Standalone Sounds: ERP Correlates of Duration, Amplitude Envelope, and Tonal Perception
Abstract
Abstract
Background and problem. Musical tension has traditionally been studied through the lens of tonal syntax, while the contributions of sound intensity and duration, the physical “power” of sound, remain largely unexplored in electrophysiology. It is unknown how the brain processes a sound’s duration when no other acoustic events occur, and how this processing interacts with tonal meaning. Objective. This study aimed to identify event‑related potential (ERP) correlates of duration‑induced tension, compare ERPs for chords with and without natural amplitude decay (thereby isolating tonal emotional meaning), and assess the influence of musical training. Methods. Forty‑four participants listened passively to four types of isolated 1‑s stimuli: natural major and minor chords (with amplitude decay) and their modified versions possessing a rectangular amplitude envelope. EEG was recorded from 21 electrodes; ERPs were analysed via amplitude, spectra, event‑related spectral perturbations (ERSP), and inter‑trial coherence (ITC). Subjective ratings of “Merry”, “Sad”, “Tensing”, and other scales were collected. Participants were divided into amateur musicians (n = 13) and non‑musicians. Results. Modified chords without amplitude decay were perceived as more tense and failed to convey the major/minor emotional distinction. A late positive potential peaking at ~1200 ms (LPP1200) was significantly larger for modified chords and correlated with subjective tension. ITC differences between chord types were most pronounced in the 3–6 Hz band at 1100–1300 ms. Natural chords elicited N400 and N600 components that were absent for modified chords. Amateur musicians showed smaller differences between conditions and higher ITC values, whereas non‑musicians exhibited stronger early (P1–N1) responses to modified chords. Conclusion. Amplitude decay is essential for communicating tonal emotion to non‑experts. The LPP1200 component reflects the resolution of temporal uncertainty and the subjective tension associated with an isolated sound. Studying standalone auditory stimuli unveils late potentials that are typically concealed by active task demands, thus offering a new window into the perception of musical tension.
Изолированные звуки: ERP-корреляты длительности, амплитудной огибающей и тонального восприятия
Николай А. Алмаев¹, Станислав О. Скорик¹, Дарья Л. Петрович², Ольга В. Мурашева¹
¹Институт психологии РАН, Москва, Россия
²Московский институт психоанализа, Москва, Россия
Резюме. Актуальность и проблема. Музыкальное напряжение традиционно исследуется сквозь призму тонального синтаксиса, тогда как вклад интенсивности и длительности звука – физической «силы» звучания – в электрофизиологических работах остаётся практически неизученным. Остаётся неизвестным, каким образом мозг обрабатывает длительность звука в отсутствие других акустических событий и как эта обработка взаимодействует с передачей тонального значения. Цель. Настоящее исследование направлено на выявление коррелятов вызванной длительностью напряжённости в связанных с событиями потенциалах (ERP), сравнение ERP для аккордов с естественным затуханием амплитуды и без него (что позволяет изолировать тональное эмоциональное содержание) и оценку влияния музыкального обучения.
Методы. Сорок четыре участника пассивно прослушивали четыре типа изолированных стимулов длительностью 1 с: натуральные мажорный и минорный аккорды (с затуханием амплитуды) и их модифицированные версии с прямоугольной амплитудной огибающей. ЭЭГ регистрировали с 21 электрода; ERP анализировали по амплитуде, спектральным характеристикам, вызванным спектральным возмущениям (ERSP) и межпробной когерентности (ITC). Собирались субъективные оценки по шкалам «Весёлый», «Грустный», «Напрягающий» и другим. Участники были разделены на любителей-музыкантов (n = 13) и немузыкантов. Результаты. Модифицированные аккорды без затухания амплитуды воспринимались как более напряжённые и не передавали мажорно-минорного эмоционального различия. Поздний положительный потенциал с пиком около 1200 мс (LPP1200) был значимо большим для модифицированных аккордов и коррелировал с субъективным напряжением. Различия в ITC между типами аккордов были наиболее выражены в полосе 3–6 Гц на интервале 1100–1300 мс. Натуральные аккорды вызывали компоненты N400 и N600, отсутствовавшие для модифицированных аккордов. Любители-музыканты демонстрировали меньшие различия между условиями и более высокие значения ITC, тогда как немузыканты обнаруживали усиленные ранние (P1–N1) ответы на модифицированные аккорды. Заключение. Затухание амплитуды необходимо для передачи тональной эмоции не-экспертам. Компонент LPP1200 отражает разрешение временной неопределённости и субъективное напряжение, связанное с изолированным звуком. Изучение одиночных слуховых стимулов раскрывает поздние потенциалы, которые обычно маскируются требованиями активных заданий, открывая тем самым новое окно в восприятие музыкального напряжения.
Ключевые слова: связанные с событиями потенциалы (ERP), поздний положительный потенциал (LPP), музыкальное напряжение, длительность звука, амплитудная огибающая, мажорные и минорные аккорды, музыкальное обучение, пассивное слушание, межпробная когерентность (ITC), эмоциональное восприятие.
Introduction
Since Kurth (1931), the communication of emotion through music has been understood as an interplay of tensions and resolutions. Tension can arise from multiple sources: the intensity of sounds, their duration, and tonal structures that produce consonance/dissonance or stable/unstable progressions. In ERP research, however, the tonal dimension has overwhelmingly prevailed. Inspired by Lerdahl and Jackendoff’s (1983) generative theory of tonal music, which drew a formal parallel with Chomskian generative grammar (Chomsky, 1957, 1995), numerous studies have employed paradigms in which expectations are violated within tonal sequences (Koelsch et al., 2003; Halpern et al., 2017; Doelling & Poeppel, 2015). Many of these investigations also compared music and language processing (Koelsch et al., 2005; Fedorenko et al., 2009; Calma‑Roddin & Drury, 2020; Sun et al., 2018; Slevc et al., 2009). Active tasks are typically included, which may themselves elicit additional ERP components and mask those of interest (Cozzi et al., 2019). Investigations of tempo, too, generally require active detection of changes (Graber & Fukuoka, 2019; Xiao et al., 2020).
In experimental psychology, tonal research (Lerdahl, 1996; Krumhansl, 1997; Lerdahl & Krumhansl, 2007) has long been complemented by studies of rhythm (Fernández‑Sotos et al., 2016; McAuley, 2010; Parncutt, 1994; Hausen et al., 2013), tempo (Kamenetsky et al., 1997; Levitin & Cook, 1996; Moelants, 2002), isolated chords (Lahdelma & Eerola, 2016a, 2016b; Arthurs et al., 2018; Ellison et al., 2015), and more complex phenomena (Küssner et al., 2014). Juslin et al. (2022) enumerated eight mechanisms underlying musical emotions, among which “musical expectancy” (relying on syntactic structure) and “brain stem reflex” as well as “rhythmic entrainment” (dependent on intensity and duration) are particularly relevant. The existence of a distinct “Intense” factor in musical preferences, characterised by loud, forceful music (Rentfrow et al., 2011), together with research on preferences for listening at high volumes (Welch & Fremaux, 2017; Manchaiah et al., 2019; Petrescu, 2008), indicates that coping with the physical power of sound constitutes a separate source of tension. Hence, “tension” as a response to physical sound properties must be distinguished from “tension” as an expectancy violation (Lehne & Koelsch, 2015), and their mutual relations may vary across listeners.
Accounting simultaneously for intensity, duration, and tonality is challenging (Farbood & Upham, 2013; Lehne et al., 2013). While the effect of sound intensity has been addressed in ERP studies (Barry et al., 2022), the effect of duration has not been the subject of a dedicated ERP investigation, to our knowledge. The influence of duration necessarily accumulates towards the end of a sound; to observe it without interference, stimuli must be presented in isolation. Moreover, the amplitude envelope of a sound can takes countless forms, complicating the study of duration. To isolate the temporal dimension, we previously introduced auditory stimuli with a rectangular (orthogonal) amplitude envelope (Almaev & Skorik, 2015). These behavioural experiments revealed that perceived tension varied nonlinearly with period length: it decreased up to ~950 ms, then increased from 950 to 1350 ms, and became non‑significant beyond 1350 ms. Consequently, the search for an ERP correlate of duration needed to extend to at least 1500 ms, and stimuli had to be presented in isolation.
Such late components are known as late positive potentials (LPP). A growing body of research implicates the LPP in processing emotionally charged stimuli (Dickey et al., 2021; Desatnik et al., 2017), primarily in the visual domain. In the auditory modality, late potentials are typically considered as latencies between 350 and 800–1000 ms (Koelsch, 2011; Halpern et al., 2017; Yu et al., 2022). ERP components beyond 1000 ms are rarely discussed and are mostly found in studies of language (Proverbio et al., 2020; Solomon et al., 2012; Sanders & Neville, 2003).
Further experiments with an orthogonal amplitude envelope (Almaev et al., 2018; Skorik & Almaev, 2019; Skorik & Almayev, 2023) revealed a peculiar feature: amplitude decay is critically important for the transmission of major and minor chords’ emotional content among ordinary listeners, but not among professional musicians. Professionals can perceive the emotional meaning (“sad”, “merry”) of stimuli that are tonally identical to a chord yet lack amplitude decay, whereas amateurs and non‑musicians cannot. Nevertheless, amplitude decay also enhanced professionals’ results (Skorik & Almaev, 2019; Skorik & Almayev, 2023). The existing data on ERP differences between ordinary major and minor chords (Halpern et al., 2008; Virtala et al., 2014; Bakker & Martin, 2015; Ellison et al., 2015) can therefore be enriched by comparing ERPs to stimuli that convey specific emotions with those that do not.
Hence, comparing chords with and without amplitude decay allows one to separate the contributions of duration and tonality to ERP components. The main idea of the present study can be summarised as follows: to observe what happens to a sound’s ERP when no other sounds occur. We focus on the comparison of stimulus ERPs “as they are”, obtained through passive listening without a context of other acoustic events or active tasks, i.e. we examine the structure of expectations and tensions when nothing interrupts them. The specific goals are: (1) to identify the effect of auditory stimulus duration on subjective tension (coping with the power of sound) and on the ERP, presumably in the LPP range; (2) to search for differences between ERPs of stimuli with and without amplitude decay; (3) to assess the effect of musical training on the obtained results.
Method
2.1. Stimuli
Stimuli were adopted from previous studies (Almaev et al., 2018; Skorik & Almaev, 2019). Four types were presented: two ordinary grand piano chords (A5 and Am5) downloaded from a Yamaha soundbank, and two modified chords (Plain A5 and Plain Am5). The modified chords were synthesised from sinusoidal waves (fundamental 880 Hz, natural scale: A, C#, E and A, C, E) using Cool Edit Pro 2.0. The natural scale was chosen to avoid additional throbs that arise with the tempered scale. The duration of each sample was 1 s, with an inter‑stimulus interval of 4 s. No detectable click occurred at the end of either ordinary or modified chords at the presentation volume. All stimuli were presented at approximately 80 dB SPL, measured at the participant’s seat.
2.2. Presentation order
Stimuli were presented in the order: Plain A, Ordinary A, Ordinary Am, Plain Am. Because of fatigue effects and the consequent increase in artifacts that reduced the number of valid epochs for minor stimuli, the order was planned to be reversed after the 20th participant (Plain Am, Ordinary Am, Ordinary A, Plain A).

