One hundred and twenty excerpts of Chinese pop song lyrics were used in this
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experiment and were divided into two conditions, familiar and unfamiliar songs. Familiar
songs were collected based on the yearly billboards constructed by KKBOX4, the biggest and
most used online music database in Taiwan. The ranking of the billboards was based on how
many times the songs were listened to by the online members; therefore, the billboards were
assumed to present most familiar songs to the general public. As for unfamiliar songs, they
were chosen from non-mainstream Chinese albums released in Taiwan that were supposed to
be relatively less known by most audience.
In terms of the lyrics stimuli, the first or first two lines in the chorus part of a familiar
song that conveyed complete sense and ended with a noun were extracted since usually the
line(s) is/are regarded as the most familiar line(s) to people. The same manner of extracting
lyrics materials was also applied to unfamiliar song condition. The chosen line(s) were
limited to 25 characters to control for the length of all the lyrics stimuli and still to provide
enough contextual information for processing. Take one song called “Not That Easy (沒那麼
簡單)” by a female singer, Xiao-Hu Huang (黃小琥), for example. The first two lines in the
chorus part which are within 25 characters and also making complete sense are:
4 People who join KKBOX to become members would be able to listen to songs collected in this database. In the yearly billboard, the songs mostly listened by members of KKBOX in that year would be ranked from 1 to 100, except that there are only 10 songs ranked in 2007 and 20 songs ranked in 2008. In KKBOX, there are different types of billboard categorized by its language (e.g. Chinese, English, Taiwanese, Korean and Japanese songs, etc.) or the music genre (e.g. Jazz, Rock n’ roll, electronic and classical music, etc.). In this study, the five yearly billboards from 2007 to 2011 were used for song selection.
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相愛沒有那麼容易，每個人有他的脾氣。
(It is not that easy to love each other because everyone has his/her characteristics.)
Based on the aforementioned rationale for lyrics selection, 127 excerpts of familiar
songs and 129 excerpts of unfamiliar songs were first chosen. A pilot test on song familiarity
was conducted to further ensure the degree of familiarity in both familiar and unfamiliar song
conditions. The 256 song excerpts from both song conditions were equally separated into four
questionnaires5 for subjects to rate. For each questionnaire, 30 Chinese-speaking subjects,
who reported to have a habit of listening to Chinese pop music, participated to do the song
familiarity rating. The subjects were asked to indicate their familiarity toward the song
excerpts on a 5-point scale (5: most familiar, 1: least familiar). Statistical analysis on the
results showed that there was a significant difference in familiarity between the chosen
familiar and unfamiliar songs (t(126)=13.62, p<.001).
In addition to the degree of familiarity, the factor of cloze probability was also taken into
consideration. It was assumed that the cloze probability of the sentence-final words in
familiar songs were high, i.e. people would commonly use the same words to finish the lyrics
since they were familiar with the songs. Thus, the cloze probability of the sentence-final
words in unfamiliar songs was measured to see if there was a possibility to match the high
5 There were 64 song excerpts in three questionnaires and 63 song excerpts in one questionnaire. All the songs had been randomized before they were displayed to the subjects.
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cloze probability in familiar songs. To make this control, a pilot questionnaire on cloze
probability of unfamiliar lyrics was designed with 194 excerpts of unfamiliar lyrics6. To
reduce subjects' load in answering the questionnaire, the 194 unfamiliar lyrics were
randomized and equally divided into two questionnaires with the sentence-final position left
blank. For each questionnaire, 20 Chinese native speakers were recruited to fill in the
sentence-final position to complete the unfamiliar lyrics. The results showed a wide range of
cloze probability distribution: only 3% of the lyrics completions had a cloze probability of
more than 70% and almost half of the completions of the lyrics had zero cloze probability. As
it became difficult to make the same control on the cloze probability between familiar and
unfamiliar song conditions, only those unfamiliar lyrics whose completions had an at least
5% cloze probability (i.e. at least one subject filled in the blank with the expected word) were
further selected for experimental materials. The overall cloze probability for the unfamiliar
lyrics was 31%.
In addition to song familiarity and cloze probability, the number of fast and slow songs
used in both song conditions was also controlled for by calculating the ratio of the number of
characters to the length of the song excerpt. In the end, a total of 120 song excerpts were
selected: 60 familiar and 60 unfamiliar songs, with each familiarity category containing 35
slow (about 1 character per second) and 25 fast (about 2 characters per second) songs (see
6 Among the 194 unfamiliar lyrics, some of the lyrics were in fact from the same song but different chorus part in that song. This way, the songs would not be deleted due to prime repetition by comparing its first chorus part only. It was also to increase the variety of primes.
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Table 1). No sentence-final words were repeated in the selected lyrics materials. No
significant difference was found in terms of character number (t(59)=1.83, p=.072) and
character to lyrics length ratio (t(59)= -1.51, p=.135) across the familiar and unfamiliar
conditions. (See Appendix I for the details of the 120 lyrics stimuli.)
Table 1. The numbers of fast and slow song excerpts in the two experimental conditions.
