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Running head: MEMORY AND CREATIVE COGNITION
Memory and creativity: A meta-analytic examination of the relationship between memory
systems and creative cognition
Courtney R. Gerver, Jason W. Griffin, Nancy A. Dennis, & Roger E. Beaty
Department of Psychology, Pennsylvania State University
Author names and affiliations
Courtney R. Gerver, Department of Psychology, Pennsylvania State University, 441 Moore
Building, University Park, PA, 16802, cxg428@psu.edu
Jason W. Griffin, Department of Psychology, Pennsylvania State University, 141 Moore Building,
University Park, PA, 16802, jxg569@psu.edu
Nancy A. Dennis, Department of Psychology, Pennsylvania State University, 450 Moore Building,
University Park, PA, 16802, nad12@psu.edu
Roger E. Beaty, Department of Psychology, Pennsylvania State University, 140 Moore Building,
University Park, PA, 16802, rebeaty@psu.edu
Corresponding Author
Correspondence concerning this article should be addressed to Courtney R. Gerver,
Department of Psychology, Pennsylvania State University, 441 Moore Building, University Park,
PA, 16802, cxg428@psu.edu.
Acknowledgements
N.A.D. is supported by a grant from the National Science Foundation [2000047 BSC].
R.E.B. is also supported by a grant from the National Science Foundation [DRL-1920653]. We
would also like to thank Rebecca Henry for help with text screening and data entry.
Conflict of Interest
There are no conflicts of interest to be declared.
Data and Code Availability
All data and code are publicly available through the Open Science Framework. Available
at https://osf.io/kudvy/?view_only=cd19b0a438a4486b8b5a0dab39339e1d.
MEMORY AND CREATIVE COGNITION
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Abstract
Researchers have been studying creativity for decades, and yet controversy still surrounds the
cognitive basis of creative thought. A longstanding question in the creativity literature concerns
the role of memory in creative cognition. Increasing evidence suggests that specific memory
systems (e.g., episodic vs. semantic) may support specific creative thought processes. However, a
number of inconsistencies in the literature remain with respect to the strength and direction of the
relationship between memory and creativity, and key questions persist concerning the influence of
specific memory types (semantic, episodic, working, and short-term) and creativity (divergent and
convergent thinking) as well as external factors (age, stimuli modality) on this purported
relationship. In this meta-analysis, we examined 525 correlations from 79 published studies and
unpublished datasets, representing data from 12,846 individual participants. We found a small but
significant (r = .19) correlation between memory and creative cognition. Among semantic,
episodic, working, and short-term memory, semantic memory—particularly verbal fluency, the
ability to strategically retrieve information from long-term memory—was found to drive this
relationship. Further, working memory capacity was found to be more strongly related to
convergent than divergent creative thinking. We also found that within visual creativity, the
relationship with visual memory was greater than that of verbal memory, but within verbal
creativity, the relationship with verbal memory was greater than that of visual memory. Finally,
there was no overall impact of age on the overall effect size, though the memory-creativity
correlation was larger for children compared to young adults. These results help to resolve over a
half century of research on memory and creativity, with three key conclusions: 1) semantic
memory supports both verbal and nonverbal creative thinking, 2) working memory supports
convergent creative thinking, and 3) the cognitive control of memory is central to performance on
creative thinking tasks.
Keywords: semantic memory, working memory, creative cognition, divergent thinking,
convergent thinking
MEMORY AND CREATIVE COGNITION
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Public Significance Statement
We synthesize over 50 years of research on creativity and memory to clarify their relationship.
Our findings indicate creativity is positively related to memory, with semantic memory supporting
both verbal and visuospatial creativity. People’s ability to think creatively is therefore reliably
related to their ability to selectively retrieve information from long-term memory. Our findings
have implications for education and interventions aimed at fostering creative thinking.
MEMORY AND CREATIVE COGNITION
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Introduction
General Overview
The act of remembering is an attempt to retrieve concepts or events that have been learned
or experienced at some point in the past. In contrast, generating creative ideas and discoveries
involves combining learned concepts into new information and perspectives that were not
previously apparent (Stein, 1989). How does remembering the past impact creative thinking?
While at first glance memory and creativity may appear distinct, creative thought is often
conceptualized as a high-level cognitive ability that is supported by “lower-level” cognitive
processes, including memory, attention, and cognitive control (c.f., Abraham, 2014; Benedek &
Fink, 2019; Finke et al., 1992). Understanding the nature of creative thought ultimately requires
mapping relevant underlying constructs, including identifying how and when memory may support
or constrain creative thought. At the same time, examining the memory-creativity link provides
critical insight into consequences and higher-level functions of different memory systems (e.g.,
the episodic system supports both remembering the past and imagining the future; Schacter &
Addis, 2007b).
Yet studying the memory-creativity relationship is a complex endeavor: there are many
types of memory (e.g., semantic, episodic, working, short-term) and creativity (e.g., divergent,
convergent thinking) that can be elicited and influenced depending on the demands of a given task.
Additionally, while classic creativity theories assume a central role of memory in generating
creative thoughts (Mednick, 1962), memory is also error-prone, and it can act as a source of
interference—particularly when people are reminded of old and unoriginal ideas. Further, the
relationship between memory and creativity can also be influenced by individual factors such as
age (e.g., Arenberg, 1973; Hess, 2005; Palmiero et al., 2017) or task-specific factors such as
stimulus modality (verbal vs. visuospatial response format; Chrysikou et al., 2016; Farah et al.,
1989; Freides, 1974; Penney, 1989). Therefore, understanding the relationship between memory
and creativity must involve defining the type of memory and creativity under investigation, as well
as task parameters and individual differences that can influence the strength and direction of the
relationship.
Here, we assess the links between memory and creativity by examining how each memory
type uniquely relates to creative performance across a wide range of task contexts. Specifically,
our review focuses on the following memory systems: semantic memory (meanings, concepts, and
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their relations; Collins & Loftus, 1975), episodic memory (memory for unique experiences;
Tulving & Patterson, 1968), working memory (temporary storage and manipulation of information;
Baddeley & Hitch, 1974), and short-term memory (holding limited information in a temporarily
accessible, non-manipulated state; Atkinson & Shiffrin, 1968). Regarding creativity, our review
focuses on divergent thinking (solving open-ended problems with multiple solutions; Guilford,
1950) and convergent thinking (solving problems with only one correct solution; Runco et al.,
2010). Together, we leverage meta-analytic tools to identify which memory systems reliably
support specific modes of creative thought, thus providing clarity on over 50 years of cognitive
research on creativity.
Overview of “Domain-General” Creativity Tasks and Metrics
Although the study of creativity is a broad and diverse field of research, the study of
“domain-general” creativity—the ability to come up with ideas and solve problems that do not
require domain-specific knowledge or expertise—has converged on a handful of measures to
assess divergent and convergent creative thinking. One of the most common measures of divergent
thinking is the Alternate Uses Task (AUT; Torrance, 1972), which requires people to think of
unusual uses for everyday objects. AUT scores can reflect the number of ideas generated (fluency;
Runco et al., 2011), the variety of ideas across categories or themes (flexibility; Guilford, 1968;
Runco & Okuda, 1991), and the novelty (statistical infrequency or quality) of an idea (Wallach &
Kogan, 1965), among others. Divergent thinking tasks have shown evidence for predictive
validity, including moderate to large correlations with real-world creative achievement (Beaty et
al., 2018; Jauk et al., 2014). Convergent thinking is often assessed with the Remote Associates
Test (RAT; Mednick, 1962), which presents a triplet of apparently unrelated words (e.g., cream,
skate, water) and requires people to find a fourth word that conceptually unites them (e.g.,
ice). Scoring the RAT and other convergent thinking tasks typically involves simply counting the
number of problems correctly solved. Convergent thinking tasks can be solved either by analysis
or insight. Analysis is the deliberate search of a problem space to find solutions (Ericsson & Simon,
1998; Kounios et al., 1987; Newell & Simon, 1972), whereas with insight, a solution emerges
spontaneously into awareness (i.e., the “aha” experience; Metcalfe & Wiebe, 1987; Smith &
Kounios, 1996). However, divergent and convergent creative thinking are both susceptible to
fixation, or a mental block to problem solving (Smith & Blankenship, 1991.) Despite increasing
attempts to map the cognitive mechanisms of divergent and convergent creative thinking, the roles
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of specific memory systems in specific modes of creative cognition remains inconclusive. Below,
we synthesize previous research efforts toward this goal.
Explicit Long-Term Memory and Creative Cognition
Semantic Memory and Divergent Thinking
Semantic memory, or the organization of facts and concepts into networks, is embedded in
classic theories of creative cognition. According to the associative theory (Mednick, 1962),
creativity involves combining weakly related, remote concepts in semantic memory into novel and
useful ideas, a process that is thought to occur through spreading activation (Collins & Loftus,
1975). On this view, as the relative “semantic distance” between two concepts increases, so does
the likelihood a conceptual combination will be perceived as creative. The associative theory also
suggests that highly creative individuals have a more efficient, “flat” associative hierarchy
(numerous and weakly related associations to a given concept) compared to less creative people,
who have more “steep” associative hierarchies (few, strong associations to a given concept;
Mednick, 1962).
The associative theory has received support from empirical investigations linking
individual semantic memory structure to creative task performance, particularly divergent
thinking. For example, the semantic networks of highly creative individuals, defined by divergent
thinking performance, have higher connectivity (the extent to which two neighbors of a node in a
network will be neighbors), shorter path length (average number of steps (edges) between any pair
of nodes in a network), and fewer subcommunities (subcategories, or smaller networks, within the
overall network) than less creative individuals (Kenett et al., 2014, 2018). That is, denser, highly
connected, and less modular networks facilitate more efficient activation spread beyond closely
connected (unoriginal) semantic concepts to more remote ones (Kenett et al., 2014, 2018), which
in turn leads to the formation of creative ideas (Mednick, 1962; Schilling, 2005). Highly creative
individuals have also exhibited a more complex lexical network structure, and they tend to activate
a wider range of associations, potentially increasing the number of novel ideas from which to
choose (Gruszka & Necka, 2002; Kenett et al., 2018, 2018).
Further support for associative processes in creative thought comes from studies of the
serial order effect (Parnes, 1961; Ward, 1969), a phenomenon in which idea production often
follows a temporal tendency where ideas become less frequent and more original over time during
divergent thinking tasks (Beaty & Silvia, 2012). Initially high idea fluency is attributed to
MEMORY AND CREATIVE COGNITION
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activating the dense semantic neighborhood directly surrounding the stimuli prompt (e.g., a brick,
during the AUT) and producing responses similar to the prompt (Gilhooly et al., 2007). Then, as
spreading activation unfolds with time, originality increases later in the task, when distant concepts
within the semantic networks can be reached (Collins & Loftus, 1975; Mednick, 1962).
Alongside such passive activation spread, contemporary theories of semantic memory also
emphasize the importance of top-down, strategic, and controlled processes to guide memory
retrieval (Rosen & Engle, 1997; Unsworth & Engle, 2009). Indeed, there is now considerable
evidence linking creative cognition to aspects of controlled semantic retrieval, with several studies
reporting positive effects of verbal fluency (a measure of sematic retrieval ability) on divergent
thinking ability (Benedek et al., 2012; Forthmann et al., 2019; Gilhooly et al., 2007; Silvia et al.,
2013). For example, category fluency tasks require people to produce as many unique words as
possible within a semantic category (e.g., animals), and phonological fluency tasks require people
to produce as many unique words starting with a given letter (e.g., F, A, and S); the tasks are scored
by summing the unique/correct words (Shao et al., 2014). Given the close resemblance in task
requirements for verbal fluency and divergent thinking tasks—both involve open-ended retrieval
from memory—classic models of intelligence have viewed divergent thinking as a lower-level
factor of broad retrieval ability (McGrew, 2009). A critical distinction, however, is that divergent
thinking tasks often consider the quality of response, whereas canonical verbal fluency tasks only
consider the number of responses. Together, top-down retrieval strategies are thought to facilitate
divergent creative thinking, in addition to the aforementioned passive process of spreading
activation.
While access to semantic memory can facilitate divergent thinking, prior knowledge can
also constrain creative thought. Indeed, the semantic system is organized to facilitate efficient and
appropriate linguistic functions, many of which do not call for creativity. Within the spreading
activation framework (Collins & Loftus, 1975), one must overcome strongly activated semantic
interference to generate a creative response, as reflected in the beginning stages of the serial order
effect (Beaty & Silvia, 2012; Christensen et al., 1957). One type of interference associated with
semantic retrieval is functional fixedness, whereby stereotypical object information impedes
generating novel ideas during creative problem solving (Duncker, 1945), including on open-ended
tasks (Glucksberg & Weisberg, 1966) such as the AUT (Chrysikou et al., 2016). Further, increased
knowledge can lead to the fan effect (Anderson, 1974), whereby increasing knowledge about
MEMORY AND CREATIVE COGNITION
8
concepts leads to increased interference from related information (Beaty et al., 2019). Although
the fan effect is known as an episodic phenomenon, a semantic analog—increasing associative
elements linked to a given cue—has been shown to impact the quality and quantity of responses
on the AUT: low-association AUT cues yield higher originality but less fluency than high-
association AUT, potentially due to less interference from closely-related concepts in semantic
memory (Beaty et al., 2019). Thus, semantic memory has shown both costs and benefits to
divergent creative thinking.
Semantic Memory and Convergent Thinking
The semantic system has also been implicated in convergent thinking. The RAT was
constructed in such a way that only one solution is possible and that the first solution is commonly
incorrect, thus requiring one to overcome the incorrect solution and identify the correct, “remote”
association (Akbari Chermahini & Hommel, 2012). Spreading activation (Collins & Loftus, 1975)
accounts of the RAT (Smith et al., 2013) suggest the cue words activate close associates in
semantic space, and the activation spreads until people ultimately converge on a solution. If the
solution to a RAT problem readily comes to mind, then the cues are considered to be “closer”
together in the underlying network. According to this research (Smith et al., 2013), the RAT can
be solved using two semantic search strategies. First, participants will select a set of possible
answers constrained by just one word from the triplet at a time. Second, they’ll adopt a local search
strategy and make new guesses based in part on their previous guesses (Smith et al., 2013). This
semantic search approach also applies to other cognitive tasks such as generating hypotheses
(Thomas et al., 2008) and analogies (Forbus et al., 1995). If this search process is biased in any
way, such as forcing participants to respond quickly, then high-frequency words are produced even
if they are not correct (Gupta et al., 2012). Successful convergent creative thinking is therefore
thought to require bypassing high-frequency responses that passively activate in semantic space
via spreading activation.
