The effect of Chinese pop background music on Chinese poetry reading comprehension

Abstract

Students prefer to listen to music while reading because they believe it will help them focus on constructing a contextual mental picture. However, the effect of background music on the reading comprehension of primary school new readers remains unclear. This study examines the effects of two musical factors (familiarity and tempo) on the construction of poetry mental picture in 129 Chinese primary school readers with attention deficit and hyperactivity disorder. The study controlled for nonverbal intelligence, age, gender, working memory, and receptive vocabulary, and results showed that background music had a negative effect on poetry reading performance. Specifically, students had similar performance in easy poetry reading with background music but performed better in difficult poetry reading with unfamiliar music and slower melody. The effect size of unfamiliar background music was larger than that of melody tempo. This study provided literature on the effect of background music on surface decoding in poetry reading and suggested that the appropriate approach for readers who are in the learning to read stage should be to refrain from listening to music while reading.

Keywords

pop music poetry reading word reading semantic inference ADHD primary school

Reading comprehension refers to a cognitive activity that constructs a mental picture through interaction and involvement with written language (Dong et al., 2020; Muntoni & Retelsdorf, 2018; Rouet et al., 2017; Snow, 2002). Past studies reported that reading comprehension plays a vital role in the knowledge acquisition (Catts, 2018; Snow, 2002; Wood et al., 2018), career development (Chow et al., 2011; Rouet et al., 2017; Spencer & Wagner, 2018), and even mental health (Catts, 2018; Rouet et al., 2017; Snow, 2002) of students. Previous surveys showed that more than 30% of students worldwide are diagnosed with reading comprehension difficulties (Cartwright et al., 2017; Tong et al., 2018). The majority of studies attempted to address reading problems by investigating the mechanism from the reader, the text, and the reading activities (Catts, 2018; Dong et al., 2020; Snow, 2002). As suggested by reading stage theory, learning to read is an essential stage for new readers during which they learn how to identify the reading text sufficiently and accurately. Past studies had demonstrated that insufficient abilities in text surface decoding will result in reading difficulties and other relevant academic learning problems (Catts, 2018; Snow, 2002; Wood et al., 2018). Moreover, students with limited reading experiences construct the mental picture through the identification and inference of visual surface word semantic meaning (e.g., Dong et al., 2020). Interference factors (e.g., external stimulation) inhibit the function of semantic meaning identification and inference process and result in comprehension difficulties.

Classical Chinese poems, as a representative agent of the Chinese reading context, are composed of aesthetic and rhythmic language. They are recognized as the core component of Chinese literature and are incorporated into the Chinese language curriculum (Erbaugh, 1990; Gao & Guo, 2018; Shu, 2018). The effect of background music on reading comprehension does not have a consistent conclusion. The majority of studies demonstrated that background music benefits readers through mitigating reading anxiety and increasing the comfort level of the reading situation (Fassbender et al., 2012; Lehmann & Seufert, 2017; Standley, 2008). Other studies reported that background music has a negative or insignificant effect on reading progress (Jäncke & Sandmann, 2010; Kallinen, 2002; Thompson et al., 2012).

Students who are diagnosed with symptoms of attention deficit and hyperactivity disorder (ADHD) have attention problems (e.g., distraction behaviors) in reading, which harm their cognitive processing of surface characters. ADHD is associated with a greater risk of emotional, academic, and social difficulties and is comorbid with primarily specific learning disorders and opposition defiant, conduct, anxiety, and substance use disorders with an onset during early childhood education (Denton et al., 2020; Dursun et al., 2021). Most ADHD students have more problems in sensory-motor abilities, sensory integration, or sensory processing, which refer to impairments in the interpretation of text information compared to typical-developing students. These problems are also related to multiple cognitive functions (e.g., auditory selectivity/discrimination and localization) and reading comprehension abilities (Dursun et al., 2021; Madjar et al., 2020). The reason for ADHD students’ reading comprehension difficulties may arise from comorbid reading comprehension disability and/or the auditory cognitive profile associated with ADHD. However, the effect of background music on the poetry reading of students with ADHD remains unknown. In addition, few studies have investigated the effect of background music on reading progress in early childhood students. To address the aforementioned problems, this study aims to investigate the effect of background music on the mental construction in poetry reading of Chinese primary school students with ADHD, thereby providing literature on how background music interacts with the poetry reading progress of students with ADHD.

Literature review

Classical Chinese poems

Classical Chinese poems are considered to be important in the Chinese curriculum because their linguistic features are unique compared with other narrative materials (Gao & Guo, 2018; J. Li et al., 2018; Zheng et al., 2019). However, 41% of Chinese elementary school students have found classical Chinese Tang poems difficult to comprehend, which affects their academic performance (e.g., H. Li & Wang, 2017). As narrative texts, Chinese Tang poems differ from typical narrative reading materials with respect to its unique features, contributing to the development of surface character identification and inference.

The ability to identify characters is reflected in tonal and rhyme regularity (Chung & Ho, 2010; Shen et al., 2020; Yu et al., 2020), which is identified as the single character semantic meaning in poetry. Poetry character inference is reflected in syntactic continuity and the use of non-declarative sentences (Catts, 2018; Lee et al., 2018; Snow, 2002), which identify the representative agents from two or three characters’ lexicon in poetry. Construction of the poetry mental picture is associated with both character identification and inference.

