Creativity is a vital aspect of cognition underpinning activities such as innovative product design, scientific advancement, and effective advertising and marketing communications. Background music is an environmental stimulus known to influence cognitive performance, which has also been claimed to enhance people's creativity for tasks involving spatial abilities such as drawing (see Schellenberg, Nakata, Hunter, & Tamoto, 2007). We argue, however, that there is limited empirical support for the claimed benefits of background music on creativity, with to our knowledge only one other study (i.e., Ritter & Ferguson, 2017) demonstrating a facilitatory effect on creativity of background music that participants were free to attend to for a task that involved participants listing novel, alternative uses for a common object (i.e., a brick). Another reason to be cautious regarding the notion that background music can enhance performance on tasks tapping creative cognition is the presence of a substantial research base demonstrating that to‐be‐ignored background sound impairs task performance (Beaman, 2005; Hughes & Jones, 2003).

In the present paper, we critically examine the claim that background music enhances creativity by employing variants of widely used verbal problem solving tasks that are typically used to study creativity (Ansburg, 2000; Fodor, 1999; Mednick & Mednick, 1967; Mehta, Zhu, & Cheema, 2012; Mikulincer & Sheffi, 2000; Storm, Angello, & Bjork, 2011) being indexed by, and solved via, a process of insight: Compound Remote Associate Tasks (CRATs; e.g., see Bowden, Jung‐Beeman, Fleck, & Kounios, 2005; see below for further explanation). We contrast two competing accounts of the impact of background music on creative problem solving: (a) the processing disfluency account (Mehta et al., 2012), in which background music potentially enhances creativity by engendering processing disfluency and thence increased task engagement; and (b) the auditory distraction (interference‐by‐process) account (e.g., Jones & Tremblay, 2000; Marsh, Hughes, & Jones, 2009; Perham & Vizard, 2010), which assumes that the presence of any type of auditory distractor sequence will disrupt cognitive task performance providing it demonstrates changing‐state characteristics. That is, auditory sequences in which a series of elements differ from one element to the next (such as tones, syllables, and words) in terms of frequency/pitch/timbre are more disruptive than a series within which the same element is repeated such as the same tone, syllable, or word. It has been shown, for example, that the latter, steady‐state stimuli typically fail to disrupt short‐term memory performance (e.g., Jones & Macken, 1993). It is worth noting here that in addition to this “acoustic interference‐by‐process,” an interference‐by‐process can also operate at a semantic level due to a clash between two concurrent semantic processes: a deliberate one applied to the to‐be‐remembered material and one applied automatically to the to‐be‐ignored auditory material (Marsh et al., 2009; Marsh, Hughes, & Jones, 2008). The focus of the current paper, however, is on the acoustic interference‐by‐process (e.g., Jones & Tremblay, 2000).

Prior to considering the relationship between background music and creative problem solving performance, it is useful to note that researchers have traditionally made a key distinction between two types of creative thinking, that is, divergent thinking versus convergent thinking (Guilford, 1967). Divergent thinking refers to a strategy whereby multiple creative ideas are produced and appraised within a short period of time in order to generate potential solutions for a given problem. A typical task involving divergent thinking is the Alternative Uses Task, wherein participants are required to think of as many uses as possible for a simple, everyday object such as a brick or paperclip (cf. the aforementioned study of music and creativity by Ritter & Ferguson, 2017). Convergent thinking, on the other hand, permits the connection of different ideas to determine a single, correct solution to a problem. Importantly, tasks involving creative convergent thinking—including the CRATs that we employed in the present study, as discussed below—may do so on the basis of associations and potential solutions generated through divergent thought.

It is additionally important to note that creative problem solving, whether underpinned by divergent or convergent thinking, is often characterised by the ability to perceive a problem space in new ways by discovering hidden patterns or by connecting seemingly unrelated ideas (e.g., Ohlsson, 2011). One key way in which creative problem solving comes about is by means of so‐called insight, with tasks involving creative thinking typically being solved via insight processes. Accounts of insight in problem solving such as the “special‐process theory” (e.g., Ball & Stevens, 2009; Bowden et al., 2005) argue that problems that tend to be solved via an insight process call upon very different processing mechanisms to “noninsight” problems. For example, Jung‐Beeman et al. (2004) identified neural patterns just prior to the emergence of insight that demonstrate a hemispheric shift in processing occurring at this point. Jung‐Beeman et al. (2004) propose that during insight problem solving loose associative processing occurring nonconsciously in the right temporal lobe takes precedence over finer‐grained processing in the left hemisphere, implying that neural areas linked with diffuse associative processing are critical for the emergence of creative insight (for a recent review of related findings, see Shen, Yuan, Liu, & Luo, 2017).

