Specifying a relationship between semantic and episodic memory in the computation of a feature-based familiarity signal using MINERVA 2.

Approaches to modeling episodic recognition memory often imply a separability from semantic memory insofar as an implicit tabula rasa (i.e., blank slate) assumption is apparent in many simulations. This is evident in the common practice of having new test probes correspond to zero memory traces in the store while old test probes correspond to traces representing instances of items' occurrence on a study list. However, in list-learning studies involving word lists, none of the test items would actually correspond to zero items in the person's memory, as all of the test words are generally known to participants, whether old or new. By focusing on a list-learning recognition phenomenon that likely results from feature-based familiarity detection and necessarily involves a role of preexisting knowledge in its mechanisms-the semantic-feature-based recognition without cued recall phenomenon-we show how incorporating preexisting knowledge into the MINERVA 2 model enables it to simulate previously shown empirical patterns with this phenomenon. The simulation patterns reported here raise new theoretical implications worth further exploration, such as the extent to which the variances change in the signal versus the noise distribution when preexisting knowledge is present versus absent in the simulations.