Effects of music listening on creative cognition and semantic memory retrieval

Abstract

Listening to music can affect cognitive abilities and may impact creative cognition. This effect is believed to be caused by music’s impact on arousal and mood. However, this causal relationship has been understudied. Furthermore, the strength of semantic knowledge associations has also been linked to creativity and provides an alternative hypothesis for increases in creative cognition. The relationship between music, mood, semantic knowledge, and creative cognition is not well understood. The present study consisted of two experiments. The first examined the relationship between music listening and creative cognition, the second additionally sought to examine whether the effect of music on semantic memory and/or mood are mechanisms that promote creative cognition. In the first experiment, participants completed 15 items of the Remote Associates Test of Creativity after listening to hip-hop music, classical music, and babble. In addition to replicating the first experiment, the second also measured mood and semantic memory. In both experiments participants displayed greater creativity after listening to music. Semantic memory retrieval was enhanced after listening to music, but creative cognition and semantic memory were not significantly correlated with mood. The findings show parallel, positive effects on creative cognition, semantic retrieval, and mood when subjects listen to music.

Keywords

arousal creativity listening memory mood regulation

Creativity was named the most important economic resource of the 21st century (Florida, 2002). It has been shown to improve psychological functions such as coping, emotional growth, problem solving, and interpersonal relationships, and contributes to vocational success, workplace leadership, and technological advancement (Plucker, Beghetto, & Dow, 2004). As a result, there is great interest in the study of creativity—a quick PsycINFO search reveals over 22,346 publications over the past decade with the keyword of “creativity.” Despite this attention, the underlying cognitive mechanisms and techniques to improve creativity are not well understood. Given its importance for both individuals and larger social structures, it is essential to understand factors that may affect creativity. Creativity is enhanced in a number of ways including exercise, mood, and linguistic cues (Steinberg et al., 1997; Isen, 2005; Bass et al., 2008). Given the degree of neurophysiological overlap between verbal and musical stimuli, we explore whether listening to music can enhance creative cognition.

Creativity

Creativity is the process through which individuals make a product that is both original and appropriate for a given situation (Runco & Jaeger, 2012). Traditionally, creativity researchers have distinguished between two types of creativity: little-c and Big-C (Richards, 1990). Products that are original in everyday situations, like a creative Halloween costume or putting a branch under a tire on a snowy day to gain traction, are grouped together as little-c. Products that are original for an epoch of time or a field, like Beethoven’s Fifth symphony or Einstein’s theory of relativity, are called Big-C (Richards, 1990). Simonton’s (1991a, 1991b) seminal works studying the career trajectories of artists, composers, and scientists concluded that Big-C contributors follow a pattern. Most begin making contributions to their prospective fields around 20 years of age (30 for scientists), make their most significant products around 40 years of age, and then slowly decline to zero from that high point. Simonton notes that the reason scientists take a little longer is that they need that time to build expertise, which has been confirmed by subsequent research (Ericsson & Lehmann, 1996; Csikszentmihalyi, 1999). On the other hand, little-c creativity is a vibrant area of research that includes traditional work on the role of semantic memory (Mednick, 1962), as well as more recent ideas concerning the role of attention control (Carson, Peterson, & Higgins, 2003), working memory (Beaty & Silvia, 2012; De Dreu, Niijstad, Baas, Wolsink, & Roskes, 2012), and dual process (type I and II) cognitive systems (Barr et al., 2015). Everyday creativity manifests itself across all sorts of commonplace events: using rhyming patterns in poems, finding novel ways to diagram organizational processes, or developing simple metaphors to explain complex ideas in classrooms. In all of these cases, people use standard cognitive tools to creatively solve problems. One of the most “standard” of all cognitive tools is language, a faculty in which sound, experience, and memory coalesce to create meaning. Recent data from the field of cognitive neuroscience suggests that language and music have many cognitive aspects in common (Jäncke, 2012). To the degree that music represents the dynamic convergence of verbal processing and semantic memory, it may be that listening to music can transiently improve creative cognition by facilitating the search of semantic memory, and in the current study we test this idea directly.

Music listening and cognition

Music is a powerful and deeply loved human pastime, at least partly because of its ability to influence our feelings and behavior. The arousal–mood hypothesis (Husain, Thompson, & Schellenberg, 2002) states that improved cognitive performance is a function of two constructs: activation and valence. Activation refers to the level of arousal that a mood produces, and valence refers to how pleasing the mood is. Moods can be positive and activating (e.g., happy), positive and deactivating (e.g., calm), negative and activating (e.g., angry), or negative and deactivating (e.g., sad).

The effect of music exposure on spatial abilities, mood, and arousal has been investigated. In two studies, Western participants listened to 10 minutes of music that varied in tempo (fast, slow) and expression of mood (major key, minor key). It was found that participants reported a more positive mood and performed better on spatial tasks (Husain et al., 2002) and non-spatial tasks (Schellenberg, Nakata, Hunter, & Tamoto, 2007) after listening to music that was in a major key and up-tempo, suggesting that mood and cognition might share in the same cognitive space.

A meta-analysis found that positive moods produce more creativity than neutral moods, and the most effective moods for enhancing creativity were those with high activation and a positive valence, such as happiness (De Dreu, Bass, & Nijstad, 2008). In a related line of work, Schellenberg et al. (2007) found that 5-year-olds spent more time working on drawings after listening to or singing familiar children’s songs than after listening to classical music. Furthermore, the children’s drawings were deemed more creative by adult raters when the children had been exposed to familiar children’s songs.

In a more applied setting, Lesiuk (2010) explored the effects of music on mood and job performance. Computer information software developers were studied using a repeated measures design that varied the amount of music listening (music, no music). Participants reported a significantly better mood than in the no music condition, and believed they were more creative and effective at solving work problems, though the self-report measure might not index objective increases in creativity. Other research has shown that music can affect creative performance. Adaman and Blaney (1995) used music to prime students to be in an elated, neutral, or depressed mood, and found that students in the elated or depressed mood performed better on measures of creativity.

