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The neuro-oscillatory profiles of static and dynamic music-induced visual imagery

January 2024
International Journal of Psychophysiology 199(12):112309

DOI:10.1016/j.ijpsycho.2024.112309

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Authors:

Sarah Hashim

Goldsmiths University of London

Mats B. Küssner

Humboldt-Universität zu Berlin

Diana Omigie

Paris Brain Institute, French National Centre for Scientific Research

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The neuro-oscillatory profiles of static and dynamic music-

induced visual imagery

Sarah Hashim, Mats B. Küssner, André Weinreich, Diana Omigie

PII: S0167-8760(24)00013-8

DOI: https://doi.org/10.1016/j.ijpsycho.2024.112309

Reference: INTPSY 112309

To appear in: International Journal of Psychophysiology

Received date: 14 July 2023

Revised date: 22 December 2023

Accepted date: 12 January 2024

Please cite this article as: S. Hashim, M.B. Küssner, A. Weinreich, et al., The neuro-

oscillatory profiles of static and dynamic music-induced visual imagery, International

Journal of Psychophysiology (2023), https://doi.org/10.1016/j.ijpsycho.2024.112309

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The neuro-oscillatory profiles of static and dynamic music-induced visual imagery

Sarah Hashim*a, Mats B. Küssner*a,b, André Weinreichc, and Diana Omigiea

* Shared first authorship

a Department of Psychology, Goldsmiths, University of London, United Kingdom

b Department of Musicology and Media Studies, Humboldt-Universität zu Berlin, Germany

c Department of Psychology, Humboldt-Universität zu Berlin, Germany

Corresponding author:

Sarah Hashim

Department of Psychology

Goldsmiths, University of London

New Cross Road

New Cross

London, United Kingdom

SE14 6NW

Email: shash002@gold.ac.uk

Abstract

Visual imagery, i.e., seeing in the absence of the corresponding retinal input, has been

linked to visual and motor processing areas of the brain. Music listening provides an ideal

vehicle for exploring the neural correlates of visual imagery because it has been shown to

reliably induce a broad variety of content, ranging from abstract shapes to dynamic scenes.

Forty-three participants listened with closed eyes to twenty-four excerpts of music, while a 15-

channel EEG was recorded, and, after each excerpt, rated the extent to which they experienced

Present address

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static and dynamic visual imagery. Our results show both static and dynamic imagery to be

associated with posterior alpha suppression (especially in lower alpha) early in the onset of

music listening, while static imagery was associated with an additional alpha enhancement later

in the listening experience. With regard to the beta band, our results demonstrate beta

enhancement to static imagery, but first beta suppression before enhancement in response to

dynamic imagery. We observed a positive association, early in the listening experience, between

gamma power and dynamic imagery ratings that was not present for static imagery ratings.

Finally, we offer evidence that musical training may selectively drive effects found with respect

to static and dynamic imagery and alpha, beta, and gamma band oscillations. Taken together, our

results show the promise of using music listening as an effective stimulus for examining the

neural correlates of visual imagery and its contents. Our study also highlights the relevance of

future work seeking to study the temporal dynamics of music-induced visual imagery.

Keywords: music listening, EEG, visual imagery, motor processing, cross-modal processing

1. Introduction

Visual imagery refers to the representation of visual mental images in the absence of

corresponding visual input from the external world (Kosslyn, 1975; Kosslyn et al., 2006). It has

been shown to be highly prevalent during music listening (Dahl et al., 2022; Hashim et al., 2023;

Küssner & Eerola, 2019; Vuoskoski & Eerola, 2015) and to play a key role in both music’s

aesthetic appeal (Belfi, 2019) and emotional power (Balteș & Miu, 2014; Hashim et al., 2020;

Juslin, 2013; Juslin & Västfjäll, 2008; see also, Taruffi & Küssner, 2019). There is evidence that

the content of music-induced visual imagery can take a variety of forms: content analyses have

revealed that they can manifest as concrete or abstract concepts (Küssner & Eerola, 2019), and

other exploratory investigations suggest that significant amounts of imagery are formulated as

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narratives or story-like sequences (Dahl et al., 2022; Hashim et al., 2023; Margulis, 2017).

However, despite the relevance of such an endeavour, there have been very few attempts to use

music to examine the neural signatures of visual imagery and to explore how these signatures

may vary as a function of content.

1.1. Neural correlates of visual imagery

It is well documented that visual imagery involves similar neural substrates to those

implicated in visual perception (Cichy et al., 2012; Dijkstra et al., 2019; Lee et al., 2012; Mutha

et al., 2014; Schaefer et al., 2013; Xie et al., 2020; Zacks, 2008). Indeed, while early studies

tended to emphasize the role of alpha oscillations (Salenius et al., 1995; Williamson et al., 1997;

Xie et al., 2020) and posterior brain regions (Drever, 1955; Gale et al., 1972; Kaufman et al.,

1990; Williamson et al., 1997) in visual imagery, it is increasingly clear that, as for visual

perception, visual imagery of complex content may implicate a wide range of oscillatory

frequency bands and brain areas.

For instance, in addition to the EEG studies emphasising suppression of occipital alpha

(indicating increased neural firing in visual areas) as a signature of visual imagery formation,

enhanced gamma power in the occipital brain region has also been associated with the

experience of creative and vivid spontaneous visual imagery (Luft et al., 2019), the content-

specific features of visual imagery (Lehmann et al., 2001), and with working memory load

during motor imagery (De Lange et al., 2008; Sepúlveda et al., 2014). Similarly, theta and beta

oscillations have (along with alpha) been shown to be successful in discriminating the contents

of visual imagination (Xie et al., 2020), while lower (8-10 Hz) and upper (11-13 Hz) bands of

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alpha appear to show nuances in terms of how they relate to visual imagery formation (Gualberto

Cremades, 2002; Petsche et al., 1997).

With regard to implicated areas, a large body of studies has shown that (in addition to

posterior areas), parietal, central, and frontal areas of the brain are also involved in visual

imagery, particularly in the context of spatial and motor aspects (de Borst et al., 2012; Menicucci

et al., 2020; Mutha et al., 2014; Sousa et al., 2017; Thompson et al., 2009; Villena-González et

al., 2018; Zabielska-Mendyk et al., 2018; Zacks, 2008, 2008). For instance, mental rotation tasks

have been shown to be associated with increased activity in parietal and precentral areas that are

involved with motor simulation (Thompson et al., 2009; Zacks, 2008). Further, it has been

suggested that frontal regions may be essential for integrating the “what” and “where” contents

of visual thought, that are processed by occipitotemporal and parietal areas respectively (de Borst

et al., 2012). Finally, in a metanalysis seeking to clarify what, if any, shared components exist for

kinaesthetic and visual imagery in the context of sports (Filgueiras et al., 2018), it was shown

that athletes’ visual motor imagery (i.e., visualizing a movement execution) of sports actions was

similar to their kinaesthetic motor imagery (i.e., imagining the sensations of the movement

execution) in recruiting, amongst others, frontal motor (including premotor and supplementary

motor areas) and parietal areas involved in feeling own movements (somatosensory cortex,

inferior and superior parietal lobule). The authors argued that the surprising finding of

somatosensory cortex activity being present during visual motor imagery may be because

athletes cannot help but have their visual imagery influenced by their bodily sensations.

1.2. Comparing electrophysiological correlates of static and dynamic visual imagery

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Given evidence that motoric aspects of visual imagery may implicate additional brain

areas, an interesting question is how the electrophysiological correlates of static and dynamic

forms of visual imagery compare. In one electroencephalography study testing the possibility

that pure visual motion imagery can be used as a tool for brain computer interfacing (Sousa et

al., 2017), participants were asked to imagine a dot in three modes: static, moving in two

opposing directions, and moving in four opposing directions. Compared to observing a static dot

on a screen, observing a moving dot led to a greater decrease of alpha levels in posterior brain

areas (parietal, parieto-occipital, and occipital areas) supporting the role of visual cortices in

visual imagery. However, imagery of the moving dots (compared to the static dot) was also

characterised by greater alpha in frontal as well as decrease of 26 Hz (beta activity) in fronto-

central channels. The authors accounted for their findings of increased frontal alpha to visual

motion imagery (compared to static imagery), by frontal alpha’s purported role in tasks with high

internal processing demands (e.g., working memory and creative thinking), and by the

association of frontal alpha with reduced external processing and increased task complexity

(Cooper et al., 2003; Klimesch et al., 2007; Sauseng et al., 2005; Schomer & Silva, 2012).

