improving your creative potential without awareness: overinclusive thinking training

Accepted Manuscript

Title: Improving Your Creative Potential without Awareness:Overinclusive Thinking Training

Author: Fa-Chung Chiu

PII: S1871-1871(14)00056-XDOI: http://dx.doi.org/doi:10.1016/j.tsc.2014.11.001Reference: TSC 275

To appear in: Thinking Skills and Creativity

Received date: 25-1-2014Revised date: 30-7-2014Accepted date: 9-11-2014

Please cite this article as: Chiu, F.-C.,Improving Your Creative Potential withoutAwareness: Overinclusive Thinking Training, Thinking Skills and Creativity (2014),http://dx.doi.org/10.1016/j.tsc.2014.11.001

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http://dx.doi.org/doi:10.1016/j.tsc.2014.11.001

http://dx.doi.org/10.1016/j.tsc.2014.11.001

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Improving Your Creative Potential without Awareness: Overinclusive Thinking Training

Fa-Chung Chiu*

Department of Psychology and Social Work, National Defense University, 112, Taiwan

(R.O.C.)

E-mail addresses:

[email protected]

*Corresponding author. Tel.: +886 2 2892 9194 ext.12; fax: +886 2 2891-4169

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Highlights

>We manipulate the overinclusive thinking training and measure participants’ performance in

creativity. >We examine the effects of the overinclusive thinking training on creativity

improvement. > The complete overinclusive thinking training can improve creativity.

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ABSTRACT

The purpose of this study was to examine the effects of overinclusive thinking training (OTT)

on creativity improvement. In Experiment 1, 40 undergraduates were randomly assigned to

the OTT group or the control group. After the training, the participants were required to

complete categorization tasks. The results show that the OTT enhanced participants’ ability to

engage in overinclusive thinking. In Experiment 2, 42 undergraduates were randomly

assigned to the OTT group or the control group. After the training, the participants were

required to complete the Creative Thinking Test. The results show that the performance of the

OTT group regarding fluency and originality was higher than that of the control group. In

Experiment 3, 56 undergraduates were randomly assigned to three groups: the control group,

the long-distance semantic OTT group, or the short-distance semantic OTT group. After the

training, the participants were required to solve insight problems. The results show that the

performance of the long-distance semantic OTT group in insight problem solving was

superior to that of the short-distance semantic OTT and the control group. In Experiment 4,

50 undergraduates were randomly assigned to the OTT group or the control group. The

Creative Thinking Test was performed 7 days after training. The results show that the training

effect on originality remained; however, no training effect was observed on either fluency or

flexibility.

Keywords: creativity, divergent thinking, insight problems, overinclusive thinking training

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1. Introduction

In a knowledge-based economy, creativity is considered a core indicator of the

competitiveness of countries, organizations, and people (Egan, 2005).Creativity is an

essential part of organizational innovation (Amabile & Khaire, 2008). In addition, it helps

people manage social conflicts and disputes (De Dreu, Baas, & Nijstad, 2008). Therefore,

improving creativity has become a critical concern; specifically, how people generate creative

ideas and solutions has attracted considerable attention from scholars (Roskes, De Dreu, &

Nijstad, 2012). For improving creativity studies, numerous researchers have developed

methods to improve creativity (e.g., Cheng, Wang, Liu, & Chen, 2010; Chiu, 2012, 2014;

Chiu &Tu, 2014; Chrysikou, 2006; Fleith, Renzulli, & Westberg, 2002; Koppel & Storm,

2013; Lewis & Lovatt, 2013; Nusbaum, Silvia, & Beaty, 2014; Oppezzo & Schwartz, 2014;

Patrick & Ahmed, 2014).

In the past, most of the creativity training methods were categorized as explicit creative

skills because the purpose of the training was to teach participants to apply creative skills

consciously to enhance creativity. However, occasionally, using creative skills did not

improve creativity because consciously applying creative thinking skills might interrupt the

search for creative answers and further hinder creative thinking (Zhong, Dijksterhuis, &

Galinsky, 2008). In addition, although explicit creative skills (rule-based training) could

provide new thinking directions, remote association ability is still required to be able to

associate unrelated concepts and subsequently generate original and creative ideas. If the

association between one person’s remote knowledge nodes is weak, the effect of explicit

skills would not be significant. For example, when asking participants with a weak

knowledge node connection to perform free association, despite their ability to use creative

skills consciously, they are not able to generate creative ideas or products; for example,

people who experience difficulty in connecting houses and chopsticks are not able to imagine

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that a house could be built with chopsticks.

To overcome these limitations, developing non-rule-based training of creative thinking,

which could directly strengthen the connections between knowledge nodes in the human

brain, is necessary. The purpose of this study was to develop non-rule-based training methods

based on Eysenck’s theory of overinclusive thinking (Eysenck, 1995).

1.1 Creativity

Generally, regarding the characteristics of created products, creativity has been defined

as the ability to develop novel and appropriate ideas (Sternberg & Lubart, 1999). A novel

creative product refers to a product or idea that has never appeared before or has never been

applied in a certain manner. Regarding the characteristics of the creative process, creativity is

considered as a process involving knowledge activation (Eysenck, 1995), remote association

(Mednick, 1962), divergent thinking (Guilford, 1957), and insight ability (Mayer, 1995).

Scholars have commonly adopted creative thinking tests, including the divergent thinking test

(Guilford, 1957), insight problems (Schooler & Melcher, 1995), the remote association test

(Mednick, 1962), the creative generation task (Friedman & Förster, 2002), and the

dominance-to-rank ratio (Leung & Chiu, 2010), to measure creative potential and

differentiate people with high and low creativity. In this study, the divergent thinking test

(Guilford, 1957) and insight problems were used to measure the participants’ creative

potential. Divergent thinking is defined as the ability to produce numerous diverse creative

ideas (Guilford, 1956). Guilford (1956) developed an alternative uses test that can be used to

examine participants’ divergent thinking and includes fluency, originality, and flexibility

indices. Fluency refers to the ability to create substantial number of ideas, originality refers to

the ability to generate novel ideas, and flexibility refers to the ability to produce multiple

conceptual categories. Regarding insight problems, Dominowski (1995) stated that “problem

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solvers usually encounter obstacles at first, then later invent a sudden ‘a-ha!’ solution.” An

example of this is the candle problem (Duncker, 1945; details are provided in Section 3.1.2

Materials).

