Individuals’ food preferences can be influenced by the Music styles: An ERP study

doi:10.21203/rs.3.rs-4538510/v1

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Individuals’ food preferences can be influenced by the Music styles: An ERP study

https://doi.org/10.21203/rs.3.rs-4538510/v1

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Studies have shown that there is a cross-modal association between listening to music and eating. This study aims to explore the influence of music style on individuals’ food preferences and provide evidence for understanding multi-sensory research. Twenty seven participants participated in the experiment. The experiment consisted of two parts. Firstly, participants completed basic information; and then completed the food choice task after being stimulated by four different styles of music and simultaneously recorded EEG data. The behavioural results showed that: compared with low-calorie foods, individuals selected more high-calorie foods. In addition, individuals selected more high-calorie foods than low-calorie foods during the jazz music; while individuals selected more low-calorie foods than high-calorie foods during the classical music. The ERP results showed that: The N1 amplitudes were smallest during the classical music and greatest during the rock music; the N450 amplitudes were smallest during the jazz music. P2 amplitudes were smallest during the rock music and greatest during the classical music. P3 amplitudes during jazz music were the greatest. Pearson analysis showed that body satisfaction was positively related to classical-P3, Jazz-P3 and Rock-P3; BMI was negatively correlated with body satisfaction. Our study provides innovative practical perspectives for healthy eating.

Cross-modal connection

Multi-sensory

Music style

Food preference

ERPs

Individuals’ food preferences, recognized as pivotal factors influencing dietary behaviours (Macht, 2008; Singh, 2014), are shaped not only by the palatability of food itself but also by its inherent attributes, including color, aroma, and taste (Pérez-Rodrigo et al., 2003). Furthermore, food preferences are one of the most important factors in disordered eating (French et al., 1994). Thus, exploring the food preferences and their influence factors may pave the way for healthy eating.

Food preferences can be influenced by internal psychological factors and external factors (Tao et al., 2016; Wardle & Cooke, 2008). Music, as an external factor that impacts individuals’ food preferences, is increasingly being valued by individuals. Music has also been shown to influence human emotions, cognition, and behaviours (Guedes et al., 2023a), This suggests a potential connection between music and food (J. Wang et al., 2022). A recent meta-analysis found that music was positively related to food intake (Cui et al., 2021). Previous studies focused on basic auditory elements like volume, rhythm, and pitch to explore the effect of music on eating behaviours (Peng-Li et al., 2021a). Some studies have reported that lower (vs. higher) volume (Biswas, Szocs, et al., 2019) and higher (vs. lower) pitched music increased people’s preferences for healthy foods (Huang & Labroo, 2020). However, the influence of complex musical features on food choice and eating behavior is much more diverse (Spence et al., 2019). Music style, with its various components including pitch, rhythm, timbre, and complexity, offers researchers new insights. For example, Fiegel et al. (2014) found jazz music more accurately predicts participants’ preference for high-calorie emotional foods (e.g., milk chocolate) compared to hip-hop or rock music. This finding suggests jazz music may promote the intake of high-calorie foods. Background music’s impact on food preference depends on the interplay between music style and food health, for instance, listening to jazz and classical music increases preference for healthy salty foods (e.g., vegetable sandwiches) (Motoki et al., 2022). The influence of background music on overall impression was present in the emotional food, but not in the non-emotional food. This is because music genre can alter flavor pleasantness and overall impression of food stimuli. For example, classical and jazz music pieces are close to the “reflective and complex” dimension (e.g., tender longing, amazement, spirituality, and peace), whereas rap music pieces are likely to be attributed to the “energetic and rhythmic” dimension (e.g., activation)(Rentfrow & Gosling, 2003; Zentner et al., 2008). The current research presents conflicting findings regarding whether jazz music promotes high or low-calorie food choices. This discrepancy arises due to different perspectives and approaches in previous studies. Furthermore, the underlying neural mechanisms behind this phenomenon have not been thoroughly explored.

With the advancement in living standards, sustenance has transcended its mere physiological function, evolving into a vessel of culture and artistry, bestowing joy and fulfilling individuals' spiritual yearnings. Likewise, music, as a form of artistic expression, holds a pivotal role in catering to emotional and cultural aspirations. Hence, when music and cuisine converge to satiate the soul, a profound interconnection emerges between the two(Y. Wang, 2022). In retail stores and restaurants, background music is a common element (Spence et al., 2019). Consumers often choose, evaluate and consume food or drinks under the influence of music (Spence & Shankar, 2010). Multiple studies have shown that the rhythm of background music can affect consumers’ purchasing behavior and eating speed. For example, faster music rhythms can promote consumers’ purchasing behavior (Caldwell & Hibbert, 2002) and speed up eating speed (Milliman, 1986). Additionally, background music can also influence people’s different preferences or choices for food. However, many other kinds of music genres have been studied (e.g., Blues, Folk, Country, Religious, and Electronica; see Helwig & Palmer, 2018;Levitan et al., 2015; Kantono et al., 2016a, 2016b). Certain ethnic melodies, such as French or Japanese compositions, have the potential to influence individuals towards making nutritious or indulgent culinary selections. The Japanese cultural heritage, for instance, is linked to the perception of wholesome eating practices (Areni & Kim, 1993; Dobrenova et al., 2015). Immersing oneself in Japanese music could evoke a health-conscious frame of mind, consequently guiding individuals towards healthier dietary preferences. Notably, when music is played at a lower volume, individuals tend to lean towards healthier food options. For instance, research has shown that when the volume is decreased, individuals tend to opt for healthier food choices (Biswas, Lund, et al., 2019; Biswas, Szocs, et al., 2019). Classical music has been associated with reducing individuals' consumption of salty foods (Hussain et al., 2021). Conversely, studies indicate that patrons are more inclined to consume larger quantities of food in eateries playing classical or jazz music as opposed to pop music or no background music (North et al., 2003). The propensity for individuals to spend more money or purchase pricier items when classical music is played (Areni & Kim, 1993; North et al., 2003), combined with the notion that healthy foods typically command higher prices than high-calorie options (Haws et al., 2017), suggests that classical music may align with the attributes associated with low-calorie foods. Consequently, various music genres can influence individuals' eating habits and food consumption patterns. Moreover, different types of music can impact emotional responses and approach-avoidance behaviors (Mehrabian & Russell, 1974), thereby influencing the perception and acceptance of food when presented under different musical backgrounds (Fiegel et al., 2014).

