A computational model of item-based directed forgetting.

Abstract

Montagliani and Hockley (2019) presented evidence that item-method directed forgetting not only leads to worse recognition of forget-cued targets than remember-cued targets but also better rejection of foils associated with forget-cued targets than remember-cued targets. Based on that result, they proposed that participants elaboratively encode more category-level information about R-cued targets. We present a retrieval-based explanation of the result within an instance-based memory model. The model imports word representations from two distributional semantic models, latent semantic analysis (LSA) and random permutation model (RPM), into an instance-based model of memory, MINERVA 2. The model reproduced Montagliani and Hockley’s results without requiring assumptions about elaborated encoding of category-level information at study. The simulations demonstrate that whereas Montagliani and Hockley’s findings are consistent with an account grounded in elaborated encoding of words at study, the results do not force that conclusion. Instead, better encoding of remember-cued targets at study establishes the conditions for retrieval-time effects at test to produce a corresponding influence on false recognition for category-related foils. Our model can be used as a formal tool to think about and study the incidental consequences of item directed forgetting in recognition memory.

Scalable cognitive modelling: Putting Simon’s (1969) ant back on the beach.

“…This model, entitled the recognition through semantic synchronization model, can account for a variety of false memory effects, including item-level variability in the amount of false recognition that different DRM lists elicit (Gallo & Roediger, 2002; Roediger & McDermott, 1995), effects of associative and thematic strength (Cann et al, 2011), and developmental reversals in false recognition (Brainerd et al, 2002), among others. Additionally, Johns et al (2021) recently expanded the model to also account for recollection-based data (see also Reid & Jamieson, 2022, for an application of the method to false recognition of unstudied but related lures).…”

Two dichotomies of recognition memory.

“…For example, Johns et al (2012) showed that their RSS model could successfully simulate both the standard effects of recognition memory as well as false memory for associated lures in the DRM procedure and the increase in the rate of false recognition of category exemplars as the number of studied exemplars from a category is increased. Reid and Jamieson (2022) were able to simulate the effects of category membership in item-based directed forgetting reported by Montagliani and Hockley (2019) with their semantic-based MINERVA-S model. It is reasonable to expect that more models of recognition memory will be based on real-world semantic representations and they likely will represent the next generation of models that combine episodic and semantic memory.…”