The advantage of the divergent thinking test is that it can be used to measure divergent

thinking responses (Runco, 1999). However, divergent thinking tests do not include specific

solution objectives; consequently, solution appropriateness is not considered (Lin, Lien, &

Jen, 2005). To overcome the disadvantage of divergent thinking tests in that they do not

emphasize response appropriateness, we used insight problems that focus on solution

appropriateness to measure creativity (i.e., creative insight problems combine divergent

elements with convergent elements [De Dreu, Nijstad, Baas, Wolsink, & Roskes, 2012]).

Moreover, if consistent results are obtained from various creative measurement tools, the

robustness of the research outcome can be enhanced.

1.2 Overinclusive Thinking Training

According to Scott, Leritz, and Mumford (2004), the results of meta-analysis regarding

the effectiveness of creativity training revealed that the effective instruction of creative skills

or rules is a core factor of effective creativity training. Several explicit skills for creativity

improvement have been proposed, such as the association skill (Cheng et al., 2010; Gruszka

& Necka, 2002; Piers & Morgan, 1973), creative problem solving (Parnes, 1962), cognitive

stimulation (Fink et al., 2010), divergent thinking (Fleith et al., 2002; Runco, 1991),

conceptual combination (Kohn, Paulus, &Korde, 2011), Synectics (Gordon, 1961); future

thinking (Chiu, 2012); instruction on how to be creative (Nusbaum et al., 2014),

improvisation (Lewis & Lovatt, 2013), and representation change (Patrick & Ahmed, 2014).

These creative skills provide rules for participants to apply to perform creative thinking.

For example, by using association skills, participants can associate two ideas to create novel

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products (Cheng et al., 2010). An example of this application is the association between

vehicles and houses, which results in the creation of mobile homes. Thus, when people apply

these creativity skills, they are in a state of controllable consciousness and awareness

(Dorfman, Shames, & Kihlstrom, 1996). However, consciously using explicit creative skills

might hinder the search for new ideas (Zhong et al., 2008). Zhong et al. (2008) found that in a

state of unconscious thought, participants were distracted when taking the Remote Associates

Test (RAT). Compared with the state of conscious thought (not including distracter tasks),

the state of unconscious thought increased the accessibility of RAT answers, indicating that

when participants are searching strategies to achieve problem-solving objectives, creativity

performance may be hampered.

From the perspective of working memory (WM), when a person engages in two

cognitive activities simultaneously, the performance of WM in conducting the main activity

diminishes (Lorist, Boksem, & Ridderinkhof, 2005). Retaining novel information in creative

thinking and discriminating between task-relevant and task-irrelevant information are two

crucial WM processes (Unsworth & Engle, 2007); therefore, WM capacity is essential to

creative thinking. The empirical study conducted by De Dreu et al.(2012) reported that when

participants were asked to complete each RAT item, they were also asked to remember two

(low load) or five (high load) digit strings. After completing the RAT items, the participants

were required to recall all the strings. The results indicated that participants under low-load

conditions performed more favorably than did those under high-load conditions. According to

the results, the wider the WM span is, the higher the creativity performance is. Therefore,

when people use rule-based (e.g., association skills) strategies to perform creative thinking,

rule operation may occupy WM capacity and prevent them from retrieving novel information

from the WM and applying task-related information. Thus, rule-based strategies hamper

creative thinking. To overcome the limitations of rule-based creative skills, developing

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non-rule-based creative training methods is necessary. In the following sections, we discuss

the possibility of non-rule-based creative training from an overinclusive thinking perspective.

The concept of overinclusive thinking was first proposed by Cameron (1944) to describe

the thinking pattern of schizophrenic patients. Overinclusive thinking is regarded as a

personality trait. Generally, overinclusive thinking is defined as the inability to preserve

conceptual boundaries (Andreasen & Powers, 1974); thus, people who engage in

overinclusive thinking have a broader conceptual framework. In addition, overinclusive

thinking can be described as increased generalization (Eysenck, 1993, 1995). When

completing the questions related to the categorization inclusion task, such as “Is a camel a

vehicle?”, overinclusive thinkers generally conclude that a camel is a vehicle based on its

similarity to cars or buses in that they are used to transport people or objects from one

location to another, whereas non-overinclusive thinkers generally exclude camels from the

vehicle category because they consider wheels to be a necessary feature of vehicles

(Friedman & Förster, 2002). Individuals who possess the overinclusive thinking trait are

highly capable of freeing their minds from conceptual boundaries, improving their creativity

by considering concepts that other people deem unrelated to certain categories, and thereby

providing an increased number of options (Campbell, 1960). Moreover, people with loose

and extensive associative networks are highly capable of performing divergent thinking and

generating highly original ideas (Eysenck, 2003).The empirical results of a previous study

indicated that participants with high psychoticism who exhibited the trait of overinclusive

thinking demonstrated superior originality in conceptual expansion and creative imagery than

participants with low psychoticism did (Abraham, Windmann, Daum, & Güntürkün, 2005).

Additionally, Andreasen and Powers (1975) discovered that creative writers generally exhibit

relatively strong overinclusive thinking. In summary, overinclusive thinking is correlated to

creativity.

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These relevant studies have indicated that although overinclusive thinking is considered

as a trait, this type of cognitive pattern can increase creativity through manipulation.

