As we take … a general view of the wonderful stream of
our consciousness, what strikes us first is this different
pace of its parts. Like a bird’s life, it seems to be made
of an alternation of flights and perchings … The
resting-places … can be held before the mind for an
indefinite time … The places of flight … obtain between
the matters contemplated in the periods of comparative
rest. William James, Principles of Psychology, 1890.
The ‘flights’ and ‘perchings’ of our thought, so poeti-
cally described by William James1, are as mysterious to
us as they are intimately familiar. To James, a perching
represented a mental state including contents such as
imaginings, worries and inner speech, whereas a flight
represented the ‘movement’ from one mental state to
another. Although the forefather of psychology empha-
sized the spontaneous and dynamic nature of thoughts,
research in the century that followed left these topics
In the past 15years, mind-wandering and spontane-
ous thought have become prominent topics in cognitive
psychology and neuroscience2. However, most theories
of mind-wandering still overlook the dynamic nature
of thought that James viewed as central. By focusing on
these dynamics, in this Review, we formulate a novel
framework for understanding spontaneous thought
and mind-wandering. By introducing this framework,
we bring together a diverse range of relevant findings
from psychology, neuroscience and the clinicalarea.
Mind-wandering: the forgotten dynamics
Until the mid-1990s, cognitive psychology and the
emerging field of cognitive neuroscience were dom-
inated by a task-centric view of mental processes.
Experimental designs were carefully constructed to min-
imize the effects of task-unrelated thoughts that were gen-
erally viewed as experimental ‘noise’. Indeed, cognitive
neuroscientists commonly used ‘rest’ (that is, a period
during which participants did not perform any experi-
mental tasks) as a baseline condition. This practice was
predicated on the assumption that any mental processes
that occur during periods of rest would essentially con-
stitute such noise. This assumption, however, was called
into question by observations that periods of rest con-
sistently recruit brain regions involved in memory3–5 and
complex reasoning6, and by an influential meta-analysis
by Shulman and colleagues7 showing that a specific set of
brain regions — that later became known as the default
network (DN)8 — are consistently more active during
baseline conditions than during experimentaltasks.
Although topics such as daydreaming, mind-wandering,
stimulus-independent thought and task-unrelated thought
had been studied for decades9–19, they had been rele-
gated to the backwaters of psychological research2. The
advent of the DN created a major shift in scientific atten-
tion: mind-wandering research came into prominence
within both mainstream psychology20,21 and cognitive
neuroscience22,23. However, this new research inherited
a historical legacy24 from previous task-centric views:
mind-wandering became predominantly defined as
the opposite of task-related and/or stimulus-related
thought. For example, a recent theoretical review25
defines mind-wandering as “a shift in the contents of
thought away from an ongoing task and/or from events
in the external environment”. This prominent definition
regards mind-wandering as a type of thought charac-
terized by its contents (or, in William James’s terms, the
bird’s perchings rather than its flights).
1Department of Psychology,
University of British
Columbia, 2136 West Mall,
Vancouver, British Columbia,
V6T 1Z4, Canada.
2Centre for Brain Health,
University of British
Columbia, 2211 Wesbrook
Mall, Vancouver, British
Columbia, V6T 2B5, Canada.
3Departments of Philosophy
and Psychology, University of
California 94720, USA.
4Laboratory of Brain and
Cognition, Department of
Institute, Cornell University,
Ithaca, New York 14853,
6Institute of Cognitive Science,
University of Colorado
Boulder, UCB 594, Boulder,
Colorado 80309–0594, USA.
Correspondence to K.C.
Published online 22 Sep 2016
Mind-wandering as spontaneous
thought: a dynamic framework
Kalina Christoff1,2, Zachary C.Irving3, Kieran C.R.Fox1, R.Nathan Spreng4,5
and Jessica R.Andrews-Hanna6
Abstract | Most research on mind-wandering has characterized it as a mental state with contents
that are task unrelated or stimulus independent. However, the dynamics of mind-wandering —
how mental states change over time — have remained largely neglected. Here, we introduce a
dynamic framework for understanding mind-wandering and its relationship to the recruitment of
large-scale brain networks. We propose that mind-wandering is best understood as a member of a
family of spontaneous-thought phenomena that also includes creative thought and dreaming. This
dynamic framework can shed new light on mental disorders that are marked by alterations in
spontaneous thought, including depression, anxiety and attention deficit hyperactivity disorder.
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Nature Reviews | Neuroscience
Rumination and obsessive thought
A mental state, or a sequence
of mental states, including the
transitions that lead to each
A transient cognitive or
emotional state of the organism
that can be described in terms
of its contents (what the state is
‘about’) and the relation that
the subject bears to the
contents (for example,
perceiving, believing, fearing,
imagining or remembering).
Thoughts with contents that
are unrelated to what the
person having those thoughts
is currently doing.
Thinking that is
characteristically fanciful (that
is, divorced from physical or
social reality); it can either be
spontaneous, as in fanciful
constrained, as during
deliberately fantasizing about
This definition has been implicitly or explicitly
endorsed by most of the empirical investigations on
mind-wandering so far26. Although it has generated a
wealth of empirical findings about task-unrelated and
stimulus-independent thought, this content-based
definition fails to capture what is arguably the key fea-
ture of mind-wandering27,28, reflected in the term itself:
to wander means to “move hither and thither without
fixed course or certain aim” (REF.29).
To say that one’s mental states are task unrelated or
stimulus independent tells us nothing about how such
states arise or change over time27. Only once we consider
the dynamics of thought are we able to make crucial dis-
tinctions between different types of thought. One such
distinction is between rumination and mind-wandering.
Rumination is sometimes viewed as negatively valenced
mind-wandering20 (or mind-wandering gone awry).
In one way, this makes sense: both mind-wandering
and rumination tend to be stimulus independent and
unrelated to the current task (that is, what the subject
is currently doing)21,30. However, when we consider the
dynamics of thought, mind-wandering and rumination
seem antithetical: although thoughts during mind-
wandering are free to ‘move hither and thither’, thoughts
during rumination tend to remain fixed on a single
theme or topic27. Furthermore, the content-based view of
mind-wandering relies on a relatively narrow definition
of the term ‘task’ as being confined to the goals of the
current experiment. However, if we define the term task
more broadly to also include one’s personal concerns (for
example, completing an essay by the end of the week),
then mind-wandering is often task related because spon-
taneously occurring thoughts often reflect personal goals
Spontaneous thought: a definition
Here, we define spontaneous thought as a mental state,
or a sequence of mental states, that arises relatively freely
due to an absence of strong constraints on the contents
of each state and on the transitions from one mental state
to another. We propose that there are two general ways
in which the content of mental states, and the transitions
between them, can be constrained (FIG.1). One type of
constraint is flexible and deliberate26, and implemented
through cognitive control33,34. For example, we can deliber-
ately maintain our attention on a dry and boring lecture,
bringing our thoughts back to the lecture whenever they
begin to stray. Another type of constraint is automatic in
nature. Automatic constraints can be thought of as a fam-
ily of mechanisms that operate outside of cognitive con-
trol to hold attention on a restricted set of information27.
