A framework for development of fuzzy GOMS model for human-computer interaction

• Published in: International journal of human-computer interaction 1990-01, Vol.2 (4), p.287-305
• Main Authors: Karwowski, Waldemar, Kosiba, Eric, Benabdallah, Salah, Salvendy, Gavriel
• Format: Article
• Language: English
• ISSN: 1044-7318; 1532-7590
• DOI: 10.1080/10447319009525987

The objective of this study was to examine usefulness of fuzzy methodologies in the analysis and design of human-computer interaction. A framework for generalization of the Goals-Operators-Methods-Selection Rules (GOMS) model, and its fuzzy version was proposed. An experimental verification of the fuzzy GOMS model was also provided. A total of six subjects participated in two laboratory experiments. These experiments were performed in order to validate the proposed fuzzy GOMS model for the text editing task described in information processing terms. The subjects were not familiar with the text files to be edited, and the task was performed from the subject's own office and desk. All subjects were familiar with and regularly used the VI screen editor. The experiments consisted of the following steps: (1) the subject performed a familiar text editing task using a screen editor (VI); (2) the methods by which the subject achieved his goals (word location) as well as selection rules were elicited; (3) several compatibility functions for fuzzy terms used by the subject were derived; and (4) once all the rules, methods, and corresponding membership functions have been elicited, the theory of possibility was used to model the expert's rule selection process. For this purpose, each of the potential rules was assigned a possibility measure equal to the membership value(s) derived during the elicitation phase of experiment Finally, the selected methods were compared to non-fuzzy predictions and actual experimental data. It was shown that overall, across all subjects and trials of the main editing task, the fuzzy-based COMS model predicted significantly more of the subject responses, than did the non-fuzzy COMS model.