Chrysikou (2006) requested participants to complete the Alternative Categories Task (ACT)

in an experimental study. The participants were presented with 12 common items (e.g., shoes

and forks) and were asked to describe the common uses of the presented items. For example,

for shoes, the common category was an item used as footwear. In this task, the participants

were asked to describe the category of each item as specifically as possible. For example, a

shoe could also be an object used to pound a nail into a wall. When the participants

completed the ACT, they were asked to solve seven insight problems. The results indicated

that compared with the control group, the ACT group exhibited superior performance in

insight problem solving. The researcher asked the participants in the ACT group to propose

unusual uses for the items. Although Chrysikou did not state that the ACT was used for

overinclusive thinking training (OTT), this activity was similar to those involving

overinclusive thinking. For example, the participants included shoes in the category of

hammers, which represents the cognitive attribute of overinclusive thinking. Wen, Butler, and

Koutstaal (2013) also reported that participants who completed the ACT tasks improved their

performance in insight problem solving. Based on these relevant empirical studies, we

inferred that enhancing creativity training by using an overinclusive thinking perspective is

possible.

The concept of overinclusive thinking can also be explained by the spreading activation

theory proposed by Collins and Loftus (1975). Collins and Loftus indicated that semantic

memory nodes (e.g., as in birds) connect with each other and become conceptual networks

(e.g., an animal conceptual network). Hence, the activation of nodes in a network may spread

to related nodes or concepts. Semantic nodes from the same conceptual network possess a

strong association (e.g., cats and dogs, both are common domestic animals); by contrast,

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semantic nodes from different conceptual networks possess a weak association (e.g., cats and

the ocean). The spreading activation theory has been supported by empirical studies that

employed the semantic priming paradigm (Copland et al., 2003; Meyer & Schvaneveldt,

1971).

People with high creativity appear to be highly capable of spreading activation among

various conceptual networks. For example, creative people can generate the idea that fruit

and vehicles share common characteristics. In an empirical study, Rychlicka (cited in Necka,

1994) instructed participants to assess whether two words were associated. In the experiment,

half of the word pairs were closely associated (e.g., chair and table) and the other half were

remotely associated (e.g., chair and lawn). The results indicated that the semantic distance

between the word pairs influenced the participants’ judgments. For paired words with a short

semantic distance, the participants exhibited a significantly higher tendency to consider the

words as being related to each other. In addition, compared with participants with lower

creativity, highly creative participants generally considered paired words with a substantial

semantic distance as being related to each other. The ability to associate paired words with

long semantic distance is similar to the characteristic of overinclusive thinking; namely, it is

less likely to be limited by semantic conceptual boundaries and it facilitates the spread of

node activation throughout various conceptual networks. In addition, in an empirical study,

Mednick (1962) used the RAT to measure participants’ remote association capacity (e.g.,

question: rat, blue, and cottage; answer: cheese). The results suggested that individuals who

exhibited high ability in conducting remote association had high flexibility. Other studies

have also emphasized the relationship between remote association abilities and creativity (Cai,

Mednick, Harrison, Kanady, & Mednick, 2009; Vul & Pashler, 2007; Ward, Thompson-Lake,

Ely, & Kaminski, 2008). Therefore, participants with high creativity have a wide range of

conceptual nodes and can connect seemingly unrelated concepts.

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The aforementioned studies demonstrate that overinclusive thinking improves creative

performance. However, Chrysikou (2006) and Wen et al. (2013) requested participants to

classify items into untypical categories. This type of training requires participants to classify

an item into a certain category through active thinking; however, this type of training is

negatively affected by people’s existing knowledge structure. To avoid such an effect, the

OTT developed in this study does not require individuals to think. The proposed method only

requires individuals to classify irrelevant items passively into one category. For example, the

unrelated items, watch and paper, were classified into a category. This procedure of

classifying unrelated items into a category loosens the boundaries of knowledge concept

categories (Andreasen & Powers, 1974) and develops people’s ability to engage in

overinclusive thinking. Thus, the training effect of the method proposed in this study is not

limited by the existing overinclusive thinking capacity of people; instead, it is determined by

training materials and procedures.

OTT simply requires participants to classify items. In addition to participants not being

able to perceive training purposes during OTT, participants are not required to learn creative

rules (i.e., non-rule-based training), which is different from previous creative skill training

(e.g., Fleith et al., 2002; Patrick & Ahmed, 2014; Kohn et al., 2011; Runco, 1991). Thus, the

situation in which creativity performance is reduced because rule-based creative thinking

hampers WM operative performance can be avoided. The Experiment 1 section further details

the OTT procedure.

2. Experiment 1

Based on the hypothesis that overinclusive thinking improves creativity, we aimed to

improve participants’ creativity by conducting OTT. In this study, for example, the

participants were asked to press the “left” keyboard when words related to furniture (e.g.,

chair) or fruit (e.g., strawberry) appeared on the screen. They were asked to press the “right”

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keyboard when words related to housing space (e.g., bedroom) or vegetables (e.g., celery)

appeared on the screen. Although “bedroom” and “vegetables” belong to two unrelated

conceptual categories, the participants were trained to place “bedroom” and “celery” in the

same category (i.e., the right keyboard) during the OTT. In other words, OTT enabled the

participants to ignore existing knowledge categories and classify seemingly unrelated

exemplars into the same category, thereby expanding the inclusivity or scope of their

conceptual networks. Through this training, the participants’ overinclusive thinking could be

enhanced and their conceptual boundaries could be loosened. This resulted in improved

creativity and the improved ability to associate novel knowledge nodes after the training.

Experiment 1 was conducted to explore whether OTT could improve the participants’

overinclusive thinking. When the results of Experiment 1 verified that OTT improved

overinclusive thinking, the manipulation training task was confirmed to conform to the

argument (i.e., OTT improved overinclusive thinking) in the literature. Subsequently, we

explored how OTT enhances creativity in Experiments 2–4.