Affective salience35–37 and sensory salience38 can both act as
sources of automatic constraints. Despite our efforts, for
example, we may find ourselves unable to disengage our
attention from a fly buzzing in a quiet library or from a
preoccupying emotional concern.
Within our framework, mind-wandering can be
defined as a special case of spontaneous thought that
tends to be more-deliberately constrained than dream-
ing, but less-deliberately constrained than creative
thinking and goal-directed thought39 (BOX1; FIG.1). In
addition, mind-wandering can be clearly distinguished
from rumination and other types of thought that are
marked by a high degree of automatic constraints, such
as obsessive thought.
Recent advances have begun to reveal the neural
underpinnings of spontaneous thought and mind-
wandering. We review these advances through the lens
of our framework, which explains the contrast between
spontaneous and constrained thought in terms of the
dynamic interactions between large-scale brain net-
works. Using this framework, we also discuss a number
of clinical conditions that are marked by excessive varia-
bility or excessive stability of thought and the way mental
states change overtime.
Brain networks and their interactions
Among brain networks that are currently recognized
in cognitive neuroscience, the DN (FIG.2a) is most fre-
quently brought up in relation to mind-wandering and
spontaneous thought. The DN was originally identified7,8
as a set of regions that are consistently deactivated across
a range of externally oriented experimental tasks. This
network has been linked to spontaneously occurring,
internally oriented mental processes22,23,40. However, DN
recruitment is not specific to spontaneous cognition: it
is also consistently observed during internally oriented,
but deliberate, goal-directed tasks, including episodic
memory retrieval, autobiographical future thinking
The DN is composed of several functionally distinct
subsystems45 (FIG.2a). The core DN subsystem (DNC ORE)
is characterized by its hub-like properties and its con-
tributions to internally oriented cognition45. The second
DN subsystem is centred around the medial temporal
lobe (MTL) and is known for its roles in memory and
Figure 1 | Conceptual space relating different types of thought. Deliberate and
automatic constraints serve to limit the contents of thought and how these contents
change over time. Deliberate constraints are implemented through cognitive control,
whereas automatic constraints can be considered as a family of mechanisms that operate
outside of cognitive control, including sensory or affective salience. Generally speaking,
deliberate constraints are minimal during dreaming, tend to increase somewhat during
mind-wandering, increase further during creative thinking and are strongest during
goal-directed thought39. There is a range of low-to-medium level of automatic
constraints that can occur during dreaming, mind-wandering and creative thinking, but
thought ceases to be spontaneous at the strongest levels of automatic constraint, such as
during rumination or obsessive thought.
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Nature Reviews | Neuroscience
More active during REM sleep
More active during waking rest
More active during creative-idea generation
More active during creative-idea evaluation
A thought with contents that
are unrelated to the current
A deliberate guidance of
current thoughts, perceptions
or actions, which is imposed in
a goal-directed manner by
currently active top-down
constructive mental simulations43,44,46,47. Here, we refer to this
subsystem as DNMTL. The third DN subsystem seems to
be linked to a wide range of functions, including mental-
izing, conceptual processing and emotional processing47.
We refer to this subsystem using the generic designation
‘DNSUB3’ because its precise role in the DN has yet to be
clarified. The DNMTL and DNSUB3 are both closely con-
nected to the DNCORE, which serves as a major conduit
for information flow through the overall DN system45.
In contrast to the DN, which seems to be primarily
involved in internally oriented mental processes, the
dorsal attention network (DAN) (FIG.2b) becomes pref-
erentially recruited when we turn our attention towards
the external world48. The DAN is thought to support
selective attention to sensory features of the environment
and link this sensory information to motor responses48.
We hypothesize that the DAN increases the stability of
attention over time by constraining the spontaneous
movement of attention.
Attention and the focus of thoughts frequently shift
back and forth between the internal and external environ-
ment49,50, and there seem to be corresponding reciprocal
shifts between DN and DAN recruitment: when regions
of the DAN are active, there is often a simultaneous deac-
tivation of the DN in many different task paradigms7,51.
This antagonism has been observed in intrinsic fluctu-
ations in the functional MRI (fMRI) brain signal during
rest52 and in neuronal populations recorded using elec-
trocorticography in people with epilepsy53, although the
stability of this antagonism across different conditions has
not yet been systematically investigated.
One way in which thoughts can be triggered to shift
between an internal and an external focus is when some-
thing salient captures attention in an automatic or ‘bottom-
up’ manner. A right-lateralized ventral attention network
(VAN) (FIG.2c) may function to automatically direct (or
re-orient) attention towards salient perceptual stimuli48.
A more general salience network54 (FIG.2c) has been
Box 1 | Dreams and creativity as spontaneous thought
The similarities between waking spontaneous thought and dreaming while
asleep have been noted for decades183. Both waking thought and dreams
are instantiated mainly in the audiovisual modalities, centre on one’s
current goals and concerns, draw heavily on semantic and episodic
memory in constructing simulations and future plans, and are laden with
a wide range of affect184. Within our framework, dreaming is a type of
spontaneous thought that is highly unconstrained, hyperassociative and
highly immersive, and therefore it is predicted to be associated with very
low or absent deliberate constraints (although lucid dreaming is an
important exception). Dreaming should also be associated with a strong
influence from internal sources of variability, combined with low to
medium influence from automatic constraints. At the neural level,
dreaming should be accompanied by a strong recruitment of default
network (DN) medial temporal lobe (MTL)-centred subsystem (DNMTL)
regions, relatively weak to medium recruitment in regions of the core DN
subsystem (DNCORE) and strong deactivations in frontoparietal control
network (FPCN) regions. A recent meta-analysis184 of
studies of rapid-eye-movement (REM) sleep, the sleep
stage associated with, by far, the highest rate of dreaming,
reveals a pattern of activation that is consistent with these
predictions (see the figure, parta). Whereas regions of the
FPCN, including the rostrolateral prefrontal cortex
(rlPFC)–dorsolateral PFC (dlPFC), show deactivation
during REM sleep relative to waking rest (areas in blue),
regions within the DNMTL, including the hippocampal
formation (HF) and parahippocampal cortex (PHC), show
greater recruitment in REM sleep versus rest (areas in red).
By contrast, the DNCORE seems to be recruited to
a comparable degree by REM sleep and waking rest.
