RUNNING HEAD: MENTAL IMAGERY in MUSICIANS Auditory and Visual

RUNNING HEAD: MENTAL IMAGERY IN MUSICIANS

Auditory and Visual Mental Imagery in Musicians and Nonmusicians

Francesca Talamini1,2, Julia Vigl1, Elizabeth Doerr2, Massimo Grassi2, Barbara Carretti2

1Institute of Psychology, University of Innsbruck, Austria

2Department of General Psychology, University of Padova, Italy

Corresponding author: Francesca Talamini, email: [email protected] ABSTRACT The present research investigated auditory and visual mental imagery and how this ability differs in people with and without musical training. In a first part, the characteristics of a new auditory imagery self-report questionnaire (the Vividness of Auditory Imagery Questionnaire, VAIQ) were reported. The questionnaire was composed of 16 items assessing mental vividness of auditory everyday sounds and it was administered to 147 participants, demonstrating good psychometric properties. In a second part, self-reported vividness of auditory and visual images was assessed in people with and without music expertise. Thirty-six formally trained musicians, 33 self-taught musicians, and 33 nonmusicians completed the questionnaires. The newly built questionnaire VAIQ and the Vividness of Visual

Imagery Questionnaire (VVIQ, Marks, 1973) were administered. Music aptitude and general cognitive abilities were also assessed in all participants as control measures. We observed that both groups of musicians self-reported greater vividness of mental imagery for auditory nonmusical sounds than nonmusicians, but not for visual images. The study confirmed that music expertise is linked to enhanced self-reported auditory mental imagery for everyday sounds, illustrating that such advantage is selective for auditory imagery; no difference concerning visual imagery between the groups of musicians and nonmusicians emerged.

Keywords: musicians and nonmusicians; auditory imagery; visual imagery; VVIQ Introduction

A mental image is an internally generated representation of an object, event or sensation. In contrast to perception, it happens when perceptual information is accessed from memory and is often described as

‘seeing with the mind’s eye’ or ‘hearing with the mind’s ear’ (Kosslyn, Ganis, & Thompson, 2001), depending on the sensory modality. Mental images can be internally generated as representations of things, experiences or scenes (Schifferstein, 2009) allowing people to recreate the past and simulate the future (Moulton & Kosslyn, 2009). Here, the term "vividness" refers to how clearly and real an image is experienced (McAvinue & Robertson, 2007).

Mental imagery plays a functional role in various contexts involving cognitive resources such as memory, learning, spatial representation and reasoning. For instance, imagery is an effective method for promoting medical adherence (Liu & Park, 2004); indeed, people who mentally implement through imagination specific health procedures they intend to carry out are more likely to perform the procedure more accurately and constantly, also for an extended period of time.

Mental imagery is also related to increased involvement in planned behavior and promotion of task engagement (Renner, Murphy, Manly, & Holmes, 2019; Vasquez & Buehler, 2007), more effective learning outcomes (Guarnera, Pellerone, Commadari, Valenti, & Buccheri, 2019), positive mood training (Holmes, Mathews, Dalgleish, & Mackintosh, 2006), improved sports performance

(Mizuguchi et al., 2012), motor control (Isaac & Marks, 1994) and plays a major role in the field of music (Aleman, Nieuwenstein, Böcker, & de Haan, 2000; Brochard, Dufour, & Després, 2004; Brown

& Palmer, 2013).

Although many studies focus on visual mental imagery, which is also the modality with the highest vividness ratings (Schifferstein, 2009), it is known from literature that mental imagery can have all other kinds of sensory forms (e.g. tactile, olfactory, motor, or auditory, see Andrade, May, Deeprose,

Baugh, & Ganis, 2014). The vividness of mental images depends not only on the sensory and affective qualities of the imagined stimulus (Bywaters, Andreade, & Turpin, 2004), or the capacity of short-term and long-term memory systems (Baddeley & Andrade, 2000), but also on personal experience in different areas. Isaac and Marks (1994) showed that specific expert groups, such as elite athletes, physical education students, air traffic controllers and pilots, reported more vivid visual and movements imagery than matched controls.

Mental imagery in musicians

Musicians are often regarded as a model for studying brain and behavioural changes after a prolonged and intense training (Schlaug, 2001; Münte, Altenmüller, & Jäncke, 2002). Music training implies a high cognitive load, caused by the integration of information coming from different sources, planning and execution of appropriate motor actions (Sergent, 1993). In order to plan any musical performance it is important to create a mental representation and imagine a desired interpretation (Holmes, 2005). In the case of music, these representations are mostly auditory images generated in the anticipation of actual auditory feedback (Bishop, Bailes, & Dean, 2013a; Keller & Koch, 2008).

Musicians use mental imagery as part of their typical learning and performing routines, or when reading written music silently (Bailes, 2006; Bishop, Bailes & Dean, 2014; Brodsky, Kessler,

Rubinstein, Ginsborg, & Henik, 2008; Gregg, Clark, & Hall, 2008). When musicians are mentally practicing, they use visual, acoustic and kinesthetic imagery. Mental imagery can help in the creation of anticipatory images enabling action planning and movement execution (Keller, 2012), thus gaining an enhanced expressive and interpretive understanding of the musical piece (Connolly & Williamon,

2004). In ensembles, it facilitates interpersonal coordination through simulating one’s own and other’s actions during the performance (Keller & Appel, 2010; Pecenka & Keller, 2009). Mental imagery could also enhance music compositional creativity (Wong & Lim, 2017). Therefore, it is not surprising that some studies found that musicians possess better auditory imagery abilities than nonmusicians, for both musical and nonmusical sounds, either at a behavioral level and at brain activity level (Aleman et al., 2000; Herholz, Lappe, Knief, & Pantev, 2008; Bishop, Bailes, & Dean, 2013b). These findings raise the question whether musicians have a generally stronger imagery or whether this ability mainly concerns the auditory domain. Only few studies tried to verify if this advantage of mental imagery in musicians could be selective for auditory stimuli or not, with some results supporting a general advantage (i.e., enhanced visual imagery in musicians vs nonmusicians, Brochard et al., 2004), and some supporting a selective auditory advantage (Aleman et al., 2000). The advantage of musicians in auditory imagery tasks might be explained by their use of mental imagery when practicing (Gregg et al., 2008), by the possible positive effects of musical training on cognitive functions (for a review see

Swaminathan & Schellenberg, 2019), or by their more effective processing of imagery representations in auditory cortical areas (Aleman et al., 2000).

