Stochastic sampling provides a unifying account of visual working memory limits

Schneegans, Sebastian; Taylor, Robert; Bays, Paul M.

doi:10.1073/pnas.2004306117

Cited by 76 publications

(94 citation statements)

References 67 publications

(90 reference statements)

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“…These tasks can be considered implicit in the sense that there is a nontrivial mapping from uncertainty to performance-maximizing behavior in a post-perceptual decision, but explicit in the sense that this decision is related to a conscious feeling of trust in a memory. Conversely, a whole-report experiment by Adam and others (2017) analyzed by Schneegans and others (2020) clearly demonstrates an implicit use of uncertainty by showing participants reported remembered items in decreasing order of memory precision. However, unlike in our study, this use was not behaviorally beneficial; Adam and colleagues found a nonsignificant performance difference when allowing participants to freely report versus being probed on which items to report their memory of.…”

Section: Discussionmentioning

confidence: 96%

Uncertainty is Maintained and Used in Working Memory

Yoo

Acerbi

2020

Preprint

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1AbstractWhat are the contents of working memory? In both behavioral and neural computational models, the working memory representation of a stimulus is typically described by a single number, namely a point estimate of that stimulus. Here, we asked if people also maintain the uncertainty associated with a memory, and if people use this uncertainty in subsequent decisions. We collected data in a two-condition orientation change detection task; while both conditions measured whether people used memory uncertainty, only one required maintaining it. For each condition, we compared an optimal Bayesian observer model, in which the observer uses an accurate representation of uncertainty in their decision, to one in which the observer does not. We find that this “Use Uncertainty” model fits better for all participants in both conditions. In the first condition, this result suggests that people use uncertainty optimally in a working memory task when that uncertainty information is available at the time of decision, confirming earlier results. Critically, the results of the second condition suggest that this uncertainty information was maintained in working memory. We test model variants and find that our conclusions do not depend on our assumptions about the observer’s encoding process, inference process, or decision rule. Our results provide evidence that people have uncertainty that reflects their memory precision at an item-specific level, maintain this information over a working memory delay, and use it implicitly in a way consistent with an optimal observer. These results challenge existing computational models of working memory to update their frameworks to represent uncertainty.

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Section: Discussionmentioning

confidence: 96%

Uncertainty is Maintained and Used in Working Memory

Yoo

Acerbi

2020

Preprint

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“…This suggests that the prioritized item was represented with enhanced fidelity in the pre-saccadic store. Both the decline in fidelity with set size ( Bays & Husain, 2008 ; Schneegans et al, 2020 ; van den Berg et al, 2012 ; Zhang & Luck, 2008 ) and the flexibility in allocation ( Gorgoraptis et al, 2011 ; Oberauer & Lin, 2017 ; Schmidt et al, 2002 ; Yoo et al, 2018 ) are characteristic qualities of visual working memory, often theorized to be the storage medium underlying transsaccadic integration (see Aagten-Murphy & Bays, 2019 for a review).…”

Section: Discussionmentioning

confidence: 99%

“…One of the defining features of visual working memory is that the information it can hold is very limited ( Alvarez & Cavanagh, 2004 ; Cowan, 1998 ; Luck & Vogel, 1997 ). In analogue report tasks, this limit manifests as a decline in recall fidelity as the number of items in memory increases ( Ma, Husain, & Bays, 2014 ; Schneegans, Taylor, & Bays, 2020 ; van den Berg, Shin, Chou, George, & Ma, 2012 ; Zhang & Luck, 2008 ). Additionally, working memory allocation is flexible, so resources can be preferentially directed to particular items based on behavioral priority ( Bays, 2014 ; Bays & Husain, 2008 ; Oberauer & Lin, 2017 ; Schmidt, Vogel, Woodman, & Luck, 2002 ; Yoo, Klyszejko, Curtis, & Ma, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

Transsaccadic integration relies on a limited memory resource

et al. 2021

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Saccadic eye movements cause large-scale transformations of the image falling on the retina. Rather than starting visual processing anew after each saccade, the visual system combines post-saccadic information with visual input from before the saccade. Crucially, the relative contribution of each source of information is weighted according to its precision, consistent with principles of optimal integration. We reasoned that, if pre-saccadic input is maintained in a resource-limited store, such as visual working memory, its precision will depend on the number of items stored, as well as their attentional priority. Observers estimated the color of stimuli that changed imperceptibly during a saccade, and we examined where reports fell on the continuum between pre- and post-saccadic values. Bias toward the post-saccadic color increased with the set size of the pre-saccadic display, consistent with an increased weighting of the post-saccadic input as precision of the pre-saccadic representation declined. In a second experiment, we investigated if transsaccadic memory resources are preferentially allocated to attentionally prioritized items. An arrow cue indicated one pre-saccadic item as more likely to be chosen for report. As predicted, valid cues increased response precision and biased responses toward the pre-saccadic color. We conclude that transsaccadic integration relies on a limited memory resource that is flexibly distributed between pre-saccadic stimuli.

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“…One explanation for this is that time during retrieval is costly, and that rewards motivate people to pay that cost to maximise rewards. There are many forms this cost could take, for example in a recent WM model retrieval is proposed to occur via sampling spikes from neurons storing the items, and greater accuracy can be achieved at the cost of longer sampling times and more energy spent on generating spikes (Schneegans, Taylor, & Bays, 2020). Alternatively, time spent on one process is time not spent on another process, leading to opportunity costs for every cognitive function, which are balanced depending on the values associated with their outcomes (Kurzban, Duckworth, Kable, & Myers, 2013;Shenhav et al, 2017).…”

Section: Discussionmentioning

confidence: 99%

Motivation Improves Working Memory by Two Processes: Prioritisation and Retrieval Thresholds

Grogan¹,

Randhawa²,

Kim³

et al. 2021

Preprint

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Motivation can improve performance when the potential rewards outweigh the cost of effort expended. In working memory (WM), people can prioritise rewarded items at the expense of unrewarded items, suggesting a fixed memory capacity. But can capacity itself increase with motivation? Across four experiments (N = 30-34) we demonstrate motivational improvements in WM even when all items were rewarded. However, this was not due to better memory precision, but rather better selection of the probed item within memory. Motivational improvements operated independently of encoding, maintenance, or attention shifts between items in memory. Moreover, motivation slowed responses. This contrasted with the benefits of rewarding items unequally, which allowed prioritisation of one item over another. We conclude that motivation can improve memory recall, not via precision or capacity, but via speed-accuracy trade-offs when selecting the item to retrieve.

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Stochastic sampling provides a unifying account of visual working memory limits

Cited by 76 publications

References 67 publications

Uncertainty is Maintained and Used in Working Memory

Uncertainty is Maintained and Used in Working Memory

Transsaccadic integration relies on a limited memory resource

Motivation Improves Working Memory by Two Processes: Prioritisation and Retrieval Thresholds

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