In contrast to passive activation, another line of research suggests individuals take a more
controlled, top-down memory search approach when problem-solving known as information
foraging. Foraging theory was first applied to non-human animals searching for food (Stephens &
Krebs, 1986), and it has since been adopted to explain information foraging in cognitive systems
(Hills et al., 2012; Pirolli, 2007). Foraging has specifically been used to describe semantic memory
search behaviors when solving creative problems, such as the RAT. Specifically, the three RAT
MEMORY AND CREATIVE COGNITION
9
cues may activate adjacent semantic neighborhoods and eventually intersect. Information within
this intersection is activated more strongly than the individual neighborhoods, but not to the point
where individual cue-specific items would get excluded. An optimal memory forage would involve
focusing one’s search on the intersection of the cues’ semantic neighborhood to maximize the
difference in activation between targets and distractors (Davelaar, 2015). In contrast, when
completing a verbal fluency task (e.g., listing animals), search behavior typically involves staying
within a “patch,” or neighborhood cluster, until it is exhausted (Hills et al., 2012). Searching the
intersection of a RAT triplet is particularly advantageous when the target is weak and cue patches
contain strong interference (Davelaar, 2015). Thus, compared to passive activation spread and
controlled retrieval, memory foraging may allow one to intentionally bypass distractors, allowing
more efficient retrieval of the target solution.
On the other hand, more spontaneous, insight-based problem-solving is thought to be the
result of using shortcuts (or creating links) between semantic concepts when searching semantic
memory (Schilling, 2005). One study (Samsonovich & Kuznetsova, 2018) attempted to map
memory search processes when solving classic insight problems (DeYoung et al., 2008) and found
people take a less linear approach through semantic concepts, particularly at the very end of the
task—just prior to the insight experience—compared to moving linearly towards a single solution
(Samsonovich & Kuznetsova, 2018). These findings suggest the anticipated end of an insight
problem is enough to alter a semantic search path. Notably, performance on classic insight
problems has shown questionable validity evidence—including near-zero correlations reported
between insight problem solving and creative achievement (Beaty et al., 2014)—so the
generalization of such findings to real-world creativity is currently unclear.
Similar to the relationship between semantic memory and divergent thinking, semantic
memory can also lead to mental fixation and impede problem solving (Duncker & Lee, 1945;
Maier, 1931). For example, simply exposing participants to inappropriate or misleading semantic
associates can impair performance on the RAT, leading to fixation (Smith & Blankenship, 1991).
People also tend to naturally fixate on salient but incorrect solutions (e.g., high-frequency words),
a phenomenon that can be redirected with clues and other types of priming (Vul & Pashler, 2007).
Therefore, bypassing inappropriate ideas to formulate new and creative ones appears to be relevant
to problem solving (Storm et al., 2011). Convergent thinking is also susceptible to negative
transfer, which is when prior learning causes poorer subsequent performance. For example, when
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the test words of RAT problems (e.g., cottage, swiss, cake) are paired with conceptually related
words (e.g., hut, chocolate, icing) that are unrelated to solutions (e.g., cheese), performance on the
RAT decreases due to fixating on what had recently been learned (Beda & Smith, 2018). Bypassing
or forgetting fixation-inducing semantic associates thus seems to be important for solving creative
problems in this task (Storm et al., 2011). Yet the literature is still mixed on the broader role of
semantic memory in convergent creative thinking, particularly regarding whether individual
differences in semantic memory abilities (e.g., verbal fluency) reliably predict performance on
convergent thinking tasks, such as the RAT.
Episodic Memory and Divergent Thinking
Although a majority of research on memory and creativity has focused on the semantic
system, recently, researchers have begun to explore the potential role of episodic memory in the
creative process. Episodic memory retrieval is considered to be a constructive process, wherein
past events are reconstructed by piecing together individual-stored memories of people, contexts,
and actions. The constructive episodic simulation hypothesis (Schacter & Addis, 2007a, 2007b)
suggests episodic memory provides a source of details for the retrieval of past events. The
hypothesis also contends that the constructive nature of the episodic memory system allows for
the recombination of such details into a simulation of a novel event, like when one imagines future
experiences that have not yet occurred (Schacter & Addis, 2007a). There is considerable evidence
demonstrating an overlap between memory retrieval and imagination, including neuroimaging
studies showing a substantial overlap in the brain regions engaged during tasks involving episodic
retrieval and future simulation (Addis et al., 2009; Schacter et al., 2012; Szpunar & Schacter,
2018). Regarding creativity, more recent evidence has demonstrated individuals sometimes draw
on episodic memories when performing divergent thinking tasks (e.g., Addis et al., 2016; Benedek,
Jauk, Fink, et al., 2014; Duff et al., 2013; Ellamil et al., 2012), suggesting that the constructive
nature of the episodic system may extend to creative tasks that similarly require flexibly combining
information.
Divergent thinking may also benefit from direct recall of solution-relevant past experiences
(Sheldon et al., 2011; Vandermorris et al., 2013). For example, participants who completed a think
aloud version of the AUT occasionally drew on their personal past experiences when generating
object uses, though this type of retrieval primarily occurred at the beginning of the task (Gilhooly
et al., 2007). Drawing on previous experiences can also be beneficial for real-world creative
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problems, such as when experienced engineers educate novice engineers by sharing hints and
previously used examples (Smith et al., 1993). Neuroimaging work has found that brain regions
typically associated with episodic memory, including the hippocampus, show increased activity
when performing divergent thinking tasks such as the AUT (Benedek, Jauk, Fink, et al., 2014) and
when generating ideas on a drawing task (Ellamil et al., 2012). Researchers have also causally
tested this relationship using inhibitory transcranial magnetic stimulation (TMS; Thakral et al.,
2020). Specifically, inhibitory TMS to the hippocampus (via the angular gyrus)—core regions of
the episodic system—led participants to produce fewer ideas on the AUT and fewer episodic
details when imagining future events.
To assess the extent to which episodic memory contributes to divergent creative thinking,
researchers have used an experimental procedure known as episodic-specificity induction (ESI).
ESI trains participants in recollecting specific details of recent experiences (e.g., recalling the
details of events from a video), which activates constructive retrieval mechanisms and thus can be
used to test the involvement of the episodic system on a subsequent behavioral task. Across several
studies, ESI has been found to specifically boost the number of episodic details (but not semantic
details) in both young and older adults (Madore et al., 2014, 2015; Madore & Schacter, 2014),
despite the observation that age-related differences in remembering the past extend to imagining
the future (Schacter et al., 2013). At the individual level, performance on the AUT was shown to
positively correlate with the amount of episodic details when younger and older adults imagine
future personal scenarios (Addis et al., 2016). Further, an fMRI study of divergent thinking found
that the ESI engages the hippocampus (Madore et al., 2019). Notably, however, the behavioral
effects of ESI appear to be limited to increasing the number (i.e., fluency) of ideas on divergent
tasks, and not their creative quality, indicating that episodic memory may make people more
generative but not necessarily more original (Madore et al., 2016). Together, ESI studies lend
further support to the constructive episodic hypothesis and the involvement of episodic memory
in divergent thinking (van Genugten et al., 2021).
While the above research supports the role of episodic memory in divergent creative
thinking, access to past experiences can also negatively impact creative output as well. For
example, past experiences can bias people toward schemas that are not conducive to creativity. In
the aforementioned study of engineers (Smith et al., 1993), biasing retrieval through conformity
was found to render expert engineers unable to think beyond hints and examples to generate novel
MEMORY AND CREATIVE COGNITION
12
designs (Linsey et al., 2010). In another study (Smith et al., 1993), participants were asked to create
new toys and new animals to inhabit a foreign planet. Participants who were shown pictorial
examples prior to creation tended to conform to these examples, despite explicitly being asked to
avoid using components of the examples. Such effects hold even if the examples contain design
flaws, and participants will replicate such flaws even when explicitly instructed not to (Chrysikou
& Weisberg, 2005). Thus, prior experience can be both a cost and benefit to divergent thinking.
Episodic Memory and Convergent Thinking
Episodic memory may also influence convergent thinking skills such as problem-solving
(Roediger et al., 2007). For example, insights are typically incorporated into long-term memory,
facilitating more efficient problem-solving in the future (Holland & Gallagher, 2006; Ludmer et
al., 2011). One line of research suggests that solving RAT problems with insight necessitates a
fundamental, unconscious changes to the initial problem representation (Ohlsson, 1992; Ohlsson,
2011). A separate line of research examines how episodic false memories, or erroneously
remembering an experience that did not actually happen, interact with higher cognitive abilities
such as problem-solving. In the Deese/Roediger-McDermott (DRM) paradigm, for example,
participants are given word lists (e.g., bed, rest, awake) whose members are all associates of an
unpresented critical lure (e.g., sleep). Despite having never been presented during the study phase,
participants often falsely remember the critical lure as being presented in the list (Deese, 1959;
Roediger & McDermott, 1995). Of relevance to creativity, researchers primed RAT performance
with a preceding DRM list whose critical lure was also the solution to one of the RAT problems
(Howe et al., 2010). They found that when the critical lure was falsely recalled in the DRM, RAT
problems were solved more often and faster than when problems were not primed. Critically, there
were no differences between primed and unprimed RAT problem solution rates and reaction times
when the critical lure was not falsely recalled. These results demonstrate that episodic false
memory can influence performance on creative problem-solving tasks.
On the other hand, previous experiences can negatively influence how one solves a single-
solution problem. For example, in the classic “water jug problem” (the Einstellung effect; Luchins,
1942), participants were required to take jugs filled with water and find a sequence of pouring that
would produce a prespecified amount of water in each jug. After the researchers performed a
demonstration, participants would continuously attempt to use the solution they saw demonstrated,
even when it was not practical. Such experience-induced inflexibility can become an even greater
MEMORY AND CREATIVE COGNITION
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problem when someone is an expert in a topic: coming up with new ideas may be challenging
simply because one knows how things should be done (De Bono, 1968). Further, even if one
generates a solution via insight, memory for the solution decays (Ormerod et al., 2002). Thus,
moving beyond previous experiences stored in memory appears to be important for creatively
solving single-solution problems.
Explicit Short-Term Memory and Creative Cognition
Working Memory and Divergent Thinking
A longstanding question in the creativity literature concerns the role of attention control
via working memory in creative thought. Does creative thinking require focused attention, or rather
a relaxation of attention control? The controlled-attention theory of working memory (Engle,
2002) suggests working memory capacity contributes to higher order cognition, such as language
comprehension (King & Just, 1991) and reasoning (Kyllonen, 1996). On this view, attentional
control is critical to facilitating more efficient maintenance of task-relevant information in working
memory (Drabant et al., 2006), efficient switching between tasks (Baddeley et al., 2001), and
sustaining general attention (Unsworth & Engle, 2009)—abilities strongly related to fluid
intelligence, or the ability to solve novel problems (Unsworth et al., 2014). Although working
memory plays a critical role in such cognitive abilities, the contribution of working memory to
divergent thinking is less clear.
The controlled attention theory has been adopted by some creativity researchers. According
to this theory, attention control facilitates divergent thinking by directing search processes away
from strong, common associates (Beaty et al., 2014; Benedek et al., 2014; Jauk et al., 2013). In
other words, controlled attentional processes may intervene in an otherwise spontaneous process
of spreading activation within semantic memory networks by suppressing unoriginal mnemonic
information (Frith et al., 2021). Additional findings suggest working memory capacity supports
divergent thinking through cognitive persistence, or sustained task-relevant processing that is
robust to proactive interference (De Dreu et al., 2012). In addition, because creativity appears to
involve pulling concepts from long term-memory into working memory, which are then
manipulated to find a solution, working memory may allow for the discrimination of task-relevant
and -irrelevant information (De Dreu et al., 2012; Unsworth & Engle, 2009). Together, working
memory has been hypothesized to benefit divergent thinking through attentional control
mechanisms that manage and direct complex search processes.
MEMORY AND CREATIVE COGNITION
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A number of individual differences studies have investigated links between working
memory, executive functions, and divergent thinking (e.g., Beck et al., 2016; Lee & Therriault,
2013; Menashe et al., 2020; Vartanian et al., 2013). In one study on divergent thinking, researchers
(Benedek, Jauk, Sommer, et al., 2014) examined three executive functions utilized within working
memory: shifting, updating, and inhibition. They found shifting (switching between different tasks
and mental sets) did not relate to divergent thinking, but updating (monitoring and revising
working memory content) and inhibition (suppressing dominant but irrelevant response
tendencies) showed significant and positive associations with divergent thinking originality. The
researchers concluded working memory (as assessed by updating tasks) uniquely contributes to
divergent thinking (Benedek, Jauk, Sommer, et al., 2014). On the other hand, several studies found
no specific relationship between working memory capacity and divergent thinking tasks (Furley
& Memmert, 2015; Smeekens & Kane, 2016). Still others maintain top-down control actually
harms divergent thinking because it restricts mind wandering, which can sometimes facilitate
creativity, particularly during incubation periods (Gable et al., 2019; Leszczynski et al., 2017).
These conflicting findings motivate a meta-analytic investigation into the relationship between
working memory capacity and divergent thinking.
Working Memory and Convergent Thinking
Because working memory involves the storing and processing of information online
(Baddeley, 1986), and given its strong association with novel problem solving (i.e., fluid
intelligence; Shelton et al., 2010), one might assume working memory is relevant for creative
problem-solving (Ericsson & Simon, 1998). Notably, although early reports examining working
memory’s benefit to convergent thinking were mixed (see Wiley & Jarosz, 2012 for a review),
recent studies point to a stronger relationship between the two constructs (Chein & Weisberg,
2014; Chuderski & Jastrzębski, 2018; Lee et al., 2014). The executive-attention framework
suggests maintaining information in working memory is critical to success across higher-order
cognitive domains by sustaining attentional focus in the face of distraction (Kane & Engle, 2002).
