Abstract

This article presents a top-down approach for analyzing sequential events in behavioral data. Analysis of behavioral sequential data often entails identifying patterns specified by the researchers. Algorithms were developed and applied to analyze a kind of behavioral data, called discrete action protocol data. Discrete action protocols consist of discrete user actions, such as mouse clicks and keypresses. Unfortunately, the process of analyzing the huge volume of actions (typically, > 10(5)) is very labor intensive. To facilitate this process, we developed an action protocol analyzer (ACT-PRO) that provides two levels of pattern matching. Level one uses formal grammars to identify sequential patterns. Level two matches these patterns to a hierarchical structure. ACT-PRO can be used to determine how well data fit the patterns specified by an experimenter. Complementarily, it can be used to focus an experimenter's attention on data that do not fit the prespecified patterns.

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