Description Usage Format Details Note Source References See Also

Wish's (1967) Morse-code-like data of discrimination
probabilities among *32* auditory Morse-code-like signals.

1 |

The `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 `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 *32* Morse-code-like signals
consists of *S*, *U*, *W*, *X*, *0*, *1*,
..., *5* (see `fechner`

).

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
minimality/maximality; `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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