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Why the Mind Wanders: How Spontaneous Thought’s Default Variability
May Support Episodic Efficiency and Semantic Optimization
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Abstract and Keywords
This chapter offers a functional account of why the mind—when free from the demands of
a task or the constraints of heightened emotions—tends to wander from one topic to
another, in a ceaseless and seemingly random fashion. We propose the default variability
hypothesis, which builds on William James’s phenomenological account of thought as a
form of mental locomotion, as well as on recent advances in cognitive neuroscience and
computational modeling. Specifically, the default variability hypothesis proposes that the
default mode of mental content production yields the frequent arising of new mental
states that have heightened variability of content over time. This heightened variability in
the default mode of mental content production may be an adaptive mechanism that (1)
enhances episodic memory efficiency through de-correlating individual episodic
memories from one another via temporally spaced reactivations, and (2) facilitates
semantic knowledge optimization by providing optimal conditions for interleaved
learning.
Keywords: mind-wandering, default variability hypothesis, episodic memory, semantic memory, learning,
neuroscience
Why doesn’t the mind grind to a halt when we are not doing anything? Why does it keep
moving instead? And why does this movement tend to proceed in a seemingly haphazard
manner, with thoughts jumping from one topic to another, often distant, seemingly
unrelated topic—creating a variability in thought content to which the mind seems to
default?
Why the Mind Wanders: How Spontaneous Thought’s
Default Variability May Support Episodic Efficiency and
Semantic Optimization
Caitlin Mills, Arianne Herrera-Bennett, Myrthe Faber, and Kalina Christoff
The Oxford Handbook of Spontaneous Thought: Mind-Wandering, Creativity,
and Dreaming
Edited by Kalina Christoff and Kieran C.R. Fox
Print Publication Date: May 2018 Subject: Psychology, Cognitive Neuroscience
Online Publication Date: Apr 2018 DOI: 10.1093/oxfordhb/9780190464745.013.42
Oxford Handbooks Online
Why the Mind Wanders: How Spontaneous Thought’s Default Variability
May Support Episodic Efficiency and Semantic Optimization
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More than 100 years ago, William James described thought as a form of mental
locomotion. Here we build on James’s phenomenological account and on recent advances
in cognitive neuroscience and computational modeling to offer a functional account of
why the mind, when free from the demands of a task or the constraints of heightened
emotions, ceaselessly moves from one topic to the next. We introduce the default
variability hypothesis, which highlights the continuous change and heightened variability
of the contents of spontaneous thought as they unfold over time. The default variability
hypothesis proposes that our default mode of mental content production, with its
continuous change and heightened variability over time, may be an adaptive mechanism
that (1) enhances episodic storage efficiency by helping de-correlate individual episodic
memories from one another via temporally spaced reactivations, and (2) facilitates
semantic knowledge optimization by providing optimal conditions for interleaved
learning.
Overview of the Default Variability
Hypothesis
People report highly variable moment-to-moment experiences during “resting states” that
facilitate spontaneous thought (Hurlburt, Alderson-Day, Fernyhough, & Kühn, 2015). For
example, a thought about the scallops one had for dinner the day before might be
followed by a memory of a bus ride taken a week ago, followed by an image of a sunny
beach. The mental states that form our thought flow need not be events that have actually
occurred (Addis, Wong, & Schacter, 2008; Schacter, Addis, & Buckner, 2007). In addition
to conjuring up veridical episodic events, details from the past can also be recombined in
novel ways to produce episodic mental simulations and other mental states that become
part of the stream of thought.
We operationalize content variability as the extent to which consecutive mental states in
the stream of thought are episodically and/or semantically distinct from each other. The
greater the semantic/episodic distance between consecutive mental states, the more
variable thought content would be over time. We propose that a default mode of
variability in thought contents serves two purposes: to facilitate efficient encoding of
separate episodic events (the episodic efficiency hypothesis), and to support the
integration and transformation of episodic memories into semantic knowledge (the
semantic optimization hypothesis).
In what follows, we elucidate the two sub-hypotheses that together make up the default
variability hypothesis. We draw on the episodic and semantic memory formation and
consolidation literature to explain how these processes are inextricably connected to
spontaneous thought’s default variability. Finally, we integrate our hypothesis into
(p. 12)
Why the Mind Wanders: How Spontaneous Thought’s Default Variability
May Support Episodic Efficiency and Semantic Optimization
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existing accounts of mind-wandering, and offer some suggestions for empirically testing
each sub-hypothesis.