Sousa and colleagues did not interpret their findings in the beta band. However, other

studies have reported and interpreted beta band involvement in visual imagery even though the

literature remains relatively unclear. Villena-González et al. (2018) found an enhancement of

beta power during a passive listening task, where participants were asked to attend to a series of

beep tones, following an instruction to visually imagine anything they wanted. They suggested

that the presence of beta band activity may indicate that cross-modal processing was taking place

to process visual imagery and an auditory task simultaneously. In contrast, beta power

suppression has been associated with imagination of complex movement (Zabielska-Mendyk et

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al., 2018). In a study by Menicucci et al. (2020), parallels were reported between the profiles of

beta and alpha band amplitude were shown, with greater beta suppression in fronto-central and

centro-parietal areas (along with alpha suppression in fronto-central areas) during a visual motor

imagery task. Here, it is also relevant to note studies that emphasise a rebound of beta power –

suppression followed by enhancement – following both real and imagined movements (Neuper

& Pfurtscheller, 1996; Salmelin et al., 1995). Indeed, one possibility is that both beta suppression

and enhancement effects may be expected in dynamic imagery depending on whether motor-

associated imagery is being, or conversely, has just been experienced.

In sum, research to date suggests that while visual imagery leads to the involvement of

visual cortices, particularly with respect to suppression of alpha activity, incorporating complex

features such as motion may engage additional motoric processes in other areas of the brain in

distinct ways (for a meta-analysis of the neural correlates of motor imagery, see also Hardwick et

al., 2018). In other words, research to date corroborates the idea that static and dynamic visual

imagery experiences may be neurally dissociable. Here we suggest that music, with its capacity

for inducing both static and dynamic forms of visual imagery, may be a useful stimulus for

throwing light on this hard-to-grasp phenomenon.

1.3. Music-induced static and dynamic visual imagery

As previously mentioned, visual imagery is a common experience during music listening

(Küssner & Eerola, 2019; Vuoskoski & Eerola, 2015) with the average latency of music-induced

visual imagery reported as being about 12 to 13 seconds after music onset (Day & Thompson,

2019), and with music-induced imagery evidenced to have a wide breadth of content (Küssner &

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Eerola, 2019; Taruffi & Küssner, 2019; Vuoskoski & Eerola, 2015).

Indeed, studies requiring participants to describe details of their music-induced visual

imagery experiences have begun to shed light on the qualitative details of imagery content,

including how visual imagery can incorporate motoric aspects. For instance, in one recent study

by Dahl et al. (2022), a content analysis of music-induced visual imagery descriptions, showed

that Movement and Events was a prominent category of listeners’ experience. Similarly, Küssner

and Eerola (2019) reported on how visual imagery can vary from static scenes to fast changing

storylines, while other work has cited dynamic forms of imagery in association with perceived

motion and metaphor in musical contexts (Eitan & Granot, 2006; Johnson & Larson, 2003).

Within this literature on the cross-modal experience of music, Zhou (2015), for instance, showed

that music can express a sense of movement as a function of acoustic parameters such as pitch

range and intensity. Further, other authors have emphasized that contours in melodic lines and

forces evoked by changes in tempo are what may drive mental imagery of movement (Eitan &

Granot, 2006).

However, despite the relevance of using music listening as a vehicle to study the neural

correlates of the content of visual imagery (for a general overview of neuroscientific measures of

music and mental imagery, see Belfi, 2022), only one such study exists: Fachner et al. (2019)

recorded EEG during a guided imagery and music (GIM) session – where GIM involves the

induction of visual imagery in response to a specialised GIM soundtrack – from both a therapist

and client simultaneously. Comparing moments of interest (characterised by imagery) to

moments of non-interest, they found greater posterior alpha suppression during moments of

visual imagery formation.

The findings from Fachner and colleagues are promising in that they are in line with

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previous work on visual imagery. However, as that study examined only one participant and as it

focused primarily on examining power in the alpha band, it is clear that further work is needed.

Indeed, while it seems likely that visual imagery to music activates largely similar brain areas as

visual imagery to non-music related stimuli, and while there is preliminary evidence that

occipital alpha should be a key signature of interest, it seems relevant to ask whether different

forms of music-induced visual imagery – here, static versus dynamic imagery – may be reflected

by different neural patterns, with respect to different brain regions and frequency bands.

1.4. The current research

The current study aimed to throw light on the extent to which static and dynamic music-

induced visual imagery may be associated with differing neural patterns with respect to three

regions of interests (frontal, centro-parietal, and parieto-occipital) and three frequency bands of

interest (alpha (8-13 Hz), beta (14-30 Hz), and gamma (30-45 Hz)). Based on evidence that

visual imagery tends to occur within the first 15 seconds of listening (Day & Thompson, 2019)

and in light of evidence that contrasting effects may occur within the same oscillatory frequency

bands (e.g., as a function of whether imagery is ongoing or recently completed; Neuper &

Pfurtscheller, 1996), we also explored how imagery-related neural activity differed in key phases

of the listening experience (the first and second halves of the piece) separately.

Participants were instructed to listen, with closed eyes and while EEG was recorded, to

twenty-four excerpts of music that had been shown in a previous study to induce either joyful,

neutral or fearful emotions in the listener (Koelsch et al., 2013). After each excerpt, they rated –

on a continuous scale – the amount of static and dynamic visual imagery experienced in response

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to the music. In line with previous research, in general (Cooper et al., 2003; Drever, 1955; Gale

et al., 1972; Salenius et al., 1995; Williamson et al., 1997; Xie et al., 2020) and in the context of

music listening (Fachner et al., 2019), we expected to see a negative relationship between the

amount of music-induced visual imagery reported and alpha band activity particularly in the

parieto-occipital area of the brain; thus reflecting the notion that there is enhanced neural firing

in visual areas during imagery.

Critically, we also predicted, in line with past findings regarding beta activity during

motor and visual-motor processing (Menicucci et al., 2020; Zabielska-Mendyk et al., 2018), that

dynamic imagery may involve both beta suppression (desynchronisation) and enhancement (due

to rebound effects), where suppression reflects motor processing and enhancement reflects a

rebound of beta power following such processing (Neuper & Pfurtscheller, 1996), as well as the

potential cross-modal processing of visual imagery and the auditory listening task (Villena-

González et al., 2018). Finally, we predicted we may find parieto-occipital gamma enhancements

(held to reflect increased neural firing), in response to visual imagery generally; this is in line

with previous results examining vivid spontaneous visual imagery in single case studies

(Lehmann et al., 2001; Luft et al., 2019), but potentially in response to dynamic imagery more

specifically; this in line with past relationships found between motor imagery tasks and gamma

enhancement (De Lange et al., 2008; Sepúlveda et al., 2014).

2. Methods

2.1. Participants

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Forty-three participants took part in the experiment. One participant was excluded due to

extreme values that could not be accounted for. This resulted in forty-two participants (27

female, 15 male; Age M = 28.85, SD = 4.85; note that age data was missing from one

participant) being included in the analyses. About 95% (40 of 42) of participants reported no

hearing issues, one participant mentioned having a hearing aid or implant (i.e., corrected hearing

that should not pose any issues for the data), and the final participant provided no response but

reported that the musical selection was pleasant and varied and that they listened to all musical

stimuli ‘pretty attentively’. Thus, these two participants were retained for further analyses.

Ethical approval (Ref. 2017-32) for this research was granted by the Ethics Committee of

the Institute of Psychology at Humboldt-Universität zu Berlin, Germany. All participants

provided written consent to be included in this study and were given monetary compensation or

course credit for their time.