Creativity can also be seen as a form of spontaneous
thought. Creative thinking may be unique among other
spontaneous-thought processes because it may involve
dynamic shifts between the two ends of the spectrum of
constraints. The creative process tends to alternate
between the generation of new ideas, which would be
highly spontaneous, and the critical evaluation of these
ideas, which could be as constrained as goal-directed
thought in terms of deliberate constraints and is likely to
be associated with a higher degree of automatic
constraints than goal-directed thought because creative
individuals frequently use their emotional and visceral
reactions (colloquially often referred to as ‘gut’ reactions)
while evaluating their own creative ideas185. Consistent with our
framework, studies demonstrate186,187 that the DNMTL, including the HF and
PHC, is more active during creative-idea generation than during the
evaluation of these ideas (see the figure, partb; areas in red). By contrast,
regions within the FPCN and the DNCORE are more active during the
evaluation of creative ideas than during their generation (see the figure,
partb; areas in blue). The study from which the findings in partb come
from used functional MRI (fMRI) to examine brain activation in artists while
they were drawing visual art in the scanner using an fMRI-compatible
AI, anterior insula; dACC, dorsal anterior cingulate cortex; FEF, frontal eye field;
IFG, inferior frontal gyrus; IPS, intraparietal sulcus; LingG, lingual gyrus; LTC,
lateral temporal cortex; MOG, medial occipital gyrus; MT+, middle temporal
motion complex; OFC, orbitofrontal cortex; PCC, posterior cingulate cortex;
pIPL, posterior inferior parietal lobule; Prec, precuneus; rACC, rostral ACC;
rmPFC, rostromedial PFC; SPL, superior parietal lobule; TPC, temporopolar
cortex. Partb is adapted with permission from REF.186, Elsevier.
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Nature Reviews | Neuroscience
PHC HF vmPFC
rlPFC dAI ITG
a The DN and its subcomponents b The DAN
c The salience network and VAN d The FPCN and COCN
The emotional significance of
percepts, thoughts or other
elements of mental experience,
which can draw and sustain
attention through mechanisms
outside of cognitive control.
Features of current perceptual
experience, such as high
perceptual contrast, which can
draw and sustain attention
through mechanisms outside of
The process of spontaneously
or deliberately inferring one’s
own or other agents’ mental
proposed to detect both external and internal salient
events. Both the VAN and the general salience network
are involved in automatic bottom-up salience detection,
and there is substantial anatomical overlap between them,
especially within areas around the anterior insula. This
has led some scientists to view the VAN and the sali-
ence network as the same network55, although others
conceptualize them as distinct networks56,57.
Shifts in attention can also occur through deliber-
ate cognitive control. Such cognitive control34 is closely
linked to the frontoparietal control network (FPCN)58,59
(FIG.2d), which is involved in both internally and exter-
nally oriented goal-directed thought60,61. The FPCN can
couple (that is, display positive functional connectivity)
with the DN, to support internally focused deliberate
autobiographical planning, or with the DAN, to support
externally focused visuospatial planning60. We there-
fore hypothesize that the FPCN implements deliberate
constraints on thought. It also seems to mediate the
interactions between other networks57,60.
Finally, cognitive control can be implemented at dif-
ferent timescales62,63, which may distinguish between the
FPCN and another putative control network that has been
described in the literature, the cingulo-opercular control
network (COCN)64 (FIG.2d). Regions of the FPCN show
relatively transient activity that is associated with the ini-
tiation of cognitive control and short-term adjustments
of cognitive control as the demands of a task change from
one trial to another; by contrast, regions of the COCN
show more temporally sustained activity that may be
related to temporally extended cognitive-control pro-
cesses such as the maintenance of a task set over time62–64.
The rostrolateral prefrontal cortex (rlPFC) seems to
participate in both the FPCN58,65 and the COCN62,63.
This overview of large-scale brain networks rep-
resents only the current consensus about different
networks and their constituent regions. The precise ana-
tomical boundaries and the extent of functional separa-
tion66 between different networks remain active topics
of current investigation. There may be several conver-
gent brain zones where multiple networks intersect. For
example, the area centred around the temporoparietal
junction and inferior parietal lobule and the area centred
around the inferior frontal gyrus and opercular region
seem to act as such convergence zones. Nonetheless, the
evidence for functional specificity in the contributions
Figure 2 | Main large-scale brain networks with relevance to
spontaneous thought. a | The default network (DN) is centred on the
medial prefrontal cortex (mPFC), the medial parietal cortex and
the lateral parietal cortex, and extends into the temporal lobe and lateral
PFC. Three subcomponents within the DN have been identified. The first
of these subcomponents, the core DN subsystem (DNCORE), includes the
anterior mPFC (amPFC), posterior cingulate cortex (PCC) and posterior
inferior parietal lobule (pIPL). The second subcomponent, the DN
subsystem centred around the medial temporal lobe (MTL) (DNMTL),
includes the hippocampal formation (HF) and parahippocampal cortex
(PHC). The DNMTL also includes a number of MTL cortical projections, such
as the retrosplenial cortex (RSC), the ventral mPFC (vmPFC) and the pIPL.
The third subcomponent, DNSUB3, extends more dorsally and includes the
dorsomedial PFC (dmPFC), the lateral temporal cortex (LTC) extending
into the temporopolar cortex (TPC), and parts of the inferior frontal gyrus
(IFG). All three DN subsystems seem to include subsections of the IPL.
b | The dorsal attention network (DAN) comprises a distributed set of
regions centred around the intraparietal sulcus (IPS)–superior parietal
lobule (SPL), the dorsal frontal cortex along the precentral sulcus near, or
at, the frontal eye field (FEF) and the middle temporal motion complex
(MT+). c | The ventral attention network (VAN) comprises a ventral frontal
cluster of regions, including the inferior frontal gyrus (IFG), the anterior
insula (AI) and the adjacent frontal operculum (not shown); the VAN also
includes the ventral temporoparietal junction (vTPJ). Although the VAN is
predominantly right lateralized, a bilateral salience network has also
been defined. The most prominent regions of the salience network are
the AI and the anterior cingulate cortex (ACC). These regions are densely
connected with subcortical structures involved in interoception and
autonomic functions, which are also considered to be part of the salience
network. d | Two ‘control’ networks have been discussed in the literature.
The frontoparietal control network (FPCN) includes, most prominently,
the dorsolateral PFC (dlPFC) and the anterior IPL (aIPL). Under a broader
definition, the FPCN extends to regions including the rostrolateral PFC
(rlPFC), the region anterior to the supplementary motor area (preSMA)
and the inferior temporal gyrus (ITG). The cingulo-opercular control
network (COCN) includes the dorsal ACC (dACC)–medial superior
frontal cortex (msFC) and bilateral AI–frontal operculum. The rlPFC
contributes to both the FPCN and COCN. dAI, dorsal AI.
NEUROSCIENCE VOLUME 17
Flexible combinations of
distinct elements of prior
experiences, constructed in the
process of imagining a novel
(often future-oriented) event.
A type of dreaming during
which the dreamer is aware
that he or she is currently
dreaming and, in some cases,
can have deliberate control
over dream content and
The ability to produce ideas
that are both novel (that is,
original and unique) and useful
(that is, appropriate and
A method in which participants
are probed at random intervals
and asked to report on aspects
of their subjective experience
immediately before the probe.