In terms of the representative agent in poetry semantic coherence, all lines in classical Chinese poems are structured to be semantically coherent to ensure that the terms share high contextual coherence, for instance, ensuring the meaning of the first line in the couplet corresponds with the second line through number, color, and location for parallelism. Narrative texts also tend to have words reflecting temporal and grammatical connection, such as before and after (e.g., Pappas, 1991), whereas syntactic discontinuity is obvious in Chinese Tang poems with noun fragments (Kao & Mei, 1971; Lee et al., 2018). Specifically, the abundance of nouns dominates with few function words, appearing as fragments with no grammatical connection, which exemplifies syntactic discontinuity (Cai, 2008). Moreover, common nouns and function words are often used in the first and second couplets, while the middle couplets tend to include sensory-rich image language of content words (Cai, 2008). With regard to the use of non-declarative sentences, past data have shown that emotional/mood-inference sentences are structured in classical Chinese poems to indicate the voice of the poets/authors, indicating that readers could utilize the information of poets/authors to imagine the content emotion and background information for mental structure inference (Kao & Mei, 1971; Shu, 2018). The title of the poetry also summarizes the main characteristics (e.g., season, poetry emotion, location, and event) of the poetry content, enabling readers to predict the mental picture information from the title.

The Chinese subject syllabus in Grade 1 requires students to recite the top 20 most frequent Chinese Tang poems. Past studies (e.g., B. H. Wang et al., 2019) defined the difficulty of poetry comprehension through the frequency of poetry items on the Chinese poetry list, the number (three to seven) of characters in each sentence, the target character in the Chinese vocabulary list, and the frequency of two or three characters’ lexicon in the Chinese database. Past studies likewise demonstrated that picture drawing is an effective approach for reading comprehension (Fiorella & Zhang, 2018; Lin et al., 2017; Schleinschok et al., 2017), especially for primary school students who have already gained the ability to draw a picture to reflect their comprehension level through surface word/character identification and inference.

Effect of background music on reading comprehension

Findings on the effect of background music on reading comprehension are not consistent. For example, Mozart theory and Yerkes–Dodson law theory suggested that music benefits listeners’ psychological factors (e.g., downgrades anxiety and frustrated emotions and reduces aggressive and disruptive behaviors) and increases readers’ reading comprehension performance (Fassbender et al., 2012; Lehmann & Seufert, 2017; Standley, 2008). However, by analyzing detailed internal (i.e., tempo, lyric presentation, intensity sound size, style) and external musical factors (i.e., musical identity effect and reader preference), extensive studies demonstrated that listening to music has a negative effect on reading performance (Chou, 2010; Furnham & Bradley, 1997; Hu et al., 2019) and that these factors have a larger distracting effect on attention, which decreases the level of reading comprehension performance. Students perform worse in reading comprehension with background music presentation in faster tempo (Jäncke & Sandmann, 2010), loud sound (Thompson et al., 2012), lyric presentation (Shih et al., 2012), contradicted personal interest (Cassidy & MacDonald, 2007; Furnham & Strbac, 2002; Huang & Shih, 2011; Perham & Vizard, 2011), and high familiarity (e.g., Du et al., 2020) because these factors have a negative significantly negative effect on text concentration and attention (e.g., disturbs reading comprehension activities). In addition, previous studies showed that visual working memory, nonverbal intelligence and receptive vocabulary have a significant moderation effect between background music and reading comprehension (Chew et al., 2016; Kämpfe et al., 2011; Lehmann & Seufert, 2017; Miller & Schyb, 1989; Ransdell & Gilroy, 2001; Ravaja & Kallinen, 2004).

However, the current literature has four obvious limitations. First, the majority of studies provided literature on those participants who were in the reading to learn stage. All these readers had huge amounts of reading experiences yet few studies investigated the entire picture with those students in the learning to read stage. Learning to read emphasizes the development of word semantic meaning identification (e.g., surface word semantic meaning identification and inference) from the given sentences (e.g., Dong et al., 2020), but the effect of background music in new readers remains unclear. Second, few studies specified the instrument (e.g., piano) or musical style (e.g., pop music) effect of background music on reading activities. Third, few studies ranked the effect size of internal musical factors (e.g., tempo, familiarity) on reading comprehension performance. Finally, students who were diagnosed with ADHD syndrome had serious problems with attention, yet the distracting effect of background music on students with ADHD remains unknown.

The current study

To address the aforementioned research gaps, the current study aims to investigate the effect of specific internal musical factors (familiarity and tempo) on Chinese Tang poetry reading comprehension. It provides literature regarding background music on the poetry mental picture construction process of students with ADHD through surface character identification and inference cognition activity.

Method

Participants

After obtaining consent forms from the participants and their parents, this study received the registration information of 1,997 Grade 1 students through online poster recruitment. Initial eligibility screening consisted of the inattention items from the Chinese Diagnostic and Statistical Manual of Mental Disorders, fourth edition (DSM-IV) rating scale (Su, 2017) completed by parents.Those who self-reported a clinical diagnosis of ADHD were asked to bring an up-to-date clinical evaluation by a trained project coordinator before the day of the experiment. Based on the clinical evaluation, through random selection, this study recruited 129 Grade 1 students (60 boys and 69 girls, mean age = 6.16 years old, SD = 0.48) with ADHD in Shenzhen, China. All participants came from lower income families (families’ income less than 6,000 RMB per month) that families’ income situated at the bottom 25% in Shenzhen, and no participant had received any prior psychological interventions. Preliminary analysis showed that all 129 students had similar interests/background in pop music, F(1, 127) = 1.09, p > .10, academic grade-level record at the primary school entrance test, χ²(128) = 1.91, p > .10, and age, F(1, 127) = .81, p > .10. All students were diagnosed only with ADHD syndrome and had no other physical or psychological difficulties (e.g., deafness or autistic spectrum disorder [ASD]).

