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The Creative Brain: Brain Correlates Underlying the Generation of Original Ideas
It is commonly believed that the ability to think creatively is advantageous in a variety of areas of our everyday life. Creativity—defined as the ability to produce work that is both novel (original, unique) and useful within a social context (e.g., Flaherty, 2005; Stein, 1953; Sternberg & Lubart, 1996)—appears to be crucial in culture, science, and education, as well as the economical or industrial domain. Employees are required to produce novel and innovative ideas. By the same token, pedagogues and teachers instruct their students to produce creative work or achievements. Similarly, in constructing buildings, furnishing workplaces or homes, or even in creating our outfit we continually rely on creativity-related skills.
Even though the striking role of creativity in these areas appears to be beyond dispute, our scientific understanding of this topic lags behind. In fact, creativity has (unlike other mental ability constructs such as intelligence) long been viewed as a “difficult” trait that is hardly amenable to research, and empirical studies on this topic were extremely scarce. In 1950, Guilford’s seminal address to the American Psychological Association brought about a resurgence in this research field. His most influential contribution to this field was presumably in a conceptual as well as in a psychometrical sense, inasmuch as he specified several characteristics of creative people that could be measured by means of psychometric tests. According to Guilford (1950), creative people might be characterized by ideational fluency (i.e., showing a large quantity of ideas), originality (i.e., novelty or uniqueness of ideas), or the ability to think flexibly (i.e., the ability to produce different types of ideas). Stimulated by Guilford’s work, many creativity measures have been developed and empirically tested, among the most prominent being the Torrance Tests of Creative Thinking (TTCT; Torrance, 1966), or the divergent production tests by Guilford (1967). The availability of creativity measures has in turn stimulated relevant research activities in several scientific disciplines and, in the meanwhile, creativity has been addressed from a variety of different perspectives. It has, for instance, been studied in the cognitive sciences (e.g., Smith et al., 1995; Ward, 2007), in pedagogy or the educational domain (e.g., Sawyer, 2006), from the perspective of social psychology (e.g., Amabile, 1983; Hennessey & Amabile, 2010), in the context of mental illness (e.g., Kaufman, 2005), and most recently in the field of neuroscience (see, e.g., Arden et al., 2010; Dietrich, 2007; Dietrich & Kanso, 2010; Fink et al., 2007; Jung et al., 2010).
This chapter attempts to show how neuroscientific approaches in this field can help us to learn more about this extremely important research topic that has been neglected for a relatively long period of time. In doing so, we will first briefly summarize recent research in this field which aims at investigating how different facets of creative cognition are manifested in our brains. Second, we will address the crucial research question as to how creative cognition can be improved effectively by training or cognitive stimulation, and whether any intervention effects are also observable at the level of the brain. We hope to demonstrate that neuroscientific research approaches in this field are a valuable tool for improving our scientific understanding of this complex but nevertheless important and fascinating mental domain.
The Neuroscientific Study of Creative Cognition
Neuroimaging techniques such as functional magnetic resonance imaging (fMRI), near infrared spectroscopy (NIRS), the measurement of the brain’s glucose metabolism via positron emission tomography (PET), or the analysis of different parameters in the electroencephalogram (EEG) allow us to investigate brain activity during a broad range of different cognitive demands. Each of these neuroscientific measurement methods has its pros and cons in the particular context of the study of creative cognition. The primary advantage of fMRI lies in its high spatial accuracy, but it does not allow for the study of cognition with high temporal resolution (as opposed to EEG techniques). The observed changes in brain activity (e.g., blood-oxygen-level dependent [BOLD] response from a prestimulus reference condition to an activation interval) occur rather slowly, thereby complicating the analysis of time-related brain activity patterns during the process of creative thinking. EEG techniques, in contrast, show lower spatial resolution but allow for a fine-grained temporal analysis of brain activation that is observed in response to a particular cognitive event (e.g., immediately prior to the production of an original idea). Also, in analyzing the functional cooperation (or functional coupling, respectively) between different cortical areas EEG techniques have turned out to be a valuable tool in the study of creative cognition (see e.g., Bhattacharya & Petsche, 2005; Grabner et al., 2007; Jaušovec, 2000; Jaušovec & Jaušovec, 2000; Mölle et al., 1999; Petsche, 1996; Razumnikova, 2000; Sandkühler & Bhattacharya, 2008).
The EEG signal represents oscillations observed across a wide range of frequencies that are commonly divided into distinct frequency bands (e.g., alpha band: 8–13 Hz, beta band: 13–30 Hz). Spectral analyses of the EEG can be used to compute the band-specific frequency power for given periods of time. Additionally, task- or event-related power changes can be quantified by contrasting the power in a specified frequency band during a cognitive task with a preceding reference interval. Event-related power decreases from a reference to an activation interval are referred to as event-related desynchronization (ERD), while power increases are referred to event-related synchronization (ERS; Pfurtscheller, 1999). ERD/ERS of the alpha band has been found to be especially sensitive to cognitive task performance and higher cognitive abilities (for a review, see Klimesch, 1999). In several studies of our laboratory we investigated task- or event-related power changes in the EEG alpha band while individuals were engaged in the performance of different types of creative idea-generation tasks. The construction of the creativity tasks (or the modification of selected tasks from literature, respectively) was strictly guided by the aim to realize them appropriately in the neurophysiological laboratory. Relevant studies in this field (regardless of whether fMRI, PET, NIRS, or EEG is used) are challenged by the realization of suitable, ecologically valid experimental paradigms or procedures that try to resemble “real-life” creativity (outside the lab) to the best possible extent. At the same time, however, drawing, free-hand writing, speaking, or any other body movements and the like that could negatively influence the quality of the neurophysiological measurements have to be minimized or avoided. In this particular context one might be well advised to separate a creativity task into time intervals during which participants are required to think creatively (creative idea generation phase) and time intervals during which participants are required to (orally) respond to the given stimulus. This would enable the experimenter to investigate functional patterns of brain activity during time periods of creative thinking that are free of, or at least less prone to, artifacts caused by speaking or free-hand writing. At the same time, the oral responses that are recorded subsequent to the creative idea generation period (and transcribed for analyses by the experimenter) allow for a reliable analysis of behavioral task performance.
In following such an approach, four different types of creative idea-generation tasks were adapted and empirically tested in our lab. In the classic alternative uses test (AU), the participants’ task is to name as many and original uses of a conventional, everyday object (e.g., unusual uses of a “brick” or “tin”). In the insight task (IS), participants are confronted with unusual, hypothetical situations in need of explanation (e.g., “A light in the darkness”). They are required to think of as many different causes, reasons, or conditions as possible that may explain the given situation. Similarly, in the utopian situation task (US), participants are instructed to put themselves in the given utopian situations and to produce as many and as original consequences as possible that would arise from this situation (e.g., “Imagine there were a creeping plant rising up to the sky. What would await you at the end of this plant?”). And finally, in the word ends task (WE), German suffixes are presented that have to be completed by the participants in many different ways. In all of these tasks participants were instructed to produce many different (i.e., ideas out of different categories) and original ideas (i.e., ideas no one else would think of). Brain activity during creative idea generation (i.e., during the creative idea generation period, in which participants were requested to [silently] think of possible solutions) was quantified by means of task- or event-related power changes in different EEG alpha-frequency bands.