These studies show that music listening can affect both mood and creativity, but the relationship between music listening, creativity, and mood is underexplored. The current study addresses this gap by examining the relationship between mood and creative cognition as mediated by music. If music affects creative cognition through mood, then changes in mood and creative cognition after music listening are expected to be linked. Conversely, if music is affecting creative cognition and mood separately through different processes, then an association is not expected. We also examine the role of semantic memory as a possible mechanism for the effects of the music on measures of creative cognition.

Mechanisms for the creative boost

Although many cognitive processes are involved in the construct of creativity, much of the research has focused on the theory of blind variation and selective retention (BVST) (Campbell, 1960). This theory identifies two important aspects of creativity. The first is a blind variation system that reflects aspects of divergent thinking where participants generate many ideas and focuses on the flexibility, fluency, and originality aspects of creativity. These creative skills are commonly tested using divergent thinking tasks such as the Alternative Uses Task, where participants are asked to produce as many uses as they can for a paper bag (Guilford, 1967; Runco, 2010). The second component of selective retention evaluates and selectively retains good ideas and contains elements of convergent thinking as one converges on a final product. Research on convergent cognition highlights the importance of combining information into new solutions, and relies on the ability to associate separate and remote ideas. Convergent ability can be investigated using the Remote Associates Test (RAT), where participants find a word that links three stimulus words to measure combinatorial processes in creative thinking (Mednick, 1962; Mednick, Mednick, & Jung, 1964; Ward & Kolomyts, 2010). Mednick proposes that people with strong associations among common items and few distant or uncommon associations are less creative than people with a greater number of concepts that are moderately associated. One prediction that follows from Mednick’s model is that creative people should produce more responses in a creative task than less creative people, which has held up in more recent research (Benedek & Neubauer, 2013; Kenett, Anaki, & Faust, 2014) These findings suggest that creativity is not a function of the strength of association, but instead the result of number of responses.

The question remains: Are people generating more responses by searching deeply within semantic categories, or is it the ability to quickly search between different categories that affords the advantage? If asked to name as many supermarket items as possible, creative people may generate more responses because they 1) have more items in a subcategory (e.g., apples, grapes, strawberries, cherries), or 2) because they can quickly switch between categories (apples, bread, chicken, broccoli, crackers), or 3) a combination of both. Fluency tasks rely on numerous cognitive processes, including accessing semantic memory stores and using executive processes like monitoring, organizing, rule implementation, inhibition, and set shifting. Therefore, it is important to understand the contributions of semantic knowledge versus executive control features in fluency tasks.

The roles of executive control and depth of semantic knowledge in verbal fluency tasks have been examined in patients with frontal and temporal lobe lesions. Specifically, Troyer, Moscovitch, Winocur, Alexander, and Stuss (1998) measured the influence of executive control by calculating switching, which was how often a patient switched between semantic memory stores (apples, bread, chicken, broccoli, crackers). Troyer et al. measured the depth of the semantic search using clustering, by counting the number of items in a subcategory (apples, grapes, strawberries, cherries). They found that patients with temporal lobe lesions (right or left) switched less frequently than controls, and that left temporal lobe patients produced smaller clusters than right temporal lobe patients. These findings highlight the use of the right temporal lobe for looking deeply with-in a store.

In the current study, in addition to measuring the effects of music on creativity and mood, we examine the relationship between convergent creativity and associative semantic memory and executive functions. Semantic memory was chosen because combining existing ideas and drawing upon existing knowledge are important for creative cognition. We and others (see Scott, Leritz, & Mumford, 2004; DeHann, 2009) theorize that interventions that can improve the basic cognitive processes that underlie creativity are likely to also improve creativity itself.

The goal of the current study was to test whether creativity is greater after listening to music relative to listening to non-musical control sounds. This is the first time to our knowledge that the effects of music listening on creative cognition measured by the RAT has been investigated. A second goal was to explore why listening to music might influence creativity scores. Two experiments were conducted. The first sought to examine the effect of music on creativity. The second tested the impact of music on creativity but also examined the relationship between semantic memory, executive function, and creativity, and the effects of music and mood on sematic memory and creativity. The hypothesis of the first study was that participants will display more creativity after listening to music than after listening to simulated background noise. The second study had two additional hypotheses: 1) those in a positive mood will display greater creativity and better semantic memory retrieval, or 2) semantic memory retrieval scores and/or executive function will mediate the effect of music on creativity scores.

Experiment I

Method

Participants

The participants for this experiment were 35 undergraduate students between the ages of 18 and 23 (M = 20.00, SD = 1.32, Male / Female = 5 / 30) enrolled at Xavier University of Louisiana. Thirty-one of the participants were African-American (88.6%). The participants were recruited via the participant pool through the university’s department of psychology, and participants currently enrolled in a psychology course were awarded extra credit for participation in the study. Of the 35 participants in the study, 71.4% reported a preference for the hip-hop or rhythm ‘n’ blues genres of music, 14.3% had other musical preferences, and 14.3% noted no preference.

Materials

Musical selections

One 260-second track was used for each genre of audio presented during the study. For hip-hop, participants listened to the 2011 single “Can’t Hold Us” by Macklemore and Ryan Lewis featuring Ray Dalton. Participants then listened to the first movement of Mozart’s 1787 composition “Eine Kleine Nachtmusik.” Participants also listened to simulated background noise, emulating the murmur of a crowd, for 260 seconds. The MixMeister BPM Analyzer was used to determine the beats per minute (BPM) for the audio tracks, and librosa.piptrack version 0.6.2 was used to calculate mean pitch (Librosa, n.d.). “Can’t Hold Us” had 146 BPM, is in D major and has a mean pitch of 590 Hz. “Eine Kleine Nachtmusik” has a BPM of 105.52, is in G major and has a mean pitch of 490 Hz. The simulated background music was not analyzed for BPM and has a mean pitch of 327 Hz.