Measures

Nonverbal intelligence

The full 60-item Raven’s (1986) test (Sections A–F) was applied to measure the intelligence performance of the participants. Participants were required to select one piece from the three to five pieces given that match the blank space in the provided pictures. Each section had 12 test items arranged in order of increasing difficulty. Each correct answer was awarded 1 point. The maximum score was 60, and Cronbach’s alpha was .91.

Chinese receptive vocabulary

For the Chinese vocabulary test, the test of Chow et al. (2011) was adopted and revised into a simplified Chinese version. This test had 2 practice items and 80 test items. For each item, students were presented with a spoken target character along with four pictures. They were required to select one piece that matches the description of the character. Each correct answer was awarded 1 point. The maximum score was 80, and the reliability index of Cronbach’s alpha was .85.

Working memory

This study conducted a verbal working memory test and a visual-spatial working memory test. As suggested by the working memory characteristics of poetry reading (Z. Liu et al., 2019; Yi et al., 2018), digit span and span board were used to test verbal and visual working memories, respectively. Regarding digit span, participants were required to repeat verbally presented digits forward and backward. Two digits were at the start level, and the number of digits increased every second item (the maximum number of digits was 7). Digits were repeated in the same order as presented in the first section (30 items), and the students were required to repeat the reverse order of digits in the second section (30 items). No feedback was given to the child throughout the task. Each correct answer was awarded 1 point. The maximum score was 60, and Cronbach’s alpha was .82.

Regarding span board, students were required to watch the screen, in which only one piece was colored in nine blocks, and then watch the second nine-block picture and make a judgment on whether the color piece was the same order as in the previous picture. Each correct answer was awarded 1 point. The maximum score was 21, and Cronbach’s alpha was .75. An example item is listed in Appendix A.

Pop music preference questionnaire

The music preference questionnaire for the students with ADHD was revised from T. Wang’s (2005) music preference questionnaire. This questionnaire had 14 items that measure music preference through four factors: reflective-complex, intense-rebellious, energetic-rhythmic, and upbeat-conventional. The trained instructor read the items, and students were required to select the best description from a 5-point Likert-type scale (1 = strongly disagree and 5 = strongly agree) to address the question. An example item is “Music is very important to me.” The maximum mean score was 5, and the minimum score was 1. The overall Cronbach’s alpha was .80.

Experimental materials

Chinese Classical poem

This study defined the difficulty levels of classical Chinese Tang poems through an interview with 20 parents and 20 Chinese subject teachers in a pilot survey. The parents and Chinese subject teachers evaluated the difficulties of Chinese Tang poems through the frequency in lexical application in daily activities of two or three characters from the poems, the frequency of the poem in the Chinese Tang poetry list and the length of each sentence. This study included 3 five-character practice poetry reading items, namely, (1) Jingyesi, (2) Chunxiao, and (3) Minnong (see Appendix D), and 50 seven-character test poetry reading items. All characters in these 53 poems were in the top 1,000 most frequent Chinese receptive character list, which ensures that students can identify the semantic meaning of every single character. The title and author of all 53 poems were removed, which ensured that all cues for mental picture construction were surface characters coding from the given content. Each poem only provided one central scenery agent (e.g., willow, fall, moon) from the descriptions of four sentences. Practice items were selected from the top 20 most frequent Chinese Tang poems taught in the Chinese subject before the experiment implementation.

A total of 50 test poetry reading items, including 25 easy items and 25 difficult items, consisted of seven characters in each sentence. Easy items were all selected from the frequency range of 50–100 of the Chinese Tang poetry list. Through software ROST-CM analysis, all two or three characters’ lexicon applications in easy items had more than 70 frequencies in the Chinese lexical database, ensuring that all 25 easy items shared similar difficult levels. In a similar vein, difficult items were all selected from the frequency range of 150–200 of the Chinese Tang poetry list, which ensures that the frequency of two or three characters’ lexicon application was appropriate (ranging from 20 to 30) to reach the minimum requirement of recognition for Chinese primary school students.

Chinese pop music

To remove the music identification effect from students (Schäfer & Sedlmeier, 2009; Vella & Mills, 2017), this study selected Chinese pop music as the background music. To remove the lyric effect on the poetry reading task and ensure the quality of melody cognition, all background music consisted of piano pieces. In total, this study selected 20 familiar pieces of pop music (10 fast melodies and 10 slow melodies) and 20 unfamiliar pop music (10 fast melodies and 10 slow melodies).

This study defined the familiarity with pop music through interviewing the parents of the 20 participants and recording the ranking list of pop music most frequently listened to from 2010 to 2020. The top 200 songs most frequently listened to from the ranking list were regarded as familiar songs, and the songs that ranged between 500 and 800 were regarded as unfamiliar songs. Parents were required to select from the song list the titles of possible songs that their child might have heard in the past years. The song title would be removed from the list of unfamiliar songs to reduce the error risk if any parent reported that their child might have heard the song.