Presently, there exists conflicting research regarding the impact of music on food preferences. While some studies suggest that genres like jazz and classical music could heighten individuals' inclination towards high-calorie foods (Fiegel et al., 2014), others indicate that exposure to classical music may actually lead individuals to opt for healthier, low-calorie food choices (Hussain et al., 2021). This inconsistency in findings could be attributed to various factors, including the diversity of music genres studied, variations in experimental methodologies, and individual variances among participants.

Event-related potential (ERP) studies have revealed that both early (N1 and N2) and late (P3) components are sensitive to the processing of food cues (Jing et al., 2024a). Early ERPs, such as occipital N1 occurring at 150–200 ms, have shown sensitivity to food type, reflecting early attention towards food stimuli (Meule et al., 2013; Schwab et al., 2017). An increase in N2 magnitude, signifying enhanced inhibitory control towards high-calorie foods, has been linked to higher self-reported mean calorie and carbohydrate intake. This suggests that individuals with lower efficiency in mobilizing inhibitory control resources are more prone to consuming a greater number of calories (Carbine et al., 2017a)

The N2 component, characterized by a negative deflection and stimulus reactivity, exhibits a greater amplitude when more conflicts are detected (Botvinick et al., 2001; Clayson & Larson, 2013). Individuals with music training have been found to display larger N2 amplitudes (Hao et al., 2023). Moreover, soothing and cheerful music have been shown to regulate negative emotions and reduce N2 amplitude (X. Liu et al., 2021). Interestingly, one study indicated that inhibitory responses to high-calorie stimuli elicited larger N2 amplitudes, while inhibitory responses to low-calorie stimuli resulted in smaller N2 amplitudes (Carbine et al., 2017b). Research has demonstrated that N2 amplitudes were greater during exposure to rap music compared to jazz music (Jing et al., 2024a). A decrease in N2 response strength may indicate weakened inhibitory control (Y. Liu et al., 2019)

Furthermore, the N450 component, characterized by a frontal-central slow wave around 500ms post-stimulus onset, has been observed to be larger in incongruent trials compared to other trial types, suggesting its involvement in inhibition processes during word processing suppression (West & Alain, 1999). Some researchers have proposed that the N450 serves as a measure of response conflict, as it is enhanced only under conditions of high conflict, such as with rare incongruent trials (Tillman & Wiens, 2011). Studies involving ERP responses in food-related conflict tasks have shown a reduction in N2 and N450 response strength in overweight Chinese females (Y. Liu et al., 2019).

The variation in P2 amplitude is contingent on the stimulus value (Sänger, 2019). In a study by Liu et al. (Y. Liu et al., 2020) involving food no-go and flower no-go tasks, overweight individuals exhibited higher P2 amplitudes compared to normal-weight individuals in both tasks. Nijs et al. (Nijs et al., 2010) reported that an escalation in P2 amplitude among obese participants indicates a heightened bias towards food-related stimuli. Conversely, Hachl et al. (Hachl et al., 2003) discovered that food intake leads to a reduction in P2 amplitude in individuals following a restricted diet. Stockburger et al. (2008) instructed participants to passively view a series of images, including both food and non-food items, revealing that hungry individuals displayed elevated P2 amplitudes compared to satiated participants.

Food cues have been linked to increased frontocentral P3 and N2 ERP amplitudes in comparison to neutral or less appetizing food cues, indicating heightened recruitment of inhibitory control and conflict monitoring resources (Carbine, Rodeback, et al., 2018). P3 amplitudes were found to be greater for food cues compared to non-food cues, reflecting a bias towards food cues (Wu et al., 2018). In contrast to normal-weight adolescents, overweight/obese individuals exhibited similar P3 amplitudes for both high- and low-calorie foods, with the most pronounced event-related alpha band desynchronization observed for low-calorie stimuli. Additionally, P3 amplitudes and state craving for low-calorie foods were predictive of snack intake in this group (Biehl et al., 2020). The response strength of P3 and the late positive component (LPC) was enhanced (Y. Liu et al., 2019).

In summary, individuals with weak inhibitory control and feelings of hunger displayed higher P2 amplitudes. The presentation of food cues elicited increased P3 and N2 amplitudes, reflecting the engagement of inhibitory control and conflict monitoring resources.

Prior research has predominantly concentrated on the influence of fundamental music parameters on food preferences (Huang & Labroo, 2020; Peng-Li et al., 2021b; Quekel, 2016), and the classification of food preferences has focused on either calorie or taste alone (Guedes et al., 2023b; Huang & Labroo, 2020; Quekel, 2016). There was significant heterogeneity in the literature, as experimental tasks, stimuli, and examined ERP components varied widely across studies.

Building upon the existing research gap, this study integrates Jing et al.(2024b) combined music genre categories (i.e., classical, rock, jazz, and rap) with food calorie information, employing event-related potentials (ERPs) analysis to explore the influence of music genres on food preferences. Through a modified food choice task, the current study aims to uncover the neural mechanisms underpinning food preference genres posits that individuals demonstrate a preference for high-calorie foods across all music genres, with classical music specifically prompting a greater selection of low-calorie options. It is suggested that the impact of music on food choices may manifest through alterations in the amplitude of EEG components.