Creativity assessment

Items from the original Remote Associates Test (RAT) of Creativity (Mednick & Mednick, 1967) and items adapted from the Kent–Rosanoff Word Association Test (see Russell & Jenkins, 2000) were used to measure participants’ creative cognition after listening to each type of audio recording. The RAT is a widely used measure of convergent creativity, but there is debate about whether aspects of divergent thinking are not also measured by the RAT (Taft & Rossiter, 1966; Benedek, Franz, Heene, & Neubauer, 2012; Smith, Huber, & Vul, 2013; Forbach & Evans, 1981). Results indicated that the RAT is significantly related to fluency, flexibility, and originality of the ideas generated, indicating aspects of divergent thinking (see also Kenett et al., 2014), and thus was used as a comprehensive measure of general creative cognition. A total of 45 items assessing creativity were divided into three sections of equal length and approximately equal difficulty (p unsolved; M = .439, SD = .007).

Procedures

Participants first provided informed consent and then completed the demographic survey. Using a counterbalanced repeated measures design, participants listened to audio, reported their arousal, and completed a 15-item section of the creativity test for all three conditions (hip-hop, classical, and simulated background noise). The order of condition as well as the 15-item test sections were counterbalanced. Participants completed the study online using the Qualtrics program, and listened to the audio through headphones. After listening to the audio, participants were asked, “After listening to the [genre] track, how excited/exhilarated do you feel?”, and were told to rate their feelings on a scale from 0 to 100. Participants then took the creativity assessment (15 RAT items). In the instructions, participants were provided with an example of an item and a proper response, and also informed of the five-minute time limit. After proceeding to the next section of the study or after five minutes had passed (whichever occurred first), participants listened to another audio track and the process was repeated until all three phases of the study were completed.

Results and discussion

The results for the RAT as a function of listening condition are shown in Figure 1. Mauchley’s test (Field, 2013) indicated that the assumption of sphericity had been violated, χ²(2) = 8.82, p = .012, and degrees of freedom were corrected using Greenhouse–Geisser estimates of sphericity (ε = .81). A one-way repeated measures analysis of variance indicated that there was a significant effect of the type of audio on the listeners’ creativity, F(1.62, 55.09) = 12.32, p < .001. Post hoc comparisons using a Bonferroni correction revealed significantly higher creativity scores after listening to hip-hop as compared to both classical music (p < .001) and background noise (p < .001). There was no significant difference in creativity between listening to classical music and simulated background noise.

Figure 1.

Comparison of the mean creativity scores for each type of audio for both Experiments 1 and 2. Error bars = SEM. *p < .05.

The effect of musical preference (hip-hop or other) on RAT performance for the three conditions (hip-hop, classical, background noise) was analyzed using independent samples t-tests. No differences were found in performance on the RAT after listening to hip-hop (p >. 05), classical (p >. 05), or background noise (p >. 05). Of note, none of the participants indicated that classical was their preferred musical choice.

The excitation measure was also examined among the three listening conditions. Using a one-way repeated measures analysis of variance revealed a significant difference in excitation reported between each type of audio, F(2, 68) = 79.94, p < .001. Hip-hop produced the most excitation (M = 60.57, SD = 26.11), then classical music (M = 34.74, SD = 25.45), with participants experiencing the least excitation after listening to simulated background noise (M = 6.06, SD = 8.33). The correlation between excitation and creativity was not significant for each type of audio, whether hip-hop (r = .144, p > .05), classical (r = .284, p > .05), or simulated background noise (r = -.093, p > .05).

The hypothesis that participants would display more creativity after listening to music than after listening to simulated background noise was supported.

Despite following similar trends, correlations between excitation and creativity were not significant for each type of audio. For example, participants displayed both the highest creativity and the highest excitation after listening to hip-hop, but the correlation between creativity and excitation was not significant. It is possible that a single measure for emotion was not sufficient to examine the effect of arousal and mood on creativity. As excitation is an activating mood with positive valence, it was expected that this particular emotion would have the desired effect on creativity. Apart from methodological explanations, this finding may be very telling. An emphasis has been placed on musical preference and the elevation of cognitive effects that occur when the music listened to is preferred (Lesiuk, 2010). It is possible that participants performed better the more they enjoyed the music they were listening to, and Experiment 2 will also take measures to investigate the relationship between musical preference and creativity.

In summary, Experiment 1 revealed that listening to music can induce transient improvements in creativity in adults. In Experiment 2 we will seek to explain the processes involved in music facilitating creativity by examining semantic memory search and affective state. Creativity will be measured with the same RAT that was used in Experiment 1. Experiment 2 will examine a much broader range of activation and valence in an effort to determine if a particular emotion or type of emotion affects creativity.

Experiment 2

Method

Participants

Participants for this experiment were 16 undergraduate and high school students, mean age 19.8 (SD = 2.2, Male/Female = 4/12), of which 12 (75%) were African-American. Participants were recruited via convenient sampling at a small liberal arts school for a summer course given through the psychology department, and were rewarded extra course credit for completing the experiment. Of the 16 participants, 62.5% indicated a preference for the hip-hop /rhythm ‘n’ blues genre, and 37.5% indicated a preference for other genres.

Materials

Musical selections

Audio tracks (260 s) were presented prior to completing each task. Macklemore’s “Can’t Hold Us” featuring Ryan Lewis or Macklemore’s “Gold” featuring Eighty4 Fly was presented for the music conditions. A 260-second clip of simulated background noise, which emulates the murmur of a crowd without any identifiable words, was used for the control conditions. We also used the MixMeister BPM Analyzer to determine the beats per minute (BPM) and librosa.piptrack version 0.6.2 was used to calculate mean pitch (Librosa, n.d.). “Gold” had a BPM of 80 and a mean pitch of 588 Hz.

Creativity assessment

The Remote Associates Test (RAT) was used to measure creativity. Thirty items from both the original RAT (Mednick & Mednick, 1967) and items adapted by Mednick (1962) from the Kent–Rosanoff Word Association Test (see Russel & Jenkins, 2000) were divided into two 15-item sections of approximately equal difficulty. During the study, participants were allowed to answer the RAT of Creativity questions in any order, and change their answers until the five-minute time limit for submission.

