R package: https://rdrr.io/cran/RateDistortion/

Code and data from Sims, C. R. (2018). Efficient coding explains the universal law of generalization in human perception. Science, 360(6389), 652-656.

Each dataset consists of an n x n confusion matrix, where rows indicate stimuli and columns indicate responses, and each value in the matrix indicates the empirical frequency for that stimulus–response pair.

Experimental data from:

• T. P. Hettinger, J. F. Gent, L. E. Marks, M. E. Frank, A confusion matrix for the study of taste perception, Perception & Psychophysics, 61(8), 1510–1521 (1999).
• F. G. Ashby, W. W. Lee, Predicting similarity and categorization from identification. Journal of Experimental Psychology: General, 120(2), 150 (1991).
• M. Azadi, L. Jones, Identification of vibrotactile patterns: Building blocks for tactons. In World Haptics Conference (WHC), IEEE, pp. 347-352 (2013).
• N. Forrest, S. Baillie, H. Z. Tan, Haptic stiffness identification by veterinarians * and novices: A comparison. In EuroHaptics 2009, IEEE, pp. 646–651 (2009).
• D. E. Kornbrot, Theoretical and empirical comparison of Luce’s choice model and logistic Thurstone model of categorical judgment. Attention, Perception, & Psychophysics, 24(3), 193-208 (1978).
• G. A. Miller, P. E. Nicely, An analysis of perceptual confusions among some English * consonants. The Journal of the Acoustical Society of America, 27(2), 338-352 (1955).
• R. M. Nosofsky, Attention and learning processes in the identification and categorization of integral stimuli. Journal of Experimental Psychology: Learning, Memory, and Cognition, 13(1), 87–108 (1987).
• J. M. Grey, Multidimensional perceptual scaling of musical timbres. Journal of the Acoustical Society of America, 61(5), 1270–1277 (1977).
• J. N. Rouder, R. D. Morey, N. Cowan, M. Pealtz, Learning in a unidimensional absolute identification task. Psychonomic Bulletin & Review, 11(5), 938–944 (2004).