In the context of convergent problem-solving, working memory is hypothesized to help focus
attention, narrow search through a problem space, and inhibit distractions. This ability may be
particularly useful, since such problems typically yield initial failed attempts at finding a solution,
requiring subsequent iterative attempts to more remote solutions. For example, when reaching an
impasse in solving, one might make incremental modifications by back-tracking and
MEMORY AND CREATIVE COGNITION
15
systematically searching semantic space. This search process, enabled in part by working memory,
ultimately results in solutions that are generally weakly related to their initial representations and
hence more creative (Kaplan & Simon, 1990).
The Present Study
Decades of research has sought to characterize the relationship between memory and
creativity. And yet, the field remains marked by inconsistent findings, with no clear view on which
memory systems reliably support creative cognition. For the field to progress, a systematic analysis
of the literature is necessary. Despite longstanding interest in the topic of memory and creativity,
to our knowledge, only two book chapters have provided qualitative overviews (Nęcka, 1999;
Stein, 1989). Critically, no attempt has been made to quantitatively summarize the memory-
creativity relationship. Here, we conduct a systematic meta-analysis to synthesize and quantify the
overall association between memory and creative cognition. Across 50 years of research, we aim
to clarify the strength and direction of the memory-creativity relationship. We also examine
whether any relationship is affected by memory system (episodic, semantic, short-term, working)
and creativity type (convergent, divergent); we also examine whether other study-specific factors
affect the strength of the relationship, such as stimulus modality of response (visual (e.g., drawing,
selecting shapes), verbal (e.g., writing words, selecting multiple choice)) and participant age.
In our view, the psychology of creativity has matured to a point where a quantitative review
of memory and creativity is warranted and necessary. There is now a critical mass of data available
to reliably assess the memory-creativity relationship, and clarifying this association is critical to
resolving persistent controversies in the field. Therefore, in this meta-analysis, we sought to
answer the following questions:
1. What is the general relationship between memory and creative cognition (across all types
of memory and creativity)?
2. If a general relationship between memory and creative cognition exists, is the summary
effect size between constructs influenced by the type of memory (e.g., semantic) or
creativity (e.g., divergent thinking)?
3. If a general relationship between memory and creative cognition exists, is the summary
effect size influenced by study-specific factors, like age or stimulus modality (verbal versus
visual)?
MEMORY AND CREATIVE COGNITION
16
For transparency, we will first define and operationalize all variables to be used in
subsequent analyses. Semantic memory will refer to a person’s capacity to remember facts,
meanings, and general knowledge about the world, including comprehension of characteristic item
properties and the semantic labels used to describe them (Barsalou, 2003; Menon et al., 2002;
Patterson et al., 2007; Quillan, 1966; Smith, 1978; Squire & Zola, 1998; Tulving, 1972).
Specifically, semantic memory concerns representing and retrieving, or mentally operating on,
stored information about the world that is abstracted from episodic experiences and is describable
(e.g., not present to the senses; Barsalou, 2003; Menon et al., 2002; Patterson et al., 2007; Smith,
1978; Tulving, 1972). Common semantic memory tasks (Saumier & Chertkow, 2002) emphasize
retrieving as many items as possible related to a specific cue (e.g., fluency tasks) and are typically
scored by the total number of valid, unique responses provided.
Episodic memory will refer to the ability to remember personally experienced events
(Baddeley, 2001; Craik, 2002; Ezzyat & Davachi, 2011; Hassabis & Maguire, 2007; Squire &
Zola, 1998; Tulving, 1972, 1983, 1993, 2002). Specifically, episodic memory receives and stores
spatial and temporal information (and spatial-temporal relationships) among events experienced
between event boundaries for later retrieval (Hassabis & Maguire, 2007; Squire & Zola, 1998;
Tulving, 1972, 1983, 1993, 2002). Episodic memories can be recalled, which is when the memory
of stimulus items is evaluated without the presence of the to-be-remembered information available
(e.g., “Tell me all the words you remember”), or recognized, when memory evaluation of stimulus
items occurs in the presence of the to-be-remembered items (e.g., “Did you see this word
previously?”; Tulving & Thomson, 1973). Common episodic memory tasks involve encoding
stimuli (e.g., word lists, picture presentations) and a later recognition or recall phase. While these
tasks can be scored on several types of metrics, we will focus on veridical memory “hits”, which
represents how much information during encoding was accurately remembered during retrieval.
Working memory will refer to the ability to maintain and manipulate a limited amount of
information held in a highly accessible mental state (Cowan, 2008). Although not completely
distinct from short-term memory, it is thought to uniquely function as an interface between
perception, long-term memory, and action (Aben et al., 2012; Andrade, 2001; Baddeley, 2003;
Baddeley & Hitch, 1974; Conway et al., 2007; Miyake & Shah, 1999). Recalling information from
working memory requires engaging in an activity interleaved between the presentation of to-be-
remembered information and recall (Unsworth & Engle, 2007). Working memory tasks involves
MEMORY AND CREATIVE COGNITION
17
the simultaneous demands of short, uninterrupted sequences of information for immediate recall
(e.g., assessed via backwards digit span, complex span, n-back tasks) and are scored on the correct
recognition or recall of one set of information.
Short-term memory will refer to the ability to temporarily hold and recall a limited amount
of information in a highly accessible mental state, including sensory events, movements, and
information from long-term memory (R. C. Atkinson & Shiffrin, 1971; Cowan, 1988, 2008; Kail
& Hall, 2001). Short-term memory tasks often involve the presentation of short, uninterrupted
sequences of information for immediate recall or recognition (e.g., serial recall tasks such as
forwards digit span) and are commonly scored on the number or length of correctly recalled or
recognized consecutively presented sequences of information.
Divergent creative thinking will refer to the ability to solve open-ended problems with
multiple solutions (Guilford, 1950). This ability is often tested with ill-defined problems, where
multiple solutions are often requested (Mumford & Gustafson, 1988) and are traditionally scored
on the number of ideas generated (fluency; Runco et al., 2011), the variety of ideas across
categories or themes (flexibility; Guilford, 1968; Runco & Okuda, 1991), and the originality
(statistical infrequency or quality) of an idea (Wallach & Kogan, 1965), though labels may differ
by researcher. Common divergent thinking tasks (e.g., Alternate/Unusual Uses Task, Torrance
Test of Creative Thinking) are scored by fluency, originality, flexibility, cleverness, or uniqueness,
variably operationalized by different researchers of the original studies presented here.
Convergent creative thinking is the ability to solve problems with only one correct solution
(Runco et al., 2010). Convergent thinking tasks can be solved either by analysis or insight.
Analysis is the deliberate search of a problem space to find solutions (Ericsson & Simon, 1998;
Kounios et al., 1987; Newell & Simon, 1972), whereas with insight, a solution emerges
spontaneously into awareness (i.e., the “aha” experience; Metcalfe & Wiebe, 1987; Smith &
Kounios, 1996). Common convergent creative thinking tasks (e.g., Remote Associates Test, classic
insight problems) are measured by summing the number of problems correctly solved.
Method
Power Analysis
To determine the feasibility of this meta-analysis, we conducted an a priori power analysis
using the R package metapower version 0.2.0 (Griffin, 2020). Based on the current state of the
MEMORY AND CREATIVE COGNITION
18
literature, we expected that 40 studies would meet inclusion criteria with an average study size of
100 and moderate-large heterogeneity among effect sizes. Overall, we expected that the correlation
between measures of creative cognition and memory would be small (i.e., r = .25). Under these
expectations, power to detect a statistically significant summary effect size was 100%. For two-
group moderator analysis (divergent vs. convergent thinking), power to detect group differences
was 90.2%. Finally, for three-group moderator analysis by age (children, adult, and older adults),
power to detect group differences was 59%. Since power for subgroups is generally low for meta-
analyses, we had no stopping rules and intended to include as many studies as possible to generate
a representative dataset of the relevant literature (see Cuijpers et al., 2021; Griffin, 2021).
Literature Search
Figure 1. PRISMA flow diagram illustrating study identification, screening, and selection processes. Blue boxes = records
interrogated for inclusion; Red boxes = excluded records; Green boxes = included in meta-analysis.
With the aim of adhering to transparent and rigorous psychological practices (Johnson,
2021), we identified, screened, and determined eligibility of empirical studies in accordance with
all Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA; (Page et al.,
MEMORY AND CREATIVE COGNITION
19
2021) guidelines, including the content checklist (see Supplemental File 1) and study search flow
diagram. On April 21, 2020, we conducted searches using the online databases PsycINFO,
PubMed, Web of Science, and Scopus (Bramer, 2017). Each search contained the following terms
and Boolean operators: ((memory OR recollection OR familiarity OR recognition OR recall OR
retrieval OR “verbal fluency”) AND (creativity OR creative OR “divergent thinking” OR
“convergent thinking” OR “idea generation” OR originality)). We collected peer-reviewed
published/in-press research articles, preprints (e.g., unreviewed work posted to PsyArxiv, bioRxiv,
etc.), and dissertations/theses. Additionally, we solicited relevant unpublished datasets using topic-
relevant listservs and Twitter to reduce selective reporting bias. From these hits, we first removed
duplicates, then applied our inclusion criteria in a sequential three-step screening procedure: title
only screening, title and abstract screening, and full-text review. Finally, we conducted a manual
reference screen by extracting the references of all articles meeting inclusion criteria from the
database search and applied the same three step screening procedure. The entire study
identification, screening, and eligibility process is shown in Figure 1. The review and protocol for
this study were unregistered and were considered exempt by the XXX Institutional Review Board.
Study Selection
Inclusion and Exclusion Criteria
For inclusion, articles must have (1) administered at least one direct, observable measure
of memory and one direct, observable measure of creativity; (2) administered memory and
creativity tasks that reflect semantic memory, episodic memory, working memory, short-term
memory, divergent creative thinking, and/or convergent creative thinking; (3) reported the
correlation coefficient between memory and creativity performance (or information to calculate
the correlation coefficient); (4) been published in English, and (5) included a neurotypical sample.
Articles were excluded if (1) memory or creativity tasks were measured via self-report or
interview; (2) performance could not be verified by a researcher, as is the case for tasks reflecting
autobiographical memory, dream recall, prospective memory, etc.; and (3) the creativity task was
domain-specific (e.g., musical).
Database Search
The searches from PsycINFO, PubMed, Web of Science, and Scopus were concatenated,
and all duplicates were removed. All titles and abstracts were then screened for inclusion. This
step was completed such that records were excluded only if there were clear examples of exclusion,
MEMORY AND CREATIVE COGNITION
20
such as the full-text record was not available, or it was non-empirical. After this, full-text articles
were screened for the aforementioned primary inclusion and exclusion components. We used a
double-screening approach where the first author and a research assistant completed all screening
steps independently of each other. Inclusion disagreements were resolved by the second author.
Data Extraction
For each included study, we extracted values for the sample size and correlation
coefficients between measures of creativity and veridical memory. Specifically, we extracted data
computed from raw scores (e.g., summative totals) on semantic memory and convergent thinking
tasks; recall or recognition for episodic, short-term, or working memory tasks; and fluency,
originality, flexibility, cleverness, or uniqueness for divergent thinking tasks—all operationalized
by the researchers of the original study.
When access to the raw data were provided (5 unpublished datasets), we calculated the
correlation coefficient manually. Since meta-analysis of correlation coefficients are performed on
Fisher’s r-to-z transformation scores, we used the metafor package (Viechtbauer, 2010) to convert
all study-specific correlation coefficients to Fisher’s z scores with the respective variances. Many
studies included more than one effect size that met inclusion criteria. To maximize and include as
much data as possible, we accounted for this multilevel structure by coding each study and unique
effect size.
Moderator Variables. To evaluate the influence of age-related variation among effect
sizes, we extracted participant information on age (in years) by categorizing each study.
Specifically, we categorized age group based on the average sample age into children (4-10),
adolescents (10-17), young adults (18-30), and older adults (60+). There were not enough data to
test an infant or middle-aged adult groups. Additionally, to assess the influence of methodological
variation among effect sizes, we extracted information related to the type of memory (semantic
memory, episodic memory, short-term memory, working memory) and creativity (divergent
thinking, convergent thinking), as well as the paradigm modality used to measure memory and
creativity (i.e., verbal, visual).
Statistical Analysis
We used the metafor package version 2.40 for the statistical software R to analyze all data
(R Core Team, 2020; Viechtbauer, 2010). To estimate a summary effect size for the correlation
between memory and creative cognition, we fit a three-level model to partition the sampling
MEMORY AND CREATIVE COGNITION
21
variance of the observed effect sizes, between-study variability, and within-study variability.
Unlike traditional univariate meta-analysis, this modeling approach allows for the inclusion of
multiple effect sizes per study, while accounting for the interdependency among effect sizes within
studies (Assink & Wibbelink, 2016; Cheung, 2014, 2019). To determine if there was significant
between-study heterogeneity among effect sizes, we evaluated Cochran’s Q and calculated the
percentage of variation across studies that is not due to sampling variability (i.e., I2), with values
of 75%, 50%, and 25% reflecting large, moderate, and small degrees of heterogeneity (Higgins &
Thompson, 2002). In the presence of moderate-large heterogeneity, we evaluated potential sources
of heterogeneity through moderator analyses and sensitivity to small-study effects (e.g.,
publication bias).
Moderator Analyses
We extended the three-level model to evaluate the influence of sample- and
methodological-related study characteristics, including sample age, type of memory, type of
creativity, and paradigm modality. Since some studies did not report information for all moderator
variables, we evaluated the influence of each moderator separately to maximize statistical power
(Assink & Wibbelink, 2016; Cheung, 2014; Viechtbauer, 2010). Lastly, we only included a
moderator category if there was a substantive cluster (k > 5).