2.1 Participants

To achieve sufficient statistical power, the required sample size was calculated before the experiment using G*Power 3.1(Faul et al., 2007). This study was a within-subjects experimental design, and a priori power analyses indicated that a minimum of 24 subjects were required to achieve 80% statistical power to predict a moderate effect (f = 0.25) with a significance level of α = 0.05. In this experiment, data from a total of 30 Chinese participants were collected. We excluded extreme values beyond the three standard deviations. The final data incorporated 27 respondents (8 males; M_age= 20.60, SD_age= 1.28) who were recruited on a social media platform from Southwest University in China. All participants reported having no history of major psychological disorders, normal weight (M_BMI= 21.68, SD_BMI= 2.96), and normal or corrected-to-normal vision. All participants provided written consent before completing the experiment. The present study was approved by the Southwest University Ethics Committee (IRB No. H22020).

2.2 Procedure

For standardization, all participants were denied eating for at least 3 hours before the experiment (Y. Liu et al., 2019, 2019, 2020). After entering the laboratory, participants firstly measured height and weight (calculating BMI), and then filled in a questionnaire (including basic information, the State Food Craving Questionnaire (G-FCQ-S) and the Visual Analogue Scale (VAS)). Finally, all participants completed the modified food choice task while the EEG data were recorded.

2.3 Questionnaires

In this study, we primarily utilized two measurement instruments.Hunger and body satisfaction: Participant rated their hunger and body satisfaction on a 100 mm visual analog scale (VAS), ranging from “not at all” to “very”.

Food Craving Questionnaires: State (FCQ-S): The FCQ-S is a 15-item questionnaire that measures the state food craving (Nijs et al., 2007). For each item, participants rated their degrees of food craving using a Likert 5-point scale ranging from 1(never) to 5 (always). In the present study, the Cronbach’s α coefficient of the scale was 0.79.

2.4 Stimuli and food choice task

As demonstrated in Fig. 1, consistent with Pantoja and Borges’s (2021) research, we employed four music segments. The food images used in the experiment, categorized as high or low calorie, were selected from the Chinese food image database developed by our research team (Y. Liu et al., 2022). A total of 160 food images were included, consisting of 80 images each of high-calorie and low-calorie foods.

During the modified food choice task, the fixation appeared on the centre of the screen for 500ms. Then, different music appeared for 30,000 ms. After the music presentation, there was a fixation for 500ms. Finally, two food images were presented on the screen. Participants were asked to select one food based on their real thoughts. They would press the button “F” if they selected the left one, or press “J” to select the right one. The food images will not disappear until the participants respond. Following this, a white screen would appear for 500 ms after the stimulus screen. During the task, after each music segment, participants are required to react to four pairs of food images. Music segments were presented randomly, while food images were presented pseudo-random.

2.5 EEG recording analysis

Brain electrical activities were recorded by a 64-channel wireless amplifier with a sampling frequency of 1000 Hz (NeuSen. W64, Neuracle, Changzhou, China). EEG data processing was processed with MATLAB R2022a using the EEGLAB toolbox (Delorme & Makeig, 2004). EEG data was filtered with a bandpass finite impulse response (FIR) filter between 0.1 and 30 Hz. The left and the right mastoids were taken as re-reference sites. The continuous EEG data were divided into trials (-100 ms to 1000 ms) according to the different marks of stimuli, a baseline correction (-100 ms to 0 ms) was applied to each trial, and the quality of EEG data was inspected trial-by-trial. Trials with abnormal fluctuations (too large or too frequent fluctuations) were eliminated, and trials with bad channels were corrected by averaging the amplitudes of their adjacent electrodes. The independent components analysis (ICA) method was applied to the EEG data to remove interference factors (e.g., eye movements, electrocardio, etc.) from the data. In the ICA results, components with EOG artifacts and head movement were removed after visual inspections (Liu, Zhao, et al., 2019). In the current study, sites Fz, FCz, and Cz was selected for the data analysis. Based on the grand-averaged ERP activities, the ERPs and their time window were as follows: P2, 200–300 ms; P3, 150–200 ms; N1, 80–120 ms; N2, 200–260 ms; N450, 300–500 ms.

We conduct five 4 music (Classical, Jazz, Rap, and Rock)× 3 sites (Fz, Cz, FCz) repeated-measures analyses (ANOVA, Greenhouse-Geisser-corrected) on the P2, P3, N1, N2, and N450 amplitudes. All analyses were conducted via SPSS 21.0. Based on the Greenhouse-Geisser method, p-values were computed for deviation in all analyses. Post-hoc t-tests were conducted with Bonferroni correction for multiple pairwise comparisons.

3.1 Behavioral results

Repeated-measures analyses of variances (ANOVA, Greenhouse-Geisser-corrected), with music types and calorie as within-subjects factors, revealed significant main effects of calorie, F (1, 26) = 6.92, p = 0.014, partial η_p² = 0.21, the selection ratio of the high-calorie foods was greater than that of the low-calorie foods. Results also revealed an interaction of music types and calories, F (3, 24) = 11.07, p < 0.001, partial η_p² = 0.58, the follow-up simple analysis revealed that the selection ratio in high-calorie foods was greatest in the jazz music, while the selection ratio in the low-calorie foods was greatest in the classical music. In addition, there was a difference between the high-calorie and low-calorie foods in jazz music and rock music, all ps < 0.001. The details of demographic information and questionnaire results are demonstrated in Table 1. Table 2 shows the descriptive statistics for all the observed variables in the present study.

Table 1

Demographic information and questionnaire results
Variables	minimum	maximum	M(SD)
Age	18.80	23.00	20.51(1.28)
BMI	17.39	29.52	21.71(2.85)
G-FCQ-S	28.00	61.00	48.82(8.04)
Gender	1.00	2.00	1.68(0.48)
Hunger	8.00	91.00	55.36(25.82)
body satisfaction	4.00	100.00	50.11(27.28)
Note. General Food Craving Questionnaires-State (G-FCQ-S) was utilized to assess current appetite; Visual-analog Scale(VAS) was utilized to assess hunger and body satisfaction.