Experiment 1 was a one-factor between-subject design. The independent variable was

the group (i.e., the OTT group and the control group), and the dependent variable was the

participants’ performance in conducting the categorization task. Because conceptual

boundaries cannot be maintained during overinclusive thinking (Andreasen & Powers, 1974),

when people with high overinclusive thinking ability were requested to evaluate several

exemplars in the prototypical category, they generally classified nontypical and typical

exemplars into the same category (e.g., categorizing handbags as apparel). Individuals with

high overinclusive thinking ability perceive relatively ambiguous boundaries among

categories; therefore, they demonstrate increased category inclusiveness, which is the

characteristic of overinclusive thinking. In this study, overinclusive thinking was measured

using the categorization task (Isen & Daubman, 1984). The purpose of using the task was to

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observe how participants evaluate the category of non-typical exemplars. If the participants

exhibited a high tendency to classify non-typical exemplars into a typical concept category,

they were considered to demonstrate a high level of overinclusive thinking.

2.1 Method

2.1.1 Participants

A total of 40 undergraduates (29 men and 11 women) at the National Defense University

in Taiwan were recruited in Experiment 1. The mean of their ages was 20.01 years with a

standard deviation (SD) of 1.08. After eliminating one participant’s data because his training

accuracy was lower than 70%, the data of the remaining 39 participants were used in

statistical analyses.

2.1.2 Materials

2.1.2.1 Overinclusive Thinking Training Task

The purpose of developing the OTT task was to improve the participants’ overinclusive

thinking. In this task, the participants were asked to categorize exemplars of fruit (e.g.,

strawberry), vegetables (e.g., celery), furniture (e.g., chair), and housing space (e.g.,

bedroom), with 10 words in each category. These exemplars were selected from those

provided by Chiu (2010). SuperLab Pro 4.01 was employed to design this experimental task

and the training was conducted using computers (Fig. 1, Part A). Two labels (i.e., “left” and

“right”) were pasted on two keyboards that indicated the left and right keyboards, which the

participants used to make responses. The OTT task consisted of four blocks, with 120 trials in

each block. In the first and second blocks, during the beginning of each trial, a fixation point,

“+,” appeared for 500 ms on the screen to inform the participants that the stimuli would soon

appear. Subsequently, the participants pressed the “left” keyboard (the category label

appeared in the upper-left corner of the screen) when the exemplars belonging to the fruit or

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furniture category appeared (the word size was 4 cm in length and width). The participants

pressed the “right” keyboard when the exemplars belonging to the vegetable or housing space

category appeared (the category label appeared in the upper-right corner of the screen). The

only difference between the trials in the third and fourth blocks and those in the first and

second blocks was the keyboard used to provide a response. The participants was required to

press the “left” keyboard instead of the “right” keyboard when the exemplars belonging to the

vegetable or housing space category appeared, whereas they were required to press the

“right” keyboard instead of the “left” keyboard when the exemplars belonging to the fruit or

furniture category appeared. Six practice trials in which feedback was obtained were

conducted before formal training was provided. After confirming that the participants

understood the experimental procedure, formal training was conducted. In addition, a break

that lasted 3 min at most was allowed between each block.

+ strawberry

Fixation

fruit or furniture

vegetable orhousingspaces

500 ms Press the “left” or

“right”keyboard(s elf-paced)

ISI

Next Trial500 ms

Part A

Part B

Discrimination Task

+ strawberry

housingspaces

vegetable

Figure 1. OTT task procedures. Part A is the procedure used in each trial in the OTT task (e.g.,

Blocks 1 and 2); Part B is the procedure used in each trial for the control group (e.g., Block1).

Fixation is the cue used to remind the participants to begin the Discrimination Task. In the

Discrimination Task, the participants were asked to categorize the exemplars and respond by

pressing keyboards. ISI is the inter stimulus interval.

Regarding the training provided for the control group (Fig. 1, Part B), the participants

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were asked to categorize the exemplars belonging to the fruit, vegetable, furniture, and

housing space categories. However, the rules for categorization were different from those

used in the OTT task. In the first block, the participants were instructed to press the “left”

keyboard when an exemplar from the housing space category appeared and the “right”

keyboard when an exemplar from the vegetable category appeared. In the second block, they

were asked to press the “left” keyboard when an exemplar from the furniture category

appeared and the “right” keyboard when an exemplar from the fruit category appeared. In the

third block, they were asked to press the “left” keyboard when an exemplar from the

vegetable category appeared and the “right” keyboard when an exemplar from the housing

space category appeared. In the fourth block, the participants were instructed to press the

“left” keyboard when an exemplar from the fruit category appeared and the “right” keyboard

when an exemplar from the furniture category appeared. Participants in both groups

completed trials with identical exemplars in approximately 20 min (including breaks).

In summary, for the OTT task, the exemplars from two categories (e.g., the vegetable

and the housing space categories) were combined into one category (e.g., the “right”

keyboard), whereas the control group task required the participants to simply place exemplars

from the same category into one category. The OTT task was conducted to train the

participants in overinclusive thinking, whereas the control group task did not produce any

training effect.

2.1.2.2 Categorization Task

To measure overinclusive thinking, a categorization task (Isen & Daubman, 1984) was

employed and revised by applying typical and nontypical exemplars of clothing and vehicles

as stimuli in the task (Rosch, 1975). Three typical and three non-typical exemplars were

included in each category. Typical exemplars of the clothing category included suit, shirt, and

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pants, and non-typical exemplars included ring, purse, and cane; typical exemplars of the

vehicle category included train, automobile, and bus, and the non-typical exemplars included

camel, feet, and elevator. The participants were instructed to rate the exemplars on a 10-point

Likert scale, in which 1 indicated definitely does not belong to the clothing (or vehicle)

category and 10 indicated definitely belongs to the clothing (or vehicle) category. The scale

assumed that people who demonstrate overinclusive thinking exhibit loose conceptual

boundaries (Andreasen & Powers, 1974) and high flexibility; therefore, they would classify

the non-typical clothing exemplars as clothes. Participants’ overinclusive thinking was scored

according to the sum of the typicality rating of the non-typical exemplars of clothing and

vehicles. People with a high score exhibited a high level of overinclusive thinking. The

typical exemplars were used as a reference. The scoring of the typical exemplars was

identical to that of the non-typical exemplars.

