1. Introduction
Music and language are complex sound systems and play important roles in our daily lives. The protolanguage hypothesis, proposed even in Darwin’s time, states that the two might have the same origin (Darwin, Reference Darwin1871). Evidence from musicology (Feld & Fox, Reference Feld and Fox1994), acoustics (Besson et al., Reference Besson, Chobert and Marie2011; Patel, Reference Patel2007, Reference Patel2011) and cognitive neuroscience (Bidelman et al., Reference Bidelman, Gandour and Krishnan2011; Koelsch et al., Reference Koelsch, Gunter, Cramon, Zysset, Lohmann and Friederici2002; Maess et al., Reference Maess, Koelsch, Gunter and Friederici2001; Patel, Reference Patel2007; Slevc et al., Reference Slevc, Rosenberg and Patel2009) suggests that music and language share many similarities. For example, both are hierarchically organized, rule-governed system and unfolding with acoustic events over time (Lerdahl & Jackendoff, Reference Lerdahl and Jackendoff1985; Lutz, Reference Lutz2012; Patel, Reference Patel2011). In addition, language-specific brain regions, such as Broca’s area and Wernik’s area, are activated when music is being processed (Koelsch et al., Reference Koelsch, Gunter, Cramon, Zysset, Lohmann and Friederici2002; Maess et al., Reference Maess, Koelsch, Gunter and Friederici2001), indicating the cross-domain relationship between music and language.
Stress refers to a prominent syllable or note marked by pitch, intensity, duration, or other enhanced acoustic characteristics (e.g., speech intelligibility) (Breen et al., Reference Breen, Fedorenko, Wagner and Gibson2010; Cutler, Reference Cutler1976; Lin et al., Reference Lin, Yan and Sun1984; Xu, Reference Xu1956). Stress carries important information in both music and language (Plack et al., Reference Plack, Oxenham, Fay, Popper, Plack, Oxenham, Fay and Popper2005), and its regularity makes auditory signals highly predictable (Ellis & Jones, Reference Ellis and Jones2010; Jones et al., Reference Jones, MacKenzie and Puente2002). In music, the stress on different scales (i.e., beats) repeats cyclically in a certain order to organize music, and the beats largely determine the sense of harmony of tonal music (Cooper & Meyer, Reference Cooper and Meyer1971; Krumhansl, Reference Krumhansl1991). In language, stress is often used by listeners for word recognition (Ye & Connine, Reference Ye and Connine1999). Stressed (i.e., strong) and unstressed (i.e., weak) syllables of words help listeners form the rhythm of the language, making the upcoming information predictable to a certain extent (Pitt & Samuel, Reference Pitt and Samuel1990). The current study employed event-related potentials (ERPs) to examine the cross-domain relationship between music and language in an auditory priming experiment. Specifically, we utilized Mandarin Chinese as a test case to investigate how lexical stress and musical stress influence the cortical processing of the upcoming lexical stress.
Mandarin Chinese, henceforth Mandarin, is a tonal language, which uses four types of pitch variations to distinguish word meanings (Kratochvil & Chao, Reference Kratochvil and Chao1970; Yip, Reference Yip2002). A famous example is that the syllable “ma” means mother, hemp, horse, and scold when carrying T1 (level tone), T2 (rising tone), T3 (falling-rising tone) and T4 (falling tone), respectively. In addition, Mandarin has a neutral tone that signals an unstressed syllable and always occurs in the final syllable of a word. The syllable carrying a neutral tone is shorter than that carrying one of the four citation tones (Chen & Xu, Reference Chen and Xu2006; Lee & Zee, Reference Lee and Zee2010; Lin, Reference Lin1985). For example, the word [’tʊ̄ŋ ’ɕīī] (meaning west and east) and the word [’tʊ̄ŋ ɕi] (meaning stuff) contrast in the stress pattern, with the unstressed second syllable of [’tʊ̄ŋ ɕi] (i.e., stuff) having a shorter duration and a mid-falling contour (Chao, Reference Chao1968; Duanmu, Reference Duanmu2007).
In addition to the words with neutral tone, there are unstressed and stressed syllables in Mandarin disyllabic words. Although unstressed and stressed syllables in Mandarin can be judged based on the listener’s subjective perception of phonological prominence, the judgment is closely related to acoustic parameters such as pitch, intensity and duration (Cao, Reference Cao1986; Lin, Reference Lin1962; Lin et al., Reference Lin, Yan and Sun1984). Compared to unstressed syllables, stressed syllables have a longer duration and a wider F0 range (Lu, Reference Lu1984; Sun, Reference Sun1999). Mandarin disyllabic words have two stress patterns, namely, the Strong-Weak and the Weak-Strong pattern (Lu, Reference Lu1984; Yin, Reference Yin1982). The first syllable of the Strong-Weak pattern is more prominent in terms of intensity, duration, pitch and energy, while the second syllable of the Weak-Strong pattern is more prominent in terms of the same acoustic parameters (Xu, Reference Xu1956). In recent years, research on the lexical stress patterns of Mandarin has shown contradictory results. Some researchers believe that both Strong-Weak and Weak-Strong patterns are equally common in Mandarin, while others argued that the Strong-Weak pattern is predominant (Wang & Feng, Reference Wang and Feng2006). There are also researchers considering that the Weak-Strong pattern is the principal pattern (Lin et al., Reference Lin, Yan and Sun1984; Xu, Reference Xu1982), although it is argued that there is no fixed stress pattern in Mandarin (Liu, Reference Liu2007; Zhou, Reference Zhou2018). The controversy in the literature may arise from the fact that Mandarin is a tonal language. Therefore, lexical stress in Mandarin has receded to a relatively minor function (Yin, Reference Yin2021). Therefore, it is often difficult to distinguish the stressed and unstressed syllables of words when no syllable bears a neutral tone. The primary function of Mandarin lexical stress is to distinguish linguistic symbols and semantic information (Lu, Reference Lu1984; Yin, Reference Yin1982). Mandarin lexical stress also has a metrical rhythmic function, as most syllables are tonalized, creating a syllable-timed rhythmic feature with syllables as units (Luo & Wang, Reference Luo and Wang2002).
