Traffic Jams: Music and Traffic Noise Interact to Influence the Vividness, Sentiment, and Spatiotemporal Properties of Directed Mental Imagery

Traffic Jams: Music and Traffic Noise Interact to Influence the Vividness, Sentiment, and Spatiotemporal Properties of Directed Mental Imagery | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Traffic Jams: Music and Traffic Noise Interact to Influence the Vividness, Sentiment, and Spatiotemporal Properties of Directed Mental Imagery Jon B. Prince, Joanna Delalande, Ceren Ayyildiz, Steffen A. Herff This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4285253/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Music and traffic noise are nearly ubiquitous components of our modern auditory landscape. However, much remains unknown on how they interact in influencing higher-order cognitive processes such as mental imagery. Understanding this influence is important because music is a candidate to support mental imagery-based therapies, and traffic noise is often unavoidable in urban environments. Here, 107 participants performed a directed mental imagery task of imagining the continuation of a journey towards a landmark. Each trial had either silence, traffic noise, music, or combined music and noise. Bayesian Mixed Effects models reveal that compared to silence, participants reported enhanced imagery vividness for music in all conditions. Only music increased positive emotional sentiment of the imagined content, and adding noise to music diminished the effect. The auditory landscape further shaped the physical properties of the imagined content; both music and traffic affected the imagined distances travelled, but only music affected the imagined time travelled. Furthermore, elevated traffic-related themes occurred in both conditions involving traffic noise. Overall, both music and noise can modulate aspects of mental imagery and interact in complex ways to reveal dissociations between imagery components. We discuss practical implications for applied contexts, such as imagery-based therapies. Music cognition Mental imagery Noise Auditory perception Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Perhaps because music and traffic noise constantly surround us, there is substantial research on their processing and perception. However, there is considerably less research on how such auditory stimulation can influence mental imagery (internal perception in the absence of corresponding physical stimuli) 1 . Understanding the role of auditory processing and perception in mental imagery is important because of the enormous application of mental imagery in both voluntary (recreational or athletic) and clinical contexts 2–6 . Accordingly, the goal of this paper is to explore how background music and noise contribute (and potentially interact) in affecting mental imagery, in terms of imagery vividness, the emotional sentiment of imagery contents, and imagined time/distance travelled. Although mental imagery is not the same as true perception, it is arguably just as important as it is involved in everyday cognitive capacities such as memory, future planning, language comprehension, decision-making and self-regulation 7–10 . Mental imagery is also strongly linked to emotion, in fact, it can be more efficacious for emotion regulation than verbal processing 11–13 . Mental images evoke emotional responses at a physiological 14 as well as a subjective level 7 , similar neural structures appear to be employed during both mental imagery and perception 3,15 , and spatiotemporal relationships are preserved during imagery 16 . Here we distinguish between undirected and directed mental imagery, where the former is akin to mind-wandering 17 , whereas the latter refers to a goal-directed or intentional mental imagery. Directed imagery is more relevant to psychotherapeutic contexts in which the imagery is voluntary and often directly guided by a therapist 3 . Music and Mental Imagery There are several clinical uses of mental imagery in cognitive behavioural therapy (CBT), including imagery rescripting and imaginal exposure, which rely on the manipulation of mental images. These therapies are particularly effective when altering specific characteristics of mental imagery, such as vividness (self-reported by the individual) and emotional sentiment (inferred through content analysis of the imagined episode) 18,19 . Therefore, techniques for manipulating imagery characteristics hold potential to optimise therapeutic outcomes. Music is one such tool, as research overwhelmingly supports its ability to enhance these imagery characteristics 20–22 . Indeed, music can radically alter the content and nature of mental imagery, likely through evoking complex and nuanced emotions and moods 23–26 . Music-evoked emotions also activate visual and motor brain areas, suggesting that music triggers sensory responses in the absence of appropriate external cues— i.e., mental imagery 27 . There have also been suggestions of a more complex, bidirectional relationship whereby low-level affect in music listening triggers visual imagery, which in turn predicts higher-level emotions 24 . Perhaps because of the dissociation between felt and perceived emotion in music 28 , the powerful emotional responses that music evokes bring significantly less negative emotionality than silence, such as during recall of autobiographical memories 29 . Music-evoked memories are accompanied by significantly higher ratings of vividness and emotional intensity than memories evoked by television 30 . They are also richer and more vivid, on average, than face-evoked memories, as measured through the relative number of internal (directly pertaining to a personal memory) and perceptual (pertaining to sensory experiences) details 31,32 . Further, whereas mental imagery preserves spatiotemporal relationships 16 , adding music dilates imagined space and time 21,22,33,34 , meaning that clinicians could use it to manipulate time and distance in imagery-based therapies (e.g., closer or further from a traumatic event). Experiencing visual mental imagery during music listening occurs in 77% of music listeners 35–37 . Compared to silence, Herff et al. 21,22 found that music significantly increased imagery vividness in directed imagery following a visual inducer, effected greater positive emotional sentiment, and increased both imagined time and distance. Using scores on the Clinician-Administered PTSD Scale in Mota et al. 19 , Herff et al. estimated that the use of music during imaginal exposure could result in an average increased efficacy per exposure session of 2.2%. Given findings that imagined vividness and emotional sentiment affect outcomes in imagery-based interventions, music could be a powerful aid in psychological therapy. Most music and imagery research focuses on undirected imagery, so Herff et al. 21,22 used a directed paradigm. They did this through a visual inducer that presented a starting point for participants’ mental imagery, as well as instructions for what to focus on during the imagined period (see the present study’s procedure for details). Directed imagery evoked from music conditions was significantly more vivid, with an average of 2.7 times greater vividness ratings, and with greater positive emotional sentiment than that evoked in silence. Noise and Mental Imagery The effects of other, more disruptive auditory stimuli on mental imagery are largely unknown, despite the ubiquity of environmental noise in day-to-day life 38,39 , and its ability to trigger emotional responses 40 . Further, the existing results paint an unclear picture: acoustic noise from a functional magnetic resonance imaging (fMRI) machine disrupted imagery vividness on a directed mental imagery task 41 . However, adding crowd noise to music recordings did not affect non-directed mental imagery 42 , suggesting noise’s disruptive effects vary based on the type of noise, imagery, and context 43 . This is an important issue of ecological validity; whereas potentially confounding background sounds tend to be tightly controlled in experimental studies, in a real-world therapy setting there may be effects from potentially disruptive sounds, such as outside street noise 44 . Road traffic is the most common source of environmental noise, and is often overheard in offices or home office workplaces, particularly where windows are open or tilted 44,45 . Such noise disrupts performance on lower order cognitive tasks involving processing and memory, including text and verbal recall, reading speed, reading comprehension, mental arithmetic, and Stroop tests 44,46,47 . Beyond basic cognitive functions, road traffic may also affect higher-order cognition (potentially mediated by annoyance), as demonstrated by associations between noise exposure and increased depression and anxiety 48,49 . Traffic noise may be a particularly disruptive type of sound because it represents a near-constant stimulus with high sound level variability (ranging from soft engine hums to significantly louder roars as vehicles speed up) and unpredictable sound bursts from car horns and tire friction. Evidence suggests variability in noise signals (see the changing-state hypothesis) 50 and the presence of “deviant sounds” (i.e., unexpected, infrequent sound in an otherwise homogeneous auditory background) predict levels of disturbance from noise 51,52 . The present study The precise effects of noise on imagery characteristics, including when these are paired with a stimulus known to enhance imagery (such as music), are important to understand for two reasons. First, although music has powerful effects on imagery, it is unclear how resistant these effects are to competing noise stimuli. Determining the degree of music relative to background noise (signal-to-noise ratio, or SNR) required to produce significant effects on imagery would enhance the ecological validity of using music to enhance imagery characteristics during therapy. Efforts toward quantifying the musical SNR required for therapeutically meaningful change in imagery characteristics to occur has significant practical implications for clinical environments in which disruptive background noise may be unavoidable. If the use of music for modulating imagery in imagery-based interventions is to be scalable, it is important to understand the effects of background noise. Second, if noise does disrupt mental imagery, this effect may actually help rather than hinder therapeutic outcomes in some instances. In interventions where vividness positively predicts treatment success, it is important to adapt vividness based on patient distress levels, as too-vivid imagery could trigger an anxiety episode. Additionally, for interventions that seek to gradually reduce imagery vividness and associated negative emotions, it is worth exploring what other sensory modalities (other than visual as used in eye movement desensitisation and reprocessing (EMDR), for instance) might facilitate this process. Extrapolating from theories that closing one’s eyes facilitates visual imagery by reducing cognitive load and freeing resources that would otherwise be used to monitor the external environment 53,54 , disruptive noise may produce an additional cognitive load, and therefore, impair imagery intensity or quality. The present study comprises an experimental test of how music, traffic noise, and their combination affect the perceived vividness and sentiment of participants’ directed imagery. Adapting the paradigm of Herff et al. (2021, see Fig. 4 ), participants heard either music, traffic noise, both, or silence while watching a short video clip (see Fig. 4 ) and subsequently closing their eyes. They then reported imagery vividness, the imagined time and distance travelled, and wrote a description of the contents of their imagery episode. The hypotheses are that ( 1 ) compared to silence, imagery during music listening will be more vivid, have more positive emotional sentiment, and greater imagined time and distance. Additionally, ( 2 ) compared to silence, the disruptive effects of traffic noise will decrease imagery vividness, have more negative emotional sentiment, yet greater imagined time and distance (due to the implied presence of motion). Further, ( 3 ) we predict that compared to music alone, the combined music and traffic noise will decrease evoked imagery vividness and positive emotional sentiment, but imagined time and distance will be comparable. Results Vividness We observed strong evidence that, compared to the Silence condition, participants reported more vivid imagery in all other conditions: Music ( β = .82, EEβ = .08, Odds ( β > 0) > 9999*), Traffic ( β = .77, EEβ = .1, Odds ( β > 0) > 9999*), and Music + Traffic ( β = .78, EEβ = .08, Odds ( β > 0) > 9999*). Vividness between the Music, Traffic, and Music + Traffic conditions was comparable (all Odds 0) > 9999*), and Music + Traffic conditions ( β = .26, EEβ = .11, Odds ( β > 0) = 105.67*) led to more positive sentiment in the imagined content compared to the Silence condition. However, in the Traffic alone condition, imagined content sentiment