Several researchers suppose that an insight sequence defines creative thinking and that any advance in thought that is not characterised by such a sequence is therefore not creative (e.g., Ohlsson, 2011; Perkins, 2000; Wiley & Jarosz, 2012; but see Weisberg, 2015). This unique sequence of events that defines insight in problem solving comprises: presentation of the problem, repeated failure, impasse, restructuring, and an “Aha!” experience that is associated with solution generation. According to this sequence of events, failed attempts to solve a problem can lead to an impasse, whereby the participant, after several unsuccessful attempts at solving the problem, feels they are unable to move forward to reach a solution. After a period of failing to make progress, an abandoning of the original problem structure occurs and a new representation of the problem is formed through restructuring, which may itself be based on processes such as spreading activation in associative networks (see Shen et al., 2017). Such problem restructuring may then lead to the emergence of a solution. Crucially, problems that are typically solved by insight often cannot readily be solved via routine search processes. This is because the starting conditions, goals, and possible sequences of actions are ambiguous (i.e., a heuristic‐type search within the original problem representation will not yield a solution).

As we have noted, our present research used CRATs as a measure of insight‐based creative problem solving (Bowden & Jung‐Beeman, 1998). A CRAT involves a participant being shown three words (e.g., dress, dial, and flower), with the requirement being to find a single associated word (in this case “sun”) that can be combined with each presented word (either being placed before it or after it) to make a common word or phrase (i.e., sundress, sundial, and sunflower in the present example). CRATs are variants of problems referred to as Remote Associate Tasks (RATs; see Mednick, 1962; Mednick & Mednick, 1967), for which the solution can be associated with each of the provided three words in different ways. For example, a RAT (e.g., same, tennis, and head), in contrast to a CRAT, can be solved by means of semantic association (tennis match), synonymy (same = match) and, as with CRATs, the formation of compound words (matchhead).

Nowadays, both RATs and CRATs are commonly used tests of creativity within psychology and cognitive neuroscience. They have been employed, for example, to examine creativity in relation to sleep (e.g., Cai, Mednick, Harrison, Kanady, & Mednick, 2009), memory (e.g., Storm et al., 2011), attention (e.g., Ansburg & Hill, 2003), and attentional deficit hyperactivity disorder (e.g., White & Shah, 2011), and they have additionally been employed in neuroimaging studies of creativity (e.g., Arden, Chavez, Grazioplene, & Jung, 2010). According to Bowden and Jung‐Beeman (2003), the popularity of these problems resides in the fact that they have an unambiguous, single‐word answer, and that multiple items can be solved in a single session. Furthermore, RATs and CRATs are less complex than classic insight problems such as the candle problem or two‐string problem (see Weisberg, 1995), such that they are less susceptible to confounding of variables. These characteristics made these problems very appealing for the current investigation.

Problem solving performance on RATs and CRATs has been found to correlate with performance on other creative tasks such as rebus puzzles (MacGregor & Cunningham, 2008; see Threadgold, Marsh, & Ball, 2018, for further discussion) and classic insight tasks (Schooler & Melcher, 1995; but see Webb, Little, Cropper, & Ruze, 2017). Such patterns of association suggest that RATs and CRATs represent effective tests of creativity. Moreover, these problems also appear to involve “the same component processes critical for, and the same phenomenological experience of, insight solutions to more complex problems” (Bowden & Jung‐Beeman, 2003, p. 634; see also Bowden & Jung‐Beeman, 2007). For example, the problems initially misdirect or fail to direct retrieval processes, thereby leading to an impasse. In addition, solvers often report an “Aha!” experience on task completion. As well as being characterised by the insight sequence, RATs and CRATs also appear to be underpinned by a range of other processes, including unconscious spreading activation in associative networks (Smith, Huber, & Vul, 2013), conscious verbal processes such as subvocal rehearsal (Ball & Stevens, 2009), and executive processes such as those that inhibit incorrect solution ideas and enable the active manipulation of information in working memory (Chein & Weisberg, 2014; Storm & Angello, 2010).