As for the role of stress in rhythm processing, Jones and Boltz (Reference Jones and Boltz1989) proposed the Dynamic Attentional Theory (DAT). According to the theory, internal oscillators would synchronize with external rhythms, and such coupling would prevent listeners from evenly distributing attentional resources to every part of the auditory signals. Instead, attention is selectively focused on the important information to facilitate auditory signal processing and prediction of upcoming information (Jones & Boltz, Reference Jones and Boltz1989; Koelsch et al., Reference Koelsch, Gunter, Cramon, Zysset, Lohmann and Friederici2002). DAT is equally applicable in music (Large et al., Reference Large, Herrera and Velasco2015; Large & Palmer, Reference Large and Palmer2002) and language (Kotz & Schwartze, Reference Kotz and Schwartze2010; Pitt & Samuel, Reference Pitt and Samuel1990). For example, when hearing a triple meter sequence with the accented pulse falling on the last beat (123, 123, 123), listeners would predict that a “123” (weak-weak-strong) rhythm will occur next (Mirka, Reference Mirka2004); when hearing a number of trisyllabic words with final stress, listeners would expect the upcoming words to have the same stress pattern (Pitt & Samuel, Reference Pitt and Samuel1990). In short, prediction is the main factor that makes listeners pay attention to a particular part of the speech signal. These findings are also consistent with the predictive coding theory and the theory of expectancy-driven speech processing. Predictive coding theory states that the brain does not passively receive bottom-up inputs, but uses predictive coding to process hierarchical information. During information processing, comprehenders would receive both bottom-up input and top-down prediction. Predictive errors would be generated when the predicted results are inconsistent with the actual input. In this case, the error signal would be transmitted upward and adjust the following prediction. The adjusted prediction would then be transmitted downward to generate expectations at a lower level, suppressing the prediction error (Garrido et al., Reference Garrido, Kilner, Kiebel and Friston2007, Reference Garrido, Kilner, Stephan and Friston2009; Parmentier et al., Reference Parmentier, Elsley, Andrés and Barceló2011; Todorović et al., Reference Todorović, van Ede, Maris and de Lange2011). Therefore, processing unexpected information consumes more cognitive resources (Friston, Reference Friston2005; Rubin et al., Reference Rubin, Ulanovsky, Nelken and Tishby2016). The theory of expectancy-driven speech processing also suggests that the temporal elements of auditory input and the subcortical speech perception system in neural networks work together to optimize the prediction of speech acts. The neural network can coordinate speech perception and production in a timely and accurate manner. The auditory rhythm-driven prediction may help enhance the perception of stressed syllables, thus facilitating language processing at these time points (Kotz & Schwartze, Reference Kotz and Schwartze2010, Reference Kotz, Schwartze, Hickok and Small2015).
Auditory rhythm, such as speech rhythm or music with a significant metrical structure, are characterized by stress patterns of syllables and notes; these syllables and notes are repeated (quasi-) periodically and are considered to be highly regular (Lehiste, Reference Lehiste1977; London, Reference London2004). Repeated and regular stress makes the rhythm predictable and enhances listeners’ attention (Ellis & Jones, Reference Ellis and Jones2010; Jones et al., Reference Jones, MacKenzie and Puente2002). Previous studies using the fragment priming paradigm have shown that listeners responded more quickly when the stress patterns of prime fragments and that of target words were matched (e.g., mu-muSEUM) than when they were mismatched (e.g., mu-MUsic) (Cooper et al., Reference Cooper, Cutler and Wales2002; Cutler & Donselaar, Reference Cutler and Donselaar2001; Soto-Faraco et al., Reference Soto-Faraco, Sebastián-Gallés and Cutler2001). At the cortical level, previous studies have found larger P350 amplitudes when the stress patterns of prime fragments and target words were mismatched. Given that the P350 component is associated with facilitating word recognition, the observed P350 effect suggests that stress is used in spoken language recognition (Friedrich et al., Reference Friedrich, Kotz, Friederici and Gunter2004). In addition to syllable priming experiments, the word priming paradigm has also been employed to investigate the role of stress in word recognition. For example, Böcker et al. (Reference Böcker, Bastiaansen, Vroomen, Brunia and de Gelder1999) examined the effect of word priming on stress recognition in Dutch by manipulating the congruency of the stress pattern between the prime word sequences and target words. Their behavioral results showed shorter reaction times and higher accuracy rates in the congruent stress condition. For the ERP results, greater LPC amplitudes were elicited in the incongruent stress condition relative to those yielded in the congruent stress condition. In addition, the participants’ performance was modulated by the stress pattern. The Weak-Strong primes elicited greater P2, N325, and N400 amplitudes than the Strong-Weak primes (Böcker et al., Reference Böcker, Bastiaansen, Vroomen, Brunia and de Gelder1999). However, this study did not control the semantic relationship between the primes and targets. Therefore, it is possible that the N400 effect was due to their semantic relatedness rather than due to their stress patterns. Based on Böcker et al. (Reference Böcker, Bastiaansen, Vroomen, Brunia and de Gelder1999), Magne et al. (Reference Magne, Jordan and Gordon2016) controlled the semantic relationship between the primes and targets. They observed larger negative ERP components in the 288–567 ms and 398–594 ms time windows when the Strong-Weak primes and Weak-Strong primes had incongruent stress patterns with their corresponding targets, respectively. These ERP components were still argued to be N400 in this study even though the semantic relatedness between the primes and targets was eliminated, since previous studies have shown that the N400 effect can also be elicited by disharmony or unexpected stress in rhythm/prosody (Bohn et al., Reference Bohn, Knaus, Wiese and Domahs2013; Domahs et al., Reference Domahs, Wiese, Bornkessel-Schlesewsky and Schlesewsky2008; Magne et al., Reference Magne, Astésano, Aramaki, Ystad, Kronland-Martinet and Besson2007; Marie et al., Reference Marie, Magne and Besson2011; McCauley et al., Reference McCauley, Hestvik and Vogel2013; Rothermich et al., Reference Rothermich, Schmidt-Kassow and Kotz2012; Schmidt-Kassow & Kotz, Reference Schmidt-Kassow and Kotz2009).
Given a plethora of similarities between music and language, a large amount of research has been conducted to investigate the interactions between these two domains. For example, some studies have shown that long-term musical training has a positive effect on listeners’ speech recognition, memory, and segmentation (Elmer et al., Reference Elmer, Klein, Kühnis, Liem, Meyer and Jäncke2014, Reference Elena, Luisa, Chiara, Marcella, Stefania and Daniele2015; François et al., Reference François, Chobert, Besson and Schön2013; François & Schön, Reference François and Schön2014). Others have shown that music can shape the coding of language in a relatively short period of time (Cason et al., Reference Cason, Astésano and Schön2015a, Reference Cason, Hidalgo, Isoard, Roman and Schön2015b; Cason & Schön, Reference Cason and Schön2012; Kotz & Gunter, Reference Kotz and Gunter2015). Of particular interest to the current study is Cason and Schön (Reference Cason and Schön2012), in which participants were first presented with a few musical beats as primes, then with pseudowords as targets, while their behavioral (reaction times) and EEG data were recorded. Results showed that participants’ reaction times to the targets were facilitated when the beat primes and pseudoword targets shared the same stress pattern. When they had incongruent stress patterns, N100 and P300 effects were observed. These results suggest that subsequent speech recognition can be enhanced if listeners have been exposed to the musical beats with the same stress pattern as the following words or short sentences (Cason et al., Reference Cason, Astésano and Schön2015a; Cason & Schön, Reference Cason and Schön2012).