2.2. Materials

Twenty-four musical excerpts that had been shown to induce joyful, fearful, and neutral

emotions were chosen (eight excerpts per emotion) for the listening task. They were obtained

from a set of stimuli used in a previous study by Koelsch et al. (2013).

The joyful excerpts consisted of CD-recorded pieces derived from a variety of musical

styles and genres (classical, South American, and Balkan music, Irish jigs, jazz, reggae). The

fearful excerpts were obtained from soundtracks of suspense movies and video games. Their

fearful qualities were further enhanced by creating two copies from each original excerpt: one

copy pitch-shifted a semitone upwards and the other shifted a tritone downwards. The original

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excerpt and both copies were merged into a single wav file. The neutral excerpts comprised of

sequences of isochronous tones selected at random from a pentatonic scale, which were set using

high quality natural instrument libraries from Ableton (https://www.ableton.com/en/) to ensure

ecological validity.

The 7-item Musical Training subscale of the Goldsmiths Music Sophistication Index

(Gold-MSI; Müllensiefen et al., 2014) was included to gauge prior training in music. Examples

of ratings items include ‘I have had formal training in music theory for __ years.’ and ‘I would

not consider myself a musician’. Each item was rated on a 7-point Likert scale.

2.3. Procedure

The experiment consisted of two main 28-trial counterbalanced blocks, consisting of 4

practice trials and 24 experimental trials. In one block, participants provided self-report ratings

related to visual imagery (analysed here and discussed exclusively henceforth), and in the other,

they provided ratings regarding emotion-related experiences, which will be analysed and

discussed elsewhere. In both blocks, participants were presented with the same set of twenty-four

excerpts (i.e., two repetitions of the musical stimuli). Note that while this means that half of our

participants (who were presented with the visual imagery block second in the counterbalancing

order) provided their imagery ratings in a second round of listening to the stimuli, this should not

affect the conclusions we seek to draw with this study.

Each main block took approximately 25 minutes to complete. During the experimental

trials, participants listened to the twenty-four 30-second musical excerpts aloud from two

speakers set to a volume they found comfortable and, after each excerpt, were instructed to rate

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their static and dynamic visual imagery experience (‘As soon as the piece of music has finished,

please open your eyes and rate how strongly it evoked still and moving images in your mind’s

eye’) using a visual analogue scale from 0 to 100. Participants always rated their static imagery

experience first, followed by their dynamic imagery experience. The static imagery rating ranged

from 0 = “Did not trigger any still images in me at all” to 100 = “Triggered a lot of still images

in me” (translated from German, see Table S1 for the original anchor points used). The dynamic

imagery rating ranged from 0 = “Did not trigger any moving images in me at all” to 100 =

“Triggered a lot of moving images in me” (translated from German).

Participants were instructed at the beginning of each block that they should keep their

eyes closed for the duration of each music excerpt to promote concentration and introspection

(‘So that you can always close your eyes in time, a visual countdown will appear before each

piece of music, announcing the beginning of the respective piece of music’). Once ratings were

provided (no time limit was given for providing ratings), a 4-s visual countdown (a short

horizontal line that decreased in length with each second to become a dot at music onset)

announced the start of the next listening trial. See Figure 1 for a summary of the trial procedure.

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Figure 1. Procedure summary of each trial.

2.4. EEG recording

EEG was recorded using sintered Ag/AgCl active electrodes (suitable for reducing noise

by amplifying the signal close to the source) and two 16-channel USB biosignal amplifiers

(g.USBamp, g.tec medical engineering GmbH, Austria). A 530 Hz antialiasing filter was applied

during recording, whereas the EEG recording sampling rate was set to 1200 Hz. A 15-channel

10-20 system cap was used, consisting of the following electrodes: AF3, AF4, F3, Fz, F4, C3,

Cz, C4, P3, Pz, P4, POz, PO7, Oz, and PO8. The reference channel was placed on the right

mastoid, and the ground electrode was placed on the atlas (i.e., the top of the spine/back of the

neck). For re-referencing offline to a non-lateralized reference, an additional electrode was

placed on the left mastoid. Electrode impedance was maintained beneath 10 kΩ.

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2.5. EEG data analyses

The EEG data was imported into MATLAB using EEGLAB (Delorme & Makeig, 2004)

functions and pre-processed using the FieldTrip toolbox (Oostenveld et al., 2011). The data was

downsampled to a sampling rate of 200 Hz and filtered using a low-pass filter at 50 Hz. The data

was subsequently segmented into epochs of 32 seconds, which comprised a 2-sec pre-trial

baseline phase and a 30-sec main trial phase. All data was re-referenced to the average activity of

the right and left mastoid channels.

The data (1,008 trials: 42 participants, 24 visual imagery main trials per participant

(excluding practice trials)) was visually assessed for visible artefacts. This allowed identification

of channels that were faulty or that displayed extreme levels of variance. Across all participants,

rejected channels (on average 1.7 and no more than four per participant) were interpolated with

the average of neighbouring channels. Next, an independent component analysis (ICA) using the

Runica algorithm (which implements the logistic infomax algorithm from EEGLAB; Delorme &

Makeig, 2004) was run. Spatial topographies were plotted, and individual components that were

visually identified as eye movements, eye blinks, or localised electrode activity were noted and

rejected from the data (on average 1.24 components per participant removed).

A fast Fourier transform frequency decomposition was carried out using a Hanning taper,

with power computed in 1-second non-overlapping segments. Frequencies were extracted

between 8 and 13 Hz for alpha band, between 14 and 30 Hz for beta band, and between 30 and

45 Hz for gamma band. For exploratory analyses, the alpha band was further subdivided into

lower (8-10 Hz) and upper alpha (11-13 Hz). The oscillatory power was baseline-corrected by

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subtracting the mean power of the two-second pre-stimulus interval, separately for each trial

within each channel. Specifically, for each channel per trial, the average power of the prior two

seconds of each trial (baseline period) was subtracted from power in each 1-second segment of

the 30-sec main trial.

2.6. Statistical analyses

Our primary aim was to examine the relationship between visual imagery ratings (static

and dynamic) and different forms of oscillatory activity (alpha, beta, and gamma power), and to

determine how these relationships grossly differed as a function of brain areas and time. To this

end, EEG channels were grouped into three regions of interest (ROI): Frontal (AF3, AF3, F3, Fz,

and F4), Centro-Parietal (C3, Cz, C4, P3, Pz, and P4), and Parieto-Occipital (POz, PO7, Oz, and

PO8) and the 30-second time windows of the main trials were divided into two phases: the First

Half, comprising the first 15 seconds of a trial, and the Second Half, comprising the final 15

seconds of a trial. Here it is important to note that due to only having a single static and dynamic

imagery rating for the whole trial (rather than continuous imagery ratings over time), our

consideration of time effects are necessarily on a macro-level and are largely motivated by the

finding that music-induced visual imagery occurs on average within about 12 to 13 seconds of

music onset (Day & Thompson, 2019). Given that this estimation of imagery onset lies within

the first half of our musical trials and given that dynamic imagery may nevertheless be expected

to continue to change over time relative to static imagery, it seemed relevant to ask how patterns

of activity in the first and second halves of our trials differed for the two types of imagery.

All statistical analyses were carried out using R (Version 4.2.3; R Core Team, 2018) and

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linear mixed models (LMMs) were estimated using the lme4 (Bates et al., 2015) and lmerTest

(Kuznetsova et al., 2017) R packages, the latter of which provided the t and p-values for our

models. Given our main aim (to observe how static and dynamic ratings influenced oscillatory

power differently as function of brain areas and time), we estimated, for each frequency band, a

restricted maximum likelihood linear mixed model analysis with oscillatory power as dependent

variable, and static and dynamic imagery, and the interactions between each of these rating types

with time period (First Half and Second Half) and ROI (Frontal, Centro-Parietal, and Parieto-

Occipital), as fixed effects. Random effects included were an intercept for participant and a

nested random intercept between musical excerpt and excerpt type (joyful, neutral, fearful).