Different ways of categorizing a
thought based on its contents,
including stimulus dependence
(whether the thought is about
stimuli that one is currently
perceiving), task relatedness
(whether the thought is about
the current task), modality
(visual, auditory, and so on),
valence (whether the thought is
negative, neutral or positive) or
temporal orientation (whether
the thought is about the past,
present or future).
of different networks seems to be relatively robust. In the
following sections of this Review, we discuss the putative
relevance and functionality of different networks with
respect to spontaneous thought and its clinical disorders.
Content-based views of mind-wandering
Most empirical research to date has examined mind-
wandering from a content-based perspective by assessing
the contents of thoughts in terms of their relationship to
an ongoing task or activity. In this approach, researchers
use thought probes that ask, for example, “are you think-
ing about something other than what you are currently
doing?” (REF.21). Answering “yes” to this question would be
categorized as being in a state of mind-wandering. Using
this approach, research has suggested a striking prevalence
of task-unrelated thought in everyday life: it accounts for
as much as 30–50% of our waking cognition15,21,30.
As tasks get easier and external demands on attention
become lower, the frequency of task-unrelated thoughts
tends to increase10,12,17 and so does DN recruitment22.
Because of these parallels, early research into DN func-
tions hypothesized a link between this network and
task-unrelated thought3,7,8. Initial empirical support for
this link came from neuroimaging studies4,22,67,68 linking
the reported frequency of task-unrelated thoughts to DN
activation during conditions of low cognitive demand
and showing stronger DN activation during highly prac-
tised tasks compared with novel tasks in people with a
higher propensity for mind-wandering 22.
This initial empirical evidence for a link between
the DN and mind-wandering was tentative because it
relied on indirect retrospective reports about the overall
frequency of mind-wandering or on indirect inferences
about its frequency based on data from independent
studies. Furthermore, it did not distinguish between
task-unrelated and stimulus-independent thought, leav-
ing open the possibility that the DN might be involved
in task-unrelated but still stimulus-oriented thought69.
Subsequent research helped to address both of these issues
by using online experience sampling measures to capture
the moment-by-moment occurrence of specific instances
of mind-wandering23,70. This research demonstrated con-
clusively a consistent link between DN activation and both
task-unrelated and stimulus-independent thought.
However, the DN is not the only brain network that
is consistently involved in task-unrelated thought. The
FPCN, especially the lateral PFC, is also consistently
recruited71. Indeed, lateral PFC recruitment during
rest was one of the earliest observations in functional
neuroimaging, dating back to work by Ingvar72 in the
1970s. It continued to be reported in subsequent stud-
ies3,4,23,67,70,73–77 exploring rest, task-unrelated thought
and/or spontaneous thought.
The lateral PFC is closely linked to executive pro-
cessing78–81 and is consistently recruited during difficult
tasks involving deliberate task-directed thought6,79,81,82.
Its recruitment during task-unrelated thought and rest
therefore seems counterintuitive and requires an expla-
nation. One such explanation is the control failure hypo-
thesis83,84. According to this hypothesis, task-unrelated
thoughts occur because of a failure of executive control
to keep attention on the current task. Once this failure
and task-unrelated thoughts have occurred, executive
resources are recruited to suppress those thoughts and
redirect attention to the task athand.
Although this theory seems to be plausible, some of its
key predictions are at odds with empirical findings. For
example, the control failure hypothesis predicts that, when
executive resources are reduced, task-unrelated thoughts
should increase. However, individuals with higher
working-memory capacity (a major component of execu-
tive ability) show an increased frequency of task-unrelated
thoughts during easy tasks85 such as breath monitoring or
identifying a target among highly dissimilar distractors.
Another prediction of this theory is that, with advancing
age and associated declines in executive function-
ing86, the frequency of task-unrelated thoughts should
increase. Instead, research shows that task-unrelated
thought decreases in frequency with advancing age16,87.
At the neural level, stimulation of executive regions using
transcranial direct current stimulation increases task-
unrelated thought88, whereas the control failure hypo-
thesis would predict the opposite. Although it is possible
that executive resources can, in principle, be used to sup-
press task-unrelated thought, it seems unlikely that this
is the main role they play during task-unrelated thought.
An alternative explanation is that executive resources
are used to direct task-unrelated thoughts towards per-
sonal goals20. One development of this view, the decou-
pling hypothesis50,89, proposes that executive resources
suppress perceptual processing during task-unrelated
thought. This suppression serves to decouple attention
from the immediate external perceptual environment
and thus ‘insulates’ an internally oriented thought flow
against perceptual distractions. The decoupling hypo-
thesis is consistent with electroencephalography find-
ings of reduced cortical analysis of the external sensory
environment during task-unrelated thought90 and atten-
uated sensory responses in visual and auditory cortices
during task-unrelated compared with task-related mental
states91. It is also consistent with fMRI findings showing
that, during task-unrelated thought, activation in the
posterior cingulate cortex (a key region of the DNC ORE)
is inversely correlated with activation in the primary
sensorimotor and extrastriate visual cortices26.
However, the decoupling hypothesis equates task-
unrelated thought with internally oriented thought.
Although task-unrelated thought can sometimes be inter-
nally oriented, it can also be externally oriented towards
stimuli in the current perceptual environment. In princi-
ple, task relatedness, internal versus external orientation
and goal directedness are separable dimensions of thought
(BOX2). Nonetheless, most investigations so far have
used the terms ‘task-unrelated’, ‘internally oriented,’ and
‘stimulus-independent’ interchangeably26. Furthermore,
mind-wandering has, so far, been defined25 largely based
on these content-based dimensions of thought. Although
mind-wandering is often task unrelated, internally ori-
ented and/or stimulus independent, none of these content-
based features captures the defining dynamic quality of
mind-wandering: the relatively free and spontaneous
arising of mental states as the mind wanders.
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Mind-wandering as spontaneous thought
Although cognitive neuroscience research has not yet
directly investigated thought’s spontaneity using expe-
rience sampling probes, a growing body of related find-
ings hints at the potential neural basis of spontaneous
thought. Not all subnetworks within the DN seem to
be involved in spontaneous thought to the same extent
(FIG.3). Although the DNCORE and DNSUB3 are more
active during task-unrelated than task-related thought
and during internally oriented than externally oriented
thought, the DNMTL does not seem to be differentially
recruited along these dimensions23,70 (FIG.3a). Instead, the
DNMTL seems to be recruited when deliberate constraints
on thought are relatively weak. For example, the DNMTL
shows stronger recruitment when participants are una-
ware that they are having task-unrelated thoughts than
when they are aware of them23 (FIG.3b). This suggests
a link between the DNMTL and spontaneity because, in
the absence of meta-awareness (that is, awareness of
one’s ongoing mental state), deliberate constraints are
likely to be minimal.