Although there is a paucity of research examining the effects of background music on creativity, there is a small literature on the impact of noise on creative cognition, with this research having typically used RATs, but occasionally other creative tasks too (Hillier, Alexander, & Beversdorf, 2006; Kasof, 1997; Martindale & Greenough, 1973; Mehta et al., 2012). For example, aperiodic noise such as white noise and pink noise has been shown to affect creativity, as measured using RATs. For example, Martindale and Greenough (1973; 75 dB) and Hillier et al. (2006; 90 dB) showed that a high intensity white noise, compared with a no noise control condition, impaired task performance. Moreover, Kasof (1997) reported that a high level (85 dB[A]) of intermittent, compared with continuous, pink noise reduced creativity as measured with a poetry writing task. In contrast, Toplyn and Maguire (1991) found that highly creative participants (as gauged by their performance on RATs) demonstrated greater creativity on other tasks when exposed to 80 dB white noise, compared with when exposed to 60 or 100 dB white noise.

Mehta et al. (2012) used more naturalistic, ambient noises to resemble restaurant noise, wherein distant construction noise, multitalker babble, and roadside traffic were blended and reported that a moderate level of noise (70 dB), as compared with low level noise (50 dB), improved performance on creative tasks. These tasks included RATs (Experiment 1), a task wherein participants generated novel ideas for improving mattress comfort (Experiment 2), a task requiring the generation of alternative uses for a brick (Experiment 3), and a task concerning how to clean scuffed shoes with no polish (Experiment 4). Of relevance to the present study, participants generated more correct answers to RATs in the presence of moderate noise compared with a low level of noise and a high level of noise (85 dB).

We note here, however, that in contrast with the RATs, the other tasks used by Mehta et al. (2012) arguably make less demands on verbal working memory. Indeed, these tasks tap divergent thinking in that they require the production of multiple responses in a manner similar to standard verbal fluency tasks. Verbal fluency tasks require the production of numerous responses given a phonemic (produce words beginning with the letter “F”) or semantic (produce as many examples of “Fruit”) cue within a time limit (Jones, Marsh, & Hughes, 2012; Marsh, Crawford, Pilgrim, Sörqvist, & Hughes, 2017). Although some aspects of the task, such as the requirement to maintain memory for previously produced responses to avoid repetition tap verbal working memory, these tasks are not characterised by continuous generation and testing of word combinations and maintenance of intermediate solutions that distinguish the convergent thinking underpinning the RAT. Indeed, perhaps it is no surprise that tasks that tap divergent thinking such as category fluency tend to be immune to disruption produced by changing‐state background sound, unless it conveys semantic content (Jones et al., 2012). In this respect, our focus was on the variant of the RAT (i.e., the CRAT), since in contrast to divergent thinking tasks, CRATs should be more sensitive to disruption produced by the changing‐state acoustic properties of background sound.

An alternative account of the relationship between background sound and creativity holds that benefits to cognitive task performance can be observed through mood and arousal (for a review, see Schellenberg, 2005). For example, Thompson, Schellenberg, and Husain (2001) showed that performance on tests of spatial abilities was improved when the tasks were executed after listening to music rated as “liked” by participants, as opposed to being exposed to quiet. Moreover, the improvement in performance was driven by changes in arousal and mood produced by listening to the music. It is important to note that mood and arousal are not the same construct. For example, mood can be decreased and arousal can be increased when music is disliked. It is possible that the effects of music on cognitive task performance are driven by changes to both mood and arousal, with increases in both leading to enhanced performance.

A recent study by Ritter and Ferguson (2017) required participants to undertake tasks involving creative cognition while concurrently listening to music or exposure to quiet. In a between‐participants design, Ritter and Ferguson showed that a beneficial effect of music on creative task performance was limited to a comparison between a silent condition and a so‐called “happy music” condition (Vivaldi's “Four Seasons”). Exposure to “calm music,” “sad music,” and “anxious music” had no impact on creative task performance compared with quiet (but see Perham & Withey, 2012, for evidence of enhanced spatial rotation performance following listening to slow‐tempo, sad music of a participant's own choosing compared with a slow‐tempo control excerpt). In line with the notion that changes to mood and arousal may collectively enhance creative task performance, participants in Ritter and Ferguson's (2017) study assigned more positive mood and higher arousal to the happy music condition in comparison with the other conditions. Therefore, the benefit to creative task performance could have been driven by increases in mood and arousal rather than the presence of the music per se.