Where this extra dimension in the random effects led to failed convergence in models, random

effects were simplified in those cases by excluding the nested intercept and retaining only

participant and excerpt type as random effects.

A Pearson’s correlation coefficient showed static and dynamic imagery to correlate

negatively but very weakly with one another, r(1006) = –0.094, p = 0.003. We included both

ratings within the same models to allow us to observe the potentially different influences of each

on the dependent variable.

See Table S2 for a full summary of all omnibus models across all our frequency bands of

interest. Follow-up models were run to explore any significant interactions emerging from the

above models for each frequency band.

In an additional set of exploratory analyses, we analysed the influence of musical training

on static and dynamic imagery ratings while EEG was recorded. To this end, musical training

was dichotomised into high and low scores using a median split. Once again, we estimated for

each frequency band a linear mixed model with oscillatory power as dependent variable, and

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static and dynamic imagery, with interactions defined between each of these rating types,

musical training (High and Low), and time period (which was included to explore potential

temporal changes in neural activity as a function of training). Random effects included in each

model were an intercept for participant and a nested random intercept between musical excerpt

and excerpt type (joyful, neutral, fearful).

3. Results

3.1. Analysis of alpha power

The overall model predicting alpha band showed a main effect of static imagery, F(1,

435460) = 4.89, p = 0.027, and of dynamic imagery, F(1, 728) = 21.79, p < 0.001, whereby high

ratings were associated with suppression in alpha (see Table 1 for means and standard deviations

of the two rating types), a main effect of time period, F(1, 453518) = 151.35, p < 0.001, whereby

there was less alpha power in the second compared to the first half of the trial, and also a main

effect of ROI, F(2, 453518) = 295.10, p < 0.001, whereby there was less alpha power in the

frontal area, followed by the parieto-occipital area, then the centro-parietal area (see Table 2 and

Figure 2 for means and standard deviations of oscillatory power for the three frequency bands

across the two time periods and three regions of interest. Also see Figure S1 for a frequency

power plot displaying power across all trials and all frequency bands).

There were also interactions found between static imagery and time period, F(1, 453518)

= 32.62, p < 0.001, and between dynamic imagery and time period, F(1, 453518) = 38.28, p <

0.001. To explore the significant interactions between static imagery and time period, a model

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was run to examine the relationship between static imagery ratings and alpha power for each

time period separately. As also illustrated in Figures 3A and 3C, these revealed a significant

negative association between alpha and static imagery (i.e., higher ratings in static imagery were

associated with reduced alpha power) in the first half of the trial, ß = –0.00189, SE = 0.00134,

t(1.61) = –3.65, p < 0.001, but a significant positive relationship in the second half, ß = 0.00328,

SE = 0.00128, t(2.10) = 2.57, p = 0.010. Similarly, to explore the significant interaction between

dynamic imagery and time period, a model was once more run for each time period separately.

These revealed that dynamic imagery ratings’ negative relationship with alpha power was

significant in the first half, ß = –0.00550, SE = 0.00129, t(1290.48) = –4.27, p < 0.001, but non-

significant in the second half, ß = –0.00040, SE = 0.00123, t(2.56) = –0.32, p = 0.746.

There was a significant interaction between ROI and both static, F(2, 453518) = 33.10, p

< 0.001, and dynamic imagery, F(2, 453518) = 121.88, p < 0.001. To explore the significant

interaction between static imagery and ROI, a model examining the relationship between power

and ratings was run for each ROI separately. While no significant effects were found with

respect to the frontal, ß = –0.00098, SE = 0.00080, t(1.41) = –1.23, p = 0.218, centro-parietal, ß

= –0.00008, SE = 0.00161, t(1.51) = –0.05, p = 0.961, or parieto-occipital areas, ß = 0.00092, SE

= 0.00209, t(9.04) = –0.440, p = 0.660, the interaction likely reflected a tendency for the

relationship to be more systematically negative in frontal areas than in the other two ROIs (see

also Figure 3B). Finally, following up the significant interaction between dynamic imagery and

ROI, it was revealed that, as for static imagery, there was a tendency for the relationship to be

more negative in frontal and posterior areas; specifically, while dynamic imagery ratings were

significantly negatively linked to frontal alpha, ß = –0.00267, SE = 0.00076, t(1.38) = –3.49, p <

0.001, and parieto-occipital alpha, ß = –0.00441, SE = 0.00199, t(956.51) = –2.22, p = 0.027,

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there was no significant effect for the centro-parietal area, ß = –0.00106, SE = 0.00121, t(1.81) =

–0.87, p = 0.383.

Finally, there was a significant interaction between ROI and time period, F(2, 431941) =

8.01, p < 0.001. However, this was not explored further due to the current investigation’s focus

towards observing visual imagery effects. No other main effects or interactions were significant.

Table 1. Summarising the descriptive statistics of static and dynamic visual imagery across the

three types of musical excerpts presented to participants (joyful, neutral, and fearful).

Mean Rating (Standard Deviation)

Joyful

Neutral

Fearful

Overall

Static Imagery

28.26 (22.98)

34.75 (29.25)

30.10 (22.65)

31.04 (25.29)

Dynamic Imagery

68.66 (24.91)

26.43 (24.92)

51.90 (27.85)

49.00 (31.21)

Table 2. Summarising the descriptive statistics of the frequency bands of interest across the two

time periods (First half and Second Half) and the three regions of interest (ROI; Frontal,

Centro-Parietal, and Parieto-Occipital).

Mean Power (Standard Deviation)

Time Period

ROI

First Half

Second Half

Frontal

Centro-Parietal

Parieto-Occipital

Alpha

–4.08 (32.84)

–4.52 (32.55)

–5.14 (30.85)

–3.59 (33.17)

–4.30 (34.15)

Beta

–0.99 (7.18)

–1.05 (6.64)

–1.09 (5.17)

–0.97 (7.25)

–1.01 (8.20)

Gamma

–0.62 (1.81)

–0.64 (2.16)

–0.74 (2.51)

–0.59 (1.92)

–0.55 (1.23)

(A) (B)

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Figure 2. Illustrating oscillatory power across the three frequency bands (alpha, beta, and

gamma), including error bars depicting ± standard error. (A) Average power across the two time

periods (First Half and Second Half). (B) Average power across the three regions of interest

(ROI; Frontal, Centro-Parietal, and Parieto-Occipital).

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Figure 3. Alpha power associated with static and dynamic imagery. Asterisks denote model

significance levels: n.s. = non-significant, * p < 0.05, ** p < 0.01, *** p < 0.001. (A)

Scatterplots showing alpha power as a function of static and dynamic imagery ratings, once the

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other had been regressed out, for two different time windows (First Half and Second Half). For

illustrative purposes, the x-axis is scaled to between 0 and 1. Individual electrodes are

represented by different colours. Thick red lines illustrate aggregated mean electrode power

values, with shaded standard error. (B) Scatterplots showing alpha power as a function of

imagery ratings, once the other rating had been regressed out, for three regions of interest

(Frontal: AF3, AF4, F3, Fz, and F4; Centro-Parietal: C3, Cz, C4, P3, Pz, and P4; and Parieto-

Occipital: POz, PO7, Oz, and PO8). For illustrative purposes, the x-axis is scaled to between 0

and 1. Individual electrodes are represented by different colours. Thick red lines illustrate

aggregated mean electrode power values, with shaded standard error. (C) For illustrative

purposes, we present topoplots showing patterns of alpha power (baselined to 2 seconds prior to

the music) from trials associated with the upper 10% (denoted High) and lower 10% (denoted

Low) of static and dynamic imagery ratings for the first half only.

3.2. Analysis of beta power

Next, the overall model for beta band showed a significant main effect of static imagery,

F(1, 453488) = 176.64, p < 0.001, and of dynamic imagery, F(1, 281826) = 9.50, p = 0.002,

whereby high static imagery ratings were associated with higher beta power whereas high

dynamic imagery ratings were associated with lower beta power. There was also a main effect of

Time Period, F(1, 453518) = 9.92, p = 0.002, whereby there was higher beta power in the first

half than in the second, as well as a main effect of ROI, F(2, 453518) = 22.69, p < 0.001,

whereby there was less beta in the frontal area, followed by parieto-occipital, then the centro-

parietal area.