Overall, a growing body of evidence suggests that the
generation of spontaneous thought may be closely linked
to the DNMTL and especially its central component, the
MTL itself. Converging evidence from humans and
rodents suggests that spontaneous memories and spon-
taneous mental simulations (both of which can be con-
sidered types of spontaneous thought), during periods
of awake rest, are initiated by the MTL and supported
by hippocampal–cortical interactions. Using single-cell
recordings in humans, one study92 found that the sponta-
neous recall of film clips following a film-viewing period
was preceded by an elevated firing rate in many of the
same medial temporal neurons that responded while first
viewing the film. The DNMTL also seems to be recruited
immediately before the spontaneous arising of thoughts,
as revealed by a recent fMRI study93 that used experienced
mindfulness practitioners to detect the precise onset of
spontaneous thoughts. In another fMRI study94, differ-
ences in resting-state connectivity within the DNMTL
predicted the propensity for spontaneous memories and
future thoughts during these periods of rest. Furthermore,
recent findings95 suggest that people with an increased
propensity to mind-wander in daily life (as measured with
a standard trait daydreaming questionnaire) exhibit more
variable (that is, more dynamic) functional connectivity
within the DNMTL in particular. In rodents, during peri-
ods of waking rest, hippocampal place cells demonstrate a
replay of previously encountered routes96–98 and a preplay
of future routes that are yet to be visited99–101.
The hippocampus, which is a central part of the MTL,
has long been linked to episodic memory102,103. Recent
findings have also linked it to a broad range of constructive
mental processes such as imagining novel scenarios and
situations43,44,104–106, constructing new spatial scenes107
and imagining potential future experiences108. Based
on these findings, it has been proposed that the hippo-
campus is involved in ‘episodic simulation’ — the imag-
inative construction of hypothetical events or scenarios
that might occur in one’s personal future109.
Of particular relevance to our dynamic framework
is the component process model110 of episodic memory.
According to this model, memory traces are encoded
in ensembles of neurons distributed throughout the
MTL and neocortex. Such ensembles are groups of
spatially distributed neurons capable of firing in a
coordinated manner. Hippocampal representations are
proposed to have an indexing function111, capable of
reactivating the ensembles that were active during the
original experience. During retrieval, cues rapidly and
unconsciously trigger the activation of hippo campal
representations, which then activate the ensembles that
they index112. This model also proposes that memory
becomes constrained and goal-directed only when
Box 2 | Varieties of task-unrelated thought
The terms ‘task-unrelated’, ‘stimulus-independent’ and ‘spontaneous’ are sometimes
used interchangeably in the cognitive and neuroimaging literature. This usage,
however, is problematic because these terms designate separable dimensions
of thought. To illustrate this independence, here, we list examples of task-unrelated
thought that is either stimulus independent or stimulus oriented. Within each of these
categories, we also list examples of task-unrelated thought that is highly constrained
(in a deliberate or automatic manner) or spontaneous.
In general, the term ‘stimulus’ is usually used to mean ‘external perceptual stimulus’. In
addition, ‘stimulus-independent thought’ is typically equated with ‘internally oriented
thought’, and ‘stimulus-dependent thought’ is typically equated with ‘externally
oriented thought’. Finally, the term ‘goal-directed thought’ refers to thought that is
deliberately directed by any goals, including personal goals that may be unrelated to the
task at hand. Although not included in the examples below, the contents of spontaneous
thought can also shift between being externally oriented (for example, a forest trail) and
being internally oriented (for example, reminiscence about one’s childhood).
Stimulus-independent (internally oriented)
Deliberately constrained (goal-directed)
• While in the shower, a bobsledder deliberately and systematically visualizes each
turn they will take on an upcoming run.
• While re-painting the walls of their room, a person plans their afternoon, figuring out
how to combine multiple errands into a single car ride.
• While trying to fall asleep, a job candidate keeps imagining the terrors and triumphs
of tomorrow’s interview.
• Despite their best attempts to write a research article, a professor keeps fixating on
a nasty teaching evaluation.
• While driving in their car, a writer suddenly thinks of a line for the book they are
writing, then remembers that they must pick up dog food on the way home, before
reminiscing about the winters of their childhood and fantasizing about the career
they might have had as a bobsledder.
Stimulus-oriented (externally oriented)
Deliberately constrained (goal-directed)
• To entertain himself during a boring earnings report, a manager tries to estimate who
has the most expensive suit in the room.
• While listening to harsh criticism by her teacher, a student starts counting the tiles on
the floor of the classroom as a means to stop herself from crying.
• While studying in a quiet library, a student finds herself unable to ignore a buzzing fly.
• A pedestrian loses the thread of his friend’s conversation when he cannot help but
gawk at a naked man walking down Main Street.
• While hiking on a forest trail, a woman’s thoughts move from the gravel on the path in
front of her to a slug crawling up a stump, and then to a leaf floating in a puddle.
NEUROSCIENCE VOLUME 17
Nature Reviews | Neuroscience
b Areas more active when unaware than when aware of task-unrelated thoughts
a Areas more active during task-unrelated than during task-related thoughts
c Neural mechanisms of contextual associative processing
these hippocampal outputs are further processed by
slower and conscious control mechanisms mediated
by the neocortex103.
We propose that a similar sequence of processes may
operate during episodic retrieval, episodic simulation
and constructive mental processes in general. Within our
framework (FIG.4), the hippocampus acts as an internal
source of variability in thought by reactivating old or
activating novel (re-combined) hippocampal–neocortical
ensembles. A transition from the activation of one ensem-
ble to another would correspond to a transition between
mental states. In Jamesian terms, each activated ensem-
ble would be a perching, and the transition from one
activated ensemble to another would be aflight.
The DNMTL may also contribute to thought var-
iability by its involvement in contextual associative
processing113,114 (FIG.3c). The DNMTL may contribute to
conceptual variability in the contents of thought over
time when one activated ensemble cues the activation of
another because they partially overlap at the neural level.
This may lead to a stream of conceptually disconnected
(but contextually connected) mentalstates.
There may also be differences within the FPCN in
how it contributes to constraining thought through cog-
nitive control. In particular, the rlPFC and the dorso-
lateral PFC (dlPFC) may have a role in implementing
deliberate constraints at different timescales64 or levels
of abstraction115,116. The rlPFC is preferentially recruited
when thought is broadly constrained towards internal
mental events, such as when directing attention towards
one’s own thoughts and away from one’s perceptual
sensations117. The rlPFC is also preferentially recruited
when thought is guided towards highly abstract con-
cepts, such as during the solving of anagrams that are
known to subjects to have highly abstract nouns as
their solutions115. This suggests that the rlPFC may be
involved in an abstract ‘top-level management’ control,
constraining thought in a relatively general, nonspecific
manner: for example, when the goal of thinking is to
generate novel ideas for an essay topic, without limit-
ing the nature of ideas any further than their suitability
as an essay topic. This top-level control may implement
relatively weak- or medium-level deliberate constraints
on thought, thus allowing for some degree of spontane-
ous variability. By contrast, the dlPFC may be better con-
ceptualized as being involved in ‘mid-level management’
— carrying out adaptive online adjustments in cognitive
control based on relatively specific rules33,34 and in direct
response to specific feedback63,118. This mid-level control
may result in some of the strongest deliberate constraints
We propose that automatic constraints on thought
can be exerted by multiple brain networks and structures,
such as the DNCORE, the salience networks (including the
VAN) and the DAN (FIG.4). The FPCN can exert deliber-
ate constraints on thought by flexibly coupling with the
DNCORE, the DAN or the salience networks, thus reinforc-
ing or reducing the automatic constraints being exerted
by the DNCORE, the DAN or the salience networks. The
level and type of constraints can change dynamically. For
example, thought may at first be spontaneous and there-
fore subject to relatively weak constraints, then it may shift
to become highly automatically constrained, and then it
may shift again to become highly deliberately constrained
(FIG.5). We propose that these fluctuations in the level and
type of constraints on thought correspond to changing
interactions between large-scale brain networks (FIG.5).
