Graph-based Reasoning: From Task Analysis to Cognitive Explanation

Models of graph-based reasoning have typically accounted for the variation in problem solving performance with different graph types in terms of a task analysis of the problem relative to the particular visual properties of each graph type (e.g. Lohse, 1993; Peebles, Cheng & Shadbolt 1999, submitted). This approach has been used to explain response time and accuracy differences in experimental situations where data are averaged over experimental conditions. A recent experiment is reported in which participants’ eye movements were recorded while they were solving various problems with different graph types. The eye movement data revealed fine grained scanning and fixation patterns that are not predicted by standard task analytic models. From these eye-movement studies it is argued that there is a missing level of detail in current task analytic models of graph-based reasoning.