This study defined the tempo of melody based on the mark (presto, allegro, andante, adagio, and lento) from the original music score. Owing to the limitation in listening cognition ability of Chinese primary school students (Feiss et al., 2020; Y. Liu et al., 2018), the study only selected and regarded allegro music scores as fast melodies and adagio music scores as slow melodies. All music scores were in 4/4 meter because the majority of Chinese pop music had 4/4 meter in the scores. The current study also uses beats per minute (BPM) as a parameter to represent music tempo. The BPM range of the allegro music was 112–130 and the BPM range of the adagio music was 48–68 in the current study.

Pictures for multiple choices

In total, 212 pictures were selected and divided into 53 groups: 3 groups for practice items and 50 groups for test items. In each group, only one picture completely matched the description of the given poetry content.

Research design

All experiments were implemented through the software E-prime 1.0 by Lenovo Y50, and the music size was set to 37% of the maximum bar. This study applied a quasi-experimental and within-subject design, where all students received all reading treatments. All the students received the experimental trials in one silent classroom. The two trained instructors supervised the entire experiment process. Students were tested individually and students were required to read the given Chinese Tang poems and answer the mental picture construction questions by the coding of surface characters.

Before the experiment, each student received three practice items to familiarize themselves with the experimental procedure. After the practice items, the student needed to report whether they were already familiar with the experimental procedure; if not, all three practice items would be repeated again until they were familiar with the experimental requirement. Each practice item only contained one poetry reading and one question, which were the same requirement and procedure as in the formal experiment. An example item was provided in Appendix B.

The formal experiment included 50 items, with each item consisting of one poetry reading content in the first slide and one question in another slide. A total of 50 poetry items were divided into 10 sections: Sections A–E were easy items, Sections F–J were difficult items, and each section had five items. A total of 40 piano pieces were divided into eight sections: Sections b, c, g, and h were familiar music and Sections d, e, i, and j were unfamiliar music. In addition, Sections b, d, g, and i had fast melody and Sections c, e, h, and j had slow melody. Each section had five music piano pieces. In total, there were 10 treatments, except for poetry Sections A and F, which were regarded as the control group without background music. Each poetry section corresponded with the small-letter section of the music piece in the rest of the groups (i.e., B-b, C-c, D-d, E-e, G-g, H-h, I-i, and J-j). All poem and music combination groups are presented in Appendix C.

With regard to the experimental procedure, each item had one reading slide (with or without music) and one question slide (without music). Students were required to follow a silent reading mode. If a student had already constructed a mental picture from the given reading content, they needed to type on the keyboard to go to the question slide, which cannot go backward. The question slide only had one multiple-choice question item, which requires students to select the appropriate picture from the four options provided.

The two control group items (Sections A and F) only provided poetry reading content. Students needed to move to the next question slide when they had constructed a mental picture from the given content. The items for the rest of the groups were a combination of background music and poetry reading. When students moved to the poetry reading slide, the background music would play at the same time and the play mode was repeated; the background music stops when students typed on the keyboard to move to the question slide. A 30-s break was given between every two items, and students could type on the keyboard to advance to the next item. After five group items (25 items total), students received a 10-min break in the silent classroom. After the break, students were required to continue the experiment until all the remaining five group items (25 items) were finished.

Regarding the poetry reading presentation, two random presentation levels were applied. First, all 10 groups of poetry reading items were presented randomly by group (five items in each group). Second, within one group treatment, five items were presented randomly. The combination of background music and poetry reading content was fixed so that one music piece matched only one poetry reading content.

To remove the random error of matching effect from music and reading item, students’ reading time for each treatment section (A–J) of poetry items were counted only if all five answers in one section were correct. Any incorrect answers in one section (e.g., Section A) would remove the record of the performance in that section. The mean time for each treatment group was counted and used for further statistical analysis. Each student took approximately 70 min to finish all poetry reading. After the experiment, each student was required to report the familiarity level of every music used in the current study by a music list presented. If students reported that the music was familiar before received experiment, but this music was used in one unfamiliar section’s design, students’ performance in that section was removed from the record. In a similar vein, if unfamiliar music was used in one familiar music section, student’s performance in that section was also removed from the record. Finally, each student received a prize as a reward.

Data analysis

The current study used a univariate mixed-model approach to do further data analysis. A univariate mixed-model approach was suitable for within-subjects design and the univariate mixed-model approach had advantages in controlling both fixed and random effects to separate the error term (Cotton, 1998; Maxwell & Delaney, 2004). In the current study, students’ variability in students’ reading performance can be separated from the error term. Measures included main effect, interaction effect, and post hoc test among task difficult level, music tempo, and music familiarity level.

Partial η² was used to report the effect size in univariate analysis because every explanatory factor can account only for unique variance partitioned from the total variance in the dependent variable. The statistic is useful in describing how variables are behaving within the selected students. In addition, partial η² is useful for comparing effects for the same factor tested across different designs when the designs differ by additional experimental inductions that cause increased total variance (Cohen, 1973; Maxwell & Delaney, 2004; Olejnik & Algina, 2000). Hedge’s g was used to report the comparison results of poetry reading performance across different sections because the Hedge’s g statistic has better properties when the sample sizes used for group comparison are significantly different (Grissom & Kim, 2005; Hedges, 1981). Hedge’s g statistic could report a standardized effect that was measured across different sections which consisted of various background music and reading items (Hedges, 1981).