Whereas deliberate constraints are relatively well
characterized and specifically linked to executive func-
tions and control networks, automatic constraints are
much more diverse and therefore probably subserved by
diverse neural correlates. It is also likely that the neural
Figure 3 | Different patterns of recruitment in the DNCORE and DNMTL during
mind-wandering. a | Regions within the core default network (DN) subsystem (DNCORE)
are more active during task-unrelated thought than during task-related thought,
whereas regions within the DN subsystem centred around the medial temporal lobe
(MTL) (DNMTL) show similar levels of activity for task-unrelated and task-related thought.
The data are from a functional MRI study23 that used experience sampling during an
ongoing task, the sustained attention to response task (SART). b | Regions within the
DNMTL, including the parahippocampal cortex (PHC), are more active when participants
are unaware of their task-unrelated thoughts than when they are aware of them. Lack of
awareness is likely to be associated with minimal constraints on thought, suggesting
a specific link between DNMTL and spontaneity. By contrast, regions within the DNCORE
show similar levels of activity for unaware and aware task-unrelated thought. The data
are from the same study23 as in parta. c | The DNMTL may also contribute to spontaneous
thought by its involvement in contextual associative processing. A network for
contextual associative processing has been identified113,114 that closely resembles the
DNMTL and includes the PHC, the retrosplenial cortex (RSC) with its associated medial
parietal cortex, and the medial prefrontal cortex (mPFC). Areas within this network show
greater activation when people see pictures of objects that elicit relatively strong
contextual associations (for example, a traffic light) compared with pictures of objects
that are not unique to any particular context and are therefore not highly associative
(for example, a bag). Partc is adapted with permission from REF.114 , Elsevier.
VOLUME 17 www.nature.com/nrn
Nature Reviews | Neuroscience
Sources of variability
basis of automatic constraints extends beyond the net-
works that we discuss here. For example, the basal ganglia
and their associated cortico–thalamic–striatal circuits are
known to be crucially involved in habit formation119 and
may exert habitual automatic constraints on thought (an
excess of which may be linked to obsessive–compulsive
disorder120). Therefore, an important goal for future
research is to improve our knowledge of different types
of automatic constraints and their neural basis. As we dis-
cuss next, dysfunctions in automatic constraints may be
a common factor across multiple mental health disorders.
Spontaneous thought is altered in a wide range of clini-
cal conditions, including depression, anxiety, attention
deficit hyperactivity disorder (ADHD) and schizophre-
nia. We propose that clinically significant alterations in
spontaneous thought can be subdivided into two major
categories: those that are marked by excessive variability
of thought contents over time and those that are marked
by excessive stability.
Within our framework, thought becomes spontane-
ous and more variable when deliberate and automatic
constraints are relaxed. Whereas excessive constraints
may reduce the dynamic flow of thoughts, excessive vari-
ability may prevent thoughts from developing coherence
(that is, meaningful interconnectedness among succes-
sive mental states). Therefore, both excessive constraints
and excessive variability, especially when they become
chronic, might have detrimental effects on cognitive
functioning and emotional well-being.
Depression and rumination. Overall, depression seems
to be characterized by excessive stability in thought. It is
marked by increased elaboration of negative information
and by difficulties in disengaging from negative material
such as negative words or pictures121,122. One hallmark
of depression is rumination, which is defined as “repet-
itively and passively focusing on symptoms of distress”
and remaining “fixated” on one’s problems and one’s feel-
ings about them123. People with depression experience
thoughts that tend to be inflexible, perseverative124 and
characterized by excessively self-focused, mostly negative
content125,126. Rumination is largely involuntary: individ-
uals with depression may want to stop themselves from
ruminating but are often unable to do so, suggesting that
the constraints on thought in rumination are primarily
When engaged in experimental tasks, individuals with
depression show several differences in neural recruitment
compared with healthy controls. The DN shows greater
activation in individuals with depression across a range of
tasks127,128. Moreover, people with depression show greater
activation of the salience network (specifically, the frontal
insula, dorsal anterior cingulate cortex and amygdala) but
lower activation of the FPCN (specifically, the dlPFC and
dorsal caudate) when they are presented with negative
stimuli129. There is also enhanced task-related coupling
between the DN and salience regions in individuals with
subclinical depression130. These results are consistent with
our hypothesis that depression involves a preponderance
of automatic affective constraints on thought.
Individuals with depression also show altered pat-
terns of resting-state functional connectivity. A recent
meta-analysis131 found that, compared with healthy con-
trols, patients with depression show increased connec-
tivity within the DN and reduced connectivity within
the FPCN. Moreover, in cases of depression, the FPCN
shows increased coupling with the DN but decreased
coupling with the DAN, which may reflect depressive
biases towards internal thoughts at the cost of engaging
with the external world131. We hypothesize that an overly
connected DN allows the DNCORE to place greater auto-
matic constraints on the DNMTL, promoting an overly
constrained thought flow with an exaggerated internal
orientation. Consistent with this idea, recent findings132
suggest that patterns of resting-state connectivity in peo-
ple with depression tend to be less variable over time,
particularly between the medial PFC (within the DNCORE)
and the parahippocampus (within the DNMTL).
Anxiety disorders. Like depression, anxiety disorders are
characterized by repetitive negative thoughts124,133, often
accompanied by severe worry about events that might
happen in the future134. There are both commonalities
Figure 4 | Neural model of the interactions among
sources of variability, automatic constraints and
deliberate constraints. Arrows represent the influences
that large-scale networks have on the dynamics of thought:
networks can be sources of variability (in purple), sources of
automatic constraints (in blue) or sources of deliberate
constraints (in red). The default network (DN) subsystem
centred around the medial temporal lobe (MTL) (DNMTL) and
sensorimotor areas can act as sources of variability in
thought content over time. The salience networks, the
dorsal attention network (DAN) and the core DN
subsystem (DNCORE) can exert automatic constraints on the
output of the DNMTL and sensorimotor areas, thus limiting
the variability of thought and increasing its stability over
time. The frontoparietal control network (FPCN) can exert
deliberate constraints on thought by flexibly coupling with
the DNCORE, the DAN or the salience networks, thus
reinforcing or reducing the automatic constraints being
exerted by the DNCORE, the DAN or the salience networks.
