wish {fechner} | R Documentation |
Wish's Morse-code-like Data
Description
Wish's (1967) Morse-code-like data of discrimination
probabilities among 32
auditory Morse-code-like signals.
Usage
wish
Format
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.
Details
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) 32\times 32
Morse-code-like data
gives the same-different judgements of subjects in response to the
32\times 32
auditorily presented pairs of codes.
Note
The original Wish's (1967) 32\times 32
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
).
Source
Wish, M. (1967) A model for the perception of Morse code-like signals. Human Factors, 9, 529–540.
References
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/.
See Also
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.