morse: Rothkopf's Morse Code Data

Description Usage Format Details Note Source References See Also


Rothkopf's (1957) Morse code data of discrimination probabilities among 36 auditory Morse code signals for the letters A, B, ..., Z and the digits 0, 1, ..., 9.




The morse data frame consists of 36 rows and 36 columns, representing the Morse code signals for the letters and digits A, ..., Z, 0, ..., 9 presented first and second, respectively. Each number, an integer, in the data frame gives the percentage of subjects who responded ‘same’ to the row signal followed by the column signal.


Each signal consists of a sequence of dots and dashes. A chart of the Morse code letters and digits can be found at

Rothkopf's (1957) 36x36 Morse code data gives the same-different judgements of 598 subjects in response to the 36x36 auditorily presented pairs of Morse codes. Subjects who were not familiar with Morse code listened to a pair of signals constructed mechanically and separated by a pause of approximately 1.4 seconds. Each subject was required to state whether the two signals presented were the same or different. Each number in the morse data frame is the percentage of roughly 150 subjects.


The original Rothkopf's (1957) 36x36 dataset does not satisfy regular maximality. There are two maximal entries in row \#2, of value 84, which are p\_BB and p\_BX. Following the argument in Dzhafarov and Colonius (2006), a statistically compatible dataset is obtained by replacing the value of p\_BX with 83 and leaving the rest of the data unchanged. The latter is the dataset accompanying the package fechner.

For typographic reasons, it may be useful to consider only a small subset of the stimulus set, best, chosen to form a ‘self-contained’ subspace: a geodesic loop for any two of the subset's elements (computed using the complete dataset) is contained within the subset. For instance, a particular self-contained 10-code subspace of the 36 Morse codes consists of the codes for the letter B and the digits 0, 1, 2, 4, ..., 9 (see fechner).


Rothkopf, E. Z. (1957) A measure of stimulus similarity and errors in some paired-associate learning tasks. Journal of Experimental Psychology, 53, 94–101.


Dzhafarov, E. N. and Colonius, H. (2006) Reconstructing distances among objects from their discriminability. Psychometrika, 71, 365–386.

Dzhafarov, E. N. and Colonius, H. (2007) Dissimilarity cumulation theory and subjective metrics. Journal of Mathematical Psychology, 51, 290–304.

Uenlue, A. and Kiefer, T. and Dzhafarov, E. N. (2009) Fechnerian scaling in R: The package fechner. Journal of Statistical Software, 31(6), 1–24. URL

See Also for checking data format; check.regular for checking regular minimality/maximality; fechner, the main function for Fechnerian scaling. See also wish for Wish's Morse-code-like data, and fechner-package for general information about this package.

fechner documentation built on May 2, 2019, 8:49 a.m.