was comparable to the Silence condition ( β = − .01, EEβ = .13, Odds ( β 0) > 9999*) and the Music + Traffic condition ( β = .26, EEβ = .10, Odds ( β > 0) = 172.91*) showed more positive imagined content when compared to the Traffic condition, and the Music condition also led to more positive imagined content when compared to the Music + Traffic condition ( β = .33, EEβ = .07, Odds ( β > 0) > 9999*), as shown in Fig. 1 (C and D). Time Compared to the Silence condition, we observed strong evidence that the imagined time travelled was greater in both the Music ( β = .34, EEβ = .1, Odds ( β > 0) = 1999*) and the Music + Traffic conditions ( β = .26, EEβ = .1, Odds ( β > 0) = 169.21*) but not in the Traffic alone condition ( β = .02, EEβ = .13, Odds ( β > 0) = 1.35). Imagined time travelled was comparable between the Music and Music + Traffic conditions ( β = .08, EEβ = .07, Odds ( β > 0) = 7.18), and both showed greater imagined time travelled compared to the Traffic condition (Music β = .32, EEβ = .1, Odds ( β > 0) = 1599*; Music + Traffic β = .23, EEβ = .1, Odds ( β > 0) = 77.43*). See Fig. 2 (A and B) for these results Distance We observed strong evidence that the Music ( β = .64, EEβ = .1, Odds ( β > 0) > 9999*), the Traffic ( β = .29, EEβ = .13, Odds ( β > 0) = 86.91*), as well as the Music + Traffic conditions ( β = .62, EEβ = .1, Odds ( β > 0) > 9999*) all showed greater imagined distance travelled compared to the Silence condition. Both the Music ( β = .35, EEβ = .1, Odds ( β > 0) > 9999*) as well as the Music + Traffic conditions ( β = .33, EEβ = .1, Odds ( β > 0) = 7999*) led to greater imagined distances travelled compared to the Traffic alone condition, but were comparable with each other ( β = .02, EEβ = .07, Odds ( β > 0) = 1.5). Figure 2 (C and D) depicts these findings. Imagined Traffic Content We observed clear support that the imagined content was influenced by the auditory condition. The probability of imagining traffic-related content was comparable between the Silent and the Music condition ( β = .07, EEβ = .4, Odds ( β > 0) = 1.25). However, both the Music + Traffic and in particular the Traffic alone conditions showed higher probabilities of imagining traffic-related content when compared to Silence (Traffic β = 3.67, EEβ = .41, Odds ( β > 0) > 9999*; Music + Traffic β = 2.55, EEβ = .37, Odds ( β > 0) > 9999*, as well as Music alone conditions (Traffic β = 3.6, EEβ = .3, Odds ( β > 0) > 9999*; Music + Traffic β = 2.48, EEβ = .24, Odds ( β > 0) > 9999*. In addition, we observed strong evidence that participants in the Traffic condition imagined more traffic-related content than participants in the Music + Traffic condition ( β = 1.12, EEβ = .19, Odds ( β > 0) > 9999*). See Fig. 3 for a depiction of these findings. Discussion The present study investigated whether music, traffic, or both sounds affected the vividness, emotional sentiment, and imagined time/distance travelled in a directed mental imagery task. Compared to silence, music significantly enhanced imagery vividness, positive sentiment, imagined time, and imagined distance, confirming our first hypothesis. However, contradicting our second hypothesis, traffic noise had a similar effect as music on vividness and a lesser effect on imagined distance, while affecting neither sentiment nor imagined time. Deleterious effects of adding traffic noise to music only occurred in sentiment (neither vividness, imagined time, nor imagined distance), largely confirming our third hypothesis (except for vividness). None of these effects were mediated by musical expertise, similar to previous studies 21,22 . In addition, traffic noise is also reflected in participants’ imagined content. Vividness Music, traffic, and their combination had comparable effects on mental imagery vividness. Thus, multiple forms of auditory input can increase directed imagery vividness. Interestingly, noise on its own enhanced vividness to a similar degree to music – why? Juslin and Västfjäll 23 propose that the mechanisms through which music evokes emotions— including mental imagery and episodic memory— may be present in non-musical stimuli. Indeed, an exploration of different sensory cues used to retrieve autobiographical memories suggests that non-musical auditory information (including environmental sounds) is a dominant mechanism for probing rich memories 55 . Environmental sounds can also evoke more memories than music, although with less positive valence 56 . Our findings align with the idea that music is not unique in its ability to evoke vivid mental images, and that the acoustic features of a variety of non-musical sounds can induce rich imagery— including potentially disruptive environmental noise. Despite the effects of both music-alone and noise-alone conditions, their combination did not have an additive effect, such that all non-silence conditions showed similar levels of vividness. Perhaps participants were able to ignore the traffic noise in the combined condition, given that we set the noise level to be 15dB quieter than music. These results may illustrate a phenomenon similar to the ‘cocktail party’ effect, in which listeners suppress speech from competing sources so they can better concentrate on a target speech stimulus through auditory stream segregation 57,58 . However, this cannot explain why vividness in the noise condition alone was equal to all conditions except silence. Furthermore, vehicle and traffic-related words featured in many of the mental imagery descriptions in the trials with traffic noise, so traffic noise contributed to the imagined content, yet did not decrease imagery vividness. Instead of tuning out unexpected or competing stimuli, it is possible to ‘tune them in’ - to incorporate them into the signal of interest. Testing listening comprehension in the presence of an attention-demanding stimulus, Russo and Pichora-Fuller 59 found older adults focused their attention on the speech foreground and attempted to tune out the music background, whereas younger adults attended to both. In this study, 91% of the participants were younger adults (< 30 years old), perhaps enabling them to tune in and incorporate the traffic noise into their imagined episodes, particularly because they were unconstrained in their imagination content. However, it seems unlikely that noise was the sole cause of the effects on imagery vividness in the combined trials, given that music was significantly louder than the background noise and itself is a powerful enhancer of imagery vividness. Perhaps a lower (music) signal to (traffic) noise ratio could reveal disruptive effects of noise, which future studies could explore. Alternatively, perhaps the mechanisms involved are more dichotomous in that mental imagery might draw from any form of concurrent auditory signal to enrich the vividness and overall quality of mental imagery its vividness (provided the input is sufficiently rich and not overly disruptive). The current paradigm does not enable disentangling the contributions of noise and music on vividness. Sentiment Unlike vividness, the effects of music and traffic noise on sentiment mix together. Although participants’ imagery was similarly vivid in the music trials regardless of whether they contained noise, their associated sentiment was more negative in the presence of noise. This finding supports the notion that participants were unable to tune out the traffic noise and instead allowed it to shape their imagery content (thus associated sentiment). Noise alone, however, resulted in sentiment comparable to silence. Because environmental sounds can generate emotional responses 40 , noise could have decreased emotional sentiment relative to silence. However, silence itself is not a truly ‘neutral’ condition given that it also results in sentiment content 60 . Moreover, given that the content differs across conditions, the effects of music, noise, and silence may have different mechanisms. Additionally, effects of auditory stimuli (musical and non-musical) on imagery characteristics tend to be stimulus-specific. Herff et al. 21 found that different pieces evoked different degrees of sentiment, and Herff et al. 22 found a relaxing piece evoked more positive sentiment than silence, despite no difference between silence and an arousing piece. Accordingly, the effects of a non-musical signal likely depend on specific and as-yet unclear acoustic and associative features. Tempo is also a likely modulator, as only the faster rendition of a piece induced positive sentiment in Herff et al. 21 . Valence, familiarity, and contrast of auditory stimuli (primarily music) also affect imagery characteristics 35,61–65 . Thus, a noise stimulus with different acoustic features might shape emotional sentiment more dramatically. Time and Distance We extend the existing findings on imagined time and distance in directed mental imagery 21,22 by showing that despite its potential cognitive demand, traffic noise had no influence on imagined time travelled, either by itself or combined with music. Accordingly, the present SNR did not decrease the time-dilating effects of music in a mental imagery context 21,33,34 . Imagined distance, however, shows that both the music and traffic noise conditions (separately) increased the imagined distance travelled compared to silence, with music having a more pronounced effect than traffic noise. Similar to imagined time, the combined condition yielded similar effects as music alone. A likely explanation for traffic noise affecting imagined distance but not time is the familiarity with vehicles and their tendency to travel at higher speed (more distance over the same time) than other means of transport, such as walking (shown as part of the visual inducer). The increased mentions of traffic-related words in conditions involving traffic noise mean that participants incorporated vehicles into their imagined journeys. Although traffic noise does not distort time like music, a faster vehicle would travel greater distances than other transportation modes within the same given time. Yet, participants imagined greater distances travelled in the music-only compared to the traffic-only condition (see Fig. 2 C). Therefore, either (a) the additional imagined travel time in the music condition compared to the traffic condition is sufficient to outpace the vehicle in the traffic condition, or (b) in the music condition, participants also utilise alternative modes of transportation (perhaps by flying, mountain biking, etc.), or (c) a combination of both. Fortunately, the combined music-traffic condition can help disentangle these possible explanations as it yields similar distances travelled compared to the music-only condition. Perhaps this means that music overrides the effect of traffic (explaining the equivalence between music and combined conditions), which then discards the distance boost of using cars (over silence), in favour of whichever modes participants use in the music-only condition, as well as the advantage of additional travel time. Yet this is unlikely because in the combined condition, traffic-related themes remain strong in the imagined content (Fig. 2 ). Perhaps then, the participants are using music’s stretching of imagined time to travel extra distance with a vehicle? This is certainly possible given the high prevalence of traffic related themes in the combined condition. However, this prevalence is still meaningfully lower in the combined condition compared to the Traffic-only condition, suggesting that there is less vehicle usage in the Music-only compared to the combined condition. Now, if the music-only condition outdistanced the Traffic-only condition solely due to the increased travel time (possible explanation (a) above), then the combined condition should show greater distances travelled than the music condition, as it combines increased travel time (Fig. 2 , A, B) with faster modes of transportation. Yet, they are comparable, which suggests that the average speed of these alternative modes of transportation is roughly equivalent to that of the vehicles imagined in the traffic condition. In summary, the present pattern of results suggest that explanation (c) is best, that participants use a combination of alternative modes of transportation and greater travel times in the music condition to outpace the silent and traffic condition. In addition, the mean travel speed of these modes is roughly equivalent to that of the vehicles in the traffic condition, and when combining music with traffic noise, some of those alternative modes of transportations are replaced by vehicles. Clinical implications Given that combining music and traffic noise did not demonstrate additive effects on vividness (or imagined distance), negative effects on imagery vividness from unwanted background noise are unlikely in a clinical context. However, background noise alone can enhance imagery vividness, which could be useful when higher vividness is desirable. Given recent WHO 38,39 reports of the ubiquity of traffic noise in urban centres (more than half of the EU population experience road noise levels above