Enhancing Creativity on Different Complexity Levels by Eliciting Mental Models

Abstract

Creativity is the result of applying and combining existing knowledge in a new way. This process becomes ever more challenging with increasing task complexity because of the concomitant increase in information density and interconnectedness. When one’s mental model is explicated, his or her underlying knowledge and the (network-like) relationships that constitute it becomes perceptible and thus easier to be applied creatively. In a 3 × 2 factorial experimental design with 121 students we tested the effect of making mental models explicit by mind-mapping on creative problem-solving. We assumed that the beneficial effects of mind-maps can be further enhanced by applying mind-map templates, which are presketched, blank mind-maps that are filled in by the user. As the first factor, we varied the type of support: A control, a classic mind-map, and a mind-map template group were compared. We also assumed that task complexity has a negative impact on creative problem-solving, which can be mitigated by mental models and their structural relationships which help the user to deal with increased complexity. As the second factor, we varied task complexity. Results showed a significant positive influence of mind-maps on creative problem-solving. Mind-map templates led to the highest levels of fluency and originality in more complex tasks. They seem to release the whole potential of the mind-map technique in such a way that the increase in information density and interconnectedness of more complex tasks is optimally exploited.

Full text

Enhancing Creativity on Different Complexity Levels by
Eliciting Mental Models
Charlotte P. Malycha and Günter W. Maier
Bielefeld University
Creativity is the result of applying and combining existing knowledge in a new way. This process
becomes ever more challenging with increasing task complexity because of the concomitant increase in
information density and interconnectedness. When one’s mental model is explicated, his or her under-
lying knowledge and the (network-like) relationships that constitute it becomes perceptible and thus
easier to be applied creatively. In a 3 ⫻2 factorial experimental design with 121 students we tested the
effect of making mental models explicit by mind-mapping on creative problem-solving. We assumed that
the beneficial effects of mind-maps can be further enhanced by applying mind-map templates, which are
presketched, blank mind-maps that are filled in by the user. As the first factor, we varied the type of
support: A control, a classic mind-map, and a mind-map template group were compared. We also
assumed that task complexity has a negative impact on creative problem-solving, which can be mitigated
by mental models and their structural relationships which help the user to deal with increased complexity.
As the second factor, we varied task complexity. Results showed a significant positive influence of
mind-maps on creative problem-solving. Mind-map templates led to the highest levels of fluency and
originality in more complex tasks. They seem to release the whole potential of the mind-map technique
in such a way that the increase in information density and interconnectedness of more complex tasks is
optimally exploited.
Keywords: mental models, creativity technique, mind-map, task complexity, TTCT
As creativity is seen as “one of the key factors that drive
civilization forward” (Hennessey & Amabile, 2010, p. 570), many
organizations seek to constantly foster it to meet the innovative
demands of the competitive global market (Anderson, Potocnik, &
Zhou, 2014). At the same time, the growing complexity of every-
day work and low predictability of situational requirements places
high demands on a person’s mental workload (Jacko & Ward,
1996), as ever increasing amounts of highly interconnected infor-
mation has to be processed (Jacko & Ward, 1996;Liu & Li, 2012).
This increased complexity has a detrimental effect on creativity
because less free capacity in working memory is available for
combining existing knowledge effectively and thus for solving
tasks creatively (Mumford, Medeiros, & Partlow, 2012;Wiley &
Jarosz, 2012). One way to cope with this complexity is by using
and making underlying mental model explicit. Mental models
represent an individuals’ complex form of conceptual knowledge
in which interrelations among embedded concepts are articulated
(Goldvarg & Johnson-Laird, 2001). This knowledge about con-
cepts, their exemplars, and their relationships is used to under-
stand, predict, and explain events (Bradley, Paul, & Seeman, 2006;
Rouse & Morris, 1986) and thus helps to solve problems creatively
(Byrne, Shipman, & Mumford, 2010).
In the following sections, we first focus on creativity and how its
processes can be enhanced by eliciting one’s mental model. To
explicate the mental model, the widely known mind-map tech-
nique (Buzan & Buzan, 2010) was used. Mind-maps help people
activate and combine knowledge systematically and display un-
derlying mental models in a clear perceptible way (Davies, 2011;
Nesbit & Adesope, 2006). Mind-maps are therefore a widely
recommended creativity technique (Buzan & Buzan, 2010;Maly-
cha & Maier, 2012;Michalko, 2006). Although techniques such as
mind-mapping are very useful, they unfortunately entail certain
costs. Until the user has routinized the technique, substantial
cognitive resources are directed toward its application instead on
the actual task (see Renkl & Nückles, 2006;Ruiz-Primo, Schultz,
Li, & Shavelson, 2001;Schau & Mattern, 1997). To support users
in their application of the mind-map technique until an appropriate
level of routinization is reached, we provided mind-map templates
(predrawn mind-maps that only has to be filled in) and compared
them with classic mind-maps. Furthermore, we provided evidence
for the notion that task complexity is a specific environmental
press which can inhibit creative thinking by draining off cognitive
resources (Runco, 2014). Task complexity has already been shown
as to be a key determinant of information processing, decision
making strategy, or cognitive load (Gill & Hicks, 2006), but it has
not yet been examined in the context of creative behavior. Thus,
This article was published Online First April 6, 2017.
Charlotte P. Malycha and Günter W. Maier, Department of Work and
Organizational Psychology, Bielefeld University.
Correspondence concerning this article should be addressed to Charlotte
P. Malycha, Department of Work and Organizational Psychology,
Bielefeld University, P.O. Box 100131, 33615 Bielefeld, Germany. E-mail:
chmalycha@gmx.de
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2017, Vol. 11, No. 2, 187–201 1931-3896/17/$12.00 http://dx.doi.org/10.1037/aca0000080
187