Table 2

*Descriptive statistics (M ± SD) for all participants in the food choice task.*
Music	High	Low	Mean
Classical	0.501 (0.045)	0.499 (0.045)	0.50
Jazz	0.668 (0.026)	0.342 (0.023)	0.50
Rap	0.506 (0.042)	0.494 (0.042)	0.50
Rock	0.663 (0.029)	0.337 (0.029)	0.50
Mean	0.585	0.415

3.2 ERP results (Fig. 3)

Stimulus-locked, grand average ERPs for N1, P2, N2, P3, and N450 (A) and topography plots of N1, P2, N2, P3, and N450 during the modified food choice task

Results on N1 amplitudes showed a main effect of music, F (3, 78) = 4.09, p = 0.04, partial η_p² = 0.14, N1 amplitudes were smallest during the classical music and greatest during the rock music. Results also revealed a main effect of brain, F (2, 52) = 23.202, p < 0.001, partial η_p² = 0.472. N1 amplitudes in the site Cz were greater than that in the site Fz (p < 0.001); N1 amplitudes in the site Cz were greater than that in the site FCz (p < 0.001).

Results on N2 amplitudes revealed a main effect of brain, F (2, 53) = 43.969, p < 0.001, partial η_p² = 0.628, post hoc t-test showed that N2 amplitudes in the site Cz were greater than that in the site FCz, p < 0.001, N2 amplitudes in the site Cz were greater than that in the site Fz, p = 0.009 and N2 amplitudes in the site FCz were greater than that in the site Fz, p < 0.001.

N450

Results on N450 amplitudes revealed an interaction of music and brain, F (6, 156) = 5.419, p = 0.008, partial η_p² = 0.17. The follow-up simple effect analysis showed that N450 amplitudes at Fz, FCz, and Cz during the Jazz music were smaller than that during the rap and rock music (all p < 0.05). Results on N450 amplitudes also revealed a main effect of music, F (3, 78) = 4.29, p = 0.03, partial η_p² = 0.14, post hoc t-test showed that N450 amplitudes were smallest during the jazz music, and there was no significant difference among the classical, rap, and rock music (all p > 0.05). In addition, results on N450 amplitudes revealed a main effect of brain, F (2, 52) = 50.08, p < 0.001, partial η_p² = 0.66, and the decreasing order of the magnitude of N450 amplitudes was as follows: Fz > FCz > Cz (all p < 0.01).

Results on P2 amplitudes revealed a main effect of music, F (3, 78) = 3.96, p = 0.04, partial η_p² = 0.13, P2 amplitudes were smallest during the rock music and greatest during the classical music (all p < 0.05). There was no significant difference among the classical, jazz, and rap music, all p > 0.05.

Results on P3 amplitudes revealed an interaction of music and brain, F (6, 156) = 4.665, p = 0.01, partial η_p² = 0.15. The simple effects analysis indicated that P3 amplitudes during jazz music were greatest at Fz, FCz, and Cz, all p < 0.05. Results on P3 amplitudes revealed a main effect of brain, F (2, 52) = 61.600, p < 0.001, partial η_p² = 0.703, and the decreasing order of the magnitude of P3 amplitudes was as follows: Cz > FCz > Fz, all p < 0.01. Results also showed that the main effect of music was marginally significant, F (3, 78) = 3.32, p = 0.054, partial η_p² = 0.11, post hoc t-test showed that P3 amplitudes during jazz were greater than that during the rap and rock music, all p < 0.01. There was no significant difference among the classical, rap, and rock music, all p > 0.05.

4.3 Relationships among Self-report data, Behavioral and ERP Results

To investigate the relationships between behavioural variables and neural markers of food-related responses, we examined Pearson’s correlation between self-report, behavioural variables and ERPs for four types of background music (Fig. 4). As is shown in Fig. 4., it was found that Body Satisfaction was positively related to classical-P3 (r = 0.416, p = 0.035), Jazz-P3 (r = 0.410, p = 0.037) and Rock-P3 (r = 0.440, p = 0.024), suggesting that there is a link between body satisfaction and P3 amplitude. Additionally, there was a significant positive correlation between N1 amplitude and jazz high-calorie food choices (p < 0.05), suggesting that N1 amplitude may be related to high calorie food choices. None of the other correlations were significant, all p > 0.05.

The current study delved into the impact of music on individuals' preferences for high-calorie and low-calorie foods, as well as its neural correlates. Existing literature has presented conflicting findings regarding the influence of music on individuals' inclinations towards high-calorie and low-calorie foods. By examining music genres (Classical, Jazz, Rap, and Rock) and food categories (high-calorie, low-calorie), we aim to unravel the mixed outcomes and elucidate, for the first time, the subtle effects of music genres on individuals' preferences for high-calorie and low-calorie foods. Our findings indicated that exposure to jazz music heightened participants' preferences for high-calorie foods, whereas classical music promoted healthier dietary choices (evidenced by an increased selection of low-calorie foods during classical music). The event-related potential (ERP) analysis revealed that N1 amplitudes were minimal and P2 amplitudes peaked during classical music; P3 amplitudes were highest, and N450 amplitudes were smallest during jazz music. Additionally, N1 amplitudes peaked, and P2 amplitudes were at their lowest during rock music. Notably, the preference for high-calorie foods during the jazz music condition exhibited a significant positive correlation with N1 amplitudes.