Mental imagery can be measured in different ways; one of the most common approaches is to use self- report questionnaires such as the Vividness on Visual Imagery Questionnaire (VVIQ, Marks, 1973), the

Plymouth Sensory Imagery Questionnaire (Andrade, May, Deeprose, Baugh & Ganis, 2014 ), the

Movement Imagery Questionnaire (Hall & Martin, 1997) or the Spontaneous Use of Imagery Scale

(Nelis et al., 2014). Another option is to evaluate mental imagery through objective testing, where imagery is needed to obtain the right solution (e.g., Aleman et al., 2000; Brochard et al., 2004). As mentioned earlier, it still remains unclear whether musicians possess general enhanced mental imagery abilities, or selective for auditory stimuli, and if these enhanced abilities could also emerge in self- report questionnaires about vividness. Pfordresher and Halpern (2013) found that people with higher self-reported vividness of imagery are more likely to sing in tune than those with lower imagery. A recent research by Di Nuovo and Angelica (2016) showed that trained and expert musicians self- reported more vivid motor imagery compared to untrained counterparts. Campos and Fuentes (2016) found that music-students self-reported higher mental imagery clarity of cutaneous, kinaesthetic, gustatory, visual, and auditory imagery vividness scores than students of other subjects. However, another study, which examined only musicians, could not find any connection between musical experience and self-reported vividness of visual imagery (Clark & Williamon, 2012).

Objectives of the study

Due to the ambiguous results from previous studies, we wanted to take a closer look at whether musicians differ from nonmusicians in terms of visual and auditory imagery, specifically for what concerns the self-reported vividness of mental imagery. To do so, in the first part of our study we created a new self-report questionnaire assessing auditory mental imagery for everyday sounds (the

VAIQ). We selected sounds that are well known by the majority of people and not specific to the music domain (e.g., the sound of teeth brushing, for the complete list of the sounds see Appendix A1). The questionnaire was modeled on the Vividness of Visual Imagery Questionnaire VVIQ (Marks, 1973), as our purpose was to have comparable measures of visual and auditory mental imagery. The VVIQ was chosen as a model because it is a short test with good reliability, and it was already used as a model to create other imagery questionnaires, such as the Vividness of Movement Imagery Questionnaire

(VMIQ, Isaac, Marks, & Russell, 1986) but an auditory version was still missing. The VAIQ was administered to a large group of people to assess its psychometric properties.

In the second part of the study we investigated the vividness of mental auditory and visual imagery in different music-skilled participants (i.e., formal-trained musicians, self-taught musicians, and nonmusicians). Including differently trained musicians (self-taught vs formal-trained) could help differentiate eventual self-reported enhanced imagery abilities due to formal training from those linked to making music in general, even by nonprofessional musicians. We also assessed music aptitude to verify the relationship between auditory imagery and musical perception regardless of musical expertise (i.e., nonmusicians or amateur musicians could possess good music perception skills even if they did not undergo any musical training). General cognitive abilities were controlled for with two subtests of the WAIS-IV (Wechsler, 2008), specifically, verbal abilities and nonverbal reasoning abilities, as they might play a role in explaining individual differences in mental abilities, including imagery (Shaw, & DeMers, 1986)

STUDY 1a: Validation of the Vividness of Auditory Imagery Questionnaire

Method

Participants

One hundred and forty-eight bachelor students from the School of Psychology of the University of

Padova, Italy, volunteered (37 males, 110 females). Participants were aged from 18 to 26 (M = 19.4 SD

= 1.37).

Materials

Auditory imagery

Vividness of Auditory Imagery Questionnaire (VAIQ): this self-report questionnaire, written in

Italian language, rates vividness of auditory mental imagery for everyday sounds. The structure of the questionnaire was modelled on the Vividness of Visual Imagery Questionnaire (VVIQ, Marks, 1973) which evaluates vividness of mental imagery for visual stimuli. For practical purposes, we also built an online version of this questionnaire with Google Forms. Sixteen items divided into four sets of questions, are presented to measure auditory imagery created when thinking about specific contexts. Each set refers to a specific sound object that has to be imagined, each individual item of the set refers to a particular detail of the object. For example the first question refers to tooth brushing, and each item assesses the vividness of specific details (e.g., the sound of the toothbrush on your teeth; the sound of running water; the sound of the toothbrush in different parts of the mouth; the sound of water in the mouth when you rinse, see supplemental materials for the complete list of items). Next to each item, there are two empty boxes (one for the open eyes condition, and the other for closed eyes) which the participant has to fill in with a number from 1 to 5, depending on how much the mental image created was vivid (1 =“no image at all, I only thought of the situation” to 5 = “ very vivid and clear image, like I really heard the sound”). The evaluation is done twice: the first time with open eyes, the second time with closed eyes, in accordance with the VVIQ, which assesses mental imagery in these two different conditions.

Procedure

During a university lecture, students were informed about the objective of the study and asked if they were willing to participate. Those who signed a written informed consent participated in the study during the same lecture. Students completed the online version of the VAIQ and the demographic questionnaire, where information about gender, age and education was collected. Students who could not access the online version completed the paper version.

The present study was approved by the local ethics committee.

Results

VAIQ properties

The questionnaire demonstrated good reliability with Cronbach’s α = .85 for items with open eyes and Cronbach’s α = .87 for items with closed eyes. Corrected item-total correlations were also carried out for both versions of items concerning open and closed eyes (Tables 1. The items shared similar levels of reliability, although general higher reliability can be noticed in the items of the eyes closed version.