the WHO recommendations), this has positive implications for the use of music and imagery in potentially noisy therapeutic office environments. Ecological validity informed the use, choice, and level of traffic noise, thus presenting compelling evidence that, unwanted background traffic noise is unlikely to jeopardise outcomes in imagery therapy that are predicted by increased imagery vividness. The other side of the coin is that clients are likely to incorporate any auditory stimulation into their imagery. Practitioners should be aware of how traffic noise may shape aspects of mental imagery, particularly emotional sentiment and content. Though noise does not eliminate music’s enhancing effects on positive sentiment, it significantly reduces them. Thus, disruptive sound may reduce the efficacy of music-aided imagery therapy by suppressing emotional shifts that are key to treatment success 18 . Further, disruptive noise alone may shape emotional sentiment in a slightly negative way — though no worse than completely silent therapeutic conditions. Depending on how participants engage with the background stimulus (tuning out or in), practitioners should consider how background noise might shape the content of patients’ mental imagery. Even if they do not reduce the vividness of mental imagery, clients are likely to incorporate environmental sounds into imaginings, which could have unforeseen or unwanted effects on the therapy. There are also cases where reducing within-session vividness is the goal (e.g., EMDR). Here, the nature of the trauma and the sound may be critical. Here, traffic noise enhanced imagery even being irrelevant to the visual inducer, so traffic noise may enhance vividness more if the sound is a potential trigger for patients. Loud sounds can induce intrusive memories in PTSD 66 , and these findings highlight how noise can enhance imagery vividness. Overall, the present findings solidify music’s utility for manipulating imagery. Compared to silence, music can significantly enhance both imagery vividness and positive emotional content even in the presence of a competing disruptive stimulus, whereas traffic noise enhances imagery vividness but with more negative emotional sentiment than music (albeit no worse than silence). Interestingly, mild levels of traffic noise do not reduce imagery vividness. Even though other forms of stimuli (e.g., eye movements) remain strong contenders for this purpose, it is useful to explore effects from other auditory stimuli, offering imagery therapists a way to enhance or minimise imagery characteristics as needed. Limitations Mental imagery is almost certainly a complex cognitive phenomenon with multiple dimensions, but we are effectively collapsing across them by using a few simple measurements. Because we cannot generalise beyond the samples of traffic noise and music used, future research should consider a more detailed analysis of imagined content to explore how acoustic properties influence imagined time and distance. There is evidence that the disruptive effects of noise depend on noise type, task type, and broader context 43,67 , so the cautious interpretation is that this study presents an example of how background traffic noise samples affect directed mental imagery. Future research should explore other noise conditions (both disruptive and not) on various facets of mental imagery to understand how sound can manipulate imagery in clinical contexts. Although we used directed imagery in the present study, the paradigm retains a significant degree of spontaneity. Participants were free to imagine anything from the point of the figure arriving on top of the hill in Fig. 4 , and imagery descriptions accordingly varied widely within and between participants. This lack of constraint allowed participants to incorporate the traffic noise stimuli into their mental imagery, potentially making it less disruptive. It is unclear how the present results would differ in a paradigm prompting participants to imagine a specific scenario or recall a particular memory. Replicating the present experiment with imagery tasks designed to more closely resemble those featured in imagery-based interventions may produce evidence with greater clinical applicability. A significant majority of participants completed the experiment online on their own device, where there was no control over external environmental noise. We also cannot be certain that participants adhered to the instruction of using headphones. Some participants participated in the lab, so this extraneous factor would be less influential for them, but there were not enough of these participants to make a systematic comparison across format. The within-subjects manipulation somewhat mitigates this limitation, but future studies could provide valuable contributions by exerting greater control over extraneous environmental noise. Conclusion We show how the presence of background noise compares to – and interacts with – music’s known effects on mental imagery 21,22,36,37,62,68 . This is an important first step towards understanding the effects of noise on mental imagery, but the mechanisms remain unclear. These findings also suggest music and/or imagery therapists may need to consider and mitigate potential effects from traffic noise pollution. Subsequent steps involve investigating the mechanisms driving the effects of auditory stimuli on imagined content, and the acoustic features, musical and non-musical, that shape the qualities of mental imagery. Such investigation may also shed light on the dissociation between vividness and sentiment, as well as between imagined time and distance in this context. Method Participants Data were collected from 122 undergraduate students at Murdoch University, Perth, Australia. Of these, 15 were excluded for incomplete data (N = 12) or failing to engage with the task (N = 3), leaving a final sample of 107. The final sample included 84 females and 23 males aged 17 to 50 (M = 22.89, SD = 6.47). Participants were reimbursed for their time with either course credit (N = 101), or $ 20 payment (N = 6). All participants reported normal or corrected-to-normal hearing and provided informed consent. The experiment was approved by the Murdoch University Human Research Ethics Committee (2023/050), and was performed in accordance with the NHMRC National Statement on Ethical Conduct in Human Research (2023). Music and Noise Stimuli The auditory stimuli comprised four samples of traffic noise and three musical pieces. The musical stimuli were a subset from a prior study 21 , specifically Ravel’s orchestral rendition of Claude Debussy’s ‘Tarantelle Styrienne’, conducted by L. Slatkin, Orchestre National de Lyon, 2016; ‘My Favourite Things’ performed by The John Coltrane Quartet, 1961; and Bach’s ‘O Haupt voll Blut und Wunden’, conducted by J. E. Gardiner, Monteverdi Choir, English Baroque Soloists, 1989. Participants heard only the first 105 seconds of each musical piece. These clips had previously been loudness-normalised to the common value of − 23 ± 5*10 − 7 LUFS, as per European Broadcasting Union R-128 69 . Traffic noise was selected here as a pervasive environmental noise pollutant likely to be heard in the background of a therapy session 44,45 . The specific noise clip used was the low-diversity city noise stimulus from Stobbe et al. 70 . In that study, these traffic noise soundscapes had no effects on lower order cognition (accuracy and speed on a digit-span recall task and a continuous recognition n-back task) but did have effects on self-reported depressiveness, suggesting an impact on higher order cognitive processes. The stimulus features horns, engines, and a constant subtle traffic flow, creating high noise signal variability and frequent deviant sounds (engine revving, vehicle horns, brakes, etc.), which have been found to contribute to noise disruptiveness 50–52 . The six-minute clip was split into four separate clips of 105 seconds. This meant that, although participants heard the same disruptive noise type throughout all noise conditions in the experiment, they did not hear the exact same exemplar more than once. Each 105-second clip was loudness normalised with the pyloudnorm Python library and had a brief (5-millisecond) fade-out applied to avoid clipping. In the combined noise-music conditions, the noise was set to be 15dB softer than the music condition. The music was louder than the noise to mimic the effects of traffic noise as background. Pilot work revealed that the specific SNR of 15dB avoided potential auditory masking of the music stimuli while still being disruptive – (other noise types disrupt lower-order cognitive tasks with SNRs between − 5 to + 15 dB) 71,72 . The combined noise-music clips were also loudness normalised to the same LUFS. Procedure Participants completed the experiment individually, either online on their own device or in-person at the Murdoch University Music Cognition Lab. As the experiment was a within-subject manipulation, this hybrid approach did not affect the design validity. Participants completed the experiment via the web-based platform Pavlovia. Participants viewed the visual inducer previously used in Herff et al. 21 , a 15-second video of the opening sequence of the video game ‘Journey’ (with written permission of Jenova Chen, CEO of ThatGameCompany, https://thatgamecompany.com , see Fig. 4 ). The video features a figure ascending a small hill (Fig. 4 a). At the top of the hill, a vague landmark (an illuminated mountain) appears in the far distance (Fig. 4 b). This visual inducer offered a clear start and direction for the guided mental imagery task. After viewing the inducer, participants were instructed to close their eyes and imagine the figure continuing to walk towards the landmark. A gong sound was played signalling the start of the mental imagery task. After 90 seconds, a second gong sound instructed participants to open their eyes and stop the mental imagery task. In each trial, from the beginning of the video until the end of the imagined period, participants heard either the disruptive noise condition, one of the music conditions, one of the mixed music and noise conditions, or silence. After each imagined period, participants were asked to indicate how vivid their imagery was (on a scale from 0 = Not very clear to 100 = Very clear) and describe their mental imagery in as much detail as possible (in a free-text response format). To avoid introducing bias, no formal definition of ‘imagination’ was provided— participants were left to interpret the instructions freely. Participants repeated the above process for each condition for a total of eight trials. Each participant heard, in a random order, one silent trial, one randomly assigned noise sample, three musical pieces, and the same three musical pieces with added noise. In the combined music and noise conditions, assignment of the four noise samples to the three musical clips was also randomised to ensure effects were not specific to a particular noise/music combination. Figure 5 shows spectrograms and energy spectra for each of the possible conditions. The final stimuli can be accessed on the Open Science Framework ( https://osf.io/pmvb9/ ). After the last mental imagery trial, participants completed two self-report scales as detailed below. The experimental session from start to finish took around one hour to complete, depending on the detail of participants’ descriptions. Measures To determine participants’ musical background, we administered the Goldsmith-Musical Sophistication Index (Gold-MSI) 73 , a well-validated, widely used measure of musical training developed using a large sample of English-speaking adult participants from the general population. Only the musical training subscale (seven items, M score = 3, SD = 1.3) was used in analysis. There were no hypotheses for musical training effects, instead this subscale was included to control for potential effects from formal training as have been identified in prior studies 74 . The short form of the Depression Anxiety Stress Scale (DASS-21) 75 was also administered to investigate the effects of mood symptoms on mental imagery across multiple experiments 76 . The DASS-21 has demonstrated good reliability and validity in assessing depression, anxiety, and stress in non-clinical populations, based on large samples representative of the general adult population 77 . The 21-item version of the measure also has several advantages over the full-length version, being shorter with a cleaner factor structure and smaller inter-factor correlations 78 . The DASS results were not included in this analysis as they are part of a larger study and will be reported elsewhere. To determine participants’ musical background, we administered the Goldsmith-Musical Sophistication Index (Gold-MSI) 73 , a well-validated, widely used measure of musical training developed using a large sample of English-speaking adult participants from the general population. Only the musical training subscale (seven items, M score = 3, SD = 1.3) was used in analysis. There were no hypotheses for musical training