Results

Descriptive analysis

The mean and standard deviation of each variable are presented in Table 1. Results showed that both skewness and kurtosis were within ±3, indicating that all data had a normal distribution.

Table 1.

Descriptive Analysis.

	n	M	SD	Skewness	Kurtosis
Receptive vocabulary	129	42.47	2.40	.13	−.95
Verbal working memory	129	6.55	0.50	−.21	−1.99
Visual-spatial working memory	129	5.53	0.50	−.11	−2.02
Nonverbal intelligence	129	23.93	1.83	−.50	−.48
Music preference	129	3.62	0.58	−.48	−.09
RT_Easy_without music (Section A)	129	36.30	3.14	.25	.12
RT_Easy_familiar and faster melody (Section B)	117	36.56	3.38	.06	−.90
RT_Easy_familiar and slower melody (Section C)	119	36.42	3.46	.09	−.65
RT_Easy_unfamiliar and faster melody (Section D)	113	36.50	3.33	.04	−.77
RT_Easy_unfamiliar and slower melody (Section E)	126	36.68	3.46	.10	−.59
RT_Diff_without music (Section F)	114	43.53	4.64	.04	−1.02
RT_Diff_familiar and faster melody (Section G)	108	52.00	5.55	.01	−.71
RT_Diff_familiar and slower melody (Section H)	107	48.57	5.12	.12	−.54
RT_Diff_unfamiliar and faster melody (Section I)	111	45.47	4.99	.06	−.77
RT_Diff_unfamiliar and slower melody (Section J)	105	45.92	5.11	−.03	−.93

mean; RT: poetry reading time; Diff: difficult; n: the number of students who got five correct answers in that treatment section.

Research Question 1: music playing or not on reading

To investigate whether background music playing impacted ADHD students’ reading performance (Table 2), a larger analysis of variance (ANOVA) approach was used for poetry performance comparison across Sections A–J by controlling gender, age, nonverbal intelligence, music preference, receptive vocabulary, and verbal and visual-spatial working memory. Results showed that students had significantly different performances across sections, F(9, 119) = 250.15, p < .001, partial η² = .80. Post hoc tests were applied by two poetry difficulty levels. Regarding easy poetry reading, students had similar performance across Sections A–E (Sections A vs. B [mean difference = .26, p > .10, Hedge’s g = .08], Sections A vs. C [mean difference = .12, p > .10, Hedge’s g = .04], Sections A vs. D [mean difference = .20, p > .10, Hedge’s g = .06], Sections A vs. E [mean difference = .38, p > .10, Hedge’s g = .12]), indicating that on the easy poetry reading task, whether background music playing or not did not significantly enhance or inhibit students’ poetry reading. Regarding difficult poetry reading, students performed significantly better in the control group (Section F) than in Sections G (mean difference = 8.48, p < .001, Hedge’s g = 1.65), H (mean difference = 5.05, p < .001, Hedge’s g = 1.03), I (mean difference = 1.94, p < .001, Hedge’s g = .40), and J (mean difference = 2.39, p < .001, Hedge’s g = .49). Results indicated that background music playing inhibited students’ poetry reading at the difficult level of poetry reading.

Table 2.

Poetry Reading Performance With or Without Background Music.

Difficult level	Group	Mean difference	Hedge’s g	95% CI
Difficult level	Group	Mean difference	Hedge’s g	Lower	Upper
Easy	Section A vs. Section B	−0.26	0.08	−1.02	.51
	Section A vs. Section C	−0.12	0.04	−.87	.63
	Section A vs. Section D	−0.20	0.06	−.89	.49
	Sections A vs. E	−0.38	0.12	−1.12	.36
Difficult	Section F vs. Section G	−8.48***	1.65	−7.73	<.01
	Section F vs. Section H	−5.08***	1.03	−4.35	<.01
	Section F vs. Section I	−1.94***	0.40	−1.25	<.01
	Section F vs. Section J	−2.39***	0.49	−1.69	<.01

CI: confidence interval.

Section A = easy poetry reading without background music, Section B = easy poetry reading with both familiar and faster melody, Section C = easy poetry reading with both familiar and slower melody, Section D = easy poetry reading with both unfamiliar and faster melody, Section E = easy poetry reading with both unfamiliar and slower melody, Section F = difficult poetry reading without background music, Section G = difficult poetry reading with both familiar and faster melody, Section H = difficult poetry reading with both familiar and slower melody, Section I = difficult poetry reading with both unfamiliar and faster melody, and Section J = difficult poetry reading with both unfamiliar and slower melody.

***

p < .001.

Research Question 2: background music effects on poetry reading

Univariate measures were used for statistics (Table 3). Gender, age, nonverbal intelligence, music preference, receptive vocabulary, and verbal and visual-spatial working memory were set as control variables, poetry reading reaction time among Sections B–E and Sections G–J were set as the dependent variable, task difficult level, music tempo, and music familiarity level were set as fixed factors. Results showed that significant interaction effects were found among task difficult level, music tempo, and music familiarity level, F(2, 126) = 11.01, p < .001, partial η² = .01, between task difficult level and music familiarity level, F(1, 127) = 76.66, p < .001, partial η² = .06, between task difficult level and music tempo, F(1, 127) = 7.93, p < .01, partial η² = .01, and between music familiarity level and music tempo, F(1, 127) = 15.25, p < .001, partial η² = .01. Main effect analysis showed that significant effects were found among task difficulty level, F(1, 127) = 1,713.67, p < .001, partial η² = .57, music familiarity level, F(1, 127) = 70.01, p < .001, partial η² = .05, and music tempo, F(1, 127) = 7.52, p < .01, partial η² = .01. Results indicated that task difficulty level, music familiarity level, and music tempo were significantly associated with the poetry reading performance of Chinese students with ADHD. In addition, the effect size was larger in familiarity level than music tempo on students’ poetry reading performance.