we sought to discover whether the enhancement of creative pro-
cesses changes when a specific environmental press—task com-
plexity—is varied.
Fostering Creativity
According to Ma (2009) “creativity is the ability to reorganize
the available knowledge, information, cues, facts and/or skills in a
person’s reservoir to generate new ideas or useful solutions” (p.
39). Numerous techniques and programs have been developed to
support creative processes and thus enhance creative outcomes
(e.g., Michalko, 2006;Scott, Leritz, & Mumford, 2004a;Runco,
2014). Contrary to the common belief that creativity is a dichot-
omous variable (i.e., one is either creative or not at all), many
researchers assume that creativity is distributed on a continuum
(Makel, 2009;Sternberg & Lubart, 1991) and that the creative
achievement of a person can be fostered to a certain extent by
specific techniques, exercises, or stimulations (see Mumford, Mo-
bley, Uhlman, Reiter-Palmon, & Doares, 1991;Nickerson, 1999;
Scott et al., 2004a). According to Mumford et al. (1991), eight core
processes play a critical role when people develop creative ideas
(see also Scott et al., 2004a;Smith & Ward, 2012): (a) After a
problem or task is clearly defined, (b) knowledge is gained or
activated and (c) organized in appropriate categories. (d) Novel
combinations of these categories results in (e) the generation of
ideas, which are (f) evaluated afterward. (g) Then the best ideas are
implemented and (h) the solution is monitored. These eight core
processes are normally enhanced by various creativity techniques,
differing only with respect to the applied processing activities and
the number of processes targeted for development (Scott, Leritz, &
Mumford, 2004b). In their meta-analyses, Ma (2009) and Scott et
al. (2004a) found that the effective and efficient handling of
knowledge constitutes an important part in creative behavior.
Retrieving or activating knowledge and combining concepts in
such a way that more original ideas are generated are actually
found to be the most important processes (Mumford, Medeiros, et
al., 2012;Scott et al., 2004a;Smith & Ward, 2012). Moreover, it
was found that they make the strongest contributions to training
effects of creativity (Ma, 2009;Scott et al., 2004a). Thus, enhanc-
ing these knowledge processes by making explicit one’s underly-
ing mental models should result in greater creative achievements
(Scott et al., 2004a).
Mental Models
Mental models are mental representations of the surrounding
world, sets of assertions, and relationships between them (Bradley
et al., 2006;Johnson-Laird, 2010). As they are an explanation of
someone’s thoughts about how something works in the real-world,
they represent a complex form of domain-specific knowledge
(Hmelo-Silver & Pfeffer, 2004), which provides a framework for
the storage and recall of past experiences (He, Erdelez, Wang, &
Shyu, 2008). Mental models are believed to represent complex
cognitive structures (Goldvarg & Johnson-Laird, 2001), to direct
attentional resources to critical concepts which have an impact on
the particular outcome (Rouse & Morris, 1986), and to influence
people’s thinking. For example, research showed that they affect
the execution of cognitive processing activities such as informa-
tion search and encoding (He et al., 2008), or idea generation and
evaluation (Weick, 1995). People often rely on their mental mod-
els when they have to solve a novel, complex, and ill-defined task
creatively (Mumford & Gustafson, 2007). The kind of mental
model a person applies might be critical to his or her performance
on a problem-solving task (e.g., Mumford, Feldman, Hein, &
Nago, 2001). Various pieces of evidence on how these mental
models affect creative problem-solving in experimental (e.g.,
Finke, Ward, & Smith, 1992), historical (e.g., Carlson & Gorman,
1992), and field (e.g., Mumford et al., 2001) studies can be found
in Mumford, Hester, et al. (2012).
The potential impact of mental models on people’s creative
thinking suggests that training interventions designed to improve
creative problem-solving should focus on fostering strategies
which require people work with the information embedded in their
mental models (Scott et al., 2004a,2004b). Mumford, Hester, et al.
(2012) found that objective and subjective features of a mental
model were related to a more creative solution. Superior mental
models and more creative solutions can both be fostered by a
causal analysis training (Hester et al., 2012) or a training about the
uses of ideas and idea implementation (Barrett et al., 2013).
Another beneficial way to increase the creative potential of a
solution by working with information embedded in a mental model
is to use visualization techniques (see Malycha & Maier, 2012).
Visualization Techniques
Visualization techniques are characterized by their net-like
structure, which is used to graphically illustrate complex relation-
ships and thus elicit underlying mental models. There are different
types of visualization techniques such as concept-maps (Novak &
Gowin, 1984) or mind-maps (Buzan & Buzan, 2010). Both visu-
alize a person’s mental model and only differ with regard to their
specific function and scope of application: Concept-maps are
mainly used in learning contexts because they depict new knowledge
with conceptual links in a hierarchically structured pattern (see
Figure 1). Mind-maps are used to enhance divergent thinking by
linking diverse aspects to each other (see Figure 2) and are highly
recommended in areas in which divergent thinking is important
(Buzan & Buzan, 2010;Malycha & Maier, 2012).
Despite the higher degree of practical relevance of the mind-
map technique and its widespread use, more research has been
conducted on concept-maps (Davies, 2011;Wheeldon, 2010). The
results of several meta-analyses show that using concept-maps was
associated with increased knowledge retention (Nesbit & Adesope,
2006), higher student achievement and better student attitude (Hor-
ton et al, 1993). These positive effects have been generalized to the
whole group of visualization techniques (see Davies, 2011;Renkl
& Nückles, 2006): Visualization techniques enhance the process-
ing depth of the content and therefore positively influence learning
(Horton et al., 1993;Nesbit & Adesope, 2006;Renkl & Nückles,
2006). On a map, the learner constructs relationships between new
content and prior knowledge. While explicating logical-semantic
meaning, the content is elaborated and organized in a highly
beneficial way. Maps can reduce complexity as the visual repre-
sentation directs attention toward structural relationships and es-
sential information (Hardy & Stadelhofer, 2006;Nesbit &
Adesope, 2006). Externalizing these relationships on a map re-
duces cognitive load because less memory search is required to
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188 MALYCHA AND MAIER

combine them creatively (Mandl & Fischer, 2000;Nesbit &
Adesope, 2006;Renkl & Nückles, 2006).
The application of knowledge, the creation of relationships
between concepts, and the combination of new ideas with existing
knowledge are not only fundamental to knowledge acquisition and
learning but also to creative achievement (see Scott et al., 2004a).
Thus, we assigned the above mentioned mechanisms of visualiza-
tion techniques on creativity tasks in the present study. Some of the
first evidence to support this notion was shown by Malycha and
Maier (2012), who found that using the mind-map visualization
technique on creative problem-solving tasks increased both the
quantity and diversity of generated ideas.
Creativity Fostered by Eliciting Mental Models
Three processes in particular are assumed to ensure a positive
effect of eliciting a mental model on creative problem-solving (see
also Malycha & Maier, 2012): First, eliciting a mental model may
enhance processing depth and the elaboration of the problem
(Horton et al., 1993;Nesbit & Adesope, 2006;Renkl & Nückles,
2006). The underlying semantic processes behind a visualization
technique seem to be similar to those of the theory of spreading
activation (see Bower, 2008;Collins & Loftus, 1975;Tehan,
2010). Within the strong differentiation of concepts in a visual-
ization technique, the user elaborates aspects more profoundly.
Visually depicted ideas are more likely to inspire new ideas
(Smith, 1998). Thus, knowledge activation is extended by eliciting
mental models (Hardy & Stadelhofer, 2006;Nesbit & Adesope,
2006;Renkl & Nückles, 2006). The more knowledge is activated,
the greater the basis for conceptual combination (Smith & Ward,
2012), which, in turn, is a prerequisite condition for creative
problem-solving (Ma, 2009;Wiley & Jarosz, 2012). As creativity
is always based on recalled information to some extent (Ward,
1995), the elicitation of mental model fosters the generation of
creative ideas.
Second, externalizing core information and their relationships
on a map reduces the need for memory search while the users are
combining those concepts (Hardy & Stadelhofer, 2006;Nesbit &
Adesope, 2006). Because visualization techniques help their users
retain complex knowledge structures active for further processing,
they reduce cognitive load (Hardy & Stadelhofer, 2006;Nesbit &
Adesope, 2006;Smith & Ward, 2012). The freed cognitive re-
sources can then be used to focus on the task as well as activate
and combine further concepts and thus generate a greater amount
of original ideas (Vandervert, Schimpf, & Liu, 2007).
Third, the strong focus of visual representations on essential
information and their structural relationships illustrates the logical-
semantic meaning between different concepts and reduces com-
plexity (Hardy & Stadelhofer, 2006;Nesbit & Adesope, 2006;
Ruiz-Primo et al., 2001). Because explicit construction rules of
visualization techniques force their users to focus on critical causes
(e.g., only one word on a branch), crucial aspects of the task are
Figure 1. Example of a concept-map.
Figure 2. Example of a mind-map.
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189
ENHANCING CREATIVITY WITH MENTAL MODELS