Episodic Efficiency Hypothesis
We propose that a default mode of content variability in the stream of thought improves
episodic memory efficiency by optimizing the distinctiveness of different episodic
memories. In this section, we give a brief description of two pivotal episodic memory
mechanisms—pattern separation and pattern completion—followed by an account of how
the content variability of spontaneous thought may lead to increased episodic memory
efficiency. We propose that this process is twofold: First, pattern separation processes
produce separable (i.e., distinct) episodic memories, through de-correlating (i.e., making
distinct) the corresponding activation patterns in the hippocampus and neocortex. A
default content variability in spontaneous thought may directly support pattern
separation processes via mental simulations (reactivations or novel recombinations) that
adaptively separate the memories over time by providing dissimilarities in consecutive
representations over time. Second, pattern completion may help strengthen
representations of the separately encoded memories through multiple, similar re-
instantiations of the same memories (which could be triggered by either external or
internal cues).
Why the Mind Wanders: How Spontaneous Thought’s Default Variability
May Support Episodic Efficiency and Semantic Optimization
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Pattern Separation
Pattern separation is a process through which distinct representations of episodic
experiences, and their contextual properties, are indexed in the hippocampus as separate
and discrete events (Rolls, 2016). Pattern separation plays a vital role in episodic memory
storage and retrieval by helping us create distinct neural representations for individual
episodic events. Here, we propose that a fundamental function of a default variability in
mental contents over time is to support pattern separation by helping de-correlate
distinct memories from one another.
At the neural level, pattern separation is considered to be dependent on hippocampal
processes (Leutgeb, Leutgeb, Moser, & Moser, 2007; Rolls, 2016; Yassa & Reagh, 2013).
It begins with input from the entorhinal cortex (see Figure 2.1), which feeds into the
granule cells of the dentate gyrus (DG) by way of the perforant path (Witter, 1993, 2007).
The DG cells are proposed to serve as a modifiable network that ultimately produces
sparse, orthogonalized outputs to Cornu Amonis region 3 (CA3). DG granule cells exhibit
unique functional properties: They have relatively sparse firing rates, yet exert a strong
influence on CA3 cells (Jung & McNaughton, 1993; Leutgeb et al., 2007). Moreover, only
a very small number of connections are received at each CA3 cell. For example, it is
presumed that the small number of connections (approximately 46 mossy fiber
connections to each CA3 cell) creates a randomizing effect (i.e., for any given event, a
random set of CA3 neurons is activated). In other words, there is an extremely low
probability that any two CA3 neurons would receive input from a similar set of DG cells
(Kesner, 2007). As a result, event (i.e., episodic) representations should be as highly
differentiated as possible from one another (Rolls, 1989, 1989; Rolls & Kesner, 2006;
Rolls & Treves, 1990), which affords optimal storage capacity of distinct event
representations (Hunsaker & Kesner, 2013; Myers & Scharfman, 2009, 2011; Treves &
Rolls, 1992, 1994).
Click to view larger
Figure 2.1. Diagram representing the pathways for
pattern completion and pattern separation from the
(p. 13)
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May Support Episodic Efficiency and Semantic Optimization
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The mechanisms in pattern
separation ultimately
contribute to the
orthogonalization of
episodic memory
representations,
characterized by reduced
overlap or redundancy
between distinct event
representations.
Sometimes referred to as
dilution or diluted
connectivity,
orthogonalization is
characterized by low levels
of correlation between
different encoded episodic
memories and a low
number of synaptic connections between each of the CA3 neurons themselves—as little as
one connection between any pair of randomly connected CA3 neurons within the network
(Rolls, 2013). Supporting evidence comes from both rodent and human studies
suggesting that the DG and CA3 can update the de-correlated network after exposure to
even slight deviations in previously encountered contexts or stimulus (Bakker, Kirwan,
Miller, & Stark, 2008; Gilbert, Kesner, & Lee, 2001; Leutgeb et al., 2007). For example,
novelty is associated with increased firing rates from certain inhibitory neurons in the DG
(Nitz & McNaughton, 2004), which may serve as a filtering mechanism for determining
when new events should be encoded as such (Jones & McHugh, 2011).