Table 3.

Univariate Measures on Poetry Reading and Background Music.

Reading task	F	Partial η²
Difficult level	1,713.67***	.57
Familiarity level	70.01***	.05
Tempo	7.52**	.01
Difficult Level × Familiarity Level	76.66***	.06
Difficult Level × Tempo	7.93**	.01
Familiarity Level × Tempo	15.25***	.01
Difficult level × Familiarity Level × Tempo	11.01**	.01

p < .01, ***p < .001.

Post hoc tests were used to do a simple effect analysis (Table 4). Regarding easy poetry reading, listening to a fast melody or a slow melody had a similar effect on poetry reading reaction time regardless of whether it was familiar music playing (mean difference = .14, p > .10, Hedge’s g = .04) or unfamiliar music playing (mean difference = .18, p > .10, Hedge’s g = .05). Furthermore, listening to familiar or unfamiliar music had a similar effect on poetry reading reaction time regardless of whether it was a fast melody playing (mean difference = .05, p > .10, Hedge’s g = .02) or a slow melody playing (mean difference = .26, p > .10, Hedge’s g = .08). These results indicated that background music had an insignificant effect on the cognition process in the easy poetry reading task.

Table 4.

Poetry Reading Performance Comparison in Different Background Music Melodies.

Difficult level			F		Mean difference	Hedge’s g	95% CI
Difficult level			F		Mean difference	Hedge’s g	Lower	Upper
Easy	FL	Familiar	.07	Fast vs. slow	.14	.04	−0.91	1.19
	FL	Unfamiliar	.11	Fast vs. slow	.18	.05	−1.23	.88
	ML	Fast	.01	Familiar vs. unfamiliar	.05	.02	1.00	1.11
	ML	Slow	.24	Familiar vs. unfamiliar	.26	.08	−1.32	.79
Difficult	FL	Familiar	40.84^***	Fast vs. slow	3.43***	.64	2.37	4.48
	FL	Unfamiliar	.69	Fast vs. slow	−.45	.09	−1.50	.61
	ML	Fast	148.18^***	Familiar vs. unfamiliar	6.53***	.81	5.48	7.59
	ML	Slow	24.50^***	Familiar vs. unfamiliar	2.66***	.52	1.60	3.71

CI: confidence level; FL: familiarity level; ML: melody level.

***

p < .001.

In the difficult poetry reading task, students had varying performances in doing poetry reading with different familiarity levels and tempo levels of melody. With familiar music playing, students had significantly better performance while listening to a slow melody than a fast melody (mean difference = 3.43, p < .001, Hedge’s g = .64), indicating that a slow melody had a less negative effect than fast melody on poetry reading when the poetry reading task was difficult. By contrast, students had a similar mental construction speed between fast and slow melodies when listening to unfamiliar music (mean difference = .45, p > .10, Hedge’s g = .09), indicating that the speed of the melody did not have significant impacts on mental picture construction process when the poetry reading task was difficult. Furthermore, regardless of whether fast (mean difference = 6.53, p < .001, Hedge’s g = .81) or slow (mean difference = 2.66, p < .001, Hedge’s g = .52) melodies were playing, ADHD students had significantly better performance in listening to unfamiliar music than familiar music, indicating that unfamiliar music had a less negative effect than familiar music on poetry reading when the poetry reading task was difficult.

Discussion

Results showed that students’ performance had insignificant differences in the easy poetry reading task whatever the background music. This result was consistent with those of previous studies (Chou, 2010; Furnham & Bradley, 1997; Hu et al., 2019), which suggested that the cognition process for easy tasks would not be interrupted significantly by the interfering factors. The result partially supported the statement that listening to music would benefit the reading task because music does not have a significantly negative effect on the reading task and will help downgrade the level of reading anxiety (Fassbender et al., 2012; Lehmann & Seufert, 2017; Standley, 2008). Given that the easy poetry reading task does not cost a huge amount of cognitive resources, students can easily construct the mental picture through cognitive interaction activity by identifying the given characters and surface coding with familiar content. The alternative reason should be the lower cognitive load in identifying double- or three-character-lexicon semantic meanings, which would result in a lower level of reading anxiety as the reader’s attention would not be significantly distracted by irrelevant stimulus (Chou, 2010; Furnham & Bradley, 1997; Hu et al., 2019). The result indicated that in easy poetry reading, the mental coding picture construction would not be inhibited significantly by background music because students would not consume more cognitive resources in the process of identifying the lexical semantic representation. The result also indicated that if the reading task would not be a great challenge to ADHD students, then the effect of background situation or atmosphere would not be the determining factor in the character surface inference task.