organized, clearly perceptible, and easily combinable (Akinoglu &
Yasar, 2007;Hardy & Stadelhofer, 2006;Renkl & Nückles, 2006).
Moreover, subsequently added ideas can be easily integrated such
that thematically alike ideas are colocated. Visualization tech-
niques help their users organize a large body of knowledge in a
relatively small area (Huba & Freed, 2000) in which both the
general framework and the details of the problem are visualized
simultaneously (Akinoglu & Yasar, 2007). The ability to flexibly
switch between different levels of abstraction is related to advan-
tages in creative problem-solving (Vandervert et al., 2007;Wiley
& Jarosz, 2012). Taking all of these arguments together, eliciting
a underlying mental model by mind-mapping seems to foster three
of the four key stages of the creative process: activating/retrieving
knowledge, generating ideas, and combining concepts (Ma, 2009;
Scott et al., 2004a). Thus, in the present study we assumed:
Hypothesis 1: The mind-map technique enhances creativity.
Facilitating the use of a Visualization Technique
Although mapping techniques are tools that have only few
construction rules, they may seem unfamiliar and “untrustworthy”
to many users at first due to their inherent deviation from more
common, “serial” note-taking practices (Schau & Mattern, 1997).
As it can be initially difficult to produce a substantial visualization,
the expedient acquisition of procedural knowledge may be im-
peded (see Renkl & Nückles, 2006;Ruiz-Primo et al., 2001;Schau
& Mattern, 1997). Drawing a map binds substantial cognitive
capacities until the technique has been routinized (Renkl & Nück-
les, 2006;Ruiz-Primo et al., 2001;Schau & Mattern, 1997). To
overcome these starting difficulties, the application of the tech-
nique can be facilitated by using, for example, a presketched map
(i.e., a template; see Hardy & Stadelhofer, 2006;Ruiz-Primo et al.,
2001;Schau, Mattern, Zeilik, Teague, & Weber, 2001). Templates
can support the user of a visualization technique to concentrate on
the task at hand rather the construction rules and thus facilitate
one’s familiarization with the technique.
In several studies researchers have concordantly found that
mapping templates supported their users in applying the novel
technique successfully because they lowered the cognitive load of
the participants while they generated their maps (e.g., Hardy &
Stadelhofer, 2006;Ruiz-Primo et al., 2001;Schau & Mattern,
1997). The combination of self-construction and sketched aid has
been found to be particularly effective for the visual depiction of
knowledge (Hardy & Stadelhofer, 2006). Templates have a posi-
tive influence on the substantive understanding of new content
(Mandl & Fischer, 2000). Chang, Sung, and Chen (2002) found
that when students had to improve a given template, their com-
prehension and summarization of the texts were enhanced.
As completely filled maps can divert attention away from the
relationships between the concepts, McCagg and Dansereau
(1991) recommended fill-in-the-gap maps. These maps contain an
extract of important concepts and have to be completed by the
user. Thus, the preselected structure focuses the user’s attention on
both the most important concepts and the relational learning con-
tent (see Hardy & Stadelhofer, 2006;Ruiz-Primo et al., 2001;
Schau et al., 2001). While focusing on creativity tasks, an idea can
be inspired from any given word. Thus, the templates in the
present study were blank skeleton maps, which had to be filled in
entirely by its user. The given templates were not rigid maps into
which the users had to squeeze their ideas. Instead, the users were
able to individualize them: Given branches could be left free or
further branches could be added wherever they were needed. Thus,
we hypothesized:
Hypothesis 2: Mind-map templates enhance creativity more
than classic mind-maps.
Task Complexity and Creativity
Regardless of the creativity technique, applying and combining
knowledge becomes more challenging as task complexity in-
creases. Task complexity is used as an umbrella term that is
associated with multiple task characteristics (Liu & Li, 2012). The
most widely used complexity model is Wood’s (1986) triadic
componential model (see Liu & Li, 2012). This model pertains to
structuralist models (Liu & Li, 2012), which define task complex-
ity objectively from the structure of a task.
In Wood’s model, three dimensions, which may vary autono-
mously, are used to describe task complexity: Component com-
plexity refers to the number of information cues, activities, or
events that an individual needs to be aware of and be able to
perform. This type of complexity derives from the amount of
distinct acts or information cues that have to be processed during
task performance. Coordinative complexity refers to the degree of
relatedness of task inputs (e.g., cues or acts) and task products.
Besides the form and strength of the relationship, the sequencing
of input is also a factor of this type of complexity. Dynamic
complexity reflects changes in acts or cues. It indicates the dy-
namic relationship between task input and the products to which
individuals have to adapt constantly.
In his cognitive load theory, Sweller (2006) states that complex
materials include a large number of interacting elements that must
be considered simultaneously in order to be understood. Thus, the
more complex tasks are, the more resources the task performer has
to invest (Liu & Li, 2012;Wood, 1986). If a task performer
perceives a task as being more difficult, he or she must exert
greater effort to manage the task (Liu & Li, 2012). Wood (1986)
assumed if the demands of a task begin to exceed an individual’s
ability to respond, there will be a condition of cognitive “overload”
and performance will suffer as a result (see also Liu & Li, 2012).
This impact of task complexity on a person’s mental workload
leads to a more narrowed focus and hinders successful task
achievement (Jacko & Ward, 1996). Thus, we hypothesized:
Hypothesis 3: Creative achievement is lower on more com-
plex tasks than on less complex tasks.
When it comes to creative tasks, increasing complexity is even
more severe: In order to be creative, it is essential for individuals
to be able to think flexibly and combine different and remote
concepts with each other to generate creative solutions (Scott et al.,
2004a). Free working memory capacity is needed to solve tasks
creatively (Wiley & Jarosz, 2012). Thus, an individual needs
strategies to mitigate the effects of complexity on working mem-
ory to solve tasks creatively. We assumed that creativity tech-
niques that reduce cognitive load are particularly helpful for more
complex tasks. As argued above, visualization techniques such as
mind-maps reduce cognitive load by helping their users focus on
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190 MALYCHA AND MAIER