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There was also a significant interaction between static imagery and time period, F(1,

453518) = 90.52, p < 0.001, and between dynamic imagery and time period, F(1, 453518) =

99.62, p < 0.001. To explore the significant interactions between time period and both static and

dynamic imagery, we ran four follow-up models. These showed a significant positive

relationship between static ratings and beta power in the first time period, ß = 0.01193, SE =

0.00065, t(2.26) = 18.39, p < 0.001, but no significant relationship in the second, ß = 0.00085,

SE = 0.00059, t(2.27) = 1.44, p = 0.150. In contrast, dynamic imagery was seen to have a

significant negative relationship with beta power in the first time period, ß = –0.00674, SE =

0.00063, t(7.55) = –10.70, p < 0.001, and a significant positive relationship in the second time

period, ß = 0.00133, SE = 0.00058, t(1.54) = 2.31, p = 0.021. See Figures 4A and 4C which

visualise beta power against dynamic imagery ratings across the two time periods.

Further, there was a significant interaction between static imagery and ROI, F(2, 453518)

= 6.67, p = 0.001. Exploration of the significant interaction between static imagery and ROI

revealed significant positive relationships between static imagery and beta power in all the ROIs

that nevertheless decreased in size from the front to the back of the head (frontal, ß = 0.00642,

SE = 0.00048, t(1.51) = 13.31, p < 0.001; centro-parietal area, ß = 0.00623, SE = 0.00074,

t(1.80) = 8.42, p < 0.001; parieto-occipital, ß = 0.00657, SE = 0.00104, t(1.21) = 6.30, p <

0.001).

There was further a significant interaction between dynamic imagery and ROI, F(2,

453518) = 7.17, p < 0.001, as well as a significant three-way interaction between dynamic

imagery, time period, and ROI, F(2, 453518) = 9.19, p < 0.001. We summarise the latter

interaction as it provides an extra dimension of detail. Exploration of this three-way interaction

revealed a significant negative relationship between dynamic imagery and beta power in the

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frontal area in the first half, ß = –0.00612, SE = 0.00052, t(3.91) = –11.74, p < 0.001, but a non-

significant negative relationship in the second half, ß = 0.00043, SE = 0.00078, t(4.40) = –0.55, p

= 0.581. Further, there was a significant negative relationship between dynamic imagery and

beta power in the centro-parietal area in the first half, ß = –0.00494, SE = 0.00105, t(4.52) = –

4.72, p < 0.001, but a non-significant positive relationship in the second half, ß = 0.00152, SE =

0.00098, t(3.45) = 1.55, p = 0.120. Finally, there was also a significant negative relationship

between dynamic imagery and beta power in the parieto-occipital area in the first half, ß = –

0.01031, SE = 0.00161, t(1.92) = –6.41, p < 0.001, as well as significant positive relationship in

the second half, ß = 0.00305, SE = 0.00121, t(3.63) = 2.52, p = 0.012. No other main effects or

interactions were significant.

Finally, there was a significant interaction between ROI and time period, F(2, 453518) =

3.59, p = 0.027. However, again, this was not explored due to the current investigation’s focus

towards observing visual imagery effects. No other main effects or interactions were significant.

See also Figure 4B which visualises beta power against dynamic imagery ratings across regions

of interest.

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Figure 4. Beta power associated with static and dynamic imagery. Asterisks denote model

significance levels: * p < 0.05, *** p < 0.001. (A) Scatterplots showing beta power as a function

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of static and dynamic imagery ratings, once the other had been regressed out, for two different

time windows (First Half and Second Half). For illustrative purposes, the x-axis is scaled to

between 0 and 1. Individual electrodes are represented by different colours. Thick red lines

illustrate aggregated mean electrode power values, with shaded standard error. (B) Scatterplots

showing beta power as a function of imagery ratings, once the other rating had been regressed

out, for three regions of interest (Frontal: AF3, AF4, F3, Fz, and F4; Centro-Parietal: C3, Cz,

C4, P3, Pz, and P4; and Parieto-Occipital: POz, PO7, Oz, and PO8). For illustrative purposes,

the x-axis is scaled to between 0 and 1. Individual electrodes are represented by different

colours. Thick red lines illustrate aggregated mean electrode power values, with shaded

standard error. (C) For illustrative purposes, we present topoplots showing patterns of beta

power (baselined to 2 seconds prior to the music) from trials associated with the upper 15%

(denoted High) and lower 15% (denoted Low) of dynamic imagery ratings across the two

different time windows.

3.3. Analysis of gamma power

The overall model predicting gamma band revealed a significant main effect of dynamic

imagery, F(1, 14847) = 5.73, p = 0.017, whereby an increase in dynamic imagery ratings was

related to an enhancement of gamma power, as well as a significant main effect of ROI F(2,

453518) = 475.81, p < 0.001. There was also a significant interaction between dynamic imagery

ratings and time period, F(1, 453518) = 14.89, p < 0.001. To examine the interaction between

dynamic imagery ratings and time period, a model was run for each time period separately.

Dynamic imagery ratings showed a significant positive relationship with gamma power in the

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first time period, ß = 0.00038, SE = 0.00011, t(2.04) = 3.34, p < 0.001, but no effect in the

second half, ß = –0.00007, SE = 0.00016, t(5.55) = –0.42, p = 0.673. See Figures 5A and 5C

which visualise gamma power against static and dynamic imagery ratings across the two time

periods.

There was further a significant interaction between static imagery ratings and ROI, F(2,

453518) = 222.20, p < 0.001, and between dynamic imagery and ROI, F(2, 453518) = 24.45, p <

0.001. Following up the interaction between static imagery and ROI revealed non-significant

positive relationships between static imagery ratings and gamma in all three ROIs but indicated

that these positive relationships were stronger in the centre than in the front and back of the head

(frontal area, ß = 0.00011, SE = 0.00019, t(1.41) = 0.59, p = 0.554; centro-parietal area, ß =

0.00026, SE = 0.00015, t(1.76) = 1.72, p = 0.085; and parieto-occipital area, ß = 0.00002, SE =

0.00012, t(1.17) = 0.15, p = 0.883). Finally, following up the interaction between dynamic

imagery ratings and ROI revealed a significant relationship in the centro-parietal area only,

showing that effects generally became more enhanced in the centre of the head than in the front

and back (frontal area, ß = 0.00004, SE = 0.00018, t(1.67) = 0.22, p = 0.826; centro-parietal, ß =

0.00036, SE = 0.00015, t(5.35) = 2.44, p = 0.015; parieto-occipital, ß = 0.000002, SE = 0.00011,

t(3.10) = 0.02, p = 0.982). No other main effects or interactions were significant.

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Figure 5. Gamma power associated with static and dynamic imagery. Asterisks denote model

significance levels: * p < 0.05, *** p < 0.001. (A) Scatterplots showing gamma power as a

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function of static and dynamic imagery ratings, once the other had been regressed out, for two

different time windows (First Half and Second Half). For illustrative purposes, the x-axis is

scaled to between 0 and 1. Individual electrodes are represented by different colours. Thick red

lines illustrate aggregated mean electrode power values, with shaded standard error. (B)

Scatterplots showing gamma power as a function of imagery ratings, once the other rating had

been regressed out, for three regions of interest (Frontal: AF3, AF4, F3, Fz, and F4; Centro-

Parietal: C3, Cz, C4, P3, Pz, and P4; and Parieto-Occipital: POz, PO7, Oz, and PO8). For

illustrative purposes, the x-axis is scaled to between 0 and 1. Individual electrodes are

represented by different colours. Thick red lines illustrate aggregated mean electrode power

values, with shaded standard error. (C) For illustrative purposes, we present topoplots showing

patterns of gamma power (baselined to 2 seconds prior to the music) from trials associated with

the upper 25% (denoted High) and lower 25% (denoted Low) of dynamic imagery ratings for the

first half only.