2.3. Participants and procedure
Participants were recruited from a larger sample of individuals who volunteered for a study on musical preferences with continuous EEG recording; the recruitment was conducted via social networks and personal invitation. A main motivation was the opportunity to listen through “legendary JBL 4345” monitors, so the participants were, in a sense, people who value sound quality. They were optionally invited to take part in the ERP study for a fee of 500 RUB (approximately 8 USD) and/or to complete personality tests for the same fee. The conditions were clearly stated in the initial offer. Filling in a survey after the instructions served as informed consent. Some individuals participated only for the fee and did not listen to music. The broader study included an extensive survey on musical education, skills, and preferences. ERP recording always preceded the continuous EEG session. Before and after stimulus presentation, resting‑state EEG was registered (1 min eyes open, 1 min eyes closed).
During the ERP study, participants were instructed to listen to the sounds with their eyes closed, seated in a sound‑ and echo‑proof room. The light was dimmed or switched off according to individual comfort. No tasks were associated with the stimuli during their administration. After the end of each stimulus series (which varied from 70 to more than 100 epochs, depending on artifact rejection), participants evaluated the presented sounds on six unipolar scales ranging from 0 to 6: “Merry”, “Sad”, “Confident”, “Pleading”, “Anxious”, “Tensing” (0 = absence of the quality, 6 = its greatest extent). Bilateral communication was maintained via speakerphones. Subjective evaluations were made on a tablet PC using Google Forms. ERP stimuli were presented in stereo mode via the aforementioned JBL 4345 monitors together with “Elektronika 50 AS‑61M” loudspeakers; both systems were active during presentations.
According to a priori power analysis (Faul et al., 2009), a total sample size of N = 45 was required for a paired t‑test to achieve power (1–β) = 0.95; for a Wilcoxon matched‑pairs test, N = 47. Data collection was stopped at 53 participants, knowing that some recordings served as training, some lacked ERP data, and some persons participated twice. After data verification, a total of 44 unique participants were selected for the ERP study. Each participant contributed to four conditions, yielding 176 ERP datasets.
2.4. ERP recording and preprocessing
Sound administration and ERP recording were synchronised using a Mitsar‑201 encephalograph (Mitsar‑Phono v.200). The sampling rate was 250 Hz, and impedance was kept below 40 kΩ. Twenty‑one Ag/AgCl electrodes were mounted on an elastic cap according to the 10–20 system. Reference electrodes were A01 and A02 (earlobes). EOG was not recorded. Automated artifact removal (Mitsar software) rejected epochs with amplitude >200 µV (any frequency), slow‑wave amplitude >100 µV (0–1 Hz), or fast‑wave amplitude >100 µV (20–35 Hz). Data were converted to. edf format and imported into EEGLAB (Delorme & Makeig, 2004). Epochs from –500 to 2000 ms were extracted and visually inspected. Epochs containing myograms, pronounced slow waves (up to ~3 Hz), or considerable spikes were excluded to the maximum extent possible. A minimum of 50 artifact‑free epochs per condition was required to maximise the signal‑to‑noise ratio. Rheograms and overt eye‑movement artifacts were not specifically excluded.
After this selection, data from 29 participants (19 females, age M = 33.7, SD = 11.4; 10 males, age M = 30.2, SD = 5.41) were retained for statistical analyses in EEGLAB. Two designs were employed: (1) one‑way repeated measures with Condition (Ordinary vs. Plain) as independent variable, using paired t‑tests (1450 epochs per condition × 21 channels), time range –200 to 1500 ms; (2) a mixed design adding Musical Training as a between‑subjects factor (amateur musicians: n = 13, 5 males, age M = 34.2, SD = 10.8; non‑musicians: remainder). This second design used two‑way ANOVA with marginal statistics. Averaged channel potentials were used instead of RMS. False Discovery Rate (FDR) correction was applied primarily; if no differences survived FDR, uncorrected results were considered. Four analysis types were performed: average amplitude, spectra, event‑related spectral perturbations (ERSP), and inter‑trial coherence (ITC).
2.5. Musical training classification
Inclusion in the amateur musician group required a self‑reported ability to perform on a musical instrument. Participants with uncertain statements (e.g., “tried the piano”) were excluded. The group included: conservatory (1), sound engineer (1), musical college (2), completed music school (8 years; 3); the remainder were amateurs trained by personal tutors and/or parents. Six participants were personally known to the authors; the others could be verified in most cases via social networks. The level generally corresponds to that of amateur musicians.
Results
3.1. Subjective appraisals
The scales “Merry”, “Sad”, and “Tensing” are particularly relevant: the first two refer to the most common attributes of major and minor chords, while “Tensing” indexes coping with the physical power of sound. Medians of subjective ratings are given in Table 1. For the total sample, differences between Plain major and Plain minor were not significant, which is typical for non‑musicians and amateurs. Based on estimated musical training, only 2–3 participants could possibly differentiate between the Plain chords.

Wilcoxon matched‑pairs tests (Table 2) revealed significant differences between Plain major and Ordinary major on “Merry”, “Sad”, “Pleading”, “Anxious”, and “Tensing” (Bonferroni‑corrected). In the ERP subsample, the differences were even more robust. For Ordinary major vs. Ordinary minor (Table 3), “Merry” and “Sad” differed significantly in the ERP subsample, but the effect sizes were smaller than those for the Plain vs. Ordinary contrast. These results indicate that conveying tonal emotional information depends on amplitude decay, while subjective tension is linked to the rectangular envelope and the physical power of sound.

3.2. ERP amplitude
Because the presentation order could not be fully balanced (only 10 participants had the minor series first, vs. 19 with the major series first), and noticeably more slow‑wave artifacts remained in minor‑series epochs, ERP results for Ordinary minor vs. Ordinary major are not reported here. The most pronounced differences were observed between Ordinary A5 and Plain A5.
Average ERP amplitude for Ordinary A5 significantly exceeded that for Plain A5 at the P2 latency. Conversely, Plain A5 elicited a significantly larger positivity peaking at approximately 1200 ms (LPP1200; Fig. 3). This LPP1200 began around 1100 ms, lasted until ~1350 ms, and its central portion (1150–1250 ms) was particularly sensitive to subjective tension (see Table 2).

3.3. Spectral, ERSP, and ITC analyses
Spectral components of the two stimuli were almost identical at all leads; no significant differences were found. ERSP analyses revealed a pattern similar to the amplitude data: significant differences were observed at F3, Fz, F4, Cz, and C4, mainly in frequencies below 4 Hz (after FDR).
Inter‑trial coherence (ITC) proved the most informative measure. Differences at ~1200 ms were found not only at the electrodes listed in Table 4 but also at P4 (Figs. 4a–c). Remarkably, the ITC differences became more distinct from frontal to parietal leads, despite decreasing absolute ITC values. This observation may relate to the role of the intraparietal sulcus in transforming musical pitch information (Foster & Zatorre, 2010). Based on the P4 plot, the difference between ERPs for stimuli with and without amplitude decay – i.e. between perceived and non‑perceived emotional tonal information – lies in the 6–9 Hz band at latencies of ~160–290 ms. The LPP1200 effect (1050–1350 ms) was most prominent in the 3–6 Hz band (p < 0.001, FDR‑corrected).

- Anterior negativity
In the full ERP sample, the negativity waveforms of Plain and Ordinary chords were similar. At Cz, the Plain chord showed an N400, whereas the Ordinary chord displayed an N600 (Fig. 3). The largest anterior negativity differences emerged at F7 and F8 (Fig. 5): at F7, a difference at 600–650 ms (p < 0.05), and at F8, a tendency at 890 ms (p = 0.058).

- Musical training effects
A two‑way ANOVA (Condition × Group) on ITC data showed that amateur musicians had smaller differences between Ordinary and Plain chords than non‑musicians. At P4, the musicians’ ITC for Ordinary chords showed a well‑defined LPP1200, whereas non‑musicians’ ITC was less focal (Fig. 6a–b). Without FDR correction, musicians also exhibited less pronounced P2 and LPP1200 differences.

In anterior leads, particularly Fp1 and Fp2, musicians showed marked differences between the two chord types in N1 and P2 (small N1 and large P2 for Ordinary; articulated N1 and small P2 for Plain), whereas non‑musicians showed almost no such differences (Fig. 7). Musicians also had larger LPP1200 peaks than non‑musicians.

Comparing the two groups for Ordinary chords revealed differences in the N400, N600, and N950 ranges (Table 5). In contrast, during Plain chord presentation, the groups differed primarily in early components (P1–N1a–N1b; Näätänen & Picton, 1987; Näätänen, 1990) and at N500 and LPP1200 (Table 6). Non‑musicians consistently showed greater negativity than amateurs in these early latencies.