structural relationships and externalizing key information on a
well-organized map (Hardy & Stadelhofer, 2006;Nesbit &
Adesope, 2006;Smith & Ward, 2012). Consequently, more inter-
connected information is structured in a meaningful and clearly
arranged way so that it can be processed easily. We therefore
hypothesized that eliciting mental models help the user to deal
with increasing complexity, change, and diversity.
Hypothesis 4a: Task complexity moderates the effect of mind-
maps on creative achievement. In comparison with note-
taking, the beneficial effect of mind-maps is even higher when
task complexity is high.
As argued above, templates enhance the (creative) potential of a
visualization technique. Resources have to be allocated to two
processes—the management of knowledge to be creative and the
application of the technique. If the cognitive demands of the latter
are reduced by templates, more resources are available for the first
process—the generation of creative ideas from the retrieved
knowledge base. These more demanding resources are especially
needed on more complex tasks as they can easily lead to cognitive
overload (Liu & Li, 2012). Thus, users of templates should benefit
even more on more complex tasks from the mind-map technique.
Hypothesis 4b: Task complexity moderates the effect of mind-
map templates on creative performance. In comparison with
classic mind-maps and note-taking, the beneficial effect of
mind-map templates is even higher when task complexity is
high.
Method
We tested our hypotheses in a 3 ⫻2 factorial design. The first
factor consisted of two different kinds of mind-maps and a control
group in which participants had to solve the tasks without any
specific technique; these groups were contrasted with each other.
The participants had to solve the tasks in the first mind-map
condition by drawing a mind-map, and in the second mind-map
condition by filling in a mind-map template (described in detail
below). As the second factor, we varied the complexity of the tasks
(low vs. high complexity). Creative achievement was assessed as
the dependent variable.
Dependent Variable
Creativity was measured by using two different tasks that were
either taken from or based off of the Torrance Tests of Creative
Thinking (TTCT; Torrance, 1966) and which were selected in a
pilot study we conducted (described below). The TTCT is the most
commonly used test for measuring creativity (for an overview, see
Kim, 2011b). Its ill-defined tasks have been used in a number of
experiments to test creative performance (e.g., Cramond,
Matthews-Morgan, Torrance, & Zuo, 1999;Kim, 2011b;Runco,
Millar, Acar, & Cramond, 2010). According to Cramond et al.
(1999), the TTCT scores have very good interrater (⬎.90) and
good retest reliability (⬎.60; see Kim, 2011a).
The TTCT uses three dimensions to measure creativity: fluency,
flexibility, and originality. Based on Torrance’s (2008b) recom-
mendations, these dimensions were interpreted individually. Flu-
ency scores are defined as the total number of interpretable,
meaningful, and relevant ideas generated in response to the spe-
cific task, whereas flexibility scores refer to the different numbers
of categories represented by the responses. Originality scores
depict the uniqueness of the responses measured by statistical
rarity with regard to the large normative sample.
The first task was the product improvement task of the TTCT.
It belongs to the domain of problem-solving specified in the Baer
and Kaufman’s (2005) Amusement Park Theoretical Model of
Creativity. The participants had to “List the smartest, most inter-
esting, and most unusual ways [they]...canthink of for changing
[a] . . . toy elephant so that children have more fun playing with it.”
The second task was based off the TTCT and was also a product
improvement task. The participants had to “List the smartest, most
interesting, and most unusual ways [they]...canthink of for
changing [a]...baby stroller so that parents or children like it
more.” In both tasks, a picture illustrated the corresponding object.
The answers to the first tasks were evaluated following the scoring
manual of the TTCT (Torrance, 2008a,2008b). For the second
task, the same three measures were used: The fluency scores
counted the number of relevant ideas generated. The flexibility
ratings were based off of the same categories as the first task (e.g.,
“change colors,” “minification,” or “rearrangement”). The mea-
sure of originality was derived from a frequency distribution of the
responses given by all participants of the study. Responses given
by less than 5% of the sample were scored as original responses;
all other responses were scored as unoriginal ones (see Chris-
tensen, Guilford, Merrifield, & Wilson, 1960;Cropley & Cropley,
2009;Goller, 2011).
Independent Variables
Mind-maps. In a mind-map, ideas and thoughts are connected
divergently on so called “branches.” As ideas branch out into
subsections, mind-maps generally take a hierarchical or tree-
branch format. The instructions of the mind-map technique were
composed according to Buzan and Buzan (2010). The technique
was presented to the participants as it is normally described, that is,
as a method to let their ideas flow and thus improve the quality and
the quantity of the ideas they generate. The likelihood of generat-
ing really good ideas increases proportionately to the quantity of
ideas that are generated. It was made clear to the participants that
it was not necessary to work on their maps linearly. They were told
to start their mind-maps with a central image or word reflecting the
main issue of the task in the middle of a tabloid-sized blank page
in horizontal format. Branching out from that central image, they
were supposed to write their concepts in printed letters on slightly
curved branches, with each branch containing only one keyword.
As they generate further aspects linked to the first keywords, the
network of branches and ideas would grow. They were also told
that each aspect only had to correspond to the next higher aspect.
That way, many different concepts could be integrated into a
mind-map, even if they did not directly correspond to the central
image. To highlight important aspects, participants could use dif-
ferent colors or symbols. Connections could be displayed by using
arrows or codes. Furthermore, they were allowed to add images to
the written aspects or substitute any aspect with an image.
Mind-map templates. In the mind-map template condition,
the application of the mind-map technique was facilitated by using
a blank mind-map on a blank tabloid-sized page that had to be
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191
ENHANCING CREATIVITY WITH MENTAL MODELS

filled in by the user (see Figure 2). The users were allowed to draw
additional branches or to leave some of the presketched branches
blank. The mind-map template was created on the basis of a
reanalysis of a previous study (N⫽40) we conducted in which
mind-maps and the same task from the TTCT was applied (Ma-
lycha & Maier, 2012). The drawn maps were evaluated in terms of
their degree of interconnection and hierarchy. The average number
of branches—not the maximum number—was taken as a basis for
the mind-map templates because performance should not be influ-
enced by the templates. The average number of first- (M⫽4.58),
second- (M⫽10.85), and further-order (M⫽5.05) branches were
taken to create the map template, which contained five first-, 11
second-, and five third-order branches, respectively. In the present
study, nearly the same hierarchy was depicted in the classic mind-
maps by the users: The maps contained an average of M⫽4.73
(SD ⫽1.61) first-order and M⫽10.18 (SD ⫽3.50) second-order
branches.
Task complexity. We assigned tasks with varying degrees of
complexity by varying coordinative complexity (Wood, 1986).
Specifically, we varied the relationship between the degree of
interdependency between various components of the tasks (Liu &
Li, 2012;Wood, 1986). This variation of task complexity is
frequently used in experimental settings (Boag, Neal, Loft, &
Halford, 2006;Lazzara, Pavlas, Fiore, & Salas, 2010;Nadkarni &
Gupta, 2007).
Task complexity was increased by interconnecting various fea-
tures of the particular product the participants dealt with (e.g., the
toy elephant or the baby stroller). In the more complex condition,
the interdependency of the task components was increased: some
features of the task were supposed to be changed simultaneously.
For each task, five features of the objects were chosen; these had
to be changed in either groups of twos or threes (e.g., for the toy
elephant, the size and the weight had to be changed simultane-
ously). Any ideas violating this rule were excluded from analyses
because they did not conform to the instructions (i.e., changing the
size but not the weight was not counted as a relevant idea;
Torrance, 2008a). On average, approximately one idea (M⫽.93,
SD ⫽1.28) had to be excluded from each task due to this scoring
rule. Beside these particular features, all other features of the
objects could be changed freely.
In the less complex condition, the participants were asked to
change the toy elephant or the baby stroller with no particular
stipulations on how to do so. Before conducting the study, we were
aware of a potential confounding factor: The participants in the
more complex condition might have had an informational advan-
tage because the categories that had to be changed together were
named explicitly. To counter this potential confound, the same
categories were named in the less complex conditions in the
descriptions of the objects (e.g., “the toy elephant is about 15 cm
tall, weighs about 200 g”).
Control variables. Creativity seems to be influenced by di-
verse personal characteristics such as intelligence, intrinsic moti-
vation, or mood (Ma, 2009). Thus, these are integrated as control
variables in the present study.
Intelligence was measured using a trail making test (the so
called Zahlen-Verbindungstest, ZVT; Oswald & Roth, 1978). The
ZVT measures cognitive processing speed, which is the basis for
all cognitive abilities and which is independent from cultural
experience such as language or knowledge. The ZVT consists of
two practice and four test matrices which contain 90 numbers in a
disorganized order that have to be connected in ascending order.
The intelligence score was measured by the number of correctly
connected numbers. As the group test version was applied, partic-
ipants had 30 seconds for each test matrix. The total time for the
ZVT was about 5 min.
Intrinsic motivation was measured using the short inventory of
motivation (the so called Kurzskala intrinsischer Motivation, KIM;
Wilde, Bätz, Kovaleva, & Urhahne, 2009), which is an adapted
and time saving version of Deci and Ryan’s (2003) Intrinsic
Motivation Inventory. The scale measures intrinsic motivation
with three items on a 5-point Likert scale (1 ⫽strongly disagree
to5⫽strongly agree).
Positive and negative mood was measured using the German
version of the Positive and Negative Affective Scale (PANAS;
Krohne, Egloff, Kohlmann, & Tausch, 1996). Depending on the
instructions, the PANAS can be used to measure mood states (“at
the moment”) or traits (“in general/ during longer periods of
time”). In the present study, the current mood (state) was
measured with 20 adjectives on a 5-point Likert scale (1 ⫽very
slightly to5⫽very much). Half of the presented words were
related to negative affect (e.g., distressed, upset, guilty), the
other half to positive affect (e.g., alert, enthusiastic, inspired).
Pilot Study
An independent pilot study was conducted to test the manipu-
lation of complexity. Different creativity tasks with a less and
more complex condition were compared with each other. The tasks
were taken from or based off of the TTCT (Torrance, 1966). The
two tasks with the greatest variation in complexity rating between
the less and the more complex condition were selected from these
tasks for the main study. Besides the two tasks already described
above, a third task required the participants to list the smartest,
most interesting, and most unusual ways to increase the sales of a
common board game. The three tasks were applied randomly in a
between-subjects design of an online survey. The participants
(N⫽69; age: M⫽33.0 years, SD ⫽13.11; 57.7% female) had
about 5 min to solve one of the tasks and were asked to note down
their creative solutions. Afterward, they rated the complexity level
of the task on a 5-point Likert scale (1 ⫽very simple to5⫽very
complex).
Based on the participants’ complexity ratings, independent-
samples ttests were conducted to compare the more and the less
complex mode of each task. The means of complexity ratings were
higher in the more complex mode, Task 1: M
less
⫽2.0 (SD ⫽.707)
versus M
more
⫽2.9 (SD ⫽1.101); Task 2: M
less
⫽2.0 (SD ⫽.926)
versus M
more
⫽3.22 (SD ⫽1.202); Task 3: M
less
⫽2.7 (SD ⫽
1.337) versus M
more
⫽2.8 (SD ⫽.855). The results showed that
the differences in complexity rating were significant in only two of
the three tasks (Task 1: t(21) ⫽⫺2.385, p⫽.03; Task 2:
t(15) ⫽⫺2.325, p⫽.04; Task 3: t(13) ⫽⫺.192, p⫽.85). The
magnitude of the differences for the first two tasks was large, while
it was small for the third one (Task 1: d⫽1.05; Task 2: d⫽1.2;
Task 3: d⫽.09). Therefore, the first two tasks were used in the
main study. Additionally, the two more complex and the two less
complex conditions of the selected tasks were compared and no
significant difference were found, M
1less
⫽2.0 (SD ⫽.707) versus
M
2less
⫽2.0 (SD ⫽.926); t(19) ⫽.000, p⫽1.0; M
1more
⫽2.9
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192 MALYCHA AND MAIER

