Evaluating expert created, idiographic command sets for novice, nomothetic purposes

Abstract

Several studies have demonstrated performance benefits assoicated with self-defined computer commands (aliases). This study further investigated the possibility of empirically establishing pools of self-generated (idiographic) command names for novice (nomothetic) use by others. Experiment 1 showed that N when given the commands and functions from which they were derived, independent expert (E) and novice (N) groups were able to discriminate between bona fide and bogus aliases at above chance levels, despite surface heterogeneity, although Es were more able to do this than Ns. Experiment 2 compared the understandability of E and N created aliases for independent groups of Es and Ns. Results showed that E aliases were more understandable than N aliases and that Es understood all aliases better. That is, Es exhibited a decoding advantage (due to experience) and an advantage in encoding semantic content in their aliases. In Experiment 3 Rosenberg's ‘command suggestiveness’ index showed that the mean suggestiveness of E aliases was significantly higher than that of N aliases. Moreover, for experiments 1 and 2, subjects’ confidence in matching aliases to their parent functions was significantly correlated with suggestiveness. To test the utility of the suggestiveness metric, lists of high, medium and low suggestiveness aliases were constructed and subjects learned all lists in counterbalanced order. Recall using command functions as cues showed that more of the higher suggestiveness aliases were remembered. It was concluded that despite aliases surface heterogeneity, they possess sufficient semantic content to allow identification of their original functions. Moreover, Es produce more meaningful aliases and experiment 4 revealed that this may be due to greater suggestiveness. Generalizing from these findings, it appears appropriate that for complex systems where novice understanding is limited, performance may be facilitated by establishing E alias pools from which the most efficacious are empirically selected using the methods proposed by Rosenberg.