Table 1 Corrected item-total correlations for items with open and closed eyes

Item Open eyes: Open eyes: α (if Closed eyes: Closed eyes: α (if item dropped) item dropped) Item-rest Item-rest correlation correlation

1a 0.38 0.84 0.41 0.87

1b 0.44 0.84 0.47 0.86

1c 0.25 0.85 0.37 0.87

1d 0.50 0.84 0.61 0.86

2a 0.52 0.84 0.56 0.86

2b 0.51 0.84 0.55 0.86

2c 0.54 0.83 0.54 0.86

2d 0.47 0.84 0.50 0.86

3a 0.50 0.84 0.56 0.86

3b 0.59 0.83 0.57 0.86

3c 0.44 0.84 0.50 0.86

3d 0.58 0.83 0.61 0.86

4a 0.48 0.84 0.52 0.86

4b 0.40 0.84 0.43 0.87

4c 0.44 0.84 0.51 0.86

4d 0.42 0.84 0.44 0.87 We examined the difference between males and females, as a meta-analysis suggested that females report slightly higher vividness of mental imagery than males (Richardson, 1995). A Welch t-test showed that there was no statistically significant difference between females and males, neither with eyes open, t(66.77) = -0.15, p = .882, d = -.03, nor with eyes closed, t(69.49) = -0.11, p = .913, d = -.02.

Moreover, participants generally gave higher scores of vividness with eyes closed (M = 56.49, SD =

11.06) than with eyes open (M = 52.03, SD = 10.59), t (146) = -9.62, p <.001. d = .41.

Discussion

The first part of the study assessed the auditory version of the VVIQ we created (i.e., the VAIQ), and verified its reliability. The VAIQ was administered to a large sample of young psychology students with results demonstrating good reliability for both open and closed eyes. Corrected item-total correlations were also carried out, highlighting similar values in the items, with slightly higher values for items with eyes closed. Generally, participants reported higher vividness when keeping their eyes closed. Despite Marks (1973) not hypothesizing any differences across the two conditions in the original paper, the advantage of the closed eyes condition could be explained by the lower amount of visual interference. Indeed, the literature has demonstrated the influence of concurrent visual interference tasks in visual imagery (Baddeley & Andrade, 2000), speculating the involvement of different components of working memory for visual and auditory imagery. During a concurrent task, the vividness of a visual stimulus was impaired due to a disruption of the visuospatial sketchpad; likewise the phonological loop was disrupted by the concurrent task when the stimulus was auditory.

Furthermore, visual imagery has proven to be helpful for learning and text recall; specifically, participants with good imagery and mastery of imagery-based strategies remembered text passages more easily, especially those rich of imagined details (De Beni & Moè, 2003). Conversely, the relationship of working memory and visual imagery remained unclear despite their involvement in manipulating visual material (Tong, 2013). Compelling evidence supporting the concept of shared mechanisms for imagery and working memory was brought by fMRI analyses, identifying the early visual cortex as the main area supporting both imagery and working memory processes (Albers, Kok,

Toni, Dijkerman & de Lange, 2013).

No gender differences emerged, despite the contradictory results of several studies applying the

Vividness of Visual Imagery Questionnaire. Particularly a review of the literature revealed no significant gender difference on the VVIQ (McKelvie; 1995), whereas Richardson (1995) concluded that women obtained slightly higher scores than men on this test.

The properties of the VAIQ were in line with other versions of the test. Indeed, the VVIQ alpha was high in several studies, ranging from an alpha of .88 to an alpha of .96 (see Campos & Pérez-Fabello,

2009). Newer versions of the VVIQ were created and tested (Campos, 2011) such as the Vividness of

Visual Imagery Questionnaire-2 (VVIQ-2) and the Vividness of Visual Imagery Questionnaire-Revised

Version (VVIQ-RV). Both questionnaires were evaluated for reliability and construct validity, demonstrating high internal consistency reliability with Cronbach’s α = .91 for the VVIQ-2 and α = .96 for the VVIQ-RV; moreover they correlated positively with other instruments for imagery evaluation such as the Object-Spatial Imagery and Verbal Questionnaire (OSIVQ; Blazhenkova & Kozhevnikov,

2009) and the Betts’ Questionnaire Upon Mental Imagery (Betts’ QMI; Sheehan, 1967). Compared to other versions of the questionnaire, the reliability of the VAIQ was slightly higher than the Vividness of Movement Imagery Questionnaire (VMIQ, Isaac, Marks, & Russell, 1986) , which had an r value of .74 (acceptable reliability). Unfortunately, we were not able to collect test-retest data, therefore future studies should assess these missing properties.

STUDY 1b: auditory mental imagery in musicians and nonmusicians

Method Participants

One hundred and two young adults participated in the study. The mean age was 22.7 years (min =

19; max = 33). There were 35 females and 67 males. Participants were: (1) thirty-six formally trained musicians (i.e., conservatory students, music schools’ students, and/or professional musicians); (2) thirty-three self-taught musicians that never underwent a music training (apart from the basics learnt at school), or took minimal years of music lessons when they were children (i.e., less than 2 years); (3) thirty-three nonmusicians that never underwent a music training (apart from the basics learnt at school), or took minimal years of music lessons when they were children (i.e., less than 2 years). The three groups completed two subtests from the WAIS-IV (the Visual Puzzles and the Vocabulary subtests,

Wechsler, 2008) to obtain a baseline measure of general cognitive abilities. Demographic details and the scores obtained in the WAIS subtests are reported separately for each group in Table 2 Formally trained musicians had slightly more years of music experience (i.e., years of training and/or years of musical activity) compared to self-taught musicians, and were substantially playing more hours per week than the self-taught musicians (see table 2). A one-way ANOVA comparing group scores in the

Vocabulary subtest of the WAIS-IV revealed no statistically significant effect of group, F(2,99) =

1.051, p = .354, η2 = .02; in the Visual Puzzles subtests of the WAIS-IV, however, the group effect was significant, F(2,99) = 3.39, p = .037, η2 = .06. Post-hoc tests revealed that formally trained musicians performed better than nonmusicians, t(63) = 2.32, p = .035, d =.56, and that self-taught musicians performed better than nonmusicians, t(62) = 2.44, p = .035, d = .60, however no statistically significant difference between formally trained musicians and self-taught musicians emerged, t(67) = 0.04, p

= .965, d = .01 (see Table 2.