Which Brain Correlates Are Associated with the Generation of Creative Ideas?
Behavioral analyses of the employed creative idea generation tasks revealed that they differ notably with respect to their task demands. This was evident by the finding that performance in the IS, US, and AU task (as opposed to the WE task) was more strongly correlated with the NEO FFI (Neuroticism Extraversion Openness Five Factor Inventory) factor “openness to experiences” which is seen in relation to creativity (e.g., Feist, 1998; King et al., 1996). In contrast, completing suffixes (i.e., performance of the WE task) was significantly correlated with verbal intelligence (Benedek et al., 2006; Fink et al., 2006, 2007), whereas in the IS, US, and AU tasks no correlation with verbal ability was apparent at all. Thus, the IS, US, and AU tasks seem to rely more strongly on divergent, free-associative demands, whereas the WE task rather involves convergent, intelligence-related demands.
Most interestingly, task differences were not only apparent on the behavioral level but were observed on the neurophysiological level as well. The IS, US, and AU tasks were accompanied by relatively strong synchronization of alpha activity, whereas in the WE task, which was correlated with verbal intelligence, the lowest synchronization of alpha activity was observed. Though topographically less restricted, these increases in alpha activity were most apparent in posterior (parietal) regions of the brain. These findings suggest that the more creativity-related a task is (e.g., finding original alternate solutions as opposed to completing suffixes) the stronger is the synchronization of alpha activity (Fink et al., 2007; a discussion of the functional meaning of task or event-related alpha synchronization can be found later on in this chapter). This finding is nicely in line with other EEG studies suggesting that convergent (i.e., more intelligence-related) versus divergent (i.e., more creativity-related) modes of thinking are accompanied by different activity patterns of the brain. The study by Mölle et al. (1999), for instance, yields evidence that divergent thinking tasks (e.g., alternate uses task) evoke higher EEG complexity (i.e., estimated correlation dimension of EEG as determined by means of singular value decomposition of the EEG signal) than convergent thinking tasks strongly drawing on intelligence (e.g., mental arithmetic), which could be the result of a larger number of independently oscillating neural assemblies during the former type of thinking. Further evidence for differences in EEG patterns between convergent and divergent thinking tasks was also reported by Jauk et al. (2012) and Jaušovec and Jaušovec (2000), as well as Razumnikova (2000, 2004).
The finding that brain activity during creative cognition might be different from brain activity observed in response to other types of cognition could be also confirmed in a study by Carlsson et al. (2000) that measured regional cerebral blood flow (rCBF). The authors showed that when participants were required to name as many different uses of bricks, they displayed a higher level of prefrontal brain activation than during the performance of a more intelligence-related verbal fluency task (naming words that begin with a given letter). Furthermore, using fMRI, Goel and Vartanian (2005) report evidence that solving match problems (known as a creative problem-solving task) exhibited activation (relative to a convergent baseline condition) in the left dorsal lateral as well as in the right ventral lateral prefrontal cortex. Thus, neuroscientific studies that contrast the performance of more creativity-related tasks with the performance of more intelligence-related problem-solving tasks provide evidence that these different modes of thinking are accompanied by qualitatively different activity patterns of the brain.
Investigation of Brain Activity in Response to the Generation of More versus Less Original Ideas
Another promising approach in the neuroscientific study of creative cognition is to investigate brain activity patterns that are associated with the production of highly creative (as opposed to less creative) ideas. This exciting research question has been stimulated by Jung-Beeman et al. (2004), who investigated brain correlates underlying the subjective experience of a flash of insight, or “aha!” The authors had their participants work on remote associate problems (finding a compound to three given unrelated words; e.g., for “rat—blue—cottage,” the solution would be “cheese”) and compared brain activity during solutions that were accompanied by a subjective experience of “aha!” with those that were solved without the subjective experience of insight. Stimulated by Jung-Beeman et al.’s approach, Fink and Neubauer (2006) investigated how brain states during the production of highly original ideas might be differentiated from those observed during the production of less original ideas. To obtain a measure of originality of the responses given during the performance of experimental tasks, we applied an external rating procedure similar to Amabile’s (1982) consensual assessment technique that has frequently been employed in this field of research. To this end, three female and three male raters were asked to evaluate the responses of the participants given during the experiment with respect to their originality on a five-point rating scale (ranging from 1, “highly original,” to 5, “not original at all”). Subsequently the ratings were averaged over all raters (who displayed satisfying internal consistency in their ratings), separately for each idea, so that one originality score was available for each single response of a task. Based on the external ratings, we compiled lists of high-original versus low-original ideas within each single task and participant (by means of a median split). Analyses revealed that more original (as opposed to less original) ideas were accompanied by a stronger synchronization of alpha activity in centroparietal regions of the cortex. This finding is in agreement with Jung-Beeman et al.’s (2004) “alpha effect” observed during subjective experience of “aha!” (viz., an increase of parietal alpha activity in insight as compared to noninsight solutions). Moreover, this result fits into previous research reports which also found parietal brain regions being critically involved in divergent or creative cognition tasks (e.g., Bechtereva et al., 2004; Razumnikova, 2004).
In Grabner et al. (2007), we aimed at extending the Fink and Neubauer (2006) findings in two important ways. First, we assessed brain activity in relation to self-rated originality of ideas. Participants were asked to evaluate each idea they gave during the experiment with respect to its originality. This was realized subsequent to the performance of the creative idea-generation tasks (i.e., subsequent to the recording session). Again, a five-point rating scale ranging from 1 (highly original) to 5 (not original at all) was used, but, in contrast to Fink and Neubauer (2006), the originality scores were now based on subjective instead of external ratings. And second, as creative cognition presumably requires different functional neural networks distributed over the whole brain, we also used measures informing us about the functional cooperation between different brain areas. For this reason, we calculated functional coupling or the phase locking value (PLV) between selected pairs of electrodes. As outlined by Lachaux et al. (1999), the PLV measures the covariance of phase between two different neuroelectrical signals. Because we were interested in changes of phase synchrony in response to the generation of creative ideas, we computed event-related phase-locking values in applying the same formula as for calculating ERD/ERS.