Fotidzis et al. (Reference Fotidzis, Moon, Steele and Magne2018) conducted a cross-modal priming experiment to examine whether auditory beat primes would affect visual word processing. Results showed that target words with inconsistent stress patterns with the beat primes elicited a greater negative ERP component with a fronto-central distribution (Fotidzis et al., Reference Fotidzis, Moon, Steele and Magne2018). Consistent with Fotidzis et al. (Reference Fotidzis, Moon, Steele and Magne2018), Hilton and Goldwater (Reference Hilton and Goldwater2020) obtained similar results using sentences as the target stimuli. These findings support the idea that music and language interact in stress processing, and the effect can be cross-modal.
As shown above, previous studies on lexical stress recognition mainly used words and beats as primes and found that the stress pattern of both types of primes influenced the stress recognition of word/pseudoword targets similarly (Böcker et al., Reference Böcker, Bastiaansen, Vroomen, Brunia and de Gelder1999; Cason & Schön, Reference Cason and Schön2012, Cason et al., 2015a, 2015b; Fotidzis et al., Reference Fotidzis, Moon, Steele and Magne2018; Magne et al., Reference Magne, Jordan and Gordon2016). For example, Böcker et al. (Reference Böcker, Bastiaansen, Vroomen, Brunia and de Gelder1999) and Magne et al. (Reference Magne, Jordan and Gordon2016) showed ERP components indicating processing difficulty at the cortical level when word prime sequences and word targets shared different stress patterns relative to when they shared the same stress pattern. Cason and Schön (Reference Cason and Schön2012, Cason et al. (2015a, 2015b), and Fotidzis et al. (Reference Fotidzis, Moon, Steele and Magne2018) demonstrated higher demand for cognitive resources when the stress patterns of beat primes and pseudoword/word targets were incongruent. Although it is relatively certain that word primes with the same stress pattern as word targets facilitate the recognition of target lexical stress (Böcker et al., Reference Böcker, Bastiaansen, Vroomen, Brunia and de Gelder1999; Magne et al., Reference Magne, Jordan and Gordon2016), the facilitative effect of beat priming on lexical stress recognition is still unclear for tone language speakers (Bidelman et al., Reference Bidelman, Gandour and Krishnan2011, Reference Bidelman, Hutka and Moreno2013; Peretz et al., Reference Peretz, Nguyen and Cummings2011; Zheng & Samuel, Reference Zheng and Samuel2018). The majority of studies examining the effect of musical stress on lexical stress recognition have been conducted in non-tonal languages such as French, German and English. Few studies investigated these issues in tonal languages, such as Mandarin, Cantonese and Thai. Given that some studies have demonstrated that non-musicians of tonal languages and musicians of non-tonal languages have similar abilities in processing stress (Bidelman et al., Reference Bidelman, Gandour and Krishnan2011, Reference Bidelman, Hutka and Moreno2013; Hutka et al., Reference Hutka, Bidelman and Moreno2015; Tong et al., Reference Tong, Choi and Man2018), while others revealed that listeners of tonal languages have more difficulty in distinguishing the contours of musical stress (Bent et al., Reference Bent, Bradlow and Wright2006; Chang et al., Reference Chang, Hedberg and Wang2016; Peretz et al., Reference Peretz, Nguyen and Cummings2011; Zheng & Samuel, Reference Zheng and Samuel2018), it is warranted to further investigate the effect of musical stress and lexical stress on the processing of lexical stress in tonal languages, such as Mandarin.
Taken together, the present study used ERPs to investigate the effects of word priming and beat priming on the lexical stress recognition of Mandarin disyllabic words. At the behavioral level, we predict that the recognition of the stress pattern of disyllabic targets would be facilitated if beat and word prime sequences have the same stress pattern as the target words (Cason et al., Reference Cason, Astésano and Schön2015a; Cason & Schön, Reference Cason and Schön2012; Fotidzis et al., Reference Fotidzis, Moon, Steele and Magne2018; Hilton & Goldwater, Reference Hilton and Goldwater2020). We also predict that shorter reaction times and higher accuracy rates would be observed if the stress pattern of the prime sequences is congruent with that of the target words (Cooper et al., Reference Cooper, Cutler and Wales2002; Cutler & Donselaar, Reference Cutler and Donselaar2001; Soto-Faraco et al., Reference Soto-Faraco, Sebastián-Gallés and Cutler2001). At the cortical level, we predict that greater N400 and LPC amplitudes would be elicited if the stress pattern of the prime sequences is incongruent with that of the target words (Böcker et al., Reference Böcker, Bastiaansen, Vroomen, Brunia and de Gelder1999; Fotidzis et al., Reference Fotidzis, Moon, Steele and Magne2018; Magne et al., Reference Magne, Jordan and Gordon2016). In addition, given that Brochard et al. (Reference Brochard, Abecasis, Potter, Ragot and Drake2003) and Potter et al. (Reference Potter, Fenwick, Abecasis and Brochard2009) showed greater P300 amplitudes for the Weak-Strong pattern than for the Strong-Weak pattern (Brochard et al., Reference Brochard, Abecasis, Potter, Ragot and Drake2003; Potter et al., Reference Potter, Fenwick, Abecasis and Brochard2009), we therefore predict that Weak-Strong prime sequences would induce larger P300 amplitudes than Strong-Weak prime sequences for beat priming (Brochard et al., Reference Brochard, Abecasis, Potter, Ragot and Drake2003; Potter et al., Reference Potter, Fenwick, Abecasis and Brochard2009); for word priming, Weak-Strong prime sequences may induce larger P2 and N400 amplitudes, as well as stronger late positive components (P600 or LPC) than Strong-Weak prime sequences (Böcker et al., Reference Böcker, Bastiaansen, Vroomen, Brunia and de Gelder1999; Breen et al., Reference Breen, Fitzroy and Oraa Ali2019; Marie et al., Reference Marie, Magne and Besson2011; McCauley et al., Reference McCauley, Hestvik and Vogel2013).