3.4. Exploratory analyses: lower and upper alpha

Current evidence is mixed regarding a potentially nuanced role of upper and lower alpha

in visual imagery: for example, while Petsche and colleagues (1997) linked visual imagination to

greater power suppression in upper (than lower) alpha, Gualberto Cremades (2002) conversely

suggested that there is greater attenuation of lower (than upper) alpha particularly in those

imagery tasks for which high attention and arousal is required.

In a set of exploratory analyses, we therefore examined how upper and lower alpha differ

in their relationship to visual imagery by estimating the same modelling analysis (as carried out

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for alpha, beta, and gamma bands) for lower and upper alpha bands separately (see

Supplementary Materials for a detailed outline of the results, and see Table S3 and Figure S2 for

means and standard deviations of lower and upper alpha power across the two time periods and

three regions of interest).

Analysis of lower alpha showed a pattern of results that was almost identical to that seen

for the full alpha band, whereby significant lower alpha suppression was found most clearly in

the first half of a trial than in the second half in response to static imagery and trending for

dynamic imagery, in addition to trends towards lower alpha suppression in almost all ROIs in

response to high static and dynamic imagery, apart from enhanced frontal lower alpha power in

response to static imagery. In contrast, analysis of the upper alpha band, while largely similar to

overall alpha, failed to show the strength of static and dynamic imagery related alpha

suppression that was seen in lower alpha.

Further, while the frontal alpha suppression associated with dynamic ratings was,

similarly to the overall alpha band, significant in lower (but not upper) alpha, a frontal alpha

suppression effect for static ratings – that was not seen in the overall alpha model – was found

when considering upper alpha alone.

In sum, the findings suggest that parieto-occipital lower alpha may be an effective

reflection of both visual imagery types but that differences between lower and upper alpha may

reflect separate functionalities with regard to static and dynamic imagery. See Figures S2 and S3

for visualisations of these results.

3.5. Exploratory analyses: effects of musicianship

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There is little neuroscientific research concerning the relationship between musical

training and visual imagery, though some behavioural investigations propose training enhances

the vividness of music-induced visual imagery (Küssner & Eerola, 2019), enables faster visual

imagery formation through improved sensorimotor integration (Brochard et al., 2004) and leads

to mental rehearsal of motor performance through kinaesthetic imagery (Lotze, 2013). Although

links between visual imagery and training are scarce, EEG research supports the notion that

musical training improves coactivation of auditory and sensorimotor processes in anterior brain

areas (Bangert & Altenmüller, 2003; Klein et al., 2015; Trainor et al., 2009).

In a final set of exploratory analyses, we examined the influence that musical training

scores may have on the patterns of neural activity that accompany visual imagery and found

some nuanced effects that suggest the relevance of further studies (see Supplementary Materials

for a detailed outline of the results).

In brief, we observed, with regard to alpha power and static imagery that those with more

musical training drove the suppression effect (in the first time window) to a greater extent than

those with less training (although the rebound in the second was greater for those with less

training). Interestingly, with regard to alpha and dynamic imagery, less trained participants

showed the alpha suppression effect more in the first time window while more trained showed it

more in the second time window.

Further, while beta in response to static imagery did not show much difference between

the two groups, the beta suppression effect in response to dynamic imagery (in the first time

window) was greater in less trained than trained individuals, and with regard to gamma, it was

seen that the previously reported positive relationship between gamma and dynamic imagery was

driven particularly by those with low training.

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4. Discussion

Behavioural studies have demonstrated music’s propensity to elicit visual imagery

(Balteș & Miu, 2014; Hashim et al., 2020; Juslin, 2013; Juslin & Västfjäll, 2008; Küssner &

Eerola, 2019; Taruffi & Küssner, 2019) that varies in terms of dynamicity (Eitan & Granot,

2006; Johnson & Larson, 2003). The aim of the current study was to examine the neural

signatures of music-induced visual imagery and to determine the extent to which static and

dynamic imagery can be seen reflected in differing patterns of neural oscillations.

Based on literature implicating modulation of different frequency bands in the visual and

motor brain regions in response to static and dynamic imagery (Fachner et al., 2019; Luft et al.,

2019; Schaefer et al., 2011, 2013), we explored three oscillatory bands (alpha, beta, and gamma)

in three main brain regions of interest (Frontal, Centro-Parietal, and Parieto-Occipital). Further,

based on preliminary evidence regarding the timeframe within which music-induced visual

images can occur (Day & Thompson, 2019), we also considered how patterns of activity differ in

the first (by which point visual imagery has likely occurred) and second half of the thirty-second

listening experience.

First, in line with the notion that visual imagery recruits similar brain areas and

mechanisms to visual perception, we predicted that both static and dynamic visual imagery

ratings would show a negative relationship with alpha as well as (particularly for dynamic

imagery) a positive relationship with gamma in the parieto-occipital region in particular (De

Lange et al., 2008; Fachner et al., 2019; Lehmann et al., 2001; Luft et al., 2019; Sepúlveda et al.,

2014). We further predicted that dynamic imagery, due to its recruitment of motor areas of the

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brain, would be associated with potentially complex patterns of beta desynchronisation and

synchronisations (Menicucci et al., 2020; Neuper & Pfurtscheller, 1996; Salmelin et al., 1995;

Villena-González et al., 2018; Zabielska-Mendyk et al., 2018).

4.1. Posterior alpha suppression during visual imagery

In line with a large body of past literature that has found strong links between visual

imagery generation and occipital alpha activity (Cooper et al., 2003; Fachner et al., 2019;

Schaefer et al., 2011, 2013), we found evidence for parieto-occipital alpha suppression as a

function of visual imagery, although this suppression effect with regard to time period more

specifically i) was only present in the first half of the piece for both imagery types (i.e., within

the 15 second period by which visual imagery is held to occur; Day & Thompson, 2019) and ii)

for static imagery, even turned to an enhancement effect in the second half of the listening

experience.

Here, we speculate that the ratings-related alpha suppression effect was limited to the

earlier time window due to visual imagery having already emerged (or not) within this 15 second

period (whether due to the affordances of the stimuli or even deliberate action on the part of the

listener). Indeed, it is possible that this earlier period (as opposed to the latter one) is what

listeners base their imagery ratings on. In turn, we speculate that the consequent positive

relationship between alpha power and static (but not dynamic) imagery ratings seen in the second

half may reflect a rebound (increase) in alpha levels that is commensurate with the initial drop

seen in the first half.

The visual imagery ratings and alpha relationships in the second half of the listening

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experience are interesting as they likely reflect the intrinsic difference between music-induced

static and dynamic visual imagery. Indeed, while we did not make this explicit prediction, it is

possible that (in contrast to the tendency for alpha levels to show a rebound effect in the second

half of the listening experience with respect to static imagery ratings), dynamic imagery ratings

do not show an alpha rebound effect because dynamic imagery is constantly changing over the

course of the listening experience and thus keeps alpha levels low (potentially at floor levels).

Here it is however important to note that the low temporal resolution of the rating reports

prevents us from drawing detailed conclusions on the differences between static and dynamic

imagery, and thus remains a key limitation of the current research. The current design, whereby

ratings are provided only after each listening trial, fails to provide information on the dynamics

of the prevalence and magnitude of visual imagery over the course of a music excerpt. Thus, our

conclusions remain largely correlative, and further studies would greatly benefit from designs

that enable the experience of visual imagery to be collected over time. Such designs would allow

the neural correlates of static and dynamic imagery to be more carefully disentangled and would

also throw light on how different types of imagery may be induced by different musico-acoustic

features.

4.2. Dynamic imagery associated with different patterns of activity from static imagery

We predicted that dynamic imagery would be similar to static imagery in terms of

posterior alpha suppression but potentially more notably associated with gamma enhancement

than static imagery. These predictions proved to be mostly accurate. We observed that dynamic

imagery was associated with alpha suppression although unlike static imagery, dynamic imagery

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was not associated with a tendency to return to original alpha levels over the course of the

imagery experience.