effects, instead this subscale was included to control for potential effects from formal training as have been identified in prior studies 74 . The short form of the Depression Anxiety Stress Scale (DASS-21) 75 was also administered to investigate the effects of mood symptoms on mental imagery across multiple experiments 76 . The DASS-21 has demonstrated good reliability and validity in assessing depression, anxiety, and stress in non-clinical populations, based on large samples representative of the general adult population 77 . The 21-item version of the measure also has several advantages over the full-length version, being shorter with a cleaner factor structure and smaller inter-factor correlations 78 . The DASS results were not included in this analysis as they are part of a larger study and will be reported elsewhere. Statistical analysis Imagined sentiment was assessed by applying the National Language Tool Kit (NLTK) 79 and the Valence Aware Dictionary for sEntiment Reasoning (VADER) model 80 to detailed descriptions of the mental imagery provided in the free-format responses. The VADER works by mapping lexical features to emotional valence and intensity on a continuum ranging from negative to positive emotionality. Sentiment scores were given a numerical value, with higher values indicating more positive sentiment. Descriptions were also content-analysed for the inclusion of traffic-related terminology by the first author, who was blinded to the respective experimental conditions whilst performing the annotation. The statistical approach closely follows the approach used in prior work deploying the same paradigm 21,26,76 . We used Bayesian Mixed Effects models to predict our variables of interest (Vividness; Sentiment; Time and Distance Travelled; Whether the descriptions of imagined content contained mentions of traffic) whilst controlling for cross-random effects in participants and trial number random effects 81 . We also included a musical expertise as a predictor, however, we did not observe strong evidence that it influenced the models’ predictions. We implemented all models in R 82 using the brms package 83 . Similar to prior work in auditory perception 15,76,84–89 , all continuous variables were standardised (Mean = 0, SD = 1), and all models were provided with a weakly informative prior (a t-distribution with a mean of 0, a standard deviation of 1, and 3 degrees of freedom) 90 . Because participants varied dramatically in their reported imagined time and distance travelled, we natural log-scaled participants’ responses, before performing participant-wise normalisation (M = 0, SD = 1). We ran all models with 1,000 warm-ups and 3,000 iterations on four chains. All models converged (all R-hats = 1.00). To evaluate the evidence, we performed hypothesis tests and report model coefficients ( β ) relevant to the specific hypotheses, the estimated error of this coefficients ( EE β ), as well as the evidence ratio in favour of a given hypothesis ( Odds β ). For convenience we denote effects than can be considered ‘significant’ under an alpha level of 5% with * (i.e., evidence ratio ≥ 19) 91 . Data availability statement The datasets, analytical scripts, and fitted models from the current study are available in the OSF repository, https://osf.io/pmvb9/ Declarations Acknowledgements: This research was supported by the Australian Government through the Australian Research Council (ARC) under the Discovery Early Career Researcher Award (DECRA, DE220100961), by the Swiss National Science Foundation (SNF) under the SPARK grant scheme (CRSK-1_196567 / 1), and by the University of Sydney through a Sydney Horizon Fellowship awarded to SAH. Author contributions: JBP contributed to the theoretical and methodological design of the experiment, and prepared the first draft of the manuscript. JD contributed to the theoretical and methodological design of the experiment, collected the data, and wrote a thesis that contributed to the first draft of the manuscript. CA contributed to the theoretical and methodological design of the experiment and revised the manuscript. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4285253","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":297519397,"identity":"56aa1c2c-0f5d-4e08-9dfb-ba9cff292727","order_by":0,"name":"Jon B. 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Herff","email":"","orcid":"","institution":"Western Sydney University","correspondingAuthor":false,"prefix":"","firstName":"Steffen","middleName":"A.","lastName":"Herff","suffix":""}],"badges":[],"createdAt":"2024-04-18 05:30:45","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4285253/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4285253/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":55770379,"identity":"f370e1b3-46c9-4c03-a943-070a58d871d7","added_by":"auto","created_at":"2024-05-02 20:58:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":32041,"visible":true,"origin":"","legend":"\u003cp\u003eSentiment and Vividness differences between conditions. The left column (A, C) shows standardised effects, and the right column (B, D) shows posterior predictions based on the auditory condition (Silence, Music, Music+Traffic, Traffic) on Vividness (A, C) and Sentiment (C, D). Error bars show 95% CI.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4285253/v1/7a3e1868ce0d4ff7a8aab65a.png"},{"id":55771257,"identity":"f62897ee-bb1d-4ffc-bb0a-41ba65dd1ae1","added_by":"auto","created_at":"2024-05-02 21:06:46","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":36123,"visible":true,"origin":"","legend":"\u003cp\u003eImagined Time and Distance travelled differ between conditions. The left column (A, C) shows standardised effects, and the right column (B, D) shows posterior predictions based on the auditory condition (Silence, Music, Music+Traffic, Traffic) on Imagined Time (A, C) and Distance (C, D) travelled. Error bars show 95% CI.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4285253/v1/34b24149f3cab59637885510.png"},{"id":55770381,"identity":"b3044b84-1856-4aba-a4cf-343fd580e9a5","added_by":"auto","created_at":"2024-05-02 20:58:46","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":11110,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted probability of Imagined Traffic-related Content by auditory condition. Error bars show 95% CI.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4285253/v1/eeef1f5fac87e648422495ab.png"},{"id":55771258,"identity":"0e027424-275d-4be4-a446-a88e6b17253a","added_by":"auto","created_at":"2024-05-02 21:06:46","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":81414,"visible":true,"origin":"","legend":"\u003cp\u003eStills From the Visual Inducer, Reproduced with Permission from Herff et al. (2021)\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4285253/v1/fc4468ae4c94c2dc64379f91.png"},{"id":55770383,"identity":"255b8893-7f49-4df6-8647-83c2511f3bfa","added_by":"auto","created_at":"2024-05-02 20:58:46","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":501420,"visible":true,"origin":"","legend":"\u003cp\u003eSpectrograms (Left) and Energy Spectra (Right) for the 20 Stimuli Used to Create the Conditions. The spectrograms represent changes in frequency (measured in kilohertz) as a function of time. The energy spectra show the energy in each frequency over the entire stimulus.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4285253/v1/fab138149838818ab23c9c7d.png"},{"id":57202328,"identity":"af2ce59c-ae8d-4e3e-8582-45e4d762dc87","added_by":"auto","created_at":"2024-05-27 10:14:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1435340,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4285253/v1/f6b848e1-39bc-4288-b9d8-d4a4fdfde92f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Traffic Jams: Music and Traffic Noise Interact to Influence the Vividness, Sentiment, and Spatiotemporal Properties of Directed Mental Imagery","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePerhaps because music and traffic noise constantly surround us, there is substantial research on their processing and perception. However, there is considerably less research on how such auditory stimulation can influence mental imagery (internal perception in the absence of corresponding physical stimuli)\u003csup\u003e1\u003c/sup\u003e. Understanding the role of auditory processing and perception in mental imagery is important because of the enormous application of mental imagery in both voluntary (recreational or athletic) and clinical contexts\u003csup\u003e2\u0026ndash;6\u003c/sup\u003e. Accordingly, the goal of this paper is to explore how background music and noise contribute (and potentially interact) in affecting mental imagery, in terms of imagery vividness, the emotional sentiment of imagery contents, and imagined time/distance travelled.\u003c/p\u003e \u003cp\u003eAlthough mental imagery is not the same as true perception, it is arguably just as important as it is involved in everyday cognitive capacities such as memory, future planning, language comprehension, decision-making and self-regulation\u003csup\u003e7\u0026ndash;10\u003c/sup\u003e. Mental imagery is also strongly linked to emotion, in fact, it can be more efficacious for emotion regulation than verbal processing\u003csup\u003e11\u0026ndash;13\u003c/sup\u003e. Mental images evoke emotional responses at a physiological\u003csup\u003e14\u003c/sup\u003e as well as a subjective level\u003csup\u003e7\u003c/sup\u003e, similar neural structures appear to be employed during both mental imagery and perception\u003csup\u003e3,15\u003c/sup\u003e, and spatiotemporal relationships are preserved during imagery\u003csup\u003e16\u003c/sup\u003e. Here we distinguish between undirected and directed mental imagery, where the former is akin to mind-wandering\u003csup\u003e17\u003c/sup\u003e, whereas the latter refers to a goal-directed or intentional mental imagery. Directed imagery is more relevant to psychotherapeutic contexts in which the imagery is voluntary and often directly guided by a therapist\u003csup\u003e3\u003c/sup\u003e.\u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003eMusic and Mental Imagery\u003c/h2\u003e \u003cp\u003eThere are several clinical uses of mental imagery in cognitive behavioural therapy (CBT), including imagery rescripting and imaginal exposure, which rely on the manipulation of mental images. These therapies are particularly effective when altering specific characteristics of mental imagery, such as vividness (self-reported by the individual) and emotional sentiment (inferred through content analysis of the imagined episode)\u003csup\u003e18,19\u003c/sup\u003e. Therefore, techniques for manipulating imagery characteristics hold potential to optimise therapeutic outcomes. Music is one such tool, as research overwhelmingly supports its ability to enhance these imagery characteristics\u003csup\u003e20\u0026ndash;22\u003c/sup\u003e. Indeed, music can radically alter the content and nature of mental imagery, likely through evoking complex and nuanced emotions and moods\u003csup\u003e23\u0026ndash;26\u003c/sup\u003e. Music-evoked emotions also activate visual and motor brain areas, suggesting that music triggers sensory responses in the absence of appropriate external cues\u0026mdash; i.e., mental imagery\u003csup\u003e27\u003c/sup\u003e. There have also been suggestions of a more complex, bidirectional relationship whereby low-level affect in music listening triggers visual imagery, which in turn predicts higher-level emotions\u003csup\u003e24\u003c/sup\u003e. Perhaps because of the dissociation between felt and perceived emotion in music\u003csup\u003e28\u003c/sup\u003e, the powerful emotional responses that music evokes bring significantly less negative emotionality than silence, such as during recall of autobiographical memories\u003csup\u003e29\u003c/sup\u003e. Music-evoked memories are accompanied by significantly higher ratings of vividness and emotional intensity than memories evoked by television\u003csup\u003e30\u003c/sup\u003e. They are also richer and more vivid, on average, than face-evoked memories, as measured through the relative number of internal (directly pertaining to a personal memory) and perceptual (pertaining to sensory experiences) details\u003csup\u003e31,32\u003c/sup\u003e. Further, whereas mental imagery preserves spatiotemporal relationships\u003csup\u003e16\u003c/sup\u003e, adding music dilates imagined space and time\u003csup\u003e21,22,33,34\u003c/sup\u003e, meaning that clinicians could use it to manipulate time and distance in imagery-based therapies (e.g., closer or further from a traumatic event).\u003c/p\u003e \u003cp\u003eExperiencing visual mental imagery during music listening occurs in 77% of music listeners\u003csup\u003e35\u0026ndash;37\u003c/sup\u003e. Compared to silence, Herff et al.\u003csup\u003e21,22\u003c/sup\u003e found that music significantly increased imagery vividness in directed imagery following a visual inducer, effected greater positive emotional sentiment, and increased both imagined time and distance. Using scores on the Clinician-Administered PTSD Scale in Mota et al.