Several findings of the Grabner et al. (2007) study appear to be noteworthy: First, similar to Fink and Neubauer (2006), creative idea generation was generally accompanied by an event-related synchronization (ERS) of alpha activity. Second, and more importantly, the obtained findings also suggest that the production of ideas that were subjectively rated as more original was reflected in a different activity pattern of the brain than the production of less original ideas. Analyses revealed that the production of more original ideas exhibited a larger right-hemispheric ERS in the lower alpha band (8–10 Hz) than the production of less original ideas, whereas in the left hemisphere no ERS differences in relation to self-rated originality of ideas were found. Interestingly, differences between subjectively more versus less original responses were observed not only with respect to ERS but also with respect to event-related phase locking. Findings suggest that more original ideas were associated with a larger event-related PLV in anterior cortices of the right hemisphere, while in the left hemisphere no significant PLV differences between more and less original responses emerged. As will be discussed in more detail below, these findings support the view of the frontal cortex as key brain region in creative cognition (for reviews, see Dietrich & Kanso, 2010; Heilman et al., 2003). Frontal cortices are believed to be involved in processes such as cognitive flexibility, attention, semantic information processing, and working memory, which may likewise play a crucial role in creativity. Also, increased alpha activity at frontal recording sites has been shown to reflect a state of heightened internal processing demands by inhibiting sensory (bottom-up) information, which may—along with alpha increases in parietal regions (primarily in the right hemisphere)—facilitate the combination of information that is normally widely distributed over the whole brain.
The Role of Individual Differences
Brain correlates underlying creative cognition have been also studied from an individual differences perspective. In this context, individuals high versus low in creativity were compared with respect to functional patterns of brain activity during the performance of different creativity-related tasks (Bhattacharya & Petsche, 2005; Carlsson et al., 2000; Chávez-Eakle et al., 2007; Jaušovec, 2000; Martindale et al., 1984). For instance, the pioneering work by Martindale and Hines (1975) revealed evidence that highly creative individuals were more likely to exhibit higher alpha wave activity while performing the alternate uses test than less creative individuals. Similarly, in Martindale and Hasenfus (1978) highly creative individuals showed higher levels of alpha than less creative subjects during an inspirational phase (e.g., while they had to make up a creative story) but not during an analogue of creative elaboration (e.g., writing down the story). Interestingly, this effect was more pronounced when individuals were explicitly instructed to be original in generating their responses (see Martindale & Hasenfus, 1978). In a more recent study, Jaušovec (2000) also observed evidence that highly creative individuals showed higher EEG alpha power measures than did average creative individuals while they were engaged in the performance of creativity problems.
The findings of our laboratory nicely fit into this picture. In Fink et al. (2009a), we investigated EEG alpha activity while participants were required to generate alternative, original uses of common, everyday objects (e.g., “umbrella,” “cap,” “pencil,” “vase of flowers”). Based on the participants’ originality of ideas, the total sample was divided into groups of lower and higher originality. We observed evidence that higher original individuals exhibited a comparatively strong hemispheric asymmetry with respect to alpha activity, with a stronger task-related alpha synchronization in the right than in the left hemisphere, while in less original individuals no hemispheric differences with respect to alpha activity emerged. Similarly, during imagining an improvisational dance, professional dancers exhibited more right-hemispheric alpha synchronization in parietotemporal and parieto-occipital areas than novices (Fink et al., 2009b). This finding is particularly interesting in view of the fact that the creativity-related brain states which we have repeatedly observed in our studies on creative cognition seem not to be restricted to the comparatively basic AU task; rather, they are apparent in more applied creativity-related domains as well.
The Role of Intelligence
A well-established finding in the neuroscientific study of human intelligence is that brighter individuals use their brains more efficiently when engaged in the performance of cognitively demanding tasks than less intelligent people do (Jung & Haier, current volume). This phenomenon, referred to as neural efficiency (Neubauer et al., 2002, 2005; for a recent review see Neubauer & Fink, 2009), has been confirmed in a variety of studies employing a broad range of cognitive task demands. However, some studies in this field of research revealed evidence that neurally efficient brain functioning appears to be moderated by task content and individuals’ sex (Neubauer et al., 2002, 2005). Motivated by these findings, which indicate that females and males of varying verbal ability show different patterns of brain activity when engaged in the performance of verbal tasks, Fink and Neubauer (2006) have also investigated sex- and intelligence-related effects on brain activity in the context of creative idea generation (as the production or generation of ideas also falls into the verbal stimulus domain). The findings of this study suggest that males and females of varying verbal intelligence level exhibit different patterns of alpha synchronization, particularly apparent in frontal regions of the cortex. While verbally proficient females (in contrast to those of average verbal intelligence) displayed a stronger synchronization of alpha activity during the production of original ideas, in males the opposite pattern was observed: The production of original ideas in verbally intelligent males was accompanied by a lower synchronization of alpha activity than in the group of average verbal ability. The finding that females and males of varying verbal ability showed different patterns of alpha synchronization during the generation of creative ideas is particularly exciting inasmuch as it resembles the result pattern that we have tentatively also observed on the behavioral level. Analyses of performance data yielded a higher ideational fluency (viewed as a prerequisite of high originality; Guilford, 1950) in verbally intelligent females than in females with average verbal ability, whereas in the male sample exactly the opposite was found, that is, a higher fluency in the group of average than in the group of verbally more intelligent individuals.
Both during the performance of intelligence-related tasks (verbal and visuospatial task employed in Neubauer et al., 2005) and during creative idea generation (Fink & Neubauer, 2006) we observed intelligence and sex-related effects on EEG alpha activity. As has been revealed by the Neubauer et al. (2005) study, during the performance of a verbal matching task only in females were verbal intelligence and ERD negatively correlated (i.e., neural efficiency). In contrast, when males were engaged in the performance of the verbal task, verbally more intelligent males displayed more brain activation than did less intelligent males. With respect to creative idea generation, males and females again displayed a contrary neurophysiological result pattern as it was evident by different patterns of alpha synchronization in women and men. Though these latter findings certainly await replication in larger samples, they could, along with the Neubauer et al. (2005) findings, point to some interesting sex differences in the processing of verbal stimulus material. Presumably as a result of their higher proficiency in this domain, females seem to process verbal information more efficiently than males do. In terms of the alpha inhibition hypothesis (Klimesch et al., 2007), our findings would be compatible with the interpretation that females are more capable of maintaining a state of heightened internal attention or top-down control on internal information processing (i.e., creative idea generation) by inhibiting interference from external input. However, irrespective of the possible meaning of the observed sex differences, the Fink and Neubauer (2006) study provides some preliminary evidence of an interaction between intelligence and creativity on the neurophysiological level (see also Jaušovec, 2000).
Can Creative Thinking Be Trained Effectively?
