In two experiments, the empirical parsing of melodies was compared with predictions derived from four grouping preference rules of A Generative Theory of Tonal Music (F. Lerdahl & R. Jackendoff, 1983). In Experiment 1 (n = 123), listeners representing a wide range of musical training heard two familiar nursery-rhyme melodies and one unfamiliar tonal melody, each presented three times. During each repetition, listeners indicated the location of boundaries between units by pressing a key. Experiment 2 (n = 33) repeated Experiment 1 with different stimuli: one familiar and one unfamiliar nursery-rhyme melody, and one unfamiliar, tonal melody from the classical repertoire. In all melodies of both experiments, there was good within-subject consistency of boundary placement across the three repetitions (mean r = .54). Consistencies between Repetitions 2 and 3 were even higher (mean r = .63). Hence, Repetitions 2 and 3 were collapsed. After collapsing, there was high between-subjects similarity in boundary placement for each melody (mean r = .62), implying that all participants parsed the melodies in essentially the same (though not identical) manner. A role for musical training in parsing appeared only for the unfamiliar, classical melody of Experiment 2. The empirical parsing profiles were compared with the quantified predictions of Grouping Preference Rules 2a (the Rest aspect of Slur/Rest), 2b (Attack-point), 3a (Register change), and 3d (Length change). Based on correlational analyses, only Attack-point (mean r = .80) and Rest (mean r = .54) were necessary to explain the parsings of participants. Little role was seen for Register change (mean r = .14) or Length change (mean r = ––.09). Solutions based on multiple regression further reduced the role for Register and Length change. Generally, results provided some support for aspects of A Generative Theory of Tonal Music, while implying that some alterations might be useful.

Tonality induction is the natural outcome of acoustic redundancies in music and the predisposition of the brain to represent these redundancies. In the simplest case, tonality induction relies on frequency resolution and a memory accumulator. A review of the literature suggests that these and other more sophisticated building blocks (analysis of complex tones and sensitivity to sequential characteristics of musical patterns) are in place to contribute to tonality induction in the first year of life. As further revealed by life-span studies of preference and recognition for stylistically different popular music excerpts, two other constraints must also be considered: (1) brain plasticity and (2) degree of exposure to music of particular styles. The importance of a final factor, (3) formal music performance training, is shown in studies of (a) the benefits of the major triad frequency ratio relations (4: 5: 6) on memory (absolute judgment) for tones in an unfamiliar context (b) the applicability of a model based on a key-finding algorithm to pitch memory in a tonal context, and (c) the probe-tone task.

We examined two models that quantified the effects of tonality on accuracy and reaction time in an intervening-tone pitch-comparison task. In each of 16 task conditions (standard tone-interpolated sequence-test tone, abbreviated as S-seq-T), the S and T tones, C₄ and/or C#₄, were separated by a three-tone sequence that was a random arrangement of one of the four triads, ${\rm{C}}_{{\rm{4Major}}} ,{\rm{C}}_{{\rm{4Minor}}} ,{\rm{C\# }}_{\rm{4}} _{{\rm{Major}}} $ or ${\rm{C\# }}_{{\rm{4Minor}}} $ . Both models were based on the tonal hierarchy (Krumhansl, 1990a; Krumhansl & Shepard, 1979) and the key-finding algorithm (Krumhansl & Schmuckler, cited in Krumhansl, 1990a); the key- finding algorithm was used to determine the best-fitting key for the first four notes of the condition (i.e., the S-seq combination). Model 1 (S-Tone Stability) determined the stability of the S tone given that key. Model 2 (T-Tone Expectancy) determined the expectancy for the T tone given that key. Over the 16 conditions, for three groups of 12 subjects, differing by level of training, mean proportion correct discrimination ranged from .53 to .95 and increased significantly across levels of musical experience. For the musically trained subjects, both models predicted performance well but neither model was dramatically more effective than the other; the combination of both models did produce an increase in predictability. For untrained subjects, tonality, as assessed by the key-finding algorithm in either model, was not significantly correlated with performance.

We investigated the effects of musical soundtracks on attitudes to figures in a short animated film. In a preliminary study and in the main experiment, subjects saw the film accompanied by one of two soundtracks or with no soundtrack, or they heard one of the two soundtracks alone. In the main experiment, Semantic Differential judgments on Activity and Potency dimensions, obtained for the music, predicted effects of the soundtracks on corresponding ratings of the film as compared to ratings in a no soundtrack condition. As well, ratings on the Activity dimension of the film characters themselves were altered by the soundtracks. It is hypothesized that congruent auditory and visual structure directs the encoding of particular visual features of the film. In addition, associations generated by the music provide a context for the interpretation of the action in the film. As a result, stimulus features and concepts that are initially encoded as disjunctive conjoin in perception and memory.