All participants had normal audiometric thresholds (assessed with a pure tone audiometry, for frequencies of 500, 1500, and 4000 Hz) and reported normal (or corrected to normal) vision. Table 2. Age, Education, Performance (Raw Scores) in the WAIS-IV Visual Puzzles and Vocabulary Subtests, music education and practice hours. Self-taught Trained Musicians Musicians Nonmusicians N =36 (13 females) N = 33 (4 females) N = 33 (18 females) Age (yrs) 22.7 (2.1) 23.2 (2.1) 22.1 (1.6) p = .137 Education (yrs) 16.3 (1.9) 16.5 (1.6) 15.9 (1.4) p = .333 Visual Puzzles (max score 26) 17.53 (4.34) 17.48 (3.72) 15.42 (3.1) p = .037 Vocabulary (max score 57) 45.75 (8.14) 47.18 (5.97) 44.76 (6.03) p = .354 Music Experience (yrs) 11.8 (3,7) 10.2 (2.3) p = .032 Weekly Practice (hrs) 14.2 (9.2) 7.2 (4.9) p < .001 Note. Mean (SD). In the last column on the right, the p-value reflects the analysis between (t-test) or among groups (ANOVA). In bold characters, significant p-values. In the Visual puzzles, post-hoc comparisons revealed that the groups that differ significantly were readers > nonmusicians, p = .034, and nonreaders > nonmusicians, p = .034.

Materials

Mental Imagery Questionnaires

VVIQ: Visual mental imagery was assessed with the VVIQ, Vividness of Visual Imagery

Questionnaire, Marks, 1973). The questionnaire consists of 16 items divided into four groups, each group representing a different subject to be imagined (i.e.,, a familiar person, the rising sun, a familiar storefront, and a countryside). Within each group, each item refers to a specific characteristic of the subject to be imagined. For example, for the “familiar person” group, the four different items ask to imagine the color of the clothes, the contour of the face and the body, the walking pace, and the posture. For each item, the vividness of the image is given on a 5-point scale (1 = perfectly clear and vivid as normal vision, 5 = no image at all, you only "know" that you are thinking of an object). The questionnaire is completed twice, once with eyes open and once with eyes closed.

VAIQ: Auditory mental imagery was assessed with the newly built VAIQ, Vividness of auditory imagery questionnaire (see Study 1a for psychometric properties). The VAIQ has the same structure of the VVIQ (as reported in Study 1a), with 16 total items, grouped into four different questions about everyday auditory objects. Mini PROMS (Law & Zentner, 2012). This online test was used to assess music aptitude of all participants and includes four subtests (Melody, Tuning, Beat and Speed) investigating different aspects of music perception. In each subtest, the participant listens twice to a standard stimulus, followed by a comparison stimulus. The participant has to judge whether the comparison stimulus is identical or different from the standard. The Melody subtest assesses the ability of recognizing whether two short melodies are identical or not. The Tuning subtest requires comparing chords. In the case of different trials, one of the middle notes of the comparison chord is different in frequency (i.e., it is mistuned). The Beat subtest requires comparing rhythmic patterns of clicks: the rhythm is produced by giving an accent (i.e., by changing the intensity) to a subset of the clicks. In the Speed subtest the participant compares the speed (i.e., beats per minute, BPM) of either a synthetic rhythmic structure, or a recorded sample of music.

Apparatus & procedure

The computer used for the PROMS test was an ASUS (Cpu Intel i5 650 3.20 GHz, Motherboard

Asus P7H55-V RAM 4 GB, Graphic Card AMD Radeon HD 5700 Series, OS Windows 7 Professional

64 bit). The computer was connected to a monitor (NEC MultiSync FE950) and M-AUDIO FastTrack

Pro sound card. The headphones were a pair of Sennheiser HD 580. Computer tests were delivered inside a single-walled IAC sound proof booth. The PROMS test was administered on its website. The level of the test was comfortable (we did not measure the intensity at the listener’s ear) and fixed for all participants.

Participants began by signing a written consent form, then information about demographic details

(e.g., age, sex, education) was collected. Participants then sat into the soundproof booth, where the audiometry test was run. The mental imagery questionnaires were administered, with counterbalanced order (i.e., half of the subjects completed first the VVIQ and then the VAIQ, half of the subjects followed the opposite order). Participants then completed the PROMS test. Finally, a questionnaire about music was administered, including questions about music habits for all participants (e.g., listening to music, dancing) and specific questions about music education for the musician groups (e.g., years of training, hours of practice, type of instrument).

The present study was approved by the local ethics committee.

Analysis

To assess group differences in mental imagery abilities, we ran a repeated measures ANOVA with group (formal-trained musicians”, “self-taught musicians”, “nonmusicians”) as a between factor, and modality (i.e., visual imagery, auditory imagery) and condition (i.e., open eyes, closed eyes) as within factors. Moreover, we ran some correlations to see whether the ability of creating vivid images is related to music perception skills (i.e., PROMS test).

All statistics for the main effects and significant interactions were reported. Means and standard deviations were reported only in case of absent graphical representations. For multiple comparisons, p- values were adjusted for False Discovery Rate (i.e., FDR, Benjamini, & Hochberg, 1995). As effect sizes, we reported partial eta-squared for ANOVAs and Cohen’s d for post-hoc tests.

Results

Mental imagery questionnaires. An ANOVA was calculated, with the score at the imagery questionnaires as dependent variable and condition (i.e., eyes opened, eyes closed), modality (i.e., visual imagery, auditory imagery), and group (i.e., formally trained musicians, self-taught musicians, nonmusicians) as independent variables. There was a significant effect of condition, F(1, 99) = 43.23, p

<.001, η2 = .29, meaning that the vividness of the images created was higher when the task was performed with closed eyes (M = 123.5, SD = 18.59) than with open eyes (M = 114.5, SD = 18.26).