Discussion
This study is exploratory in nature, and most of its results were unexpected. Although the design is simple, it departs substantially from the dominant syntactic paradigm; therefore, the correspondence of our findings to those obtained within that paradigm requires careful consideration. Moreover, the experiment jointly addresses two phenomena such as duration and tonality, each deserving separate investigation.
The first major finding is that the effect of a sustained sound accumulates in an LPP peaking at approximately 1200 ms. The positive rise begins after 1000 ms and stabilises around 1350 ms (Fig. 3). Late positive potentials beyond 1000 ms are mostly reported in the context of visual stimulation (Dickey et al., 2021; Solomon et al., 2012), but they also appear in studies of auditory, mainly verbal perception. Shahin et al. (2006) identified a “P1000” between 700 and 1500 ms for semantic targets. Männel et al. (2013) observed a positive shift peaking at ~1500 ms in 6‑year‑olds. A similar rise, even if not explicitly discussed, is visible in the waveforms of several published articles. For instance, Cozzi et al. (2019, their Figs. 1–3) demonstrate that an active task suppresses the late positivity, whereas a passive task releases it; the passive‑task waveform closely resembles that obtained in the present study. In Müller et al. (2010, Fig. 6), the requirement of an aesthetic judgement an active task apparently prevents the positivity from crossing the zero line.
Studies of word and speech recognition employ relatively long stimuli and can therefore illuminate late auditory positivity. Sanders and Neville (2003) showed that sentences containing pseudowords of sufficient duration elicit a positive rise after 1000 ms, whereas acoustically and semantically varied sentences do not. Similar patterns can be discerned in works conducted within the syntactic paradigm, both musical and linguistic (Malins et al., 2013, Fig. 4; Ma et al., 2018, Fig. 4).
The dynamics observed for the Ordinary chord (audible until ~250–270 ms; Fig. 1) and for the Plain chord (audible until ~950–970 ms; Fig. 2) are similar (Fig. 3). An intense, durable sound evokes negativity while it lasts – this is expected for a rectangular envelope – but why does the same occur for the Ordinary chord in both amateurs and non‑musicians? One cannot exclude the possibility that the fixed interval between stimuli, and the fact that Plain chords were always presented first, created specific expectations and influenced the regularity of the peaks. On the other hand, the observed components may reflect the inner dynamics of expectations and tensions that are inherent to the late stages of perceiving a standalone sound. What psychological processes could they represent? Converting latencies into tempo yields suggestive correspondences: 1000 ms ≈ 60 bpm (lentamente), 1200 ms ≈ 50 bpm (lento), 1350 ms ≈ 44 bpm (largo/grave – the slowest musical tempo). We propose that LPP1200 indexes the decision of whether the perceived sound is part of a very slow rhythmic structure or a standalone (though possibly repeating) event. This very uncertainty about the tempo of the sequence likely underlies the subjective tension found earlier (Almaev & Skorik, 2015). The mechanism may also underpin performance practices in which slowing the tempo enhances expressivity.
LPP1200 may additionally correspond to the finalisation of a subjective appraisal of tempo and, in some cases, of rhythmic structure. The results of Joucla et al. (2018), who investigated neural signatures of musical preference during silence, corroborate this suggestion: they identified three main components, the second of which occurred between 1000 and 1300 ms. The importance of P2 for chord processing has been acknowledged before (Bakker & Martin, 2015; Ellison et al., 2015; Müller et al., 2010); accordingly, the P2 rise observed here may be interpreted as the accumulation of information about the event, but not yet its full semantic identification.
Amateurs and non‑musicians. Elements such as intensity, duration, fill factor, timbre, and “sound quality” clearly play an important role in perception, and their role appears to grow as musical training decreases. How should the differences between amateur musicians and non‑musicians be interpreted in the absence of any syntactic context? The semantic differences between Ordinary and Plain chords are reflected in ERP components in both groups (Fig. 7, Tables 5 and 6). The differences between groups therefore seem to lie in the consumption of cognitive resources by the sound itself. As noted in the Method, the sample was initially biased towards individuals sensitive to “sound quality”. Consequently, non‑musicians may simply have been “more impressed”, reacting more chaotically and extravagantly, whereas more experienced amateurs responded in a more economical manner. The higher ITC values in amateurs for Ordinary‑chord LPP1200 (Fig. 6a–b, upper panels) could be interpreted as a better sense of rhythm, i.e. expectations being active in the relevant time window despite the lower fill factor of the period. Taken together, musical training might be viewed as a progressive abstraction from everything unnecessary for performance – an abstraction that, in professional musicians, reaches the level of discarding amplitude decay (Skorik & Almaev, 2019; Skorik & Almayev, 2023). This topic certainly requires further investigation, particularly regarding the interaction between instrument type and professionalism.
The ERP differences between amateurs and non‑musicians in response to the Ordinary chord included the N400 (Fig. 7, left panels; Table 5), a component extensively discussed in relation to both linguistic and musical semantics (Kutas & Federmeier, 2011; Cummings et al., 2006; Miranda & Ullman, 2007; Calma‑Roddin & Drury, 2020). No analogous differences were detected for the Plain chord. According to the prevailing interpretation (Kutas & Federmeier, 2011), N400 reflects the identification of an event in semantic memory. The contrast between Ordinary and Plain chords can therefore be understood as the difference between meaningful and meaningless acoustic events. Furthermore, instead of the P600 often associated with musical expectancy violations (Patel et al., 1998; Tanner & Van Hell, 2014) or the P700 (Shahin et al., 2006), we detected a rarer N600 in most frontal leads – both as the negativity maximum for the Plain chord at Cz (Fig. 3) and in the comparisons between non‑musicians and musicians. According to Cummings et al. (2006), the N600 is related to general stimulus processes such as maintenance of task demands and response monitoring, an interpretation that fits both phenomena in the sense of searching for neural resources.
Plain chord presentation highlighted the focus on early (P1–N1) sound processing (Table 6). This observation confirms that decisions about the meaningfulness of sounds are first taken during these latencies (Fiveash et al., 2018; Virtala et al., 2014). In addition, the group comparison revealed the rather rare N500 component, which, according to Koelsch (2011, part 5), corresponds to the processing of intra‑musical meaning – the reference of one musical element to at least one other musical element. N500 is related to the complex interplay between Early Right Anterior Negativity (ERAN, linked to syntactic processing) and mismatch negativity (MMN, linked to expectancy violations). Initially, Koelsch et al. (2001) estimated the MMN to lie in the 125–185 ms range and the ERAN in the 170–230 ms range (i.e., MMN earlier). A recent thorough study by Ishida and Nittono (2024) placed the ERAN at 103–143 ms and the syntactic MMN at 135–175 ms (i.e., ERAN earlier). Thus, these latencies partially overlap. In the present study, four of the differences between amateurs and non‑musicians fell within the MMN window in both schemes (136, 146, 152, 154 ms; Table 6), which can be interpreted as a stronger reaction in musically trained participants to the absence of expected meaning. Meanwhile, the N500 (larger in non‑musicians) indicates that some form of syntactic or, in this case, semantic processing still occurs. This pattern supports the view (Koelsch et al., 2001; Ishida & Nittono, 2024) that ERAN and MMN are similar in nature, while N500 is closer to N400.
Data on P1–N1a activity at F3, and a tendency at F7 (Table 6), suggest left‑lateralised tonal processing, whereas the late negativity at F8 (Fig. 5) and baseline differences indicate that coping with sound is predominantly right‑lateralised.
Conclusions
The importance of amplitude decay for broadcasting a chord’s emotional meaning was confirmed for amateur musicians and non‑musicians. A late positive potential peaking at ~1200 ms (LPP1200) is associated with the subjective sense of tension produced by a single sound. The same negative components that have been reported for words, musical excerpts, environmental sounds, and other meaningful acoustic events are present during passive listening to a single chord with amplitude decay. A chord without amplitude decay is perceived as meaningless by amateurs and non‑musicians, and its negativity peaks in the LP range at about 900–1000 ms. ERP differences between amateurs and non‑musicians are significant and widespread across components and analysis techniques. Studying standalone sounds permits one to focus on late potentials in sound perception, unveiling neural dynamics that are normally concealed by active task demands.
Ethics approval: The study was conducted in accordance with the Declaration of Helsinki. All participants provided written informed consent after receiving a complete description of the procedures. Approval of the experimental protocol by the ethics committee is not required
Data availability: The datasets generated and/or analysed during the current study are available from the corresponding author on reasonable request.
Funding: This work was supported by the Ministry of Education and Science of the Russian Federation, topic № 0138‑2023‑0004. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Author contributions: N.A.A. and S.O.S. conceived and designed the study. S.O.S. collected the data. N.A.A. and S.O.S. performed the data analysis. N.A.A. drafted the manuscript.
Competing interests: The authors declare no competing interests.
Author statement: Both authors have read and approved the final version of the manuscript and take full responsibility for its content. The work described has not been published previously, nor is it under consideration for publication elsewhere.
References
- Almaev, N. A., Skorik, S. O., & Bessonova, Y. V. (2018). Towards psychophysiological mechanisms of major and minor chords perception [Abstract]. International Journal of Psychophysiology, 131(Suppl.), S163. https://doi.org/10.1016/j.ijpsycho.2018.07.432
- Almaev, N. A., & Skorik, S. O. (2015, August). Expectations and tensions induced by primitive rhythms[Paper presentation]. Ninth Triennial Conference of the European Society for the Cognitive Sciences of Music, Manchester, UK. https://www.researchgate.net/publication/331311663
- Arthurs, Y., Beeston, A. V., & Timmers, R. (2018). Perception of isolated chords: Examining frequency of occurrence, instrumental timbre, acoustic descriptors and musical training. Psychology of Music, 46(1), 136–152. https://doi.org/10.1177/0305735617720834
- Bakker, D. R., & Martin, F. H. (2015). Musical chords and emotion: Major and minor triads are processed for emotion. Cognitive, Affective, & Behavioral Neuroscience, 15(1), 15–31. https://doi.org/10.3758/s13415-014-0309-4
- Barry, R. J., De Blasio, F. M., Rushby, J. A., MacDonald, B., Fogarty, J. S., & Cave, A. E. (2022). Stimulus intensity effects and sequential processing in the passive auditory ERP. International Journal of Psychophysiology, 176, 149–163. https://doi.org/10.1016/j.ijpsycho.2022.03.005
- Calma-Roddin, N., & Drury, J. E. (2020). Music, language, and the N400: ERP interference patterns across cognitive domains. Scientific Reports, 10, Article 11222. https://doi.org/10.1038/s41598-020-66732-0
- Chomsky, N. (1957). Syntactic structures. Mouton.
- Chomsky, N. (1995). The minimalist program. MIT Press.
- Cozzi, J., Angel, R., & Herdman, A. (2019). How can no change in an auditory stimulus generate an N2b-P3a? Brain and Cognition, 129, 9–15. https://doi.org/10.1016/j.bandc.2018.12.002
- Cummings, A., Čeponienė, R., Koyama, A., Saygin, A. P., Townsend, J., & Dick, F. (2006). Auditory semantic networks for words and natural sounds. Brain Research, 1115(1), 92–107. https://doi.org/10.1016/j.brainres.2006.07.050
- Delorme, A., & Makeig, S. (2004). EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of Neuroscience Methods, 134(1), 9–21. https://doi.org/10.1016/j.jneumeth.2003.10.009
- Desatnik, A., Bel-Bahar, T., Nolte, T., Crowley, M., Fonagy, P., & Fearon, P. (2017). Emotion regulation in adolescents: An ERP study. Biological Psychology, 129, 52–61. https://doi.org/10.1016/j.biopsycho.2017.08.001
- Dickey, L., Politte-Corn, M., & Kujawa, A. (2021). Development of emotion processing and regulation: Insights from event-related potentials and implications for internalizing disorders. International Journal of Psychophysiology, 170, 121–132. https://doi.org/10.1016/j.ijpsycho.2021.10.003
- Doelling, K. B., & Poeppel, D. (2015). Cortical entrainment to music and its modulation by expertise. Proceedings of the National Academy of Sciences, 112(45), E6233–E6242. https://doi.org/10.1073/pnas.1508431112
- Ellison, D., Moisseinen, N., Fachner, J., & Brattico, E. (2015). Affective versus cognitive responses to musical chords: An ERP and behavioral study. Psychomusicology: Music, Mind, and Brain, 25(4), 423–434. https://doi.org/10.1037/pmu0000127
- Farbood, M. M., & Upham, F. (2013). Interpreting expressive performance through listener judgments of musical tension. Frontiers in Psychology, 4, Article 998. https://doi.org/10.3389/fpsyg.2013.00998
- Faul, F., Erdfelder, E., Buchner, A., & Lang, A.-G. (2009). Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, 41(4), 1149–1160. https://doi.org/10.3758/BRM.41.4.1149
- Fedorenko, E., Patel, A., Casasanto, D., Winawer, J., & Gibson, E. (2009). Structural integration in language and music: Evidence for a shared system. Memory & Cognition, 37(1), 1–9. https://doi.org/10.3758/MC.37.1.1
- Fernández-Sotos, A., Fernández-Caballero, A., & Latorre, J. M. (2016). Influence of tempo and rhythmic unit in musical emotion regulation. Frontiers in Computational Neuroscience, 10, Article 80. https://doi.org/10.3389/fncom.2016.00080
- Fiveash, A., Thompson, W. F., Badcock, N. A., & McArthur, G. (2018). Syntactic processing in music and language: Effects of interrupting auditory streams with alternating timbres. International Journal of Psychophysiology, 129, 31–40. https://doi.org/10.1016/j.ijpsycho.2018.05.003
- Foster, N. E. V., & Zatorre, R. J. (2010). A role for the intraparietal sulcus in transforming musical pitch information. Cerebral Cortex, 20(6), 1350–1359. https://doi.org/10.1093/cercor/bhp199
- Friederici, A. D. (2002). Towards a neural basis of auditory sentence processing. Trends in Cognitive Sciences, 6(2), 78–84. https://doi.org/10.1016/S1364-6613(00)01839-8
- Graber, E., & Fukuoka, T. (2019). Endogenous expectations for sequence continuation after auditory beat accelerations and decelerations revealed by P3a and induced beta-band responses. Neuroscience, 413, 11–21. https://doi.org/10.1016/j.neuroscience.2019.06.004
- Halpern, A. R., Martin, J. S., & Reed, T. D. (2008). An ERP study of major-minor classification in melodies. Music Perception, 25(3), 181–191. https://doi.org/10.1525/mp.2008.25.3.181
- Halpern, A. R., Zioga, I., Shankleman, M., Lindsen, J., Pearce, M. T., & Bhattacharya, J. (2017). That note sounds wrong! Age-related effects in processing of musical expectation. Brain and Cognition, 113, 1–9. https://doi.org/10.1016/j.bandc.2016.12.006
- Hausen, M., Salmela, V. L., Vainio, M., & Särkämö, T. (2013). Music and speech prosody: A common rhythm. Frontiers in Psychology, 4, Article 566. https://doi.org/10.3389/fpsyg.2013.00566
- Herholz, S. C., & Zatorre, R. J. (2012). Musical training as a framework for brain plasticity: Behavior, function, and structure. Neuron, 76(3), 486–502. https://doi.org/10.1016/j.neuron.2012.10.011
- Ishida, K., & Nittono, H. (2024). Relationship between schematic and dynamic expectations of melodic patterns in music perception. International Journal of Psychophysiology, 196, Article 112292. https://doi.org/10.1016/j.ijpsycho.2023.112292
- Joucla, C., Nicolier, M., Giustiniani, J., Brunotte, G., Noiret, N., Monnin, J., Magnin, E., Pazart, L., Moulin, T., Haffen, E., Vandel, P., & Gabriel, D. (2018). Evidence for a neural signature of musical preference during silence. International Journal of Psychophysiology, 125, 50–56. https://doi.org/10.1016/j.ijpsycho.2018.02.007
- Juslin, P. N., Sakka, L. S., Barradas, G. T., & Lartillot, O. (2022). Emotions, mechanisms, and individual differences in music listening: A stratified random sampling approach. Music Perception, 40(1), 55–86. https://doi.org/10.1525/mp.2022.40.1.55
- Kamenetsky, S. B., Hill, D. S., & Trehub, S. E. (1997). Effect of tempo and dynamics on the perception of emotion in music. Psychology of Music, 25(2), 149–160. https://doi.org/10.1177/0305735697252005
- Koelsch, S. (2000). Brain and music: A contribution to the investigation of central auditory processing with a new electrophysiological approach. Max Planck Institute of Cognitive Neuroscience. https://pure.mpg.de/rest/items/item_720506/component/file_720505/content
- Koelsch, S. (2011). Toward a neural basis of music perception—A review and updated model. Frontiers in Psychology, 2, Article 110. https://doi.org/10.3389/fpsyg.2011.00110
- Koelsch, S., Gunter, T. C., Schröger, E., Tervaniemi, M., Sammler, D., & Friederici, A. D. (2001). Differentiating ERAN and MMN: An ERP study. NeuroReport, 12(7), 1385–1389. https://doi.org/10.1097/00001756-200105250-00025
- Koelsch, S., Gunter, T., Schröger, E., & Friederici, A. D. (2003). Processing tonal modulations: An ERP study. Journal of Cognitive Neuroscience, 15(8), 1149–1159. https://doi.org/10.1162/089892903322598111
- Koelsch, S., Gunter, T. C., Wittfoth, M., & Sammler, D. (2005). Interaction between syntax processing in language and in music: An ERP study. Journal of Cognitive Neuroscience, 17(10), 1565–1577. https://doi.org/10.1162/089892905774597290
- Krumhansl, C. L. (1997). An exploratory study of musical emotions and psychophysiology. Canadian Journal of Experimental Psychology / Revue canadienne de psychologie expérimentale, 51(4), 336–352. https://doi.org/10.1037/1196-1961.51.4.336
- Kurth, E. (1931). Musikpsychologie. Max Hesses Verlag.
- Küssner, M. B., Tidhar, D., Prior, H. M., & Leech-Wilkinson, D. (2014). Musicians are more consistent: Gestural cross-modal mappings of pitch, loudness and tempo in real-time. Frontiers in Psychology, 5, Article 789. https://doi.org/10.3389/fpsyg.2014.00789
- Kutas, M., & Federmeier, K. D. (2011). Thirty years and counting: Finding meaning in the N400 component of the event-related brain potential (ERP). Annual Review of Psychology, 62, 621–647. https://doi.org/10.1146/annurev.psych.093008.131123
- Lahdelma, I., & Eerola, T. (2016a). Mild dissonance preferred over consonance in single chord perception. i-Perception, 7(3). https://doi.org/10.1177/2041669516655812
- Lahdelma, I., & Eerola, T. (2016b). Single chords convey distinct emotional qualities to both naïve and expert listeners. Psychology of Music, 44(1), 37–54. https://doi.org/10.1177/0305735614552006
- Lehne, M., & Koelsch, S. (2015). Toward a general psychological model of tension and suspense. Frontiers in Psychology, 6, Article 79. https://doi.org/10.3389/fpsyg.2015.00079
