Prototype Theory: Central Tendency vs. Attribute Frequency

In chapter 4 (Pattern Recognition) of the textbook, prototype formation (not part of the course reading) is one of the theories mentioned to account for the cognitive process of pattern recognition in the human brain. Prototype formation asserts that pattern perception occurs as a result of abstraction of stimuli, which are stored in LTM and serve as idealized forms against which patterns are evaluated.

Two models are proposed by prototype theory: (a) the central tendency model, which states that a prototype presents the mean or an average of the set of exemplars, and (b) the attribute-frequency model, which states that a prototype represents the mode or a summation of the most frequently experienced attributes.

Now, let's consider the central tendency model, which states that the prototype is the mean of a set of exemplars. Instead of thinking of the mean as something static and "central", if we allow the mean to be a measure that can evolve with time, then as more and more and exemplars are introduced, the prototype is modified to reflect the "current" mean or average of all the exemplars sensed so far. From this perspective, the time-varying mean will converge towards the exemplar that is most frequently sensed. Thus, the two models can perhaps be subsumed under one model?