True and false recognition in MINERVA2: Integrating fuzzy-trace theory and computational memory modeling

Previous research suggests that the MINERVA2 model can capture basic Deese/Roediger/McDermott (DRM) false recognition findings with either randomized representations or distributional semantic representations. In the current article, we extended this line of research by showing that MINERVA2 can accommodate not only basic DRM recognition findings but also the effects of various theory-driven manipulations. Importantly, we incorporated two assumptions of fuzzy-trace theory into MINERVA2: the verbatim-gist distinction and hierarchies of gist. To implement the verbatim-gist distinction, we represented local gist traces with distributional semantic vectors and verbatim traces with holographic word-form vectors. With separate representations incorporated, MINERVA2 successfully simulated a wide range of empirical effects in the DRM illusion, as well as remember/know and source judgments. To incorporate hierarchies of gist into the framework, we added an assumption that an item's storage quality depends on its semantic similarity to the preceding item. This accommodated the effect of global gist beyond that of local gist and solved the problem of storage independence in multitrace models of episodic memory. Our findings provided extensive evidence that MINERVA2 is a viable candidate for scalable modeling of the DRM illusion and strengthened the connection between computational modeling and substantive theories of false memory. (PsycInfo Database Record (c) 2025 APA, all rights reserved).