2. Methods
2.1. Participants
Before participant recruitment, G∗Power 3.1 software (Faul et al., Reference Faul, Erdfelder, Buchner and Lang2009) was used to calculate the required number of participants. At the significance level α = 0.05 and the medium effect (f = 0.25), the total sample size required to reach the 80% statistical power level was predicted to be at least 24 participants. We randomly recruited 27 non-music major students from Liaoning Normal University to participate in the experiment. Among them, the data of 3 participants were excluded due to excessive artifacts or incorrect responses (the error rate of an experimental condition was greater than 35%). The remaining data of the 24 participants were subject to subsequent analyses (10 male students, ranging in age from 18 to 27, with an average age of 20.52 ± 2.77 years). According to their self-reports, all the participants were right-handed and native speakers of Mandarin Chinese. They all had normal vision or corrected-to-normal vision, no hearing impairment, dyslexia, or any neurological disorder. The study was approved by the Ethics Committee of Liaoning Normal University. The participants signed an informed consent form before the experiment and were paid after the experiment.
2.2. Materials
The priming materials include word primes and beat primes. A total of 84 word primes were selected from the Mandarin Concise Light and Heavy Format Dictionary (Song, Reference Song2009), half of which had the Strong-Weak stress pattern. The other half of them had the Weak-Strong stress pattern. In order to reduce the influence of tones on stress perception, we balanced the number of times each tone occurred at the initial syllable or final syllable positions, resulting in 16 combinations (T1 + T1, T1 + T2, T1 + T3, T1 + T4, T2 + T2, T2 + T3, T2 + T4, T3 + T1, T3 + T3, T3 + T4, T4 + T1, T4 + T2, T4 + T3, T4 + T1, T4 + T2, T4 + T3, T4 + T4, see Table 1). The beat primes were created by combining 7 notes (‘do’, ‘re’, ‘mi’, ‘fa’, ‘sol’, ‘la’ and ‘si’) into all possible pair combinations with both Strong-Weak and Weak-Strong patterns, resulting in 84 tokens. The 40 pairs of disyllabic target words were also selected from the Mandarin Concise Light and Heavy Format Dictionary. They were all inverse morpheme words (for example, 中期 ‘middle stage’ and 期中 ‘midterm’, with the bold characters representing stressed syllables). In addition, another 40 words with initial stress and 40 words with final stress were selected as filler targets (e.g., 西瓜 ‘watermelon’, 血脉 ‘blood relationship’).
Examples of experimental materials
Note: Bolded words or beats indicate stressed conditions.
Each word/beat prime was presented at a different position of a prime sequence of three words using the Latin Square design, producing a total of 160 word-prime sequences and 160 beat-prime sequences. Within a sequence, the three word/beat primes had the same stress pattern, with either initial stress or final stress. The congruency of the stress pattern between the prime sequence and the target was also manipulated, with half of the targets having a congruent stress pattern with the prime sequences and half of the targets having an incongruent stress pattern with the prime sequences. To prevent the participants from hearing the same target words in both congruent and incongruent conditions, all the experimental materials were divided into two lists, with each participant receiving only one of the lists. There was no semantic relatedness between the prime words and target words. All word materials were recorded in a soundproof studio at 44.1 kHz sampling rate and 16-bit resolution by a phonetically trained female native speaker of Mandarin. The musical stimuli were sung by a female musician and recorded at 44.1 kHz sampling rate and 16-bit resolution (see Figure 1 for the examples). All the recorded materials were saved as. wav files. The intensity of the materials was normalized to 70 dB using Praat (http://www.fon.hum.uva.nl/praat/).
Oscillograms and spectrograms of the stimulus sample.
To ensure the validity of the recorded experimental materials, 20 participants (10 males, 19–24 years old, mean age = 22.2) who did not participate in the priming experiment were recruited to evaluate the materials. The auditory stimuli were presented using the E-prime 2.0 software, and the participants were asked to judge the stress pattern of the materials (initial stress or final stress). Whenever there was a stimulus that did not have a 90%-or-higher accuracy rate, this particular stimulus was re-recorded and rated until it met the standard. To ensure that all the word stimuli were familiar to the participants, a familiarity rating experiment was conducted in which the participants were asked to score the familiarity of words on a 7-point scale (1 being very unfamiliar and 7 being very familiar). The rating results showed that the participants were pretty familiar with the word stimuli (M = 5.09 ± 1.46). Finally, acoustic analyses were also conducted on the prime words, target words, and prime beats to evaluate the acoustic features of the stressed and unstressed syllables, and those of the strong and weak beats. Duration, F0 range, and intensity data were analyzed using paired sample t-tests (see Table 2). Results showed that the stressed syllables/strong beats had longer duration, wider F0 range, and greater intensity than the unstressed syllables/weak beats. These results are consistent with the acoustic characteristics observed for stressed/unstressed syllables and strong/weak beats in previous studies (Chen & Gussenhoven, Reference Chen and Gussenhoven2008; Li et al., Reference Li, Deng, Yang and Wang2018; Sun et al., Reference Sun, Sommer and Li2022).
Acoustic parameters of words and beats
*** p < 0.001.
2.3. Procedure
The auditory–auditory priming experiment was conducted in a dim-lit, cozy and soundproof room. The participants were seated in a comfortable chair in front of the LCD screen (23 inches, 60 Hz refresh rate), with headphones in both ears and the volume adjusted to suit individuals’ hearing needs. In each trial, the fixation cross (+) was first presented for 500 ms. Then the fixation cross stayed in the center of the screen to reduce the eye movement of the participants while three-word primes/beat primes and a target word were being presented auditorily one after another. The participants were required to pay attention to the stress pattern of the target word. The time interval between the stimuli was 600 ms, which is considered the most spontaneous and natural interval without bias (Fraisse, Reference Fraisse1982; Krumhansl, Reference Krumhansl2000). The participants’ task was to determine whether or not the stress pattern of the prime sequence and that of the target word were congruent. Half of the participants would need to press “F” for the congruent condition and “J” for the incongruent condition. The other half of the participants would need to press “F” for the incongruent condition and “J” for the congruent condition on the keyboard (see Figure 2 for the experimental procedure). The whole experiment consisted of 4 blocks of word priming and 4 blocks of beat priming. Each block comprised 40 trials, with each experimental condition containing 10 trials (see Table 1). To minimize the possibility that prior exposure to music may enhance cognitive processing (Ladányi et al., Reference Ladányi, Lukács and Gervain2021) and prevent such effects from influencing our results, the orders of the word priming and beat priming were counterbalanced across participants. Before the main experiment, 8 practice trials were provided to familiarize the participants with the procedure.
Schematic illustration of the trial schemes.