2.1.3 Procedures

First, the participants were randomly assigned to the OTT group or the control group.

The first stage of the experiment was the training session. Following the training, the

participants were asked to complete the categorization task. At the end of the experiment, the

purpose of the experiment was explained and a NT$50 coupon was given as a token of

gratitude.

2.2 Results and Discussion

2.2.1 Accuracy Rates of the Training Task

Data with accuracy below 70% were deleted to ensure the effectiveness of the training

(after eliminating one participant’s data, data from the remaining 39 participants were

analyzed). No significant difference existed in the accuracy of the OTT task between the OTT

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(M = 92.84%, SD = 6.19) and control groups (M = 92.03%, SD = 3.88; t(37) = .47, p = .65).

Therefore, the potential threat to internal validity caused by varying accuracy in the two

groups was excluded. In addition, the high accuracy of both groups (an average of 92%)

indicated that the manipulation of Experiment 1 was effective.

2.2.2 Effect of Overinclusive Thinking Training on the Categorization Task

The results of the t-test for the non-typical exemplars revealed that the performance of

the OTT group (M = 24.45, SD = 5.92) was superior to that of the control group (M = 21.47,

SD = 5.23; t[37] = 1.67, p = .05 [one-tailed], and Cohen’s d = .53). However, no significant

difference in the accuracy of the typical exemplars between the OTT group (M = 59.20, SD =

2.02) and the control group (M = 59.53, SD = .90; t [37] = 0.65, p = .52) was observed. The

results of Experiment 1 showed that after OTT, the typicality level of the non-typical

exemplars increased but that of the typical exemplars did not. The results further indicated

that the OTT improved the participants’ overinclusive thinking.

3. Experiment 2

The results of Experiment 1 revealed the effect of OTT on participants’ overinclusive

thinking. According to previous research, manipulating overinclusive thinking can enhance

creativity (Chrysikou, 2006; Wen et al., 2013) because overinclusive thinking is correlated

with creativity (Eysenck, 2003).Therefore, we explored whether the participants’ creativity

was improved after OTT. In Experiment 2, the training for both groups was the same as that

used in Experiment 1, but the dependent variable was changed from overinclusive thinking to

creativity. Divergent thinking is considered to be a core component of creativity (Guilford,

1967); hence, the divergent thinking task was applied to measure creativity. In addition,

previous studies have demonstrated that variables such as positive emotion (Lyubomirsky,

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King, & Diener, 2005), motivation (Amabile, 1996), and interest (Hirt, Melton, McDonald, &

Harackiewicz, 1996) may influence creativity. These confounding variables were measured

after the training stage for examining whether the manipulation of Experiment 2 influenced

the three variables and, thus, validate whether these confounding variables diminished the

internal validity of Experiment 2. In this experiment, the single-item questionnaire format

proposed by Friedman and Förster (2000, 2001, 2002, 2005) was adopted to measure the

three variables.

3.1 Method

3.1.1 Participants

A total of 42 undergraduates (36 men and 6 women) from the National Defense

University in Taiwan were recruited in Experiment 2.The mean of their ages was 21.03 years

with an SD of 1.22. After eliminating two participants, who had a training accuracy lower

than 70%, the data of the remaining 40 participants were used in statistical analyses.

3.1.2 Materials

3.1.2.1 Creative Thinking Test

This study applied the figural subscale of the Creative Thinking Test (Wu et al., 1998),

which was a divergent thinking test, to measure creative potential. The participants were

asked to draw a picture or an object on the Chinese character “人,” which means “human,”

and name the images they had drawn. Three indicators were assessed in the task: (a) fluency

(the number of responses); (b) originality (unusual responses); and (c) flexibility (the number

of categories of responses).Wu et al. developed the norm of the Creative Thinking Test.

Scores for originality and flexibility could be obtained according to the norm (the score for

fluency is the number of responses). An experienced rater rated the divergent thinking

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abilities of the participants on the basis of the grading standards and the norms. The score for

fluency was calculated according to the total number of responses; and the score for

flexibility was calculated based on the number of categories of responses. Originality was

scored based on the incidence of a certain response in the total normative sample. If 5% or

more of the sample provided the same response, no point was awarded; if 2% to 5% provided

the same response, one point was allocated; and if less than 2% provided the same response,

two points were awarded. The final score for originality was the sum of the points awarded

for each item. The raters began scoring the experimental results after they fully understood

the scoring process. This subscale has a high interrater reliability on fluency, flexibility, and

originality (rs> .94; Wu et al., 1998).

3.1.3 Procedures

The participants were randomly assigned to the OTT group or the control group.

Participants completed the experimental task in a laboratory. The first stage of the experiment

was the training session. After the training, the participants’ mood, their motivation to

participate in the experiment, and their interest in the experiment were measured. The

participants were requested to rate each item on a 7-point Likert scale. For example, “How

happy are you at this moment?” (1 = extremely unhappy, 7 = extremely happy), “How much

effort did you put into this experiment?” (1 = extremely minimal, 7 = extremely substantial),

and “How much were you interested in this experiment?”(1 = extremely disinterested, 7 =

extremely interested). The participants were then required to perform a 10-min Creative

Thinking Test. Following the task, they were asked whether the computerized tasks

influenced their responses on the written test (i.e., Creative Thinking Test). At the end of the

experiment, the purpose of the experiment was explained and a NT$50 coupon was given as a

token of gratitude.

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(after eliminating two participants’ data, data from the remaining 40 participants were

analyzed). No significant difference was observed in the accuracy of the OTT task between

the OTT (M = 92.75%, SD = 6.04) and control groups (M = 91.98%, SD = 3.63; t[38] = 0.49,

and p = .63). Therefore, the potential threat to internal validity caused by varying the

accuracy of the two groups was excluded. In addition, the high accuracy of both groups (an

average of 92%) indicated that the manipulation was effective.