From Pattern Separation in the Hippocampus to Neocortical
Competition
Although the exact neural details of pattern separation are still a subject of debate, one
thing is clear: the ability to separate different episodic memory patterns requires an
efficient storage mechanism. In addition to the sparse encoding in the DG and CA3,
efficient storage capacity is also proposed to be achieved through hippocampo-cortical
interactions, according to the hippocampal indexing theory (Teyler & DiScenna, 1986).
While individual memory traces are indexed separately in the hippocampus, additional
details of the memory are thought to be stored elsewhere in the cortex. Yassa and Reagh
(2013) have described this process by comparing the neocortex to a library where
information is stored, and the hippocampus as the librarian who can refer to where the
information is stored.
cortex to the medial temporal lobe (and back to the
cortex). (See Color Insert)
Click to view larger
Figure 2.1. Diagram representing the pathways for
pattern completion and pattern separation from the
cortex to the medial temporal lobe (and back to the
cortex).
Why the Mind Wanders: How Spontaneous Thought’s Default Variability
May Support Episodic Efficiency and Semantic Optimization
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Based on this idea, the competitive trace theory (Yassa & Reagh, 2013) makes a specific
prediction about the benefits of episodic memory reactivation. According to this theory,
certain episodic features of a memory are preserved through multiple reactivations of this
memory over time (Figure 2.2). Every reactivation causes a trace to be re-encoded in the
DG, so that this trace does not completely overlap with the traces created by other
reactivations or by the original event. Across multiple reactivations over time, some
features of the memory will overlap (i.e., will be the same), while others will not (i.e., they
will differ; Figure 2.2). Over time, overlapping features are strengthened with respect to
their corresponding hippocampal and neocortical representations, which results in their
higher fidelity during retrieval. On the other hand, non-overlapping features compete for
representation in the cortex (unlike overlapping features), and mutually inhibit
one another through anti-Hebbian learning (i.e., active neurons initiate inhibitory
competition, and weakly activated neurons are subsequently inhibited). Therefore, non-
overlapping features—which are presumably likely to be the less common and less
important features of the memory—will have a reduced likelihood of being retrieved.
The idea that both the
hippocampus and
neocortex are involved in
pattern separation is
important for considering
how temporally variable
mental simulations
(reactivations and novel
recombinations) can aid in
efficient separation. The
neocortex, where much of
the episodic memory
information is stored, is
associatively modifiable
through competitive
learning so that given
some input, competition is
generated among neural
representations (i.e.,
multiple representations of
a memory receive some
level of activation, resulting in a competition between them to win total activation,
resulting in an action potential). The “winner” of the competition then becomes activated,
thus strengthening the association between the input and particular neural activations in
the neocortex. Indeed, the mossy fiber system in the DG and its connections to CA3 also
exhibit an associative Hebbian learning network (Treves & Rolls, 1994), where
concurrent presynaptic activity and postsynaptic action potentials result in a
Click to view larger
Figure 2.2. Graphic illustration of how overlapping
features are preserved and non-overlapping features
are suppressed. (See Color Insert)
Click to view larger
Figure 2.2. Graphic illustration of how overlapping
features are preserved and non-overlapping features
are suppressed.
(p. 14)
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May Support Episodic Efficiency and Semantic Optimization
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strengthened connection and increased synaptic efficiency (Treves & Rolls, 1994). In
turn, this type of synaptic strengthening supports the sparse coding in the DG and CA3,
which may be a sufficient mechanism for orthogonalization in the hippocampus.
The idea that memories can become de-correlated over time in the neocortex also bears
relation to other proposed mechanisms. For example, Hulbert and Norman (2015)
propose a process similar to pattern separation, called differentiation, in which episodic
memories can become de-correlated through competitive learning in the hippocampus
and cortical regions. Their explanation distinguishes between pattern separation, which
is asserted to be automatic, and differentiation, which is driven by competition (in the
neocortex) after pattern separation has already occurred (in the hippocampus). Hulbert
and Norman (2015) present functional magnetic resonance imaging (fMRI) evidence that
a reduction in similarity in the hippocampus between memories is correlated with
retrieval-induced facilitation, which is the opposite of retrieval-induced forgetting (e.g.,
impaired memory for related items). This pattern of results supports the idea that when
memories are differentiated from one another, they do not hinder retrieval due to
similarity. Further support also comes from Favila et al. (2016), who showed that
reducing the similarity between memories can be an adaptive process: learning serves to
reduce the amount of overlap in hippocampal representations of highly similar stimuli,
which in turn prevents interference during subsequent retrieval.