\u003csup\u003e19\u003c/sup\u003e, Herff et al. estimated that the use of music during imaginal exposure could result in an average increased efficacy per exposure session of 2.2%. Given findings that imagined vividness and emotional sentiment affect outcomes in imagery-based interventions, music could be a powerful aid in psychological therapy. Most music and imagery research focuses on undirected imagery, so Herff et al.\u003csup\u003e21,22\u003c/sup\u003e used a directed paradigm. They did this through a visual inducer that presented a starting point for participants\u0026rsquo; mental imagery, as well as instructions for what to focus on during the imagined period (see the present study\u0026rsquo;s procedure for details). Directed imagery evoked from music conditions was significantly more vivid, with an average of 2.7 times greater vividness ratings, and with greater positive emotional sentiment than that evoked in silence.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eNoise and Mental Imagery\u003c/h2\u003e \u003cp\u003eThe effects of other, more disruptive auditory stimuli on mental imagery are largely unknown, despite the ubiquity of environmental noise in day-to-day life\u003csup\u003e38,39\u003c/sup\u003e, and its ability to trigger emotional responses\u003csup\u003e40\u003c/sup\u003e. Further, the existing results paint an unclear picture: acoustic noise from a functional magnetic resonance imaging (fMRI) machine disrupted imagery vividness on a directed mental imagery task\u003csup\u003e41\u003c/sup\u003e. However, adding crowd noise to music recordings did not affect non-directed mental imagery\u003csup\u003e42\u003c/sup\u003e, suggesting noise\u0026rsquo;s disruptive effects vary based on the type of noise, imagery, and context\u003csup\u003e43\u003c/sup\u003e. This is an important issue of ecological validity; whereas potentially confounding background sounds tend to be tightly controlled in experimental studies, in a real-world therapy setting there may be effects from potentially disruptive sounds, such as outside street noise\u003csup\u003e44\u003c/sup\u003e. Road traffic is the most common source of environmental noise, and is often overheard in offices or home office workplaces, particularly where windows are open or tilted\u003csup\u003e44,45\u003c/sup\u003e. Such noise disrupts performance on lower order cognitive tasks involving processing and memory, including text and verbal recall, reading speed, reading comprehension, mental arithmetic, and Stroop tests\u003csup\u003e44,46,47\u003c/sup\u003e. Beyond basic cognitive functions, road traffic may also affect higher-order cognition (potentially mediated by annoyance), as demonstrated by associations between noise exposure and increased depression and anxiety\u003csup\u003e48,49\u003c/sup\u003e. Traffic noise may be a particularly disruptive type of sound because it represents a near-constant stimulus with high sound level variability (ranging from soft engine hums to significantly louder roars as vehicles speed up) and unpredictable sound bursts from car horns and tire friction. Evidence suggests variability in noise signals (see the changing-state hypothesis)\u003csup\u003e50\u003c/sup\u003e and the presence of \u0026ldquo;deviant sounds\u0026rdquo; (i.e., unexpected, infrequent sound in an otherwise homogeneous auditory background) predict levels of disturbance from noise\u003csup\u003e51,52\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eThe present study\u003c/h2\u003e \u003cp\u003eThe precise effects of noise on imagery characteristics, including when these are paired with a stimulus known to enhance imagery (such as music), are important to understand for two reasons. First, although music has powerful effects on imagery, it is unclear how resistant these effects are to competing noise stimuli. Determining the degree of music relative to background noise (signal-to-noise ratio, or SNR) required to produce significant effects on imagery would enhance the ecological validity of using music to enhance imagery characteristics during therapy. Efforts toward quantifying the musical SNR required for therapeutically meaningful change in imagery characteristics to occur has significant practical implications for clinical environments in which disruptive background noise may be unavoidable. If the use of music for modulating imagery in imagery-based interventions is to be scalable, it is important to understand the effects of background noise.\u003c/p\u003e \u003cp\u003eSecond, if noise does disrupt mental imagery, this effect may actually help rather than hinder therapeutic outcomes in some instances. In interventions where vividness positively predicts treatment success, it is important to adapt vividness based on patient distress levels, as too-vivid imagery could trigger an anxiety episode. Additionally, for interventions that seek to gradually reduce imagery vividness and associated negative emotions, it is worth exploring what other sensory modalities (other than visual as used in eye movement desensitisation and reprocessing (EMDR), for instance) might facilitate this process. Extrapolating from theories that closing one\u0026rsquo;s eyes facilitates visual imagery by reducing cognitive load and freeing resources that would otherwise be used to monitor the external environment\u003csup\u003e53,54\u003c/sup\u003e, disruptive noise may produce an additional cognitive load, and therefore, impair imagery intensity or quality.\u003c/p\u003e \u003cp\u003eThe present study comprises an experimental test of how music, traffic noise, and their combination affect the perceived vividness and sentiment of participants\u0026rsquo; directed imagery. Adapting the paradigm of Herff et al. (2021, see Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), participants heard either music, traffic noise, both, or silence while watching a short video clip (see Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) and subsequently closing their eyes. They then reported imagery vividness, the imagined time and distance travelled, and wrote a description of the contents of their imagery episode. The hypotheses are that (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) compared to silence, imagery during music listening will be more vivid, have more positive emotional sentiment, and greater imagined time and distance. Additionally, (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) compared to silence, the disruptive effects of traffic noise will decrease imagery vividness, have more negative emotional sentiment, yet greater imagined time and distance (due to the implied presence of motion). Further, (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) we predict that compared to music alone, the combined music and traffic noise will decrease evoked imagery vividness and positive emotional sentiment, but imagined time and distance will be comparable.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eVividness\u003c/h2\u003e \u003cp\u003eWe observed strong evidence that, compared to the Silence condition, participants reported more vivid imagery in all other conditions: Music (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.82, \u003cem\u003eEEβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.08, \u003cem\u003eOdds\u003c/em\u003e(\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0)\u0026thinsp;\u0026gt;\u0026thinsp;9999*), Traffic (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.77, \u003cem\u003eEEβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.1, \u003cem\u003eOdds\u003c/em\u003e(\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0)\u0026thinsp;\u0026gt;\u0026thinsp;9999*), and Music\u0026thinsp;+\u0026thinsp;Traffic (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.78, \u003cem\u003eEEβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.08, \u003cem\u003eOdds\u003c/em\u003e(\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0)\u0026thinsp;\u0026gt;\u0026thinsp;9999*). Vividness between the Music, Traffic, and Music\u0026thinsp;+\u0026thinsp;Traffic conditions was comparable (all \u003cem\u003eOdds\u0026thinsp;\u0026lt;\u0026thinsp;3).\u003c/em\u003e Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e (A and B) depicts these results.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eSentiment\u003c/h2\u003e \u003cp\u003eWe also observed strong evidence that the Music (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.59, \u003cem\u003eEEβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.11, \u003cem\u003eOdds\u003c/em\u003e(\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0)\u0026thinsp;\u0026gt;\u0026thinsp;9999*), and Music\u0026thinsp;+\u0026thinsp;Traffic conditions (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.26, \u003cem\u003eEEβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.11, \u003cem\u003eOdds\u003c/em\u003e(\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0)\u0026thinsp;=\u0026thinsp;105.67*) led to more positive sentiment in the imagined content compared to the Silence condition. However, in the Traffic alone condition, imagined content sentiment was comparable to the Silence condition (\u003cem\u003eβ\u003c/em\u003e = \u0026minus;\u0026thinsp;.01, \u003cem\u003eEEβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.13, \u003cem\u003eOdds\u003c/em\u003e(\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0)\u0026thinsp;=\u0026thinsp;1.13). Additionally, both the Music (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.60, \u003cem\u003eEEβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.1, \u003cem\u003eOdds\u003c/em\u003e(\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0)\u0026thinsp;\u0026gt;\u0026thinsp;9999*) and the Music\u0026thinsp;+\u0026thinsp;Traffic condition (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.26, \u003cem\u003eEEβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.10, \u003cem\u003eOdds\u003c/em\u003e(\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0)\u0026thinsp;=\u0026thinsp;172.91*) showed more positive imagined content when compared to the Traffic condition, and the Music condition also led to more positive imagined content when compared to the Music\u0026thinsp;+\u0026thinsp;Traffic condition (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.33, \u003cem\u003eEEβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.07, \u003cem\u003eOdds\u003c/em\u003e(\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0)\u0026thinsp;\u0026gt;\u0026thinsp;9999*), as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e (C and D).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eTime\u003c/h2\u003e \u003cp\u003eCompared to the Silence condition, we observed strong evidence that the imagined time travelled was greater in both the Music (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.34, \u003cem\u003eEEβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.1, \u003cem\u003eOdds\u003c/em\u003e(\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0)\u0026thinsp;=\u0026thinsp;1999*) and the Music\u0026thinsp;+\u0026thinsp;Traffic conditions (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.26, \u003cem\u003eEEβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.1, \u003cem\u003eOdds\u003c/em\u003e(\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0)\u0026thinsp;=\u0026thinsp;169.21*) but not in the Traffic alone condition (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.02, \u003cem\u003eEEβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.13, \u003cem\u003eOdds\u003c/em\u003e(\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0)\u0026thinsp;=\u0026thinsp;1.35). Imagined time travelled was comparable between the Music and Music\u0026thinsp;+\u0026thinsp;Traffic conditions (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.08, \u003cem\u003eEEβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.07, \u003cem\u003eOdds\u003c/em\u003e(\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0)\u0026thinsp;=\u0026thinsp;7.18), and both showed greater imagined time travelled compared to the Traffic condition (Music \u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.32, \u003cem\u003eEEβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.1, \u003cem\u003eOdds\u003c/em\u003e(\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0)\u0026thinsp;=\u0026thinsp;1599*; Music\u0026thinsp;+\u0026thinsp;Traffic \u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.23, \u003cem\u003eEEβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.1, \u003cem\u003eOdds\u003c/em\u003e(\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0)\u0026thinsp;=\u0026thinsp;77.43*). See Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e (A and B) for these results\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eDistance\u003c/h2\u003e \u003cp\u003eWe observed strong evidence that the Music (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.64, \u003cem\u003eEEβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.1, \u003cem\u003eOdds\u003c/em\u003e(\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0)\u0026thinsp;\u0026gt;\u0026thinsp;9999*), the Traffic (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.29, \u003cem\u003eEEβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.13, \u003cem\u003eOdds\u003c/em\u003e(\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0)\u0026thinsp;=\u0026thinsp;86.91*), as well as the Music\u0026thinsp;+\u0026thinsp;Traffic conditions (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.62, \u003cem\u003eEEβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.1, \u003cem\u003eOdds\u003c/em\u003e(\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0)\u0026thinsp;\u0026gt;\u0026thinsp;9999*) all showed greater imagined distance travelled compared to the Silence condition. Both the Music (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.35, \u003cem\u003eEEβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.1, \u003cem\u003eOdds\u003c/em\u003e(\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0)\u0026thinsp;\u0026gt;\u0026thinsp;9999*) as well as the Music\u0026thinsp;+\u0026thinsp;Traffic conditions (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.33, \u003cem\u003eEEβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.1, \u003cem\u003eOdds\u003c/em\u003e(\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0)\u0026thinsp;=\u0026thinsp;7999*) led to greater imagined distances travelled compared to the Traffic alone condition, but were comparable with each other (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.02, \u003cem\u003eEEβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.07, \u003cem\u003eOdds\u003c/em\u003e(\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0)\u0026thinsp;=\u0026thinsp;1.5). Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e (C and D) depicts these findings.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eImagined Traffic Content\u003c/h2\u003e \u003cp\u003eWe observed clear support that the imagined content was influenced by the auditory condition. The probability of imagining traffic-related content was comparable between the Silent and the Music condition (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.07, \u003cem\u003eEEβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.4, \u003cem\u003eOdds\u003c/em\u003e(\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0)\u0026thinsp;=\u0026thinsp;1.25). However, both the Music\u0026thinsp;+\u0026thinsp;Traffic and in particular the Traffic alone conditions showed higher probabilities of imagining traffic-related content when compared to Silence (Traffic \u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.67, \u003cem\u003eEEβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.41, \u003cem\u003eOdds\u003c/em\u003e(\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0)\u0026thinsp;\u0026gt;\u0026thinsp;9999*; Music\u0026thinsp;+\u0026thinsp;Traffic \u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.55, \u003cem\u003eEEβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.37, \u003cem\u003eOdds\u003c/em\u003e(\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0)\u0026thinsp;\u0026gt;\u0026thinsp;9999*, as well as Music alone conditions (Traffic \u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.6, \u003cem\u003eEEβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.3, \u003cem\u003eOdds\u003c/em\u003e(\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0)\u0026thinsp;\u0026gt;\u0026thinsp;9999*; Music\u0026thinsp;+\u0026thinsp;Traffic \u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.48, \u003cem\u003eEEβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.24, \u003cem\u003eOdds\u003c/em\u003e(\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0)\u0026thinsp;\u0026gt;\u0026thinsp;9999*. In addition, we observed strong evidence that participants in the Traffic condition imagined more traffic-related content than participants in the Music\u0026thinsp;+\u0026thinsp;Traffic condition (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.12, \u003cem\u003eEEβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.19, \u003cem\u003eOdds\u003c/em\u003e(\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0)\u0026thinsp;\u0026gt;\u0026thinsp;9999*). See Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e for a depiction of these findings.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study investigated whether music, traffic, or both sounds affected the vividness, emotional sentiment, and imagined time/distance travelled in a directed mental imagery task. Compared to silence, music significantly enhanced imagery vividness, positive sentiment, imagined time, and imagined distance, confirming our first hypothesis. However, contradicting our second hypothesis, traffic noise had a similar effect as music on vividness and a lesser effect on imagined distance, while affecting neither sentiment nor imagined time. Deleterious effects of adding traffic noise to music only occurred in sentiment (neither vividness, imagined time, nor imagined distance), largely confirming our third hypothesis (except for vividness). None of these effects were mediated by musical expertise, similar to previous studies\u003csup\u003e21,22\u003c/sup\u003e. In addition, traffic noise is also reflected in participants\u0026rsquo; imagined content.\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eVividness\u003c/h2\u003e \u003cp\u003eMusic, traffic, and their combination had comparable effects on mental imagery vividness. Thus, multiple forms of auditory input can increase directed imagery vividness. Interestingly, noise on its own enhanced vividness to a similar degree to music \u0026ndash; why? Juslin and V\u0026auml;stfj\u0026auml;ll\u003csup\u003e23\u003c/sup\u003e propose that the mechanisms through which music evokes emotions\u0026mdash; including mental imagery and episodic memory\u0026mdash; may be present in non-musical stimuli. Indeed, an exploration of different sensory cues used to retrieve autobiographical memories suggests that non-musical auditory information (including environmental sounds) is a dominant mechanism for probing rich memories\u003csup\u003e55\u003c/sup\u003e. Environmental sounds can also evoke more memories than music, although with less positive valence\u003csup\u003e56\u003c/sup\u003e. Our findings align with the idea that music is not unique in its ability to evoke vivid mental images, and that the acoustic features of a variety of non-musical sounds can induce rich imagery\u0026mdash; including potentially disruptive environmental noise.\u003c/p\u003e \u003cp\u003eDespite the effects of both music-alone and noise-alone conditions, their combination did not have an additive effect, such that all non-silence conditions showed similar levels of vividness. Perhaps participants were able to ignore the traffic noise in the combined condition, given that we set the noise level to be 15dB quieter than music. These results may illustrate a phenomenon similar to the \u0026lsquo;cocktail party\u0026rsquo; effect, in which listeners suppress speech from competing sources so they can better concentrate on a target speech stimulus through auditory stream segregation\u003csup\u003e57,58\u003c/sup\u003e. However, this cannot explain why vividness in the noise condition alone was equal to all conditions except silence. Furthermore, vehicle and traffic-related words featured in many of the mental imagery descriptions in the trials with traffic noise, so traffic noise contributed to the imagined content, yet did not decrease imagery vividness.\u003c/p\u003e \u003cp\u003eInstead of tuning out unexpected or competing stimuli, it is possible to \u0026lsquo;tune them in\u0026rsquo; - to incorporate them into the signal of interest. Testing listening comprehension in the presence of an attention-demanding stimulus, Russo and Pichora-Fuller\u003csup\u003e59\u003c/sup\u003e found older adults focused their attention on the speech foreground and attempted to tune out the music background, whereas younger adults attended to both. In this study, 91% of the participants were younger adults (\u0026lt;\u0026thinsp;30 years old), perhaps enabling them to tune in and incorporate the traffic noise into their imagined episodes, particularly because they were unconstrained in their imagination content. However, it seems unlikely that noise was the sole cause of the effects on imagery vividness in the combined trials, given that music was significantly louder than the background noise and itself is a powerful enhancer of imagery vividness. Perhaps a lower (music) signal to (traffic) noise ratio could reveal disruptive effects of noise, which future studies could explore. Alternatively, perhaps the mechanisms involved are more dichotomous in that mental imagery might draw from any form of concurrent auditory signal to enrich the vividness and overall quality of mental imagery its vividness (provided the input is sufficiently rich and not overly disruptive). The current paradigm does not enable disentangling the contributions of noise and music on vividness.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eSentiment\u003c/h2\u003e \u003cp\u003eUnlike vividness, the effects of music and traffic noise on sentiment mix together. Although participants\u0026rsquo; imagery was similarly vivid in the music trials regardless of whether they contained noise, their associated sentiment was more negative in the presence of noise. This finding supports the notion that participants were unable to tune out the traffic noise and instead allowed it to shape their imagery content (thus associated sentiment). Noise alone, however, resulted in sentiment comparable to silence. Because environmental sounds can generate emotional responses\u003csup\u003e40\u003c/sup\u003e, noise could have decreased emotional sentiment relative to silence. However, silence itself is not a truly \u0026lsquo;neutral\u0026rsquo; condition given that it also results in sentiment content\u003csup\u003e60\u003c/sup\u003e. Moreover, given that the \u003cem\u003econtent\u003c/em\u003e differs across conditions, the effects of music, noise, and silence may have different mechanisms.\u003c/p\u003e \u003cp\u003eAdditionally, effects of auditory stimuli (musical and non-musical) on imagery characteristics tend to be stimulus-specific. Herff et al.\u003csup\u003e21\u003c/sup\u003e found that different pieces evoked different degrees of sentiment, and Herff et al.\u003csup\u003e22\u003c/sup\u003efound a relaxing piece evoked more positive sentiment than silence, despite no difference between silence and an arousing piece. Accordingly, the effects of a non-musical signal likely depend on specific and as-yet unclear acoustic and associative features. Tempo is also a likely modulator, as only the faster rendition of a piece induced positive sentiment in Herff et al.\u003csup\u003e21\u003c/sup\u003e. Valence, familiarity, and contrast of auditory stimuli (primarily music) also affect imagery characteristics\u003csup\u003e35,61\u0026ndash;65\u003c/sup\u003e. Thus, a noise stimulus with different acoustic features might shape emotional sentiment more dramatically.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eTime and Distance\u003c/h2\u003e \u003cp\u003eWe extend the existing findings on imagined time and distance in directed mental imagery\u003csup\u003e21,22\u003c/sup\u003e by showing that despite its potential cognitive demand, traffic noise had no influence on imagined time travelled, either by itself or combined with music. Accordingly, the present SNR did not decrease the time-dilating effects of music in a mental imagery context\u003csup\u003e21,33,34\u003c/sup\u003e. Imagined distance, however, shows that both the music and traffic noise conditions (separately) increased the imagined distance travelled compared to silence, with music having a more pronounced effect than traffic noise. Similar to imagined time, the combined condition yielded similar effects as music alone.\u003c/p\u003e \u003cp\u003eA likely explanation for traffic noise affecting imagined distance but not time is the familiarity with vehicles and their tendency to travel at higher speed (more distance over the same time) than other means of transport, such as walking (shown as part of the visual inducer). The increased mentions of traffic-related words in conditions involving traffic noise mean that participants incorporated vehicles into their imagined journeys. Although traffic noise does not distort time like music, a faster vehicle would travel greater distances than other transportation modes within the same given time. Yet, participants imagined \u003cem\u003egreater\u003c/em\u003e distances travelled in the music-only compared to the traffic-only condition (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Therefore, either (a) the additional imagined travel time in the music condition compared to the traffic condition is sufficient to outpace the vehicle in the traffic condition, or (b) in the music condition, participants also utilise alternative modes of transportation (perhaps by flying, mountain biking, etc.), or (c) a combination of both.