However, background music had different effects on the difficulty level of the poetry reading task. First, students with ADHD performed better with unfamiliar music than with familiar music. This result was consistent with those of previous studies in selected attention findings (Chew et al., 2016; Fassbender et al., 2012; Patston & Tippett, 2011; Shih et al., 2009). The results indicated that familiar background music automatically attracted the attention of students with ADHD and aroused their memory to recognize and identify the name and relevant information from the sound cues. The familiar music contained cues that could be used to identify the characteristics of the music, and students might also remember and assume what would be happen or listen in after. The music identification consumed cognitive resources that prolong the reading progress in surface character coding on poetry mental picture construction. With regard to unfamiliar music, the music information may only arrest the students’ attention and be processed in their sense memory after the judgment of students’ self-value because the information would not be further identified, hence the cognition progress on this unfamiliar music would stop. Therefore, students did not consume more cognitive resources on the further progress with unfamiliar music; as a result, the saved cognitive resources enabled the students with ADHD to identify the poetry mental picture faster. This result implicated a selection mechanism of reading; that is, if the reading task is complicated, then students with ADHD would consume more cognitive resources on the given poetry reading task rather than process the complicated information. Students would concentrate more on the surface coding of characters and may even ignore the unfamiliar music background.

Second, students performed better in slow melody music than fast melody music if the music was familiar. This result remained consistent with those studies that demonstrated the cognition progress was impacted by the number of stimuli provided at the same time (Jäncke & Sandmann, 2010; Kallinen, 2002; Thompson et al., 2012). A slow melody could provide more opportunities for students with ADHD to capture the sound signal and more time for students to achieve cognition (identify the signal and retrieve the memory by signal cues). By contrast, a fast melody provided more stimuli to students at the same time, and these stimuli continuously attracted students’ attention and cost more cognitive resources at the same time than did the slow melody (Jäncke & Sandmann, 2010; Kallinen, 2002; Thompson et al., 2012). The results indicated that students regarded the familiar music cognition to be easier than the poetry reading task, and they preferred to allocate more cognitive resources on their musical cognition progress.

An interesting finding was that students had similar performances in unfamiliar background music whatever the melody tempo. This result partially supported the findings of previous studies (e.g., Dobbs et al., 2011; Fassbender et al., 2012) which suggested that students with ADHD faced difficulties in the complicated problem-solving process due to the effect of cognitive resources being distracted by irrelevant work. This result demonstrated that the familiarity of the background music likely had a larger selection effect size on task attention and cognition than did music melody on poetry mental picture construction, indicating that familiarity selection had higher priority than specific music characteristic cognition. If the personal selection mechanism of the students defined the background music as not useful or complicated, there is the potential risk that it can impact the given task cognition, and the information of background music would be waived.

Limitations, future directions, and implications

This study controlled for the sound size, but the best range of sound size for background music remains unclear. Future studies could further categorize background music into detailed sound size groups and test the effect size of every sound size group on students’ academic performance. There are various music styles (e.g., jazz, classical) around the world, and different music styles contain various tunes and tones that affect the listening comprehension process through rhyme. The effect, however, remains unclear for other music styles. Future studies should involve more music styles and compared the effect of different music styles on students’ academic performance by testing the effect amongst parameters (e.g., tunes, tones). This study also did not investigate the correlation between poetry content emotion and background music style, specifically, whether or not the contradicting emotion between music and reading associated the reading comprehension performance (e.g., Johansson et al., 2012). Future studies should specify emotion in both reading materials and music, testing the different emotion combination groups’ effect on students’ academic performance.

The implication of the results for Chinese poetry reading is that readers should refrain from listening to music while reading. In addition, this study indicated that unfamiliar music has a less negative impact on the reading process; hence, if necessary, readers can select unfamiliar music to listen to when the given reading task is difficult. Finally, the slow melody has a less negative impact on poetry reading than the fast melody.

Conclusion

This study added to the explanation on the early reading stage of Chinese students with ADHD through the interaction effect between background music and surface character coding in Chinese poetry reading comprehension. With regard to the surface inference for mental picture construction from the given passage comprehension, the familiarity of background music and the difficulty level of the task would impact the surface inference coding process in Chinese participants. Background music has a significant impact on the reading process of readers in difficult tasks that require a huge amount of cognition resources. Unfamiliar music has less negative impact on the reading process.

Footnotes

Appendix D

Translation of Chinese Poetry Used in the Current Study

练习题目(Practice Items)

No.1

jìnɡ yè sī tánɡ lǐ bái 静夜思 (唐) 李白

chuánɡ qián mínɡ yuè ɡuānɡ yí shì dì shànɡshuānɡ 床前明月光，疑是地上霜。

jǔ tóu wànɡ mínɡ yuè dī tóu sī ɡù xiānɡ举头望明月，低头思故乡。

A Tranquil Night

Li Bai

Before my bed a pool of light,

I wonder if it’s frost aground.

Looking up, I find the moon bright,

Bowing, in homesickness I’m drowned.

No. 2

chūn xiǎo tánɡ mènɡ hào rán 春晓 (唐) 孟浩然

chūn mián bù jué xiǎo chù chù wén tí niǎo 春眠不觉晓，处处闻啼鸟。

yè lái fēnɡ yǔ shēnɡ huā luò zhī duō shǎo夜来风雨声，花落知多少?

SPRING MORNING

Meng Haoran

This spring morning in bed I’m lying,

Not to awake till birds are crying.

