Discrete fixed-resolution representations in visual working memory

By Weiwei Zhang, Steven J. Luck. Nature, 2008

1,585 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/34c6405fd95cabd6fc05c2db6ef5db2475d6b41f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

Citations in the corpus

Dynamic Shifts of Limited Working Memory Resources in Human Vision (2008, neutral): “Comparison with Zhang & Luck (2008).”
Discrete Capacity Limits in Visual Working Memory (2010, background): “Interactions between number and resolution in WM One empirical pattern that has sometimes been argued to support flexible resource accounts is the inverse relationship between resolution in WM and set size [11 ,12 ,14].”
Consciousness and cognitive access (2008, neutral): “A recent article in Nature (Zhang and Luck 2008) purported to settle the issue.”
An Interference Model of Visual Working Memory (2017, methodology): “…range (1, 2, 4, 6, 8), and in addition included cues pointing to one of the visual objects as the one most likely to be tested (Griffin & Nobre, 2003; Zhang & Luck, 2008).4 The cueing manipulations served to control the orientation of the focus of attention, which plays an important part in the IM.”
Visual working memory representation as a topological defined perceptual object. (2019, positive): “Downloaded from jov.arvojournals.org on 11/21/2021 (Luck & Vogel, 1997, 2013; Zhang & Luck, 2008).”
The precision of visual working memory is set by allocation of a shared resource (2009, methodology): “The final component consisted of a uniform distribution (Figure 3c), corresponding to the probability of responding at random, as in Zhang and Luck’s (2008) model. The results of this analysis are shown in Figures 3d–3f. As the number of items in the sample array increased from one to six, the stand”
The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) study protocol: a cross-sectional, lifespan, multidisciplinary examination of healthy cognitive ageing (2014, methodology): “As with the Stage 2 VSTM task, for each set-size, behavioural measures of VSTM quantity and quality are estimated by fitting the error distribution with a mixture model approach developed by Zhang and Luck [57].”
Neural substrates of cognitive capacity limitations (2011, methodology): “This model is supported by human psychophysical experiments indicating graded information resources within slots (30, 31).”
The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) data repository: Structural and functional MRI, MEG, and cognitive data from a cross-sectional adult lifespan sample (2017, background): “…participants were required to be willing to continue, be cognitively healthy (MMSE N 24), to meet hearing, vision, and English language ability criteria necessary for completing experimental tasks, and to be free of MRI or MEG contraindications and neurological or serious psychiatric conditions.”
How much of reinforcement learning is working memory, not reinforcement learning? A behavioral, computational, and neurogenetic analysis (2012, background): “We hypothesize that this might be due to not taking into account an increase in WM performance when set size is under capacity, thus affording possible redundant encoding of information [e.g. the slots + averaging model of Zhang & Luck (2008)].”
Hierarchical Encoding in Visual Working Memory (2010, methodology): “…independent unit and assume that items do not influence each others’ representations (Alvarez & Cavanagh, 2004; Bays, Catalao, & Husain, 2009; Luck & Vogel, 1997; Rouder et al., 2008; Wilken & Ma, 2004; Zhang & Luck, 2008; although see Lin & Luck, 2008, and Johnson, Spencer, Luck, & Schöner, 2009).”
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].”
The association between working memory precision and the nonlinear dynamics of frontal and parieto-occipital EEG activity (2022, background): “Since the κ value measured the quality of WM representation 38 , our results suggest that maintaining fine-grained perceptual information in WM requires engaging the parieto-occipital region 14 .”