(SD ⫽1.101) versus M
2more
⫽3.22 (SD ⫽1.202); t(17)⫽⫺.610,
p⫽.55. Thus, the two tasks were found to be comparable
regarding their complexity level.
Main Study
Sample. The sample of our study consisted of N⫽121, which
corresponded to the optimal size of the sample following a calcu-
lation of power (Faul, Erdfelder, Buchner, & Lang, 2009). For the
calculation, an average effect size of f⫽.33 (Scott et al., 2004b),
a significance level of .05, a statistical power of .80, and a group
size of six were taken into account.
Seven participants from our sample were excluded for either
showing a lack of motivation (no relevant answer given or chatting
while working on the tasks; N⫽4) or for voluntarily drawing
mind-maps even though they belonged to the control group (N⫽
3). Further analyses are thus based on the results of 114 individ-
uals. Of these, 37 individuals belonged to the control group (less
complex tasks: n⫽20; more complex tasks: n⫽17), 37 to the
classic mind-map group (less complex tasks: n⫽18; more com-
plex tasks: n⫽19), and 40 to the mind-map template group (less
complex tasks: n⫽20; more complex tasks: n⫽20). The
participants were mainly students (96.5%) of various fields of
studies (main fields: law 13.2%, educational science 19.2%, psy-
chology 7.9%, history 6.1%, and business 5.3%). They had an
average age of 23.94 years (SD ⫽4.73). About half of them were
female (56.1%). All participants voluntarily participated in the
study and either took part in a raffle to win one of 15 vouchers or
received credit hours toward experimental participation (required
for all undergraduates in psychology). Although most of the par-
ticipants (85.8%) indicated that they were familiar with some
creativity techniques, a majority of them (69.9%) indicated that
they had not used any technique for a month’s time prior to the
study.
Procedure. The participants were guided through the study,
which lasted for about 1.5 hr, by a trained experimenter. Up to
eight participants were tested simultaneously; however, they
worked individually on the tasks and no interaction between par-
ticipants took place. In the between-subjects design, the partici-
pants were randomly assigned to one of the six experimental
conditions. Those who participated at the same time were assigned
to the same experimental condition to avoid confusion while
explaining the procedure.
At the beginning of the study, the participants were encouraged
to think creatively and not to be afraid of giving wrong answers.
They were also assured that all data would be collected anony-
mously. After the intelligence screening phase, the participants in
both mapping conditions were informed on how to use the mind-
map technique via written instructions. Meanwhile, the partici-
pants in the control condition had to read a neutral text concerning
innovation and employee suggestion systems, which was identical
to the mind-map instructions with regard to length and word count.
Thereafter, the participants familiarized themselves with the diver-
gent thinking tasks (all groups) and had the opportunity to practice
the mind-map technique (mind-map groups) with an exercise from
the Alternated Uses Task (AUT; Christensen et al., 1960). They
were asked to generate as many, different, and original ideas on the
alternative uses of a paper clip. During the first 7 min, the parti-
cipants in the map conditions were supposed to draw a mind-map
or fill in a given mind-map template depending on their condition,
whereas the participants in the control condition had the chance to
take some notes. In the following 3 min, the participants were told
to write down their answers on an extra sheet of paper. After that,
creativity was measured by the two randomly assigned tasks that
were derived from the pilot study. The participants had to generate
as many, different, and original ideas that they could think of to
creatively improve their particular object. For each task, the par-
ticipants were supposed to use the first 12 min to draw a mind-map
(see Figure 3), fill in a mind-map template (see Figure 4), or take
some notes. In the following 8 min, they had to write down their
answers to the task assigned to them. Later on, these responses
were scored for creativity. If someone wanted to finish the task
early, the examiner encouraged him or her to continue with the
task by saying that good ideas sometimes need time to come into
fruition.
After completing the two tasks, the participants were asked
about their mood, their intrinsic motivation, and demographic
characteristics (e.g., age, gender, major subject, and years of
study). Further, they were asked to rate three statements concern-
ing the ease of the use of the mind-map technique (“The instruc-
tions distracted me from being creative;” “The preparation was
difficult for me;” “I would have preferred to work on the tasks in
Figure 3. Mind-maps drawn by different participants. Please See the
online article for the color version of this figure.
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193
ENHANCING CREATIVITY WITH MENTAL MODELS