After one night of wind and showers,

How many are the fallen flowers?

No. 3

mǐn nónɡ tánɡ lǐ shēn 悯农 (唐)李绅

chú hé rì dānɡ wǔ hàn dī hé xià tǔ 锄禾日当午，汗滴禾下土。

shuí zhī pán zhōnɡ cān lì lì jiē xīn kǔ 谁知盘中餐，粒粒皆辛苦。

Sympathy for the peasants

Li Bai

Hoeing millet in mid-day heat,

Sweat dripping to the earth beneath:

Do you know the food on your plate,

Each grain was hard-earned.

Appendix A.

Appendix B.

Appendix C.

Poetry List and Matched Materials.

Section no.	Poem	Piano music piece
Practice	静夜思 (jingyesi)
	春晓 (chunxiao)
	悯农 (minnong)
Section A	劝学 (quanxue)
	凉州词其一 (liangzhouci_Qi yi)
	出塞 (chusai)
	芙蓉楼送辛渐 (furonglousongxinjian)
	咏柳 (yongliu)
Section B	凉州词其二 (liangzhouci_Qi er)	日不落 (ribuluo)
	九月九日忆山东兄弟 (jiuyuejiuriyishandongxiongdi)	沙漠骆驼 (shamoluotuo)
	望洞庭 (wangdongting)	少年 (shaonian)
	浪淘沙(langtaosha)	少年游 (shaonianyou)
	送元二使安西 (songyuanershianxi)	朋友的酒 (pengyoudejiu)
Section C	望庐山瀑布 (wanglushanpubu)	清明雨上 (qingmingyushang)
	赠汪伦 (Zengwanglun)	庐州月 (luzhouyue)
	黄鹤楼送孟浩然之广陵 (huanghelousongmenghaoranzhiguanglin)	老男孩 (laonanhai)
	早发白帝城 (zaofabaidicheng)	真的爱你 (zhendeaini)
	望天门山 (wangtianmenshan)	画 (hua)
Section D	别董大 (biedongda)	彩虹 (caihong)
	绝句其一 (jueju_Qi yi)	火花 (huohua)
	回乡偶书 (huixiangoushu)	不哭 (buku)
	枫桥夜泊 (fengqiaoyebo)	萤火虫 (yinghuochong)
	山行(shanxing)	爱之玄 (aizhixuan)
Section E	清明 (qingming)	同桌的你 (tongzhuodeni)
	江南春 (jiangnanchun)	好大一棵树 (haodayikeshu)
	江畔独步寻花其一 (jiangpandubuxunhua Qi yi)	风中有朵雨做的云 (fengzhongyouduoyuzuodeyun)
	早春 (zaochun)	我愿意 (woyuanyi)
	江南逢李龟年 (jiangnanfengliguinian)	爱江山更爱美人 (aijiangshangengaimeiren)
Section F	泊秦淮(boqinhuai)
	寄扬州绰判官 (jiyangzhouchuopanguan)
	寒食其一 (hanshi Qi yi)
	乌衣巷 (wuyixiang)
	滁州西涧 (chuzhouxijian)
Section G	春晴 (chunqing)	浪花一朵朵 (langhuayiduoduo)
	登山 (dengshan)	我的楼兰 (wodeloulan)
	嫦娥 (chang’e)	黑桃皇后 (heitaohuanghou)
	金缕衣 (jinlvyi)	专注 (zhuanzhu)
	闺怨 (guiyuan)	出发 (chufa)
Section H	逢入京使 (fengrujingshi)	稻香 (daoxiang)
	从军行其一 (congjunxing Qiyi)	摇滚怎么了 (yaogunzenmele)
	从军行其二 (congjunxing Qier)	淡淡 (dandan)
	从军行其三 (congjunxing Qisan)	女孩当自强 (nvhaidangziqiang)
	客中行 (kezhongxing)	舍子花 (shezihua)
Section I	春夜洛城闻笛 (chunyeluochengwendi)	爱之旅 (aizhilv)
	军行 (junxing)	庆祝 (qingzhu)
	清平调其一 (qingpingdiao Qiyi)	节日 (festival)
	清平调其二 (qingpingdiao Qier)	冰河 (binghe)
	清平调其三 (qingpingdiao Qisan)	斗牛要不要(douniuyaobuyao)
Section J	城东早春 (chengdongzaochun)	洪湖水浪打浪 (hongshushuilangdalang)
	江畔独步寻花其二 (jiangpandubuxunhua)	涛声依旧 (taoshengyijiu)
	蜂 (feng)	弯弯的月亮 (wanwandeyueliang)
	金谷园 (jinguyuan)	就是现在 (jiushixianzai)
	小儿垂钓 (xiaoerchuidiao)	献给阿妈的歌 (xiangeiamadege)

Acknowledgements

The authors thank the students for their participation in this study.

Author’s Note

Xiao-Ying Wu is now affiliated to New York University, New York City, USA.

Skylar Yuan-Ke Sun is now affiliated to The Education University of Hong Kong, Tai Po, Hong Kong.

Author contributions

Yang Dong and Hao-Yuan Zheng had same contribution to this paper.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The work described in this paper was fully supported by a grant from the Guangzhou, China (Project No. 2020WQNCX120).

Research ethics and participant consent

This project received approval from the ethics committee of Guangzhou University. Consent forms were collected from both participants and their parents.

ORCID iD

Yang Dong

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