wish: Wish's Morse-code-like Data
In fechner: Fechnerian Scaling of Discrete Object Sets

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.

wish

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.