Although the notion that increases in both mood and arousal can benefit creativity has some appeal, we note that Ritter and Ferguson (2017) did not report statistical comparisons between all of the music conditions in their between‐participants design, which potentially undermines their conclusions. Furthermore, Mehta et al. (2012) propose that arousal‐based explanations of the impact of to‐be‐ignored noise on creativity are insufficient because over a longer period of exposure to the sound, physiological arousal levels should normalise and cease to have a consistent influence. Thus, Mehta et al. argue that arousal is not the key contributing factor to the impact of to‐be‐ignored noise on creativity. They instead propose that moderate noise levels increase processing disfluency, with this processing disfluency increasing construal levels, thereby promoting more abstract thinking. More specifically, when construal levels are high, then individuals will engage in abstract thought to consider the “bigger picture” rather than focus on specific details (e.g., see Burgoon, Henderson, & Markman, 2013). Such high‐level construal involves a focus on the commonality and central features of a situation such that its overall gist can be extracted. In contrast, the overall gist of a situation is less likely to be extracted when construal levels are low because people focus on peripheral (or secondary) features. In support of the influence of high‐level construal on creativity, research has demonstrated that performance on a wide range of creativity tasks can benefit from the experimental induction of abstract levels of thought (Friedman & Fӧrster, 2002; Fӧrster, Friedman, & Liberman, 2004).

The processing disfluency account has its conceptual basis within research on metacognition, which focuses on processes that monitor and control cognition (Ackerman & Thompson, 2017a,b). Such metacognitive processes are involved in people's subjective judgements of how well a current task is being, could be, or has been performed. Metacognitive control processes about one's current task can be applied to initiate, terminate, or change the allocation of time, effort, and cognitive resources to the task (Ackerman & Thompson, 2017a). One of a variety of cues on which metacognitive monitoring is based is the subjective ease of processing (fluent vs. disfluent; easy vs. difficult) that derives from one's own experience at attempting the task. Subjective experiences of task difficulty can catalyse a shift in processing and engender increased task engagement (e.g., Alter, Oppenheimer, Epley, & Eyre, 2007; Rummer, Schweppe, & Schwede, 2016).

Attempts to comprehend metacognitive modulation of thought have typically evoked dual‐process theories, which posit the existence of two qualitatively distinct types of thinking: Types 1 and 2 processes (Evans & Stanovich, 2013a,b). Type 1 processes are autonomous and undemanding of working memory (a concept used in ways that links to notions of executive and attentional control) and tend to be fast, nonconscious, intuitive, and associative. On the other hand, Type 2 processes rely on working memory (including executive and attentional control) and are focused on cognitive decoupling and mental simulation, critical for hypothetical thinking. Type 2 processes also tend to be slow, conscious, analytic, and deliberative. Type 2 processes can be activated if the monitoring system—as part of the metacognitive architecture—judges that a task is difficult (e.g., Bjork, Dunlosky, & Kornell, 2013; see also Thompson, 2010). Mehta et al. (2012) argue that the presence of noise creates processing disfluency and supports a processing shift inducing higher construal levels and more abstract thinking that is presumably linked to more diffuse associative processing of the type that is known to arise in creative insight. For example, in the case of CRATs, diffuse associative processing could cause spreading semantic activation within a network of associates yielding convergent activation on the word that the three seemingly unrelated words have in common, thereby yielding the solution (see Bowden & Beeman, 1998; Shen et al., 2017).

That background sound can improve performance on creative tasks contrasts with a large literature relating to distraction of human cognition through exposure to noise (for reviews, see Beaman, 2005; Hughes & Jones, 2003). The task typically used to illustrate the vulnerability of cognition to disruption by the mere presence of to‐be‐ignored background sound is short‐term visual–verbal serial recall (Colle & Welsh, 1976; Jones & Macken, 1993; Salamé & Baddeley, 1982). This task involves the visual presentation of verbal items (e.g., seven or eight letters or digits) with the requirement to recall these items according to the serial order in which they were presented. Initial work suggested that this disruption arose because the sound was composed of speech. However, the semantic properties of speech were found to be impotent in their capacity to disrupt serial recall: speech presented in a language understood by the participant produces no more disruption than that produced in a language incomprehensible to the participant (Jones, Miles, & Page, 1990). Thus, semantic properties of the to‐be‐ignored background sound were irrelevant to the level of disruption caused. Similarly, the notion that the disruption by background sound arose due to a confusion between phonemes derived from the visual items (via their covert articulation) that gain direct (spoken items) and indirect (visual items) access into a phonological store (Salamé & Baddeley, 1982) was undermined by findings that serial recall was shown to be susceptible to disruption by the presence of background music without lyrics, and therefore phonemes (Klatte, Kilcher, & Hellbrück, 1995; Klatte, Lachmann, Schlittmeier, & Hellbrück, 2010; Nittono, 1997; Salamé & Baddeley, 1989; Schlittmeier, Hellbrück, & Klatte, 2008), and by the presence of sequences of tones, provided they change from one successive tone to the next (Divin, Coyle, & James, 2001; Elliott, 2002; Jones & Macken, 1993).