- Lehne, M., Rohrmeier, M., Gollmann, D., & Koelsch, S. (2013). The influence of different structural features on felt musical tension in two piano pieces by Mozart and Mendelssohn. Music Perception, 31(2), 171–185. https://doi.org/10.1525/mp.2013.31.2.171
- Lerdahl, F., & Jackendoff, R. (1983). A generative theory of tonal music. MIT Press.
- Lerdahl, F., & Krumhansl, C. L. (2007). Modeling tonal tension. Music Perception, 24(4), 329–366. https://doi.org/10.1525/mp.2007.24.4.329
- Lerdahl, F. (1996). Calculating tonal tension. Music Perception, 13(3), 319–363. https://doi.org/10.2307/40286174
- Levitin, D. J., & Cook, P. R. (1996). Memory for musical tempo: Additional evidence that auditory memory is absolute. Perception & Psychophysics, 58(6), 927–935. https://doi.org/10.3758/BF03205494
- Ma, X., Ding, N., Tao, Y., & Yang, Y. F. (2018). Syntactic complexity and musical proficiency modulate neural processing of non-native music. Neuropsychologia, 121, 164–174. https://doi.org/10.1016/j.neuropsychologia.2018.10.005
- McAuley, J. D. (2010). Tempo and rhythm. In M. R. Jones, R. R. Fay, & A. N. Popper (Eds.), Music perception(pp. 165–199).
- McLean, M. A., Van den Bergh, B. R. H., Baart, M., Vroomen, J., & Van den Heuvel, M. I. (2020). The late positive potential (LPP): A neural marker of internalizing problems in early childhood. International Journal of Psychophysiology, 155, 78–86. https://doi.org/10.1016/j.ijpsycho.2020.06.005
- Malins, J. G., Desroches, A. S., Robertson, K. E., Newman, R. L., Archibald, L. M. D., & Joanisse, M. F. (2013). ERPs reveal the temporal dynamics of auditory word recognition in specific language impairment. Developmental Cognitive Neuroscience, 5, 134–148. https://doi.org/10.1016/j.dcn.2013.02.005
- Manchaiah, V., Zhao, F., & Ratinaud, P. (2019). Young adults’ knowledge and attitudes regarding “music” and “loud music” across countries: Applications of social representations theory. Frontiers in Psychology, 10, Article 1390. https://doi.org/10.3389/fpsyg.2019.01390
- Männel, C., Schipke, C. S., & Friederici, A. D. (2013). The role of pause as a prosodic boundary marker: Language ERP studies in German 3- and 6-year-olds. Developmental Cognitive Neuroscience, 5, 86–94. https://doi.org/10.1016/j.dcn.2013.01.003
- Miranda, R. A., & Ullman, M. T. (2007). Double dissociation between rules and memory in music: An event-related potential study. NeuroImage, 38(2), 331–345. https://doi.org/10.1016/j.neuroimage.2007.07.034
- Moelants, D. (2002). Preferred tempo reconsidered. In C. Stevens, D. Burnham, G. McPherson, E. Schubert, & J. Renwick (Eds.), Proceedings of the 7th International Conference on Music Perception and Cognition(pp. 1–4). Sydney, Australia.
- Müller, M., Höfel, L., Brattico, E., & Jacobsen, T. (2010). Aesthetic judgments of music in experts and laypersons—An ERP study. International Journal of Psychophysiology, 76(1), 40–51. https://doi.org/10.1016/j.ijpsycho.2010.02.002
- Näätänen, R., & Picton, T. (1987). The N1 wave of the human electric and magnetic response to sound: A review and an analysis of the component structure. Psychophysiology, 24(4), 375–425. https://doi.org/10.1111/j.1469-8986.1987.tb00311.x
- Näätänen, R. (1992). Attention and brain function.
- Näätänen, R. (1990). The role of attention in auditory information processing as revealed by event-related potentials and other brain measures of cognitive function. Behavioral and Brain Sciences, 13(2), 201–288. https://doi.org/10.1017/S0140525X00078407
- Parncutt, R. (1994). A perceptual model of pulse salience and metrical accent in musical rhythms. Music Perception, 11(4), 409–464. https://doi.org/10.2307/40285633
- Patel, A. D., Gibson, E., Ratner, J., Besson, M., & Holcomb, P. J. (1998). Processing syntactic relations in language and music: An event-related potential study. Journal of Cognitive Neuroscience, 10(6), 717–733. https://doi.org/10.1162/089892998563121
- Petrescu, N. (2008). Loud music listening. McGill Journal of Medicine, 11(2), 169–176. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2582665/
- Pinheiro, A. P., Vasconcelos, M., Dias, M., Arrais, N., & Gonçalves, Ó. F. (2015). The music of language: An ERP investigation of the effects of musical training on emotional prosody processing. Brain and Language, 140, 24–34. https://doi.org/10.1016/j.bandl.2014.10.009
- Proverbio, A. M., Santoni, S., & Adorni, R. (2020). ERP markers of valence coding in emotional speech processing. iScience, 23(3), 100933. https://doi.org/10.1016/j.isci.2020.100933
- Rentfrow, P. J., Goldberg, L. R., & Levitin, D. J. (2011). The structure of musical preferences: A five-factor model. Journal of Personality and Social Psychology, 100(6), 1139–1157. https://doi.org/10.1037/a0022406
- Sanders, L. D., & Neville, H. J. (2003). An ERP study of continuous speech processing I. Segmentation, semantics, and syntax in native speakers. Cognitive Brain Research, 15(3), 228–240. https://doi.org/10.1016/S0926-6410(02)00195-7
- Shahin, A. J., Alain, C., & Picton, T. W. (2006). Scalp topography and intracerebral sources for ERPs recorded during auditory target detection. Brain Topography, 19(1–2), 89–96. https://doi.org/10.1007/s10548-006-0015-9
- Skorik, S. O., & Almaev, N. A. (2019). Limitations of musicians and non‑musicians in differentiating between major and minor chords. In Proceedings of the First International Conference Psychology and Music – Interdisciplinary Encounter(p. 273). Belgrade, Serbia. https://www.researchgate.net/publication/337306945
- Skorik, S. O., & Almayev, N. A. (2023). The role of the amplitude decay for the evaluation of major and minor chords in amateur listeners and professionals. Natural Systems of Mind, 3(1), 64–80. https://doi.org/10.38098/nsom_2023_03_01_04
- Slevc, L. R., Rosenberg, J. C., & Patel, A. D. (2009). Making psycholinguistics musical: Self-paced reading time evidence for shared processing of linguistic and musical syntax. Psychonomic Bulletin & Review, 16(2), 374–381. https://doi.org/10.3758/PBR.16.2.374
- Solomon, B., DeCicco, J. M., & Dennis, T. A. (2012). Emotional picture processing in children: An ERP study. Developmental Cognitive Neuroscience, 2(1), 110–119. https://doi.org/10.1016/j.dcn.2011.04.002
- Sun, Y., Lu, X., Ho, H. T., Johnson, B. W., Sammler, D., & Thompson, W. F. (2018). Syntactic processing in music and language: Parallel abnormalities observed in congenital amusia. NeuroImage: Clinical, 19, 640–651. https://doi.org/10.1016/j.nicl.2018.05.032
- Tanner, D., & Van Hell, J. G. (2014). ERPs reveal individual differences in morphosyntactic processing. Neuropsychologia, 56, 289–301. https://doi.org/10.1016/j.neuropsychologia.2014.02.002
- Virtala, P., Huotilainen, M., Partanen, E., & Tervaniemi, M. (2014). Musicianship facilitates the processing of Western music chords—An ERP and behavioral study. Neuropsychologia, 61, 247–258. https://doi.org/10.1016/j.neuropsychologia.2014.06.028
- Welch, D., & Fremaux, G. (2017). Why do people like loud sound? A qualitative study. International Journal of Environmental Research and Public Health, 14(8), 908. https://doi.org/10.3390/ijerph14080908
- Xiao, R., Liu, C., Chen, J. J., & Chen, J. (2020). The influence of music tempo on inhibitory control: An ERP study. Frontiers in Behavioral Neuroscience, 14, Article 48. https://doi.org/10.3389/fnbeh.2020.00048
- Yu, K., Chen, Y., Yin, S., Li, L., & Wang, R. (2022). The roles of pitch type and lexicality in the hemispheric lateralization for lexical tone processing: An ERP study. International Journal of Psychophysiology, 177, 83–91. https://doi.org/10.1016/j.ijpsycho.2022.04.013
- Zhang, J., Zhou, X., Chang, R., & Yang, Y. (2018). Effects of global and local contexts on chord processing: An ERP study. Neuropsychologia, 109, 149–154. https://doi.org/10.1016/j.neuropsychologia.2017.12.016
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Background and problem. Musical tension has traditionally been studied through the lens of tonal syntax, while the contributions of sound intensity and duration, the physical “power” of sound, remain largely unexplored in electrophysiology. It is unknown how the brain processes a sound’s duration when no other acoustic events occur, and how this processing interacts with tonal meaning. Objective. This study aimed to identify event‑related potential (ERP) correlates of duration‑induced tension, compare ERPs for chords with and without natural amplitude decay (thereby isolating tonal emotional meaning), and assess the influence of musical training. Methods. Forty‑four participants listened passively to four types of isolated 1‑s stimuli: natural major and minor chords (with amplitude decay) and their modified versions possessing a rectangular amplitude envelope. EEG was recorded from 21 electrodes; ERPs were analysed via amplitude, spectra, event‑related spectral perturbations (ERSP), and inter‑trial coherence (ITC). Subjective ratings of “Merry”, “Sad”, “Tensing”, and other scales were collected. Participants were divided into amateur musicians (n = 13) and non‑musicians. Results. Modified chords without amplitude decay were perceived as more tense and failed to convey the major/minor emotional distinction. A late positive potential peaking at ~1200 ms (LPP1200) was significantly larger for modified chords and correlated with subjective tension. ITC differences between chord types were most pronounced in the 3–6 Hz band at 1100–1300 ms. Natural chords elicited N400 and N600 components that were absent for modified chords. Amateur musicians showed smaller differences between conditions and higher ITC values, whereas non‑musicians exhibited stronger early (P1–N1) responses to modified chords. Conclusion. Amplitude decay is essential for communicating tonal emotion to non‑experts. The LPP1200 component reflects the resolution of temporal uncertainty and the subjective tension associated with an isolated sound. Studying standalone auditory stimuli unveils late potentials that are typically concealed by active task demands, thus offering a new window into the perception of musical tension.
Изолированные звуки: ERP-корреляты длительности, амплитудной огибающей и тонального восприятия
Николай А. Алмаев¹, Станислав О. Скорик¹, Дарья Л. Петрович², Ольга В. Мурашева¹
¹Институт психологии РАН, Москва, Россия
²Московский институт психоанализа, Москва, Россия
Резюме. Актуальность и проблема. Музыкальное напряжение традиционно исследуется сквозь призму тонального синтаксиса, тогда как вклад интенсивности и длительности звука – физической «силы» звучания – в электрофизиологических работах остаётся практически неизученным. Остаётся неизвестным, каким образом мозг обрабатывает длительность звука в отсутствие других акустических событий и как эта обработка взаимодействует с передачей тонального значения. Цель. Настоящее исследование направлено на выявление коррелятов вызванной длительностью напряжённости в связанных с событиями потенциалах (ERP), сравнение ERP для аккордов с естественным затуханием амплитуды и без него (что позволяет изолировать тональное эмоциональное содержание) и оценку влияния музыкального обучения.
Методы. Сорок четыре участника пассивно прослушивали четыре типа изолированных стимулов длительностью 1 с: натуральные мажорный и минорный аккорды (с затуханием амплитуды) и их модифицированные версии с прямоугольной амплитудной огибающей. ЭЭГ регистрировали с 21 электрода; ERP анализировали по амплитуде, спектральным характеристикам, вызванным спектральным возмущениям (ERSP) и межпробной когерентности (ITC). Собирались субъективные оценки по шкалам «Весёлый», «Грустный», «Напрягающий» и другим. Участники были разделены на любителей-музыкантов (n = 13) и немузыкантов. Результаты. Модифицированные аккорды без затухания амплитуды воспринимались как более напряжённые и не передавали мажорно-минорного эмоционального различия. Поздний положительный потенциал с пиком около 1200 мс (LPP1200) был значимо большим для модифицированных аккордов и коррелировал с субъективным напряжением. Различия в ITC между типами аккордов были наиболее выражены в полосе 3–6 Гц на интервале 1100–1300 мс. Натуральные аккорды вызывали компоненты N400 и N600, отсутствовавшие для модифицированных аккордов. Любители-музыканты демонстрировали меньшие различия между условиями и более высокие значения ITC, тогда как немузыканты обнаруживали усиленные ранние (P1–N1) ответы на модифицированные аккорды. Заключение. Затухание амплитуды необходимо для передачи тональной эмоции не-экспертам. Компонент LPP1200 отражает разрешение временной неопределённости и субъективное напряжение, связанное с изолированным звуком. Изучение одиночных слуховых стимулов раскрывает поздние потенциалы, которые обычно маскируются требованиями активных заданий, открывая тем самым новое окно в восприятие музыкального напряжения.
Ключевые слова: связанные с событиями потенциалы (ERP), поздний положительный потенциал (LPP), музыкальное напряжение, длительность звука, амплитудная огибающая, мажорные и минорные аккорды, музыкальное обучение, пассивное слушание, межпробная когерентность (ITC), эмоциональное восприятие.
Since Kurth (1931), the communication of emotion through music has been understood as an interplay of tensions and resolutions. Tension can arise from multiple sources: the intensity of sounds, their duration, and tonal structures that produce consonance/dissonance or stable/unstable progressions. In ERP research, however, the tonal dimension has overwhelmingly prevailed. Inspired by Lerdahl and Jackendoff’s (1983) generative theory of tonal music, which drew a formal parallel with Chomskian generative grammar (Chomsky, 1957, 1995), numerous studies have employed paradigms in which expectations are violated within tonal sequences (Koelsch et al., 2003; Halpern et al., 2017; Doelling & Poeppel, 2015). Many of these investigations also compared music and language processing (Koelsch et al., 2005; Fedorenko et al., 2009; Calma‑Roddin & Drury, 2020; Sun et al., 2018; Slevc et al., 2009). Active tasks are typically included, which may themselves elicit additional ERP components and mask those of interest (Cozzi et al., 2019). Investigations of tempo, too, generally require active detection of changes (Graber & Fukuoka, 2019; Xiao et al., 2020).
In experimental psychology, tonal research (Lerdahl, 1996; Krumhansl, 1997; Lerdahl & Krumhansl, 2007) has long been complemented by studies of rhythm (Fernández‑Sotos et al., 2016; McAuley, 2010; Parncutt, 1994; Hausen et al., 2013), tempo (Kamenetsky et al., 1997; Levitin & Cook, 1996; Moelants, 2002), isolated chords (Lahdelma & Eerola, 2016a, 2016b; Arthurs et al., 2018; Ellison et al., 2015), and more complex phenomena (Küssner et al., 2014). Juslin et al. (2022) enumerated eight mechanisms underlying musical emotions, among which “musical expectancy” (relying on syntactic structure) and “brain stem reflex” as well as “rhythmic entrainment” (dependent on intensity and duration) are particularly relevant. The existence of a distinct “Intense” factor in musical preferences, characterised by loud, forceful music (Rentfrow et al., 2011), together with research on preferences for listening at high volumes (Welch & Fremaux, 2017; Manchaiah et al., 2019; Petrescu, 2008), indicates that coping with the physical power of sound constitutes a separate source of tension. Hence, “tension” as a response to physical sound properties must be distinguished from “tension” as an expectancy violation (Lehne & Koelsch, 2015), and their mutual relations may vary across listeners.
Accounting simultaneously for intensity, duration, and tonality is challenging (Farbood & Upham, 2013; Lehne et al., 2013). While the effect of sound intensity has been addressed in ERP studies (Barry et al., 2022), the effect of duration has not been the subject of a dedicated ERP investigation, to our knowledge. The influence of duration necessarily accumulates towards the end of a sound; to observe it without interference, stimuli must be presented in isolation. Moreover, the amplitude envelope of a sound can takes countless forms, complicating the study of duration. To isolate the temporal dimension, we previously introduced auditory stimuli with a rectangular (orthogonal) amplitude envelope (Almaev & Skorik, 2015). These behavioural experiments revealed that perceived tension varied nonlinearly with period length: it decreased up to ~950 ms, then increased from 950 to 1350 ms, and became non‑significant beyond 1350 ms. Consequently, the search for an ERP correlate of duration needed to extend to at least 1500 ms, and stimuli had to be presented in isolation.
Such late components are known as late positive potentials (LPP). A growing body of research implicates the LPP in processing emotionally charged stimuli (Dickey et al., 2021; Desatnik et al., 2017), primarily in the visual domain. In the auditory modality, late potentials are typically considered as latencies between 350 and 800–1000 ms (Koelsch, 2011; Halpern et al., 2017; Yu et al., 2022). ERP components beyond 1000 ms are rarely discussed and are mostly found in studies of language (Proverbio et al., 2020; Solomon et al., 2012; Sanders & Neville, 2003).
Further experiments with an orthogonal amplitude envelope (Almaev et al., 2018; Skorik & Almaev, 2019; Skorik & Almayev, 2023) revealed a peculiar feature: amplitude decay is critically important for the transmission of major and minor chords’ emotional content among ordinary listeners, but not among professional musicians. Professionals can perceive the emotional meaning (“sad”, “merry”) of stimuli that are tonally identical to a chord yet lack amplitude decay, whereas amateurs and non‑musicians cannot. Nevertheless, amplitude decay also enhanced professionals’ results (Skorik & Almaev, 2019; Skorik & Almayev, 2023). The existing data on ERP differences between ordinary major and minor chords (Halpern et al., 2008; Virtala et al., 2014; Bakker & Martin, 2015; Ellison et al., 2015) can therefore be enriched by comparing ERPs to stimuli that convey specific emotions with those that do not.
Hence, comparing chords with and without amplitude decay allows one to separate the contributions of duration and tonality to ERP components. The main idea of the present study can be summarised as follows: to observe what happens to a sound’s ERP when no other sounds occur. We focus on the comparison of stimulus ERPs “as they are”, obtained through passive listening without a context of other acoustic events or active tasks, i.e. we examine the structure of expectations and tensions when nothing interrupts them. The specific goals are: (1) to identify the effect of auditory stimulus duration on subjective tension (coping with the power of sound) and on the ERP, presumably in the LPP range; (2) to search for differences between ERPs of stimuli with and without amplitude decay; (3) to assess the effect of musical training on the obtained results.
2.1. Stimuli
Stimuli were adopted from previous studies (Almaev et al., 2018; Skorik & Almaev, 2019). Four types were presented: two ordinary grand piano chords (A5 and Am5) downloaded from a Yamaha soundbank, and two modified chords (Plain A5 and Plain Am5). The modified chords were synthesised from sinusoidal waves (fundamental 880 Hz, natural scale: A, C#, E and A, C, E) using Cool Edit Pro 2.0. The natural scale was chosen to avoid additional throbs that arise with the tempered scale. The duration of each sample was 1 s, with an inter‑stimulus interval of 4 s. No detectable click occurred at the end of either ordinary or modified chords at the presentation volume. All stimuli were presented at approximately 80 dB SPL, measured at the participant’s seat.
2.2. Presentation order
Stimuli were presented in the order: Plain A, Ordinary A, Ordinary Am, Plain Am. Because of fatigue effects and the consequent increase in artifacts that reduced the number of valid epochs for minor stimuli, the order was planned to be reversed after the 20th participant (Plain Am, Ordinary Am, Ordinary A, Plain A).

