Changing the interval content of algorithmically generated music changes the emotional interpretation of visual images

Fernando Bravo

pp. 494-508

in: Mitsuko Aramaki, Olivier Derrien, Richard Kronland-Martinet, Sølvi Ystad (eds), Sound, music, and motion, Berlin, Springer, 2014

Abstract

The ability of music to influence the emotional interpretation of visual contexts has been supported in several psychological studies. However, we still lack a significant body of empirical studies examining the ways in which specific structural characteristics of music may alter the affective processing of visual information. The present study suggests a way to use algorithmically generated music to assess the effect of sensory dissonance on the emotional judgment of a visual scene. This was examined by presenting participants with the same abstract animated film paired with consonant, dissonant and no music. The level of sensory dissonance was controlled in this experiment by employing different interval sets for the two contrasting background music conditions. Immediately after viewing the clip, participants were asked to complete a series of bipolar adjective ratings representing the three connotative dimensions (valence, activity and potency). Results revealed that relative to the control group of no music, consonant background music significantly biased the affective impact by guiding participants toward positive valence ratings. This finding is discussed in terms of interval content theory within the general perspective of post-tonal music theory and David Temperley's probabilistic framework (model of tonalness).