There was a significant interaction between modality and group, F(2, 99) = 4.20, p = .018, η2 = .08, suggesting that the three groups performed differently depending on the imagery modality of the questionnaire (see Figure 4). When adding the Visual Puzzles as a covariate, the covariate resulted nonsignificant, F(1, 98) = .50, p = .481, η2 = .005; the interaction remained significant, meaning that this result did not depend on the difference among groups in nonverbal reasoning abilities, F(2, 98) =

3.59, p = .031, η2= .07. Post hoc analyses revealed that formally trained musicians differed significantly from the nonmusicians group only in the auditory imagery total score, t(67) = 2.54, p =.039, d = .61 , and not in the visual one, t(67) = 1.28, p = .310, d = .31. Just as the formally trained musicians, self- taught musicians differed significantly from nonmusicians in the auditory imagery questionnaire, t(64)

= 3.55, p =.004, d = .87, but did not differ in the visual imagery questionnaire, t(59) = 1.58, p =.240, d

= .39. Finally, formally trained- and self-taught musicians did not differ in the auditory questionnaire, t(67) = -.9, p = .445, d = .22, nor in the visual questionnaire, t(64) = -.42, p = .679, d = .10. This suggests that musicians, especially self-taught musicians, could create more vivid auditory mental imagery than nonmusicians. Figure 1. Self-report scores in the mental imagery questionnaires (sum of eyes open and eyes closed conditions): the higher the value, the higher the vividness reported. For each modality, the score for the opened eyes condition and closed eyes condition was combined, to evidence the interaction between group and modality. The maximum score obtainable is 160. Inside each box, the horizontal line indicates the median. The red diamond represents the mean. The edges of the box represent the 25th and the 75th percentiles. The whiskers are the interquartile range (i.e., Q3-Q1) augmented by 50%. The black dots are outliers.

PROMS test. A one-way ANOVA with the total score of PROMS test as dependent variable and the group as between factor1 revealed that group was significant, F(2, 99) = 32.17, p < .001, η2 = .39.

Namely, formally trained musicians and self-taught musicians performed better in this test than nonmusicians (see Figure 3). When the Visual Puzzle score was included as a covariate, it resulted significant, F(1, 98) = 5.63, p = .02, η2 = .04, whereas the group factor remained significant, F(2, 98) =

27.17, p < .001, η2 = .34. Post-hoc tests revealed that formally trained musicians (M = 52.61, SD =

8.05) outperformed nonmusicians (M = 38.70, SD = 7.33), t(67) = 7.51, p < .001, d = 1.81 and that

1 Other four one-way ANOVAs for each one of the PROMS subtests were run, and in each one of them the group factor was always significant (p < .05) self-taught musicians (M = 49.06, SD = 6.78) outperformed nonmusicians too, t(64) = 5.96, p < .001, d

= 1.47. A significant difference between trained- and self-taught musicians was also found, with the former performing just slightly better than the latter, t(66) = 1.987, p = .05, d = .48. Moreover, the total score of PROMS was positively correlated with the Visual Puzzles test, r(102) = .33, p < .001, as expected from the significant covariate in the ANOVA.

Correlations. Several correlations were run with the purpose to explore the relationship between music training, music aptitude and mental imagery abilities. In the groups of musicians, weak correlations emerged between the years of music training and the VAIQ score, between the hours of weekly practice and the VAIQ score, and between the hours of weekly practice and the VVIQ score only in the open eyes condition (see Table 3). To investigate whether music perception skills (i.e.,

PROMS test) could be related to vividness of mental imagery, we ran further partial correlations while controlling for the Visual Puzzle score, as it was found to be a significant covariate when assessing group differences in the PROMS score. Few weak significant correlations emerged between the

PROMS test and both the VAIQ and the VVIQ, but not in the rhythmic subtests (i.e,, speed and beat) of the PROMS (see Table 3).

Table 3. Pearson correlations between years of music training, hours of weekly practice, PROMS score and the imagery questionnaires (VVIQ and VAIQ)

VVIQ VVIQ VAIQ VAIQ EO EC EO EC

Musical experience 0.03 0.17 0.20* 0.26** (yrs) Practice hours 0.07 0.22* 0.21* 0.30**

PROMS Melody 0.20* 0.25* 0.30** 0.23* PROMS Tuning 0.09 0.25* 0.20* 0.24*

PROMS Speed 0.11 0.14 0.16 0.17

PROMS Beat 0.11 0.11 0.12 0.13

PROMS Total 0.17 0.26** 0.27** 0.27**

Note. *p <.05, **p < .01, ***p< .001. EO = eyes open; EC = eyes closed. Musical experience and practice hours refer only to the musicians group.

Discussion

The present study assessed visual and auditory mental imagery in three different music-skilled groups (i.e., formally trained musicians, self-taught musicians, and nonmusicians). Music aptitude

(PROMS test) was also assessed to have an independent and objective measure of music perception skills, regardless of being a musician or not. General cognitive abilities were controlled for by administering two subtests of the WAIS-IV (i.e., Vocabulary and Visual Puzzles).

Results revealed that both formally trained musicians and self-taught musicians, reported to have more vivid mental imagery than nonmusicians, but only for auditory objects. Indeed, in the VVIQ no group difference emerged. This result is in line with the one of Aleman and colleagues, where the authors also found that musicians had better mental imagery abilities than nonmusicians, but only when they are asked to produce auditory images (Aleman et al., 2000). Note that they used a different paradigm to test mental imagery: namely they asked participants to mentally compare pitches of notes

(musical condition), the acoustic characteristics of everyday sounds (nonmusical auditory condition), and visual forms of objects (visual condition). This seems to strengthen the validity of the present self- report questionnaires as a measure of mental imagery. Surprisingly, in the VAIQ the formally trained musicians and the self-taught musicians did not differ in the reported highest vividness scores of mental images, suggesting that such ability is not the result of a formal musical training, rather something generally linked to music playing. Moreover, years of musical experience (regardless of being formally trained or not), could also play a role in enhancing auditory mental imagery. However, this role seems to be small, as in the present study we found only a weak correlation between years of musical experience and the VAIQ scores. This is in line with a previous study by Pfordresher and Halpern

(2013), where music training was only weakly correlated with a measure of auditory imagery.