my conventional manner;” 5-point Likert scale with 1 ⫽absolute
disagreement to5⫽absolute agreement).
Results
In preliminary analyses, the equality of the different experimen-
tal groups was tested: One-way between-groups ANOVAs were
conducted to explore differences in demographic data. The results
showed that there were no significant differences between the
experimental conditions in terms of age, F(5, 107) ⫽.828, p⫽
.53; gender, F(5, 108) ⫽1.885, p⫽.10; or duration of study,
F(5, 108) ⫽1.228; p⫽.30. Furthermore, no significant differ-
ences in fluency, flexibility, and originality were found due to the
order of the two tasks—fluency: t(112) ⫽.09, p⫽.93; flexibility:
t(112) ⫽.61, p⫽.55; originality: t(112) ⫽⫺.22, p⫽.82. Thus,
the different order of the tasks was no longer taken into account.
The participants’ answers were evaluated by a trained, blind
rater according to the category system of the TTCT (Torrance,
2008a). To prove rating objectivity, about 15% of the answers
were rated by a second rater. Interrater reliability ICC (2, 1;
intraclass coefficient, two-way random single measure) was .99 for
fluency, 1.0 for flexibility, and .99 for originality (all p⫽.001).
All three intraclass coefficients were slightly above the high rater
reliability scores (.95–.99) provided by Torrance (2008b).
Hypotheses Testing
Intercorrelations of the creativity dimensions, contrast and con-
trol variables are depicted in Table 1. As expected (see Torrance,
2008b), the three creativity dimensions were highly correlated with
each other. In addition, significant correlations were found be-
tween the creativity dimensions and intrinsic motivation and pos-
itive mood, respectively. Means and standard deviations of the
different groups are depicted in Table 2.
The hypotheses were tested using multiple regression analyses
of the creativity dimensions. Multiple regression analysis was
chosen because it yields identical statistical tests to those provided
by the typically used analysis of variance (ANOVA). Moreover,
with its special coding systems multiple regression analyses test
research hypotheses directly and purposefully and thus enhance
statistical power (Cohen, Cohen, West, & Aiken, 2003). In the
present study, contrast coding was used as a coding system to test
the effect on the three creativity dimensions (Cohen et al., 2003).
For the different technique conditions (control, classic mind-map,
and mind-map template), two contrast variables were needed: c
tec1
contrasts control versus both mind-map conditions (⫺2/3 vs.
1/3) testing Hypothesis 1 and c
tec2
contrasts classic mind-map
versus mind-map templates (⫺.5 vs. .5) testing Hypothesis 2.
Complexity level was coded by one contrast variable (low
Figure 4. Mind-map templates drawn by different participants. See the
online article for the color version of this figure.
Table 1
Means, Standard Deviations, Reliabilities (Cronbach’s Alpha), and Intercorrelations Among Study Variables
Variables M(SD)12345678910
1. Fluency 22.04 (8.74)
a
2. Flexibility 11.76 (3.78)
a
.83
ⴱⴱⴱ
—
3. Originality 9.96 (5.98)
a
.85
ⴱⴱⴱ
.68
ⴱⴱⴱ
—
4. c
tec1
.01 (.47) .32
ⴱⴱⴱ
.33
ⴱⴱⴱ
.37
ⴱⴱⴱ
—
5. c
tec2
.01 (.41) .21
ⴱ
.19
ⴱ
.22
ⴱ
.02 —
6. c
comp
⫺.01 (.50) .04 ⫺.06 .08 .04 ⫺.01 —
7. Intelligence 110.44 (10.95) .10 .12 .09 ⫺.01 .24
ⴱⴱ
⫺.10 —
8. Intrinsic motivation 3.38 (.93) .38
ⴱⴱⴱ
.36
ⴱⴱⴱ
.27
ⴱⴱ
.05 .12 ⫺.05 .08 (.90)
9. Positive mood 2.95 (.73) .25
ⴱⴱ
.24
ⴱⴱ
.17 ⫺.02 .16 ⫺.02 .04 .64
ⴱⴱⴱ
(.88)
10. Negative mood 1.25 (.31) ⫺.11 ⫺.11 ⫺.06 .01 .07 .02 ⫺.01 ⫺.31
ⴱⴱⴱ
⫺.28
ⴱⴱ
(.71)
Note. N ⫽114. Reliability coefficients appear in parentheses. Coding: c
tec1
:⫺2/3 ⫽control, 1/3 ⫽maps; c
tec2
:⫺1/2 ⫽classic mind-map, 1/2 ⫽
mind-map template; c
comp
:⫺1/2 ⫽low complexity, 1/2 ⫽high complexity.
a
Nonstandardized data are reported.
ⴱ
p⬍.05.
ⴱⴱ
p⬍.01.
ⴱⴱⴱ
p⬍.001 (two-tailed).
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194 MALYCHA AND MAIER

complexity: c
comp
⫽⫺.5 vs. high complexity: c
comp
⫽.5)
testing Hypothesis 3.
A control variable was retained to the analyses only if it pro-
duced a relationship significant at the .05 level. Thus, in the
multiple regression analyses, the control variables intrinsic moti-
vation and positive mood were entered as a first step, the two
contrast variables for the techniques (c
tec1
and c
tec2
) and the one
for complexity (c
comp
) were entered as a second step, and the two
interaction weights (c
tec1
⫻c
comp
and c
tec2
⫻c
comp
) were entered
as a third step. For a better overview, the results of the three
regression analyses will be reported together: Intrinsic motivation
showed a significant effect on all three creativity dimensions and
explained 15% of the variance of fluency, 13% of the variance of
flexibility, and 7% of the variance of originality. Entering the three
contrast codes (c
tec1
,c
tec2
,c
comp
) in the second step of the analyses
explained 27% of the variance of fluency, 25% of the variance of
flexibility, and 24% of the variance of originality in the second
step of the regression analyses (see Table 3). The first contrast
code for the techniques (c
tec1
) showed a large effect in each of the
three regression analyses (fluency: p⫽.000, d⫽.77; flexibility:
p⫽.000, d⫽.77; originality: p⫽.000, d⫽.90), indicating that
the two mapping conditions enhanced the quantity, diversity, and
uniqueness of ideas when compared to note-taking condition (see
Figures 5,6,7). Thus, our data confirmed Hypothesis 1. The
second contrast code for the techniques (c
tec2
) showed a medium
effect for fluency (p⫽.04, d⫽.45) and originality (p⫽.03, d⫽
.49), which means that mind-map templates are even more bene-
ficial with respect to fluency and originality than classic mind-
maps. The second contrast code showed a nonsignificant effect on
flexibility (p⫽.10, d⫽.39). Hypothesis 2 was therefore partly
confirmed. The contrast code for complexity (c
comp
) revealed a
nonsignificant, small effect on each of the three dimensions (flu-
ency: p⫽.59, d⫽.18; flexibility: p⫽.50, d⫽.12; originality:
p⫽.39, d⫽.24), indicating that the complexity of the tasks had
no significant diminishing effects on the three creativity dimen-
sions. Thus, Hypothesis 3 could not be confirmed.
In the third step, entering the two interaction weights in the three
regression analyses explained 32% of the variance of fluency, 25%
of the variance of flexibility, and 30% of the variance of original-
ity. The first interaction weight (c
tec1
⫻c
comp
) had no significant
influence on the three creativity dimensions (fluency: p⫽.22, d⫽
.30; flexibility: p⫽.50, d⫽.21; originality: p⫽.52, d⫽.20),
indicating that when the two mapping conditions are conjointly
contrasted to note-taking, no interaction existed with complexity.
As a result, Hypothesis 4a could not be confirmed. The second
interaction weight (c
tec2
⫻c
comp
) showed a significant effect of
medium size for fluency and originality (fluency: p⫽.02, d⫽.49;
originality: p⫽.01, d⫽.58). With respect to the quantity and
uniqueness of the generated ideas, participants using a mind-map
template in the more complex condition outperformed the ones in
the less complex condition. Whereas, for the participants who used
classic mind-maps or notes, achievement on more complex tasks is
slightly lower than on less complex tasks. However, this interac-
tion effect could not be found for flexibility (p⫽.83, d⫽.14).
The mind-map templates in the more and in the less complex task
conditions showed about the same amount of flexibility. Thus, the
data partly confirmed Hypothesis 4b.
Further Analyses
In order to take the comfort of applying a mind-map into
account, we additionally ran a regression analysis of the contrast
codes of the techniques (c
tec1
and c
tec2
) on the three questions
concerning the ease of the use (described above in the Procedure
section): The participants in both mapping groups significantly
preferred to work on the tasks in their conventional manner (c
tec1
:
␤⫽⫺.26, p⫽.004). Regarding this question, no significant
difference was found between classic mind-maps and mind-map
templates (c
tec2
:␤⫽.11, p⫽.21). Furthermore, no significant
difference was found between all three groups regarding their
subjective rating of the degree of difficulty of the preparation, that
is, mind-maps or notes (c
tec1
:␤⫽⫺.15, p⫽.11; c
tec2
:␤⫽⫺.01,
p⫽.89) or whether the preparation kept them from being creative
(c
tec1
:␤⫽⫺.17, p⫽.07; c
tec2
:␤⫽⫺.01, p⫽.95).
Discussion
The results of the present study provide strong support for the
notion that eliciting mental models via mind-maps enhances cre-
ativity. Compared with common note-taking, applying the mind-
map technique significantly increased each of the three creativity
dimensions (i.e., fluency, flexibility, and originality). Eliciting the
mental model via the mind-map technique showed large effect
sizes of .77 ⬍d⬍.90. These are comparable to the average effect
sizes of d⫽.80 of idea production training which were calculated
in the meta-analysis of Scott et al. (2004b). In the present study, we
were able to replicate the beneficial effects of the mind-map
technique on fluency and flexibility found by Malycha and Maier
(2012). However, there were a few significant additions: First, we
found a significant positive effect of the mind-map technique on
originality. Second, we were able to show that fluency and origi-
nality were further enhanced by mind-map templates and even
showed the highest values when these templates were used on
more complex tasks. Third, intrinsic motivation had a significant
effect as a control variable.
Table 2
Means and Standard Deviations of the Experimental Groups in
the Three Creativity Dimensions
Dependent variable Group affiliation MSE
Fluency Control group Less complex ⫺.35 .19
More complex ⫺.55 .21
Classic mind-map Less complex .12 .20
More complex ⫺.11 .20
Mind-map template Less complex .06 .19
More complex .75 .19
Flexibility Control group Less complex ⫺.32 .20
More complex ⫺.60 .22
Classic mind-map Less complex .08 .21
More complex .00 .21
Mind-map template Less complex .37 .20
More complex .39 .20
Originality Control group Less complex ⫺.51 .19
More complex ⫺.52 .21
Classic mind-map Less complex .19 .21
More complex ⫺.16 .20
Mind-map template Less complex .08 .19
More complex .85 .20
Note.N⫽114. z-standardized data is reported.
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195
ENHANCING CREATIVITY WITH MENTAL MODELS