Given the immanence of creativity in several areas of our everyday life (e.g., in education, pedagogy, science, and industry), research in this field has also addressed the question as to how creativity-related skills can be improved effectively. This has been realized from different perspectives: Krampen (1997), for instance, reports evidence that systematic relaxation exercises were effective in enhancing creative thinking in children and in elderly people. Similarly, So and Orme-Johnson (2001) observed beneficial effects of transcendental meditation techniques on cognition (including creativity) in adolescent school children. From a more cognitive perspective, there are also techniques that aim at improving creativity-related skills by providing specific problem-solving strategies or by activating existing knowledge (see Hany, 2001). In addition to this, neuroscientific studies in this research field also suggest that positive affect or humor might be favorable in the generation of novel, creative ideas. Positive affect is usually induced by giving small, unanticipated rewards to participants or by having them watch funny cartoons or films. Highly relevant literature in this field of research suggests that positive affect has a beneficial influence on cognition and creative problem solving (e.g., cognitive flexibility, verbal fluency, flexibility in thinking, breadth of attentional selection; see Ashby et al., 1999; Rowe et al., 2007). This effect has been explained by referring to increased dopamine levels of the brain (i.e., stimulation of the reward centers of the brain). Recent neuroimaging studies substantiate this view. For instance, using fMRI, Mobbs et al. (2003) report evidence that humor in response to funny cartoons appears to modulate (along with regions of the cerebral cortex) subcortical brain regions that are associated with the dopaminergic reward centers of the brain (such as the ventral tegmental area or the nucleus accumbens). The effects of humor and the comprehension of puns or jokes are also seen in close relation to brain activity in the right hemisphere (Coulson & Williams, 2005), which likewise plays a crucial role in creative cognition (Bowden et al., 2005; Fink et al., 2009a,b; Grabner et al., 2007; Jung-Beeman, 2005).
The findings briefly reported so far provide some evidence that creativity (or in a broader sense cognition) can be improved by positive affect or techniques such as transcendental meditation or relaxation exercises. However, the vast majority of interventions that are reported in relevant literature are creativity trainings designed to specifically enhance the ability to think divergently. Scott et al. (2004a) have reported a meta-analysis including seventy studies on the efficacy of such trainings and observed an overall effect size of Cohen’s Δ = 0.64 (see also Hany, 2001; Lipsey & Wilson, 1993; Rose & Lin, 1984). Additional analyses (Scott et al., 2004b) revealed that more cognitive-oriented training procedures proved to be particularly effective, whereas other commonly applied techniques such as imagery training turned out to be less effective.
We have also reported on a cognitive-oriented, computerized creative thinking training (Benedek et al., 2006). In a subsequent study (Fink et al., 2006) the efficacy of the employed creative thinking training was examined on the neurophysiological level by investigating training-induced changes in brain activity from the pre- to the post-test. We briefly describe these two training studies in the following section.
Training of Creative Thinking by Means of Computerized Divergent Thinking Exercises
Computers may not only be supportive in engineering or in the computational domain, they may also be helpful in creative work (Lubart, 2005). This becomes obvious, for instance, when a number of people (e.g., from different institutions or countries) are conjointly engaged in the production or development of new concepts or ideas. Given that traditional face-to-face creativity techniques (such as brainstorming) are usually quite difficult to realize (e.g., due to limited time and location), computer-based techniques such as electronic brainstorming (EBS—virtual brain storming with participants communicating via computers rather than face-to-face; see, e.g., DeRosa et al., 2007) represent a powerful alternative.
The application of computerized techniques in the particular context of improving creativity-related skills may be associated with several important advantages. As outlined by Benedek et al. (2006), conventional trainings are quite time-consuming and extensive as they require a large number of sessions and often necessitate the presence of a trainer or moderator. By contrast, computerized trainings can be conducted efficiently and in a highly economical way (at any time and place). They enable the experimenter to monitor and objectively document the progress in the course of the training (e.g., the time attended to the training or to specific exercises, successfully completed items, number of produced ideas) and thus obtain information on relevant variables that may explain potential training effects. In addition, a computerized creativity training also provides a setting in which idea generation is not prone to process losses (such as production blocking, social loafing, evaluation apprehension) that are typically observed in face-to-face setting (see Diehl & Stroebe, 1987).
The training employed by Benedek et al. (2006) was provided as software stored on a compact disc, along with a manual (including an installation guide) and a training schedule. The training program required participants to generate creative ideas to a broad range of verbal divergent thinking tasks. Some of the employed training exercises were adopted from well-known creativity tests, while other training exercises were constructed by the authors. Guided by the observation that the creativity tasks in the verbal domain may differ considerably with respect to their task demands, two training variants were realized: (1) In the verbal creativity training participants were requested to generate original linguistic products, such as finding slogans (e.g., slogans for the new product “orange-ice”), producing nicknames (e.g., for “coffee”), or generating sentences with three given stimulus words (e.g., “car—fish—book”). (2) By contrast, in the more abstract functional creativity training, tasks were included that focused on characteristics and functional relations of objects and situations, such as basic features (e.g., think of the basic features of an “apple”), product improvements (e.g., how could a “bicycle” be improved?), or finding explanations and consequences of given situations (e.g., “what would be the consequences of a new ice age?”). Each training version consisted of seventy-two exercises, which were organized into nine training units, each taking approximately half an hour. Individuals who participated in the training were instructed to complete not more than one training unit per day, but not less than one unit within two days, which results in a total training duration of approximately two weeks.
The efficacy of the training was investigated in a pre- and post-test design. As a measure of creativity, we used two parallel versions of the “Verbaler Kreativitäts-Test” by Schoppe (1975). In general, the results of the Benedek et al. (2006) study suggest that creative thinking (as operationalized by means of ideational fluency) can be enhanced by means of divergent thinking exercises. Analyses moreover revealed that the verbal creativity training, which focused on the generation of creative linguistic ideas, was more effective than the more abstract and difficult functional creativity training. Detailed analyses of the training protocols revealed that the verbal creativity training generally resulted in higher ideation rates than the functional creativity training, and only the former training showed a significant increase of the ideation rate over the course of the training period. According to the self-reports of the participants, the functional creativity training was less interesting and entertaining (in the second half of the training) and somewhat more exhausting than the verbal training, which could have resulted in a decrease in training motivation, thereby negatively affecting performance in the post-test.
The findings obtained in the Benedek et al. (2006) study support the view that (at least some types of) creative cognition can be improved effectively. Also, the study points to the usefulness of computerized techniques in the particular context of improving creativity-related skills. In Fink et al. (2006), we addressed the research question as to how brain activity may change as a consequence of a creative thinking training. In this study we employed only the verbal creativity training, which proved to be more effective than the functional creativity training (Benedek et al., 2006). We measured task-related changes in EEG alpha activity in an EEG pre-test and an EEG post-test while participants were engaged in the performance of different creative idea generation tasks (IS, US, AU, and WE task; see above). Between the pre- and post-test, half of the participants received a verbal creative thinking training, whereas the remaining participants received training only after the post-test (waiting control group). The training procedure was the same as in Benedek et al. (2006).
