Dynamic Shifts of Limited Working Memory Resources in Human Vision

By Paul M. Bays, Masud Husain. Science, 2008

571 citations recorded by the source corpus.

Our ability to remember what we have seen is very limited. Most current views characterize this limit as a fixed number of items—only four objects—that can be held in visual working memory. We show that visual memory capacity is not fixed by the number of objects, but rather is a limited resource that is shared out dynamically between all items in the visual scene. This resource can be shifted flexibly between objects, with allocation biased by selective attention and toward targets of upcoming eye movements. The proportion of resources allocated to each item determines the precision with which it is remembered, a relation that we show is governed by a simple power law, allowing quantitative estimates of resource distribution in a scene.

Subfields: Cognitive Neuroscience, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/7200ff3d863fcb7e92faef37f7cd133673069ecb

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Citations in the corpus

An Interference Model of Visual Working Memory (2017, methodology): “By introducing as a free parameter, Bays and Husain (2008) made their Resource model more flexible than the sample-size model.”
Neural Evidence for a Distinction Between Short-Term Memory and the Focus of Attention (2011, unknown): “…for some time, the importance of internally directed attention for selecting subsets of information within STM (Chun, Golomb, & Turk-Browne, 2011; Bays & Husain, 2008; Cowan, 1988) has only recently been recognized by neuroscience researchers (Chun, 2011; Cowan, 2011; Gazzaley, 2011; Ikkai &…”
Consciousness and cognitive access (2008, unknown): “However, a duelling article in Science (Bays and Husain 2008) purports to show the opposite, explaining away the Zhang and Luck result.”
How much of reinforcement learning is working memory, not reinforcement learning? A behavioral, computational, and neurogenetic analysis (2012, methodology): “Other model parameters implemented more continuous aspects of WM limitations, through precision or temporal stability (Bays & Husain, 2008), but the COMT effects were selective to the discrete capacity.”
Hierarchical Encoding in Visual Working Memory (2010, background): “Current theories model visual working memory as a flexible resource that is quantized into slots (Zhang & Luck, 2008) or continuously divisible (Alvarez & Cavanagh, 2004; Bays & Husain, 2008; Wilken & Ma, 2004).”
The precision of visual working memory is set by allocation of a shared resource (2009, methodology): “The power function is also consistent with previous results for memory of visual locations and orientations (Bays & Husain, 2008).”
Discrete Capacity Limits in Visual Working Memory (2010, background): “By contrast, flexible resource models of capacity suggest that mnemonic resources can be allocated in a continuous fashion, without set limits on the number of items that can be represented [12 ,13].”
Non-Target Suppression Supports the Formation of Representational Prioritization Under High Working Memory Load (2025, unknown): “Enhancing the representation of the target via a retro-cue comes at the cost of reducing resources allocated to non-targets [33,54], leading to the suppression or removal of non-targets to ensure the stability of target representation [55,56].”
Prioritizing working memory resources depends on prefrontal cortex (2024, unknown): “This predicts improved memory for high-priority targets at the cost of worse memory for low-priority ones, a pattern consistent with the behavior of human subjects 14,19,20 .”
The neurovascular coupling in the attention during visual working memory (2023, background): “A limited number 4 of available vWM “slots” [3, 4] or limited resource allocation [5, 6] requires attention 5 processes that govern access to the mnemonic processes [7, 8].”