We followed up with a stress type judgment task and a word recall test. In the stress type judgment task, we selected a total of 50 words from the formal experiment, of which 25 were Strong-Weak words and 25 were Weak-Strong words. In the word recall test, 10 words from the stress type judgment task (5 Strong-Weak words, 5 Weak-Strong words) were selected, and another 10 words from the formal experiment (5 Strong-Weak words, 5 Weak-Strong words) were selected as new vocabulary. We recruited 20 additional subjects (9 males, mean age 21.85 ± 2.21 years) to conduct a stress type judgment task and a word recall test in order. In the stress type judgment task, a fixation cross (+) was first presented for 500 ms, followed by an auditory presentation of a word that required subjects to focus only on the stress type of the word. Then they were asked to determine whether the word belonged to the Strong-Weak word or Weak-Strong word by pressing the key on a keyboard. Half of the participants would need to press “F” for the Strong-Weak word and “J” for the Weak-Strong word. The other half of the participants would need to press “F” for the Weak-Strong word and “J” for the Strong-Weak word. In the word recall test, a 500-ms fixation cross was presented, followed by an auditory presentation of a word, in which subjects were asked to determine whether the word they had just heard appeared in the stress type judgment task. They had to press the “F” key if the word appeared, or the “J” key if it did not. The other half of the subjects made the opposite keystroke.
2.4. EEG recording and data processing
Continuous EEG data were recorded using Brain Products actiCHamp in accordance with a 64 Ag/AgCl electrodes cap modified by the 10–20 international system. The signal was recorded at a sampling rate of 1000 Hz and FCz was used as an online reference. The electrodes TP9 and TP10 were placed on the left and right mastoid processes, respectively, and the impedance between the electrodes and the scalp was less than 5 kΩ.
The data were preprocessed using EEGLAB v.13.5.4b (MathWorks, Natick, USA). Off-line filtering was performed by high-pass filtering at 0.01 Hz and low-pass filtering at 30 Hz, and the mean of bilateral mastoid processes was subtracted from the EEG data of each lead as the re-reference. Artifact correction was performed using independent component analysis in EEGLAB (ICA). The onset of EEG analysis lock was at the moment the target words were presented, intercepting data 200 ms before and 800 ms after the target word presentation, and the baseline correction time was 200 ms to 0 ms before the target stimulus presentation. After excluding the trials whose amplitudes were greater than ±80 μV, the effective trials under each condition were more than 25. Finally, the ERPs of the remaining trials within each condition were averaged.
By visual inspection of the average amplitudes and previous studies on the P2 (Böcker et al., Reference Böcker, Bastiaansen, Vroomen, Brunia and de Gelder1999; Marie et al., Reference Marie, Magne and Besson2011), N400 (Breen et al., Reference Breen, Fitzroy and Oraa Ali2019; Marie et al., Reference Marie, Magne and Besson2011; Rothermich et al., Reference Rothermich, Schmidt-Kassow and Kotz2012) and LPC (Böcker et al., Reference Böcker, Bastiaansen, Vroomen, Brunia and de Gelder1999; McCauley et al., Reference McCauley, Hestvik and Vogel2013) components, we selected the electrodes and time windows corresponding to each component. For the P2 component, the selected time window was 50–150 ms, and the ROIs were FC1, FCz, FC2, F1, Fz, F2, C1, Cz, C2; for the N400 component, the selected time window was 300–450 ms, and the ROIs were P1, Pz, P2, CP1, CPz, CP2; for the LPC component, the selected time window was 500–700 ms, and the ROIs were CP1, CPz, CP2, P1, Pz, P2, PO3, POz, PO4.
3. Results
3.1. Behavioral results
A three-way repeated measures ANOVA was conducted on participants’ accuracy rates with Prime Type (word priming vs. beat priming), Stress Pattern (Strong-Weak vs. Weak-Strong), and Congruency (congruent vs. incongruent stress patterns between primes and targets) as independent variables. A significant main effect of Congruency was found, F(1, 23) = 16.158, p = 0.001, η2 = 0.413, with the accuracy rates yielded in the congruent condition (M = 92.444, 95%CI [90.924, 93.964]) being significantly higher than those elicited in the incongruent condition (M = 89.179, 95%CI [86.899, 91.459]). A significant interaction between Stress Pattern and Prime Type was also observed, F(1, 23) = 8.524, p = 0.008, η2 = 0.270. Subsequent simple effects analysis showed that the accuracy rates of word primes were significantly higher than those of beat primes for the Strong-Weak condition, F(1, 23) = 3.968, p = 0.058, η2 = 0.147, while there was no significant difference between the two types of primes for the Weak-Strong condition, F(1, 23) = 1.861, p = 0.186, η2 = 0.075. Moreover, Congruency and Prime Type had a significant interaction, F(1, 23) = 4.566, p = 0.043, η2 = 0.166. Subsequent simple effects analysis showed that the accuracy rates in the congruent condition were significantly higher than those in the incongruent condition only for the beat primes, F(1, 23) = 22.898, p < 0.001, η2 = 0.499, while there was no significant difference of the accuracy rates between the congruent and incongruent conditions for the word primes, F(1, 23) = 1.448, p = 0.241, η2 = 0.059. There was no significant difference between the word primes and beat primes for the congruent condition, F(1, 23) = 1.366, p = 0.255, η2 = 0.056. Neither was there significant difference between the two prime types for the incongruent condition, F(1, 23) = 1.435, p = 0.243, η2 = 0.059 (see Figure 3A).
Accuracy (A) and reaction times (B) for the four experimental conditions under word priming and beat priming. SW, Strong-Weak; WS, Weak-Strong.