We also saw that only dynamic imagery showed parieto-occipital gamma enhancement

(De Lange et al., 2008). The difference found between dynamic and static imagery with respect

to gamma oscillations is interesting (Luft et al., 2019), but in line with past assertions that

gamma oscillations are most evident when imagery is complex (De Lange et al., 2008;

Sepúlveda et al., 2014), as can be assumed for more dynamic forms of imagery content.

Furthermore, with regard to additional differences between dynamic and static imagery

that we predicted, we anticipated that dynamic imagery may be associated with both

synchronisation and desynchronisation of beta frequency band in frontal, centro-parietal, and

parieto-occipital areas. Indeed, here, we showed that dynamic imagery was associated with a

suppression (desynchronisation) in all regions of interest followed by an enhancement

(synchronisation) of beta power in the parieto-occipital area specifically; this is in contrast to

static imagery which was associated with beta enhancement in the first time period only and

across all regions of interest. In line with previous results, we suggest that the beta suppression

seen with dynamic imagery in the first 15 seconds reflects the recruitment of motor regions

(Menicucci et al., 2020; Zabielska-Mendyk et al., 2018) and that the subsequent enhancement

reflects the rebound in beta that has been reported to occur following such motor activity

(Neuper & Pfurtscheller, 1996; Salmelin et al., 1995).

With regard to the unpredicted beta enhancement seen as a function of static imagery

ratings in the first time period, we propose – as has previously been suggested – that this may

reflect the cross-modal processing of visual and auditory information simultaneously (Villena-

González et al., 2018). As dynamic imagery is just as likely to involve such cross-modal

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processing, it is important to put forward the possibility that – along with any beta power

rebound effect (Neuper & Pfurtscheller, 1996; Salmelin et al., 1995) – such cross-modal

processing also drives the positive relationship between beta and dynamic imagery that is seen in

the second time period.

Finally, it is important to note that unlike static imagery, dynamic imagery was associated

with a robust frontal alpha suppression. Previous work has shown dynamic imagery to be

associated with greater frontal alpha power when compared to static imagery (Sousa et al.,

2017); and others have suggested that this may be due to the higher internal processing demands

and task complexity (Cooper et al., 2003; Klimesch et al., 2007; Sauseng et al., 2005; Schomer &

Silva, 2012) of dynamic imagery compared to static imagery. Our findings are opposed to those

from Sousa and colleagues (2017), it is however important to note that direct comparison with

their work is difficult since their study compared two different conditions (static and dynamic),

whereas we measured static and dynamic imagery using a pair of subjective ratings for each.

Further work is needed to corroborate our finding of frontal alpha suppression in response to

dynamic content of visual imagery, and to illuminate what exactly the finding may reflect.

An additional exploration involved examining if there were any differences between

lower and upper alpha in their associations with static and dynamic imagery. The results

demonstrated minimal differences between overall alpha power and lower alpha power,

especially with regard to frontal alpha suppression in response to dynamic imagery, suggesting

that the lower band may be an effective reflection of this type of visual imagery content. The

findings nevertheless also show that certain effects may be present in upper but not lower alpha

(e.g., frontal alpha suppression in response to static ratings) thus suggesting that these sub-bands

may indeed play distinct roles. Taken together, our findings support past literature proposing

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distinct functions of lower and upper alpha power (Petsche et al., 1997), which further music-

related research should aim to distinguish between using a more targeted approach.

Our final exploration involved examining the influence of musical training on the effects

between oscillatory power and visual imagery ratings. Though findings in previous research have

been mixed (Aleman et al., 2000; Küssner & Eerola, 2019), patterns of results suggest that

musical training influenced patterns of neural activity in nuanced ways. The differences in

patterns of oscillatory activity – during visual imagery – that one should expect when

considering how the musical trained brain differs from the typical brain is not yet well

documented. We therefore suggest the need for further studies to examine such influence of

musical training further.

In summary, our results present unexpected differences between static and dynamic

imagery with respect to alpha modulation but are in line with our predictions that i) both imagery

types would drive posterior alpha suppression, ii) that the two may show different effects over

time (due to dynamic but not static imagery being associated with continuous changing imagery

content) and that, iii) dynamic imagery to a greater extent than static imagery, would show

complex modulation of areas involved in motion processing (Sirigu & Duhamel, 2001;

Thompson et al., 2009; Zacks, 2008).

4.3. Implications of the research

We provide preliminary evidence to show that the neural correlates of music-induced

visual imagery may include both posterior alpha suppression and beta and gamma enhancement.

This research adds to similar previous research by Fachner et al. (2019) who also showed

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evidence of posterior alpha suppression but did not investigate gamma power, and did so using a

single case study design. As such, our study highlights the fact that music is a powerful tool to

study neural correlates of the content of visual imagery.

Further, the consistency of our findings with the previous body of work on visual motor

imagery can be taken as support for the idea that motion plays a significant role in music-induced

visual imagery (Dahl et al., 2022) and, as such, in the music listening experience more generally

(Eitan & Granot, 2006; Johnson & Larson, 2003; Schaefer, 2014).

However, perhaps the most important implication of our research relates to future

investigations of the neural correlates of dynamic visual imagery. Indeed, this literature, much of

which has focused on athletes (Wilson et al., 2016) or used small samples to explore brain-

computer interfaces (Sousa et al., 2017), offers mixed results as to the directionality and cortical

localisation of effects related to the experience of dynamic or motion-related imagery. Our

findings, which are in line with the results of a meta-analysis suggesting a great overlap between

visual motor imagery and kinaesthetic motor imagery (Filgueiras et al., 2018), suggest that

music-induced visual imagery could be particularly useful for future studies seeking to explore

dynamic content of visual imagery.

That said, it is important to consider the possibility that studies using musical stimuli

show neural effects that not only reflect visual imagery content but also the music that induces

said imagery content. Here, we sought to account for the effects of music heard (on the differing

brain signatures we reported for static and dynamic imagery), by including pieces as random

effects within our mixed modelling analysis approach. However, preliminary findings of the

effects that musical features can have on visual imagery (Herff et al., 2022; Juslin, 2019) suggest

that future studies may want to control for such variables even more carefully.

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It is relevant to once more consider the extent to which music listening tasks are an ideal

stimulus for studying the neural correlates of static and dynamic imagery. Past behavioural

studies have reported high prevalence rates of visual imagery during music (Dahl et al., 2022;

Küssner & Eerola, 2019; Vuoskoski & Eerola, 2015), leading us to view it as a useful reliable

stimulus. However, a relevant concern is that insights from studying music may not be

generalisable to other instances of static and dynamic imagery. Here, we suggest that music is a

good vehicle (like many possible others, e.g., poetry; Belfi et al., 2017) for inducing visual

imagery, and that while some neural patterns may relate specifically to music’s physical

properties, this is not the case for all other stimuli types that may be used for inducing imagery.

Further, even though we point out that music-induced visual imagery has been related to music’s

aesthetic appeal (Belfi, 2019), differing levels of aesthetic evaluation to differing imagery

content is something that would be present in other imagery induction paradigms, including

simpler paradigms where people are asked to imagine different scenes in the absence of any

inducer. Future studies where such additional factors are a potential concern could seek to track

such variables carefully and account for them in the analysis approach.

As previously mentioned, future investigations should further seek to corroborate and

extend our results by using alternative experimental designs that enable the explicit comparison

between music-induced static and dynamic visual imagery. While the current study touches upon

partially distinct processes that may underpin these two types of visual imagery, further

refinements to the design could provide insight into the magnitude of their differences and allow

conclusions to be made regarding the strength of the associations between different brain areas

and distinct types of visual imagery content.

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4.4. Conclusion

We aimed to further our understanding of the oscillatory characteristics underlying two

types of content of visual imagery during music listening: static and dynamic. In line with our

predictions, we corroborate past literature on the oscillatory signatures of visual imagery and

reveal nuanced differences in signatures of static and dynamic content of visual imagery.

Investigations into what listeners tend to imagine whilst listening to music is gaining traction.

Our study opens further avenues into the operationalisation of typical content of visual imagery

and how they can be observed in neural data.

