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.
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 http://en.wikipedia.org/wiki/Morse_code.
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
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
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 http://www.jstatsoft.org/v31/i06/.
check.data for checking data format;
check.regular for checking regular
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.
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