Semantic memory measure

Semantic memory and the structure of the semantic hierarchy was examined using two category fluency tests, which are commonly used to test semantic memory and structure (Monsch et al., 1992; Nutter-Upham et al., 2008). The two categories used in this study were animals and supermarket items, and this study followed the same testing and scoring procedures as Troyer (2000). Participants were asked to list as many words as possible that belonged to a specific category (i.e., animals or supermarket items) in one minute. The total number of words generated was used as an indicator of verbal fluency. To assess semantic structure, mean cluster size and the raw number of switches were recorded for each participant. A cluster refers to items within the same subcategory (e.g., apples, grapes, and berries belong to the “fruits” cluster). Scoring for clusters begins with the second item in that cluster (the above example would be a cluster of two). Mean cluster size was calculated by dividing the number of items generated by the number of clusters generated. A switch occurs when a participant lists an item from a different subcategory than the previously mentioned items. In addition to the raw number of switches, the corrected number of switches (unique subcategory clusters) was examined in order to assess creative flexibility.

Mood and music measures

In order to assess change in mood, the Positive and Negative Affect Schedule (PANAS) was given at the start and end of the study (Watson, Clark, & Tellegen, 1988). The PANAS asks participants to indicate how they are feeling right now, at the present moment, on a list of emotions (guilty, strong, excited) on a scale of 1, very slightly or not at all to 5, extremely. The visual analog mood scales (VAMS) were also used to measure valance and arousal after listening to each auditory stimulus. Each scale has a schematic face and accompanying word at the top of a 100 mm vertical line with a specific “mood” face and word at the bottom of the line. Participants choose the point along the continuum (ranging from 0 to 100) that indicates their current level of mood. Higher numbers indicate more of the mood. For this study, measures for happiness, sadness, anger, and energy were taken after each audio task in order to assess music-induced mood.

Musical experience was also measured using the Musical Experience survey to assess a participant’s experience with music, musical training, how many hours a week spent studying a musical instrument, and any professional musical activities (not reported here).

Procedure

A within subjects repeated measures design tested the effects of auditory stimuli (music A, music B, babble) on creativity (RAT subtest 1, RAT subtest 2), semantic fluency (category fluency test; e.g., animals, supermarket items), and mood (VAMS). The participants first completed the PANAS, then listened to three audio tracks (A, B, or babble; babble was presented twice), completing the VAMS and a portion of the RAT in between each new track. For each track, they were asked to rate how much they enjoyed the track (on a scale from 0 to 100) and then complete a version of the RAT. After completing the first RAT, the participants completed the Musical Experience survey, which served as a delay to decrease carry-over effects before hearing the next auditory stimulus. The participants were also presented with the category fluency test twice, once for animals and once for supermarket items. The participants then completed the PANAS again once all the audio tracks and testing had been administered. The study was counterbalanced, so that the order of presentation for the tracks, VAMS, RAT, and category fluency test varied between participants (e.g., PANAS, Music B, VAMS, RAT1, Babble, RAT2, VAMS, Music A, VAMS, Animals, Babble, Supermarket items, VAMS, PANAS).

Results and discussion

The results for the RAT and semantic fluency measures are shown in Figure 2. A paired samples t-test revealed a significant difference in creativity between listening conditions, t(15) = 2.719, p = .016. Participants displayed higher creativity after listening to music than after listening to babble. All correlations between creativity and mood after listening to music were not significant, whether happiness, r = .181, p > .05, energy, r = .177, p > .05, sadness, r = .372, p > .05, or anger, r = -.061, p > .05.

Figure 2.

Mean scores on the semantic memory measures in Experiment 2. # words refers to the number of words generated. # switches reflects the number of unique subcategories from which a participant listed items. Cluster size refers to the average number of items a participant recalled consecutively that belonged to the same subcategory. Error bars = SEM. *p < .05.

Paired samples t-tests were also used to examine differences in music vs. babble on semantic memory. There was a significant difference of mean cluster size t(15) = 2.86, p = .017. Participants generated significantly more items per cluster after listening to music (M = .97, SD = .59) than babble (M = .61, SD = .36). Similar to creativity, mean cluster size did not correlate with happiness, r = -.073, energy, r = -.095, sadness, r = .080, or anger, r = -.034, p > .05. Also, the correlations between creativity scores and mean cluster size were not significant, both for the music condition, r = .224, p > .05, and simulated background noise, r = .108, p > .05.

Analysis of VAMS using paired samples t-tests revealed that after listening to music, participants were significantly more positive, t(15) = 3.95, p = .001 and significantly less negative compared to listening to babble, t(15) = -2.23, p > .05. This difference in mood did not correlate to a significant performance difference on either the RAT or semantic fluency tests.

Analysis of musical preference (hip-hop or other) on RAT performance using an independent samples t-test revealed no difference in performance based on musical preference.

The hypotheses of the second experiment were 1) that participants would displayer higher creativity after listening to music than after listening to simulated background noise, 2) participants would display greater creativity and semantic memory if they also reported a more positive mood, and 3) that semantic retrieval scores would mediate the effect of music on creativity scores. Only the first hypothesis was supported by the study.

The finding that participants displayed greater creativity after listening to music than simulated background noise was consistent with the finding from Experiment 1. Also consistent was the non-significant correlation between mood and creativity. If inversely scoring energy, the four moods featured on the VAMS introduced every possible combination of activation and valence to describe emotion, none of which were significantly related to creativity. Once again, the arousal–mood hypothesis was not shown to relate to the cognitive effects of creativity.

It was found that mean cluster size was increased when participants listened to music, compared to simulated background noise. This difference indicates that participants may search deeper into subcategories of semantic memory concepts after listening to music than after listening to simulated background noise.

While it was hypothesized that improvement in semantic memory would facilitate the creative process, correlations between the two areas of cognition were not significant. Failing to find a relationship between creativity and either mood or semantic memory, it is possible to assert that music positively affects creativity, but more research must be done to examine how this occurs.