In the PROMS test the two groups of musicians had different performances, with formally trained musicians performing better than self-taught musicians. If having better music perception skills was linked to more vivid auditory imagery skills, we would have observed an advantage of formally trained musicians over self-taught musicians in the VAIQ, but this was not the case. Moreover, we found only weak correlations between the PROMS test and the VAIQ, suggesting that music perception abilities alone are not responsible for the higher scores of musicians in the VAIQ. However, concerning the correlations between the PROMS, and both the VVIQ and the VAIQ, it is interesting to note that even if small, they appeared only in the tonal subtests of the PROMS test (i.e., melody and tuning) whereas the subtests assessing temporal skills (i.e., speed and beat) were not correlated with mental imagery abilities. The items of the VAIQ test (see appendix A1) indeed, are focused on specific everyday sounds but not particularly on their temporal properties (regardless a couple of items about the speed of the sounds). This could suggest that tonal and temporal properties of sounds are represented in different ways, with tonal information being perhaps easier to imagine than temporal information (Janata &

Paroo, 2006). Finally, this selective advantage of musicians over nonmusicians seems to not be explained by superior nonverbal reasoning abilities. In fact, even if musicians scored better than nonmusicians in the Visual Puzzles subtest of the WAIS-IV, when this difference was controlled for in the analysis, the advantage in the VAIQ of the two musicians’ groups over nonmusicians was still there. Even if formally trained musicians and self-taught musicians did not statistically differ in their

VAIQ scores, it is interesting to note that the effect size was larger for self-taught musicians. This could suggest that perhaps self-taught musicians rely even more on mental imagery when playing, as they don’t have a formal knowledge of reading music notation. If this was the case, it would be interesting to investigate auditory mental imagery in jazz musicians, as one could hypothesize that improvisation requires even more the use of mental imagery.

Now, the next question that should be answered is why people who play music possess higher vividness of auditory imagery than people who do not play any musical instrument. It would be interesting to understand if this is an a priori difference in people who decide to learn to play an instrument. Our results seem to point towards causality, that is, music playing could enhance mental imagery: in fact, we found a selective advantage of musicians (both groups) over nonmusicians only in auditory mental imagery. If there is an a-priori superiority in mental imagery in individuals who decide to play a musical instrument, one would expect a more general superiority in mental imagery abilities and not a selective one for auditory stimuli. One possible explanation is that musicians (better than nonmusicians) know the physical rules that govern the relationship between a sound source and its sound (see Gaver, 1993a, 1993b; Spence 2011 for an overview of crossmodal correspondences). For example, small objects are known to produce high- frequency sounds whereas large objects are known to produce low-frequency sounds (Grassi, 2005; Grassi, Pastore & Lemaitre, 2013), objects’ materials have known and distinguished sonic characteristics (Giordano & McAdams, 2006), or the shape of objects determines the sound they produce (Kunkler-Peck & Turvey, 2000). These relationships emerge evidently in musical instruments. For example, imagine the size/sound of a violin in comparison to the size/sound of a double bass or the sound of a metal vibraphone compared to a similar instrument (but made of wood) like a marimba. Thanks to the exposure to the sound of musical instruments and the constant association between the instrument (together with its physical characteristics) and its resulting sound, musicians might learn (implicitly or explicitly) the vocabulary of the physical rules that link the physics of an object and the sound it produces, exploiting these rules when asked to imagine environmental sounds. Of course, this hypothesis should be further tested, for example by developing a specific training to boost the association between the characteristics of objects and their sounds, and see if this could improve auditory mental imagery as well.

General conclusion

The present research assessed self-reported mental imagery abilities in different music-skilled participants. In particular, we wanted to compare auditory and visual imagery skills in different groups of musicians and nonmusicians, to see if music expertise could be associated with general enhanced imagery abilities or selective for auditory stimuli. To test this we built an auditory version of the VVIQ, a well known and used questionnaire to assess the vividness of mental imagery for visual objects. The newly created questionnaire, the VAIQ, showed good reliability and was sensitive in detecting a difference between musicians and nonmusicians, as already observed in previous studies using other materials. The present research strengthens the idea that music training is linked to a selective mental imagery advantage for auditory stimuli, and not to a general one. Future studies should shed light on the origin of this advantage.

Acknowledgments: We would like to thank our students Camilla, Antonio, Valentina, and Lara for their help in collecting the data. References

Albers, A. M., Kok, P., Toni, I., Dijkerman, H. C., & De Lange, F. P. (2013). Shared representations for working memory and mental imagery in early visual cortex. Current Biology,

23(15), 1427-1431.https://doi.org/10.1016/j.cub.2013.05.065

Aleman, A., Nieuwenstein, M. R., Böcker, K. B. E., & de Haan, E. H. F. (2000). Music training and mental imagery ability. Neuropsychologia, 38, 1664–1668. DOI: 10.1016/s0028-3932(00)00079-8

Andrade, J., May, J., Deeprose, C., Baugh, S.-J., & Ganis, G. (2014). Assessing vividness of mental imagery: The Plymouth Sensory Imagery Questionnaire. British Journal of Psychology, 105(4),

547–563. https://doi.org/10.1111/bjop.12050

Baddeley, A. D., & Andrade, J. (2000). Working memory and the vividness of imagery.

Journal of Experimental Psychology: General, 129(1), 126–145. https://doi.org/10.1037//0096-

3445.129.1.126

Bailes, F. A. (2006). The use of experience-sampling methods to monitor musical imagery in everyday life. Musicae Scientiae, 10(2), 173–190. https://doi.org/10.1177/102986490601000202

Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal statistical society: series B

(Methodological), 57(1), 289-300.

