicates that uncertainty monitoring begins to
emergeduring the preschool years with children as young as 3 years
reporting higher conﬁdence in accurate versus inaccurate responses on
perceptual identiﬁcation tasks (Lyons & Ghetti, in press;see also
Ruffman, Garnham, Import, & Connolly, 2001). By age 5 years, children
report higher conﬁdence in their responses on memory tests for words
studied with a picture (which ought to result in stronger memories) than
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for words studied without a picture (Ghetti et al., 2002). By age 5, children
also report higher conﬁdence in responses on memory tests for repeatedly
experienced versus singularly experienced events (Roberts & Powell,
2005). Throughout middle childhood, children become increasingly able
to provide conﬁdence judgments that differentiate ﬁne-grained distinct-
ions in the strength of their memory representations (e.g., for events imag-
ined once vs. twice; Ghetti et al., 2008) and they become increasingly able
accurately to monitor their certainty under more challenging conditions,
such as when they are asked misleading questions (e.g., Howie &
Roebers, 2007; Roebers, 2002).
Recent ﬁndings suggest that developmental changes in the ability to
monitor uncertainty may stem from age-related differences in the avail-
ability of reliable heuristic cues. For example, Koriat and colleagues
(Koriat & Ackerman, 2010)documented age-related increases between
second and ﬁfth grade in children’s reliance on response latency as a heu-
ristic for conﬁdence judgments. Response latency became an increasingly
reliable predictor of the actual accuracy of responses with increasing age,
consistent with the trend that the speed-accuracy trade-off becomes more
pronounced with age. Thus, with increasing age, children may receive
increasingly reliable signals, via fringe consciousness, as to the likely accu-
racy of their responses, perhaps as a result of age-related increases in
children’s ability iteratively to reprocess information (Zelazo, 2004;
Zelazo et al., 2007).
Developmental improvement in uncertainty monitoring may also arise
as afunction of differences in the way children of different ages respond
to and reason about the experience of subjective uncertainty. For exam-
ple, when children initially begin to experience subjective feelings of
uncertainty, they may not interpret them as such (Flavell, 2003), and thus
may not appreciate that when they feel this way, they are more likely to
provide an incorrect response. However, as children come to associate
their subjective feelings of uncertainty with higher levels of risk for the
production of incorrect responses (perhaps via the maturation of the
insula and/or age-related increases in children’s ability to concurrently
represent and reason about both subjective experiences and outcomes),
they may come to identify their feelings as indicating uncertainty, and
adjust their actions accordingly (in order to alleviate their uncertainty),
contributing to age-related improvements in self-regulation.
Awareness of subjective uncertainty (the sensation that one cannot
quickly and easily decide how to proceed) may be a highly effective self-
regulation tool. As the counterpart to ﬂuency, which is inherently pleasing
(Reber, Schwartz, & Winkielman, 2004), uncertainty is arguably an affec-
tively negative experience. As such, subjective uncertainty may serve as a
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fundamental inhibitory control mechanism, preventing individuals from
engaging in behaviors that are likely to result in negative outcomes.
Moreover, the experience associated with resolving uncertainty (e.g.,
suddenly gaining insight about the answer to a crossword clue or reading
the ﬁnal chapters of a mystery novel as the criminals are exposed)
involves a rush of clarity and ﬂuency, resulting in highly positive
sensations (Topolinski & Reber, 2010). Thus, subjective awareness of
uncertainty may not simply motivate response inhibition but may also
motivate the ﬂexible shifting of one’s thoughts or actions to clarify the
uncertainty, resulting in enhanced performance accuracy (e.g., Platt &
Research on uncertainty monitoring suggests that age-related
improvements in children’s subjective awareness of their own feelings of
uncertainty have a profound impact on the development of self-regulation,
helping children to detect instances when they should proceed with caution
and providing them with the motivation to adjust their actions as necessary
to ensure adequate performance in changing contexts.
III. Integrating Disparate Literatures
Although these four lines of research have progressed relatively inde-
pendently of one another, research on EF, error monitoring, metacogni-
tion, and uncertainty monitoring (summarized in Table I)shares several
common grounds. The processes of interest appear to be supported by
overlapping neural substrates in medial PFC, including ACC and insula
(Fernandez-Duque et al., 2000; Holroyd & Coles, 2002; Lahat, Todd,
Mahy, & Zelazo, 2010; Lamm, Zelazo, & Lewis, 2006; Shimamura,
2000). Investigations rely on overlapping methodological techniques. For
example, error monitoring studies often entail recording ERPs while par-
ticipants complete ﬂanker or go/no-go (i.e., EF) tasks, and uncertainty
monitoring, error monitoring, and metacognitive investigations often col-
lect self-monitoring reports from participants. Generally, similar patterns
of development are observed across the four literatures, with substantial
developmental improvement being observed in early childhood (in EF,
metacognition, and uncertainty monitoring) and more gradual develop-
ment continuing well into adolescence (in all four arenas).