This study contributes novel insights into how music influences individuals' preferences for specific food types. Previous research has highlighted the role of fundamental auditory parameters (e.g., volume, pitch).(Biswas, Lund, et al., 2019; Fiegel et al., 2019; Huang & Labroo, 2020) and intricate auditory parameters (e.g., music genres) (Fiegel et al., 2014; Peng-Li et al., 2021b) influence preferences for eating. Some studies have reported that lower (vs. higher) volume (Biswas, Lund, et al., 2019; Biswas, Szocs, et al., 2019) and higher (vs. lower) pitched music can heighten individuals' inclination towards nutritious foods (Huang & Labroo, 2020), while another study failed to discern any effects of pitch and volume on individuals' preferences for healthy foods (e.g., Fiegel et al., 2019).

We selected four music genres previously examined in the literature (Fiegel et al., 2014) and curated five soundtracks for each genre. While the "healthy soundtrack (featuring jazz music)" amplified the selection of nutritious foods compared to its counterpart (Peng-Li et al., 2021b), contrasting findings revealed that jazz (in comparison to Rap and rock) heightened preferences for high-calorie foods (Fiegel et al., 2014). Notably, our results diverge somewhat from Motoki et al.'s (Motoki et al., 2022) discovery that jazz/classical music (as opposed to rock) increased the preference for low-calorie savory foods. This study corroborates the notion that classical music can indeed foster healthier dietary choices.

The N1 amplitude is at its minimum during the rendition of classical music and peaks during the performance of rock music. This suggests that rock music may stimulate the most rapid neural plasticity among the four music genres, while classical music elicits the least neural plasticity (Seppänen et al., 2012). Furthermore, previous research has indicated that the N1 component is linked to processing incoming information influenced by higher cognitive functions (Wascher et al., 2009). Consequently, rock music may engage more extensive cognitive processing compared to the other genres, while classical music may prompt the least cognitive engagement. This finding contradicts our behavioral results, where the inclination towards high-calorie choices significantly increased when listening to rock music, whereas no significant differences were observed during classical music sessions. An increased N1 amplitude has been associated with primary sensory analysis and attentional focus (Luck et al., 2000), particularly heightened in response to foods essential for health and survival (Carbine, Duraccio, et al., 2018). Notably, cues for low-calorie foods have been shown to elicit larger N1 amplitudes than high-calorie food cues over the occipital region (Meule et al., 2013). Furthermore, a significant positive correlation was observed between N1 amplitude and the selection of high-calorie foods while listening to jazz music (p < 0.05). High-calorie foods may require fewer cognitive resources to process, thus eliciting less attention.

The descending sequence of N2 amplitudes was as follows: Cz > FCz > Fz. Interestingly, there was no variation in N2 amplitude across different music genres, suggesting that both conflict control and inhibitory control represent stable individual traits unaffected by music. This implies that the music did not trigger mismatch negativity, as indicated by an amplified N2 response. Notably, participants were not subjected to conflicting stimuli simultaneously: aversive music and appetizing images of food. Intriguingly, the N2 component has also been associated with response inhibition during the processing of food-related cues (Watson & Garvey, 2013).

The N450 amplitude observed during Jazz music is lower compared to that during rap and rock music. The N450 component serves as an indicator of response conflict, as it is heightened solely during instances of high conflict (e.g., rare inconsistent trials). Interestingly, the behavioral findings indicated a greater tendency towards selecting high-calorie foods in the presence of jazz music. This suggests that there is no conflict between jazz music and high-calorie food choices, implying that jazz music may be associated with high-calorie preferences.

The P2 amplitude is at its minimum during rock music and peaks during classical music. Research has demonstrated that an elevation in P2 amplitude among obese individuals signifies a heightened preference for food-related stimuli (Nijs et al., 2010), whereas food consumption results in a reduction in P2 amplitude among individuals following a restricted diet (Hachl et al., 2003).

The P3 amplitude reaches its peak during Jazz music. Previous studies have indicated that P3 amplitudes are larger when inhibiting high-calorie foods compared to low-calorie options (Carbine, Duraccio, et al., 2018). Food-related cues tend to evoke greater P3 amplitudes than cues related to less palatable foods or neutral stimuli. The heightened P3 amplitude observed during Jazz music could be attributed to the relatively weaker inhibitory effect it exerts. However, this finding contrasts with prior research indicating that high-calorie foods trigger greater P3 amplitudes compared to low-calorie foods (J. Wang et al., 2022). Our results demonstrate a positive correlation between satisfaction with body size and P3 amplitude, suggesting that individuals dissatisfied with their body size may exhibit enhanced inhibition in their food choices. This aligns with the notion that P3 serves as a neural marker of inhibitory processing (Albert et al., 2013; Hong-li et al., 2017; Wessel, 2018). Collectively, these findings shed light on the psychological mechanisms underlying the influence of specific music genres on food preferences.

Our research outcomes offer practical created by music can be easily manipulated and controlled by restaurant and store managers. Opting for specific music genres, rather than focusing solely on basic auditory parameters such as when selecting background music. For establishments primarily offering healthy food options to other genres. Conversely, restaurants and stores specializing in high-calorie savory foods (e.g., beef sandwiches), it is advisable to consider playing jazz, rap, or rock music while avoiding classical music. Similarly, during festivals or events featuring specific music genres (e.g., jazz festivals, classical concerts), event organizers can tailor menu options accordingly. Moreover, our findings suggest that sounds in food advertisements can be fine-tuned based on these insights.

Certain limitations of this study warrant acknowledgment. Firstly, the selection of music genres and soundtracks may have impacted our results. This study exclusively utilized Western music, and the potential influence of Chinese music, particularly Chinese classical music, on participants' food choices remains unexplored. Future investigations should aim to broaden the scope by incorporating a more diverse array of musical genres and soundtracks. Secondly, the study did not examine the impact of background music on actual sales. Subsequent research should delve into whether our findings hold true for real-world purchasing behaviors. Thirdly, there was a notable disparity between eating choices in a real-life setting and food selections in the experimental task, highlighting the need to enhance the ecological validity of future experiments. Additionally, certain mediating factors were not taken into account. While our study revealed varying preferences for background music concerning high and low-calorie foods, the underlying mechanisms were not thoroughly investigated. Future research should delve into the mechanisms through which music genres influence food preferences.

