Biopsychological Responses to Music Chosen by a Computer: Validation of a Music Search Engine July 31, 2009 Dwight Krehbiel, Professor of Psychology Bethel College, North Newton, KS

This material is based upon work supported by the National Science Foundation under Grant No and No from the Division of Information and Intelligent Systems and Grant No from the Division of Undergraduate Education. Acknowledgments Professor Bill Manaris, Department of Computer Science, College of Charleston, Charleston, SC and his students: Patrick RoosLuca Pellicoro Thomas ZalonisJ.R. Armstrong who created the search engine used in the experiments And Bethel student collaborators: Aimee SiebertTim Burns José RojasErin White Sonia BarreraBecky Buchta Yue YuBrittany Baker Sierra PryceElizabeth Friesen Naomi GraberLisa Penner

Natural Patterns in Music (and Many Other Phenomena)‏ Zipf’s law: The probability of an event of rank f is inversely proportional to that rank f raised to some power n, and n is close to 1. or P(f) ~ 1/f n Example events in music: pitches, durations, harmonic intervals, melodic intervals but also pairs of intervals, sets of three intervals, etc. Basic finding: Music that is Zipfian is generally judged to be more pleasant than is non-Zipfian music.

Web User Interface of the Search Engine

Do listeners like what the search engine finds?

Liking Ratings Excerpt Set A (n=25) Genre Familiarity Ratings Liking Ratings Excerpt Set B (n=25) Genre Familiarity Ratings O MS MS2 MD2 MD O: original MS: most similar MS2: 2 nd most similar MD2: 2 nd most dissimilar MD: most dissimilar

Experimental Design & Procedure

Experimental Design (cont)‏ Two sets of excerpts (one min/excerpt)‏  Set A - set of 7 presented to all participants: O = original, MS = most similar, MS2 = 2 nd most similar, MS3 = 3 rd most similar, MD3= 3 rd most dissimilar, MD2 = 2 nd most dissimilar, MD = most dissimilar  Set B - set of 5 unique to each participant (chosen from one of their three favorite genres): O, MS, MS2, MD2, MD  Random order of all 12 excerpts for each participant  40 participants  Instrumental music only

Emotion Rating Instrument

One of Our Participants

Psychological Ratings – Set A

Psychological Ratings – Set B

Posterior Frontal Asymmetry – Set A Asymmetry Near the Central Sulcus – Set A n = 38

Skin Conductance Changes during the Music – Set A (Individual Participants' Data)‏

Heart Interbeat Intervals (IBI) - Sets A & B (means and standard deviations across 60 sec of listening

All Psycho- physiological Measures – Set A (means across 60 sec of listening)‏

Summary & Conclusions A search engine based on aesthetic similarity can find music that we like, perhaps by finding music with sound patterns that are already familiar. Affective responses to similar music found by the search engine (pleasantness, activation, liking) are clearly different from those to dissimilar music. Similarity judgments by human participants show clear agreement with search engine ratings.

Summary & Conclusions (cont) Hemispheric asymmetry measures (alpha power) show significant differences between similar and dissimilar music when all participants listen to the same music, but not when preferences are controlled (i.e. asymmetry is not closely correlated with consciously reported affective responses). Peripheral psychophysiological responses also display significant differences between similar and dissimilar music. Heart rate differences do appear to be correlated with affective responses.