On the behavioral level, the creative thinking training has proven to effectively enhance the originality of ideas in three (i.e., in the IS, US, and AU tasks) out of the four creative idea generation tasks. In the more convergent or intelligence-related WE task, which has been shown to be significantly correlated with verbal intelligence (Fink et al., 2006, 2007), however, no training effects were observed. The most important finding of the Fink et al. (2006) study was that training effects were also reflected at the level of the brain. As was evident by a significant interaction between training group and cortical area in the post-test, the training group exhibited a stronger task-related synchronization of frontal alpha activity than the control group. The observed frontal alpha synchronization during creative thinking could point to the possibility that during the generation of novel, original ideas (which presumably also requires top-down processing), frontal brain regions must not become disturbed by interfering cognitive processes as long as ongoing idea generation takes place. Thus, the stronger frontal alpha synchronization due to the creativity training (Fink et al., 2006) could indicate that the participants were effectively trained to keep their attention highly focused on relevant aspects of the task (i.e., the production of novel, original ideas) by suppressing interference from task-irrelevant external input (see Klimesch et al., 2007; Sauseng et al., 2005). Though the functional significance of EEG alpha synchronization in the particular context of creative thinking certainly needs to be addressed in more detail in future studies, the obtained findings are consistent with the view that frontal cortical areas are critically involved in the production of novel, original ideas (e.g., Carlsson et al., 2000; Dietrich, 2004; Flaherty, 2005; Folley & Park, 2005; Goel & Vartanian, 2005; Heilman et al., 2003; Howard-Jones et al., 2005). From a more general perspective, the Fink et al. (2006) study also demonstrates the usefulness of neuroscientific measurement methods in the validation of cognitive trainings and generally points—along with the Benedek et al. (2006) study—to the applicability of computer-based training procedures in the particular context of improving creativity-related skills.
Enhancing Creativity by Means of Cognitive Stimulation
Relevant literature in this field also suggests that creative cognition might be improved by means of cognitive stimulation (e.g., Dugosh et al., 2000). This could be simply realized, for instance, by means of divergent thinking exercises preceding the actual idea-generation task (Coskun, 2005). In addition to this, cognitive stimulation can be also achieved by confronting people with the ideas of others. As is the case in classic group-based brainstorming techniques (Osborn, 1957), each single idea or solution a person generates for a specific problem may stimulate new ideas or solutions in others. In this context, Dugosh and Paulus (2005; see also Dugosh et al., 2000) report exciting empirical findings that the number of generated unique ideas may be enhanced through the exposure to ideas of others (provided that the individuals actively attend to the presented ideas; see also Paulus & Yang, 2000). In two fMRI experiments of our laboratory we addressed the research question of how creative cognition can be improved effectively by means of such types of interventions and whether any intervention effects are also reflected at the level of the brain. Similarly to our previous EEG studies, we presented everyday objects (such as a “tin” or an “umbrella”) during fMRI recording and participants were instructed to generate creative or original uses of the given objects, which had to be verbalized by the participants subsequent to a so-called idea-generation phase (see Fink et al., 2010, 2011). The oral responses were recorded by the experimenter and rated with respect to their originality subsequent to the fMRI recording session. In one experimental condition, participants performed the AU task subsequent to a short cognitive stimulation condition during which they were exposed to ideas produced by other people (as they were obtained in a pre-experimental pilot study). The findings of the Fink et al. (2010) study reveal performance increases as a result of the employed creativity interventions, and more importantly, effects were also apparent at the level of the brain. The employed interventions recruit a complex and widespread neural network primarily involving posterior brain regions that are known as important components of the neural network specialized for semantic information processing.
Concluding Remarks
Neuroscientific studies on creative cognition have revealed valuable insights into potential brain mechanisms that underlie various facets of creative cognition. For instance, research has shown that brain activity in response to more divergent or creativity-related tasks (such as responding creatively to hypothetical or utopian situations) differ from brain activity patterns during the performance of more convergent or intelligence-related tasks (such as completing given words or performing mental arithmetic; Fink et al., 2006, 2007; Jaušovec & Jaušovec, 2000; Mölle et al., 1999; Razumnikova, 2000). Studies on creative cognition have also yielded evidence that brain states accompanying highly original ideas differ from those observed during the production of less original, conventional ideas (as determined by external or subjective ratings; Fink & Neubauer, 2006; Grabner et al., 2007; see also research on the subjective experience of “aha!,” Jung-Beeman et al., 2004). From an individual differences perspective we could—in continuation of our work on neural efficiency (Neubauer et al., 2002, 2005)—also demonstrate that the production of original ideas seems to be moderated by participants’ sex and intelligence level (Fink & Neubauer, 2006) and by individual differences in the personality dimension of extraversion–introversion (Fink & Neubauer, 2008). Finally, research in this field also suggests that creative cognition can be improved effectively by means of training (Benedek et al., 2006) or cognitive stimulation (Fink et al., 2010, 2011) and that performance increases can be validated at the neurophysiological level (Fink et al., 2006; 2010, 2011).
It is worth noting that EEG activity in the alpha frequency band has proven to be fairly sensitive to creativity-related demands in a series of studies (for a review, see Fink & Benedek, 2013). Specifically, on the basis of existing evidence on the relationship between EEG alpha activity and creative cognition we can conclude that EEG alpha activity varies as a function of the creative demands of a task (the more creative a task, the higher the level of alpha activity; Fink et al., 2007), as a function of originality (higher originality is accompanied by more alpha; Fink & Neubauer, 2006; Grabner et al., 2007) or subjective experience of insight (more alpha in insight vs. noninsight solutions; Jung-Beeman et al., 2004), and as a function of an individual’s creativity level (more alpha in higher creative individuals; Fink et al., 2009a; Jaušovec, 2000; Martindale & Hines, 1975). Alpha synchronization has traditionally been considered as a functional correlate of cortical idling, presumably reflecting a reduced state of active information processing in the underlying neuronal networks (Pfurtscheller et al., 1996). However, in the meantime more and more studies suggest that synchronization of alpha activity does not merely reflect cortical deactivation or cortical idling (a highly readable review on this topic is given in Klimesch et al., 2007). In fact, alpha synchronization appears to be especially relevant during internal processing demands, for instance when participants are required to hold information temporarily in mind (see Sauseng et al., 2005). Along these lines, the diffuse and topographically less clear pattern of alpha synchronization in posterior parietal brain regions, which we have repeatedly observed in our studies on creative cognition (e.g., Fink et al., 2007, 2009a,b), could reflect the absence of stimulus-driven, external bottom-up stimulation and, thus, a form of top-down activity (Benedek et al., 2011; von Stein & Sarnthein, 2000) or a state of heightened internal attention facilitating the (re-)combination of semantic information that is normally distantly related.