Role of Content Variability
How does the content variability inherent to spontaneous thought contribute to episodic
memory separation? At a basic level, spontaneous reactivations can provide the
foundation for initiating competition between multiple instantiations of a given memory,
ultimately preserving the important (i.e., recurring) features of the memory; that is, since
each spontaneous thought is re-encoded as a new memory trace (Yassa & Reagh, 2013),
competition is generated among the non-overlapping features in the new and
previously encoded memories. The idea here is that essential overlapping features that
are present in both (or more) versions of the encoded memory will be strengthened and
retained for later recall due to spontaneous reactivations. At the same time, spontaneous
reactivations are unlikely to be veridical instantiations of the memory. Therefore, the non-
overlapping, and perhaps irrelevant, features of that memory will be inhibited and
potentially lost over time. See Figure 2.2 for a graphical example.
A second proposed role of content variability is that memories can become de-correlated
through continual shifts in mental content, where memory reactivations are not
temporally or spatially bound from one spontaneous thought to the next (see Figure 2.3
for an example). The variability of content over time acts to provide a time buffer between
overlapping memories. Enough time can pass between similar memory traces, such that
activation from one memory can die down before other related memories are activated,
(p. 15)
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May Support Episodic Efficiency and Semantic Optimization
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thus avoiding the “fire together, wire together” association rule originally proposed by
Hebb.
We argue therefore that
default content variability
plays a functional role in
organizing episodic
memories by optimizing
de-correlated memories in
the hippocampus and
neocortex. Although the
flow of mental states
during spontaneous
thought may seem
randomly disjointed,
spontaneous thoughts are
often tied to recent
memories, past events, or
future plans (Baird,
Smallwood, & Schooler,
2011; Klinger & Cox,
2004). Thus, spontaneous
mental simulations may
play a critical role in
separating episodic
memories that are
important to one’s life,
such as episodic
experiences that need to
be distinguished from
others and should not be
grouped with them due to
factors such as temporal
contiguity.
Click to view larger
Figure 2.3. Examples of low variability in thought
(top), corresponding to clustered learning, and
highly variable thought content (bottom),
corresponding to de-correlated memories via
temporally spaced memories in spontaneous
thoughts. (See Color Insert)
Click to view larger
Figure 2.3. Examples of low variability in thought
(top), corresponding to clustered learning, and
highly variable thought content (bottom),
corresponding to de-correlated memories via
temporally spaced memories in spontaneous
thoughts.
(p. 16)
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May Support Episodic Efficiency and Semantic Optimization
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Pattern Completion
Pattern completion is a process through which completion of a whole memory of an event
or experience is generated from recall of any of its parts. In other words, partial or
degraded cues can trigger the respective stored event representation, which then serves
to reactivate the original episodic memory and its accompanying features, including the
context in which it was originally experienced (Marr, 1971). The phenomenological
qualities of the original event—including even elements such as the emotional tone of the
initial experience—can be recaptured and reinstated, and in this sense vividly re-
experienced by the individual. As such, pattern completion is central to the notion of
episodic memory retrieval, in that it supports not only the recall of the information
surrounding a given event, but also taps into the fundamental conscious feeling of
reliving a moment as a specific, rich, and unique subjective episode (Nadel & Moscovitch,
1997; Teyler & Rudy, 2007). Pattern completion therefore elicits a sense of autonoetic
consciousness (e.g., the ability to mentally put ourselves in other situations—past,
present, and imagined—and reflect on them), a hallmark of episodic awareness (James,
1890; Tulving, 2002), which is necessary for episodic memory retrieval.
At the neural level, the hippocampus and the surrounding structures of the medial
temporal lobe (MTL) are considered to be among the key neural substrates underlying
the reinstatement of episodic memories (see Figure 2.1). While pattern separation is
thought to be mediated by the DG, areas CA1 and CA3 have been implicated as more
central components of pattern completion. Incoming information stems from the
entorhinal cortex, and perforant path projections onto CA3 cells initiate retrieval in CA3
(without passing through the DG). The process of pattern completion itself is principally
subserved by the CA3 auto-associative network architecture (a network that can
essentially retrieve a memory from partial information about the memory itself; Marr,
1971). This auto-associative CA3 architecture is considered to operate as a single
attractor network (Rolls, 2013). Because of that, a retrieval cue need not be very strong
in order to produce accurate recall—the retrieval process itself is taken over by the CA3
recurrent auto-associative system (Rolls, 2013; Treves & Rolls, 1992).
