Do familiar memory items decay?

Ricker, Timothy J.; Sandry, Joshua; Vergauwe, Evie; Cowan, Nelson

doi:10.1037/xlm0000719

Cited by 22 publications

(17 citation statements)

References 84 publications

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“…These measures rest on the same problematic assumptions as the K measure, another threshold measure. (4) Some researchers opt to simply combine hits and correct rejections (the complement of false alarms) by calculating overall "percent correct" (Cappell, Gmeindl, & Reuter-Lorenz, 2010;Gao & Theeuwes, 2020;Harthsorne & Makovski, 2019;Luck & Vogel, 1997;Luria & Vogel, 2011;Maxcey-Richard & Hollingworth, 2013;Parra, Della Sala, Logie, & Morcom, 2014;Pessoa, Gutierrez, Bandettini, & Ungerleider, 2002;Postle, Druzgal, & D'Esposito, 2003;Potter, Staub, Raud, & O'Connor, 2002;Ricker, Sandry, Vergauwe, & Cowan, 2020;Shoval, Luria, & Makovski, 2020;Sloutsky & Fisher, 2004;Tas, Luck, & Hollingworth 2016;Wagner, Kashyap, Diekelmann, & Born, 2007;Yan, Young, & Andrews, 2017). Intuitively, this measure feels objective, as if it is not dependent on any theoretical assumptions.…”

Section: For Examplementioning

confidence: 99%

Measuring memory is harder than you think: How to avoid problematic measurement practices in memory research

Tf¹,

Mm²,

Williams³

et al. 2021

Preprint

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There is a crisis of measurement in memory research, with major implications for theory and practice. This crisis arises because of a critical complication present when measuring memory using the recognition memory task that dominates the study of working memory and long-term memory (“did you see this item? yes/no” or “did this item change? yes/no”). Such tasks give two measures of performance, the “hit rate” (how often you say you previously saw an item you actually did previously see) and the “false alarm rate” (how often you say you saw something you never saw). Yet what researchers want is one single, integrated measure of memory performance. Integrating the hit and false alarm rate into a single measure, however, requires a complex problem of counterfactual reasoning that depends on the (unknowable) distribution of underlying memory signals: when faced with two people differing in both hit rate and false alarm rate, the question of who had the better memory is really “who would have had more hits if they each had the same number of false alarms”. As a result of this difficulty, different literatures in memory research (e.g., visual working memory, eyewitness identification, picture memory, etc) have settled on a variety of distinct metrics to combine hit rates and false alarm rates (e.g., A’, corrected hit rate, percent correct, d’, diagnosticity ratios, K values, etc.). These metrics make different, contradictory assumptions about the distribution of latent memory signals, and all of their assumptions are frequently incorrect. Despite a large literature on how to properly measure memory performance, spanning decades, real-life decisions are often made using these metrics, even when they subsequently turn out to be wrong when memory is studied with better measures. We suggest that in order for the psychology and neuroscience of memory to become a cumulative, theory-driven science, more attention must be given to measurement issues. We make a concrete suggestion: the default memory task should change from old/new (“did you see this item’?”) to forced-choice (“which of these two items did you see?”). In situations where old/new variants are preferred (e.g., eyewitness identification; theoretical investigations of the nature of memory decisions), receiver operating characteristic (ROC) analysis should always be performed.

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Section: For Examplementioning

confidence: 99%

Measuring memory is harder than you think: How to avoid problematic measurement practices in memory research

Tf¹,

Mm²,

Williams³

et al. 2021

Preprint

View full text Add to dashboard Cite

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“…Visual working memory (VWM) is a fundamental human capacity that enables the encoding, storing, and retrieval of information in various cognitive tasks—such as reading, writing, language learning, and measures of fluid intelligence ( Engle, 2002 ; Engle, 2018 ; Jaeggi, Buschkuehl, Jonides, & Perring, 2008 ; Luck & Vogel, 2013 ). Although the very limited capacity of VWM has been thoroughly studied ( Cowan, 2001 ; Luck & Vogel, 1997 ), there is less research on forgetting in VWM (see, however, Cohen-Dallal, Fradkin, & Pertzov, 2018 ; Honig, Ma, & Fougnie, 2020 ; Mercer & Barker, 2020 ; Pertzov, Manohar, & Husain, 2017 ; Ricker, Sandry, Vergauwe, & Cowan, 2020 ; Schneegans & Bays, 2018 ; Zhang & Luck, 2009 ). Although most studies on forgetting have demonstrated temporal decay in VWM, some have not ( Blake, Cepeda, & Hiris, 1997 ; Magnussen, Greenlee, Asplund, & Dyrnes, 1990 ; Magnussen, Greenlee, & Thomas, 1996 ).…”

Section: Introductionmentioning

confidence: 99%

Forgetting in visual working memory: Internal noise explains decay of feature representations

2022

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The precision of visual working memory (VWM) representations decreases as time passes. It is often assumed that VWM decay is random and caused by internal noise accumulation. However, forgetting in VWM could occur systematically, such that some features deteriorate more rapidly than others. There exist only a few studies testing these two models of forgetting, with conflicting results. Here, decay of features in VWM was thoroughly tested using signal detection theory methods: psychophysical classification images, internal noise estimation, and receiver operant characteristic (ROC). A modified same–different memory task was employed with two retention times (500 and 4000 ms). Experiment 1 investigated VWM decay using a compound grating memory task, and Experiment 2 tested shape memory using radial frequency patterns. Memory performance dropped some 15% with increasing retention time in both experiments. Interestingly, classification images showed virtually indistinguishable weighting of stimulus features at both retention times, suggesting that VWM decay is not feature specific. Instead, we found a 77% increase in stimulus-independent internal noise at the longer retention time. Finally, the slope of the ROC curve plotted as z -scores was shallower at the longer retention time, indicating that the amount of stimulus-independent internal noise increased. Together these findings provide strong support for the idea that VWM decay does not result from a systematic loss of some stimulus features but instead is caused by uniformly increasing random internal noise.

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“…In the context of short-term or working memory, the passage of time is usually thought of as an opportunity to forget (Donkin et al, 2015;Lewandowsky & Oberauer, 2009;Mercer & McKeown, 2014;Ricker et al, 2016Ricker et al, , 2020. A less well studied role of time is that, under some circumstances, it helps maintaining information in working memory.…”

Section: Introductionmentioning

confidence: 99%

What is time good for in working memory?

Mızrak¹,

Oberauer²

2021

Preprint

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Increasing time to process information in working memory (WM) improves performance. Free time given after an item is often assumed to enable maintenance processes to counteract forgetting of this item, suggesting that time has a retroactive benefit. Two other hypotheses – short-term consolidation, and temporal distinctiveness – entail a local effect of time on immediately preceding and following items. Here, we show instead a novel, global and proactive, benefit of time in WM. In three serial-recall experiments (21,25,26 young-adults), we varied the position and duration of the free time within a seven-item list of consonants. Experiment 1 showed that the effect is global and not local. Experiments 2a and 2b showed increased inter-item time only benefited the subsequent items, implying a proactive benefit. This finding rules out maintenance processes, short-term consolidation, and temporal distinctiveness as explanations of the free-time benefit but is consistent with the proposal of a gradually recovering encoding resource.

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Do familiar memory items decay?

Cited by 22 publications

References 84 publications

Measuring memory is harder than you think: How to avoid problematic measurement practices in memory research

Measuring memory is harder than you think: How to avoid problematic measurement practices in memory research

Forgetting in visual working memory: Internal noise explains decay of feature representations

What is time good for in working memory?

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