3.2.2 Analysis of the Levels of Happiness, Motivation, and Interest

The results regarding the happiness, motivation, and interest of the OTT group were M

= 4.10 and SD = 1.48; M = 5.35 and SD = 1.23, and M = 4.65 and SD = 1.53, respectively,

whereas those of the control group were M = 4.65 and SD = 1.18, M = 5.30 and SD = 1.08,

and M = 4.90 and SD = 1.59, respectively. No significant differences were observed between

the two groups (ts(38)< 1.30, ps> .20). We concluded that the different training or

manipulations used between the OTT and control groups did not have significant differences

in the levels of happiness, motivation, and interest. Therefore, the three variables did not

threaten the internal validity of Experiment 2.

3.2.3 Effects of Overinclusive Thinking Training on the Creative Thinking Test

The measures of three indicators (i.e., fluency, flexibility, and originality) were averaged

ratings calculated by two raters. The intraclass correlations (two-way random model,

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definition of absolute agreement) of three indicators were .99, .97, and .98. Accordingly, the

rating of the Creative Thinking Test was reliable.

The results of the t test indicated that the fluency of the OTT group (M = 17.80, SD =

7.24) was higher than that of the control group (M = 13.55, SD = 4.70; t[32.59] = 2.20, p

= .02[one tail] with Cohen’s d = .70). No significant difference was observed in the flexibility

between the OTT group (M = 10.65, SD = 3.39) and the control group (M = 9.75, SD = 2.59;

t[38] = .94, p = .35). The originality of the OTT group (M = 14.38, SD = 7.95) was higher

than that of the control group (M = 10.20, SD = 4.53; t[30.16] = 2.04, p = .02 with Cohen’s d

= .65).

In addition, when the participants were asked whether the computer task had affected

the Creative Thinking Test, all of them considered the two to be unrelated. The results

indicated that OTT implicitly improved the participants’ creativity without their awareness.

4. Experiment 3

The results of Experiments 1 and 2 revealed that after the participants received OTT,

their overinclusive thinking and divergent thinking were improved, respectively. The purpose

of Experiment 3 was to further investigate the effect of OTT on the improvement in creative

insight. The insight component of creativity was applied as the dependent variable and insight

problems were used to measure insight (Schooler, Ohlsson, & Brooks, 1993). If the effect of

OTT on various creative thinking indicators was significant, the reliability of the training

effect was considered to be robust. In addition, this study involved a discussion of the effect

of OTT comprising various semantic distance designs on creativity enhancement. When

words with long semantic distances were classified into one category, the effect on creativity

enhancement was likely to be substantial because an extensive degree of OTT was achieved.

For example, long-distance semantic OTT may require classifying fruit-related words and

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furniture-related words into a category. Conversely, short-distance semantic OTT requires

classifying words with short semantic distances into a category, such as fruit-related and

vegetable-related words. Therefore, the manipulation of Experiment 3 differed from that of

Experiments 1 and 2 because of the inclusion of an additional short-distance semantic OTT

group.

The difference between the additional condition (i.e., the short-distance semantic OTT)

in Experiment 3 and the OTT group in Experiments 1 and 2 (i.e., the long-distance semantic

OTT) was that in the first and second blocks, the participants were instructed to press the

“left” keyboard when the exemplars from the fruit or vegetable category appeared and the

“right” keyboard when the exemplars from the furniture or housing space category appeared.

In the third and fourth blocks, the participants were instructed to press the “right” keyboard

when the exemplars from the furniture or housing space category appeared and the “left”

keyboard when the exemplars from the fruit or vegetable category appeared. In Experiment 3,

to categorize exemplars from two unrelated categories into one category (e.g., categorizing

the exemplar into the fruit and housing space category by pressing the “left” keyboard), as in

Experiments 1 and 2, was defined as the long-distance semantic OTT task; and the

manipulation of the short-distance semantic OTT task involved categorizing exemplars from

two relatively similar categories into one category (e.g., categorizing the exemplar into the

fruit and vegetable category by pressing the “right” keyboard). In other words, the

long-distance semantic OTT involved categorizing exemplars from two long-distance

semantic categories into one category; however, the short-distance semantic OTT involved

categorizing exemplars from two short-distance semantic categories into one category. In

addition, the task administered to the control group was identical to that performed in

Experiments 1 and 2. In addition to examining the influence of OTT on the improvement of

insight problem solving, the other purpose of Experiment 3 was to investigate the difference

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between the effects of the short- and long-distance semantic OTT.

4.1 Methods

4.1.1 Participants


University in Taiwan participated in Experiment 3. The mean of their ages was 20.18 years

with an SD of .99. After eliminating three participants, who had a training accuracy lower

than 70%, the data of the remaining 56 participants were used in statistical analyses.

4.1.2 Materials

4.1.2.1 Insight Problems

The three features of insight problems are listed as follows: (a) insight problems can be

solved by anyone; (b) people may reach an impasse and be unable to identify a solution to the

problem; and (c) when a solution is suddenly identified, it is typically accompanied by a

“a-ha” moment (Schooler et al., 1993). To select the insight problems used in the study, we

first collected 20 items from Förster, Friedman, & Liberman (2004), Isaak & Just (1995), and

Metcalfe & Wiebe (1987). The 20 items were presented to 421 university students. Winstep

software was used to conduct a Rasch model difficulty of item analysis (Embretson & Reise,

2000). We selected five problems of medium difficulty, with a mean difficulty (β) of 0.53.

Five insight problems were used in Experiment 3 (five points in total, one point for each

correct answer). An example item is shown as follows:

A prisoner was attempting to escape from a tower. He found in his cell a rope that was

half long enough to permit him to reach the ground safely. He divided the rope in half, tied

the two parts together, and escaped. How could he have done this? (Isaak & Just, 1995).

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4.1.3 Procedures

The participants were randomly assigned to either the long-distance semantic OTT

group, the short–distance semantic OTT group, or the control group. Participants completed

the experimental task in the laboratory. After the training session, the researchers assessed the

extent of the participants’ happiness, motivation to participate, and interest in the

experimental task by using the same items employed in Experiment 2. The participants then

completed a 10-min written test containing insight problems. Following the insight problems,

they were asked to state whether the computerized tasks had influenced their responses to the

written test. At the end of the test, the purpose of the experiment was explained and a NT$50

coupon was given as a token of gratitude.