2.3. Participants and procedure
Participants were recruited from a larger sample of individuals who volunteered for a study on musical preferences with continuous EEG recording; the recruitment was conducted via social networks and personal invitation. A main motivation was the opportunity to listen through “legendary JBL 4345” monitors, so the participants were, in a sense, people who value sound quality. They were optionally invited to take part in the ERP study for a fee of 500 RUB (approximately 8 USD) and/or to complete personality tests for the same fee. The conditions were clearly stated in the initial offer. Filling in a survey after the instructions served as informed consent. Some individuals participated only for the fee and did not listen to music. The broader study included an extensive survey on musical education, skills, and preferences. ERP recording always preceded the continuous EEG session. Before and after stimulus presentation, resting‑state EEG was registered (1 min eyes open, 1 min eyes closed).
During the ERP study, participants were instructed to listen to the sounds with their eyes closed, seated in a sound‑ and echo‑proof room. The light was dimmed or switched off according to individual comfort. No tasks were associated with the stimuli during their administration. After the end of each stimulus series (which varied from 70 to more than 100 epochs, depending on artifact rejection), participants evaluated the presented sounds on six unipolar scales ranging from 0 to 6: “Merry”, “Sad”, “Confident”, “Pleading”, “Anxious”, “Tensing” (0 = absence of the quality, 6 = its greatest extent). Bilateral communication was maintained via speakerphones. Subjective evaluations were made on a tablet PC using Google Forms. ERP stimuli were presented in stereo mode via the aforementioned JBL 4345 monitors together with “Elektronika 50 AS‑61M” loudspeakers; both systems were active during presentations.
According to a priori power analysis (Faul et al., 2009), a total sample size of N = 45 was required for a paired t‑test to achieve power (1–β) = 0.95; for a Wilcoxon matched‑pairs test, N = 47. Data collection was stopped at 53 participants, knowing that some recordings served as training, some lacked ERP data, and some persons participated twice. After data verification, a total of 44 unique participants were selected for the ERP study. Each participant contributed to four conditions, yielding 176 ERP datasets.
2.4. ERP recording and preprocessing
Sound administration and ERP recording were synchronised using a Mitsar‑201 encephalograph (Mitsar‑Phono v.200). The sampling rate was 250 Hz, and impedance was kept below 40 kΩ. Twenty‑one Ag/AgCl electrodes were mounted on an elastic cap according to the 10–20 system. Reference electrodes were A01 and A02 (earlobes). EOG was not recorded. Automated artifact removal (Mitsar software) rejected epochs with amplitude >200 µV (any frequency), slow‑wave amplitude >100 µV (0–1 Hz), or fast‑wave amplitude >100 µV (20–35 Hz). Data were converted to. edf format and imported into EEGLAB (Delorme & Makeig, 2004). Epochs from –500 to 2000 ms were extracted and visually inspected. Epochs containing myograms, pronounced slow waves (up to ~3 Hz), or considerable spikes were excluded to the maximum extent possible. A minimum of 50 artifact‑free epochs per condition was required to maximise the signal‑to‑noise ratio. Rheograms and overt eye‑movement artifacts were not specifically excluded.
After this selection, data from 29 participants (19 females, age M = 33.7, SD = 11.4; 10 males, age M = 30.2, SD = 5.41) were retained for statistical analyses in EEGLAB. Two designs were employed: (1) one‑way repeated measures with Condition (Ordinary vs. Plain) as independent variable, using paired t‑tests (1450 epochs per condition × 21 channels), time range –200 to 1500 ms; (2) a mixed design adding Musical Training as a between‑subjects factor (amateur musicians: n = 13, 5 males, age M = 34.2, SD = 10.8; non‑musicians: remainder). This second design used two‑way ANOVA with marginal statistics. Averaged channel potentials were used instead of RMS. False Discovery Rate (FDR) correction was applied primarily; if no differences survived FDR, uncorrected results were considered. Four analysis types were performed: average amplitude, spectra, event‑related spectral perturbations (ERSP), and inter‑trial coherence (ITC).
2.5. Musical training classification
Inclusion in the amateur musician group required a self‑reported ability to perform on a musical instrument. Participants with uncertain statements (e.g., “tried the piano”) were excluded. The group included: conservatory (1), sound engineer (1), musical college (2), completed music school (8 years; 3); the remainder were amateurs trained by personal tutors and/or parents. Six participants were personally known to the authors; the others could be verified in most cases via social networks. The level generally corresponds to that of amateur musicians.
3.1. Subjective appraisals
The scales “Merry”, “Sad”, and “Tensing” are particularly relevant: the first two refer to the most common attributes of major and minor chords, while “Tensing” indexes coping with the physical power of sound. Medians of subjective ratings are given in Table 1. For the total sample, differences between Plain major and Plain minor were not significant, which is typical for non‑musicians and amateurs. Based on estimated musical training, only 2–3 participants could possibly differentiate between the Plain chords.