Character to lyrics length
ratio
Familiar songs Unfamiliar songs
1 character per second (i.e.
slow songs)
35 excerpts 35 excerpts
2 characters per second
(i.e. fast songs)
25 excerpts 25 excerpts
A semantic priming paradigm was adopted in this experiment, with the final nouns of
the lyrics stimuli being the prime words (e.g. “脾氣” in “相愛沒有那麼容易，每個人有他的
脾氣。”). For the target words, two different conditions were constructed in relation to the
sentence-final noun: 1) semantically related and 2) semantically unrelated. For the
semantically-related targets, a pilot test was carried out to ask subjects to come up with the
first two words they could think of that were most related to the prime words provided. The
120 prime words were divided into two questionnaires with 60 words each, and 20 Chinese
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native speakers were recruited for each questionnaire. With the answers given by the subjects,
120 words related to the primes were decided. Another 120 words not given by the subjects
as responses were constructed by the experimenter as unrelated targets. To ensure that word
frequency was not a confound, the word frequency of the 120 semantically-related and 120
semantically-unrelated target words were examined by referencing to the Corpus of Chinese
Word Frequency (http://elearning.ling.sinica.edu.tw/CWordfreq.html) constructed by Institute
of Linguistics, Academia Sinica. Statistical analysis showed no significant difference in word
frequency with regard to the factors of familiarity and relatedness (no main effect of
familiarity, F(1,59)=.20, p=.657; no main effect of relatedness, F(1,59)=.006, p=.937; no
familiarity x relatedness interaction, F(1,59)=1.50, p=.225). See Table 2 for the example
materials for the prime-target paradigm, Table 3 for the exact numbers of materials in each
experimental condition and Appendix II for the 240 target words paired with the primes and
their word frequency.
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Table 2. Example materials in the experiment.
Condition
Table 3. The numbers of the experimental materials in each experimental condition.
Condition Prime number Target Target number Trial number
Familiar
conditions, another pilot test on word pair relatedness rating was conducted (e.g. 脾氣-怒氣,
脾氣-鋼筆). The 240 prime-target word pairs were divided into two questionnaires and
randomized to avoid the same primes appearing in one questionnaire. Twenty Chinese native
speakers were recruited for each questionnaire and were asked to rate the relatedness of the
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word pairs on a 5-point scale (5: strongly related, 1: weakly related). The two-way ANOVA
showed that there was a main effect of relatedness between the prime-related target and
prime-unrelated target pairs, F(1,59)=2323.05, p<.001. There was no main effect of
familiarity, F(1,59)=2.39, p=.127 and familiarity x relatedness interaction, F(1,59)=.62,
p=.432.
In sum, the experimental materials were carefully controlled for song familiarity
(familiar vs. unfamiliar), cloze probability in the unfamiliar lyrics (the overall cloze
probability: 31%), character number of the lyrics (i.e. length of the presented lyrics),
character to lyrics length ratio (i.e. speed of the song), relatedness in the prime-target pairs,
and word frequency of the targets. Thus, the materials were ensured not to be biased by the
possible confounds as the factors illustrated above (e.g. song familiarity, cloze probability
and speed of the song, etc.). See Table 4 & 5 for the summary of the statistical results for all
the pilot tests.
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Table 4. The summary of the statistical results for the pilot tests: song lyrics materials.
Factor Comparison Mean (S.D.) t(59) p
Song familiarity
Familiar vs. Unfamiliar 2.14 (2.46) 6.73 .000*Character number of the
lyrics
Familiar vs. Unfamiliar 1.167(4.937) 1.83 .072
Character to lyrics length
ratio
Familiar vs. Unfamiliar -.100(.511) -1.51 .135
Note. p*=<.001. S.D. is indicated in parenthesis.
Table 5. The summary of the statistical results for the pilot tests: targets.
Dependent variable Factors F(1,59) p
Word frequency
Familiarity, RelatednessFamiliarity=.20 .657
Relatedness=.006 .937
Familiarity x Relatedness=1.50 .225
Relatedness ratings
Familiarity, RelatednessFamiliarity=2.39 .127
Relatedness=2323.05 .000*
Familiarity x Relatedness=.62 .432
Note. p*=<.001.
Four versions of the experimental trials were constructed so that the 120 excerpts of
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lyrics stimuli were counterbalanced across the semantically-related and
semantically-unrelated target conditions. This way, each subject only heard each excerpt once,
with the prime word pairing with either the semantically related or the unrelated target. Also,
the chance for each prime to pair with the semantically related or unrelated targets was equal
(see Table 6). All the 120 excerpts of lyrics stimuli were made with Audacity 2.0, an audio
editor and recorder, each lasting from 5 to 18 seconds, and the target words were presented
with the E-Prime 2.0 software from Psychology Software Tools, Inc..
Table 6. The summary on the four versions of trials with lyrics stimuli counterbalanced across the related and unrelated conditions.
Condition
(Familiarity)
Excerpt Version A Version B Version C Version D
Familiar
#1-30 Related Unrelated Related Unrelated
#31-60 Unrelated Related Unrelated Related
Unfamiliar
#1-30 Related Unrelated Unrelated Related
#31-60 Unrelated Related Related Unrelated