The MIDCA cognitive architecture has at its core a process sequence consisting of three phases. They are to note an anomaly, to assess what causes the anomaly, and then to guide a response to the anomaly. Here we present a novel approach to the first phase and discuss implications for the second. Our method detects a shift in streams of symbolic data that signal an anomaly and trigger in-depth understanding of the input. The approach uses a metric called the A-distance, normally used to detect shifts in distributions underlying numeric data. Instead, using a novel plan representation, we apply this metric to streams of changing predicates in various environments. Empirical results show that over a range of circumstances we are able to detect changes in the underlying domain. We suggest that these results apply to metacognitive as well as to cognitive processing.