Sensitivity to Small-Study Effects
To evaluate the sensitivity of meta-analyses to small-study effects, visual inspection of
funnel plot asymmetry and the Egger’s regression test are standard methods for evaluating the
potential presence of publication bias (Egger et al., 1997). However, visual inspection is subjective
and the Egger’s regression test is not appropriate for multilevel data (Nakagawa & Santos, 2012).
Therefore, to evaluate sensitivity of our meta-analytic estimates to small-study effects, we
objectively evaluated funnel plot asymmetry by regressing our summary effect size estimate onto
the study-specific standard errors. This method is conceptually identical to the Egger’s regression
test, but preserves the multilevel form (Nakagawa & Santos, 2012; see also Griffin et al., 2021).
In the presence of statistically significant funnel plot asymmetry, we planned to conduct sensitivity
analysis by excluding effect sizes that contributed to funnel plot asymmetry. Finally, to evaluate
the potential risk of bias due to unpublished results, we evaluated the summary effect size with
and without published data.
Results
MEMORY AND CREATIVE COGNITION
22
Study Selection
The full study identification, screening, and selection process are displayed in Figure 1. In
total, we included 525 effect sizes from 79 unique empirical articles and unpublished datasets
(indicated by the studies listed in Table 1 (presented at the end of the document) and marked with
an asterisk (*) in the Reference section) representing data from 12,846 individual participants. On
average, each study included 6.65 effect sizes (SD = 9.45). Overall, the average study sample age
was 22.09 (SD = 11.05), with 49 effect sizes from children, 24 effect sizes from adolescents, 415
effect sizes from young adults, and 12 effect sizes from older adults. Table 1 reports the included
studies’ sample sizes, study characteristics, participant demographics, memory type, creativity
type, and paradigm modality See Figure 2 for the number of studies by publication year for effect
sizes included in this meta-analysis.
Summary Effect Size
Figure 2. Number of studies by publication year for effect sizes included in this meta-analysis.
MEMORY AND CREATIVE COGNITION
23
Overall, the effect size between creativity and memory was statistically significant and
small in magnitude (r = .19, se = .02, 95% CI [.15, .22], p < .0001). Consistent with our choice of
a random-effects model, we also observed considerable heterogeneity among effect sizes (Q =
1897.64, I2 = 79.41%, p < .0001). Specifically, our three-level model revealed that 20.59% of the
total variance was attributed to sampling variability, 19.96% was attributed to variation among
effect sizes of the same study, and 59.44% was attributed to between-study variability. We
evaluated potential sources of this heterogeneity with moderator analysis.
Moderator Analyses
Figure 3. The top panel displays the summary effect sizes for each Memory Type (Short-term Memory, Working Memory,
Episodic Memory, Semantic Memory) as function of Creativity Type (Divergent Thinking, Convergent Thinking). The bottom
panel displays the summary effect sizes for Verbal and Visual memory as a function of Creativity Modality (Visual, Verbal).
Points reflect point estimates and error bars reflect 95% confidence intervals. k = number of effects contributing to summary
effect size estimates. Gray vertical bands in each panel represents a summary effect size confidence interval at 95%. * = p < .05;
*** = p < .001.
Memory Type
While all memory types (semantic, episodic, short-term, and working memory) were
related to creative cognition, the magnitude varied by type. Specifically, the summary effect size
was strongly moderated by memory type (Q = 33.56, p < .0001). The largest correlation was found
between creative cognition and semantic memory (r = .25), followed by working memory (r =
.17), episodic memory (r = .16), and short-term memory (r = .15). Statistically, the correlation
between memory and creative cognition was significantly larger for semantic memory compared
MEMORY AND CREATIVE COGNITION
24
to all other memory types, including working memory (b = -.08, se = .01, 95% CI[-.11, -.05], p <
.0001), episodic memory (b = -.09, se = .03, 95% CI[-.15, -.03], p = .002), and short-term memory
(b = -.10, se = .03, 95% CI[-.16, -.04], p = .003; see Figure 3 and Table 2). The correlation between
semantic memory and creative cognition was not different across memory paradigm modality
(verbal vs. visual; b = .34, se = .20, 95% CI[-.04, .72], p = .09).
Table 2. Summary effect size estimates for each Memory and Creativity Type.
Creativity Type
Memory Type
#
Studies
# Effect
sizes
N
r
se
Lower
Upper
p
Divergent Thinking
Working memory
29
176
4601
0.09
0.01
0.07
0.1
< .001
Short-term memory
13
45
3095
0.16
0.01
0.14
0.19
< .001
Episodic memory
15
47
3076
0.12
0.01
0.09
0.15
< .001
Semantic memory
21
138
3506
0.2
0.01
0.19
0.22
< .001
Convergent Thinking
Working memory
14
57
2140
0.16
0.01
0.14
0.18
< .001
Short-term memory
5
17
2049
0.12
0.01
0.1
0.15
< .001
Episodic memory
6
10
1828
0.17
0.02
0.13
0.22
< .001
Semantic memory
5
21
720
0.21
0.02
0.18
0.24
< .001
Note. k = number of effect sizes, N = number of participants, r = summary effect size, se = standard
error; Lower and Upper are 95% confidence intervals
Creativity Type
Similarly, the summary effect size was moderated by creativity type (Q = 16.74, p < .0001).
Specifically, the correlation between creative cognition and memory was different for convergent
(r = .23) compared to divergent (r = .17) creativity tasks (b = -.06, se = .02, 95% CI[-.09, -.03], p
< .0001). Since divergent thinking tasks evaluate numerous dimensions of creativity (e.g., fluency,
originality, flexibility, cleverness, uniqueness), we also tested for moderation among these
dimensions. We did not find evidence of significant variation across effect size metrics (Q = 3.96,
p = .78).
Memory Type as a Function of Creativity Type
We also evaluated whether the summary effect size between creative cognition and
memory was conditional on whether the creativity task was measuring either convergent or
divergent thinking. We found that working memory was more strongly correlated with convergent
thinking (r = 0.23) than divergent thinking (r = 0.15), though we did not find a significant
Creativity Type x Memory Type interaction (Q = 5.52, p = .14).
Age Group
MEMORY AND CREATIVE COGNITION
25
Overall, the summary effect size was not moderated by age group (Q = 7.31, p = .06).
However, pairwise comparisons show that the effect size was different between children and
young adult samples (b = -.13, se = .06, 95% CI [-.24, -.01], p = .03). The correlation between
memory and creative cognition was lower in young adults (r = .17) compared to children (r = .29).
Paradigm Modality
The summary effect size was not moderated by paradigm modality of the memory task (Q
= 3.15, p = .08). Overall, the summary effect size was similar for visual compared to verbal
memory tasks (b = -.03, se = .02, 95% CI [-.06, -.003], p = .08). Similarly, the summary effect size
was not different for visual and verbal creativity tasks (b = -.002, se = .02, 95% CI [-.04, .03], p =
.86). These findings were qualified by a two-way interaction (Q = 13.15, p = .0003). Specifically,
within visual creativity, the relationship with visual memory was greater than that of verbal
memory, but within verbal creativity, the relationship with verbal memory was greater than that of
visual memory. See Figure 3 for a visualization of these results.
Sensitivity to Small-Study Effects
Figure 4. Funnel plot displaying the summary effect size estimates as a function of precision (i.e., standard error). The funnel plot
is centered on the overall summary effect size for all effect sizes (k = 564) indicated by the vertical black line (r = .19).
We evaluated the influence of small-study effects on the overall summary estimate using a
modified Egger’s regression that preserves the multilevel structure of the data to quantify funnel
plot asymmetry (Nakagawa & Santos, 2012). Overall, we found no evidence of significant plot
asymmetry (b = -.11, se = .37, 95% CI [-.84, .61], p = .76; see Figure 4), suggesting that the results
MEMORY AND CREATIVE COGNITION
26
presented here were not impacted by small-study effects, including publication bias. In addition,
there were 56 effect sizes that were from unpublished studies or datasets. We found that without
these effect sizes, the overall summary effect was virtually identical (r = .19, se = .02, 95% CI
[.16, .22], p < .0001).
Discussion
The present meta-analysis sought to quantitatively summarize the relationship between
memory and creative cognition. To our knowledge, this is the first quantitative attempt to
summarize over 50 years of research in the literature. Overall, we found a small positive correlation
between memory and creative cognition. Importantly, this association varied as a function of the
type of memory and creative thinking under investigation. Semantic memory shared the largest
relationship with general overall creative cognition, convergent thinking shared a stronger
relationship with general memory, and working memory was more strongly correlated with
convergent than divergent thinking. Reliable effects were not observed for episodic or short-term
memory, across both types of creative cognition. Moreover, within visual creativity, the
relationship with visual memory was greater than that of verbal memory, but within verbal
creativity, the relationship with verbal memory was greater than that of visual memory. Our
findings thus provide insight into the role of memory in creative thinking, addressing a
longstanding question in the psychology of creativity regarding the relative importance of specific
memory systems for creative cognition.
The general relationship between memory and creative cognition
Despite decades of research describing the relationship between memory and creativity, a
consensus on the strength and direction of this relationship has never been reached. We synthesized
525 effect sizes from 79 unique empirical studies to quantitatively summarize the overall
association (correlation) between memory and creative cognition. We found that better memory—
averaged across semantic, episodic, working, and short-term memory—is related to higher
creativity (collapsed across divergent and convergent thinking tasks). This suggests memory
systems reliably support creative cognition. In addition, the magnitude of the effect size (r = .19)
suggests memory and creativity have modest similarities, with substantial variance in creative
ability left unexplained by memory ability alone. Additionally, follow up analyses showed that the
modesty of the correlation can be attributed to the variance of memory and creativity type.
MEMORY AND CREATIVE COGNITION
27
Impact of memory and creativity type on the relationship between memory and creative
cognition
Semantic memory showed the largest correlation with creative cognition compared to all
other memory types (episodic, working, and short-term). This finding provides some support for
the classic associative theory (Mednick, 1962), which first suggested creativity involves
connecting weakly related, remote concepts. However, given that semantic memory was primarily
assessed with tasks involving goal-directed retrieval (e.g., verbal fluency), our findings are perhaps
more consistent with a growing literature emphasizing the roles of strategic semantic retrieval
ability in creative cognition (Avitia & Kaufman, 2014; Forthmann et al., 2019; Silvia et al., 2013).
Importantly, the meta-analytic effect of semantic memory on creative performance was not
moderated by creativity modality (verbal vs. visuospatial) and it was consistent across creativity
type (divergent and convergent), indicating that semantic memory’s role in creative cognition is
broad and not limited to verbal tasks only. Thus, the ability to retrieve items from long-term
memory reliably predicts creative performance across a diverse range of tasks. This result suggests
that semantic memory is a cognitive system fundamentally supporting people’s ability to think
creatively.
Analyses also revealed that working memory was more strongly related to convergent than
divergent thinking. While the effect size was modest, this finding is in line with previous work
raising questions about whether convergent creative thinking tasks (such as the RAT) are measures
of creativity or intelligence (Lee & Therriault, 2013). Recently, several latent variable studies have
reported large correlations between RAT performance and working memory capacity, as well as
other cognitive abilities such as fluid and crystallized intelligence (Chein & Weisberg, 2014; Lee
& Therriault, 2013). Importantly, however, our meta-analysis could not disentangle the roles of
insight vs. analytical problem solving, which have been shown to differentially relate to working
memory (i.e., stronger working memory associations for analytical over insight; Fleck, 2008).
Nevertheless, to the extent that the RAT and other convergent thinking tasks index creative
thought, our meta-analytic finding emphasizes the importance of cognitive control processes that
allow people to maintain and manipulate multiple items from memory in an active state to solve
complex creative problems (Benedek et al., 2014).
Notably, compared to convergent thinking, the contribution of working memory to
divergent thinking was smaller, despite this being the most well-powered comparison in the
MEMORY AND CREATIVE COGNITION
28
analysis, with 179 effect sizes reported in the literature. On the one hand, relatively weaker
relationship between working memory and divergent thinking may raise questions about the
executive nature of divergent creativity; on the other hand, the finding may call for increased
specificity in the field. Perhaps some executive functions relate to divergent thinking more strongly
than others (c.f., Benedek et al., 2014). In other words, although working memory is a broad
construct that tends to correlate with other higher cognitive abilities that relate to divergent
thinking (e.g., fluid intelligence), the specific ability to update items in working memory—as
indexed by complex span tasks—appears to be less relevant to divergent creativity compared to
convergent thinking.
We also found that episodic memory was associated with divergent thinking to a weaker
degree than that of semantic memory. Although a growing number of studies have found a
contribution of the episodic system to divergent thinking, via an experimental manipulation that
boosts episodic memory known as the episodic specificity induction (Madore et al., 2014), our
meta-analytic results do not support a strong relationship between episodic memory ability and
divergent thinking. One possibility is that episodic memory can support divergent thinking in a
state-dependent manner. That is, experimentally activating the episodic system may temporarily
boost some aspects of divergent thinking, but trait-level episodic memory ability may not impact
people’s ability to think divergently. Further meta-analytic work is needed, however, before such
conclusions can be made.
Impacts of paradigm modality and age on the relationship between memory and creative
cognition
Our results showed that the summary effect size was similar for visual compared to verbal
memory tasks and for visual and verbal creativity tasks. However, a difference was found between
memory modalities for verbal compared to visual creativity tasks with respect to direction.
Specifically, within visual creativity, the relationship with visual memory was greater than that of
verbal memory, but within verbal creativity, the relationship with verbal memory was greater than
that of visual memory. This indicates that whether the task involves verbal or visual stimuli impacts
relationships between memory and creative thinking. Although our analysis did not detect a
visual/verbal difference for creativity tasks, our finding for verbal memory highlights a
consideration for future research on memory and creativity. Specifically, prior work demonstrates
functional fixedness may be differentially induced depending on stimulus modality, such as
MEMORY AND CREATIVE COGNITION
29
whether people are presented pictures or words in a divergent thinking task (Chrysikou et al.,
2016). The Matched Filter Hypothesis contends task demands influence the level of cognitive
control required to complete the task (Chrysikou, 2019). In this context, stimulus modality can
bias retrieval strategy during divergent thinking, either towards top-down (visual, e.g., pictures) or
bottom-up (verbal, e.g., words), which has implications for both the type of memory engaged and
the level of cognitive control required. We encourage future researchers to carefully consider
stimulus modality and other task parameters when designing cognitive experiments on creativity
to avoid unintentional confounds in their data.