A three-way repeated measures ANOVA was conducted on reaction times (RTs) with Prime Type (word priming vs. beat priming), Stress Pattern (Strong-Weak vs. Weak-Strong) and Congruency (congruent vs. incongruent stress patterns between primes and targets) as independent variables. A significant main effect of Stress Pattern was found, F(1, 23) = 15.776, p = 0.001, η2 = 0.407, showing that the RTs elicited by the Strong-Weak primes (M = 947.124, 95%CI [867.544, 1026.705]) were significantly longer than those yielded by the Weak-Strong primes (M = 877.019, 95%CI [958.065, 955.974]). There was a significant main effect of Congruency, F(1, 23) = 84.771, p < 0.001, η2 = 0.787, revealing that the RTs elicited by the congruent condition (M = 761.982, 95%CI [702.100, 821.864]) were significantly shorter than those yielded by the incongruent condition (M = 1062.162, 95%CI [959.260, 1165.064]). Prime Type also reached significance, F(1, 23) = 28.416, p < 0.001, η 2 = 0.553, with the RTs elicited by the beat primes (M = 842.313, 95%CI [770.885, 913.742]) being significantly shorter than those yielded by the word primes (M = 981.830, 95%CI [890.824, 1072.736]). There was a significant interaction between Stress Pattern and Congruency, F(1, 23) = 232.358, p < 0.001, η2 = 0.910. Follow-up simple effects analysis showed that while the RTs of the congruent condition were significantly shorter than those of the incongruent condition for both the Weak-Strong prime condition, F(1, 23) = 202.613, p < 0.001, η2 = 0.898, and the Strong-Weak prime condition, F(1, 23) = 6.659, p = 0.017, η2 = 0.225, the RT difference between the congruent and incongruent conditions was larger for the Weak-Strong prime condition than for the Strong-Weak prime condition. There was also a significant interaction between Stress Pattern and Prime Type, F(1, 23) = 12.691, p = 0.002, η2 = 0.356. Follow-up simple effects analysis demonstrated that the RTs elicited by the Strong-Weak primes were significantly longer than those yielded by the Weak-Strong primes only for word priming, F(1, 23) = 17.544, p < 0.001, η2 = 0.433; however, there was no significant difference of RTs between the Strong-Weak and Weak-Strong prime conditions for beat priming, F(1, 23) = 2.395, p = 0.135, η2 = 0.094. In addition, there was a significant interaction between Congruency and Prime Type, F(1, 23) = 12.000, p = 0.002, η2 = 0.343. Simple effects analysis showed that while the RTs elicited by the incongruent condition was significantly longer than those of the congruent condition for the word primes, F(1, 23) = 38.214, p < 0.001, η2 = 0.624, the RTs difference between the incongruent and congruent conditions was greater for the beat primes, with the incongruent condition producing significantly longer RTs than the congruent condition, F(1, 23) = 93.835, p < 0.001, η2 = 0.803. There was a significant three-way interaction between Prime Type, Stress Pattern, and Congruency, F(1, 23) = 23.5003, p < 0.001, η2 = 0.535. Subsequent simple effects analysis showed that for the word primes with the Strong-Weak stress pattern, the RTs in the incongruent condition were longer than those in the congruent condition, F(1, 23) = 7.946, p = 0.010, η2 = 0.257. For the word primes with the Weak-Strong stress pattern, the RTs in the incongruent condition were also longer than those in the congruent condition, F(1, 23) = 22.270, p < 0.001, η2 = 0.492. For the beat primes with the Strong-Weak stress pattern, the RTs in the congruent condition were longer than those in the incongruent condition, F(1, 23) = 26.127, p < 0.001, η2 = 0.532. For the beat primes with the Weak-Strong stress pattern, the RTs in the incongruent condition were longer than those in the congruent condition, F(1, 23) = 18.486, p < 0.001, η2 = 0.446 (see Figure 3B).
3.2. ERP results
A series of three-way repeated measures ANOVAs were conducted on the average amplitudes of the ERP components, with Prime Type (word priming vs. beat priming), Stress Pattern (Strong-Weak vs. Weak-Strong), and Congruency (congruent vs. incongruent stress patterns between primes and targets) as independent variables. Whenever sphericity was not met in a repeated measures ANOVA, the Greenhouse–Geisser was used to correct the degrees of freedom.
3.2.1. P2 (50–150 ms)
Statistical results showed that the interaction between Priming Type, Stress Pattern, and Congruency was significant, F(1, 23) = 5.684, p = 0.026, η2 = 0.198. Simple effects analysis showed that for the word primes with the Strong-Weak stress pattern, the incongruent condition elicited significantly greater P2 amplitudes than the congruent condition, F(1, 23) = 7.426, p = 0.012, η2 = 0.244. For the word primes with the Weak-Strong stress pattern, there was no significant difference between the incongruent and congruent conditions, F(1, 23) = 0.051, p = 0.823, η2 = 0.002. For the beat primes with the Strong-Weak and Weak-Strong stress patterns, there was no significant difference between the incongruent and congruent conditions (Strong-Weak: F(1, 23) = 0.553, p = 0.465, η2 = 0.023, Weak-Strong: F(1, 23) = 0.750, p = 0.395, η2 = 0.032). In addition, for word priming, the Weak-Strong congruent condition elicited larger P2 amplitudes than the Strong-Weak congruent condition, F(1, 23) = 3.952, p = 0.059, η2 = 0.147, whereas for beat priming, there was no significant difference in the P2 amplitudes elicited by the Weak-Strong congruent and Strong-Weak congruent conditions, F(1, 23) = 3.146, p = 0.088, η2 = 0.120 (Figure 4).
Word priming (A) and beat priming (B) elicited waveforms (left), violin plots of P2 wave amplitudes for each experimental condition (middle), and topography of differences for the four experimental conditions (right), where the small black dots indicate ROIs (C1, Cz, C2, FC1, FCz, FC2, F1, Fz, F2). SW, Strong-Weak; WS, Weak-Strong.
3.2.2. N400 (300–450 ms)
Statistical results revealed that the main effect of stress pattern was significant, F(1, 23) = 6.895, p = 0.015, η2 = 0.231. The Strong-Weak primes (M = −0.641, 95%CI [−1.559, 0.277]) elicited significantly greater N400 amplitudes than the Weak-Strong primes (M = 0.135, 95%CI [−0.776, 1.046]) (Figure 5).
Word priming (A) and beat priming (B) elicited waveforms (left), violin plots of N400 wave amplitudes for each experimental condition (middle), and topography of differences for the experimental conditions (right), where the small black dots indicate ROIs (P1, Pz, P2, CP1, CPz, CP2). SW, Strong-Weak; WS, Weak-Strong.
3.2.3. LPC (500–700 ms)
Statistical results illustrated that the main effect of stress pattern was significant, F(1, 23) = 10.235, p = 0.004, η2 = 0.308, with the Weak-Strong primes (M = −0.995, 95%CI [−2.028, 0.038]) elicited significantly larger LPC amplitudes than the Strong-Weak primes (M = −1.822, 95%CI [−2.842, −0.803]). The main effect of Congruency was also significant, F(1, 23) = 4.696, p = 0.041, η2 = 0.170, with the incongruent stress patterns between the primes and targets showing significantly larger LPC amplitudes than the congruent stress pattern (incongruent: M = −0.781, 95%CI [−1.997, 0.434]; congruent: M = −2.036, 95%CI [−1.997, −0.434]) (Figure 6).
Word priming (A) and beat priming (B) elicited waveforms (left), violin plots of LPC wave amplitudes for each experimental condition (middle), and topography of differences for the experimental conditions (right), where the small black dots indicate ROIs (CP1, CPz, CP2, P1, Pz, P2, PO3, Poz, PO4). SW, Strong-Weak; WS, Weak-Strong.
