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**Paper** This dataset contains the collected data and analysis script for: ###### de Fleurian, R., Blackwell, T., Ben-Tal, O., & Müllensiefen, D. (2017). Information-theoretic measures predict the human judgment of rhythm complexity. *Cognitive Science*, *41*(3), 800–813. doi: [10.1111/cogs.12347][1] ---------- **Corrigendum** In Table 2, the values for excess entropy should be `0.174` and `0.378` instead of `0.208` and `0.361` (still non-significant once Bonferroni-corrected). More details are available in the analysis script. ---------- **Data** The data collected for this experiment is available as a `.csv` file [here][2]. The variables are as follows: | Variable | Description | -------- | ----------- |`msi_all` | Gold-MSI - General Sophistication |`msi_perc` | Gold-MSI - Perceptual Abilities |`msi_train` | Gold-MSI - Musical Training |`apm` | Raven's Advanced Progressive Matrices - Set I |`algo` | Generative algorithm (full names in [Appendix][3]) |`algo_id` | Generative algorithm, coded as 1-16 |`subseq` | Sub-sequence shown to participants for each algorithm |`comp_H` | Shannon entropy |`comp_h` | Entropy rate |`comp_E` | Excess entropy |`comp_T` | Transient information |`comp_K` | Kolmogorov complexity |`score` | 1 for correct, 0 for incorrect (correct answers in [Appendix][4]) |`easy` | Easiness rating on 1-7 scale ---------- **Analysis** An analysis script written in `R` is available [here][5]. [1]: [2]: [3]: [4]: [5]:
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