Learning and Liking of Melody and Harmony: Further Studies in Artificial Grammar Learning
Topics in Cognitive Science (forthcoming)
| Abstract | Much of what we know and love about music is based on implicitly acquired mental representations of musical pitches and the relationships between them. While previous studies have shown that these mental representations of music can be acquired rapidly and can influence preference, it is still unclear which aspects of music influence learning and preference formation. This article reports two experiments that use an artificial musical system to examine two questions: (1) which aspects of music matter most for learning, and (2) which aspects of music matter most for preference formation. Two aspects of music are tested: melody and harmony. In Experiment 1 we tested the learning and liking of a new musical system that is manipulated melodically so that only some of the possible conditional probabilities between successive notes are presented. In Experiment 2 we administered the same tests for learning and liking, but we used a musical system that is manipulated harmonically to eliminate the property of harmonic whole-integer ratios between pitches. Results show that disrupting melody (Experiment 1) disabled the learning of music without disrupting preference formation, whereas disrupting harmony (Experiment 2) does not affect learning and memory but disrupts preference formation. Results point to a possible dissociation between learning and preference in musical knowledge | |||||||||
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Martin Možina, Jure Žabkar, Trevor Bench-Capon & Ivan Bratko (2005). Argument Based Machine Learning Applied to Law. Artificial Intelligence and Law 13 (1):53-73.
C. A. Seger (1998). Independent Judgment-Linked and Motor-Linked Forms of Artificial Grammar Learning. Consciousness and Cognition 7 (2):259-284.
Roger Scruton (1999). The Aesthetics of Music. Oxford University Press.
S. Russell (1991). Inductive Learning by Machines. Philosophical Studies 64 (October):37-64.
Rolf Reber (2005). Rule Versus Similarity: Different in Processing Mode, Not in Representations. Behavioral and Brain Sciences 28 (1):31-32.
Annette Kinder (2000). Can We Do Without Distributed Models? Not in Artificial Grammar Learning. Behavioral and Brain Sciences 23 (4):484-484.
Jenefer Robinson (ed.) (1997). Music & Meaning. Cornell University Press.
Joscelyn Godwin (1995). Music and the Occult: French Musical Philosophies, 1750-1950. University of Rochester Press.
Kathleen Marie Higgins (2012). The Music Between Us: Is Music a Universal Language? The University of Chicago Press.
Hartmut Giest (2008). The Formation Experiment in the Age of Hypermedia and Distance Learning. In B. van Oers (ed.), The Transformation of Learning: Advances in Cultural-Historical Activity Theory. Cambridge University Press.
Peter A. Bibby & Geoffrey Underwood (1999). Volitional Control in the Learning of Artificial Grammars. Behavioral and Brain Sciences 22 (5):757-758.
Stellan Ohlsson (1997). Old Ideas, New Mistakes: All Learning is Relational. Behavioral and Brain Sciences 20 (1):79-80.
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