Finally, the summary effect size between memory and creativity was not moderated by
age. Most memory types (Schneider & Pressley, 2013) and performance on convergent thinking
abilities (Kleibeuker et al., 2013) continue to develop well into young adulthood. However,
divergent thinking does not follow a linear developmental pattern. For example, fluency and
flexibility are already well-developed by adolescence, and in one study, adolescents excelled on a
visuospatial divergent thinking task compared to other older individuals (age range: 10-30;
Kleibeuker et al., 2013). There are also reports of slumps and jumps in creative abilities as a
function of age/school grade (Claxton et al., 2005; Saggar et al., 2019; Said-Metwaly et al., 2021;
Torrance, 1962). An alternate speculative interpretation of our finding could be that children rely
more on memory because their executive functions are less developed than younger adults aged
18-30 (Schneider & Pressley, 2013). After young adulthood, one model suggests a general decline
in creative potential as a function of old age (e.g., over age 60) due to changing underlying
cognitive processes (Simonton, 1984). Indeed, with the exception of semantic memory (Bäckman
& Nilsson, 1996), there is a general decline in cognitive abilities, particularly in the memory
domain, around the time one transitions from middle age to older age (Josefsson et al., 2012; Olaya
et al., 2017). Empirical work on creativity and aging has primarily focused on divergent thinking,
producing mixed results. Some findings suggest creativity is maintained into older adulthood
(Addis et al., 2016; Foos & Boone, 2008; Palmiero et al., 2014), perhaps related to preserved
crystallized intelligence (Palmiero et al., 2014). Others findings suggest aging is marked with a
reduction of fluency and originality (Alpaugh & Birren, 1977), with this deficit first present in
middle adulthood (Reese et al., 2001). Coupling this prior research with the findings from our
meta-analysis, individuals may differentially rely on memory for creative thinking across the
MEMORY AND CREATIVE COGNITION
30
lifespan, and this inconsistency may be too subtle to detect with binned age categories, as was
done in the current study.
Creative cognition and the cognitive control of memory
Taken together, the current findings emphasize the central importance of cognitive control
to creative cognition. Specifically, we found semantic memory—assessed primarily by verbal
fluency tasks, which require controlled semantic retrieval—consistently predicted performance on
both divergent and convergent creative thinking tasks. After semantic memory, we found working
memory to be the second strongest predictor of convergent thinking, pointing to the role of
controlled attention (Kane & Engle, 2002). Here, we explore the implications of these meta-
analytic results in the context of the ongoing debate on the role of cognitive control in creative
thought.
Longstanding theories in the creativity literature emphasized the role of unconscious
processes in creative cognition (see Abraham, 2018; Campbell, 1960; Martindale, 2007; Mednick,
1962; Mendelsohn, 1976; Wallas, 1926), particularly with respect to insight problem solving. On
this view, cognitive control plays a minimal role—and in some cases, even a detrimental role—in
solving creative problems. In a similar vein, the Blind Variation and Selective Retention (BVSR)
theory of creativity (Campbell, 1960; Simonton, 2011) posits that creative idea generation is
largely spontaneous and unpredictable (i.e., blind). Likewise, several theories propose that
cognitive disinhibition (or defocused attention) supports creative performance by “releasing”
attentional control (Martindale, 2007; Mendelsohn, 1974), allowing diffuse semantic activation
and extraneous sensory information to be entertained when thinking creatively (Zabelina et al.,
2016).
On the other hand, more recently, researchers have begun to theorize about how cognitive
control may support creative cognition, particularly in light of evidence linking the two cognitive
abilities (Silvia, 2015). For example, studies linking divergent thinking to facets of intelligence,
such as verbal fluency (or broad retrieval ability, Gr), have informed the view that divergent
thinking in part relies on controlled retrieval from long-term memory (Avitia & Kaufman, 2014;
Forthmann et al., 2019; Silvia et al., 2013). Common verbal fluency tasks require participants to
retrieve specific exemplars from memory, such as category fluency tasks (e.g., foods, animals,
etc.) and phonemic fluency (e.g., words that start with the letters F, A, or S). Verbal fluency is
considered a canonical task of cognitive control: performance reliable engages prefrontal brain
MEMORY AND CREATIVE COGNITION
31
regions, particularly the inferior frontal gyrus (Costafreda et al., 2006; Hirshorn & Thompson-
Schill, 2006; Phelps et al., 1997; Schlösser et al., 1998). Verbal fluency is thought to require
selective and goal-directed memory retrieval mechanisms, such as generating and maintaining
search cues (Unsworth et al., 2011).
In the context of divergent thinking, and given evidence linking verbal fluency to divergent
thinking, researchers have theorized that similar selective retrieval mechanisms contribute to
divergent thinking performance. Although the goals of verbal fluency and divergent thinking tasks
differ in terms of what is to-be-retrieved from memory, i.e., typical vs. atypical exemplars, how
information is retrieved from long-term memory may be at least partly similar with respect to
controlled retrieval mechanisms (e.g., maintaining a retrieval cue in mind while strategically
searching memory for candidate responses). Despite commonalties, however, selection demands
may be even higher for divergent thinking, particularly when many salient and unoriginal items
become activated during search. Of course, elaborative processing, beyond simply retrieving
information from memory, is required to formulate creative ideas, which may require more or less
controlled aspects of cognition. The current meta-analysis could not provide such mechanistic
insight into specific cognitive subprocesses of divergent thinking (e.g., generating vs. evaluating
ideas), but we see this as a fruitful direction for future research, with an eye toward dissociating
contributions of controlled vs. spontaneous semantic retrieval, or the relative roles of semantic
search processes vs. the semantic network structure (Kenett & Hills, 2022).
Regarding convergent thinking, cognitive control may likewise support performance on
tasks such as the RAT, particularly when participants solve problems analytically (compared to
insightfully). Given large correlations between performance on classic convergent thinking tasks
like the RAT and cognitive ability—including large latent correlations between convergent
creative thinking tasks and WMC (Chuderski & Jastrzębski, 2018)—researchers have recently
raised the question of whether convergent creativity tasks actually measure creativity or rather
working memory/intelligence (Chein & Weisberg, 2014; Lee et al., 2014). The present meta-
analysis indeed supports the role of working memory in solving convergent thinking tasks.
However, it is important to mention that our analysis could not dissociate insightful vs. analytical
problem solving, which may contribute to the WMC-convergent thinking relation (Kounios &
Beeman, 2014; Salvi et al., 2016). Nevertheless, our findings clearly implicate cognitive control
(via WMC) to overall performance on classic tests of convergent creative thinking, suggesting that
MEMORY AND CREATIVE COGNITION
32
the ability to actively maintain and manipulate information in working memory is a reliable path
toward successful creative problem solving.
The current meta-analysis is partly consistent with the recently proposed minimal theory
of creative ability (MTCA; Stevenson et al., 2021). According to MTCA, individual creative
performance can be largely explained by two factors: intelligence (domain-general cognitive
ability) and expertise (domain-specific knowledge). Our meta-analysis provides support for the
first factor of MTCA, with respect to general cognitive ability (e.g., verbal fluency, working
memory), and it is aligned with another recent meta-analysis by Gerwig et al. (2021), who reported
a meta-analytic correlation between general intelligence and divergent thinking. Importantly, the
current work points more directly at the cognitive control of memory; that is, although general
cognitive control abilities (e.g., fluid intelligence) have previously been shown to support creative
cognition, our findings provide specificity on the role of cognitive control by demonstrating meta-
analytic relations between creative performance and cognitive abilities that require the control of
memory (via working memory and verbal fluency).
Limitations
Some limitations of this meta-analytic review merit attention. First, while the current
review attempted to cover a wide breadth of research, the number of studies for each memory and
creativity type included for analysis was unequal, which could impact Type I error rates. Second,
with respect to aging, we also had unbalanced numbers of participants per age group; notably, we
could not analyze age continuously—due to differences in demographic reporting across studies—
which may have limited our ability to detect any true age effects. Third, Pearson correlation
coefficients cannot capture potential non-linear effects that may exist between variables. Fourth,
regarding convergent thinking, we could not examine analytical vs. insight problem solving
separately, and prior work highlights key cognitive differences between these two modes of
solving convergent thinking problems (Kounios & Beeman, 2009, 2014), with specific
implications for semantic and working memory. Fifth, the current review focused exclusively on