The putative role of each network is meant to be illustrative
rather than exhaustive. The model includes only those
interactions that are relatively well understood given the
current state of research.
NEUROSCIENCE VOLUME 17
Sources of variability Automatic constraints Deliberate constraints
Spontaneous thought Automatically constrained
While I walk to the grocery
store, I daydream about the
winter boots I’ve ordered
from an online store, recall
that blustery winter when
they shut down my elementary
school, then envision next
weekend’s ski trip to
As I cross the street, I begin
to worry about the story that
my newspaper editor wants
me to write before I leave for
Tahoe. Can I submit on time?
Will anyone read a piece on
trade unions? I picture my
scowling editor. Do I even
While I step up the curb, I
realize that my thoughts are
making me miserable. I decide
to think about something else.
Where am I heading to?
Oh yes, groceries! I imagine
myself walking down each
grocery aisle... I should get
eggs, milk and lemonade from
the freezer, potatoes and
cauliﬂower from produce...
Nature Reviews | Neuroscience
and differences between anxiety and depression135. Like
depression, anxiety is associated with attentional biases to
consciously perceived stimuli121,136. However, patients with
anxiety show biased processing of subliminally presented
threat-related stimuli, whereas individuals with depres-
sion generally do not121,122. This suggests that anxiety
biases begin in relatively early, orienting stages of infor-
mation processing, before awareness of perceptual stim-
uli137, whereas depressive biases occur primarily at later
stages of processing involving the elaboration (that is, the
conceptual interpretation) of perceptual information122.
Within our framework, both anxiety and depres-
sion are marked by excessive automatic constraints on
thought. These constraints may differ, however, in terms
of the level of cognitive processing at which they begin.
Consistent with this idea, anxiety disorders, like depres-
sion, are marked by alterations in recruitment and func-
tional connectivity within the DN, FPCN and salience
network135,138,139. What seems to be more pronounced
in anxiety, however, are functional alterations in sub-
cortical structures and their interactions with the other
networks. For instance, generalized anxiety disorder is
associated with disrupted subregional functional connec-
tivity within the amygdala, which also shows enhanced
connectivity with the FPCN but reduced connectivity
with the salience network138. In addition, the amygdala
and the globus pallidus show increased activation across
studies when individuals with specific phobias are pre-
sented with phobic stimuli139. Finally, a recent study135
examined resting-state fMRI connectivity in individu-
als with anxiety disorder, depression, both anxiety and
depression (comorbid), or neither anxiety nor depres-
sion (control subjects). In this study, greater severity of
anxiety-specific symptoms was associated with stronger
functional connectivity between the ventral striatum and
subgenual anterior cingulate cortex, whereas people with
depression had reduced connectivity in the same circuit
compared with people without depression. Because here
we focus on large-scale cortical networks, our framework
does not currently highlight the specific contributions of
these subcortical structures and their possible role in
implementing automatic constraints. However, these top-
ics undoubtedly remain important directions for future
ADHD. Within our framework, ADHD is a disorder
marked by an excessive variability in thought movement.
Clinically, ADHD is characterized by a pattern of inatten-
tion and/or hyperactivity/impulsivity, which can occur
in both children and adults140. It is associated with broad
Figure 5 | Fluctuations in the level and type of constraints may correspond to dynamically changing interactions
between large-scale brain networks. In this example, an internally oriented stream of thought, described from a
person’s subjective perspective, transitions from spontaneous thought to automatically constrained thought, and then to
deliberately constrained thought. We propose that each transition corresponds to changing interactions among
large-scale brain networks. During spontaneous, internally oriented thought, the default network (DN) subsystem centred
around the medial temporal lobe (MTL) (DNMTL) exerts a relatively strong diversifying influence on the stream of thought, in
the context of relatively low deliberate and automatic constraints exerted by the frontoparietal control network (FPCN),
core DN subsystem (DNCORE) and salience networks. During automatically constrained, internally oriented thought, the
salience networks and the DNCORE exert relatively strong automatic constraints on thought, in the context of relatively
weak internal sources of variability from the DNMTL and relatively weak deliberate sources of constraint from the FPCN.
Finally, during deliberately constrained, internally oriented thought, the FPCN exerts strong deliberate constraints on
thought, in the context of relatively weak internal sources of variability from the DNMTL and relatively weak automatic
constraints by the DNCORE and salience networks. Arrows represent influences on the dynamics of thought: sources of
variability (in purple), automatic constraints (in blue) and deliberate constraints (in red). The thickness of an arrow
represents the hypothesized relative strength of these influences during the corresponding part in the stream of thought.
VOLUME 17 www.nature.com/nrn
impairments in executive functions141,142, manifesting as
lapses in attention and heightened intra-individual (that
is, within-subject) variability in reaction time on cogni-
tive tasks143. Failures to sustain attention on a task goal
may relate to another characteristic of ADHD: excessive
task-unrelated thoughts144,145. Spontaneous thought in
ADHD has not yet been explored directly using expe-
rience sampling, but, based on our framework, we
would predict heightened variability of thought content
Neural alterations associated with ADHD146–148 are
consistent with it being a disorder marked by reduced
constraints on thought. Task-related fMRI studies indi-
cate that ADHD is associated with reduced activation
of the FPCN and DAN147,149, and failures to deactivate
regions within the DN150,151. In contrast to studies focus-
ing on depression, resting-state connectivity studies in
ADHD152–157 generally report decreased within-network
functional connectivity in the DN and DAN, as well as
weaker anti-correlations between key regions of the DN
and control networks.
ADHD has a strong developmental component140,
and many of the neural alterations that are present in
adults with ADHD are also detectable in affected chil-
dren149,151,154. During typical development, regions within
large-scale brain networks, such as the DN, are initially
only sparsely connected and gradually mature into a
cohesive, interconnected network158. Children with
ADHD show a maturational delay, which is character-
ized by hypo-connectivity within the DN and weaker
anti-correlations between key regions of the DN and con-
trol networks154,156,159,160. Crucially, resting-state functional
connectivity in ADHD varies across DN subsystems: one
study161 found increased connectivity within the DNMTL
but decreased connectivity within the DNCORE, consist-
ent with an increased generation of spontaneous men-
tal content in ADHD (from the DNMTL) combined with
decreased automatic constraints on thought (from the
DNCORE). However, these results need to be interpreted
with caution because motion-induced fMRI artefacts
have been shown162,163 to have significant influence on
resting-state functional connectivity findings in ADHD,
especially in younger populations.
In summary, the patterns of neural alterations in
ADHD suggest a general reduction in both automatic
and deliberate constraints on thought, coupled with a
possible increase in DNMTL-derived sources of variabil-
ity. Our account extends the influential hypothesis164
that patients with ADHD are unable to suppress inter-
nally oriented cognition that is supported by the DN.
This hypothesis explains why ADHD is associated with
weaker anti-correlations between the DN and other
networks but not why the disorder is associated with
reduced connectivity within some DN subsystems. Our
model explains these results, as it suggests that ADHD
reflects a reduction in constraints from sources both
within and outside of theDN.