The key empirical referent for this so‐called “irrelevant sound effect” is the changing‐state effect. This concerns the finding that a changing sequence of sounds, regardless of whether the changes occur on a speech carrier (e.g., a sequence of different verbal tokens) or a nonspeech carrier (e.g., a sequence of tones of different frequency), disrupts serial recall to a far greater extent than a nonchanging or steady‐state sound (e.g., a repeated token or tone; Jones & Macken, 1993; Jones, Madden, & Miles, 1992). According to the interference‐by‐process approach (e.g., Jones & Tremblay, 2000), the pre‐attentive processing of the order of changes within sound impairs the deliberate serial rehearsal process that supports the ordered recall of to‐be‐remembered items.

Given that the solving of CRATs appears to be underpinned by verbal processes such as subvocal rehearsal (Ball & Stevens, 2009) in addition to executive processes (Chein & Weisberg, 2014) and spreading activation in associative networks (Smith et al., 2013), we expect CRATs to be susceptible to disruption by the presence of to‐be‐ignored background music. Our rationale behind suggesting that verbal processing of CRATs will be susceptible to changing‐state distraction is supported by the findings that impairment of CRAT performance through concurrent articulatory suppression is observed within this procedure, whereas facilitation of CRAT performance occurs via encouraging verbalisation through the “think aloud” technique (Ball & Stevens, 2009). Thus, the availability of speech (inner speech or external speech) is necessary for efficient CRAT problem solving performance. In the context of serial recall, the skill of speech (or rather speech planning) is co‐opted because it provides an effective medium for retaining visual–verbal items due to its inherent sequentiality, continuity, and prosodic and co‐articulatory nature. Inner speech therefore enables the grafting of serial order constraints onto the presented list; the act of covertly co‐articulating to‐be‐remembered items generates sequential information and constraints that do not occur within the list itself. However, this motoric based serial rehearsal process is subject to interference from the automatic, pre‐attentive processing of the serial order of changes in a background auditory sequence (such as music).

In our recent work, it is becoming clearer that tasks that may not necessarily involve serial rehearsal, but that do involve the use of inner speech for effective task performance (e.g., face recognition; Marsh et al., 2018) are also vulnerable to the changing‐state effect. Of course, speech (inner or outer) involves planning of sequential motor acts, which may render it vulnerable to disruption via changing‐state speech in many settings. In the context of CRATs, inner (and outer) speech clearly supports effective performance (Ball & Stevens, 2009). It may even be that participants use serial rehearsal to test out novel solutions.

We do not claim that CRAT performance is underpinned entirely by verbal maintenance processes. Rather, it is clear that spreading semantic activation processes (Smith et al., 2013), and executive processes that are involved in generating response candidates and inhibiting misleading/incorrect solutions (Storm & Angello, 2010) are also central to the production of responses. That said, it is often not clear which component of a multicomponent task is associated with CRAT performance. For example, the finding of an association between Working Memory Capacity measures and CRAT performance (Chein & Weisberg, 2014) could be due to the role of attentional/cognitive control (which may involve executive control processes such as inhibition) or the requirement to retain serial order information: Working Memory Capacity tasks involve combining the short‐term storage of visual/verbal items with a concurrent processing task. Therefore, we hold that subvocal maintenance processes involving inner speech can underpin solution of CRATs and that this process is susceptible to disruption via processing of a changing‐state auditory sequence. The aim of the series of three experiments that we present here was to investigate the impact of to‐be‐ignored background sound (i.e., music with foreign, [unfamiliar] lyrics; instrumental music; and music with familiar lyrics) on tasks believed to measure creativity, that is, CRATs.