Bishop, L., Bailes, F., & Dean, R. T. (2013) (a). Musical Imagery and the Planning of

Dynamics and Articulation During Performance. Music Perception: An Interdisciplinary Journal,

31(2), 97–117. https://doi.org/10.1525/mp.2013.31.2.97

Bishop, L., Bailes, F., & Dean, R. T. (2013) (b). Musical expertise and the ability to imagine loudness. PloS one, 8(2). https://doi.org/10.1371/journal.pone.0056052

Bishop, L., Bailes, F., & Dean, R. T. (2014). Performing Musical Dynamics. Music Perception:

An Interdisciplinary Journal, 32(1), 51–66. https://doi.org/10.1525/mp.2014.32.1.51 Blazhenkova, O., & Kozhevnikov, M. (2009). The new object‐‐ spatial verbal cognitive style model: Theory and measurement. Applied Cognitive Psychology: The Official Journal of the Society for Applied Research in Memory and Cognition, 23(5), 638-663. https://doi.org/10.1002/acp.1473

Brochard, R., Dufour, A., & Després, O. (2004). Effect of musical expertise on visuospatial abilities: Evidence from reaction times and mental imagery. Brain and Cognition, 54(2), 103–109. https://doi.org/10.1016/S0278-2626(03)00264-1

Brodsky, W., Kessler, Y., Rubinstein, B.-S., Ginsborg, J., & Henik, A. (2008). The mental representation of music notation: Notational audiation. Journal of Experimental Psychology. Human

Perception and Performance, 34(2), 427–445. https://doi.org/10.1037/0096-1523.34.2.427

Brown, R. M., & Palmer, C. (2013). Auditory and motor imagery modulate learning in music performance. Frontiers in Human Neuroscience, 7, 320. https://doi.org/10.3389/fnhum.2013.00320

Bywaters, M., Andrade, J., & Turpin, G. (2004). Determinants of the vividness of visual imagery: The effects of delayed recall, stimulus affect and individual differences. Memory (Hove,

England), 12(4), 479–488. https://doi.org/10.1080/09658210444000160

Campos, A., & Fuentes, L. (2016). Musical Studies and the Vividness and Clarity of Auditory

Imagery. Imagination, Cognition and Personality, 36(1), 75–84. https://doi.org/10.1177/0276236616635985

Campos, A., & Pérez-Fabello, M. J. (2009). Psychometric quality of a revised version

Vividness of Visual Imagery Questionnaire. Perceptual and Motor Skills, 108(3), 798-802. DOI:

10.2466/PMS.108.3.798-802

Clark, T., & Williamon, A. (2012). Imagining the music: Methods for assessing musical imagery ability. Psychology of Music, 40(4), 471–493. https://doi.org/10.1177/0305735611401126

Connolly, C., & Williamon, A. (2004). Mental skills training. In A. Williamon (Ed.), Musical excellence (pp. 221–245). Oxford University Press. De Beni, R., & Moè, A. (2003). Presentation modality effects in studying passages. Are mental images always effective?. Applied Cognitive Psychology: The Official Journal of the Society for

Applied Research in Memory and Cognition, 17(3), 309-324. https://doi.org/10.1002/acp.867

Di Nuovo, S. F., & Angelica, A. (2016). Musical skills and perceived vividness of imagery: differences between musicians and untrained subjects. Annali Della Facoltà Di Scienze Della

Formazione Università Degli Studi Di Catania, 14, 3–13. DOI:10.4420/unict-asdf.v14i0.189

Gaver, W. W. (1993a). What in the world do we hear? An ecological approach to auditory event perception. Ecological Psychology, 5, 1–29. https://doi.org/10.1207/s15326969eco0501_1

Gaver, W. W. (1993b). How do we hear the world? Explanations in ecological acoustics.

Ecological Psychology, 5, 285–313. DOI: 10.1207/s15326969eco0504_2

Giordano B.L., McAdams S. (2006). Material identification of real impact sounds: Effects of size variation in steel, glass, wood and plexiglass plates. The Journal of the Acoustical Society of

America, 119, pp. 1171-118. DOI: 10.1121/1.2149839

Grassi, M. (2005). Do we hear size or sound: Balls dropped on plates. Perception &

Psychophysics, 67, 274–284. DOI: 10.3758/BF03206491

Grassi, M., Pastore, M., & Lemaitre, G. (2013). Looking at the world with your ears: How do we get the size of an object from its sound?. Acta Psychologica, 143(1), 96-104. DOI:

10.1016/j.actpsy.2013.02.005

Gregg, M. J., Clark, T. W., & Hall, C. R. (2008). Seeing the sound: An exploration of the use of mental imagery by classical musicians. Musicae Scientiae, 12(2), 231–247. https://doi.org/10.1177/102986490801200203

Guarnera, M., Pellerone, M., Commodari, E., Valenti, G. D., & Buccheri, S. L. (2019). Mental

Images and School Learning: A Longitudinal Study on Children. Frontiers in Psychology, 10, 2034. https://doi.org/10.3389/fpsyg.2019.02034 Hall, C. R., & Martin, K. A. (1997). Measuring movement imagery abilities: A revision of the

Movement Imagery Questionnaire. Journal of Mental Imagery, 21(1-2), 143–154.

Herholz, S. C., Lappe, C., Knief, A., & Pantev, C. (2008). Neural basis of music imagery and the effect of musical expertise. European Journal of Neuroscience, 28(11), 2352-2360.

Holmes, E. A., Mathews, A., Dalgleish, T., & Mackintosh, B. (2006). Positive interpretation training: Effects of mental imagery versus verbal training on positive mood. Behavior Therapy, 37(3),

237–247. https://doi.org/10.1016/j.beth.2006.02.002

Holmes, P. (2005). Imagination in practice: a study of the integrated roles of interpretation, imagery and technique in the learning and memorisation processes of two experienced solo performers.

British Journal of Music Education, 22(3), 217–235. DOI: https://doi.org/10.1017/S0265051705006613

Isaac, A., Marks, D. F., & Russell, D. G. (1986). An instrument for assessing imagery of movement: The Vividness of Movement Imagery Questionnaire (VMIQ). Journal of Mental Imagery,

10(4), 23–30.