Psychotic disorders. Psychotic disorders, including schiz-
ophrenia, schizoaffective disorder and psychotic bipolar
disorder, are characterized by a profound disruption
of thought. The symptoms of such disorders include
thought disorganization, hallucinations and delusions140.
Psychotic disorders are also characterized by notable
impairments in executive functioning and processing
of semantic information165. Psychotic thought can be
marked by frequent and abrupt leaps from one topic to
another166 or by stereotyped thinking, including rigid,
repetitious or barren thought content167. Psychotic dis-
orders may therefore be associated with both excessive
variability and excessive stability of thought, which may
be present in different psychotic presentations across
individuals or may occur at different times within the
At the neural level, schizophrenia is associated with
widespread structural and functional brain abnormal-
ities and with significant reductions in both grey and
white matter168. Progressive grey-matter reductions can
occur throughout the brain but are found most consist-
ently in salience network regions, the FPCN (especially
the dlPFC), and the DNMTL and DNCORE regions169–171.
Whereas grey-matter alterations may be partially linked to
antipsychotic drug treatments169,172, white-matter abnor-
malities seem to precede treatment and may therefore be
linked most directly to the disease itself168.
Consistent with these findings, fMRI studies of psy-
chotic disorders reveal a pattern of global dysconnec-
tivity173,174. In both schizophrenia and bipolar disorder,
there is reduced global functional connectivity174. In
schizophrenia, the dlPFC shows reduced connectivity
with other lateral PFC regions but increased long-range
connectivity with non-FPCN regions175, suggesting an
impairment of FPCN integrity. Consistent with this
finding, functional connectivity within the FPCN is
reduced176. Within our framework, this disruption of
FPCN integrity suggests that deliberate constraints on
thought may still be present, but they may lack coherence
and logical structure.
Schizophrenia is also associated with disruptions
of connectivity within the DN127,177. There may be greater
connectivity within the DNCORE (REFS178,179) and weaker
anti-correlations between the DN and DAN during both
rest and working-memory tasks127. Finally, there seems
to be a failure of the salience network to appropriately
regulate the interactions between the DN and FPCN180.
We hypothesize that there is an overall dysregulation
of both deliberate and automatic constraints on thought
in psychotic disorders. There may also be a blurring
between external (visual, auditory and somatosensory)
and internal (DNMTL) sources of variability, which in
turn could be linked to a breakdown of the typical
network-based functional brain organization that
maintains a relative functional segregation between the
processing of internal and external information.
Summary and future directions
Mind-wandering has recently become a prominent topic
of research within cognitive neuroscience and psychol-
ogy. However, its dynamics have been all but forgot-
ten. Rather than emphasizing the spontaneous flow
of thought, most research has instead used the terms
‘mind-wandering’ and ‘spontaneous’ as loose synonyms
NEUROSCIENCE VOLUME 17
for ‘task-unrelated’ or ‘stimulus-independent’. Our
framework offers explicit definitions of spontaneous
thought and mind-wandering that capture those largely
ignored dynamics. In doing so, we lend conceptual clar-
ity to numerous issues. We draw conceptual distinctions
between the dimensions of spontaneity, task relatedness
and stimulus relatedness. Our framework can also tease
apart antithetical phenomena such as mind-wandering
and rumination, which seem to be indistinguishable if
we focus on the static contents of thoughts to the exclu-
sion of its dynamics. We argue that mind-wandering
is best understood as a member of a family of sponta-
neous-thought processes — a family that also includes
creative thought and dreaming. Finally, we also locate
spontaneous thought within a broader conceptual space
that allows its comparison to goal-directed thought, as
well as to clinical alterations that make thought exces-
sively constrained — such as in rumination and anxiety
— or excessively variable — such as inADHD.
Our conceptual framework is empirically grounded
and thus makes falsifiable predictions. Overall, it predicts
that fluctuations between spontaneous, automatically con-
strained and deliberately const rained thought correspond to
changes in the interactions between large-scale brain net-
works. Furthermore, divisions within these large-scale
networks are predicted to have different influences on the
dynamics of thought. Thus, we predict that the DNCORE
would show increased recruitment as automatic con-
straints on internally oriented thought increase, whereas
the DNMTL would show decreased recruitment as either
deliberate or automatic constraints on thought increase.
One future direction of development for our frame-
work is to enumerate the types of automatic constraints
and link them to their neural substrates. We have focused
here on constraints from affective salience, which are
implemented, in part, by the salience network and have
clear implications for disease. However, other forms
of automatic constraints, such as habits of attention
that depend on cortico–thalamic–striatal circuits or
neuromodulatory influences on thought by midbrain
mechanisms such as the locus coeruleus noradrenaline
system181, are also likely to be of theoretical and clinical
significance. Elucidating how automatic constraints are
implemented could improve our understanding of how to
de-automatize188 them when they become detrimental
to well-being, as in clinical conditions, or how to benefi-
cially harness already existing automatic constrains182, as
in the case of creative thinking. Future research will also
be needed to clarify the role of the DNSUB3 in the dynamics
of thought. Regions within the DNSUB3 have been linked
to the processing of social, semantic and emotional infor-
mation, but it remains unclear how they contribute to the
constraining and diversifying of thought.
Future research may particularly benefit from a
neuro phenomenological approach189 that combines
online experience sampling or first-person measures
of ongoing thought dynamics with measures of neural
activity. Such approaches may greatly benefit clinical
investigations, from which a wealth of information can be
gathered regarding the subjective experiences associated
with disruptions in thought dynamics. To do so, however,
reliable methods need to be develped for measuring the
extent to which individuals’ thoughts unfold in a sponta-
neous, automatically constrained or goal-directed man-
ner. The development of such methods, combined with
theoretical, empirical and neuroscientific advances such
as those that we have reviewed here, may one day unfurl
the mystery that captivated William James more than a
century ago: what do the ‘flights of the mind’ look like,
and can we ever observe them?
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The authors are grateful to R. Buckner, P. Carruthers,
M. Cuddy-Keane, M. Dixon, S. Fazelpour, D. Stan,
E. Thompson, R. Todd and the anonymous reviewers for their
thoughtful feedback on earlier versions of this paper, and to
A.Herrera-Bennett for help with the figure preparation.
K.C. was supported by grants from the Natural Sciences and
Engineering Research Council (NSERC) (RGPIN 327317–11)
and the Canadian Institutes of Health Research (CIHR)
(MOP-115197). Z.C.I. was supported by a Social Sciences and
Humanities Research Council of Canada (SSHRC) postdoctoral
fellowship, the Balzan Styles of Reasoning Project and
a Templeton Integrated Philosophy and Self Control grant.
K.C.R.F. was supported by a Vanier Canada Graduate
Scholarship. R.N.S. was supported by an Alzheimer’s
Association grant (NIRG-14-320049). J.R.A.-H. was supported
by a Templeton Science of Prospection grant.
Competing interests statement
The authors declare no competing interests.
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