Completion is then carried out via CA3 projections to CA1 neurons, which then results in
divergent back-projections from CA1 to the entorhinal cortex and subsequent neocortical
areas. These back-projections occur through a Hebbian-like competitive learning network
(i.e., associative learning, where similar firing patterns result in strengthened
connections), so that inputs from CA3 generate competition among the cells in CA1.
Subsequently, cells with the strongest activation in CA1 instigate a winner-take-all effect,
thereby strengthening that specific pattern and suppressing shared activation among
other memory representations that were not completed. Thus, an anti-Hebbian effect
takes place when the active neurons initiate inhibitory competition, thereby depressing
activations from weakly activated neurons.
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May Support Episodic Efficiency and Semantic Optimization
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CA1 projections act as efficient retrieval cues (even partial or degraded), ultimately
eliciting activity in those areas of the cerebral cortex that initially supplied input to the
hippocampus. In other words, those areas of the brain that served to generate the initial
episodic experience are again recruited upon retrieval. In this way, pattern completion
can be conceptualized as “a reverse hierarchical series of pattern association networks
implemented by the hippocampo-cortical backprojections, each one of which performs
some pattern generalization, to retrieve a complete pattern of cortical firing in higher-
order cortical areas” (Rolls, 2013, p. 1).
Pattern Completion as a Source of Continuously Generated Mental
Content
In addition to strengthening the existing memories, we also propose that pattern
completion might serve as a source of the ceaseless change in mental content (i.e., the
frequent generation of new mental states). Indeed, a similar idea was proposed earlier by
O’Neill, Pleydell-Bouverie, Dupret, and Csicsvari (2010), where pattern completion in the
CA3 was suggested to be well suited for promoting reactivation during rest. Further,
there is evidence that spontaneous reactivation of a memory can be triggered by partial
cues from the memory’s retrieval context, as evidenced by qualitative overlap between
thought content and its cue (Berntsen, 1996; Berntsen & Hall, 2004).
We therefore consider the possibility that memories recalled during pattern
completion might provide partial or degraded cues that may then serve to trigger further
pattern completions, thus facilitating a continuous change in mental contents. For
example, one might see a chocolate cupcake. Chocolate may then become a cue to
complete a memory of a birthday party with chocolate cake. The cue of birthday might
lead to completing a memory of the Ninja Turtles, and green may serve as a cue to
remember a favorite green shirt. This continuous cue provision and pattern completion
tendency may help us understand why the mind keeps moving, with novel mental
contents emerging repeatedly.
If, as we propose here, there is a bias for consecutive spontaneous mental simulations to
be de-correlated via pattern separation processes, these partial cues are likely to trigger
patterns that are at least somewhat dissimilar to the immediately preceding pattern that
was triggered. This might be one reason that spontaneous thought exhibits a heightened
variability over time, while at the same time allowing for thematic relationships or other
partial associations to be present among consecutive mental states. In turn, the
completed patterns may also work together with pattern separation processes to further
differentiate episodic events by strengthening the hippocampal-neocortical
representations of an episodic memory when it is reactivated.
Cascades of thought might spontaneously arise within the hippocampus and propagate
throughout the brain (Ellamil et al., 2016). Some of these thoughts may end up being
experienced consciously, whereas others may fail to reach awareness. This account is
(p. 17)
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May Support Episodic Efficiency and Semantic Optimization
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consistent with the notion of thoughts shifting in and out of the foreground of one’s focus
of attention, and the accompanying subjective experience of competing or coexisting
streams of thought. It also speaks to the ease with which we experience a high level of
content variability from one moment to the next, a large proportion of which may unfold
spontaneously from partial cues in the internal or external environment.