explicit behaviors that were observed in a laboratory setting (i.e., psychometric creativity tests),
and not domain-specific creative performance. Future reviews could also focus on implicit,
primed, or subjective facets of creativity and memory.
Conclusion
MEMORY AND CREATIVE COGNITION
33
By aggregating over 50 years of research on memory and creativity, we provide the first
quantitative and conclusive meta-analytic evidence that memory supports creative cognition.
Collapsing across types of memory and creativity, we found a small but significant (r = .19) general
relationship between these two constructs. A closer examination of memory type revealed this
association to be driven largely by semantic memory, assessed primarily by performance on verbal
fluency tasks. Further, despite previously mixed evidence, we showed working memory capacity
is more strongly related to convergent than to divergent creative thinking. Regarding paradigm
modality, we found that within visual creativity, the relationship with visual memory was greater
than that of verbal memory, but within verbal creativity, the relationship with verbal memory was
greater than that of visual memory. Finally, there was no impact of age on the general effect size.
These findings provide clarity regarding the nature of the relationship between memory and
creative cognition—pointing to the cognitive control of memory as central to creative task
performance—and they help to resolve longstanding controversies around how, and to what extent,
specific cognitive systems support specific modes of creative thought.
MEMORY AND CREATIVE COGNITION
34
Table 1. Study characteristics (methodological- and sample-related) and effect sizes for studies included in the meta-analysis.
Study
Memory
Creativity
N
Age
Age
Group
r
se
95%
Lower
95%
Upper
Type
Modality
Type
Modality
Bentley (1966)[1]
EM
Verbal
DT
Verbal
75
NA
YA
0.11
0.12
-0.12
0.35
Bentley (1966)[2]
EM
Verbal
CT
Verbal
75
NA
YA
0.44
0.12
0.2
0.67
Pollert et al. (1969)[1]
STM
Verbal
DT
Visual
63
NA
NA
0.04
0.13
-0.21
0.29
Pollert et al. (1969)[2]
STM
Verbal
DT
Verbal
63
NA
NA
0.22
0.13
-0.03
0.48
Pollert et al. (1969)[3]
STM
Verbal
DT
Visual
63
NA
NA
0.05
0.13
-0.2
0.3
Pollert et al. (1969)[4]
STM
Verbal
DT
Verbal
63
NA
NA
0.17
0.13
-0.08
0.43
Pollert et al. (1969)[5]
STM
Verbal
DT
Visual
63
NA
NA
0.01
0.13
-0.24
0.26
Pollert et al. (1969)[6]
STM
Verbal
DT
Verbal
63
NA
NA
0.14
0.13
-0.11
0.4
Pollert et al. (1969)[7]
STM
Verbal
DT
Visual
63
NA
NA
0.04
0.13
-0.21
0.29
Pollert et al. (1969)[8]
STM
Verbal
DT
Verbal
63
NA
NA
0.11
0.13
-0.14
0.37
Pollert et al. (1969)[9]
EM
Verbal
DT
Visual
63
NA
NA
0.11
0.13
-0.14
0.37
Pollert et al. (1969)[10]
EM
Verbal
DT
Verbal
63
NA
NA
0.5
0.13
0.24
0.75
Pollert et al. (1969)[11]
EM
Verbal
DT
Visual
63
NA
NA
0.33
0.13
0.08
0.59
Pollert et al. (1969)[12]
EM
Verbal
DT
Verbal
63
NA
NA
0.38
0.13
0.12
0.63
Pollert et al. (1969)[13]
EM
Verbal
DT
Visual
63
NA
NA
0.22
0.13
-0.03
0.48
Pollert et al. (1969)[14]
EM
Verbal
DT
Verbal
63
NA
NA
0.24
0.13
-0.01
0.5
Pollert et al. (1969)[15]
EM
Verbal
DT
Visual
63
NA
NA
0.09
0.13
-0.16
0.35
Pollert et al. (1969)[16]
EM
Verbal
DT
Verbal
63
NA
NA
-0.15
0.13
-0.41
0.1
Piers et al. (1971)[1]
SM
Verbal
CT
Verbal
96
NA
YA
0.07
0.1
-0.13
0.27
Piers et al. (1971)[2]
SM
Verbal
CT
Verbal
44
NA
YA
0
0.16
-0.31
0.32
Hakstian et al. (1974)[1]
SM
Verbal
DT
Visual
343
23.7
YA
0.46
0.05
0.36
0.56
Hakstian et al. (1974)[2]
EM
Visual
DT
Visual
343
23.7
YA
0.28
0.05
0.18
0.37
Hakstian et al. (1974)[3]
STM
Verbal
DT
Visual
343
23.7
YA
0.18
0.05
0.08
0.28
Hakstian et al. (1974)[4]
SM
Verbal
DT
Visual
343
23.7
YA
0.42
0.05
0.33
0.52
Kaltsounis et al. (1975)[1]
STM
Verbal
DT
Visual
40
NA
CH
-0.15
0.16
-0.46
0.16
Kaltsounis et al. (1975)[2]
STM
Verbal
DT
Visual
40
NA
CH
-0.11
0.16
-0.42
0.2
Kaltsounis et al. (1975)[3]
STM
Verbal
DT
Visual
40
NA
CH
-0.1
0.16
-0.41
0.21
Kaltsounis et al. (1975)[4]
STM
Verbal
DT
Visual
40
NA
CH
-0.14
0.16
-0.45
0.17
Kaltsounis et al. (1975)[5]
STM
Visual
DT
Visual
40
NA
CH
0.51
0.16
0.2
0.82
Kaltsounis et al. (1975)[6]
STM
Visual
DT
Visual
40
NA
CH
0.55
0.16
0.24
0.86
Kaltsounis et al. (1975)[7]
STM
Visual
DT
Visual
40
NA
CH
0.31
0.16
0
0.62
Kaltsounis et al. (1975)[8]
STM
Visual
DT
Visual
40
NA
CH
0.46
0.16
0.15
0.77
Kaltsounis et al. (1975)[9]
STM
Verbal
DT
Verbal
40
NA
CH
-0.05
0.16
-0.36
0.26
Kaltsounis et al. (1975)[10]
STM
Verbal
DT
Verbal
40
NA
CH
-0.03
0.16
-0.34
0.28
Kaltsounis et al. (1975)[11]
STM
Verbal
DT
Verbal
40
NA
CH
-0.07
0.16
-0.38
0.24
Kaltsounis et al. (1975)[12]
STM
Visual
DT
Verbal
40
NA
CH
0.09
0.16
-0.22
0.4
Kaltsounis et al. (1975)[13]
STM
Visual
DT
Verbal
40
NA
CH
0.1
0.16
-0.21
0.41
MEMORY AND CREATIVE COGNITION
35
Kaltsounis et al. (1975)[14]
STM
Visual
DT
Verbal
40
NA
CH
0.07
0.16
-0.24
0.38
Lang et al. (1976)[1]
SM
Verbal
DT
Verbal
96
21.8
YA
0
0.1
-0.2
0.2
Lang et al. (1976)[2]
SM
Verbal
DT
Verbal
96
21.8
YA
0.14
0.1
-0.06
0.34
Lang et al. (1976)[3]
SM
Verbal
DT
Verbal
96
21.8
YA
-0.06
0.1
-0.26
0.14
Clabby et al. (1980)[1]
SM
Verbal
DT
Verbal
58
NA
YA
0.6
0.13
0.35
0.86
Bromage et al. (1981)[1]
EM
Verbal
CT
Verbal
26
NA
YA
0.31
0.21
-0.1
0.72
Shaw et al. (1986)[1]
EM
Verbal
CT
Verbal
54
NA
AD
0.12
0.14
-0.16
0.39
Shaw et al. (1986)[2]
EM
Verbal
DT
Visual
54
NA
AD
-0.07
0.14
-0.34
0.21
Shaw et al. (1986)[3]
EM
Verbal
DT
Verbal
54
NA
AD
0
0.14
-0.28
0.27
Shaw et al. (1986)[4]
EM
Verbal
CT
Verbal
84
NA
AD
0.22
0.11
0.01
0.44
Shaw et al. (1986)[5]
EM
Verbal
DT
Visual
84
NA
AD
0.21
0.11
-0.01
0.42
Shaw et al. (1986)[6]
EM
Verbal
DT
Verbal
84
NA
AD
-0.03
0.11
-0.24
0.19
Feingold et al. (1991)[1]
SM
Visual
CT
Verbal
59
23
YA
0.59
0.13
0.34
0.84
Bucik et al. (1996)[1]
STM
Both
NA
NA
182
28.93
YA
0.44
0.07
0.3
0.57
Bahar et al. (2000)[1]
WMC
Verbal
DT +
CT
Both
71
NA
YA
0.3
0.12
0.06
0.53
Neuman et al. (2000)[1]
EM
Visual
DT
Verbal
1390
28.3
YA
0.1
0.03
0.04
0.16
Neuman et al. (2000)[2]
STM
Verbal
DT
Verbal
1390
28.3
YA
0.15
0.03
0.09
0.21
Neuman et al. (2000)[3]
STM
Verbal
DT
Verbal
1390
28.3
YA
0.16
0.03
0.1
0.22
Neuman et al. (2000)[4]
EM
Verbal
CT
Visual
1390
28.3
YA
0.12
0.03
0.06
0.18
Neuman et al. (2000)[5]
STM
Verbal
CT
Visual
1390
28.3
YA
0.07
0.03
0.01
0.13
Neuman et al. (2000)[6]
STM
Verbal
CT
Visual
1390
28.3
YA
0.09
0.03
0.03
0.15
Weinstein et al. (2002)[1]
SM
Verbal
CT
Verbal
60
19.8
YA
0.27
0.13
0.01
0.52
Murray et al. (2005)[1]
WMC + STM
composite
Verbal
CT
Verbal
33
NA
YA
0.41
0.18
0.06
0.76
Murray et al. (2005)[2]
WMC
Verbal
CT
Verbal
33
NA
YA
0.56
0.18
0.21
0.92
Nemoto et al. (2005)[1]
EM
Verbal
DT
Visual
26
29.7
YA
0.56
0.21
0.15
0.97
Chiappe et al. (2007)[1]
WMC
Verbal
DT
Verbal
276
NA
YA
0.34
0.06
0.23
0.46
Chiappe et al. (2007)[2]
SM
Verbal
DT
Verbal
276
NA
YA
0.26
0.06
0.14
0.37
Chiappe et al. (2007)[3]
WMC
Verbal
DT
Verbal
197
NA
YA
0.48
0.07
0.35
0.62
Chiappe et al. (2007)[4]
STM
Verbal
DT
Verbal
197
NA
YA
0.16
0.07
0.02
0.3
Chiappe et al. (2007)[5]
WMC
Verbal
DT
Verbal
197
NA
YA
0.2
0.07
0.07
0.34
Chiappe et al. (2007)[6]
SM
Verbal
DT
Verbal
197
NA
YA
0.39
0.07
0.25
0.53
Freund et al. (2007)[1]
STM
Both
DT +
CT
Both
1135
14.48
AD
0.54
0.03
0.48
0.6
Ricks et al. (2007)[1]
WMC
Verbal
CT
Verbal
58
NA
YA
0.21
0.13
-0.05
0.46
DeYoung et al. (2008)[1]
WMC
Visual
CT
Verbal
103
NA
YA
0.33
0.1
0.14
0.53
DeYoung et al. (2008)[2]
WMC
Visual
DT
Verbal
103
NA
YA
0.02
0.1
-0.18
0.22
DeYoung et al. (2008)[3]
WMC
Visual
DT
Verbal
103
NA
YA
0.11
0.1
-0.09
0.31
DeYoung et al. (2008)[4]
WMC
Visual
DT
Verbal
103
NA
YA
-0.06
0.1
-0.26
0.14
DeYoung et al. (2008)[5]
WMC
Visual
DT
Verbal
103
NA
YA
0.02
0.1
-0.18
0.22
MEMORY AND CREATIVE COGNITION
36
Roskos-Ewoldsen et al.
(2008)[1]
WMC
Visual
DT
Both
39
NA
YA
0.06
0.17
-0.27
0.39
Roskos-Ewoldsen et al.
(2008)[2]
WMC
Visual
DT
Both
39
NA
YA
-0.06
0.17
-0.39
0.27
Roskos-Ewoldsen et al.
(2008)[3]
WMC
Visual
DT
Both
39
NA
YA
0.31
0.17
-0.02
0.64
Roskos-Ewoldsen et al.
(2008)[4]
WMC
Visual
DT
Both
39
NA
YA
-0.08
0.17
-0.41
0.25
Roskos-Ewoldsen et al.
(2008)[5]
WMC
Visual
DT
Both
39
NA
YA
0.22
0.17
-0.11
0.56
Roskos-Ewoldsen et al.
(2008)[6]
WMC
Visual
DT
Both
39
NA
YA
0.07
0.17
-0.26
0.4
Roskos-Ewoldsen et al.
(2008)[7]
WMC
Visual
DT
Both
39
NA
YA
0.17
0.17
-0.16
0.5
Roskos-Ewoldsen et al.
(2008)[8]
WMC
Visual
DT
Both
31
NA
OA
-0.17
0.19
-0.54
0.2
Roskos-Ewoldsen et al.
(2008)[9]
WMC
Visual
DT
Both
31
NA
OA
-0.26
0.19
-0.63
0.12
Roskos-Ewoldsen et al.
(2008)[10]
WMC
Visual
DT
Both
31
NA
OA
0.63
0.19
0.26
1.01
Roskos-Ewoldsen et al.
(2008)[11]
WMC
Visual
DT
Both
31
NA
OA
-0.16
0.19
-0.53
0.21
Roskos-Ewoldsen et al.
(2008)[12]
WMC
Visual
DT
Both
31
NA
OA
0.29
0.19
-0.08
0.66
Roskos-Ewoldsen et al.
(2008)[13]
WMC
Visual
DT
Both
31
NA
OA
0.24
0.19
-0.13
0.62
Roskos-Ewoldsen et al.
(2008)[14]
WMC
Visual
DT
Both
31
NA
OA
0.26
0.19
-0.12
0.63
Ward et al. (2009)[1]
EM
Verbal
DT
Both
114
NA
YA
0.05
0.09
-0.13
0.23
Ward et al. (2009)[2]
EM
Verbal
DT
Both
114
NA
YA
0.15
0.09
-0.03
0.33
Ward et al. (2009)[3]
EM
Verbal
DT
Both
114
NA
YA
-0.16
0.09
-0.34
0.02
Ward et al. (2009)[4]
EM
Verbal
DT
Both
114
NA
YA
-0.04
0.09
-0.22
0.14
Chein et al. (2010)[1]
WMC
Verbal
CT
Visual
54
NA
YA
0.2
0.14
-0.07
0.48
Chein et al. (2010)[2]
WMC
Visual
CT
Visual
54
NA
YA
0.49
0.14
0.21
0.76
Liminana Gras et al. (2010)[1]
EM
Both
DT
Verbal
42
NA
AD
-0.05
0.16
-0.36
0.26
Liminana Gras et al. (2010)[2]
EM
Both
DT
Verbal
42
NA
AD
-0.16
0.16
-0.47
0.16
Liminana Gras et al. (2010)[3]
EM
Both
DT
Verbal
42
NA
AD
-0.1
0.16
-0.41
0.21
Liminana Gras et al. (2010)[4]
SM
Verbal
DT
Verbal
42
NA
AD
0.07
0.16
-0.24
0.38
Liminana Gras et al. (2010)[5]
SM
Verbal
DT
Verbal
42
NA
AD
0.15
0.16
-0.17
0.46
Liminana Gras et al. (2010)[6]
SM
Verbal
DT
Verbal
42
NA
AD
0.11
0.16
-0.21
0.42
Liminana Gras et al. (2010)[7]
EM
Both
DT
Verbal
33
NA
AD
0.24
0.18
-0.12
0.59
Liminana Gras et al. (2010)[8]
EM
Both
DT
Verbal
33
NA
AD
0.1
0.18
-0.26
0.45
Liminana Gras et al. (2010)[9]
EM
Both
DT
Verbal
33
NA
AD
0.19
0.18
-0.17
0.54
Liminana Gras et al.
(2010)[10]
SM
Verbal
DT
Verbal
33
NA
AD
0.48
0.18
0.13
0.84
Liminana Gras et al.
(2010)[11]
SM
Verbal
DT
Verbal
33
NA
AD
0.24
0.18
-0.11
0.6
Liminana Gras et al.
