The Effects of Cognitive Load and Complexity on Music Preference

1. The Effects of Cognitive Load and Complexity on Music Preference Chris Buchholz, Elizabeth Hord, Kiel VanNess, Michael Bankert, & Chava Urecki Roanoke College – Salem, VA Abstract Previous studies indicate a relationship between music preference and cognitive ability. For example, individuals preferring complex music are higher in need for cognition and working memory span. The current study found that participants experiencing high cognitive load rated music lower than those experiencing low cognitive load. This research is an initial effort toward conceptualizing our aesthetic preferences in terms of the underlying cognitive features of our mental system. Results are discussed in terms of their relation to Complex Systems Theory. Introduction (cont.) We appear to seek out stimuli (e.g., friends, music, activities) that exhibit a level of complexity or uncertainty that matches our cognitive ability. Furthermore, we experience a positive emotional state when there is an effective match between complexity and ability (Nakamura & Csikszentmihalyi, 2002). Therefore, we should be able to use individual differences in our cognitive ability to predict individual differences in our preference for particular stimuli. In the context of music preferences, individuals with higher levels of cognitive resources should gravitate toward songs with more complex melodies, lyrics, and/or rhythms. However, all of us have the cognitive ability to be able to synchronize to a simple song. Why then would we ever bother with complex music like jazz? Arguably this is because we become bored with stimuli that are too predictable for our particular cognitive system, just as we can become bored with the latest pop song after hearing it too many times. We seek out stimuli that challenge us, that push the limits of our cognitive system (Cacioppo, Petty, Feinstein, & Jarvis, 1996). In previous studies, we examined the relationship between measures of cognitive ability (e.g., working memory span, need for cognition) and music preference (Buchholz, et al., 2011). Specifically, we looked at individuals preferences for music that varies in complexity. We found that a preference for more complex music (e.g., classical, blues, folk, jazz, and heavy metal) was positively correlated with a need for cognition, tolerance for uncertainty and working memory span. Overall, these results suggest that there is a link between cognitive ability and our preference for music. Individuals with more cognitive resources gravitate toward music that is more complex in structure, melody, and rhythm. The current study sought to determine if cognitive resources (or the lack of) were indeed the cause of these differences in music preference. Figure 1. The effect of song complexity on likability. Figure 2. The effect of cognitive load on likability. Introduction Why do some of us go for the more straightforward-simple rhythms and melodies that characterize most popular music, while others gravitate towards more complex music such as jazz or classical? Our culture and upbringing, of course, determine the types of music that we are exposed to; however, previous research indicates that individual differences in our cognitive ability may also play a role in our musical preferences (Buchholz, Babbitt, VanNess, Hoover, Urecki, & Bankert, 2011). In order to understand why this is the case, we need to ask a more general question: why do we like some stimuli over others? One factor is how a given stimulus makes us feel. At the simplest level, positive emotions tell us to approach, to pay attention to that particular stimulus, whether it is a butterfly, a friend’s face, or the next action thriller. Some of our favorite stimuli are those that change over time in complex ways (e.g., music, film, a waterfall, or other individuals). As we process these stimuli our cognitive system adjusts in complex ways to match the ebb and flow of information. In recent years, researchers have begun to conceptualize the social-cognitive and affective processes of our mind as emergent properties of a complex system (Shoda, Tiernan, & Mischel, 2002; Vallacher, Nowak, Froehlich, & Rockloff, 2002). Complex systems, which have been successfully utilized in such diverse areas as fluid dynamics, economics, ecology, and the prediction of weather patterns, are typically defined as a set of interacting elements that give rise to higher-order properties over time (Nowak & Vallacher, 1998). At a physiological level, individual neurons are the interacting