Though the findings summarized in this chapter may uncover some brain correlates underlying creative cognition, some important issues remain unresolved. First and foremost, the employed creativity tasks used in neuroscientific studies on creative cognition are essentially basic types of tasks, which had to be modified in order to be reasonably applicable in EEG or fMRI measurements. In this particular context it can be argued that the employed tasks are too simple to be generalizable to “real-life” creative achievements. The difficulty of operationalizing creativity in neuroscientific studies of creative cognition is additionally complicated by the fact that participants are required to be creative while they are mounted with an electrode cap sitting in a shielded EEG cabin or lying supine in the fMRI scanner. Thus, future neuroscientific research on creativity may not only be challenged by the investigation of brain activity in tasks with valid psychometric properties (Arden et al., 2010), but also in more complex, ecologically valid “real-life” creativity tasks. Promising examples for this exciting new research line include the studies of Berkowitz and Ansari (2010), Bhattacharya and Petsche (2005), and Kowatari et al. (2009), who extend neuroscientific research to the domain of artistic creativity including the study of brain activity during musical improvisation, visual art, and designing new pens, respectively (for a recent EEG study on dance improvisation see Fink et al., 2009b). On the other hand, however, it has also been argued that the employed tasks might be too complex, and thus do not allow us to link the evidence with single definable neurocognitive processes (e.g., Dietrich & Kanso, 2010). That is, the neuroscientific research on creativity might also benefit from the employment of simpler tasks and paradigms, which can more easily be related to well-established concepts of cognitive neuroscience such as attention, memory, or cognitive control. This approach would thus not study creativity as a unitary construct, but would study relevant aspects of it, thereby trying to promote neurocognitive theories of creativity.
Perhaps the most important benefit of the summarized research on creative cognition is that it may also entail some relevant practical implications. The work presented in this chapter not only reveals some valuable brain correlates underlying creative cognition, it moreover suggests that at least some facets of creative cognition can be trained or stimulated effectively and that the effects of such interventions are also observable at the level of the brain. This could be viewed as a highly promising objective in the field of cognition inasmuch as relevant research not only focuses on describing the status quo of an individual in a particular variable of interest (such as intelligence or creativity) but also adopts a dynamic view of cognition that incorporates the crucial importance of learning or training in the course of expertise acquisition in a particular cognitive domain. Meanwhile, neuroscientific studies have accumulated a large body of empirical evidence substantiating this view. For instance, research has revealed that training of reasoning (Neubauer et al., 2004, 2010), mental arithmetic (Ischebeck et al., 2006), creative cognition (Fink et al., 2006), and the treatment of orthographic spelling in dyslexic children (Richards et al., 2006; Weiss et al., 2010) are accompanied by specific changes in activity patterns of the brain (for training-induced changes of structural parameters of the brain see, e.g., Maguire et al., 2000; Mechelli et al., 2004; Münte et al., 2002).
The enhancement of creativity-related skills may also be a fruitful avenue for future research. Progress in the scientific understanding of how creative cognition can be enhanced involves important practical implications, particularly for the pedagogical or educational domain. In light of the view that the “plastic” brain is sensitive to environmental stimulation (see Garlick, 2002; see also Münte et al.’s 2002 report on “the musician’s brain as a model of neuroplasticity”), we are all—practitioner and scientists—challenged to attend to the question of how the cognitive capacities of an individual can be realized to the best possible extent.
References
Amabile, T. M. (1982). Social psychology of creativity: A consensual assessment technique. Journal of Personality and Social Psychology, 43, 997–1013.
Amabile, T. M. (1983). Social psychology of creativity: A componential conceptualization. Journal of Personality and Social Psychology, 45, 357–376.
Arden, R., Chavez, R. S., Grazioplene, R., & Jung, R. E. (2010). Neuroimaging creativity: A psychometric review. Behavioural Brain Research, 214, 143–156.
Ashby, F. G., Isen, A. M., & Turken, A. U. (1999). A neuropsychological theory of positive affect and its influence on cognition. Psychological Review, 106, 529–550.
Bechtereva, N. P., Korotkov, A. D., Pakhomov, S. V., Roudas, M. S., Starchenko, M. G., & Medvedev, S. V. (2004). PET study of brain maintenance of verbal creative activity. International Journal of Psychophysiology, 53, 11–20.
Benedek, M., Bergner, S., Könen, T., Fink, A., & Neubauer, A. C. (2011). EEG alpha synchronization is related to top-down processing in convergent and divergent thinking. Neuropsychologia, 49, 3505–3511. doi:10.1016/j.neuropsychologia.2011.09.004.
Benedek, M., Fink, A., & Neubauer, A. C. (2006). Enhancement of ideational fluency by means of computer-based training. Creativity Research Journal, 18, 317–328.
Berkowitz, A. L., & Ansari, D. (2010). Expertise-related deactivation of the right temporoparietal junction during musical improvisation. NeuroImage, 49, 712–719.
Bhattacharya, J., & Petsche, H. (2005). Drawing on mind’s canvas: Differences in cortical integration patterns between artists and non-artists. Human Brain Mapping, 26, 1–14.
Bowden, E. M., Jung-Beeman, M., Fleck, J., & Kounios, J. (2005). New approaches to demystifying insight. Trends in Cognitive Sciences, 9, 322–328.
Carlsson, I., Wendt, P. E., & Risberg, J. (2000). On the neurobiology of creativity: Differences in frontal activity between high and low creative subjects. Neuropsychologia, 38, 873–885.
Chávez-Eakle, R. A., Graff-Guerrero, A., García-Reyna, J., Vaugier, V., & Cruz-Fuentes, C. (2007). Cerebral blood flow associated with creative performance: A comparative study. NeuroImage, 38, 519–528.
Coskun, H. (2005). Cognitive stimulation with convergent and divergent thinking exercises in brainwriting: Incubation, sequence priming, and group context. Small Group Research, 36, 466–498.
Coulson, S., & Williams, S. (2005). Hemispheric asymmetries and joke comprehension. Neuropsychologia, 43, 128–141.
DeRosa, D. M., Smith, C. L., & Hantula, D. A. (2007). The medium matters: Mining the long-promised merit of group interaction in creative idea generation tasks in a meta-analysis of the electronic group brainstorming literature. Computers in Human Behavior, 23, 1549–1581.
Diehl, M., & Stroebe, W. (1987). Productivity loss in brainstorming groups: Toward the solution of a riddle. Journal of Personality and Social Psychology, 53, 497–509.
Dietrich, A., & Kanso, R. (2010). A review of EEG, ERP, and neuroimaging studies of creativity and insight. Psychological Bulletin, 136, 822–848.
Dietrich, A. (2004). The cognitive neuroscience of creativity. Psychonomic Bulletin & Review, 11, 1011–1026.
Dietrich, A. (2007). Who’s afraid of a cognitive neuroscience of creativity? Methods, 42, 22–27.
Dugosh, K. L., & Paulus, P. B. (2005). Cognitive and social comparison processes in brain storming. Journal of Experimental Social Psychology, 41, 313–320.
Dugosh, K. L., Paulus, P. B., Roland, E. J., & Yang, H.-C. (2000). Cognitive stimulation in brainstorming. Journal of Personality and Social Psychology, 79, 722–735.
Feist, G. J. (1998). A meta-analysis of personality in scientific and artistic creativity. Personality and Social Psychology Review, 2, 290–309.
