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.