\u003c/p\u003e \u003cp\u003eFortunately, the combined music-traffic condition can help disentangle these possible explanations as it yields similar distances travelled compared to the music-only condition. Perhaps this means that music overrides the effect of traffic (explaining the equivalence between music and combined conditions), which then discards the distance boost of using cars (over silence), in favour of whichever modes participants use in the music-only condition, as well as the advantage of additional travel time. Yet this is unlikely because in the combined condition, traffic-related themes remain strong in the imagined content (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Perhaps then, the participants are using music\u0026rsquo;s stretching of imagined time to travel extra distance with a vehicle? This is certainly possible given the high prevalence of traffic related themes in the combined condition. However, this prevalence is still meaningfully lower in the combined condition compared to the Traffic-only condition, suggesting that there is less vehicle usage in the Music-only compared to the combined condition. Now, if the music-only condition outdistanced the Traffic-only condition solely due to the increased travel time (possible explanation (a) above), then the combined condition should show greater distances travelled than the music condition, as it combines increased travel time (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, A, B) with faster modes of transportation. Yet, they are comparable, which suggests that the average speed of these alternative modes of transportation is roughly equivalent to that of the vehicles imagined in the traffic condition. In summary, the present pattern of results suggest that explanation (c) is best, that participants use a combination of alternative modes of transportation and greater travel times in the music condition to outpace the silent and traffic condition. In addition, the mean travel speed of these modes is roughly equivalent to that of the vehicles in the traffic condition, and when combining music with traffic noise, some of those alternative modes of transportations are replaced by vehicles.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eClinical implications\u003c/h2\u003e \u003cp\u003eGiven that combining music and traffic noise did not demonstrate additive effects on vividness (or imagined distance), negative effects on imagery vividness from unwanted background noise are unlikely in a clinical context. However, background noise alone can enhance imagery vividness, which could be useful when higher vividness is desirable. Given recent WHO\u003csup\u003e38,39\u003c/sup\u003e reports of the ubiquity of traffic noise in urban centres (more than half of the EU population experience road noise levels above the WHO recommendations), this has positive implications for the use of music and imagery in potentially noisy therapeutic office environments. Ecological validity informed the use, choice, and level of traffic noise, thus presenting compelling evidence that, unwanted background traffic noise is unlikely to jeopardise outcomes in imagery therapy that are predicted by increased imagery vividness.\u003c/p\u003e \u003cp\u003eThe other side of the coin is that clients are likely to incorporate any auditory stimulation into their imagery. Practitioners should be aware of how traffic noise may shape aspects of mental imagery, particularly emotional sentiment and content. Though noise does not eliminate music\u0026rsquo;s enhancing effects on positive sentiment, it significantly reduces them. Thus, disruptive sound may reduce the efficacy of music-aided imagery therapy by suppressing emotional shifts that are key to treatment success\u003csup\u003e18\u003c/sup\u003e. Further, disruptive noise alone may shape emotional sentiment in a slightly negative way \u0026mdash; though no worse than completely silent therapeutic conditions. Depending on how participants engage with the background stimulus (tuning out or in), practitioners should consider how background noise might shape the content of patients\u0026rsquo; mental imagery. Even if they do not reduce the vividness of mental imagery, clients are likely to incorporate environmental sounds into imaginings, which could have unforeseen or unwanted effects on the therapy.\u003c/p\u003e \u003cp\u003eThere are also cases where reducing within-session vividness is the goal (e.g., EMDR). Here, the nature of the trauma and the sound may be critical. Here, traffic noise enhanced imagery even being irrelevant to the visual inducer, so traffic noise may enhance vividness more if the sound is a potential trigger for patients. Loud sounds can induce intrusive memories in PTSD\u003csup\u003e66\u003c/sup\u003e, and these findings highlight how noise can enhance imagery vividness.\u003c/p\u003e \u003cp\u003eOverall, the present findings solidify music\u0026rsquo;s utility for manipulating imagery. Compared to silence, music can significantly enhance both imagery vividness and positive emotional content even in the presence of a competing disruptive stimulus, whereas traffic noise enhances imagery vividness but with more negative emotional sentiment than music (albeit no worse than silence). Interestingly, mild levels of traffic noise do not reduce imagery vividness. Even though other forms of stimuli (e.g., eye movements) remain strong contenders for this purpose, it is useful to explore effects from other auditory stimuli, offering imagery therapists a way to enhance or minimise imagery characteristics as needed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eMental imagery is almost certainly a complex cognitive phenomenon with multiple dimensions, but we are effectively collapsing across them by using a few simple measurements. Because we cannot generalise beyond the samples of traffic noise and music used, future research should consider a more detailed analysis of imagined content to explore how acoustic properties influence imagined time and distance. There is evidence that the disruptive effects of noise depend on noise type, task type, and broader context\u003csup\u003e43,67\u003c/sup\u003e, so the cautious interpretation is that this study presents an example of how background traffic noise samples affect directed mental imagery. Future research should explore other noise conditions (both disruptive and not) on various facets of mental imagery to understand how sound can manipulate imagery in clinical contexts.\u003c/p\u003e \u003cp\u003eAlthough we used directed imagery in the present study, the paradigm retains a significant degree of spontaneity. Participants were free to imagine anything from the point of the figure arriving on top of the hill in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, and imagery descriptions accordingly varied widely within and between participants. This lack of constraint allowed participants to incorporate the traffic noise stimuli into their mental imagery, potentially making it less disruptive. It is unclear how the present results would differ in a paradigm prompting participants to imagine a specific scenario or recall a particular memory. Replicating the present experiment with imagery tasks designed to more closely resemble those featured in imagery-based interventions may produce evidence with greater clinical applicability.\u003c/p\u003e \u003cp\u003eA significant majority of participants completed the experiment online on their own device, where there was no control over external environmental noise. We also cannot be certain that participants adhered to the instruction of using headphones. Some participants participated in the lab, so this extraneous factor would be less influential for them, but there were not enough of these participants to make a systematic comparison across format. The within-subjects manipulation somewhat mitigates this limitation, but future studies could provide valuable contributions by exerting greater control over extraneous environmental noise.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWe show how the presence of background noise compares to – and interacts with – music’s known effects on mental imagery\u003csup\u003e21,22,36,37,62,68\u003c/sup\u003e. This is an important first step towards understanding the effects of noise on mental imagery, but the mechanisms remain unclear. These findings also suggest music and/or imagery therapists may need to consider and mitigate potential effects from traffic noise pollution. Subsequent steps involve investigating the mechanisms driving the effects of auditory stimuli on imagined content, and the acoustic features, musical and non-musical, that shape the qualities of mental imagery. Such investigation may also shed light on the dissociation between vividness and sentiment, as well as between imagined time and distance in this context.\u003c/p\u003e "},{"header":"Method","content":"\u003ch2\u003eParticipants\u003c/h2\u003e\u003cp\u003e Data were collected from 122 undergraduate students at Murdoch University, Perth, Australia. Of these, 15 were excluded for incomplete data (N = 12) or failing to engage with the task (N = 3), leaving a final sample of 107. The final sample included 84 females and 23 males aged 17 to 50 (M = 22.89, SD = 6.47). Participants were reimbursed for their time with either course credit (N = 101), or \u003cspan\u003e$\u003c/span\u003e20 payment (N = 6). All participants reported normal or corrected-to-normal hearing and provided informed consent. The experiment was approved by the Murdoch University Human Research Ethics Committee (2023/050), and was performed in accordance with the NHMRC National Statement on Ethical Conduct in Human Research (2023).\u003c/p\u003e\u003ch2\u003eMusic and Noise Stimuli\u003c/h2\u003e\u003cp\u003eThe auditory stimuli comprised four samples of traffic noise and three musical pieces. The musical stimuli were a subset from a prior study\u003csup\u003e21\u003c/sup\u003e, specifically Ravel’s orchestral rendition of Claude Debussy’s ‘Tarantelle Styrienne’, conducted by L. Slatkin, Orchestre National de Lyon, 2016; ‘My Favourite Things’ performed by The John Coltrane Quartet, 1961; and Bach’s ‘O Haupt voll Blut und Wunden’, conducted by J. E. Gardiner, Monteverdi Choir, English Baroque Soloists, 1989. Participants heard only the first 105 seconds of each musical piece. These clips had previously been loudness-normalised to the common value of − 23 ± 5*10 − 7 LUFS, as per European Broadcasting Union R-128\u003csup\u003e69\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eTraffic noise was selected here as a pervasive environmental noise pollutant likely to be heard in the background of a therapy session\u003csup\u003e44,45\u003c/sup\u003e. The specific noise clip used was the low-diversity city noise stimulus from Stobbe et al.\u003csup\u003e70\u003c/sup\u003e. In that study, these traffic noise soundscapes had no effects on lower order cognition (accuracy and speed on a digit-span recall task and a continuous recognition n-back task) but did have effects on self-reported depressiveness, suggesting an impact on higher order cognitive processes. The stimulus features horns, engines, and a constant subtle traffic flow, creating high noise signal variability and frequent deviant sounds (engine revving, vehicle horns, brakes, etc.), which have been found to contribute to noise disruptiveness\u003csup\u003e50–52\u003c/sup\u003e. The six-minute clip was split into four separate clips of 105 seconds. This meant that, although participants heard the same disruptive noise \u003cem\u003etype\u003c/em\u003e throughout all noise conditions in the experiment, they did not hear the exact same \u003cem\u003eexemplar\u003c/em\u003e more than once. Each 105-second clip was loudness normalised with the pyloudnorm Python library and had a brief (5-millisecond) fade-out applied to avoid clipping.\u003c/p\u003e\u003cp\u003eIn the combined noise-music conditions, the noise was set to be 15dB softer than the music condition. The music was louder than the noise to mimic the effects of traffic noise as background. Pilot work revealed that the specific SNR of 15dB avoided potential auditory masking of the music stimuli while still being disruptive – (other noise types disrupt lower-order cognitive tasks with SNRs between − 5 to + 15 dB)\u003csup\u003e71,72\u003c/sup\u003e. The combined noise-music clips were also loudness normalised to the same LUFS.\u003c/p\u003e\u003ch2\u003eProcedure\u003c/h2\u003e\u003cp\u003eParticipants completed the experiment individually, either online on their own device or in-person at the Murdoch University Music Cognition Lab. As the experiment was a within-subject manipulation, this hybrid approach did not affect the design validity.\u003c/p\u003e\u003cp\u003eParticipants completed the experiment via the web-based platform Pavlovia. Participants viewed the visual inducer previously used in Herff et al.