Wish's (1967) Morse-code-like data of discrimination probabilities among 32 auditory Morse-code-like signals.
wish data frame consists of 32 rows and 32
columns, representing the Morse-code-like signals (see
‘Details’) presented first and second, respectively. Each
number, a numeric, in the data frame gives the relative frequency of
subjects who responded ‘different’ to the row signal followed
by the column signal.
The 32 Morse-code-like signals in Wish's (1967) study were 5-element sequences T\_1P_\1T\_2P\_2T\_3, where T stands for a tone (short or long) and P stands for a pause (1 or 3 units long). As in Dzhafarov and Colonius (2006), the stimuli are labeled A, B, ..., Z, 0, 1, ..., 5, in the order they are presented in Wish's (1967) article.
Wish's (1967) 32x32 Morse-code-like data gives the same-different judgements of subjects in response to the 32x32 auditorily presented pairs of codes.
The original Wish's (1967) 32x32 dataset
does not satisfy regular minimality. There is the entry
p\_TV = 0.03, which is the same as
p\_VV and smaller than
p\_TT = 0.06. Following the argument in
Dzhafarov and Colonius (2006), a statistically compatible
dataset is obtained by replacing the value of p\_TV
with 0.07 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 32 Morse-code-like signals
consists of S, U, W, X, 0, 1,
..., 5 (see
Wish, M. (1967) A model for the perception of Morse code-like signals. Human Factors, 9, 529–540.
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
morse for Rothkopf's
Morse code data, and
fechner-package for general
information about this package.
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