(2010)[12]
SM
Verbal
DT
Verbal
33
NA
AD
0.44
0.18
0.09
0.79
Coskun et al. (2011)[1]
EM
Verbal
DT
Verbal
180
19.27
YA
0.34
0.08
0.19
0.5
MEMORY AND CREATIVE COGNITION
37
De Dreu et al. (2012)[1]
STM
Verbal
CT
Verbal
121
NA
YA
0.18
0.09
0.01
0.36
De Dreu et al. (2012)[2]
WMC
Verbal
DT
Verbal
60
NA
YA
0.46
0.13
0.21
0.71
De Dreu et al. (2012)[3]
WMC
Verbal
DT
Verbal
60
NA
YA
0.71
0.13
0.45
0.96
De Dreu et al. (2012)[4]
WMC
Verbal
DT
Verbal
60
NA
YA
0.05
0.13
-0.2
0.3
Beaty et al. (2013)[1]
SM
Verbal
DT
Verbal
191
NA
YA
0.11
0.07
-0.03
0.25
Beaty et al. (2013)[2]
SM
Verbal
DT
Verbal
191
NA
YA
0.13
0.07
-0.01
0.27
Beaty et al. (2013)[3]
SM
Verbal
DT
Verbal
191
NA
YA
0.13
0.07
0
0.27
Beaty et al. (2013)[4]
SM
Verbal
DT
Verbal
191
NA
YA
0.16
0.07
0.02
0.3
Beaty et al. (2013)[5]
SM
Verbal
DT
Verbal
191
NA
YA
0.16
0.07
0.03
0.3
Beaty et al. (2013)[6]
SM
Verbal
DT
Verbal
191
NA
YA
0.07
0.07
-0.07
0.21
Beaty et al. (2013)[1]
WMC
Verbal
DT
Verbal
10
NA
NA
-0.07
0.38
-0.81
0.67
Beaty et al. (2013)[2]
WMC
Visual
DT
Verbal
10
NA
NA
-0.23
0.38
-0.98
0.51
Beaty et al. (2013)[3]
WMC
Both
DT
Verbal
10
NA
NA
-0.19
0.38
-0.94
0.55
Fugate et al. (2013)[1]
WMC
NA
DT
Visual
20
14.44
AD
-0.35
0.24
-0.82
0.12
Lee et al. (2013)[1]
WMC
Verbal
DT
Both
265
20.33
YA
-0.02
0.06
-0.13
0.1
Lee et al. (2013)[2]
WMC
Verbal
DT
Both
265
20.33
YA
-0.02
0.06
-0.14
0.1
Lee et al. (2013)[3]
WMC
Verbal
DT
Both
265
20.33
YA
0.05
0.06
-0.07
0.16
Lee et al. (2013)[4]
WMC
Verbal
DT
Both
265
20.33
YA
0.07
0.06
-0.05
0.19
Lee et al. (2013)[5]
WMC
Verbal
DT
Verbal
265
20.33
YA
0.06
0.06
-0.06
0.17
Lee et al. (2013)[6]
WMC
Verbal
CT
Verbal
265
20.33
YA
0.26
0.06
0.14
0.38
Lee et al. (2013)[7]
WMC
Verbal
CT
Visual
265
20.33
YA
0.17
0.06
0.05
0.29
Lee et al. (2013)[8]
WMC
Verbal
CT
Verbal
265
20.33
YA
0.07
0.06
-0.05
0.19
Lee et al. (2013)[9]
WMC
Visual
DT
Both
265
20.33
YA
0.04
0.06
-0.07
0.16
Lee et al. (2013)[10]
WMC
Visual
DT
Both
265
20.33
YA
0.03
0.06
-0.09
0.15
Lee et al. (2013)[11]
WMC
Visual
DT
Both
265
20.33
YA
0.01
0.06
-0.11
0.13
Lee et al. (2013)[12]
WMC
Visual
DT
Both
265
20.33
YA
0.03
0.06
-0.08
0.15
Lee et al. (2013)[13]
WMC
Visual
DT
Verbal
265
20.33
YA
0.12
0.06
0.01
0.24
Lee et al. (2013)[14]
WMC
Visual
CT
Verbal
265
20.33
YA
0.13
0.06
0.01
0.25
Lee et al. (2013)[15]
WMC
Visual
CT
Visual
265
20.33
YA
0.16
0.06
0.04
0.28
Lee et al. (2013)[16]
WMC
Visual
CT
Verbal
265
20.33
YA
0.08
0.06
-0.04
0.2
Lee et al. (2013)[17]
SM
Verbal
DT
Both
265
20.33
YA
0.19
0.06
0.07
0.31
Lee et al. (2013)[18]
SM
Verbal
DT
Both
265
20.33
YA
0.19
0.06
0.07
0.3
Lee et al. (2013)[19]
SM
Verbal
DT
Both
265
20.33
YA
0.26
0.06
0.14
0.38
Lee et al. (2013)[20]
SM
Verbal
DT
Both
265
20.33
YA
0.12
0.06
0
0.23
Lee et al. (2013)[21]
SM
Verbal
DT
Verbal
265
20.33
YA
0.22
0.06
0.1
0.34
Lee et al. (2013)[22]
SM
Verbal
CT
Verbal
265
20.33
YA
0.23
0.06
0.11
0.34
Lee et al. (2013)[23]
SM
Verbal
CT
Visual
265
20.33
YA
0.08
0.06
-0.04
0.2
Lee et al. (2013)[24]
SM
Verbal
CT
Verbal
265
20.33
YA
0.15
0.06
0.04
0.27
Lee et al. (2013)[25]
SM
Verbal
DT
Both
265
20.33
YA
0.21
0.06
0.1
0.33
Lee et al. (2013)[26]
SM
Verbal
DT
Both
265
20.33
YA
0.09
0.06
-0.03
0.21
MEMORY AND CREATIVE COGNITION
38
Lee et al. (2013)[27]
SM
Verbal
DT
Both
265
20.33
YA
0.2
0.06
0.08
0.32
Lee et al. (2013)[28]
SM
Verbal
DT
Both
265
20.33
YA
0.13
0.06
0.01
0.24
Lee et al. (2013)[29]
SM
Verbal
DT
Verbal
265
20.33
YA
0.19
0.06
0.07
0.31
Lee et al. (2013)[30]
SM
Verbal
CT
Verbal
265
20.33
YA
0.22
0.06
0.1
0.33
Lee et al. (2013)[31]
SM
Verbal
CT
Visual
265
20.33
YA
0.16
0.06
0.05
0.28
Lee et al. (2013)[32]
SM
Verbal
CT
Verbal
265
20.33
YA
0.09
0.06
-0.03
0.2
Lee et al. (2013)[33]
SM
Verbal
DT
Both
265
20.33
YA
0.18
0.06
0.06
0.3
Lee et al. (2013)[34]
SM
Verbal
DT
Both
265
20.33
YA
0.13
0.06
0.02
0.25
Lee et al. (2013)[35]
SM
Verbal
DT
Both
265
20.33
YA
0.27
0.06
0.15
0.39
Lee et al. (2013)[36]
SM
Verbal
DT
Both
265
20.33
YA
0.05
0.06
-0.07
0.17
Lee et al. (2013)[37]
SM
Verbal
DT
Verbal
265
20.33
YA
0.24
0.06
0.12
0.35
Lee et al. (2013)[38]
SM
Verbal
CT
Verbal
265
20.33
YA
0.15
0.06
0.03
0.26
Lee et al. (2013)[39]
SM
Verbal
CT
Visual
265
20.33
YA
0.08
0.06
-0.04
0.2
Lee et al. (2013)[40]
SM
Verbal
CT
Verbal
265
20.33
YA
0.16
0.06
0.04
0.27
Lin et al. (2013)[1]
WMC
Verbal
DT
Verbal
55
20.3
YA
-0.02
0.14
-0.29
0.25
Lin et al. (2013)[2]
WMC
Verbal
DT
Verbal
55
20.3
YA
0.06
0.14
-0.21
0.33
Lin et al. (2013)[3]
WMC
Verbal
DT
Verbal
55
20.3
YA
0
0.14
-0.27
0.27
Lin et al. (2013)[4]
WMC
Verbal
CT
Both
68
20.1
YA
0.33
0.12
0.1
0.57
Lin et al. (2013)[5]
WMC
Verbal
DT
Verbal
68
20.1
YA
0.14
0.12
-0.09
0.38
Lin et al. (2013)[6]
WMC
Verbal
DT
Verbal
68
20.1
YA
0.19
0.12
-0.04
0.43
Lin et al. (2013)[7]
WMC
Verbal
DT
Verbal
68
20.1
YA
0.14
0.12
-0.09
0.38
Silvia et al. (2013)[1]
SM
Verbal
DT
Verbal
131
19.71
YA
0.23
0.09
0.06
0.41
Silvia et al. (2013)[2]
SM
Verbal
DT
Verbal
131
19.71
YA
0.23
0.09
0.06
0.41
Silvia et al. (2013)[3]
SM
Verbal
DT
Verbal
131
19.71
YA
0.21
0.09
0.04
0.39
Silvia et al. (2013)[4]
SM
Verbal
DT
Verbal
131
19.71
YA
0.16
0.09
-0.02
0.34
Silvia et al. (2013)[5]
SM
Verbal
DT
Verbal
131
19.71
YA
0.26
0.09
0.08
0.43
Silvia et al. (2013)[6]
SM
Verbal
DT
Verbal
131
19.71
YA
0.08
0.09
-0.1
0.26
Silvia et al. (2013)[7]
SM
Verbal
DT
Verbal
131
19.71
YA
0.28
0.09
0.1
0.45
Silvia et al. (2013)[8]
SM
Verbal
DT
Verbal
131
19.71
YA
0.1
0.09
-0.08
0.28
Silvia et al. (2013)[9]
SM
Verbal
DT
Verbal
131
19.71
YA
0.12
0.09
-0.06
0.3
Silvia et al. (2013)[10]
SM
Verbal
DT
Verbal
131
19.71
YA
0.15
0.09
-0.03
0.33
Silvia et al. (2013)[11]
SM
Verbal
DT
Verbal
131
19.71
YA
0.15
0.09
-0.03
0.33
Silvia et al. (2013)[12]
SM
Verbal
DT
Verbal
131
19.71
YA
0.38
0.09
0.2
0.55
Silvia et al. (2013)[13]
SM
Verbal
DT
Verbal
131
19.71
YA
0.14
0.09
-0.04
0.32
Silvia et al. (2013)[14]
SM
Verbal
DT
Verbal
131
19.71
YA
0.29
0.09
0.11
0.46
Silvia et al. (2013)[15]
SM
Verbal
DT
Verbal
131
19.71
YA
0.27
0.09
0.09
0.44
Silvia et al. (2013)[16]
SM
Verbal
DT
Verbal
131
19.71
YA
0.19
0.09
0.02
0.37
Silvia et al. (2013)[17]
SM
Verbal
DT
Verbal
131
19.71
YA
0.16
0.09
-0.02
0.34
Silvia et al. (2013)[18]
SM
Verbal
DT
Verbal
131
19.71
YA
0.12
0.09
-0.06
0.3
Silvia et al. (2013)[19]
SM
Verbal
DT
Verbal
131
19.71
YA
0.2
0.09
0.03
0.38
MEMORY AND CREATIVE COGNITION
39
Silvia et al. (2013)[20]
SM
Verbal
DT
Verbal
131
19.71
YA
0.05
0.09
-0.13
0.23
Silvia et al. (2013)[21]
SM
Verbal
DT
Verbal
131
19.71
YA
0.16
0.09
-0.02
0.34
Silvia et al. (2013)[22]
SM
Verbal
DT
Verbal
131
19.71
YA
0.13
0.09
-0.05
0.31
Silvia et al. (2013)[23]
SM
Verbal
DT
Verbal
131
19.71
YA
0.18
0.09
0.01
0.36
Silvia et al. (2013)[24]
SM
Verbal
DT
Verbal
131
19.71
YA
0.1
0.09
-0.08
0.28
Silvia et al. (2013)[25]
SM
Verbal
DT
Verbal
131
19.71
YA
0.13
0.09
-0.05
0.31
Silvia et al. (2013)[26]
SM
Verbal
DT
Verbal
131
19.71
YA
0.27
0.09
0.09
0.44
Silvia et al. (2013)[27]
SM
Verbal
DT
Verbal
131
19.71
YA
0.14
0.09
-0.04
0.32
Silvia et al. (2013)[28]
SM
Verbal
DT
Verbal
131
19.71
YA
0.29
0.09
0.11
0.46
Avitia et al. (2014)[1]
STM + EM
Composite
Both
DT
Visual
116
26
YA
0.39
0.09
0.21
0.56
Avitia et al. (2014)[2]
STM + EM
Composite
Both
DT
Verbal
116
26
YA
0.15
0.09
-0.03
0.33
Bahar et al. (2014)[1]*
NA
Both
DT
Visual
67
NA
CH
-0.05
0.12
-0.29
0.18
Beaty et al. (2014)[1]
SM
Verbal
DT
Verbal
147
19.68
YA
0.15
0.08
-0.01
0.31
Beaty et al. (2014)[2]
SM
Verbal
DT
Verbal
147
19.68
YA
0.18
0.08
0.03
0.34
Beaty et al. (2014)[3]
SM
Verbal
DT
Verbal
147
19.68
YA
0.26
0.08
0.1
0.41
Beaty et al. (2014)[4]
SM
Verbal
DT
Verbal
147
19.68
YA
0.29
0.08
0.13
0.44
Benedek et al. (2014)[1]
WMC
Visual
DT
Verbal
230
23
YA
0.16
0.07
0.02
0.29
Benedek et al. (2014)[2]
WMC
Visual
DT
Verbal
230
23
YA
0.17
0.07
0.03
0.31
Chein et al. (2014)[1]
WMC
Verbal
CT
Verbal
53
NA
YA
0.46
0.14
0.19
0.73
Chein et al. (2014)[2]
WMC
Visual
CT
Verbal
53
NA
YA
0.31
0.14
0.04
0.58
Lee et al. (2014)[1]
WMC
Verbal
CT
Visual
413
20.01
YA
0.26
0.05
0.16
0.35
Lee et al. (2014)[2]
WMC
Visual
CT
Visual
413
20.01
YA
0.17
0.05
0.07
0.27
Lee et al. (2014)[3]
WMC
Verbal
DT
Both
413
20.01
YA
-0.03
0.05
-0.13
0.07
Lee et al. (2014)[4]
WMC
Verbal
DT
Both
413
20.01
YA
-0.02
0.05
-0.12
0.08
Lee et al. (2014)[5]
WMC
Verbal
DT
Both
413
20.01
YA
0.07
0.05
-0.03
0.17
Lee et al. (2014)[6]
WMC
Verbal
DT
Both
413
20.01
YA
0.09
0.05
-0.01
0.19
Lee et al. (2014)[7]
WMC
Visual
DT
Both
413
20.01
YA
0.05
0.05
-0.05
0.15
Lee et al. (2014)[8]
WMC
Visual
DT
Both
413
20.01
YA
-0.01
0.05
-0.11
0.09
Lee et al. (2014)[9]
WMC
Visual
DT
Both
413
20.01
YA
0.02
0.05
-0.08
0.12
Lee et al. (2014)[10]
WMC
Visual
DT
Both
413
20.01
YA
0.06
0.05
-0.04
0.16
Lee et al. (2014)[11]
WMC
Verbal
DT
Verbal
413
20.01
YA
0.08
0.05
-0.02
0.18
Lee et al. (2014)[12]
WMC
Visual
DT
Verbal
413
20.01
YA
0.1
0.05
0
0.2
Lee et al. (2014)[13]
WMC
Verbal
CT
Visual
413
20.01