General discussion

The findings from Experiments 1 and 2 were consistent; creative cognition as measured by the RAT was significantly improved by listening to music beforehand, and this effect did not seem to be related to mood. Furthermore, music also affected semantic memory retrieval, once again independent of the mood measures. Although both creative cognition and semantic clustering were positively affected following music, the magnitudes of the effects were not correlated to each other. Previous work has also found that music affects creativity and mood (Baas, Nijstad, & De Dreu, 2015; Adaman & Blaney, 1995), but few have investigated the relationship between creativity and mood. Specifically, when certain moods were induced using music, such as a positive or activated mood, creativity increased. In other studies, music increased mood and self-reported creativity (Lesiuk, 2010). In both situations, the possibility of separate, possibly more direct effect of music on creative cognition (or cognition in general) has been overlooked. One recent study compared the effects of happy, calm, sad, and anxious music on measures of divergent and convergent creativity (Ritter & Ferguson, 2017). Using the valance and arousal ratings for four musical excerpts, they deemed each piece happy, calm, sad, or anxious and compared performance on divergent and convergent creativity measures to a silence condition as a control. They found that happy music increased scores on divergent thinking, as measured by the Alternative Uses Task, when compared to silence. Ritter and Ferguson had participants indicate their mood before the study began (they were not significantly different) and asked participants to indicate how arousing or activating each piece of music was, but did not directly measure the participants’ mood again. This study provides evidence that although moods may enhance creativity, mood did not mediate the effects of music on creativity. The findings also suggest that our musical selections (Macklemore’s “Can’t Hold Us” and “Gold”) may have increased scores on the RAT because the acoustical properties would be considered happy music (rated as high on valance and arousal). Future work should attempt to understand the influence of the acoustical properties on measures of creativity.

Koelsch et al. (2004) suggest that music primes information in similar manner to language. Semantic priming in and of itself is independent of emotion, and, as such, may explain why mood did not correlate with our increases in creativity and cluster size. One consideration is the implications of lyrics in our musical selection. Though it is impossible to assert if the tune or the lyrics acted as a prime, it is important to consider that the classical piece in Experiment I was devoid of lyrics. Hip-hop, which contained lyrics, produced the most significant effect on creative cognition. The classical music did not significantly differ from babble when using the conservative Bonferonni test. Therefore, it is possible that either a) both lyrics and tune prime information concurrently, or b) tunes are able to prime information, but words are more effective at priming information. Note that the number of subjects was sufficient to reliably identify robust effects of listening to music on the RAT, but statistical power for the correlational analyses would be improved with a larger sample size. Future research using larger samples should analyze distinct qualities of music.

Finally, because the increase in creativity was not correlated with mood or semantic cluster size, music may be acting on creativity through another process. The blind variation and selective retention (BVSR) theory states that the creative process involves oscillating between a divergent and convergent search to select a viable product; the product is then compared to the constraints of the problem for accuracy. Both the oscillation between convergent and divergent thinking and comparing the products to constraints are controlled by executive functions. It has been shown that increased executive functions have a positive correlation with creativity. For example, divergent thinking (Benedek, Jauk, Sommer, Arendasy, & Neubauer, 2014), working memory control (Vandervert, Schimpf, & Liu, 2007), problem solving (Mumford et al., 1991, 1996; Runco, 2004) cognitive flexibility, and inhibitory control (Gupta, Jang, Mednick, & Huber, 2012) have all been associated with creativity.

Furthermore, a recent paper has proposed a model to suggest how creative cognition may map on the human brain (Jung, Mead, Carrasco, & Flores, 2013), which builds on the BVSR theory that creative cognition is the result of oscillations between divergent and convergent processes. Jung et al. assert that three neural networks neatly map onto the BVSR theory. The first network is the default mode network (DMN), which is known to be engaged when a person is not focused on the outside world, is letting the mind wander, and is filtering out internal and external distraction (Buckner, Andrews-Hanna, & Schacter, 2008). Jung suggests that the DMN maps onto the divergent thinking aspect of the creative process. The second network identified by Jung et al. is the cognitive control network (CCN), which is engaged stimulus-dependent thought. This network aligns nicely with our idea of convergence; when converging on an idea, we focus our attention of the viability of our proposed creative product and compare it to the constraints of the problem. The last network identified is the salience network that is important for motivation, goal directed behavior, and initiation and has been associated with the medial prefrontal cortex and amygdala (mPFC; Devinsky, Morrell, & Vogt, 1995). Therefore, oscillation (controlled by the salience network) between the divergent thinking (DMN) and convergent thinking (CCN) results in creative cognition. This theory provides a possible mechanism for our finding that listening to music can elicit improved performance on a creative cognition task and deepen searches of semantic stores independent of mood.

For example, music listening might increase the DMN and induce a mind wandering state that is partly responsible for the increased performance we observed on the RAT, while at the same time music may activate the salience network which may be responsible for our changes in mood. The idea that music may differentially affect two (or possibly three) separate networks may explain why our findings show parallel, positive effects on creativity, semantic retrieval, and mood when subjects listen to music.

Another consideration in understanding the results of this study is the nature and generalizability of the test of creative cognition. The Remote Associates Test was chosen for its ability to tap into aspects of both convergent and divergent creative cognition. Semantic memory search is a vital part of the aspects of creativity measured by the RAT, making it particularly interesting that correlations between scores on the RAT and semantic cluster size were not found. It is possible that our sample size was too small to detect this difference. It is also possible that the fluency measures a more static aspect of semantic memory rather than the dynamic search process involved in finding answers for the RAT. It would enhance the generalizability to investigate if listening to music would also increase scores on measures of more purely divergent thinking measures of creativity, such as Guilford’s Alternative Uses Task (Guilford, 1967) or the Torrance Test of Creativity (Torrance, 1974).

Future research should aim to a) investigate the connection between music, mood, and creativity with a large sample size, and b) consider investigating other theories of music and cognition. A good candidate might be Rauscher, Shaw, & Ky’s priming explanation (1993, 1995) which states that music activates areas of the cortex that subserve higher brain functions. Rauscher et al.’s theory is evidenced by their finding that music listening and spatial rotation tasks are subserved by similar neural patterns and that listening to music enhances performance on spatial rotation tasks. Other research has also supported this idea. Jaušovec and Habe (2004) found that students who listened to music while they solved a simple visual task displayed greater coupling between cortical sites (mainly coherence in the EEG gamma band) than students who solved the task in silence. Using EEG, Jaušovec, Jaušovec, and Gerlic (2006) found that listening to Mozart increased alpha and gamma band synchronization that accompanied enhanced learning on a spatial–temporal rotation task. Fluctuation in gamma band power has also been implicated in the perception of music (Bhattacharya & Petsche, 2001) and is associated with the binding hypothesis—the linking of separate areas of the cortex to enable identification for the object as a whole (Singer & Gray, 1995).