We could show that creativity is about applying knowledge
meaningfully and directing resources efficiently. Strategic and
relational knowledge in particular are fundamental to creativity
(Howel & Boies, 2004;Mumford, Medeiros, et al., 2012) as they
enhance the subsequent creative processes of conceptual combi-
nation or idea generation (Mumford et al., 1991). Eliciting under-
lying mental models via mind-mapping may help people to acti-
vate a broader array of relevant knowledge, identify critical causes,
or exclude irrelevant information. A more useful and greater
network of interrelated concepts is created in the process. Al-
though, these networking abilities have a positive influence on
creativity (Baer, 2012;Wiley & Jarosz, 2012), more knowledge is
not always beneficial for the production of a viable solution. To be
supportive, knowledge has to be organized meaningfully (Mum-
ford, Hester, et al., 2012). Examining the drawn mind-maps in both
mapping conditions, we could see that concepts were highly in-
terconnected and differentiated (see Figures 3 and 4). By contrast,
the notes of the control group showed nearly no interconnections.
Even if mind-maps are not directly comparable with notes, mind-
maps seem to activate, provide, and organize knowledge in a
different way, which—as the results show—is more beneficial for
creative problem-solving. In future research, these knowledge pro-
cesses, which seem to be activated by mind-maps, should be
placed more into the focus of creativity research. For the devel-
opment of further creativity trainings, it is important to know if the
activation, the organization, or the interconnection of knowledge is
the determining factor for creativity and how these processes
influence each other.
In the template conditions, higher creative achievement was
observed compared with the classic mind-map conditions—
especially when the tasks were more complex. When mind-map
templates are used, a condition similar to routinization is
reached as the user can focus more of his or her attention on the
task at hand instead of the application of the mind-mapping
technique. Routinization normally begins after repeated execu-
tion of a behavior and leads to less required intentionality and
awareness (Kanfer & Ackerman, 1989). Ohly, Sonnentag, and
Pluntke (2006) found that routinization had positive effects on
creativity and idea implementation. In long-term studies, the
mechanisms of templates should now be investigated. If the
long-term effects of classic mind-maps gradually approaches
the superior effects of templates, the notion of routinization
would be supported. Moreover, further research has to ascertain
whether other forms of templates (e.g., with less or more
branches) have the same effect on creative problem-solving. As
the template in the present study was designed to be an average-
sized mind-map, which was derived from a previous study
(Malycha & Maier, 2012), it may carry some important infor-
Table 3
Hierarchical Regression Analyses Predicting the Three Creativity Dimensions
Dependent variable Predictor BSEB ␤R
2
⌬R
2
F
Fluency Step 1 controls variables
Intrinsic motivation .37 .11 .34
ⴱⴱⴱ
.15
ⴱⴱⴱ
.15
ⴱⴱⴱ
9.63
ⴱⴱⴱ
Positive mood ⫺.02 .15 ⫺.01
Step 2 hypotheses testing
c
tec1
.65 .17 .31
ⴱⴱⴱ
.27
ⴱⴱⴱ
.12
ⴱⴱⴱ
8.01
ⴱⴱⴱ
c
tec2
.40 .20 .17
ⴱ
c
comp
.09 .16 .04
Step 3 interaction
c
tec1
⫻c
comp
.43 .35 .10 .32
ⴱⴱⴱ
.05
ⴱ
7.03
ⴱⴱⴱ
c
tec2
⫻c
comp
.92 .40 .19
ⴱ
Flexibility Step 1 control variables
Intrinsic motivation .33 .12 .30
ⴱⴱ
.13
ⴱⴱⴱ
.13
ⴱⴱⴱ
8.18
ⴱⴱⴱ
Positive mood .03 .15 .02
Step 2 hypotheses testing
c
tec1
.67 .18 .31
ⴱⴱⴱ
.25
ⴱⴱⴱ
.12
ⴱⴱⴱ
7.05
ⴱⴱⴱ
c
tec2
.34 .21 .14
c
comp
⫺.11 .17 ⫺.06
Step 3 interaction
c
tec1
⫻c
comp
.25 .36 .06 .25
ⴱⴱⴱ
.01 5.04
ⴱⴱⴱ
c
tec2
⫻c
comp
.09 .41 .02
Originality Step 1 control variables
Intrinsic motivation .24 .12 .22
ⴱ
.07
ⴱ
.07
ⴱ
4.29
ⴱ
Positive mood ⫺.01 .15 ⫺.01
Step 2 hypotheses testing
c
tec1
.76 .17 .36
ⴱⴱⴱ
.24
ⴱⴱⴱ
.17
ⴱⴱⴱ
6.78
ⴱⴱⴱ
c
tec2
.45 .20 .19
ⴱ
c
comp
.14 .16 .07
Step 3 interaction
c
tec1
⫻c
comp
.23 .35 .05 .30
ⴱⴱⴱ
.06
ⴱ
6.35
ⴱⴱⴱ
c
tec2
⫻c
comp
1.12 .40 .23
ⴱⴱ
Note. N ⫽114. Coding: c
tec1
:⫺2/3 ⫽control, 1/3 ⫽maps; c
tec2
:⫺1/2 ⫽classic mind-map, 1/2 ⫽mind-map
template; c
comp
:⫺1/2 ⫽low complexity, 1/2 ⫽high complexity. Coefficients are taken from the last step of
regression analysis.
ⴱ
p⬍.05.
ⴱⴱ
p⬍.01.
ⴱⴱⴱ
p⬍.001 (two-tailed).
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196 MALYCHA AND MAIER