Most critically, however, all four lines of research suggest that age-
relatedimprovements in self-regulation are critically dependent upon
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age-related increase in self-reﬂection. In the EF literature, it has been
suggested that improved self-control results from age- and experience-
related increases in children’s ability to iteratively reprocess information
at lower levels of consciousness, with increased psychological distance
affording children with the ability to select their responses ﬂexibly, rather
than being limited to stimulus–response patterns of responding. In the
error-monitoring literature, age-increases in self-control are attributed to
the maturation of neural monitoring systems that evaluate response accu-
racy and age-related increases in connectivity between regions supporting
error detection and PFC regions supporting response selection. In the
metacognitive literature, there is evidence that self-regulation improves
as children become increasingly aware of their ongoing cognitive activity,
and increasingly able to control their thoughts and actions, based on the
results of metacognitive monitoring assessments. Finally, research on
uncertainty monitoring suggests that with age, children become increas-
ingly able to evaluate the likely outcomes of their responses, via increased
awareness of their subjective feelings of certainty, with corresponding
changes in behavior aimed at alleviating uncertainty (via inhibitory con-
trol or adjustments in responding).
IV. The Role of Self-Reﬂection in the Development
Taken together, these literatures suggest that developmental improvement
in self-regulation arises from two sources: (a) age-related improvements in
self-reﬂective awareness, and (b) age-related improvements in the ability to
translate information gleaned from self-reﬂection into appropriate behavioral
adjustments. Both of these are likely multifaceted processes occurring at
various levels of conscious awareness.
A. SOURCES OF AGE-RELATED IMPROVEMENTS IN
There is some evidence to suggest that, like regulation, monitoring may
shift from being externally guided (e.g., performance monitoring based on
feedback), to internally guided (e.g., based on endemic signals) (Pasquali
et al., 2010). For example, children’s learning of response contingencies is
much more disrupted by intermittently incorrect feedback than is adults’
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(Eppinger, Mock, & Kray, 2009). This ﬁnding suggests that with age,
children’s neural monitoring systems may become better calibrated, per-
haps as a result of improvements in connectivity among brain regions
feeding into monitoring evaluations.
Developmental research stemming from the somatic marker hypothesis
(Damasio, 1996)is consistent with this notion. This hypothesis posits that
ventromedial PFC associates facts (i.e., environmental cues, previous
responses in a given context) with corresponding internal physiological
and emotional sensations. When the same cues are encountered at a dif-
ferent time point, the internal emotional and physiological sensations
are automatically reactivated, supporting learning about which behaviors
are advantageous and which behaviors are disadvantageous in a given
context. Research indicates that between middle childhood and adoles-
cence, skin conductance responses preceding previously punished choices
increase signiﬁcantly (as does the ability to learn about which response
options are advantageous vs. disadvantageous; Crone & Van Der Molen,
2007). Hence, with age, children may receive increasingly reliable and
potent somatic signals concerning their performance accuracy.
Such improvements in signal quality are likely also accompanied by age-
relatedimprovements in children’s ability or propensity to “read”these
signals. Conceptualizations of EF in terms of increasing levels of con-
sciousness and iterative reprocessing (Zelazo, 2004; Zelazo &
Cunningham, 2007) as well as metacognition (Dienes & Perner, 2002)
posit that with age, children become increasingly able to take as the con-
tents of their mental activity the contents of lower levels of mental activity.
Hence, with increasing age, children may gain better access to information
concerning their own performance.
B. SOURCES OF AGE-RELATED IMPROVEMENTS IN
IMPLEMENTING REFLECTION-BASED REGULATION
Of course, increased awareness may not directly translate into improved
regulation as children’s ability to use their subjective knowledge to guide
their responding also likely improves with age. In part, this may stem from
age differences in children’s understanding of the meaning of their subjec-
tive feelings. For example, as children come to appreciate the relations
between subjective feelings of uncertainty and the likelihood of commit-
ting an error, they may be more likely to adjust their behavior in the face
of uncertainty, resulting in improvements in self-regulation. From this per-
spective, age-related improvements in self-regulation may arise from
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improvements in children’s ability to reason about the appropriate way to
respond, given their current status.
Relatedly, age-related improvements in self-regulation may arise from
increases in the motivational power of self-monitoring evaluations. That
is to say, as monitoring signals become increasingly reliable, they may also
become increasingly salient, persistent forces inﬂuencing children’s behav-
ior, directing children to adjust their behavior in order to reduce the neg-
ative affective experience associated with erroneous responses.