Semantic Optimization Hypothesis
How do we transform our fragmented episodic experiences into a meaningful
understanding of our world—or what scientists call “semantic knowledge”? Prominent
consolidation models, such as the standard consolidation theory (Scoville & Milner, 1957;
Squire, 1992; Squire & Alvarez, 1995; Squire & Zola, 1998) and the multiple trace theory
(Nadel & Moscovitch, 1997)—although not in full theoretical agreement—share a central
assumption with regard to this episodic-to-semantic transformation: at the neural level,
episodic memories for events are primarily hippocampus-dependent, whereas semantic
memories rely primarily on neocortical substrates. Here, we propose that default
variability not only supports the organization of episodic memory in the hippocampus and
neocortex, but also supports the organization of semantic memory by providing the
conditions necessary for efficient episodic-to-semantic transformation.
The creation of semantic knowledge out of episodic experiences is a gradual process that
occurs across multiple instantiations (McClelland, McNaughton, & O’Reilly, 1995).
Variability across instantiations plays an important role in semantic knowledge
acquisition. A combination of similarity and dissimilarity across representations facilitate
the extraction of regularities and the development of categorization (Gelman & Markman,
1986; Sloutsky, 2003). Similarity (i.e., overlapping features that should be extracted for
meaning-making) provides evidence for regularities within a category, whereas
dissimilarity (i.e., specific differences in individual events) provides contrasting evidence
that helps identify category boundaries. Moreover, the experience of repeated events in
various contexts aids the encoding of relationships between its typical elements
(Avrahami & Kareev, 1994). Over multiple subsequent exposures, these event elements
are stored together in one schema, affording economical representations of semantic
concepts (Nadel, Hupbach, Gomez, & Newman-Smith, 2012). As part of the default
variability hypothesis, we propose that spontaneous thought’s heightened content
variability serves to support and optimize semantic abstraction by providing multiple
mental simulations that are both similar and dissimilar in nature. A default mode of
content variability in spontaneous thought may therefore provide a mechanism for
generating contextually variable episodic simulations (both veridical and novel
recombinations). The similarity in consecutive mental simulations can provide the basis
for abstracting general meaning and overarching categories through multiple exposures,
while the dissimilarities can help ensure that one specific instance is not overlearned
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May Support Episodic Efficiency and Semantic Optimization
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(e.g., if you only saw one breed of dog, you may not realize that another breed is also a
dog).
Aside from the variability of consecutive representations, gradual exposure is also
considered to play a critical role. Gradual exposure, also referred to as interleaved
learning, affords optimal semantic abstraction (McClelland et al., 1995). Based on
evidence from connectionist models, interleaved learning is theorized to critically support
the progressive refinement of stable representations at the conceptual level. Semantic
representations resulting from interleaved learning are optimally flexible in assuming and
reflecting “the aggregate influence of the entire ensemble of patterns” elicited across
events, while simultaneously being resilient to large modifications due to exposure to a
single episodic trial (e.g., catastrophic interference; McClelland et al., 1995, p. 429).
Accuracy in neocortical conceptual representation formation is argued to be a function of
both sample size (i.e., number of experiences being aggregated across) and learning rate,
whereby a slower rate allows for a greater number of interleaved samples to be factored
into each computed estimate (White, 1989). For example, after enough gradual exposure
to the meaning of “cat,” a child would be less susceptible to fundamental
misunderstandings of the cat category (e.g., classifying a small dog as a cat). However, if
a child is shown 50 pictures of cats in one day, he or she may confuse a small dog for a
cat one week later. Interleaved learning is assumed to operate by “basically causing the
network to take a running average over a larger number of recent examples” (McClelland
et al., 1995, p. 437). We propose that a default content variability in spontaneous thought
serves as a mechanism for increasing the opportunities for interleaved episodic-to-
semantic transformation. By combining spontaneous reactivations that are highly variable
from moment to moment, but also have recurring themes over time (e.g., particular
things that are relevant to goals or current concerns), spontaneous thought may optimize
the conditions for episodic-to-semantic abstraction and semantic memory organization
overall.
Another important property of interleaved learning is that it can deter catastrophic
interference (the loss of previously learned information due to the introduction of new
information; McCloskey & Cohen, 1989). As commonly portrayed through the AB-AC
paradigm (for more details, see McClelland, McNaughton, & O’Reilly, 1995), newly
learned associations (AC) can exhibit retroactive interference upon a previously acquired
set of associations (AB). In this example, AC can interfere with the ability to recall AB
later—because AC has “replaced” our concept of the AB association. Avoiding
catastrophic interference means that we can actually distinguish AB and AC as different
instances in an overarching category, rather than letting exposure to one harm the
memory of the other.
