Arndt and Hirshman (1998) used MINERVA 2 to simulate true and false recognition in DRM-style lists and found that the model was able to capture many features of the empirical data. Here, we first replicate their simulations, but using empirically structured vectors derived from Latent Semantic Analysis rather than the randomly generated vectors characteristic of MINERVA 2. We report that the model still captures the DRM effect with fewer free parameters. We then extend our analyses to true and false recognition for full sentences and metaphorical expressions. Using a simple bag-of-words representation for sentences, we find that the MINERVA 2 model captures classic sentence false recognition findings from Bransford and Frank (1971) and a more recent finding from Reid and Katz (2018a) that demonstrates false recognition of unstudied sentences that share a metaphorical but not literal theme to studied sentences. These simulations provide evidence that an instance-based memory model, when amalgamated with structured semantic representations from a distributional semantic model, can account for true and false recognition across different types of language experiences.