In conclusion, our study illustrates the impact of various music genres on food preferences. Classical music emerges as a potential influencer of healthy eating habits, as indicated by the reduced N1 amplitudes and heightened P2 amplitudes observed. Conversely, Jazz music appears to lean towards fostering unhealthy eating tendencies, evident through the amplified P3 amplitudes and diminished N450 amplitudes. These findings underscore the significant role of music in shaping individuals' dietary choices, offering both theoretical insights and practical implications for promoting healthy eating behaviors.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Author Contributions

Authors' contribution:

Funding

The work was supported by the Chinese National Natural Science Foundation (No.32200849; 32300916); Natural Science Foundation of Chongqing (No. CSTB2022NSCQ-MSX0788); the Fundamental Research Funds for the Central Universities (SWU2209501).

Albert, V. A., Barbazuk, W. B., dePamphilis, C. W., Der, J. P., Leebens-Mack, J., Ma, H., Palmer, J. D., Rounsley, S., Sankoff, D., Schuster, S. C., Soltis, D. E., Soltis, P. S., Wessler, S. R., Wing, R. A., Albert, V. A., Ammiraju, J. S. S., Barbazuk, W. B., Chamala, S., Chanderbali, A. S., … Tomsho, L. (2013). The Amborella Genome and the Evolution of Flowering Plants. SCIENCE, 342(6165), 1467-+. https://doi.org/10.1126/science.1241089
Areni, C. S., & Kim, D. (1993). The influence of background music on shopping behavior: Classical versus top-forty music in a wine store. Advances in Consumer Research, 20(1).
Biehl, S. C., Keil, J., Naumann, E., & Svaldi, J. (2020). ERP and oscillatory differences in overweight/obese and normal-weight adolescents in response to food stimuli. Journal of Eating Disorders, 8(1), 14. https://doi.org/10.1186/s40337-020-00290-8
Biswas, D., Lund, K., & Szocs, C. (2019). Sounds like a healthy retail atmospheric strategy: Effects of ambient music and background noise on food sales. Journal of the Academy of Marketing Science, 47, 37–55.
Biswas, D., Szocs, C., & Abell, A. (2019). Extending the boundaries of sensory marketing and examining the sixth sensory system: Effects of vestibular sensations for sitting versus standing postures on food taste perception. Journal of Consumer Research, 46(4), 708–724.
Botvinick, M. M., Braver, T. S., Barch, D. M., Carter, C. S., & Cohen, J. D. (2001). Conflict monitoring and cognitive control. Psychological Review, 108(3), 624.
Caldwell, C., & Hibbert, S. A. (2002). The influence of music tempo and musical preference on restaurant patrons’ behavior. Psychology & Marketing, 19(11), 895–917.
Carbine, K. A., Christensen, E., LeCheminant, J. D., Bailey, B. W., Tucker, L. A., & Larson, M. J. (2017a). Testing food‐related inhibitory control to high‐and low‐calorie food stimuli: Electrophysiological responses to high‐calorie food stimuli predict calorie and carbohydrate intake. Psychophysiology, 54(7), 982–997.
Carbine, K. A., Christensen, E., LeCheminant, J. D., Bailey, B. W., Tucker, L. A., & Larson, M. J. (2017b). Testing food‐related inhibitory control to high‐and low‐calorie food stimuli: Electrophysiological responses to high‐calorie food stimuli predict calorie and carbohydrate intake. Psychophysiology, 54(7), 982–997.
Carbine, K. A., Duraccio, K. M., Kirwan, C. B., Muncy, N. M., LeCheminant, J. D., & Larson, M. J. (2018). A direct comparison between ERP and fMRI measurements of food-related inhibitory control: Implications for BMI status and dietary intake. NeuroImage, 166, 335–348. https://doi.org/10.1016/j.neuroimage.2017.11.008
Carbine, K. A., Rodeback, R., Modersitzki, E., Miner, M., LeCheminant, J. D., & Larson, M. J. (2018). The utility of event-related potentials (ERPs) in understanding food-related cognition: A systematic review and recommendations. Appetite, 128, 58–78. https://doi.org/10.1016/j.appet.2018.05.135
Clayson, P. E., & Larson, M. J. (2013). Psychometric properties of conflict monitoring and conflict adaptation indices: Response time and conflict N 2 event‐related potentials. Psychophysiology, 50(12), 1209–1219.
Cui, T., Xi, J., Tang, C., Song, J., He, J., & Brytek-Matera, A. (2021). The Relationship between Music and Food Intake: A Systematic Review and Meta-Analysis. Nutrients, 13(8), 2571. https://doi.org/10.3390/nu13082571
Dobrenova, F. V., Grabner-Kräuter, S., & Terlutter, R. (2015). Country-of-origin (COO) effects in the promotion of functional ingredients and functional foods. European Management Journal, 33(5), 314–321. https://doi.org/10.1016/j.emj.2015.03.003
Faul, F., Erdfelder, E., Lang, A.-G., & Buchner, A. (2007). G* Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39(2), 175–191.
Fiegel, A., Childress, A., Beekman, T. L., & Seo, H.-S. (2019). Variations in food acceptability with respect to pitch, tempo, and volume levels of background music. Multisensory Research, 32(4–5), 319–346.
Fiegel, A., Meullenet, J.-F., Harrington, R. J., Humble, R., & Seo, H.-S. (2014). Background music genre can modulate flavor pleasantness and overall impression of food stimuli. Appetite, 76, 144–152.