elements that give rise to higher-order properties such as thoughts and emotions. Recently, researchers have found specific neurons (i.e., “mirror neurons”) that appear to react to sensory stimuli in a synchronized fashion (Rizzolatti & Craighero, 2004). Likewise, Nowak, Vallacher, and Zochowski (2002) suggest that interpersonal interaction can be thought of as the synchronization of two or more complex systems. In other words, as we interact with another individual, our cognitive system adjusts in order to match the thoughts, behaviors, and emotions of that individual. A particular amount of cognitive resources are required in order to process complex sensory information that may be changing in complex ways over time. Not only are we attempting to synchronize, but we are also anticipating future behaviors. Thus, the more predicable the pattern, the easier it will be for us to synchronize our thoughts, behaviors, and/or emotions with that pattern, whether it is listening to a song or talking with a friend. For instance, we have all experienced a time when we were so well synchronized with another person that it was as if we were thinking the same thoughts and feeling the same emotions. This type of experience would be impossible if the stimulus pattern we are trying to synchronize with is too complex, too unpredictable, and/or if we lack the cognitive resources necessary to process that pattern. Figure 3. The effect of song complexity and cognitive load on likability. References Buchholz, C., Babbitt, A., VanNess, K., Hoover, B., Urecki, C., & Bankert, M. (2011, March). Cognitive ability and the complexity of music: Complex systems seeking complex stimuli. Poster presented at the eighty first annual meeting of the Eastern Psychological Association, Cambridge, MA. Cacioppo, J.T., Petty, R.E., Feinstein, J.A., & Jarvis, W.B.G (1996). Dispositional differences in cognitive motivation: The life and times of individuals varying in need for cognition. Psychological Bulletin, 119(2), 197-253. Nakamura, J., & Csikszentmihalyi, M. (2002). The concept of flow. In The Handbook of Positive Psychology, C.R. Snyder and S.J. Lopez (eds.), Oxford: University Press. Nowak, A., & Vallacher, R. R. (1998). Dynamical Social Psychology. New York: Guilford Publications. Nowak, A.J., Vallacher, R.R., & Zochowski, M. (2002). The emergence of personality: Personal stability through interpersonal synchronization. In Advances in personality science, Daniel Cervone& Walter Mischel (eds.), The Guilford Press, New York, NY. Rizzolatti, G., & Craighero, L. (2004). The mirror-neuron system. Annual Review of Neuroscience, 27, 169-92. Shoda, Y., Tiernan, S. L., & Mischel, W. (2002). Personality as a dynamical system: Emergence of stability and distinctiveness from intra- and interpersonal interactions. Personality and Social Psychology Review, 6 (4), 316-325. Vallacher, R. R., Nowak, A., Froehlich, M., & Rockloff, M. (2002). The dynamics of self-evaluation. Personality and Social Psychology Review, 6 (4), 370-379. Methods In a 2(simple/complex song) x 2(low/high cognitive load) between subjects design, participants listen to either a simple or complex song while completing a task that varied in cognitive load from low (circling instances of the letter “e” in a short story) to high (determining if sentences made sense or were nonsense, while memorizing the last word of each sentence). We also included measures of need for cognition and music preference. Results There was a significant main effect for song complexity, F(1,44) = 38.48, p<.001, 2=.47 (Figure 1). There was also a significant main effect for cognitive load, F(1,44) = 10.19, p=.003, 2=.19 (Figure 2). Participants preferred the simple song (M=3.88, SD=.19) over the complex song (M=2.3, SD=1.6) and in general liked the songs better when under the low cognitive load manipulation (M=3.50, SD=.18; M=2.71, SD=.18). The interaction failed to reach significance but was in the direction of predictions. That is, participants disliked the complex song about the same regardless of cognitive load condition, while participants liked the simple song more when in the low cognitive load condition. Simple main effects analysis indicated that cognitive load significantly affected liking ratings for the simple song F(1,18) = 8.79, p=.008, 2=.33 but not for the complex song (Figure 3). We also found significant correlations between preference for complex music and need for cognition, r(71)=.643, p<.001, as well as a negative correlation between preference for complex music and need for cognition, r(71)= -.313, p=.008.