Contributorship: MBK and AW designed the experiment and ran the data collection. SH ran

the analysis and wrote the first draft of the manuscript under the supervision of DO. All authors

edited, reviewed, and approved the final version of the manuscript.

5. Declaration of interest

None.

6. Funding

This research did not receive any specific grant from funding agencies in the public,

commercial, or not-for-profit sectors.

7. Ethical approval

Ethical approval (Ref. 2017-32) for this research was granted by the Ethics Committee of

the Institute of Psychology at Humboldt-Universität zu Berlin, Germany.

8. Consent to participate

All participants provided written consent to be included in this study and were given

monetary compensation or course credit for their time.

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Funding: This research did not receive any specific grant from funding agencies in the public,

commercial, or not-for-profit sectors.

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Highlights

 We investigate the neuro-oscillatory correlates of static and dynamic music-induced

visual mental imagery

 We show evidence of posterior alpha suppression in response to static and dynamic

music-induced visual imagery

 Dynamic imagery demonstrates additional associations with brain regions responsible for

motor processing

 Findings hint at temporal fluctuations in the formation of different types of music-

induced visual imagery

Journal Pre-proof

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Full-text available

Nov 2020

Visual mental imagery has been proposed to be an underlying mechanism of music-induced emotion, yet very little is known about the phenomenon due to its ephemeral nature. The present study utilised a saccadic eye-movement task designed to suppress visual imagery during music listening. Thirty-five participants took part in Distractor (eye-movement) and Control (blank screen) conditions, and reported the prevalence, control, and vividness of their visual imagery, and felt emotion ratings using the GEMS-9 in response to short excerpts of film music. The results show that the eye-movement task was highly effective in reducing ratings for prevalence and vividness of visual imagery, and for one GEMS item, Nostalgia, but was not successful in reducing control of imagery or the remaining GEMS items in response to the music. This represents a novel approach to understanding the potentially causal role of visual imagery on music-induced emotion, on which future research can build by considering the attentional mechanisms that a distraction task may pose during music-induced visual imagery formation.

Visual Imagery and Perception Share Neural Representations in the Alpha Frequency Band

Article

Full-text available

Jun 2020
CURR BIOL

To behave adaptively with sufficient flexibility, biological organisms must cognize beyond immediate reaction to a physically present stimulus. For this, humans use visual mental imagery, the ability to conjure up a vivid internal experience from memory that stands in for the percept of the stimulus. Visually imagined contents subjectively mimic perceived contents, suggesting that imagery and perception share common neural mechanisms. Using multivariate pattern analysis on human electroencephalography (EEG) data, we compared the oscillatory time courses of mental imagery and perception of objects. We found that representations shared between imagery and perception emerged specifically in the alpha frequency band. These representations were present in posterior, but not anterior, electrodes, suggesting an origin in parieto-occipital cortex. Comparison of the shared representations to computational models using representational similarity analysis revealed a relationship to later layers of deep neural networks trained on object representations, but not auditory or semantic models, suggesting representations of complex visual features as the basis of commonality. Together, our results identify and characterize alpha oscillations as a cortical signature of representations shared between visual mental imagery and perception.

A Review of Music-Evoked Visual Mental Imagery: Conceptual Issues, Relation to Emotion, and Functional Outcome

Article

Full-text available

Jun 2019

Visual mental imagery has been characterized as an important aspect of our mental life, which consists of “seeing” in the absence of a sensory stimulus. However, the mechanisms underlying how visual mental images unfold during music listening have remained largely neglected. Here, we review the existing literature on the relation between music-evoked emotions and images, and we draw attention to how visual mental imagery has been previously conceptualized in the music domain. We also propose to adopt a conceptual framework from research on spontaneous cognition, which will promote a more nuanced and comprehensive understanding of the different types of music-evoked visual mental imagery. Finally, we highlight how music’s capability to trigger images can be harnessed in daily life as well as in therapeutic practices to foster the benefits and minimize the costs of visual mental imagery.

'‘A huge man is bursting out of a rock'. Bodies, motion, and creativity in verbal reports of musical connotation

Article

Jan 2024
J NEW MUSIC RES

The present study proposes a new approach to musical referentiality and its alleged tendency to relate to bodily experience and movement. To address this, we collected a corpus of 38,587 words (2,265 verbal descriptions of ‘associations’ or ‘imagery’ sparked by short musical excerpts by 554 participants from 32 countries). We tested the hypotheses that verbalizations containing clear references to bodies and/or their movement will (1) be prevalent in the data; (2) tend to be more creative than descriptions not containing such references; and (3) display similar underlying image-schematic structures, themselves hypothesized in cognitive linguistics to be a consequence of bodily interactions. Our analysis suggests that (1) descriptions containing references to human or animal bodies or body parts, and to movement, significantly outnumber those not containing such references, with a strong correlation between the invocation of bodies and movement ; (2) descriptions containing references to bodies and movement are deemed more creative by an automated creativity assessment routine than those not containing them; and (3) a qualitative linguistic analysis of the most creative descriptions reveals some embodied, image-schematic similarities beneath outwardly very different verbalizations. These findings suggest that musical meaning, while open-ended, may be partly constrained by tractable principles.

Visual Mental Imagery, Music, and Emotion: From Academic Discourse to Clinical Applications

Chapter

Nov 2022

Music has the capability to trigger visual mental imagery (VMI), often leading to constructed imaginative worlds in the mind of the listener. Although VMI has been a topic of interest throughout centuries and across various disciplines including philosophy, cognitive neuroscience, and visual arts, its relationship to musical contexts remains elusive. For example, questions concerning what kind of images are commonly visualized by listeners and how the perception of the acoustical properties is linked to the evoked mental imagery are still unanswered. In this chapter, we discuss two academic debates in more depth. The first one, which was sparked in the 1970s among cognitive psychologists and philosophers, concerns the format of mental images, whether they are propositional (i.e., symbolic, linguistic representations) or depictive (i.e., pictorial representations). The second one is related to recent empirical work in music psychology investigating the relationship between VMI and emotions during music listening. It touches specifically on the association between the qualitative content of thoughts and emotions, their causal and temporal dynamics, and the role of VMI in affect regulation. We close the chapter by discussing some clinical applications that harness the emotional power of music-related VMI and by highlighting possible avenues for future music-based interventions. However, to unlock the full potential of real-world applications, a more thorough understanding of the link between music, emotion, and VMI needs to be achieved.

Neuroscience Measures of Music and Mental Imagery

Chapter

Nov 2022

Amy M. Belfi

Task-independent Electrophysiological Correlates of Motor Imagery Ability from Kinaesthetic and Visual Perspectives

Article

Jul 2020
NEUROSCIENCE

Motor imagery (MI) ability is highly subjective, as indicated by the individual scores of the MIQ-3 questionnaire, and poor imagers compensate for the difficulty in performing MI with larger cerebral activations, as demonstrated by MI studies involving hands/limbs. In order to identify general, task-independent MI ability correlates, 16 volunteers were stratified with MIQ-3. The scores in the kinaesthetic (K) and 1st-person visual (V) perspectives were associated with EEG patterns obtained during K-MI and V-MI of the same complex MIQ-3 movements during these MI tasks (Spearman’s correlation, significance at p≤<0.05, SnPM corrected). EEG measures were relative to rest (relaxation, closed eyes), and based on six electrode clusters both for band spectral content and connectivity (Granger causality). Lower K-MI ability was associated with greater theta decreases during tasks in fronto-central clusters and greater inward information flow to prefrontal clusters for theta, high alpha and beta bands. On the other hand, power band relative decreases were associated with V-MI ability in fronto-central clusters for low alpha and left fronto-central and both centro-parietal clusters for beta bands. The results thus suggest different computational mechanisms for MI-V and MI-K. The association between low alpha/beta desynchronization and V-MIQ scores and between theta changes and K-MIQ scores suggest a cognitive effort with greater cerebral activation in participants with lower V-MI ability. The association between information flow to prefrontal hub and K-MI ability suggest the need for a continuous update of information to support MI-related executive functions in subjects with poor K-MI ability.

The neuro-oscillatory profiles of static and dynamic music-induced visual imagery

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