French, S. A., Perry, C. L., Leon, G. R., & Fulkerson, J. A. (1994). Food preferences, eating patterns, and physical activity among adolescents: Correlates of eating disorders symptoms. Journal of Adolescent Health, 15(4), 286–294. https://doi.org/10.1016/1054-139X(94)90601-7
Guedes, D., Prada, M., Garrido, M. V., & Lamy, E. (2023a). The taste & affect music database: Subjective rating norms for a new set of musical stimuli. Behavior Research Methods, 55(3), 1121–1140.
Hachl, P., Hempel, C., & Pietrowsky, R. (2003). ERPs to stimulus identification in persons with restrained eating behavior. International Journal of Psychophysiology, 49(2), 111–121.
Hao, J., Zhong, Y., Pang, Y., Jing, Y., Liu, Y., Li, H., Li, J., & Zheng, M. (2023). The relationship between music training and cognitive flexibility: An ERP study. Frontiers in Psychology, 14, 1276752.
Haws, K. L., Reczek, R. W., & Sample, K. L. (2017). Healthy Diets Make Empty Wallets: The Healthy = Expensive Intuition. JOURNAL OF CONSUMER RESEARCH, 43(6), 992–1007. https://doi.org/10.1093/jcr/ucw078
Hong-li, W., Sheng-ao, J., Sheng-rong, L., Qing-yao, H., Li, L., Shi-Kang, T., Cheng, H., Li-ping, Q., & Chang-hong, C. (2017). Volatile organic compounds (VOCs) source profiles of on-road vehicle emissions in China. SCIENCE OF THE TOTAL ENVIRONMENT, 607, 253–261. https://doi.org/10.1016/j.scitotenv.2017.07.001
Huang, X., & Labroo, A. A. (2020). Cueing morality: The effect of high-pitched music on healthy choice. Journal of Marketing, 84(6), 130–143.
Hussain, M., Egan, H., Keyte, R., & Mantzios, M. (2021). Exploring the environmental manifestation of types of music on reinforcing mindfulness and concurrent calorie intake. Psychological Reports, 124(6), 2633–2650.
Jing, Y., Xu, Z., Pang, Y., Liu, X., Zhao, J., & Liu, Y. (2024a). The Neural Correlates of Food Preference among Music Kinds. Foods, 13(7), 1127. https://doi.org/10.3390/foods13071127
Kantono, K., Hamid, N., Shepherd, D., Lin, Y. H. T., Yakuncheva, S., Yoo, M. J. Y., Grazioli, G., & Carr, B. T. (2016). The influence of auditory and visual stimuli on the pleasantness of chocolate gelati. FOOD QUALITY AND PREFERENCE, 53, 9–18. https://doi.org/10.1016/j.foodqual.2016.05.008
Kantono, K., Hamid, N., Shepherd, D., Yoo, M. J. Y., Carr, B. T., & Grazioli, G. (2016). The effect of background music on food pleasantness ratings. PSYCHOLOGY OF MUSIC, 44(5), 1111–1125. https://doi.org/10.1177/0305735615613149
Levitan, C. A., Charney, S., Schloss, K. B., & Palmer, S. E. (2015). The Smell of Jazz: Crossmodal Correspondences Between Music, Odor, and Emotion. CogSci, 1, 1326–1331.
Liu, X., Liu, Y., Shi, H., Li, L., & Zheng, M. (2021). Regulation of mindfulness-based music listening on negative emotions related to COVID-19: An ERP study. International Journal of Environmental Research and Public Health, 18(13), 7063.
Liu, Y., Gao, X., Zhao, J., Zhang, L., & Chen, H. (2020). Neurocognitive correlates of food-related response inhibition in overweight/obese adults. Brain Topography, 33, 101–111.
Liu, Y., Quan, H., Song, S., Zhang, X., Yang, C., & Chen, H. (2019). Decreased conflict control in overweight Chinese females: Behavioral and event-related potentials evidence. Nutrients, 11(7), 1450.
Liu, Y., Zhao, J., Zhou, Y., Yang, R., Han, B., Zhao, Y., Pang, Y., Yuan, H., & Chen, H. (2022). High-Calorie Food-Cues Impair Conflict Control: EEG Evidence from a Food-Related Stroop Task. Nutrients, 14(21), 4593. https://doi.org/10.3390/nu14214593
Luck, S. J., Woodman, G. F., & Vogel, E. K. (2000). Event-related potential studies of attention. Trends in Cognitive Sciences, 4(11), 432–440. https://doi.org/10.1016/S1364-6613(00)01545-X
Macht, M. (2008). How emotions affect eating: A five-way model. Appetite, 50(1), 1–11.
Mehrabian, A., & Russell, J. A. (1974). An approach to environmental psychology. the MIT Press.
Meule, A., Kübler, A., & Blechert, J. (2013). Time course of electrocortical food-cue responses during cognitive regulation of craving. Frontiers in Psychology, 4. https://doi.org/10.3389/fpsyg.2013.00669
Milliman, R. E. (1986). The influence of background music on the behavior of restaurant patrons. Journal of Consumer Research, 13(2), 286–289.
Motoki, K., Takahashi, N., Velasco, C., & Spence, C. (2022). Is classical music sweeter than jazz? Crossmodal influences of background music and taste/flavour on healthy and indulgent food preferences. Food Quality and Preference, 96, 104380.
Nijs, I. M., Franken, I. H., & Muris, P. (2007). The modified Trait and State Food-Cravings Questionnaires: Development and validation of a general index of food craving. Appetite, 49(1), 38–46.
Nijs, I. M., Franken, I. H., & Muris, P. (2010). Food-related Stroop interference in obese and normal-weight individuals: Behavioral and electrophysiological indices. Eating Behaviors, 11(4), 258–265.
North, A. C., Shilcock, A., & Hargreaves, D. J. (2003). The effect of musical style on restaurant customers’ spending. Environment and Behavior, 35(5), 712–718.
Pantoja, F., & Borges, A. (2021). Background music tempo effects on food evaluations and purchase intentions. Journal of Retailing and Consumer Services, 63, 102730. https://doi.org/10.1016/j.jretconser.2021.102730