Wilcoxon matched‑pairs tests (Table 2) revealed significant differences between Plain major and Ordinary major on “Merry”, “Sad”, “Pleading”, “Anxious”, and “Tensing” (Bonferroni‑corrected). In the ERP subsample, the differences were even more robust. For Ordinary major vs. Ordinary minor (Table 3), “Merry” and “Sad” differed significantly in the ERP subsample, but the effect sizes were smaller than those for the Plain vs. Ordinary contrast. These results indicate that conveying tonal emotional information depends on amplitude decay, while subjective tension is linked to the rectangular envelope and the physical power of sound.

3.2. ERP amplitude
Because the presentation order could not be fully balanced (only 10 participants had the minor series first, vs. 19 with the major series first), and noticeably more slow‑wave artifacts remained in minor‑series epochs, ERP results for Ordinary minor vs. Ordinary major are not reported here. The most pronounced differences were observed between Ordinary A5 and Plain A5.
Average ERP amplitude for Ordinary A5 significantly exceeded that for Plain A5 at the P2 latency. Conversely, Plain A5 elicited a significantly larger positivity peaking at approximately 1200 ms (LPP1200; Fig. 3). This LPP1200 began around 1100 ms, lasted until ~1350 ms, and its central portion (1150–1250 ms) was particularly sensitive to subjective tension (see Table 2).

3.3. Spectral, ERSP, and ITC analyses
Spectral components of the two stimuli were almost identical at all leads; no significant differences were found. ERSP analyses revealed a pattern similar to the amplitude data: significant differences were observed at F3, Fz, F4, Cz, and C4, mainly in frequencies below 4 Hz (after FDR).
Inter‑trial coherence (ITC) proved the most informative measure. Differences at ~1200 ms were found not only at the electrodes listed in Table 4 but also at P4 (Figs. 4a–c). Remarkably, the ITC differences became more distinct from frontal to parietal leads, despite decreasing absolute ITC values. This observation may relate to the role of the intraparietal sulcus in transforming musical pitch information (Foster & Zatorre, 2010). Based on the P4 plot, the difference between ERPs for stimuli with and without amplitude decay – i.e. between perceived and non‑perceived emotional tonal information – lies in the 6–9 Hz band at latencies of ~160–290 ms. The LPP1200 effect (1050–1350 ms) was most prominent in the 3–6 Hz band (p < 0.001, FDR‑corrected).

- Anterior negativity
In the full ERP sample, the negativity waveforms of Plain and Ordinary chords were similar. At Cz, the Plain chord showed an N400, whereas the Ordinary chord displayed an N600 (Fig. 3). The largest anterior negativity differences emerged at F7 and F8 (Fig. 5): at F7, a difference at 600–650 ms (p < 0.05), and at F8, a tendency at 890 ms (p = 0.058).

- Musical training effects
A two‑way ANOVA (Condition × Group) on ITC data showed that amateur musicians had smaller differences between Ordinary and Plain chords than non‑musicians. At P4, the musicians’ ITC for Ordinary chords showed a well‑defined LPP1200, whereas non‑musicians’ ITC was less focal (Fig. 6a–b). Without FDR correction, musicians also exhibited less pronounced P2 and LPP1200 differences.

In anterior leads, particularly Fp1 and Fp2, musicians showed marked differences between the two chord types in N1 and P2 (small N1 and large P2 for Ordinary; articulated N1 and small P2 for Plain), whereas non‑musicians showed almost no such differences (Fig. 7). Musicians also had larger LPP1200 peaks than non‑musicians.

Comparing the two groups for Ordinary chords revealed differences in the N400, N600, and N950 ranges (Table 5). In contrast, during Plain chord presentation, the groups differed primarily in early components (P1–N1a–N1b; Näätänen & Picton, 1987; Näätänen, 1990) and at N500 and LPP1200 (Table 6). Non‑musicians consistently showed greater negativity than amateurs in these early latencies.


