A spatially continuous diffusion model of visual working memory

Fennell, Alex; Ratcliff, Roger

doi:10.1016/j.cogpsych.2023.101595

Cited by 3 publications

(2 citation statements)

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“…We first analyzed participants' recall errors in color recall tasks using the Zhang and Luck (2018) mixture model (Figure 2A). This model defines mnemonic precision as the inverse of the standard deviation (SD) of recall errors, factoring out failed memory recall potentially driven by mis-binding across random study items (Bays et al, 2009), strategic or random guessing (Fennell & Ratcliff, 2023;Huang, 2020;Xie & Zhang, 2017a;Zhang & Luck, 2008), or extremely weak memory traces (Schurgin et al, 2020;van den Berg et al, 2012) that may require millions of trials to measure (Adam et al, 2017). These errors can be approximated empirically by a uniform distribution, inversely related to the recall success probability (Pm = 1 -Probability of a uniform distribution).…”

Section: Discussionmentioning

confidence: 99%

“…In this case, a more parsimonious explanation would be favored. Clarifying these behavioral associations is crucial not only for identifying better behavioral measures to detect more subtle memory deficits in aging or clinical populations (Zokaei et al, 2015), but also for evaluating relevant cognitive models of memory quantity and quality (Fennell & Ratcliff, 2023;Koriat & Goldsmith, 1996;Xie & Zhang, 2017a;Zhang & Luck, 2008).…”

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confidence: 99%

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Discrimination of Mnemonic Similarity is Associated with Short-term and Long-term Memory Precision

Xie,

Ma,

Thakurdesai

et al. 2024

Preprint

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Remembering specific memories with precision relies on the differentiation of similar memory contents — a process commonly referred to as pattern separation and behaviorally operationalized as lure discrimination in the mnemonic similarity task. Although pattern separation is typically investigated in the context of long-term memory (LTM), recent research extends these findings to short-term memory (STM) within a mixture model framework, emphasizing the distinction between memory quality and quantity. According to this framework, pattern separation is associated with memory precision across STM and LTM, regardless of the overall memory likelihood. However, these associations among memory quality measures may persist without the mixture model assumption. In an alternative model, a unitary memory strength measure quantified as a discrimination score (d’) may also capture the association between pattern separation and memory quality, as pattern separation has been previously linked with strength-based memory performance. We tested these possibilities based on individual differences among 132 participants who underwent tasks measuring mnemonic pattern separation and STM/LTM quality. We find that behavioral estimates of pattern separation are significantly correlated with memory precision across STM and LTM, irrespective of the likelihood of STM/LTM recall success. However, these associations are absent when considering the correlation between pattern separation and memory strength under a unitary model framework. By leveraging individual differences to constrain our understanding of cognitive models, our data unravel the intricate relationship between pattern separation and memory quality across timescales. These findings may therefore contribute to identifying sensitive behavioral measures for detecting subtle memory deficits in aging/clinical populations.

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