mation for the task. But as all participants in the template
conditions individualized their given templates by adding more
branches to the templates and at the same time leaving some
given branches free, the actual form of the template seems to be
of secondary importance.
Interestingly, the users of the mind-map technique did not
feel comfortable with using it and would have preferred to use
their conventional manner of solving tasks even though they
reached higher levels of creativity. This result supported our
assumption that users need time to familiarize themselves and
become comfortable with new unfamiliar ways of organizing
their ideas (see also Hilbert et al., 2008;Schau & Mattern,
1997). Nonetheless, future research should investigate how
mind-mapping can be made more comfortable and attractive to
its users so that they voluntarily employ the beneficial tech-
nique.
The present study was the first study to vary task complexity
in experimental settings concerning creativity. Normally, cre-
ativity tasks in experimental settings are quite simple and do not
adequately depict the complex nature of real-world situations
(Cropley, 2000;Okuda, Runco, & Berger, 1991). Up to now,
the effect of increasing task complexity on creative achieve-
ment was unknown. In the present study, we found that creative
achievement on more complex tasks was slightly, but not sig-
nificantly lower than in less complex tasks. But when the
mind-map templates were applied, the creative achievement in
fluency and originality rose even above the values of less
complex tasks. This effect should be rated as more compelling
because complexity was manipulated by varying coordinative
complexity, which meant that certain features could only be
changed together. Thus, the number of possible valid responses
was reduced in the more complex condition, which is common
when complexity increases (Liu & Li, 2012). Although the base
rate of possible feature changes was a little lower in complex
tasks, the participants using templates generated the highest
number or and the most original ideas. One explanation of this
effect can be that more complex tasks provide much more
information than is visible at first glance. This information
could have only been exploited if cognitive overload was kept
in check by an appropriate strategy (Wood, 1986). Mind-map
templates seem to be an appropriate highly suitable strategy
with which the increased density of information and intercon-
nectedness of more complex tasks can be handled. Nonetheless,
we need more research that considers the influence of creativity
techniques on a diverse array of complex problems, as these
kind of tasks are the ones for which creativity techniques were
invented and are most needed.
Regarding flexibility, the templates did not show a superior
effect to classic mind-maps due to a no interaction effect between
them and complexity. In this context, flexibility is the number of
categories of the ideas generated and is therefore shaped by the
amount of mental clusters a person uses. On a mind-map, mental
clusters are represented by major facets and their subsequent ideas.
As a consequence, we assume that flexibility scores will increase
when the number of major facets increases. Further studies should
Figure 5. Interaction effects of creativity techniques and complexity level
on fluency.
Figure 6. Interaction effects of creativity techniques and complexity level
on flexibility.
Figure 7. Interaction effects of creativity techniques and complexity level
on originality.
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197
ENHANCING CREATIVITY WITH MENTAL MODELS

investigate whether a different way of explaining mind-maps can
foster the number of major facets on all complexity levels and
therefore positively influence flexibility scores under more com-
plex and thus more restricted conditions as well. As the type of
instruction can influence the type of creative achievement attained
(Lee, 2004;Niu & Liu, 2009), maybe the type of explanation (i.e.,
the way a creativity technique is explained) can influence whether
ideas will be elaborated more on in terms of depth (fluency and
originality) and breadth (flexibility). Further research should shed
light on this issue.
Limitations
The limitations of this exploratory study mainly relate to two
topics. The first major weakness is a potential limitation of exter-
nal validity. The present study is an experimental study with a
student sample. Although laboratory experiments offer advantages
in terms of control and measurement, they lack external validity. In
other words, these results cannot be easily generalized to nonstu-
dent samples or nonexperimental settings. Thus, the effects of
mind-maps as a creativity technique on real-world problems can
only be assumed based on the experimental findings. Although
external validity of experimental studies is doubtful, Mitchell
(2012) found evidence for a strong association between laboratory
and field findings in his meta-analysis. Nonetheless, further re-
search is necessary to predict the influence of the mind-map
technique in real-world settings. The application of the mind-map
technique should also be broadened to nonstudent samples and
participants from different age-groups in order to generalize our
findings.
The second limitation of the present study refers to the
operating modes of the mind-map technique. We found that
mind-maps and mind-map templates are useful for creative
problem-solving because they enhance the quantity, diversity,
and uniqueness of generated ideas. However, as the attributes or
features of the elicited mental models were not scored, we still
do not know how and why mind-maps enhance creativity and
which attributes of a mind-map are most supportive. As stated
above, we assumed an enhanced level of knowledge activation
via the identification of critical causes and the exclusion of
irrelevant information as well as conceptual combination to be
the two core processes driving the effectiveness of mind-maps.
In further studies, the features of mind-maps and these core
processes need to be tested in detail. First, an appropriate
classification system for mind-maps, similar to the one Mum-
ford, Hester, et al. (2012) used for mental models or the one
Novak and Gowin (1984) applied to concept-maps, should be
created. Second, the mechanisms of the mind-map technique
such as knowledge activation, conceptual combination, the in-
terconnectedness of ideas, their concurrent visibility, or spread-
ing activation should be tested separately to gain further in-
sights into which mechanisms are the most important ones. As
functional MRI (fMRI) studies are recently in scope of creativ-
ity researchers (e.g., Ellamil, Dobson, Beeman, & Christoff,
2012;Fink et al., 2010), the cognitive processes of the mind-
map technique should be investigated by looking “inside the
head” (Smith & Ward, 2012). Due to new technologies such as
fMRI-compatible drawing tablets, it should be possible to ex-
amine these cognitive processes in detail while subjects are
mind-mapping.
Conclusion
To fulfill one of the requirements of modern civilization—to
constantly invent creative products—specific creativity tech-
niques can be applied because many of the observed differences
in creative achievement can be explained by differences in the
use and intensity of application of certain cognitive processes
(Nickerson, 1999;Runco, 2014). As creativity is the result of
applying and combining existing knowledge in a new way and
simultaneously managing resources effectively, this study
aimed at advancing research on creativity techniques by elicit-
ing mental models via the mind-map technique. Although mind-
maps are a widely accepted and commonly used creativity
technique, research in this area is still in its infancy. Our
research showed that mind-maps can significantly foster cre-
ativity— especially when they are applied with templates on
complex tasks. Templates seem to enhance the full potential of
the mind-map technique in such a way that an increase in
information density and interconnectedness of more complex
tasks can be used to the fullest.
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Received July 15, 2015
Revision received May 30, 2016
Accepted June 30, 2016 䡲
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ENHANCING CREATIVITY WITH MENTAL MODELS