Finally, as children practice using self-reﬂection to adjust their behavior
to achieve more optimal outcomes, connectivity between regions
supporting monitoring and control operations is likely to strengthen and
become more efﬁcient, leading to an increased automatization of appro-
priate control adjustments in response to different monitoring signals.
Developmental differences in self-regulation likely arise as a conse-
quence of age-related changes in children’s self-reﬂective awareness of
their ongoing task performance, and their ability to use self-insight to
implement appropriate behavioral or cognitive adjustments. This appears
to be a multifaceted process, with changes in both automatic and con-
trolled functions contributing to improvements in children’s ability to stra-
tegically regulate their own behavior.
V. Dynamic Interactions Between Automatic and
Controlled Processes in Self-Regulation
In addition to striking developmental differences in self-regulation, lon-
gitudinal research suggests that individual differences in EF and aspects of
temperament related to self-regulation are relatively stable over child-
hood (e.g., Carlson et al., 2004, Kochanska et al., 2000), raising the
intriguing question of why some individuals are better at controlling them-
selves than are others. In part, this may arise from relatively stable
differences in children’s environments. For example, recent ﬁndings sug-
gest that parents who are generally more sensitive to their children’s
needs, providing scaffolding, appropriate pacing, and helpful feedback
that helps children to succeed on tasks, tend to have children who exhibit
better EF (Bernier, Carlson, & Whipple, 2010). From this perspective,
individuals may differ in their self-regulation ability as a result of
differences in the quality of training that self-regulatory systems receive.
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Alternatively, differences in self-regulation may arise from relatively
endemic differences in the extent to which individuals are (neurophysio-
logically and behaviorally) sensitive to errors (Rueda, Rothbart,
McCandliss, Saccomanno, & Posner, 2005). Research from the social
psychological literature indicates that tolerance of uncertainty varies
signiﬁcantly between individuals (e.g., Weary & Jacobson, 1997), and
error-monitoring research indicates that individual differences in ERN
amplitude are associated with personality in children and adolescents,
with a reduced ERN being observed in children who are poorly socialized
and adolescents who are high in risk-taking (Santesso et al., 2005). On
the opposite end of the anxiety spectrum, individuals with
obsessive–compulsive disorder and anxiety disorder, who are highly
concerned about negative outcomes, show higher ERN amplitudes than
control participants (Santesso, Segalowitz, & Schmidt, 2006). Thus, it
seems reasonable to speculate that differences in self-regulation may stem
(at least in part) from individual differences in the degree to which low-
level monitoring operations signal to the individual the need for caution.
That is to say, some individuals may simply care less about making mis-
takes, acting rather impulsively without much concern for the potentially
negative outcomes that may result from their behavior because their neu-
ral responses to risk and ambiguity may be generally dampened.
An informative new direction for future research would thus be to col-
lect an independent measure of concern for accurate performance as well
as estimates of monitoring and control ability. Results could provide
insight into the factors contributing to individual differences in self-regula-
tion, as well as how the relative contribution of these factors to self-regu-
lation changes over the course of development. For example, the
emergence of the speed-accuracy trade-off during childhood may be
attributable to age-related increases in children’s concern about commit-
There is a growing recognition that self-regulation always results from
dynamic interactions between top-down inﬂuences (e.g., EF) and bot-
tom-up inﬂuences (e.g., physiological arousal, stress, anxiety, motivation)
(Blair & Dennis, 2010; Zelazo & Cunningham, 2007). However, the inﬂu-
ence of automatic processes on children’s self-regulation remains poorly
understood. Future research elucidating the nature of this interaction
and how it changes over the course of development will provide invalu-
able new insight into the development of self-regulation.
Finally, future research is necessary to elucidate the complex
interactions between automaticity and conscious self-monitoring. While
increases in task automaticity ought to free up resources that could be
dedicated to self-monitoring (and thus, increases in automaticity may
403Self-Reﬂection and Self-Regulation
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generally be correlated with increased self-monitoring), if automaticity
surpasses a certain threshold, individuals may become less able or prone
to monitor themselves on the task. Thus, automaticity and monitoring
may interact in a nonlinear manner, and this relation may change
Over the course of childhood and adolescence, individuals become
increasingly responsible for, and increasingly capable of, regulating their
own thoughts, emotions, and actions. Converging evidence from diverse
literatures on monitoring, EF, and metacognition suggest that age-related
improvements in children’s ability to willfully alter their patterns of
thought and action may be critically dependent upon age-related
improvements in self-reﬂective awareness and the corresponding deliber-
ate adjustment of behavior. We believe that only by integrating these
approaches, theoretically and experimentally, will a comprehensive under-
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