If “what one learns about something is stored in the connection weights among the units
activated in representing it” (McClelland et al., 1995, p. 433), then abstraction or
generalization is only possible to the extent to which conceptual pattern representations
overlap (Hinton, Mcclelland, & Rumelhart, 1986). Therefore, it is imperative that a
(p. 18)
Why the Mind Wanders: How Spontaneous Thought’s Default Variability
May Support Episodic Efficiency and Semantic Optimization
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system is not only capable of—but also capitalizes upon—the ability to extract shared
properties among concepts, while simultaneously minimizing catastrophic interference.
McClelland, McNaughton, and O’Reilly (1995) aptly highlight the existence of two
independent yet complementary learning systems that meet both these needs: rapid
acquisition at the hippocampal level (pattern separation and completion), paired with
gradual interleaved learning consolidation at the neocortical level.
Taken together, we propose that the content variability that characterizes spontaneous
thought supports episodic-to-semantic transformation and semantic memory organization
by providing both increased variability and frequency over time in a set of samples—novel
combinations of elements and reinstatements of episodic memories in new contexts—
which facilitates rapid extraction of regularities and allows for generalization across
them. In this way, spontaneous thought’s default variability plays a critical role in
optimizing the efficient abstraction and organization of semantic memory.
Other Potential Benefits of Spontaneous
Thought
Novel Association Formation
Another key prediction of the semantic optimization hypothesis is that novel association
formations can arise out of MTL activity. Fox, Andrews-Hanna, and Christoff’s (2016)
expanded account of the hippocampal indexing theory (Teyler & DiScenna, 1986; Teyler &
Rudy, 2007) suggests that the generation of novel thought is supported by the same
mechanisms involved in the spontaneous reactivation of memory traces. This is consistent
with the idea that the organization of memory traces in MTL regions is considered
associative (Moscovitch, 1995). In other words, immediate temporal contiguity or
simultaneity will largely dictate which combinations of cues and ensuing memory
reactivations will arise together.
In this way, novel thought patterns that are constructive or generative in nature can be
potentially facilitated by spontaneous mental simulations, whereby randomization
of emergent thought patterns might in part promote more flexible, as opposed to fixated,
thinking (Fox, Kang, Lifshitz, & Christoff, 2016). In fact, it has been shown that noise or
variability in attractor networks is indeed beneficial for decision-making and memory,
because it causes them to be non-deterministic, which in turn can cultivate new problem
solutions and creativity (Deco, Rolls, & Romo, 2009; Rolls, 2013, 2014). Furthermore,
spontaneous thought has also been recognized as supporting many constructive cognitive
functions (Fox & Christoff, 2014; Fox, Kang, et al., 2016; McMillan, Kaufman, & Singer,
2013; Smallwood & Andrews-Hanna, 2013), including generation of creative solutions and
ideas to present problems (Baird et al., 2012; Campbell, 1960; Simonton, 1999),
(p. 19)
Why the Mind Wanders: How Spontaneous Thought’s Default Variability
May Support Episodic Efficiency and Semantic Optimization
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simulated thinking (Rice & Redcay, 2015; Spiers & Maguire, 2006), and coordination and
planning of future goals (Smallwood & Andrews-Hanna, 2013; Spreng, Stevens,
Chamberlain, Gilmore, & Schacter, 2010).
According to the default variability hypothesis, mind-wandering and spontaneous thought
activity can be considered a mechanism involved in not only consolidating past episodes,
but also processing ongoing current concerns and upcoming future events, a system that
is expected and theorized to continuously update and integrate new information into
existing semantic knowledge.
Why the Mind Wanders: How Spontaneous Thought’s Default Variability
May Support Episodic Efficiency and Semantic Optimization
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Reconciling Existing Mind-Wandering
Frameworks
Several theories of spontaneous thought and mind-wandering have been proposed, yet
there is a lack of consensus about functional role(s) and underlying mechanisms.
Smallwood (2013) recently attempted to differentiate two accounts: the first explained
why the spontaneous onset of unconstrained self-generated mental activity arises
(deemed “occurrence” hypotheses), while the second explained how the continuity of
internal thought is maintained once initiated (i.e., “process” accounts).