Isaac, A. R., & Marks, D. F. (1994). Individual differences in mental imagery experience:

Developmental changes and specialization. British Journal of Psychology (London, England : 1953),

85 (Pt 4), 479–500. https://doi.org/10.1111/j.2044-8295.1994.tb02536.x

Janata, P., & Paroo, K. (2006). Acuity of auditory images in pitch and time. Perception & psychophysics, 68(5), 829–844. https://doi.org/10.3758/bf03193705

Keller, P. E. (2012). Mental imagery in music performance: Underlying mechanisms and potential benefits. Annals of the New York Academy of Sciences, 1252, 206–213. https://doi.org/10.1111/j.1749-6632.2011.06439.x Keller, P. E., & Appel, M. (2010). Individual Differences, Auditory Imagery, and the

Coordination of Body Movements and Sounds in Musical Ensembles. Music Perception, 28(1), 27–46. https://doi.org/10.1525/mp.2010.28.1.27

Keller, P. E., & Koch, I. (2008). Action planning in sequential skills: Relations to music performance. Quarterly Journal of Experimental Psychology (2006), 61(2), 275–291. https://doi.org/10.1080/17470210601160864

Kosslyn, S. M., Ganis, G., & Thompson, W. L. (2001). Neural foundations of imagery. Nature

Reviews Neuroscience, 2(9). DOI: 10.1038/35090055

Kunkler-Peck, A. J., & Turvey, M. T. (2000). Hearing shape. Journal of Experimental

Psychology: Human Perception and Performance, 26, 279–294. https://doi.org/10.1037/0096-

1523.26.1.279

Liu, L. L., & Park, D. C. (2004). Aging and medical adherence: the use of automatic processes to achieve effortful things. Psychology and aging, 19(2), 318-325. https://doi.org/10.1037/0882-

7974.19.2.318

Marks, D. F. (1973). Visual imagery differences in the recall of pictures. British Journal of

Psychology, 64(1), 17–24. https://doi.org/10.1111/j.2044-8295.1973.tb01322.x

McAvinue, L. P., & Robertson, I. H. (2007). Measuring Visual Imagery Ability: A Review.

Imagination, Cognition and Personality, 26(3), 191–211. https://doi.org/10.2190/3515-8169-24J8-7157

Mizuguchi, N., Nakata, H., Uchida, Y., & Kanosue, K. (2012). Motor imagery and sport performance. The Journal of Physical Fitness and Sports Medicine, 1(1), 103–111. https://doi.org/10.7600/jpfsm.1.103

Moulton, S. T., & Kosslyn, S. M. (2009). Imagining predictions: Mental imagery as mental emulation. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences,

364(1521), 1273–1280. https://doi.org/10.1098/rstb.2008.0314 Münte, T. F., Altenmüller, E., & Jäncke, L. (2002). The musician's brain as a model of neuroplasticity. Nature Reviews Neuroscience, 3(6), 473-478. https://doi.org/10.1038/nrn843

Nelis, S., Holmes, E. A., Griffith, J. W., & Raes, F. (2014). Mental imagery during daily life:

Psychometric evaluation of the Spontaneous Use of Imagery Scale (SUIS). Psychologica Belgica,

54(1), 19–32. https://doi.org/10.5334/pb.ag

Pecenka, N., & Keller, P. E. (2009). The relationship between auditory imagery and musical synchronisation abilities in musicians. In Proceedings of the 7th Triennial Conference of European

Society for the Cognitive Sciences of Music (ESCOM) (pp. 409–415).

Pfordresher, P. Q., & Halpern, A. R. (2013). Auditory imagery and the poor-pitch singer.

Psychonomic Bulletin & Review, 20(4), 747–753. https://doi.org/10.3758/s13423-013-0401-8

Renner, F., Murphy, F. C., Ji, J. L., Manly, T., & Holmes, E. A. (2019). Mental imagery as a

"motivational amplifier" to promote activities. Behaviour Research and Therapy, 114, 51–59. https://doi.org/10.1016/j.brat.2019.02.002

Richardson, J. T. E. (1995). Gender differences in the Vividness of Visual Imagery

Questionnaire: A meta-analysis. Journal of Mental Imagery, 19(3-4), 177–187.

Sheehan, P. W. 1967. A shortened form of Betts' Questionnaire Upon Mental Imagery. Journal of Clinical Psychology, 23: 386–389. https://doi.org/10.1002/1097-4679(196707)23:3<386::aid- jclp2270230328>3.0.co;2-s

Schifferstein, H. N. J. (2009). Comparing Mental Imagery across the Sensory Modalities.

Imagination, Cognition and Personality, 28(4), 371–388. https://doi.org/10.2190/IC.28.4.g

Schlaug, G. (2001). The brain of musicians: a model for functional and structural adaptation.

Annals of the New York Academy of Sciences, 930(1), 281-299.

Sergent, J. (1993). Music, the brain and Ravel. Trends in Neurosciences, 16(5), 168–172. https://doi.org/10.1016/0166-2236(93)90142-9 Shaw, G. A., & DeMers, S. T. (1986). The relationship of imagery to originality, flexibility and fluency in creative thinking. Journal of Mental Imagery.

Spence, C. (2011). Crossmodal correspondences: A tutorial review. Attention, Perception, &

Psychophysics, 73(4), 971-995. https://doi.org/10.3758/s13414-010-0073-7

Swaminathan, S., & Schellenberg, E. G. (2019). Music Training and Cognitive Abilities:

Associations, Causes, and Consequences. In M. H. Thaut, D. A. Hodges, S. Swaminathan, & E. G.

Schellenberg (Eds.), The Oxford Handbook of Music and the Brain (pp. 644–670). Oxford University

Press. https://doi.org/10.1093/oxfordhb/9780198804123.013.26

Tong, F. (2013). Imagery and visual working memory: one and the same?. Trends in cognitive sciences, 17(10), 489-490. https://doi.org/10.1016/j.tics.2013.08.005

Vasquez, N. A., & Buehler, R. (2007). Seeing future success: Does imagery perspective influence achievement motivation? Personality & Social Psychology Bulletin, 33(10), 1392–1405. https://doi.org/10.1177/0146167207304541

Vredeveldt, A., Baddeley, A. D., & Hitch, G. J. (2012). The effects of eye-closure and “ear- closure” on recall of visual and auditory aspects of a criminal event. Europe’s Journal of Psychology,

8(2), 284-299. https://doi.org/10.1037/e617732012-010

Wechsler, D. (2008). Wechsler Adult Intelligence Scale–Fourth Edition. San Antonio, TX:

Pearson

Wong, S. S. H., & Lim, S. W. H. (2017). Mental imagery boosts music compositional creativity. PloS One, 12(3), e0174009. https://doi.org/10.1371/journal.pone.0174009