Keypress-Based Musical Preference Is Both Individual and Lawful

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ID: 78871
2017
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Ranked #75 of 78 articles by views in Frontiers in neuroscience

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Abstract
Musical preference is highly individualized and is an area of active study to develop methods for its quantification. Recently, preference-based behavior, associated with activity in brain reward circuitry, has been shown to follow lawful, quantifiable patterns, despite broad variation across individuals. These patterns, observed using a keypress paradigm with visual stimuli, form the basis for relative preference theory (RPT). Here, we sought to determine if such patterns extend to non-visual domains (i.e., audition) and dynamic stimuli, potentially providing a method to supplement psychometric, physiological, and neuroimaging approaches to preference quantification. For this study, we adapted our keypress paradigm to two sets of stimuli consisting of seventeenth to twenty-first century western art music (Classical) and twentieth to twenty-first century jazz and popular music (Popular). We studied a pilot sample and then a separate primary experimental sample with this paradigm, and used iterative mathematical modeling to determine if RPT relationships were observed with high R2 fits. We further assessed the extent of heterogeneity in the rank ordering of keypress-based responses across subjects. As expected, individual rank orderings of preferences were quite heterogeneous, yet we observed mathematical patterns fitting these data similar to those observed previously with visual stimuli. These patterns in music preference were recurrent across two cohorts and two stimulus sets, and scaled between individual and group data, adhering to the requirements for lawfulness. Our findings suggest a general neuroscience framework that predicts human approach/avoidance behavior, while also allowing for individual differences and the broad diversity of human choices; the resulting framework may offer novel approaches to advancing music neuroscience, or its applications to medicine and recommendation systems.
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livengood2017keypressbasedfrontiers Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Livengood, Sherri L.;Livengood, Sherri L.;Sheppard, John P.;Sheppard, John P.;Sheppard, John P.;Kim, Byoung W.;Kim, Byoung W.;Kim, Byoung W.;Malthouse, Edward C.;Malthouse, Edward C.;Bourne, Janet E.;Bourne, Janet E.;Barlow, Anne E.;Barlow, Anne E.;Lee, Myung J.;Lee, Myung J.;Lee, Myung J.;Marin, Veronica;O'Connor, Kailyn P.;Csernansky, John G.;Block, Martin P.;Block, Martin P.;Blood, Anne J.;Blood, Anne J.;Blood, Anne J.;Blood, Anne J.;Blood, Anne J.;Breiter, Hans C.;Breiter, Hans C.;Breiter, Hans C.;Breiter, Hans C.;Breiter, Hans C.;
Journal Frontiers in neuroscience
Year 2017
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