Although his process-occurrence model certainly helps unify the various accounts under
one framework, it falls short of providing a functional reason for why the mind evolved to
wander. Instead, Smallwood suggests that the prospective consolidation hypothesis might
be a possible explanation for the source and function of internally generated thought
(Smallwood, 2013). The prospective consolidation hypothesis suggests that “a core
function of the hippocampal-cortical system is to use remnants of past experiences to
make predictions about upcoming events” (Buckner, 2010, p. 42). Our hypothesis extends
this idea to also include the reactivation of past and current information. Specifically, the
default variability hypothesis provides further insight into the question of why we have
evolved to produce spontaneous thought marked by heightened variability of content over
time. First and foremost, we propose a functional account for why spontaneous thought is
such a prevalent and ongoing experience in daily waking life, and the mechanisms that
support this ongoing mental activity. Second, we suggest that from one moment to the
next, high levels of content variability—thoughts that seem unrelated to each, or only
loosely related—are capable of arising quickly, ranging and shifting between past and
current episodic reactivations to future-related simulated events. Finally, the current
account takes a step away from the traditional task-centric literature, and suggests that
this ongoing mental activity persists in both the presence and absence of external input.
As such, the content of spontaneous thought itself may be partially determined by simple
random probability of thought-pattern reactivation, as determined by any incoming
externally or internally generated partial cues, paired with the effect of constraints acting
upon the cognitive system within each given moment (Christoff, Irving, Fox, Spreng, &
Andrews-Hanna, 2016). These constraints might be a function of salience, whether
personal or perceptual in nature (e.g., the current concerns hypothesis; Klinger & Cox,
2004), the effect of attentional control (e.g., the executive failure hypothesis; McVay &
Kane, 2009), a result of one’s capacity to identify the contents of one’s consciousness
(e.g., the meta-awareness hypothesis; Schooler, 2002), or most likely the outcome of a
combination of all of those, functioning to different degrees. In this way, it can be
expected that mental contents are constantly emerging from within hippocampal
structures, whereby the extent to which they are transformed into thoughts and unfold
Why the Mind Wanders: How Spontaneous Thought’s Default Variability
May Support Episodic Efficiency and Semantic Optimization
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throughout the rest of the brain (and the extent to which they are likely to be experienced
consciously) is determined by the level and specificity of those constraints.
Conclusion
Our minds frequently tend to “wander” about, shaping a spontaneous thought flow
marked by heightened content variability over time. Since the signature of free movement
and content variability are likely to come at a considerable metabolic cost
(Laughlin, de Ruyter van Steveninck, & Anderson, 1998; Plaçais & Preat, 2013), there is
likely some evolutionary advantage of the dynamic nature of human thought. Thus, this
chapter has attempted to introduce an account of the neural and cognitive evolutionary
benefits of spontaneous thought and its inherent content variability.
Specifically, the default variability hypothesis proposes that mind-wandering is
characterized by content variability and continuous movement that support both efficient
episodic storage (episodic efficiency hypothesis) and semantic knowledge abstraction
(semantic optimization hypothesis). The episodic efficiency hypothesis suggests that the
reactivations and recombinations underlying content variability play a critical role in
pattern separation by helping to de-correlate memories in the hippocampus and
neocortex. Pattern completion, on the other hand, is proposed to strengthen the
separated episodic memory representations, while also being a potential source of
continuous mental content, where one activated memory serves as a partial cue for the
next. In addition, the semantic optimization hypothesis maintains that content variability
supports episodic-to-semantic abstraction through multiple mental simulations that are
both similar and dissimilar: The similarities provide the opportunity for repeated
exposures so that concepts and categories can be strengthened over multiple exposures,
while the dissimilarities mitigate the danger of overlearning a single instance. Through
mental simulations, stemming from novel recombinations as well as reactivations,
semantic abstraction is optimized due to increased variability and frequency over time in
a set of samples containing similar yet dissociable information.
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Caitlin Mills
Caitlin Mills, Department of Psychology, University of British Columbia, Vancouver,
British Columbia, Canada
Arianne Herrera-Bennett
Arianne Herrera-Bennett, Munich Center of the Learning Sciences, Ludwig
Maximilions University-Munich, Munich, Germany
Myrthe Faber
Myrthe Faber, Department of Psychology, University of Notre Dame, South Bend,
Indiana, United States
Kalina Christoff
Kalina Christoff Centre for Brain Health Department of Psychology University of
British Columbia Vancouver, British Columbia, Canada
