R: Wish's Morse-code-like Data

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/.