YA
0.17
0.05
0.07
0.27
Lee et al. (2014)[14]
WMC
Visual
CT
Visual
413
20.01
YA
0.15
0.05
0.05
0.25
Lee et al. (2014)[15]
WMC
Verbal
CT
Visual
413
20.01
YA
0.03
0.05
-0.07
0.13
Lee et al. (2014)[16]
WMC
Visual
CT
Visual
413
20.01
YA
0.14
0.05
0.04
0.24
Leon et al. (2014)[1]
WMC
Verbal
DT
Verbal
30
20.21
YA
0.34
0.19
-0.03
0.71
Leon et al. (2014)[2]
WMC
Verbal
DT
Verbal
30
20.21
YA
-0.12
0.19
-0.5
0.25
Leon et al. (2014)[3]
WMC
Verbal
DT
Verbal
30
20.21
YA
-0.01
0.19
-0.38
0.36
MEMORY AND CREATIVE COGNITION
40
Leon et al. (2014)[4]
WMC
Verbal
DT
Verbal
30
20.21
YA
0.15
0.19
-0.23
0.52
Leon et al. (2014)[5]
WMC
Verbal
DT
Verbal
30
72.93
OA
0.35
0.19
-0.02
0.73
Leon et al. (2014)[6]
WMC
Verbal
DT
Verbal
30
72.93
OA
0.38
0.19
0.01
0.75
Leon et al. (2014)[7]
WMC
Verbal
DT
Verbal
30
72.93
OA
0.67
0.19
0.3
1.05
Leon et al. (2014)[8]
WMC
Verbal
DT
Verbal
30
72.93
OA
0.39
0.19
0.02
0.76
Wagner et al. (2014)[1]
STM
Verbal
DT
NA
201
21.8
YA
0.13
0.07
-0.01
0.27
Furley et al. (2015)[1]
WMC
Verbal
DT
Verbal
61
23.48
YA
0.11
0.13
-0.15
0.36
Furley et al. (2015)[2]
WMC
Verbal
DT
Verbal
61
23.48
YA
0
0.13
-0.26
0.25
Furley et al. (2015)[3]
WMC
Verbal
DT
Verbal
61
23.48
YA
0.06
0.13
-0.19
0.32
Furley et al. (2015)[4]
WMC
Verbal
DT
Verbal
61
23.48
YA
0.1
0.13
-0.15
0.36
Hao et al. (2015)[1]
WMC
Verbal
DT
Verbal
90
21.49
YA
0.04
0.11
-0.18
0.26
Hao et al. (2015)[2]
WMC
Verbal
DT
Verbal
90
21.49
YA
0.48
0.11
0.27
0.7
Hass et al. (2015)[1]
SM
Verbal
DT
Verbal
72
NA
YA
0.2
0.12
-0.03
0.44
Hass et al. (2015)[2]
SM
Verbal
DT
Verbal
72
NA
YA
0.18
0.12
-0.06
0.41
Hass et al. (2015)[3]
SM
Verbal
DT
Verbal
72
NA
YA
0.26
0.12
0.02
0.49
Hass et al. (2015)[4]
SM
Verbal
DT
Verbal
72
NA
YA
0.07
0.12
-0.17
0.31
Lv et al. (2015)[1]
WMC
Visual
CT
Verbal
87
NA
YA
-0.64
0.11
-0.85
-0.42
Lv et al. (2015)[2]
WMC
Visual
CT
Verbal
87
NA
YA
0.06
0.11
-0.16
0.27
Lv et al. (2015)[3]
WMC
Verbal
CT
Verbal
87
NA
YA
-0.28
0.11
-0.5
-0.06
Lv et al. (2015)[4]
WMC
Verbal
CT
Verbal
87
NA
YA
0.11
0.11
-0.1
0.33
Lv et al. (2015)[5]
WMC
Visual
CT
Verbal
119
NA
YA
-0.28
0.09
-0.46
-0.11
Lv et al. (2015)[6]
WMC
Visual
CT
Verbal
119
NA
YA
0.24
0.09
0.06
0.41
Lv et al. (2015)[7]
WMC
Verbal
CT
Verbal
119
NA
YA
-0.21
0.09
-0.39
-0.04
Lv et al. (2015)[8]
WMC
Verbal
CT
Verbal
119
NA
YA
0.15
0.09
-0.02
0.33
Addis et al. (2016)[1]
EM
Verbal
DT
Verbal
36
NA
NA
0.1
0.17
-0.24
0.43
Addis et al. (2016)[2]
EM
Verbal
DT
Verbal
36
NA
NA
0.11
0.17
-0.22
0.44
Addis et al. (2016)[3]
EM
Verbal
DT
Verbal
36
NA
NA
0.02
0.17
-0.31
0.36
Addis et al. (2016)[4]
EM
Verbal
DT
Verbal
36
NA
NA
0.11
0.17
-0.22
0.44
Addis et al. (2016)[5]
EM
Verbal
DT
Verbal
36
NA
NA
0.26
0.17
-0.07
0.6
Dumas et al. (2016)[1]
WMC
Verbal
DT
Verbal
44
25.36
YA
0.33
0.16
0.02
0.64
Dumas et al. (2016)[2]
WMC
Verbal
DT
Both
44
25.36
YA
0.36
0.16
0.05
0.67
Dumas et al. (2016)[3]
WMC
Verbal
DT
Both
44
25.36
YA
0.51
0.16
0.2
0.83
Lunke et al. (2016)[1]
WMC
Verbal
CT
Verbal
270
NA
YA
0.06
0.06
-0.06
0.18
Lunke et al. (2016)[2]
WMC
Verbal
DT
Visual
270
NA
YA
0.05
0.06
-0.07
0.17
Lunke et al. (2016)[3]
WMC
Verbal
DT
Verbal
270
NA
YA
0.19
0.06
0.07
0.31
Necka et al. (2016)[1]
WMC
Visual
CT
Both
89
NA
YA
-0.56
0.11
-0.77
-0.34
Schweizer et al. (2016)[1]
SM
Verbal
DT
Verbal
48
22
YA
0.3
0.15
0
0.59
Schweizer et al. (2016)[2]
SM
Verbal
DT
Verbal
48
22
YA
0.35
0.15
0.06
0.65
Schweizer et al. (2016)[3]
SM
Verbal
DT
Verbal
48
22
YA
0.33
0.15
0.04
0.63
Schweizer et al. (2016)[4]
EM
Verbal
DT
Verbal
48
22
YA
0.29
0.15
-0.01
0.58
MEMORY AND CREATIVE COGNITION
41
Schweizer et al. (2016)[5]
EM
Verbal
DT
Verbal
48
22
YA
-0.3
0.15
-0.59
0
Smeekens et al. (2016)[1]
WMC
Both
DT
Verbal
173
NA
YA
0.01
0.08
-0.15
0.17
Smeekens et al. (2016)[2]
WMC
Both
DT
Verbal
173
NA
YA
0.07
0.08
-0.09
0.23
Smeekens et al. (2016)[3]
WMC
Both
DT
Verbal
173
NA
YA
0.03
0.08
-0.13
0.19
Benedek et al. (2017)[1]
SM
Verbal
DT
Verbal
89
25
YA
0.44
0.11
0.22
0.65
Benedek et al. (2017)[2]
SM
Verbal
DT
Verbal
89
25
YA
0.39
0.11
0.17
0.6
Gubbels et al. (2017)[1]
STM
Verbal
DT
Verbal
116
10.3
AD
0.34
0.09
0.16
0.51
Gubbels et al. (2017)[2]
STM
Verbal
DT
Verbal
116
10.3
AD
0.19
0.09
0.02
0.37
Shakeel et al. (2017)[1]
WMC
Verbal
DT
Visual
93
70.12
OA
0.2
0.11
-0.01
0.42
Avila et al. (2018)[1]*
WMC
Verbal
DT
Verbal
83
8.54
CH
0.42
0.11
0.21
0.64
Avila et al. (2018)[2]*
WMC
Verbal
DT
Verbal
83
8.54
CH
0.51
0.11
0.29
0.73
Avila et al. (2018)[3]*
WMC
Verbal
DT
Verbal
83
8.54
CH
0.12
0.11
-0.1
0.34
Avila et al. (2018)[4]*
WMC
Verbal
DT
Verbal
100
19.05
YA
-0.02
0.1
-0.22
0.18
Avila et al. (2018)[5]*
WMC
Verbal
DT
Verbal
100
19.05
YA
-0.01
0.1
-0.21
0.19
Avila et al. (2018)[6]*
WMC
Verbal
DT
Verbal
100
19.05
YA
0.05
0.1
-0.15
0.25
Chen et al. (2018)[1]
WMC
Verbal
DT
Verbal
159
19.58
YA
0.03
0.08
-0.13
0.19
Chen et al. (2018)[2]
WMC
Verbal
DT
Verbal
159
19.58
YA
0.09
0.08
-0.07
0.25
Chuderski et al. (2018)[1]
STM
Verbal
CT
Verbal
318
24.5
YA
0.13
0.06
0.01
0.25
Chuderski et al. (2018)[2]
STM
Verbal
CT
Visual
318
24.5
YA
0.23
0.06
0.12
0.35
Chuderski et al. (2018)[3]
STM
Verbal
CT
Visual
318
24.5
YA
0.23
0.06
0.12
0.35
Chuderski et al. (2018)[4]
STM
Verbal
CT
Verbal
318
24.5
YA
0.09
0.06
-0.03
0.21
Chuderski et al. (2018)[5]
STM
Verbal
CT
Verbal
318
24.5
YA
0.06
0.06
-0.06
0.18
Chuderski et al. (2018)[6]
STM
Verbal
CT
Visual
318
24.5
YA
0.18
0.06
0.06
0.3
Chuderski et al. (2018)[7]
STM
Verbal
CT
Visual
318
24.5
YA
0.15
0.06
0.03
0.27
Chuderski et al. (2018)[8]
STM
Verbal
CT
Verbal
318
24.5
YA
0.05
0.06
-0.07
0.17
Chuderski et al. (2018)[9]
STM
Visual
CT
Verbal
318
24.5
YA
0.11
0.06
-0.01
0.23
Chuderski et al. (2018)[10]
STM
Visual
CT
Visual
318
24.5
YA
0.22
0.06
0.11
0.34
Chuderski et al. (2018)[11]
STM
Visual
CT
Visual
318
24.5
YA
0.26
0.06
0.14
0.37
Chuderski et al. (2018)[12]
STM
Visual
CT
Verbal
318
24.5
YA
0.06
0.06
-0.06
0.18
Cushen et al. (2018)[1]
WMC
Both
CT
Verbal
120
19.39
YA
0.31
0.09
0.13
0.49
Cushen et al. (2018)[2]
WMC
Both
CT
Verbal
120
19.39
YA
0.38
0.09
0.2
0.55
Figueroa et al. (2018)[1]*
WMC
Verbal
CT
Verbal
212
NA
YA
0.16
0.07
0.02
0.3
Figueroa et al. (2018)[2]*
WMC
Verbal
CT
Verbal
212
NA
YA
0.07
0.07
-0.07
0.21
Figueroa et al. (2018)[3]*
WMC
Verbal
CT
Visual
212
NA
YA
-0.16
0.07
-0.3
-0.02
Figueroa et al. (2018)[4]*
WMC
Verbal
DT
Verbal
212
NA
YA
0.03
0.07
-0.11
0.17
Figueroa et al. (2018)[5]*
WMC
Verbal
DT
Verbal
212
NA
YA
0.06
0.07
-0.08
0.2
Figueroa et al. (2018)[6]*
WMC
Verbal
DT
Verbal
212
NA
YA
0.18
0.07
0.04
0.32
Figueroa et al. (2018)[7]*
WMC
Verbal
DT
Verbal
212
NA
YA
0.09
0.07
-0.05
0.23
Figueroa et al. (2018)[8]*
WMC
Verbal
CT
Verbal
212
NA
YA
0.21
0.07
0.08
0.35
Figueroa et al. (2018)[9]*
WMC
Verbal
CT
Verbal
212
NA
YA
0.15
0.07
0.01
0.29
MEMORY AND CREATIVE COGNITION
42
Figueroa et al. (2018)[10]*
WMC
Verbal
CT
Visual
212
NA
YA
-0.19
0.07
-0.33
-0.06
Figueroa et al. (2018)[11]*
WMC
Verbal
DT
Verbal
212
NA
YA
0.04
0.07
-0.1
0.17
Figueroa et al. (2018)[12]*
WMC
Verbal
DT
Verbal
212
NA
YA
0.27
0.07
0.13
0.4
Figueroa et al. (2018)[13]*
WMC
Verbal
DT
Verbal
212
NA
YA
0.22
0.07
0.09
0.36
Figueroa et al. (2018)[14]*
WMC
Verbal
DT
Verbal
212
NA
YA
0.09
0.07
-0.05
0.23
Figueroa et al. (2018)[15]*
WMC
Verbal
CT
Verbal
212
NA
YA
0.1
0.07
-0.04
0.24
Figueroa et al. (2018)[16]*
WMC
Verbal
CT
Verbal
212
NA
YA
0.13
0.07
-0.01
0.27
Figueroa et al. (2018)[17]*
WMC
Verbal
CT
Visual
212
NA
YA
-0.08
0.07
-0.22
0.06
Figueroa et al. (2018)[18]*
WMC
Verbal
DT
Verbal
212
NA
YA
0.08
0.07
-0.06
0.22
Figueroa et al. (2018)[19]*
WMC
Verbal
DT
Verbal
212
NA
YA
0.1
0.07
-0.04
0.24
Figueroa et al. (2018)[20]*
WMC
Verbal
DT
Verbal
212
NA
YA
0.07
0.07
-0.07
0.21
Figueroa et al. (2018)[21]*
WMC
Verbal
DT
Verbal
212
NA
YA
0.1
0.07
-0.04
0.24
Gubbels et al. (2018)[1]
STM
Visual
DT
Verbal
197
10.35
AD
0.37
0.07
0.23
0.51
Gubbels et al. (2018)[2]
STM
Verbal
DT
Verbal
197
10.35
AD
0.27
0.07
0.13
0.41
Krumm et al. (2018)[1]
WMC + STM
composite
Verbal
DT
Visual
209
9.96
CH
0.33
0.07
0.19
0.47
Krumm et al. (2018)[2]
WMC + STM
composite
Verbal
DT
Verbal
209
9.96
CH
0.47
0.07
0.34
0.61
Krumm et al. (2018)[3]
WMC + STM
composite
Verbal
DT
Visual
209
9.96
CH
0.5
0.07
0.36
0.63
Krumm et al. (2018)[4]
SM
Verbal
DT
Visual
209
9.96
CH
0.34
0.07
0.21
0.48
Krumm et al. (2018)[5]
SM
Verbal
DT
Verbal
209
9.96
CH
0.47
0.07
0.34
0.61
Krumm et al. (2018)[6]
SM
Verbal
DT
Visual
209
9.96
CH
0.45
0.07
0.31
0.58
Krumm et al. (2018)[7]
SM
Verbal
DT
Visual
209
9.96
CH
0.38
0.07
0.24
0.51
Krumm et al. (2018)[8]
SM
Verbal
DT
Verbal
209
9.96
CH
0.58
0.07
0.44
0.71
Krumm et al. (2018)[9]
SM
Verbal
DT
Visual
209
9.96
CH
0.52
0.07
0.39
0.66
Leder et al. (2018)[1]
WMC
Verbal
DT
Verbal
157
23.04
YA
0.06
0.08
-0.1
0.22
Leder et al. (2018)[2]
WMC
Verbal
DT
Verbal
157
23.04
YA
0.06
0.08
-0.1
0.22
Leder et al. (2018)[3]
WMC
Verbal
DT
Verbal
157
23.04
YA
0.09
0.08
-0.07
0.25