Furthermore, recent work investigating the relationship between binding and executive functions has demonstrated that binding shares common resources with the executive function of inhibition (Nieznańsk, Obidziński, Zyskowska, & Niedziałkowska, 2015). When participants in our study searched for a word that embodied the three stimuli words, they were also solving a binding problem. Furthermore, inhibition may enable the repression of dominant response tendencies (such as responding with the same word you are presented with; Gupta, Jang, Mednick, & Huber, 2012). This inhibition might be responsible for larger semantic clusters, as common responses need to be inhibited to allow for a deeper search. Taken together, we believe that investigations into how music is associated with executive inhibition are a worthwhile endeavor.

Although most studies in cognitive psychology give little or no consideration to whether the test conditions influence performance, context is known to matter. For example, the ambient temperature (Sellaro, Hommel, Manai, & Colzato, 2015), architecture and natural lighting (Wang & Boubekri, 2010), a natural vs urban environment (Kaplan, 1995; Berman, Jonides, & Kaplan, 2008), and the ambient soundscape (Schafer, 1977; Davies et al., 2013; Lee & Jeon, 2013) all have notable influences on cognition and performance. Although one’s surroundings intuitively relate to the likelihood of being creative, there is little empirical work on how context relates to cognitive processes involved in creativity (McCoy & Evans, 2002). The current study is a start for how soundscape can influence creativity, with a focus on a specific cognitive mechanism (semantic memory search). Music was given before task performance, and results indicated that music had a carry-over effect on RAT performance. Future work could examine music in particular, or the soundscape in general, while the creativity of subjects is tested.

In summary, the current study provided support for the idea that listening to music can elicit improved performance on a creative cognition task and deepen searches of semantic stores independent of mood. Further research should examine the possible role of executive functions, such as cognitive flexibility and inhibitory control, which may underlie both music-related improvements.

Footnotes

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

References

Adaman

J. E.

Blaney

P. H.

(1995). The effects of musical mood induction on creativity. The Journal of Creative Behavior 29(2), 95–108. https://doi.org/10.1002/j.2162-6057.1995.tb00739.x

Baas

Nijstad

B. A.

De Dreu

C. K.

(2015). The cognitive, emotional and neural correlates of creativity. Frontiers in Human Neuroscience, 9, 275. https://doi.org/10.3389/fnhum.2015.00275

Barr

Pennycook

Stolz Fugelsang

J. A.

(2015). Reasoned connections: A dual-process perspective on creative thought. Thinking & Reasoning, 21(1), 61–75. http://doi.org/10.1080/13546783.2014.895915

Beaty

R. E.

Silvia

P. J.

(2012). Why do ideas get more creative across time? An executive interpretation of the serial order effect in divergent thinking tasks. Psychology of Aesthetics, Creativity, and the Arts, 6(4), 309–319. https://dx-doi-org.web.bisu.edu.cn/10.1037/a0029171

Benedek

Franz

Heene

Neubauer

A. C.

(2012). Differential effects of cognitive inhibition and intelligence on creativity. Personality and individual differences, 53(4), 480–485. https://doi.org/10.1016/j.paid.2012.04.014

Benedek

Jauk

Sommer

Arendasy

Neubauer

A. C.

(2014). Intelligence, creativity, and cognitive control: The common and differential involvement of executive functions in intelligence and creativity. Intelligence, 46, 73–83. https://doi.org/10.1016/j.intell.2014.05.007

Benedek

Neubauer

A. C.

(2013). Revisiting Mednick’s model on creativity-related differences in associative hierarchies: Evidence for a common path to uncommon thought. Journal of Creative Behavior, 47(4), 273–289. https://doi.org/10.1002/jocb.35

Berman

M. G.

Jonides

Kaplan

(2008). The cognitive benefits of interacting with nature. Psychological Science, 19(12), 1207–1212. http://doi.org/10.1111/j.1467-9280.2008.02225.x

Bhattacharya

Petsche

(2001). Universality in the brain while listening to music. Proceedings of the Royal Society of London B: Biological Sciences, 268(1484), 2423–2433. DOI: 10.1098/rspb.2001.1802

10.

Buckner

R. L.

Andrews-Hanna

J. R.

Schacter

D. L.

(2008). The brain’s default network: Anatomy, function, and relevance to disease. Annals of the New York Academy of Sciences, 1124 (1), 1–38. doi: 10.1196/annals.1440.011

11.

Campbell

D. T.

(1960). Blind variation and selective retentions in creative thought as in other knowledge processes. Psychological Review, 67(6), 380. https://dx-doi-org.web.bisu.edu.cn/10.1037/h0040373

12.

Carson

S. H.

Peterson

J. B.

Higgins

D. M.

(2003). Decreased latent inhibition is associated with increased creative achievement in high-functioning individuals. Journal of Personality and Social Psychology, 85(3), 499–506. https://dx-doi-org.web.bisu.edu.cn/10.1037/0022-3514.85.3.499

13.

Csikszentmihalyi

(1999). Implications of a systems perspective for the study of creativity. In Sternberg

R. J.

(Ed.), Handbook of creativity (pp. 313–335). New York, NY: Cambridge University Press.

14.

Davies

W. J.

Adams

M. D.

Bruce

N. S.

Cain

Carlyle

Cusack

Hall

Hume

K. I.

Irwin

Jennings

Marselle

Plack

C. J.

Poxon

(2013). Perception of soundscapes: An interdisciplinary approach. Applied Acoustics, 74(2), 224–231. http://doi.org/10.1016/j.apacoust.2012.05.010

15.

DeHaan

R. L.

(2009). Teaching creativity and inventive problem solving in science. CBE—Life Sciences Education, 8(3), 172–181. https://doi.org/10.1187/cbe.08-12-0081

16.

De Dreu

C. K.

Baas

Nijstad

B. A

. (2008). Hedonic tone and activation level in the mood-creativity link: toward a dual pathway to creativity model. Journal of Personality and Social Psychology, 94(5), 739–756. doi: 10.1037/0022-3514.94.5.739.