Fink, A., & Benedek, M. (2013). EEG Alpha power and creative ideation. Neuroscience and Biobehavioral Reviews. Advance online publication. doi:10.1016/j.neubiorev.2012.12.002.
Fink, A., & Neubauer, A. C. (2006). EEG alpha oscillations during the performance of verbal creativity tasks: Differential effects of sex and verbal intelligence. International Journal of Psychophysiology, 62, 46–53.
Fink, A., & Neubauer, A. C. (2008). Eysenck meets Martindale: The relationship between extraversion and originality from the neuroscientific perspective. Personality and Individual Differences, 44, 299–310.
Fink, A., Benedek, M., Grabner, R. H., Staudt, B., & Neubauer, A. C. (2007). Creativity meets neuroscience: Experimental tasks for the neuroscientific study of creative thinking. Methods, 42, 68–76.
Fink, A., Grabner, R. H., Benedek, M., & Neubauer, A. C. (2006). Divergent thinking training is related to frontal electroencephalogram alpha synchronization. European Journal of Neuroscience, 23, 2241–2246.
Fink, A., Grabner, R. H., Benedek, M., Reishofer, G., Hauswirth, V., Fally, M., et al. (2009a). The creative brain: Investigation of brain activity during creative problem solving by means of EEG and fMRI. Human Brain Mapping, 30, 734–748.
Fink, A., Grabner, R. H., Gebauer, D., Reishofer, G., Koschutnig, K., & Ebner, F. (2010). Enhancing creativity by means of cognitive stimulation: Evidence from an fMRI study. NeuroImage, 52, 1687–1695.
Fink, A., Graif, B., & Neubauer, A. C. (2009b). Brain correlates underlying creative thinking: EEG alpha activity in professional vs. novice dancers. NeuroImage, 46, 854–862.
Fink, A., Koschutnig, K., Benedek, M., Reishofer, G., Ischebeck, A., Weiss, E. M., et al. (2011). Stimulating creativity via the exposure to other people’s ideas. Human Brain Mapping. doi:10.1002/hbm.21387.
Flaherty, A. W. (2005). Frontotemporal and dopaminergic control of idea generation and creative drive. Journal of Comparative Neurology, 493, 147–153.
Folley, B. S., & Park, S. (2005). Verbal creativity and schizotypal personality in relation to prefrontal hemispheric laterality: A behavioral and near-infrared optical imaging study. Schizophrenia Research, 80, 271–282.
Garlick, D. (2002). Understanding the nature of the general factor of intelligence: The role of individual differences in neural plasticity as an explanatory mechanism. Psychological Review, 109, 116–136.
Goel, V., & Vartanian, O. (2005). Dissociating the roles of right ventral lateral and dorsal lateral prefrontal cortex in generation and maintenance of hypotheses in set-shift problems. Cerebral Cortex, 15, 1170–1177.
Grabner, R. H., Fink, A., & Neubauer, A. C. (2007). Brain correlates of self-rated originality of ideas: Evidence from event-related power and phase-locking changes in the EEG. Behavioral Neuroscience, 121, 224–230.
Guilford, J. P. (1950). Creativity. American Psychologist, 5, 444–454.
Guilford, J. P. (1967). The nature of human intelligence. New York: McGraw-Hill.
Hany, E. A. (2001). Förderung von Kreativität. In K. J. Klauer (Ed.), Handbuch Kognitives Training (pp. 261–291). Göttingen: Hogrefe.
Heilman, K. M., Nadeau, S. E., & Beversdorf, D. O. (2003). Creative innovation: possible brain mechanisms. Neurocase, 9, 369–379.
Hennessey, B. A., & Amabile, T. M. (2010). Creativity. Annual Review of Psychology, 61, 569–598.
Howard-Jones, P. A., Blakemore, S.-J., Samuel, E. A., Summers, I. R., & Claxton, G. (2005). Semantic divergence and creative story generation: An fMRI investigation. Cognitive Brain Research, 25, 240–250.
Ischebeck, A., Zamarian, L., Siedentopf, C., Koppelstätter, F., Benke, T., Felber, S., et al. (2006). How specifically do we learn? Imaging the learning of multiplication and subtraction. NeuroImage, 30, 1365–1375.
Jauk, E., Benedek, M., & Neubauer, A. C. (2012). Tackling creativity at its roots: Evidence for different patterns of EEG alpha activity related to convergent and divergent modes of task processing. International Journal of Psychophysiology, 84, 219–225. doi:10.1016/j.ijpsycho.2012.02.012.
Jaušovec, N., & Jaušovec, K. (2000). EEG activity during the performance of complex mental problems. International Journal of Psychophysiology, 36, 73–88.
Jaušovec, N. (2000). Differences in cognitive processes between gifted, intelligent, creative, and average individuals while solving complex problems: An EEG Study. Intelligence, 28, 213–237.
Jung, R. E., Segall, J. M., Jeremy Bockholt, H., Flores, R. A., Smith, S. M., Chavez, R. S., et al. (2010). Neuroanatomy of creativity. Human Brain Mapping, 31, 398–409.
Jung-Beeman, M. (2005). Bilateral brain processes for comprehending natural language. Trends in Cognitive Sciences, 9, 512–518.
Jung-Beeman, M., Bowden, E. M., Haberman, J., Frymiare, J. L., Arambel-Liu, S., Greenblatt, R., et al. (2004). Neural activity when people solve verbal problems with insight. PLoS Biology, 2, 500–510.
Kaufman, J. C. (2005). The door that leads into madness: Eastern European poets and mental illness. Creativity Research Journal, 17, 99–103.
King, L. A., Walker, L. M., & Broyles, S. J. (1996). Creativity and the Five-Factor model. Journal of Research in Personality, 30, 189–203.
Klimesch, W. (1999). EEG alpha and theta oscillations reflect cognitive and memory performance: A review and analysis. Brain Research Reviews, 29, 169–195.
Klimesch, W., Sauseng, P., & Hanslmayr, S. (2007). EEG alpha oscillations: The inhibition-timing hypothesis. Brain Research Reviews, 53, 63–88.
Kowatari, Y., Lee, S. H., Yamamura, H., Nagamori, Y., Levy, P., Yamane, S., et al. (2009). Neural networks involved in artistic creativity. Human Brain Mapping, 30, 1678–1690.
Krampen, G. (1997). Promotion of creativity (divergent productions) and convergent productions by systematic-relaxation exercises: Empirical evidence from five experimental studies with children, young adults, and elderly. European Journal of Personality, 11, 83–99.
Lachaux, J.-P., Rodriguez, E., Martinerie, J., & Varela, F. J. (1999). Measuring phase synchrony in brain signals. Human Brain Mapping, 8, 194–208.
Lipsey, M. W., & Wilson, D. B. (1993). The efficacy of psychological, educational, and behavioral treatment: Confirmation from meta-analysis. American Psychologist, 48, 1181–1209.