4. Discussion
This study explored the effects of word priming and beat priming on Mandarin lexical stress recognition by manipulating the stress pattern and the congruency of stress between the prime sequences and target words. Behavioral results showed that the RTs elicited by the Strong-Weak primes were longer than those yielded by the Weak-Strong primes. In the Strong-Weak condition, the accuracy rates produced by the word primes were higher than those by the beat primes. In the Weak-Strong condition, the accuracy rates produced by the word and beat primes had no significant difference. Furthermore, the accuracy rates and RTs elicited by the congruent stress pattern between the primes and targets were higher/shorter than those yielded by the incongruent stress pattern. The ERP results revealed that the incongruent stress pattern between the primes and targets elicited larger P2 and LPC amplitudes than the congruent stress pattern (Böcker et al., Reference Böcker, Bastiaansen, Vroomen, Brunia and de Gelder1999; Bohn et al., Reference Bohn, Knaus, Wiese and Domahs2013; Domahs et al., Reference Domahs, Wiese, Bornkessel-Schlesewsky and Schlesewsky2008; Marie et al., Reference Marie, Magne and Besson2011; McCauley et al., Reference McCauley, Hestvik and Vogel2013; Schmidt-Kassow & Kotz, Reference Schmidt-Kassow and Kotz2009). Moreover, the Strong-Weak prime sequences led to larger N400 amplitudes than the Weak-Strong prime sequences (Bohn et al., Reference Bohn, Knaus, Wiese and Domahs2013; Magne et al., Reference Magne, Jordan and Gordon2016; Marie et al., Reference Marie, Magne and Besson2011). For the comparisons of beat priming and word priming, except for the difference in the early time window (50–150 ms), no effect of Prime Type was found in either the middle (300–450 ms) or late time windows (500–700 ms). The present study showed that there were cross-domain interactions between music and language in lexical stress recognition, although the neural mechanisms regarding how they influence lexical stress recognition of Mandarin were different.
For the word primes with the Strong-Weak stress pattern, significantly larger P2 amplitudes were found when the stress patterns between the primes and targets were incongruent, while for the word primes with the Weak-Strong stress pattern, there was no P2 difference between the incongruent and congruent stress patterns. Given that the P2 component is related to the early processing load, the larger P2 amplitudes for the Strong-Weak incongruent priming indicate increasing difficulty in processing the target words with the unexpected Weak-Strong stress pattern (Huang et al., Reference Huang, Liu, Yang, Zhao and Zhou2018; Marie et al., Reference Marie, Magne and Besson2011). As the brain processes information, it constantly predicts new information based on the characteristics of previously presented signals and incorporates the predicted information into the pre-existing representational structures. Therefore, when the new information is incongruent with the regularity of the previous information, the pre-existing representational structure would not be able to accommodate the new information, resulting in processing difficulties. In addition, some studies have illustrated that the P2 component may be related to the process of rapid attention (Fan et al., Reference Fan, Zhong, Li, Yang, Zhan, Cai and Fu2016; Kanske et al., Reference Kanske, Plitschka and Kotz2011). In the current study, greater P2 elicited by the Strong-Weak incongruent priming may indicate that stress violation of the Weak-Strong targets attracts more attentional resources, leading to an increase of P2 amplitudes (Fan et al., Reference Fan, Zhong, Li, Yang, Zhan, Cai and Fu2016; Zhang et al., Reference Zhang, Zhang, Wang, Zheng, Zhao and Li2021).
In addition, greater P2 amplitudes were also observed for the Weak-Strong congruent priming condition relative to the Strong-Weak congruent priming condition. This effect may be related to the subjective stress pattern. Previous studies have found that listeners have strong preference for the strong beat/strong syllable falling in the odd position (i.e., 1, 3, 5,…), showing the strong-weak binary structure (Bolton, Reference Bolton1894; Drake, Reference Drake1993; Fraisse, Reference Fraisse1982). Such stress preference has been attested in Indo-European languages such as Dutch (Böcker et al., Reference Böcker, Bastiaansen, Vroomen, Brunia and de Gelder1999), where approximately 88% of words carry the Strong-Weak stress pattern (Vroomen & de Gelder, Reference Vroomen and de Gelder1995), and in tonal languages such as Mandarin, where native Mandarin listeners showed preference for trochaic blocks in their perception of sound intensity (Yu et al., Reference Yu, Fan, Yu and Liang2019). The accuracy results of the current study also confirm native Mandarin listeners’ preference for the lexical stress pattern of Strong-Weak, in that the accuracy rates elicited by the word primes with the Strong-Weak stress pattern were higher than those yielded by the beat primes with the same stress pattern, but there was no significant accuracy difference between the word and beat primes having the Weak-Strong stress pattern, indicating that the Strong-Weak stress pattern may be the stored representation in Mandarin listeners’ mental lexicon. The Weak-Strong stress pattern of words did not conform to the default binary structure pattern of Mandarin listeners, thus producing larger P2 amplitudes, reflecting an automatic and early process of lexical stress awareness (Olson et al., Reference Olson, Chun and Allison2001).
In Mandarin, the second syllable of some words loses its original tone, becoming the so-called unaccented, or neutral tone (Lin, Reference Lin1962; Lin & Yan, Reference Lin and Yan1980). The larger P2 amplitudes elicited by the Weak-Strong words than by the Strong-Weak words may also indicate that Mandarin listeners require more resources to process words having the relatively atypical Weak-Strong pattern. However, there are controversies over the stress pattern of Mandarin disyllabic words. Some scholars believe that the Strong-Weak pattern is more typical (Wang & Feng, Reference Wang and Feng2006), while others claim that the Weak-Strong pattern is more common (Lin et al., Reference Lin, Yan and Sun1984; Xu, Reference Xu1982), and still others propose that the two are at random (Liu, Reference Liu2007; Zhou, Reference Zhou2018). The results of the present study suggest that the Strong-Weak stress pattern may be more typical in Mandarin disyllabic words.
Compared to studies of non-tonal languages (Böcker et al., Reference Böcker, Bastiaansen, Vroomen, Brunia and de Gelder1999; Marie et al., Reference Marie, Magne and Besson2011), the P2 time window in this study is relatively early, indicating that Mandarin listeners may be more sensitive to stress (Bidelman et al., Reference Bidelman, Gandour and Krishnan2011, Reference Bidelman, Hutka and Moreno2013; Hutka et al., Reference Hutka, Bidelman and Moreno2015; Tong et al., Reference Tong, Choi and Man2018), so that they were able to detect stress violation more rapidly whenever there was one.