4.2.1 Accuracy Rates of the Training Tasks


(after eliminating three participants’ data, data from the remaining 56 participants were

analyzed). No significant difference existed in the accuracy among the long-distance

semantic OTT group (M = 93.25%, SD = 6.52), the short-distance OTT group (M = 94%, SD

= 3.55), and the control group (M = 91.79%, SD = 4.26), with F (2, 53) = 0 .90 and p = .42.

Therefore, the potential threat to internal validity caused by varying the accuracy of the three

groups was excluded. In addition, the high accuracy of the three groups (an average of 94%)

indicated that the manipulation was effective.

4.2.2 Analysis of the Levels of Happiness, Motivation, and Interest

The results for the happiness, motivation, and interest of the long–distance semantic

OTT group were M = 4.81 and SD = 1.50, M = 5.15 and SD = 1.56, and M = 4.95 and SD =

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1.57, respectively; those of the short-distance semantic OTT group were M = 4.21 and SD =

1.68, M = 5.47 and SD = 1.47, and M = 4.74 and SD = 1.76, respectively; and those of the

control group were M = 3.88 and SD = .99,M = 4.88 and SD = 1.45, and M = 4.12 and SD =

1.41, respectively. The results indicated that no significant difference existed among the three

groups (Fs(2, 53)< 0.85, ps> .40). We concluded that the different manipulations for the three

groups did not cause differences in the extent of happiness, motivation, and interest among

the groups. Therefore, these three confounding variables did not threaten the internal validity

of Experiment 3.

4.2.3 Effects of Overinclusive Thinking Training on Insight Problem Solving

To examine the performance differences in insight problem solving among the three

groups, we conducted a one-way between-groups analysis of variance (ANOVA). We first

conducted a Levene’s test for homogeneity of variance and determined that the results

satisfied the assumption of homogeneity of variance, with F(2, 53) = 3.03 and p = .06, and

were suitable for further ANOVA. The results indicated that significant differences existed in

the performance demonstrated in insight problem solving among the three groups, with F (2,

53) = 3.71, p = .03, and ηp2 = .12. The post hoc comparison revealed that the performance of

the long-distance semantic OTT group (M = 2.50, SD = 1.32) was superior to that of the

control group (M = 1.47, SD = .72); whereas no significant differences existed between the

performance of the short- and long-distance semantic OTT groups (M = 1.95 and SD = 1.27),

or between the short-distance semantic OTT and control groups. In addition, when the

participants were asked whether they believed that the computer tasks had affected their

performance on the written tests, they all considered the two to be unrelated. The results of

Experiment 3 indicated that the long–distance semantic OTT improved the participants’

creativity, but the short-distance semantic OTT did not.

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5. Experiment 4

Experiments 2 to 4 revealed that OTT enhances creativity. However, regarding the

exploration of training effectiveness, the results of Experiments 2 and 3 verified only the

immediate effect of training because the participants were instructed to complete creative

tasks immediately after training. Therefore, the results of these two experiments cannot

validate the delay effect of OTT. To investigate the effect of OTT after a delay of 7 days, we

conducted Experiment 4 by using the same two-group training design and Creative Thinking

Test employed in Experiment 2. The only difference between Experiments 4 and 2 was the

administration time of the creative thinking task. In Experiment 4, the Creative Thinking Test

was administered 7 days after the training to explore the delay effect that OTT has on the

enhancement of creativity.

5.1 Method

5.1.1 Participants


University in Taiwan participated in Experiment 4. The mean of their ages was 21.26 years

with an SD of 2.10. After eliminating one participant’s data because their training accuracy

was lower than 70%, the data of the remaining 49 participants were used in statistical

analyses.

5.1.2 Procedure

The participants were randomly assigned to the OTT group or the control group. Both

groups received corresponding training, which was identical to that implemented in

Experiment 2. Following a 7-day delay after training, the participants were instructed to

complete the figural subscale of the Creative Thinking Test. Finally, they were asked whether

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the computerized tasks had influenced their responses on the written test. After administering

Experiment 4, the purpose of the experiment was explained and a NT$50 coupon was given

as a token of gratitude.




(after eliminating one participant’s data, data from the remaining 49 participants were

analyzed). No significant difference was observed in the accuracy of the OTT task between

the OTT (M = 90.15%, SD = 8.42) and control groups (M = 91.42%, SD = 3.44), with t (47)

= .51 and p = .54. Therefore, the potential threat to internal validity caused by varying the

accuracy of the two groups was excluded. In addition, the high accuracy of both groups (an

average of 91%) indicated that the manipulation was effective.

5.2.2 Effects of Overinclusive Thinking Training on the Creative Thinking Test

The t-test results indicated that the OTT (M = 15.38, SD = 6.62) and control groups (M =

14.16, SD = 5.38) did not exhibit significant differences in the fluency index (t[47] = 0.71, p

= .48). Furthermore, the OTT (M = 9.12, SD = 3.67) and the control groups (M = 10.60, SD =

8.87) did not exhibit significant differences in the flexibility index (t[47] = 0.61, p = .55).

Regarding the originality index, the performance of the OTT group (M = 13.63, SD = 8.26)

was significantly superior to that of the control group (M = 9.88, SD = 5.28; t[47] = 1.90, p

= .03 [one tail], Cohen’s d = .54). In addition, when the participants were asked whether they

believed the computer tasks had affected their performance on the written tests, they all

considered the two to be unrelated. In summary, the results of Experiment 4 indicated that

OTT had a delay effect on originality, but did not affect fluency or flexibility.