17.

De Dreu

C. K.

Nijstad

B. A.

Baas

Wolsink

Roskes

. (2012). Working memory benefits creative insight, musical improvisation, and original ideation through maintained task-focused attention. Personality and Social Psychology Bulletin, 38(5), 656–669. https://doi.org/10.1177/0146167211435795

18.

Devinsky

Morrell

M. J.

Vogt

B. A.

(1995). Contributions of anterior cingulate cortex to behaviour. Brain, 118, 279–306. doi: 10.1093/brain/118.1.279

19.

Ericsson

K. A.

Lehmann

A. C.

(1996). Expert and exceptional performance: Evidence of maximal adaptation to task constraints. Annual Review of Psychology, 47(1), 273–305. https://doi.org/10.1146/annurev.psych.47.1.273

20.

Field

(2013). Discovering statistics using IBM SPSS statistics. SAGE.

21.

Forbach

G. B.

Evans

R. G.

(1981). The Remote Associates Test as a predictor of productivity in brainstorming groups. Applied Psychological Measurement, 5(3), 333–339. https://doi.org/10.1177/014662168100500306

22.

Guilford

J. P.

(1967). Creativity: Yesterday, today and tomorrow. The Journal of Creative Behavior, 1(1), 3–14. https://doi.org/10.1002/j.2162-6057.1967.tb00002.x

23.

Gupta

Jang

Mednick

S. C.

Huber

D. E.

(2012). The road not taken: Creative solutions require avoidance of high-frequency responses. Psychological Science, 23(3), 288–294. https://doi.org/10.1177/0956797611429710

24.

Husain

Thompson

W. F.

Schellenberg

E. G.

(2002). Effects of musical tempo and mode on arousal, mood, and spatial abilities. Music Perception, 20(2), 151–171. DOI: 10.1525/mp.2002.20.2.151

25.

Isen

A. M.

(2005). A role for neuropsychology in understanding the facilitating influence of positive affect on social behavior and cognitive processes. In Snyder

C. R.

Lopez

S. J.

(Eds.), Handbook of positive psychology (pp. 528–540). London, UK: Oxford University Press.

26.

Jäncke

(2012). The relationship between music and language. Frontiers in Psychology, 3, 123. https://doi.org/10.3389/fpsyg.2012.00123

27.

Jaušovec

Habe

(2004). The influence of auditory background stimulation (Mozart’s sonata K. 448) on visual brain activity. International Journal of Psychophysiology, 51(3), 261–271. https://doi.org/10.1016/S0167-8760(03)00227-7

28.

Jaušovec

Gerlič

(2006). The influence of Mozart’s music on brain activity in the process of learning. Clinical Neurophysiology, 117(12), 2703–2714. https://doi.org/10.1016/j.clinph.2006.08.010

29.

Jung

R. E.

Mead

B. S.

Carrasco

Flores

R. A.

(2013). The structure of creative cognition in the human brain. Frontiers in Human Neuroscience, 7, 330.

30.

Kaplan

(1995). The restorative benefits of nature: Toward an integrative framework. Journal of Environmental Psychology, 15, 169–182. https://doi.org/10.1016/0272-4944(95)90001-2

31.

Kenett

Y. N.

Anaki

Faust

(2014). Investigating the structure of semantic networks in low and high creative persons. Frontiers in Human Neuroscience, 8, 407. https://doi.org/10.3389/fnhum.2014.00407

32.

Koelsch

Kasper

Sammler

Schulze

Gunter

Friederici

A. D.

(2004). Music, language and meaning: Brain signatures of semantic processing. Nature Neuroscience, 7(3), 302–307. DOI: 10.1038/nn1197

33.

Lee

P. J.

Jeon

J. Y

. (2013). Relating traffic, construction, and ventilation noise to cognitive performances and subjective perceptions. Journal of Acoustical Society of America, 134, 2765–2772. https://doi.org/10.1121/1.4818776

34.

Limrosa [computer software]. (n.d.). Retrieved from https://librosa.github.io/librosa/generated/librosa.core.piptrack.html

35.

Lesiuk

(2010). The effect of preferred music on mood and performance in a high-cognitive demand occupation. Journal of Music Therapy, 47(2), 137–154. https://doi.org/10.1093/jmt/47.2.137

36.

McCoy

J. M.

Evans

G. W.

(2002). The potential role of the physical environment in fostering creativity. Creativity Research Journal, 14(3–4), 409–426. DOI: 10.1207/S15326934CRJ1434_11

37.

Mednick

S. A.

(1962). The associative basis of the creative process. Psychological Review, 69, 220–232. https://dx-doi-org.web.bisu.edu.cn/10.1037/h0048850

38.

Mednick

S. A.

Mednick

M. T.

(1967). Examiner’s manual, Remote Associates Test: College and adult forms 1 and 2. Boston, MA: Houghton Mifflin.

39.

Mednick

M. T.

Mednick

S. A.

Jung

C. C.

(1964). Continual association as a function of level of creativity and type of verbal stimulus. Journal of Abnormal and Social Psychology, 69, 511–515. https://dx-doi-org.web.bisu.edu.cn/10.1037/h0041086

40.

Monsch

A. U.

Bondi

M. W.

Butters

Salmon

D. P.

Katzman

Thal

L. J.

(1992). Comparisons of verbal fluency tasks in the detection of dementia of the Alzheimer type. Archives of Neurology, 49, 1253–1258. doi:10.1001/archneur.1992.00530360051017

41.

Mumford

M. D.

Baughman

W. A.

Threlfall

K. V.

Supinski

E. P.

Costanza

D. P.

(1996). Process-based measures of creative problem-solving skills: I. Problem construction. Creativity Research Journal, 9(1), 63–76. https://doi.org/10.1207/s15326934crj0901_6

42.

Mumford

M. D.

Mobley

M. I.

Uhlman

C. E.

Reiter-Palmon

Doares

L. M.

(1991). Process analytic models of creative capacities. Creativity Research Journal, 4(2), 91–122. https://doi.org/10.1080/10400419109534380

43.