With regard to the N400 results, for both word priming and beat priming, we found that the Strong-Weak primes elicited greater N400 amplitudes than the Weak-Strong primes. Although the semantic relatedness between the prime sequences and targets was controlled in the current study, some studies have shown that semantic processing is human intuition (i.e., default) for language processing (Kutas & Federmeier, Reference Kutas and Federmeier2011). In the experiment of the present study, the participants’ task was quite simple, which only required them to pay attention to the stress pattern of the stimuli. Therefore, the participants may have extra attentional resources to process the meanings of the words. Moreover, the behavioral results demonstrated that the RTs induced by the beat primes were significantly shorter than those elicited by the word primes, indicating that more information, such as lexical information, for the word primes than for the beat primes has to be processed by the listeners, thus, slowing down the speed of lexical stress recognition. Importantly, the behavioral results also showed that the RTs elicited by the Strong-Weak primes were longer than those yielded by the Weak-Strong primes, which further confirms that the participants may have carried out additional semantic processing for the Strong-Weak words.
Lexical processing depends not only on the suitable semantic context, but also on regular prosody (Rothermich et al., Reference Rothermich, Schmidt-Kassow, Schwartze and Kotz2010; Rothermich & Kotz, Reference Rothermich and Kotz2013). Violation of subtle rhythmic preferences can consume additional processing resources (Bohn et al., Reference Bohn, Knaus, Wiese and Domahs2013; Henrich et al., Reference Henrich, Alter, Wiese and Domahs2014). Furthermore, atypical stress patterns are not stored in the mental lexicon, so processing these stress patterns may hinder semantic understanding at the same time (Marie et al., Reference Marie, Magne and Besson2011; Schiller et al., Reference Schiller, Fikkert and Levelt2004). Given that the Weak-Strong stress pattern may be more atypical and does not conform to listeners’ default binary structure, making it harder for listeners to form rhythm expectations (Cason et al., Reference Cason, Astésano and Schön2015a), more cognitive resources should be needed to process this stress pattern (Schröger, Reference Schröger1996). As a result, the lexical information of the Weak-Strong words may not be able to be processed so thoroughly that smaller N400 amplitudes were obtained relative to the Strong-Weak words. The greater N400 amplitudes for the Strong-Weak words may be due to the fact that the listeners not only focused on the stress patterns of the words. The greater N400 amplitudes for the Strong-Weak words (irrespective of the congruency) may be due to the fact that the listeners not only focused on the stress patterns of the words, but most likely accessed the semantic information them. Further, a paired-sample t-test on the accuracy of the word recall test revealed that the recall rate of the Strong-Weak words (87.00%) was significantly higher than that of the Weak-Strong words (77.50%), t (1,19) = 2.89, p < 0.01. This result reinforces the explanation that the participants were likely to process the semantic information of the Strong-Weak words more deeply than that of the Weak-Strong words, leading to a larger N400.
Consistent with Böcker et al. (Reference Böcker, Bastiaansen, Vroomen, Brunia and de Gelder1999), our behavioral results showed that the accuracy rates were higher and the RTs were shorter when the prime sequences and the targets shared the same stress pattern. Our ERP results showed that incongruent priming elicited larger LPC amplitudes than congruent priming. Moreover, both word and beat primes with the Weak-Strong stress pattern produced larger LPC amplitudes than those with the Strong-Weak stress pattern. Studies on explicit and implicit prosodic processing have shown that prosodic violations are likely to induce late positive components (Kriukova & Mani, Reference Kriukova and Mani2016; Rothermich et al., Reference Rothermich, Schmidt-Kassow and Kotz2012; Schmidt-Kassow & Kotz, Reference Schmidt-Kassow and Kotz2009). The LPC is thought to be related to error detection (Kolk et al., Reference Kolk, Chwilla, van Herten and Oor2003), reanalysis, and re-attention processes (Zhang et al., Reference Zhang, Li, Gold and Jiang2010), reflecting the integration process of tasks (Bornkessel & Schlesewsky, Reference Bornkessel and Schlesewsky2006). The larger LPC amplitudes for incongruent priming may be due to the fact that the participants devoted more attentional resources to reanalyzing the stress violation of the targets and integrating the unexpected stress pattern into the stress pattern of the prime sequence. This explanation is consistent with the predictive coding theory and the theory of expectancy-driven speech processing in that individuals need to readjust their predictions and re-generate expectations at a lower level to suppress predictive errors when there is unpredicted information (Friston, Reference Friston2005; Rubin et al., Reference Rubin, Ulanovsky, Nelken and Tishby2016). When the auditory input is consistent with the predicted information, the auditory rhythm drives the information processing, consuming less cognitive resources (Kotz & Schwartze, Reference Kotz and Schwartze2010, Reference Kotz, Schwartze, Hickok and Small2015). As the P2 results, larger LPC amplitudes were yielded by the Weak-Strong primes compared to the Strong-Weak primes, revealing that the Weak-Strong stress pattern may not be in line with Mandarin listeners’ expectations of the Strong-Weak binary structure and preference for rhythm perception. Hence, the listeners needed to put more psychological effort into readjusting expectations to accept new pieces of information while processing the stimuli (Friston, Reference Friston2002). Our LPC results support Jones’ dynamic attention theory (Jones et al., Reference Jones, MacKenzie and Puente2002; Jones & Boltz, Reference Jones and Boltz1989; Large & Jones, Reference Large and Jones1999), which states that attention resources would be re-adjusted to optimize the dynamic prediction and processing of events when unexpected events occur. The fact that the effect of Stress Pattern was still observed at a later time window reflects that stress processing is not only influenced by earlier attention, but also by later cognitive processing at a higher level.
There are some limitations in this study that warrant future research. First, the duration of the beat prime sequences (5061 ± 693 ms) and that of the word prime sequences (2967 ± 441 ms) were not normalized. Future studies are needed to investigate the effects of prime duration on lexical stress recognition. Second, the present study did not separate the factors of pitch, intensity, and duration; neither did it test the individual contributions of these factors to lexical stress recognition. Future studies can further investigate the contribution of each of these factors to lexical stress recognition. Finally, the cross-domain bidirectional influence between music and language, especially whether and how language affects musical processing, should be further examined.
5. Conclusions
This study found that although both word priming and beat priming facilitated the recognition of Mandarin lexical stress, beat priming had no effect on the recognition of lexical stress in the early processing stage. In the late time window, there was no significant difference between the two prime types. In addition, priming effects were also modulated by stress congruency between primes and targets, as well as stress patterns. We propose that Mandarin listeners need to consume more cognitive resources when processing the incongruent stress and Weak-Strong stress patterns. To sum up, stress in music and language can influence each other across domains.
Data availability statement
The data from the study can be found on the Open Science Framework at https://osf.io/xgjyq/?view_only=0cf01be4e6754a97816823d69fbeb9fc.
Open access