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6. Discussion

The creativity training designed in this study was based on the hypothesis that OTT can

enhance people’s overinclusive thinking ability. The results suggested that the training in

which the participants were instructed to categorize seemingly unrelated words into one

category loosened the boundary between conceptual categories, thereby facilitating

overinclusive thinking. The results of Experiment 1 verified that OTT can improve

overinclusive thinking, proving that the training task developed in this study improved

overinclusive thinking and the effectiveness of the training method. The results of

Experiment 2 revealed that OTT enhanced the fluency and originality of the participants’

divergent thinking. However, flexibility was not improved. This may have resulted from

insufficient conceptual association training in which only four word categories were included

in the OTT task of Experiment 2. Although the training improved the participants’ originality

by enabling them to develop unusual ideas through broadened and enhanced scopes of

thinking and concept searching, it failed to improve flexibility. This might be because only a

few category concepts were adopted in the OTT, which further limited the expansion of

thinking categories. The results of Experiment 2 demonstrated that OTT increased the

number of novel knowledge nodes (fluency) and the novelty of ideas. In other words, the

training can improve people’s ability to generate additional ideas and associate categories

among various semantic networks. Through OTT, the scope of knowledge categories can be

expanded, which can further increase the number of semantic associations (Arndt, Greenberg,

& Cook, 2002), thereby improving fluency. Moreover, OTT activates the associations among

remote semantic networks, facilitates the generation of novel ideas, and further improves

originality.

The results from Experiment 3 indicated that long-distance semantic OTT improved the

participants’ insight problem solving, whereas short-distance semantic OTT did not.

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Furthermore, long-distance OTT improved the participants’ creativity; in other words,

long-distance semantic OTT substantially affected creativity improvement. In Experiment 3,

the results revealed that OTT can enhance the performance of insight problemsolving. Insight

problem solving requires a representational change (Knoblich, Ohlsson, Haider, & Rhenius,

1999). After OTT, the ability to make connections among semantic networks was improved.

This might extend the range of knowledge nodes in the brain, facilitate representational

changes, and further overcome obstacles (e.g., fixation) during insight problem solving

(Duncker, 1945). Moreover, the results of Experiment 3 revealed that when the level of OTT

increased, the extent of creativity improvement also increased. In other words, OTT was

effective in creativity improvement when the participants were instructed to classify

seemingly unrelated words from long-distance semantic network categories (e.g., fruit- and

house-related words) into one category during the training. However, when the participants

were required to categorize words from related semantic network categories into the same

category (e.g., fruit- and vegetable-related words), no creativity improvement occurred. These

results provide practical implications for the development of the OTT task: providing a

training task design in which unrelated words can be categorized into one group is necessary.

However, further research is required to investigate another topic regarding the development

of the categorization task: the required length of the semantic distance between two words

from different categories for enhancing creativity.

The results of Experiment 4 revealed that, following a 7-day delay after OTT, the

training still exerted a significant effect on participants’ originality, but not on fluency and

flexibility. This indicates that OTT had both immediate and delayed effects on the originality

of divergent thinking. By contrast, only an immediate effect of OTT on flexibility and

fluency occurred. Possible reasons that delayed training effects on flexibility were not

observed are explained in the previous sections. The remainder of this section provides a

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discussion on the reason no delay effect on fluency occurred.

According to Schooler (2002) and Schooler, Fiore, and Brandimonte (1997), people’s

sequential processing of certain tasks causes processing shifts; namely, after being facilitated

by an earlier cognitive process, the activation of a cognitive process is sustained and then

shifts to subsequent tasks. During transfer-appropriate processing shifts, the residual process

activation facilitates subsequent processing. Therefore, in Experiment 2, when the

participants completed OTT in which they classified seemingly unrelated words into one

category, their relative semantic knowledge nodes were activated. This activation then shifted

to subsequent divergent thinking tasks. In other words, increasing the spread of earlier

knowledge node activation may subsequently improve the performance of fluency, which is

the effect of appropriate processing shifts. Additionally, the results of the meta-analysis on

the effect of creative-improvement training in Ma (2006) indicated that the originality of

divergent thinking had the largest effect size, which was consistent with the data obtained in

this study. In other words, if the divergent thinking test is used in evaluating

creative-improvement training, it can considerably influence originality. Additionally,

another finding of this study is that after OTT, the improvement in originality can be

maintained.

The results of this study indicated that the OTT developed in this study differed from

previous creativity training methods (i.e., rule-based training) because the participants did not

learn specific creative rules. The participants simply performed computer classification tasks

to improve overinclusive thinking and, subsequently, their creativity (changing the remote

association ability of participants directly). Moreover, the participants did not perceive that

their creative capacity was trained when they received OTT. Thus, we developed an

alternative model for creativity training. Explicit creativity training techniques can limit the

search for creative ideas (Zhong et al., 2008), whereas the OTT developed in this study

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involved no such limitations and, thus, provided an advantage when applied.

The creativity-improvement training proposed in this study was developed based

onoverinclusive thinking theory. Although Experiment 1 verified that the participants’

overinclusive thinking increased after their participation in OTT, the results of the

categorization task were used to infer and support the hypothesis of Experiment 1. However,

only indirect evidence was provided to support the hypothesis. In the future, if functional

magnetic resonance imaging (Friston, Ashburner, Kiebel, Nichols, & Penny, 2011) can be

used to examine people’s brain activation patterns during creative thinking following OTT,

relatively direct evidence regarding the effect of OTT can be obtained.

This study examined the effects of OTT on creativity improvement after a 7-day delay,

but the maintenance of effects for longer periods was not examined. Future studies should

explore the delayed effects of training following longer delay periods. In addition, only one

OTT session comprising 480 trials was conducted in this study. Multiple training sessions

conducted over relatively longer periods may increase and reinforce creativity improvement.

Future studies should also investigate the long-term training effects of OTT.

The method of OTT developed in this study produced preliminary effects on creativity

improvement. Thus, this method provides a new training technique for improving creativity.

Particularly for people who have difficulties in learning and using explicit creative skills, the

OTT proposed in this study might be another method for them to improve creative thinking or,

might produce a supplementary effect in traditional explicit creative skill training.

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