This study is exploratory in nature, and most of its results were unexpected. Although the design is simple, it departs substantially from the dominant syntactic paradigm; therefore, the correspondence of our findings to those obtained within that paradigm requires careful consideration. Moreover, the experiment jointly addresses two phenomena such as duration and tonality, each deserving separate investigation.
The first major finding is that the effect of a sustained sound accumulates in an LPP peaking at approximately 1200 ms. The positive rise begins after 1000 ms and stabilises around 1350 ms (Fig. 3). Late positive potentials beyond 1000 ms are mostly reported in the context of visual stimulation (Dickey et al., 2021; Solomon et al., 2012), but they also appear in studies of auditory, mainly verbal perception. Shahin et al. (2006) identified a “P1000” between 700 and 1500 ms for semantic targets. Männel et al. (2013) observed a positive shift peaking at ~1500 ms in 6‑year‑olds. A similar rise, even if not explicitly discussed, is visible in the waveforms of several published articles. For instance, Cozzi et al. (2019, their Figs. 1–3) demonstrate that an active task suppresses the late positivity, whereas a passive task releases it; the passive‑task waveform closely resembles that obtained in the present study. In Müller et al. (2010, Fig. 6), the requirement of an aesthetic judgement an active task apparently prevents the positivity from crossing the zero line.
Studies of word and speech recognition employ relatively long stimuli and can therefore illuminate late auditory positivity. Sanders and Neville (2003) showed that sentences containing pseudowords of sufficient duration elicit a positive rise after 1000 ms, whereas acoustically and semantically varied sentences do not. Similar patterns can be discerned in works conducted within the syntactic paradigm, both musical and linguistic (Malins et al., 2013, Fig. 4; Ma et al., 2018, Fig. 4).
The dynamics observed for the Ordinary chord (audible until ~250–270 ms; Fig. 1) and for the Plain chord (audible until ~950–970 ms; Fig. 2) are similar (Fig. 3). An intense, durable sound evokes negativity while it lasts – this is expected for a rectangular envelope – but why does the same occur for the Ordinary chord in both amateurs and non‑musicians? One cannot exclude the possibility that the fixed interval between stimuli, and the fact that Plain chords were always presented first, created specific expectations and influenced the regularity of the peaks. On the other hand, the observed components may reflect the inner dynamics of expectations and tensions that are inherent to the late stages of perceiving a standalone sound. What psychological processes could they represent? Converting latencies into tempo yields suggestive correspondences: 1000 ms ≈ 60 bpm (lentamente), 1200 ms ≈ 50 bpm (lento), 1350 ms ≈ 44 bpm (largo/grave – the slowest musical tempo). We propose that LPP1200 indexes the decision of whether the perceived sound is part of a very slow rhythmic structure or a standalone (though possibly repeating) event. This very uncertainty about the tempo of the sequence likely underlies the subjective tension found earlier (Almaev & Skorik, 2015). The mechanism may also underpin performance practices in which slowing the tempo enhances expressivity.
LPP1200 may additionally correspond to the finalisation of a subjective appraisal of tempo and, in some cases, of rhythmic structure. The results of Joucla et al. (2018), who investigated neural signatures of musical preference during silence, corroborate this suggestion: they identified three main components, the second of which occurred between 1000 and 1300 ms. The importance of P2 for chord processing has been acknowledged before (Bakker & Martin, 2015; Ellison et al., 2015; Müller et al., 2010); accordingly, the P2 rise observed here may be interpreted as the accumulation of information about the event, but not yet its full semantic identification.
Amateurs and non‑musicians. Elements such as intensity, duration, fill factor, timbre, and “sound quality” clearly play an important role in perception, and their role appears to grow as musical training decreases. How should the differences between amateur musicians and non‑musicians be interpreted in the absence of any syntactic context? The semantic differences between Ordinary and Plain chords are reflected in ERP components in both groups (Fig. 7, Tables 5 and 6). The differences between groups therefore seem to lie in the consumption of cognitive resources by the sound itself. As noted in the Method, the sample was initially biased towards individuals sensitive to “sound quality”. Consequently, non‑musicians may simply have been “more impressed”, reacting more chaotically and extravagantly, whereas more experienced amateurs responded in a more economical manner. The higher ITC values in amateurs for Ordinary‑chord LPP1200 (Fig. 6a–b, upper panels) could be interpreted as a better sense of rhythm, i.e. expectations being active in the relevant time window despite the lower fill factor of the period. Taken together, musical training might be viewed as a progressive abstraction from everything unnecessary for performance – an abstraction that, in professional musicians, reaches the level of discarding amplitude decay (Skorik & Almaev, 2019; Skorik & Almayev, 2023). This topic certainly requires further investigation, particularly regarding the interaction between instrument type and professionalism.
The ERP differences between amateurs and non‑musicians in response to the Ordinary chord included the N400 (Fig. 7, left panels; Table 5), a component extensively discussed in relation to both linguistic and musical semantics (Kutas & Federmeier, 2011; Cummings et al., 2006; Miranda & Ullman, 2007; Calma‑Roddin & Drury, 2020). No analogous differences were detected for the Plain chord. According to the prevailing interpretation (Kutas & Federmeier, 2011), N400 reflects the identification of an event in semantic memory. The contrast between Ordinary and Plain chords can therefore be understood as the difference between meaningful and meaningless acoustic events. Furthermore, instead of the P600 often associated with musical expectancy violations (Patel et al., 1998; Tanner & Van Hell, 2014) or the P700 (Shahin et al., 2006), we detected a rarer N600 in most frontal leads – both as the negativity maximum for the Plain chord at Cz (Fig. 3) and in the comparisons between non‑musicians and musicians. According to Cummings et al. (2006), the N600 is related to general stimulus processes such as maintenance of task demands and response monitoring, an interpretation that fits both phenomena in the sense of searching for neural resources.
Plain chord presentation highlighted the focus on early (P1–N1) sound processing (Table 6). This observation confirms that decisions about the meaningfulness of sounds are first taken during these latencies (Fiveash et al., 2018; Virtala et al., 2014). In addition, the group comparison revealed the rather rare N500 component, which, according to Koelsch (2011, part 5), corresponds to the processing of intra‑musical meaning – the reference of one musical element to at least one other musical element. N500 is related to the complex interplay between Early Right Anterior Negativity (ERAN, linked to syntactic processing) and mismatch negativity (MMN, linked to expectancy violations). Initially, Koelsch et al. (2001) estimated the MMN to lie in the 125–185 ms range and the ERAN in the 170–230 ms range (i.e., MMN earlier). A recent thorough study by Ishida and Nittono (2024) placed the ERAN at 103–143 ms and the syntactic MMN at 135–175 ms (i.e., ERAN earlier). Thus, these latencies partially overlap. In the present study, four of the differences between amateurs and non‑musicians fell within the MMN window in both schemes (136, 146, 152, 154 ms; Table 6), which can be interpreted as a stronger reaction in musically trained participants to the absence of expected meaning. Meanwhile, the N500 (larger in non‑musicians) indicates that some form of syntactic or, in this case, semantic processing still occurs. This pattern supports the view (Koelsch et al., 2001; Ishida & Nittono, 2024) that ERAN and MMN are similar in nature, while N500 is closer to N400.
Data on P1–N1a activity at F3, and a tendency at F7 (Table 6), suggest left‑lateralised tonal processing, whereas the late negativity at F8 (Fig. 5) and baseline differences indicate that coping with sound is predominantly right‑lateralised.
The importance of amplitude decay for broadcasting a chord’s emotional meaning was confirmed for amateur musicians and non‑musicians. A late positive potential peaking at ~1200 ms (LPP1200) is associated with the subjective sense of tension produced by a single sound. The same negative components that have been reported for words, musical excerpts, environmental sounds, and other meaningful acoustic events are present during passive listening to a single chord with amplitude decay. A chord without amplitude decay is perceived as meaningless by amateurs and non‑musicians, and its negativity peaks in the LP range at about 900–1000 ms. ERP differences between amateurs and non‑musicians are significant and widespread across components and analysis techniques. Studying standalone sounds permits one to focus on late potentials in sound perception, unveiling neural dynamics that are normally concealed by active task demands.
Ethics approval: The study was conducted in accordance with the Declaration of Helsinki. All participants provided written informed consent after receiving a complete description of the procedures. Approval of the experimental protocol by the ethics committee is not required
Data availability: The datasets generated and/or analysed during the current study are available from the corresponding author on reasonable request.
Funding: This work was supported by the Ministry of Education and Science of the Russian Federation, topic № 0138‑2023‑0004. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Author contributions: N.A.A. and S.O.S. conceived and designed the study. S.O.S. collected the data. N.A.A. and S.O.S. performed the data analysis. N.A.A. drafted the manuscript.
Competing interests: The authors declare no competing interests.
Author statement: Both authors have read and approved the final version of the manuscript and take full responsibility for its content. The work described has not been published previously, nor is it under consideration for publication elsewhere.
- Almaev, N. A., Skorik, S. O., & Bessonova, Y. V. (2018). Towards psychophysiological mechanisms of major and minor chords perception [Abstract]. International Journal of Psychophysiology, 131(Suppl.), S163. https://doi.org/10.1016/j.ijpsycho.2018.07.432
- Almaev, N. A., & Skorik, S. O. (2015, August). Expectations and tensions induced by primitive rhythms[Paper presentation]. Ninth Triennial Conference of the European Society for the Cognitive Sciences of Music, Manchester, UK. https://www.researchgate.net/publication/331311663
- Arthurs, Y., Beeston, A. V., & Timmers, R. (2018). Perception of isolated chords: Examining frequency of occurrence, instrumental timbre, acoustic descriptors and musical training. Psychology of Music, 46(1), 136–152. https://doi.org/10.1177/0305735617720834
- Bakker, D. R., & Martin, F. H. (2015). Musical chords and emotion: Major and minor triads are processed for emotion. Cognitive, Affective, & Behavioral Neuroscience, 15(1), 15–31. https://doi.org/10.3758/s13415-014-0309-4
- Barry, R. J., De Blasio, F. M., Rushby, J. A., MacDonald, B., Fogarty, J. S., & Cave, A. E. (2022). Stimulus intensity effects and sequential processing in the passive auditory ERP. International Journal of Psychophysiology, 176, 149–163. https://doi.org/10.1016/j.ijpsycho.2022.03.005
- Calma-Roddin, N., & Drury, J. E. (2020). Music, language, and the N400: ERP interference patterns across cognitive domains. Scientific Reports, 10, Article 11222. https://doi.org/10.1038/s41598-020-66732-0
- Chomsky, N. (1957). Syntactic structures. Mouton.
- Chomsky, N. (1995). The minimalist program. MIT Press.
- Cozzi, J., Angel, R., & Herdman, A. (2019). How can no change in an auditory stimulus generate an N2b-P3a? Brain and Cognition, 129, 9–15. https://doi.org/10.1016/j.bandc.2018.12.002
- Cummings, A., Čeponienė, R., Koyama, A., Saygin, A. P., Townsend, J., & Dick, F. (2006). Auditory semantic networks for words and natural sounds. Brain Research, 1115(1), 92–107. https://doi.org/10.1016/j.brainres.2006.07.050
- Delorme, A., & Makeig, S. (2004). EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of Neuroscience Methods, 134(1), 9–21. https://doi.org/10.1016/j.jneumeth.2003.10.009
- Desatnik, A., Bel-Bahar, T., Nolte, T., Crowley, M., Fonagy, P., & Fearon, P. (2017). Emotion regulation in adolescents: An ERP study. Biological Psychology, 129, 52–61. https://doi.org/10.1016/j.biopsycho.2017.08.001
- Dickey, L., Politte-Corn, M., & Kujawa, A. (2021). Development of emotion processing and regulation: Insights from event-related potentials and implications for internalizing disorders. International Journal of Psychophysiology, 170, 121–132. https://doi.org/10.1016/j.ijpsycho.2021.10.003
- Doelling, K. B., & Poeppel, D. (2015). Cortical entrainment to music and its modulation by expertise. Proceedings of the National Academy of Sciences, 112(45), E6233–E6242. https://doi.org/10.1073/pnas.1508431112
- Ellison, D., Moisseinen, N., Fachner, J., & Brattico, E. (2015). Affective versus cognitive responses to musical chords: An ERP and behavioral study. Psychomusicology: Music, Mind, and Brain, 25(4), 423–434. https://doi.org/10.1037/pmu0000127
