To formalize the human judgment of rhythm complexity, we used five measures from information theory and algorithmic complexity to measure the complexity of 48 artificially generated rhythmic sequences. We compared these measurements to human prediction accuracy and easiness judgments obtained from a listening experiment, in which 32 participants guessed the last beat of each sequence. We also investigated the modulating effects of musical expertise and general pattern identification ability. Entropy rate and Kolmogorov complexity were correlated with prediction accuracy, and highly (...) correlated with easiness judgments. A logistic regression showed main effects of musical training, entropy rate, and Kolmogorov complexity, and an interaction between musical training and both entropy rate and Kolmogorov complexity. These results indicate that information-theoretic concepts capture some salient features of the human judgment of rhythm complexity, and they confirm the influence of musical expertise on complexity judgments. (shrink)

Over the last few decades, empirical researchers have become increasingly interested in explaining the formation of “basic” aesthetic judgments, i.e. simple judgments of sensory preferability and the pleasure that seems to accompany them. To that end, Reber et al. have recently defended a “processing-fluency” view, which identifies aesthetic pleasure with one’s ability to easily process an object’s perceptual properties (e.g. Reber 2012 ). While the processing-fluency theory is certainly an improvement over its competitors, it is currently vulnerable to several serious (...) criticisms. In what follows, I aim to provide a more holistic, explanatorily robust, model of the processing-fluency theory of aesthetic pleasure by incorporating what the view neglects: the crucial role of perceptual disfluency, interest, and the underlying values that drive aesthetic appraisal. (shrink)