A recent study published in Personal Relationships found that the lyrics of our favorite songs could be clues to our attachment style.
After asking individuals to share their favorite songs about relationships, the listed song lyrics were analyzed and rated as demonstrating anxious, secure, or avoidant attachment. When the rated songs were compared to the attachment styles of participants, it was revealed that people who were avoidantly attached had favorite songs with lyrics expressing avoidant behavior.
Additionally, when the same list was compared to personality traits, those scoring high in neuroticism and conscientiousness liked songs with anxiety-based lyrics. On the other hand, those who scored high in openness did not prefer lyrics that expressed secure attachment.
Study authors Ravin Alaei and colleagues also analyzed the most popular songs from 1946-2015 and found that song lyrics were more likely to reflect anxious attachment as time went by. This finding may support the theory that recent generations are becoming more detached.
Past research has found that the type of music you prefer reflects part of your personality and that music can be used to validate personal experiences. For example, those open to new experiences frequently enjoy complex music. Alaei and colleagues were curious if the music may also indicate attachment style.
Attachment style refers to how individuals seek to maintain connections with those closest to them. Some people are uncomfortable with those getting too close (attachment avoidance), while others fear rejection or the loss of a relationship (attachment anxiety).
The research team found 502 participants through Amazon’s crowdsourcing platform Mechanical Turk. About half the participants were female, and the average age was 34. Once participants agreed to take the survey, they were asked to think of 7-15 of their favorite English language songs about relationships. Participants who could not think of at least seven songs were not included in the data. Next, they took attachment style and personality trait assessments.
Once participants listed their songs, they had to be analyzed for evidence of attachment style. The researchers called this lyric coding. Separate research assistants were trained to examine the song’s plot and label it as portraying avoidant, anxious, or secure attachment. Once coded, the songs were compared to the participants’ attachment styles and personality traits. This process was used in part two of the study when they coded 800 of the most popular songs since 1946.
Results indicate that people with avoidant attachment styles enjoy lyrics that express the same sentiment. They also discovered that those who scored high in the personality trait of neuroticism were likely to prefer lyrics expressing anxiety.
Unexpectedly, this was not true for those with an anxious attachment style. These findings did not seem to indicate that the music was influencing attachment style; instead, “individuals like music with narratives that matched what may be considered validating and self-expressive themes about relationships.”
Finally, their analysis of the most popular songs from 1946-2015 revealed that songs had become more avoidant over time. Alaei and colleagues state, “we found evidence suggesting that Western culture’s diminishing orientation toward social engagement is reflected in the rise of avoidant popular music.”
The self-report style of data collection limits the inferences one can make from this data. For example, it’s possible the list of favorite songs about relationships was sometimes a list of songs they can remember – not necessarily a favorite. Also, attachment style and personality traits were assessed through self-report, so bias is likely a part of the data.
Despite these reservations, the research team feels they found strong evidence for individuals with certain attachment styles or personality traits to gravitate toward lyrics for their power to validate their lived experience.
The study, “Individual’s favorite songs’ lyrics reflect their attachment style“, was authored by Ravin Alaei, Nicholas Rule, and Geoff MacDonald.