\u003csup\u003e21\u003c/sup\u003e, a 15-second video of the opening sequence of the video game ‘Journey’ (with written permission of Jenova Chen, CEO of ThatGameCompany, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://thatgamecompany.com\u003c/span\u003e\u003cspan address=\"https://thatgamecompany.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, see Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The video features a figure ascending a small hill (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). At the top of the hill, a vague landmark (an illuminated mountain) appears in the far distance (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). This visual inducer offered a clear start and direction for the guided mental imagery task. After viewing the inducer, participants were instructed to close their eyes and imagine the figure continuing to walk towards the landmark. A gong sound was played signalling the start of the mental imagery task. After 90 seconds, a second gong sound instructed participants to open their eyes and stop the mental imagery task. In each trial, from the beginning of the video until the end of the imagined period, participants heard either the disruptive noise condition, one of the music conditions, one of the mixed music and noise conditions, or silence. After each imagined period, participants were asked to indicate how vivid their imagery was (on a scale from 0 = Not very clear to 100 = Very clear) and describe their mental imagery in as much detail as possible (in a free-text response format). To avoid introducing bias, no formal definition of ‘imagination’ was provided— participants were left to interpret the instructions freely.\u003c/p\u003e\u003cp\u003eParticipants repeated the above process for each condition for a total of eight trials. Each participant heard, in a random order, one silent trial, one randomly assigned noise sample, three musical pieces, and the same three musical pieces with added noise. In the combined music and noise conditions, assignment of the four noise samples to the three musical clips was also randomised to ensure effects were not specific to a particular noise/music combination. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows spectrograms and energy spectra for each of the possible conditions. The final stimuli can be accessed on the Open Science Framework (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://osf.io/pmvb9/\u003c/span\u003e\u003cspan address=\"https://osf.io/pmvb9/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). After the last mental imagery trial, participants completed two self-report scales as detailed below. The experimental session from start to finish took around one hour to complete, depending on the detail of participants’ descriptions.\u003c/p\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eMeasures\u003c/h2\u003e \u003cp\u003eTo determine participants’ musical background, we administered the Goldsmith-Musical Sophistication Index (Gold-MSI)\u003csup\u003e73\u003c/sup\u003e, a well-validated, widely used measure of musical training developed using a large sample of English-speaking adult participants from the general population. Only the musical training subscale (seven items, \u003cem\u003eM\u003c/em\u003e score = 3, \u003cem\u003eSD\u003c/em\u003e = 1.3) was used in analysis. There were no hypotheses for musical training effects, instead this subscale was included to control for potential effects from formal training as have been identified in prior studies\u003csup\u003e74\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe short form of the Depression Anxiety Stress Scale (DASS-21)\u003csup\u003e75\u003c/sup\u003e was also administered to investigate the effects of mood symptoms on mental imagery across multiple experiments\u003csup\u003e76\u003c/sup\u003e. The DASS-21 has demonstrated good reliability and validity in assessing depression, anxiety, and stress in non-clinical populations, based on large samples representative of the general adult population\u003csup\u003e77\u003c/sup\u003e. The 21-item version of the measure also has several advantages over the full-length version, being shorter with a cleaner factor structure and smaller inter-factor correlations\u003csup\u003e78\u003c/sup\u003e. The DASS results were not included in this analysis as they are part of a larger study and will be reported elsewhere.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\u003cp\u003eTo determine participants’ musical background, we administered the Goldsmith-Musical Sophistication Index (Gold-MSI)\u003csup\u003e73\u003c/sup\u003e, a well-validated, widely used measure of musical training developed using a large sample of English-speaking adult participants from the general population. Only the musical training subscale (seven items, \u003cem\u003eM\u003c/em\u003e score = 3, \u003cem\u003eSD\u003c/em\u003e = 1.3) was used in analysis. There were no hypotheses for musical training effects, instead this subscale was included to control for potential effects from formal training as have been identified in prior studies\u003csup\u003e74\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe short form of the Depression Anxiety Stress Scale (DASS-21)\u003csup\u003e75\u003c/sup\u003e was also administered to investigate the effects of mood symptoms on mental imagery across multiple experiments\u003csup\u003e76\u003c/sup\u003e. The DASS-21 has demonstrated good reliability and validity in assessing depression, anxiety, and stress in non-clinical populations, based on large samples representative of the general adult population\u003csup\u003e77\u003c/sup\u003e. The 21-item version of the measure also has several advantages over the full-length version, being shorter with a cleaner factor structure and smaller inter-factor correlations\u003csup\u003e78\u003c/sup\u003e. The DASS results were not included in this analysis as they are part of a larger study and will be reported elsewhere.\u003c/p\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eImagined sentiment was assessed by applying the National Language Tool Kit (NLTK)\u003csup\u003e79\u003c/sup\u003e and the Valence Aware Dictionary for sEntiment Reasoning (VADER) model\u003csup\u003e80\u003c/sup\u003e to detailed descriptions of the mental imagery provided in the free-format responses. The VADER works by mapping lexical features to emotional valence and intensity on a continuum ranging from negative to positive emotionality. Sentiment scores were given a numerical value, with higher values indicating more positive sentiment. Descriptions were also content-analysed for the inclusion of traffic-related terminology by the first author, who was blinded to the respective experimental conditions whilst performing the annotation.\u003c/p\u003e\u003cp\u003eThe statistical approach closely follows the approach used in prior work deploying the same paradigm\u003csup\u003e21,26,76\u003c/sup\u003e. We used Bayesian Mixed Effects models to predict our variables of interest (Vividness; Sentiment; Time and Distance Travelled; Whether the descriptions of imagined content contained mentions of traffic) whilst controlling for cross-random effects in participants and trial number random effects\u003csup\u003e81\u003c/sup\u003e. We also included a musical expertise as a predictor, however, we did not observe strong evidence that it influenced the models’ predictions. We implemented all models in R\u003csup\u003e82\u003c/sup\u003e using the brms package\u003csup\u003e83\u003c/sup\u003e. Similar to prior work in auditory perception\u003csup\u003e15,76,84–89\u003c/sup\u003e, all continuous variables were standardised (Mean = 0, SD = 1), and all models were provided with a weakly informative prior (a t-distribution with a mean of 0, a standard deviation of 1, and 3 degrees of freedom)\u003csup\u003e90\u003c/sup\u003e. Because participants varied dramatically in their reported imagined time and distance travelled, we natural log-scaled participants’ responses, before performing participant-wise normalisation (M = 0, SD = 1).\u003c/p\u003e\u003cp\u003eWe ran all models with 1,000 warm-ups and 3,000 iterations on four chains. All models converged (all R-hats = 1.00). To evaluate the evidence, we performed hypothesis tests and report model coefficients (\u003cem\u003eβ\u003c/em\u003e) relevant to the specific hypotheses, the estimated error of this coefficients (\u003cem\u003eEE\u003c/em\u003e\u003csub\u003e\u003cem\u003eβ\u003c/em\u003e\u003c/sub\u003e), as well as the evidence ratio in favour of a given hypothesis (\u003cem\u003eOdds\u003c/em\u003e\u003csub\u003e\u003cem\u003eβ\u003c/em\u003e\u003c/sub\u003e). For convenience we denote effects than can be considered ‘significant’ under an alpha level of 5% with * (i.e., evidence ratio ≥ 19)\u003csup\u003e91\u003c/sup\u003e.\u003c/p\u003e\u003ch2\u003eData availability statement\u003c/h2\u003e\u003cp\u003eThe datasets, analytical scripts, and fitted models from the current study are available in the OSF repository, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://osf.io/pmvb9/\u003c/span\u003e\u003cspan address=\"https://osf.io/pmvb9/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgements: This research was supported by the Australian Government through the Australian Research Council (ARC) under the Discovery Early Career Researcher Award (DECRA, DE220100961), by the Swiss National Science Foundation (SNF) under the SPARK grant scheme (CRSK-1_196567 / 1), and by the University of Sydney through a Sydney Horizon Fellowship awarded to SAH.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAuthor contributions: JBP contributed to the theoretical and methodological design of the experiment, and prepared the first draft of the manuscript. JD contributed to the theoretical and methodological design of the experiment, collected the data, and wrote a thesis that contributed to the first draft of the manuscript. CA contributed to the theoretical and methodological design of the experiment and revised the manuscript. SAH contributed to the theoretical and methodological design of the experiment, provided technical support, did the statistical analysis, prepared the figures, and revised the manuscript. All authors reviewed and approved the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKosslyn, S. M., Ganis, G. \u0026amp; Thompson, W. L. Neural foundations of imagery. \u003cem\u003eNat. Rev. Neurosci.\u003c/em\u003e \u003cstrong\u003e2\u003c/strong\u003e, 635-642, doi:10.1038/35090055 (2001).\u003c/li\u003e\n\u003cli\u003eJanjigian, K. 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The perceptual relevance of balance, evenness, and entropy in musical rhythms. \u003cem\u003eCognition\u003c/em\u003e \u003cstrong\u003e203\u003c/strong\u003e, 104233, doi:https://doi.org/10.1016/j.cognition.2020.104233 (2020).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Music cognition, Mental imagery, Noise, Auditory perception ","lastPublishedDoi":"10.21203/rs.3.rs-4285253/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4285253/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMusic and traffic noise are nearly ubiquitous components of our modern auditory landscape. However, much remains unknown on how they interact in influencing higher-order cognitive processes such as mental imagery. Understanding this influence is important because music is a candidate to support mental imagery-based therapies, and traffic noise is often unavoidable in urban environments. Here, 107 participants performed a directed mental imagery task of imagining the continuation of a journey towards a landmark. Each trial had either silence, traffic noise, music, or combined music and noise. Bayesian Mixed Effects models reveal that compared to silence, participants reported enhanced imagery vividness for music in all conditions. Only music increased positive emotional sentiment of the imagined content, and adding noise to music diminished the effect. The auditory landscape further shaped the physical properties of the imagined content; both music and traffic affected the imagined distances travelled, but only music affected the imagined time travelled. Furthermore, elevated traffic-related themes occurred in both conditions involving traffic noise. Overall, both music and noise can modulate aspects of mental imagery and interact in complex ways to reveal dissociations between imagery components. We discuss practical implications for applied contexts, such as imagery-based therapies.\u003c/p\u003e","manuscriptTitle":"Traffic Jams: Music and Traffic Noise Interact to Influence the Vividness, Sentiment, and Spatiotemporal Properties of Directed Mental Imagery","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-02 20:58:41","doi":"10.21203/rs.3.rs-4285253/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"95d1dd12-63b4-438a-9578-da03a6456acc","owner":[],"postedDate":"May 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-27T10:06:35+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-02 20:58:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4285253","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4285253","identity":"rs-4285253","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}