- Farbood, M. M., & Upham, F. (2013). Interpreting expressive performance through listener judgments of musical tension. Frontiers in Psychology, 4, Article 998. https://doi.org/10.3389/fpsyg.2013.00998
- Faul, F., Erdfelder, E., Buchner, A., & Lang, A.-G. (2009). Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, 41(4), 1149–1160. https://doi.org/10.3758/BRM.41.4.1149
- Fedorenko, E., Patel, A., Casasanto, D., Winawer, J., & Gibson, E. (2009). Structural integration in language and music: Evidence for a shared system. Memory & Cognition, 37(1), 1–9. https://doi.org/10.3758/MC.37.1.1
- Fernández-Sotos, A., Fernández-Caballero, A., & Latorre, J. M. (2016). Influence of tempo and rhythmic unit in musical emotion regulation. Frontiers in Computational Neuroscience, 10, Article 80. https://doi.org/10.3389/fncom.2016.00080
- Fiveash, A., Thompson, W. F., Badcock, N. A., & McArthur, G. (2018). Syntactic processing in music and language: Effects of interrupting auditory streams with alternating timbres. International Journal of Psychophysiology, 129, 31–40. https://doi.org/10.1016/j.ijpsycho.2018.05.003
- Foster, N. E. V., & Zatorre, R. J. (2010). A role for the intraparietal sulcus in transforming musical pitch information. Cerebral Cortex, 20(6), 1350–1359. https://doi.org/10.1093/cercor/bhp199
- Friederici, A. D. (2002). Towards a neural basis of auditory sentence processing. Trends in Cognitive Sciences, 6(2), 78–84. https://doi.org/10.1016/S1364-6613(00)01839-8
- Graber, E., & Fukuoka, T. (2019). Endogenous expectations for sequence continuation after auditory beat accelerations and decelerations revealed by P3a and induced beta-band responses. Neuroscience, 413, 11–21. https://doi.org/10.1016/j.neuroscience.2019.06.004
- Halpern, A. R., Martin, J. S., & Reed, T. D. (2008). An ERP study of major-minor classification in melodies. Music Perception, 25(3), 181–191. https://doi.org/10.1525/mp.2008.25.3.181
- Halpern, A. R., Zioga, I., Shankleman, M., Lindsen, J., Pearce, M. T., & Bhattacharya, J. (2017). That note sounds wrong! Age-related effects in processing of musical expectation. Brain and Cognition, 113, 1–9. https://doi.org/10.1016/j.bandc.2016.12.006
- Hausen, M., Salmela, V. L., Vainio, M., & Särkämö, T. (2013). Music and speech prosody: A common rhythm. Frontiers in Psychology, 4, Article 566. https://doi.org/10.3389/fpsyg.2013.00566
- Herholz, S. C., & Zatorre, R. J. (2012). Musical training as a framework for brain plasticity: Behavior, function, and structure. Neuron, 76(3), 486–502. https://doi.org/10.1016/j.neuron.2012.10.011
- Ishida, K., & Nittono, H. (2024). Relationship between schematic and dynamic expectations of melodic patterns in music perception. International Journal of Psychophysiology, 196, Article 112292. https://doi.org/10.1016/j.ijpsycho.2023.112292
- Joucla, C., Nicolier, M., Giustiniani, J., Brunotte, G., Noiret, N., Monnin, J., Magnin, E., Pazart, L., Moulin, T., Haffen, E., Vandel, P., & Gabriel, D. (2018). Evidence for a neural signature of musical preference during silence. International Journal of Psychophysiology, 125, 50–56. https://doi.org/10.1016/j.ijpsycho.2018.02.007
- Juslin, P. N., Sakka, L. S., Barradas, G. T., & Lartillot, O. (2022). Emotions, mechanisms, and individual differences in music listening: A stratified random sampling approach. Music Perception, 40(1), 55–86. https://doi.org/10.1525/mp.2022.40.1.55
- Kamenetsky, S. B., Hill, D. S., & Trehub, S. E. (1997). Effect of tempo and dynamics on the perception of emotion in music. Psychology of Music, 25(2), 149–160. https://doi.org/10.1177/0305735697252005
- Koelsch, S. (2000). Brain and music: A contribution to the investigation of central auditory processing with a new electrophysiological approach. Max Planck Institute of Cognitive Neuroscience. https://pure.mpg.de/rest/items/item_720506/component/file_720505/content
- Koelsch, S. (2011). Toward a neural basis of music perception—A review and updated model. Frontiers in Psychology, 2, Article 110. https://doi.org/10.3389/fpsyg.2011.00110
- Koelsch, S., Gunter, T. C., Schröger, E., Tervaniemi, M., Sammler, D., & Friederici, A. D. (2001). Differentiating ERAN and MMN: An ERP study. NeuroReport, 12(7), 1385–1389. https://doi.org/10.1097/00001756-200105250-00025
- Koelsch, S., Gunter, T., Schröger, E., & Friederici, A. D. (2003). Processing tonal modulations: An ERP study. Journal of Cognitive Neuroscience, 15(8), 1149–1159. https://doi.org/10.1162/089892903322598111
- Koelsch, S., Gunter, T. C., Wittfoth, M., & Sammler, D. (2005). Interaction between syntax processing in language and in music: An ERP study. Journal of Cognitive Neuroscience, 17(10), 1565–1577. https://doi.org/10.1162/089892905774597290
- Krumhansl, C. L. (1997). An exploratory study of musical emotions and psychophysiology. Canadian Journal of Experimental Psychology / Revue canadienne de psychologie expérimentale, 51(4), 336–352. https://doi.org/10.1037/1196-1961.51.4.336
- Kurth, E. (1931). Musikpsychologie. Max Hesses Verlag.
- Küssner, M. B., Tidhar, D., Prior, H. M., & Leech-Wilkinson, D. (2014). Musicians are more consistent: Gestural cross-modal mappings of pitch, loudness and tempo in real-time. Frontiers in Psychology, 5, Article 789. https://doi.org/10.3389/fpsyg.2014.00789
- Kutas, M., & Federmeier, K. D. (2011). Thirty years and counting: Finding meaning in the N400 component of the event-related brain potential (ERP). Annual Review of Psychology, 62, 621–647. https://doi.org/10.1146/annurev.psych.093008.131123
- Lahdelma, I., & Eerola, T. (2016a). Mild dissonance preferred over consonance in single chord perception. i-Perception, 7(3). https://doi.org/10.1177/2041669516655812
- Lahdelma, I., & Eerola, T. (2016b). Single chords convey distinct emotional qualities to both naïve and expert listeners. Psychology of Music, 44(1), 37–54. https://doi.org/10.1177/0305735614552006
- Lehne, M., & Koelsch, S. (2015). Toward a general psychological model of tension and suspense. Frontiers in Psychology, 6, Article 79. https://doi.org/10.3389/fpsyg.2015.00079
- Lehne, M., Rohrmeier, M., Gollmann, D., & Koelsch, S. (2013). The influence of different structural features on felt musical tension in two piano pieces by Mozart and Mendelssohn. Music Perception, 31(2), 171–185. https://doi.org/10.1525/mp.2013.31.2.171
- Lerdahl, F., & Jackendoff, R. (1983). A generative theory of tonal music. MIT Press.
- Lerdahl, F., & Krumhansl, C. L. (2007). Modeling tonal tension. Music Perception, 24(4), 329–366. https://doi.org/10.1525/mp.2007.24.4.329
- Lerdahl, F. (1996). Calculating tonal tension. Music Perception, 13(3), 319–363. https://doi.org/10.2307/40286174
- Levitin, D. J., & Cook, P. R. (1996). Memory for musical tempo: Additional evidence that auditory memory is absolute. Perception & Psychophysics, 58(6), 927–935. https://doi.org/10.3758/BF03205494
- Ma, X., Ding, N., Tao, Y., & Yang, Y. F. (2018). Syntactic complexity and musical proficiency modulate neural processing of non-native music. Neuropsychologia, 121, 164–174. https://doi.org/10.1016/j.neuropsychologia.2018.10.005
- McAuley, J. D. (2010). Tempo and rhythm. In M. R. Jones, R. R. Fay, & A. N. Popper (Eds.), Music perception(pp. 165–199).
- McLean, M. A., Van den Bergh, B. R. H., Baart, M., Vroomen, J., & Van den Heuvel, M. I. (2020). The late positive potential (LPP): A neural marker of internalizing problems in early childhood. International Journal of Psychophysiology, 155, 78–86. https://doi.org/10.1016/j.ijpsycho.2020.06.005
- Malins, J. G., Desroches, A. S., Robertson, K. E., Newman, R. L., Archibald, L. M. D., & Joanisse, M. F. (2013). ERPs reveal the temporal dynamics of auditory word recognition in specific language impairment. Developmental Cognitive Neuroscience, 5, 134–148. https://doi.org/10.1016/j.dcn.2013.02.005
- Manchaiah, V., Zhao, F., & Ratinaud, P. (2019). Young adults’ knowledge and attitudes regarding “music” and “loud music” across countries: Applications of social representations theory. Frontiers in Psychology, 10, Article 1390. https://doi.org/10.3389/fpsyg.2019.01390
- Männel, C., Schipke, C. S., & Friederici, A. D. (2013). The role of pause as a prosodic boundary marker: Language ERP studies in German 3- and 6-year-olds. Developmental Cognitive Neuroscience, 5, 86–94. https://doi.org/10.1016/j.dcn.2013.01.003
- Miranda, R. A., & Ullman, M. T. (2007). Double dissociation between rules and memory in music: An event-related potential study. NeuroImage, 38(2), 331–345. https://doi.org/10.1016/j.neuroimage.2007.07.034
- Moelants, D. (2002). Preferred tempo reconsidered. In C. Stevens, D. Burnham, G. McPherson, E. Schubert, & J. Renwick (Eds.), Proceedings of the 7th International Conference on Music Perception and Cognition(pp. 1–4). Sydney, Australia.
- Müller, M., Höfel, L., Brattico, E., & Jacobsen, T. (2010). Aesthetic judgments of music in experts and laypersons—An ERP study. International Journal of Psychophysiology, 76(1), 40–51. https://doi.org/10.1016/j.ijpsycho.2010.02.002
- Näätänen, R., & Picton, T. (1987). The N1 wave of the human electric and magnetic response to sound: A review and an analysis of the component structure. Psychophysiology, 24(4), 375–425. https://doi.org/10.1111/j.1469-8986.1987.tb00311.x
- Näätänen, R. (1992). Attention and brain function.
- Näätänen, R. (1990). The role of attention in auditory information processing as revealed by event-related potentials and other brain measures of cognitive function. Behavioral and Brain Sciences, 13(2), 201–288. https://doi.org/10.1017/S0140525X00078407
- Parncutt, R. (1994). A perceptual model of pulse salience and metrical accent in musical rhythms. Music Perception, 11(4), 409–464. https://doi.org/10.2307/40285633
- Patel, A. D., Gibson, E., Ratner, J., Besson, M., & Holcomb, P. J. (1998). Processing syntactic relations in language and music: An event-related potential study. Journal of Cognitive Neuroscience, 10(6), 717–733. https://doi.org/10.1162/089892998563121
- Petrescu, N. (2008). Loud music listening. McGill Journal of Medicine, 11(2), 169–176. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2582665/
- Pinheiro, A. P., Vasconcelos, M., Dias, M., Arrais, N., & Gonçalves, Ó. F. (2015). The music of language: An ERP investigation of the effects of musical training on emotional prosody processing. Brain and Language, 140, 24–34. https://doi.org/10.1016/j.bandl.2014.10.009
- Proverbio, A. M., Santoni, S., & Adorni, R. (2020). ERP markers of valence coding in emotional speech processing. iScience, 23(3), 100933. https://doi.org/10.1016/j.isci.2020.100933
- Rentfrow, P. J., Goldberg, L. R., & Levitin, D. J. (2011). The structure of musical preferences: A five-factor model. Journal of Personality and Social Psychology, 100(6), 1139–1157. https://doi.org/10.1037/a0022406
- Sanders, L. D., & Neville, H. J. (2003). An ERP study of continuous speech processing I. Segmentation, semantics, and syntax in native speakers. Cognitive Brain Research, 15(3), 228–240. https://doi.org/10.1016/S0926-6410(02)00195-7
- Shahin, A. J., Alain, C., & Picton, T. W. (2006). Scalp topography and intracerebral sources for ERPs recorded during auditory target detection. Brain Topography, 19(1–2), 89–96. https://doi.org/10.1007/s10548-006-0015-9
- Skorik, S. O., & Almaev, N. A. (2019). Limitations of musicians and non‑musicians in differentiating between major and minor chords. In Proceedings of the First International Conference Psychology and Music – Interdisciplinary Encounter(p. 273). Belgrade, Serbia. https://www.researchgate.net/publication/337306945
- Skorik, S. O., & Almayev, N. A. (2023). The role of the amplitude decay for the evaluation of major and minor chords in amateur listeners and professionals. Natural Systems of Mind, 3(1), 64–80. https://doi.org/10.38098/nsom_2023_03_01_04
- Slevc, L. R., Rosenberg, J. C., & Patel, A. D. (2009). Making psycholinguistics musical: Self-paced reading time evidence for shared processing of linguistic and musical syntax. Psychonomic Bulletin & Review, 16(2), 374–381. https://doi.org/10.3758/PBR.16.2.374
- Solomon, B., DeCicco, J. M., & Dennis, T. A. (2012). Emotional picture processing in children: An ERP study. Developmental Cognitive Neuroscience, 2(1), 110–119. https://doi.org/10.1016/j.dcn.2011.04.002
- Sun, Y., Lu, X., Ho, H. T., Johnson, B. W., Sammler, D., & Thompson, W. F. (2018). Syntactic processing in music and language: Parallel abnormalities observed in congenital amusia. NeuroImage: Clinical, 19, 640–651. https://doi.org/10.1016/j.nicl.2018.05.032
- Tanner, D., & Van Hell, J. G. (2014). ERPs reveal individual differences in morphosyntactic processing. Neuropsychologia, 56, 289–301. https://doi.org/10.1016/j.neuropsychologia.2014.02.002
- Virtala, P., Huotilainen, M., Partanen, E., & Tervaniemi, M. (2014). Musicianship facilitates the processing of Western music chords—An ERP and behavioral study. Neuropsychologia, 61, 247–258. https://doi.org/10.1016/j.neuropsychologia.2014.06.028
- Welch, D., & Fremaux, G. (2017). Why do people like loud sound? A qualitative study. International Journal of Environmental Research and Public Health, 14(8), 908. https://doi.org/10.3390/ijerph14080908
- Xiao, R., Liu, C., Chen, J. J., & Chen, J. (2020). The influence of music tempo on inhibitory control: An ERP study. Frontiers in Behavioral Neuroscience, 14, Article 48. https://doi.org/10.3389/fnbeh.2020.00048
- Yu, K., Chen, Y., Yin, S., Li, L., & Wang, R. (2022). The roles of pitch type and lexicality in the hemispheric lateralization for lexical tone processing: An ERP study. International Journal of Psychophysiology, 177, 83–91. https://doi.org/10.1016/j.ijpsycho.2022.04.013
- Zhang, J., Zhou, X., Chang, R., & Yang, Y. (2018). Effects of global and local contexts on chord processing: An ERP study. Neuropsychologia, 109, 149–154. https://doi.org/10.1016/j.neuropsychologia.2017.12.016