Nieznański

Obidziński

Zyskowska

Niedziałkowska

(2015). Executive resources and item-context binding: Exploring the influence of concurrent inhibition, updating, and shifting tasks on context memory. Advances in Cognitive Psychology, 11(3), 106–117. doi: 10.5709/acp-0176-9

44.

Nutter-Upham

K. E.

Saykin

A. J.

Rabin

L. A.

Roth

R. M.

Wishart

H. A.

Pare

Flashman

L. A.

(2008). Verbal fluency performance in amnestic MCI and older adults with cognitive complaints. Archives of Clinical Neuropsychology, 23, 229–241. https://doi.org/10.1016/j.acn.2008.01.005

45.

Plucker

J. A.

Beghetto

R. A.

Dow

G. T.

(2004). Why isn’t creativity more important to educational psychologists? Potentials, pitfalls, and future directions in creativity research. Educational Psychologist, 39(2), 83–96. https://doi.org/10.1207/s15326985ep3902_1

46.

Rauscher

F. H.

Shaw

G. L.

K. N.

(1993). Music and spatial task performance. Nature, 365(6447), 611. DOI: 10.1038/365611a0

47.

Rauscher

F. H.

Shaw

G. L.

K. N.

(1995). Listening to Mozart enhances spatial-temporal reasoning: Towards a neurophysiological basis. Neuroscience Letters, 185(1), 44–47. https://doi.org/10.1016/0304-3940(94)11221-4

48.

Richards

(1990). Everyday creativity, eminent creativity, and health: “Afterview”; for CRJ Issues on creativity and health. Creativity Research Journal, 3(4), 300–326.

49.

Ritter

A. M.

Ferguson

(2017). Happy creativity: Listening to happy music facilitates divergent thinking. PLoS ONE, 12(9). https://doi.org/10.1371/journal.pone.0182210

50.

Runco

M. A.

(2004). Personal creativity and culture. In Lau

Hui

A. H.

G. Y.

(Eds.), Creativity: When east meets west. (pp. 9–22). Singapore: World Scientific Publishing. https://doi.org/10.1142/9789812567192_0002

51.

Runco

M. A.

(2010). Divergent thinking, creativity, and ideation. In Kaufman

J. C.

Sternberg

R. J.

(Eds.), The Cambridge handbook of creativity (pp. 413–446). New York, NY: Cambridge University Press.https://dx-doi-org.web.bisu.edu.cn/10.1017/CBO9780511763205.026

52.

Runco

M. A.

Jaeger

G. J.

(2012). The standard definition of creativity. Creativity Research Journal, 24(1), 92–96. https://doi.org/10.1080/10400419.2012.650092

53.

Russell

W. A.

Jenkins

J. J.

(2000). The complete Minnesota norms for responses to 100 words from the Kent-Rosanoff Word Association Test. US Department of Commerce, National Technical Information Service, Springfield, Va.

54.

Schafer

R. M.

(1977). The soundscape: Our sonic environment and the tuning of the world. Rochester, VT: Destiny Books.

55.

Schellenberg

E. G.

Nakata

Hunter

P. G.

Tamoto

(2007). Exposure to music and cognitive performance: Tests of children and adults. Psychology of Music, 35(1), 5–19. https://doi.org/10.1177/0305735607068885

56.

Scott

Leritz

L. E.

Mumford

M. D.

(2004). The effectiveness of creativity training: A quantitative review. Creativity Research Journal, 16(4), 361–388. https://doi.org/10.1080/10400410409534549

57.

Sellaro

Hommel

Manai

Colzato

L. S.

(2015). Preferred but not objective temperature predicts working memory depletion. Psychological Research, 79, 282–288. http://doi.org/10.1007/s00426-014-0558-4

58.

Singer

Gray

C. M.

(1995). Visual feature integration and the temporal correlation hypothesis. Annual Review of Neuroscience, 18(1), 555–586. https://doi.org/10.1146/annurev.ne.18.030195.003011

59.

Smith

K. A.

Huber

D. E.

Vul

(2013). Multiply-constrained semantic search in the Remote Associates Test. Cognition, 128, 64–75. doi:10.1016/j.cognition.2013.03.001

60.

Steinberg

Sykes

E. A.

Moss

Lowery

LeBoutillier

Dewey

(1997). Exercise enhances creativity independently of mood. British Journal of Sports Medicine, 31(3), 240–245. doi: 10.1136/bjsm.31.3.245

61.

Taft

Rossiter

J. R.

(1966). The Remote Associates Test: divergent or convergent thinking? Psychological Report, 19, 1313–1314. https://doi.org/10.2466/pr0.1966.19.3f.1313

62.

Troyer

A. K.

(2000). Normative data for clustering and switching on verbal fluency tasks. Journal of Clinical and Experimental Neuropsychology, 22(3), 370–378. https://doi.org/10.1076/1380-3395(20006)22:3;1-V;FT370

63.

Troyer

A. L.

Moscovitch

Winocur

Alexander

M. P.

Stuss

(1998). Clustering and switching on verbal fluency: The effects of focal frontal- and temporal-lobe lesions. Neuropsychologia, 36, 449–504. https://doi.org/10.1016/S0028-3932(97)00152-8

64.

Vandervert

L. R.

Schimpf

P. H.

Liu

(2007). How working memory and the cerebellum collaborate to produce creativity and innovation. Creativity Research Journal, 19(1), 1–18. https://doi.org/10.1080/10400410709336873

65.

Wang

Boubekri

(2010). Investigation of declared seating preference and measured cognitive performance in a sunlit room. Journal of Environmental Psychology, 30(2), 226–238. http://doi.org/10.1016/j.jenvp.2009.12.001

66.

Ward

T. B.

Kolomyts

(2010). Cognition and creativity. In Kaufman

J. C.

Sternberg

R. J.

(Eds.), The Cambridge handbook of creativity (pp. 93–112). New York, NY: Cambridge University Press.

67.

Watson

Clark

L. A.

Tellegen

(1988). Development and validation of brief measures of positive and negative affect: the PANAS Scales. Journal of Personality and Social Psychology, 54(6), 1063–1070.