Lubart, T. (2005). How can computers be partners in the creative process: Classification and commentary on the special issue. International Journal of Human–Computer Studies, 63, 365–369.
Maguire, E. A., Gadian, D. G., Johnsrude, I. S., Good, C. D., Ashburner, J., Frackowiak, R. S., et al. (2000). Navigation-related structural change in the hippocampi of taxi drivers. Proceedings of the National Academy of Sciences of the United States of America, 97, 4398–4403.
Martindale, C., Hines, D., Mitchell, L., & Covello, E. (1984). EEG alpha asymmetry and creativity. Personality and Individual Differences, 5, 77–86.
Martindale, C., & Hasenfus, N. (1978). EEG differences as a function of creativity, stage of the creative process, and effort to be original. Biological Psychology, 6, 157–167.
Martindale, C., & Hines, D. (1975). Creativity and cortical activation during creative, intellectual, and EEG feedback tasks. Biological Psychology, 3, 71–80.
Mechelli, A., Crinion, J. T., Noppeney, U., O’Dohorty, J., Ashburner, J., Frackowiak, R. S., et al. (2004). Structural plasticity in the bilingual brain. Nature, 431, 757.
Mobbs, D., Greicius, M. D., Abdel-Azim, E., Menon, V., & Reiss, A. L. (2003). Humor modulates the mesolimbic reward centres. Neuron, 40, 1041–1048.
Mölle, M., Marshall, L., Wolf, B., Fehm, H. L., & Born, J. (1999). EEG complexity and performance measures of creative thinking. Psychophysiology, 36, 95–104.
Münte, T. F., Altenmüller, E., & Jäncke, L. (2002). The musician’s brain as a model of neuroplasticity. Nature Reviews: Neuroscience, 3, 473–478.
Neubauer, A. C., Bergner, S., & Schatz, M. (2010). Two- vs. three-dimensional presentation of mental rotation tasks: Sex differences and effects of training on performance and brain activation. Intelligence, 38, 529–539.
Neubauer, A. C., & Fink, A. (2009). Intelligence and neural efficiency. Neuroscience and Biobehavioral Reviews, 33, 1004–1023.
Neubauer, A. C., Fink, A., & Schrausser, D. G. (2002). Intelligence and neural efficiency: The influence of task content and sex on the brain-IQ relationship. Intelligence, 30, 515–536.
Neubauer, A. C., Grabner, R. H., Fink, A., & Neuper, C. (2005). Intelligence and neural efficiency: Further evidence of the influence of task content and sex on the brain-IQ relationship. Cognitive Brain Research, 25, 217–225.
Neubauer, A. C., Grabner, R. H., Freudenthaler, H. H., Beckmann, J. F., & Guthke, J. (2004). Intelligence and individual differences in becoming neurally efficient. Acta Psychologica, 116, 55–74.
Osborn, A. F. (1957). Applied imagination. New York: Scribner’s.
Paulus, P. B., & Yang, H.-C. (2000). Idea generation in groups: A basis for creativity in organizations. Organizational Behavior and Human Decision Processes, 82, 76–87.
Petsche, H. (1996). Approaches to verbal, visual, and musical creativity by EEG coherence analysis. International Journal of Psychophysiology, 24, 145–159.
Pfurtscheller, G. (1999). Quantification of ERD and ERS in the time domain. In G. Pfurtscheller & F. H. Lopes da Silva (Eds.), Event-related desynchronization: Handbook of electroencephalography and clinical neurophysiology (Rev. Ed., Vol. 6, pp. 89–105). Amsterdam: Elsevier.
Pfurtscheller, G., Stancak, A., Jr., & Neuper, C. (1996). Event-related synchronization (ERS) in the alpha band—an electrophysiological correlate of cortical idling: a review. International Journal of Psychophysiology, 24, 39–46.
Razumnikova, O. M. (2000). Functional organization of different brain areas during convergent and divergent thinking: An EEG investigation. Cognitive Brain Research, 10, 11–18.
Razumnikova, O. M. (2004). Gender differences in hemispheric organization during divergent thinking: An EEG investigation in human subjects. Neuroscience Letters, 362, 193–195.
Richards, T. L., Aylward, E. H., Berninger, V. W., Field, K. M., Grimme, A. C., Richards, A. L., et al. (2006). Individual fMRI activation in orthographic mapping and morpheme mapping after orthographic or morphological spelling treatment in child dyslexics. Journal of Neurolinguistics, 19, 56–86.
Rose, L. H., & Lin, H. J. (1984). A meta-analysis of long-term creativity training programs. Journal of Creative Behavior, 18, 11–22.
Rowe, G., Hirsh, J. B., & Anderson, A. K. (2007). Positive affect increases the breadth of attentional selection. Proceedings of the National Academy of Sciences of the United States of America, 104, 383–388.
Sandkühler, S., & Bhattacharya, J. (2008). Deconstructing insight: EEG correlates of insightful problem solving. PLoS ONE, 3(1), e1459.
Sauseng, P., Klimesch, W., Doppelmayr, M., Pecherstorfer, T., Freunberger, R., & Hanslmayr, S. (2005). EEG alpha synchronization and functional coupling during top-down processing in a working memory task. Human Brain Mapping, 26, 148–155.
Sawyer, R. K. (2006). Educating for innovation. Thinking Skills and Creativity, 1, 41–48.
Schoppe, K. (1975). Verbaler Kreativitäts-Test (V-K-T). Göttingen: Hogrefe.
Scott, G., Leritz, L. E., & Mumford, M. D. (2004a). The effectiveness of creativity training: A quantitative review. Creativity Research Journal, 16, 361–388.
Scott, G., Leritz, L. E., & Mumford, M. D. (2004b). Types of creativity training: Approaches and their effectiveness. Journal of Creative Behavior, 38, 149–179.
Smith, S. M., Ward, T. B., & Finke, R. A. (1995). The creative cognition approach. Cambridge, MA: MIT Press.
So, K. T., & Orme-Johnson, D. W. (2001). Three randomized experiments on the longitudinal effects of the transcendental meditation technique on cognition. Intelligence, 29, 419–440.
Stein, M. I. (1953). Creativity and culture. Journal of Psychology, 36, 311–322.
Sternberg, R. J., & Lubart, T. I. (1996). Investing in creativity. American Psychologist, 7, 677–688.
Torrance, E. P. (1966). Torrance tests of creative thinking. Bensenville, IL: Scholastic Testing Service.
von Stein, A., & Sarnthein, J. (2000). Different frequencies for different scales of cortical integration: From local gamma to long range alpha/theta synchronization. International Journal of Psychophysiology, 38, 301–313.
Ward, T. B. (2007). Creative cognition as a window on creativity. Methods, 42, 28–37.
Weiss, S., Grabner, R. H., Kargl, R., Purgstaller, C. & Fink, A. (2010). Behavioral and neurophysiological effects of a computer-aided morphological awareness training on spelling and reading skills. Reading and Writing: An Interdisciplinary Journal, 23, 645–671.