Peng-Li, D., Mathiesen, S. L., Chan, R. C., Byrne, D. V., & Wang, Q. J. (2021a). Sounds Healthy: Modelling sound-evoked consumer food choice through visual attention. Appetite, 164, 105264.
Peng-Li, D., Mathiesen, S. L., Chan, R. C., Byrne, D. V., & Wang, Q. J. (2021b). Sounds Healthy: Modelling sound-evoked consumer food choice through visual attention. Appetite, 164, 105264.
Pérez-Rodrigo, C., Ribas, L., Serra-Majem, L., & Aranceta, J. (2003). Food preferences of Spanish children and young people: The enKid study. European Journal of Clinical Nutrition, 57(1), S45–S48.
Quekel, M. (2016). Sweets in my beat, sugar for my money?: The effects of high-pitched background music on consumers’ choice of sweet food products (Master's thesis, University of Twente).
Rentfrow, P. J., & Gosling, S. D. (2003). The do re mi’s of everyday life: The structure and personality correlates of music preferences. Journal of Personality and Social Psychology, 84(6), 1236.
Sänger, J. (2019). Can’t take my eyes off you–How task irrelevant pictures of food influence attentional selection. Appetite, 133, 313–323.
Schwab, D., Giraldo, M., Spiegl, B., & Schienle, A. (2017). Disgust evoked by strong wormwood bitterness influences the processing of visual food cues in women: An ERP study. Appetite, 108, 51–56. https://doi.org/10.1016/j.appet.2016.09.023
Seppänen, M., Hämäläinen, J., Pesonen, A.-K., & Tervaniemi, M. (2012). Music Training Enhances Rapid Neural Plasticity of N1 and P2 Source Activation for Unattended Sounds. Frontiers in Human Neuroscience, 6. https://doi.org/10.3389/fnhum.2012.00043
Singh, M. (2014). Mood, food, and obesity. Frontiers in Psychology, 5, 99252.
Spence, C., Reinoso-Carvalho, F., Velasco, C., & Wang, Q. J. (2019). Extrinsic auditory contributions to food perception & consumer behaviour: An interdisciplinary review. Multisensory Research, 32(4–5), 275–318.
Spence, C., & Shankar, M. U. (2010). The influence of auditory cues on the perception of, and responses to, food and drink. Journal of Sensory Studies, 25(3), 406–430.
Stockburger, J., Weike, A. I., Hamm, A. O., & Schupp, H. T. (2008). Deprivation selectively modulates brain potentials to food pictures. Behavioral Neuroscience, 122(4), 936.
Tao, S., Yu, L., Gao, W., & Xue, W. (2016). Food preferences, personality and parental rearing styles: Analysis of factors influencing health of left-behind children. Quality of Life Research, 25(11), 2921–2929. https://doi.org/10.1007/s11136-016-1317-3
Tillman, C. M., & Wiens, S. (2011). Behavioral and ERP indices of response conflict in Stroop and flanker tasks. Psychophysiology, 48(10), 1405–1411. https://doi.org/10.1111/j.1469-8986.2011.01203.x
Wang, J., Wang, H., Yu, H., Wang, J., Guo, X., Tong, S., Bao, Y., & Hong, X. (2022). Neural mechanisms of inhibitory control deficits in obesity revealed by P3 but not N2 event-related potential component. Appetite, 171, 105908. https://doi.org/10.1016/j.appet.2021.105908
Wang, Y. (2022). The Intersection of Musical Art and Food Culture—Review of Food Culture. Journal of Food Safety and Quality Testing (In Chinese), 13, 4409–4410.
Wardle, J., & Cooke, L. (2008). Genetic and environmental determinants of children’s food preferences. British Journal of Nutrition, 99(S1), S15–S21.
Wascher, E., Hoffmann, S., Sänger, J., & Grosjean, M. (2009). Visuo‐spatial processing and the N1 component of the ERP. Psychophysiology, 46(6), 1270–1277. https://doi.org/10.1111/j.1469-8986.2009.00874.x
Watson, T. D., & Garvey, K. T. (2013). Neurocognitive correlates of processing food-related stimuli in a Go/No-go paradigm. Appetite, 71, 40–47. https://doi.org/10.1016/j.appet.2013.07.007
Wessel, J. R. (2018). Prepotent motor activity and inhibitory control demands in different variants of the go/no‐go paradigm. Psychophysiology, 55(3), e12871. https://doi.org/10.1111/psyp.12871
West, R., & Alain, C. (1999). Event-related neural activity associated with the Stroop task. Cognitive Brain Research, 8(2), 157–164. https://doi.org/10.1016/S0926-6410(99)00017-8
Wu, J., Willner, C. J., Hill, C., Fearon, P., Mayes, L. C., & Crowley, M. J. (2018). Emotional eating and instructed food-cue processing in adolescents: An ERP study. Biological Psychology, 132, 27–36. https://doi.org/10.1016/j.biopsycho.2017.10.012
Zentner, M., Grandjean, D., & Scherer, K. R. (2008). Emotions evoked by the sound of music: Characterization, classification, and measurement. Emotion, 8(4), 494.

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Individuals’ food preferences can be influenced by the Music styles: An ERP study

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Abstract

Figures

1. Introduction

2. Methods

2.1 Participants

2.2 Procedure

2.3 Questionnaires

2.4 Stimuli and food choice task

2.5 EEG recording analysis

3. Results

3.1 Behavioral results

3.2 ERP results (Fig. 3)

4.3 Relationships among Self-report data, Behavioral and ERP Results

4. Discussion

4 Conclusions

Declarations

References

Additional Declarations

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Version 1