New research published in journal of intelligence Our findings suggest that a person’s daily music listening habits contain subtle clues about their general cognitive abilities. Scientists have discovered that the lyrics of the songs people choose to play provide more insight into their intelligence than the beat or melody of the music. These findings provide evidence that the digital footprints we leave behind in our daily lives may ultimately help us approximate cognitive skills without formal testing.
Traditional intelligence assessments rely on formal tests administered in highly controlled and stressful environments. But cognitive abilities are constantly being used outside the laboratory to navigate the complexities of everyday life.
With smartphones and digital apps capturing so much of our behavior, researchers saw an opportunity to study cognitive abilities in natural environments. They decided to focus on music listening because it is a very common daily activity that engages multiple brain networks, including memory, emotion, and auditory processing.
Previous research linking music and intelligence has relied primarily on laboratory experiments and self-report surveys. In such an environment, people may misremember what they are listening to or claim to like sophisticated music to look good. Scientists aimed to capture exactly what people are listening to in the real world by using digital tracking data.
“Most research on cognitive ability, or simply intelligence, focuses on situations in which people try to do their best, such as tests, school grades, and work tasks. Therefore, there is little research on how cognitive ability relates to achievement. “We know a lot, but we know even less about whether cognitive abilities translate into everyday low-risk behaviors,” said study author Larissa Sast, a postdoctoral researcher at the Ludwig-Maximilians-University of Munich.
“At the same time, many everyday activities now leave digital traces, making it possible to study such real-life behaviors more naturally than before. Our research was motivated by this gap. We wanted to see whether patterns in everyday digital activities also reflected differences in cognitive abilities. As a starting point, we chose music listening, a common behavior that can be easily tracked on a smartphone using a custom research application.”
Researchers tracked 185 participants’ smartphone usage over a five-month period. They utilized a custom research application installed on participants’ personal cell phones to record every song played.
Participants also completed a short cognitive ability test on their smartphones. This test measured abilities in fluid reasoning, vocabulary comprehension, and mathematical knowledge. Together, these skills make up a person’s general cognitive abilities and reflect how well that person can think rationally and adapt to new situations.
During the study period, participants listened to 58,247 unique songs. The researchers then collected detailed information about these tracks from online music databases such as Spotify. They extracted audio characteristics such as the tempo and acoustic characteristics of the sound.
They also used specialized linguistic tools to analyze the song’s lyrical content. The tool categorized words in lyrics based on psychological themes, emotional tone, and social references. Scientists collected a total of 215 different characteristics related to each participant’s audio, lyrics, and general listening habits.
To make sense of this vast amount of data, researchers turned to machine learning. Machine learning is a type of artificial intelligence in which computer programs analyze large data sets to identify complex patterns. They trained these computer models to see if music listening abilities could predict participants’ scores on cognitive ability tests.
The researchers tested different types of computer algorithms. Only complex nonlinear models were able to detect meaningful patterns in the data. This suggests that the relationship between musical habits and intelligence is not simple and direct, but is highly complex.
A computer model detected a small but reliable association between a person’s music listening behavior and cognitive test scores. The most informative predictor was not the sound of the music, but the words in the song. The participants’ lyrical preferences provided the strongest evidence of their cognitive abilities.
“As we looked more closely at how our predictive model worked and which aspects of music listening were most beneficial, one discovery surprised us,” Sust told PsyPost. “The lyrics of the songs people listened to were a better predictor of cognitive performance than the characteristics of the music.”
“In other words, the themes and language used in the lyrics appear to be more important than aspects such as tempo or musical key. This was unexpected, because previous research often suggests that melodic preferences play a larger role (for example, when predicting personality traits), and many assume that intelligence is primarily reflected in preferences for specific genres, such as classical or jazz music.”
Specifically, the model found that people who listened to songs with less positive emotional tones tended to have higher predictive intelligence scores. Researchers suggest that sad or melancholic music may appeal to people who use music for introspection and contemplation.
Songs with lyrics that focused on the present moment, honesty, and home-related topics were also associated with higher cognitive performance. On the other hand, a preference for lyrics with more social or tentative language tended to predict lower intelligence scores.
With one notable exception, phonetic characteristics contribute little to predicting cognitive ability. The model found that a preference for less lively songs was a strong predictor of higher intelligence. Liveness refers to the probability that a track was recorded in front of a live audience.
Scientists have proposed that live recordings are often highly energetic and poorly controlled. People with higher cognitive abilities may prefer studio recordings because they often use music for focused intellectual activity rather than high-energy stimulation.
Listening habits also influenced predictions. Participants who spent more time listening to music overall tended to have higher intelligence scores. Additionally, a preference for songs in a language other than German, the sample’s native language, was associated with higher cognitive performance.
“One of the key takeaways is that cognitive ability (or intelligence) may be subtly reflected not only in test and high-stakes performance, but also in everyday behavior,” Sast explained. “Our research suggests that people’s music listening patterns contain small but detectable signals related to cognitive abilities, and that the digital traces we leave behind in our daily lives could potentially help estimate intelligence.”
“Listening to music alone provides limited information, but in the future, combining multiple types of digital behavior (e.g. what books people read, what places they visit) could make such predictions more accurate and ultimately support adaptive digital tools and early detection of cognitive decline.”
Although these patterns are interesting, the researchers note several potential misconceptions and limitations. The predictive power of just listening to music is so small that an app can’t accurately determine a person’s intelligence just by looking at a playlist.
“Thus, these effects alone may not be strong enough to be of practical use,” Sust noted. “However, they suggest that everyday digital behavior may contain small signals of cognitive differences that can become more meaningful when combined with many other types of behavioral data.”
The relationships observed in this study are purely correlational, meaning that listening to certain music does not make people smarter, and vice versa. Researchers caution that other unmeasured factors, such as a person’s age, may be influencing both intelligence test scores and music preferences.
“An important caveat is that the associations we found may be influenced by other factors, known as confounding variables,” Sust said. “For example, age is related to both cognitive ability and the type of music people tend to listen to, so age may play a role. We are currently working on follow-up analyzes to better understand and explain such effects.”
The study, “Deep Beats, Deep Thoughts? Predicting General Cognitive Abilities from Natural Music Listening Behavior,” was authored by Larissa Sust, Maximilian Bergmann, Markus Bühner, and Ramona Schoedel.
