Modeling False Recognition in the Deese-Roediger-McDermott Paradigm: An Approach Using Holographic Declarative Memory

Memory tasks such as the Deese-Roediger-McDermott (DRM) paradigm demonstrate how semantic associations can induce false recognition(Roediger & McDermott, 1995). Although cognitive architectures such as ACT-R have modeled various memory processes (Stewart & West, 2007; Kelly, Arora, West, & Reitter, 2020), they often struggle with large-scale semantic representations and thus lack the capacity to reliably predict false recognition. In contrast, hyperdimensional computing approaches effectively capture semantic similarity but lack mechanisms for control processes (Reid & Jamieson, 2023; Dodhia & Metcalfe, 1999). We propose a computational model that integrates distributional semantic models with ACT-R through holographic declarative memory (HDM, Kelly et al., 2020) to simulate the DRM paradigm. Our model uses embeddings generated by the BEAGLE algorithm (Jones & Mewhort, 2007) to represent pre-experimental semantic knowledge, and incorporates control processes, including rehearsal and decision strategies, to capture recall and recognition effects (Atkinson & Shiffrin, 1968; Lehman & Malmberg, 2013). We demonstrate that the model reproduces primacy and recency effects, and we expect high false recognition rates for semantically related lures and intra-list, interlist, and extra-list phenomena during recall. This work bridges cognitive architectures like ACT-R, which model human decision-making and skilled behaviour, and distributional semantics, enhancing computational models of false memory.