There is broad agreement that working memory is closely related to attention. This article delineates several theoretical options for conceptualizing this link, and evaluates their viability in light of their theoretical implications and the empirical support they received. A first divide exists between the concept of attention as a limited resource, and the concept of attention as selective information processing. Theories conceptualizing attention as a resource assume that this resource is responsible for the limited capacity of working memory. Three versions of this idea have been proposed: Attention as a resource for storage and processing, a shared resource for perceptual attention and memory maintenance, and a resource for the control of attention. The first of these three is empirically well supported, but the other two are not. By contrast, when attention is understood as a selection mechanism, it is usually not invoked to explain the capacity limit of working memory - rather, researchers ask how different forms of attention interact with working memory, in two areas. The first pertains to attentional selection of the contents of working memory, controlled by mechanisms of filtering out irrelevant stimuli, and removing no-longer relevant representations from working memory. Within working memory contents, a single item is often selected into the focus of attention for processing. The second area pertains to the role of working memory in cognitive control. Working memory contributes to controlling perceptual attention - by holding templates for targets of perceptual selection - and controlling action - by holding task sets to implement our current goals.

/pdf/working-memory-and-attention-a-conceptual-analysis-and-23i47ifgtd.pdf

Working Memory and Attention - A Conceptual Analysis and Review.

Chunking is the recoding of smaller units of information into larger, familiar units. Chunking is often assumed to help bypassing the limited capacity of working memory (WM). We investigate how chunks are used in WM tasks, addressing three questions: (a) Does chunking reduce the load on WM? Across four experiments chunking benefits were found not only for recall of the chunked but also of other not-chunked information concurrently held in WM, supporting the assumption that chunking reduces load. (b) Is the chunking benefit independent of chunk size? The chunking benefit was independent of chunk size only if the chunks were composed of unique elements, so that each chunk could be replaced by its first element (Experiment 1), but not when several chunks consisted of overlapping sets of elements, disabling this replacement strategy (Experiments 2 and 3). The chunk-size effect is not due to differences in rehearsal duration as it persisted when participants were required to perform articulatory suppression (Experiment 3). Hence, WM capacity is not limited to a fixed number of chunks regardless of their size. (c) Does the chunking benefit depend on the serial position of the chunk? Chunks in early list positions improved recall of other, not-chunked material, but chunks at the end of the list did not. We conclude that a chunk reduces the load on WM via retrieval of a compact chunk representation from long-term memory that replaces the representations of individual elements of the chunk. This frees up capacity for subsequently encoded material. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

/pdf/how-does-chunking-help-working-memory-1sfdvt38gj.pdf

How does chunking help working memory

https://pure.knaw.nl/ws/files/37261481/Olivers2020.pdf

Attention for action in visual working memory

https://www.zora.uzh.ch/id/eprint/173131/1/Oberauer.2019.Rehearsal.TiCS.Manuscript.pdf

Is Rehearsal an Effective Maintenance Strategy for Working Memory

First Course in Probability and Statistics

Attention helps manage the information held in visual working memory (vWM). Perceptual attention selects the stimuli to be represented in vWM, whereas internal attention prioritizes information already in vWM. In the present study we assessed the spatial precision of perceptual and internal attention in vWM. Participants encoded eight colored dots for a local-recognition test. To manipulate attention, a cue indicated the item most likely to be tested (~65% validity). The cue appeared either before the onset of the memory array (precue) or during the retention interval (retrocue). The precue guides perceptual attention to gate encoding into vWM, whereas the retrocue guides internal attention to prioritize the cued item within vWM. If attentional selection is spatially imprecise, attention should be preferentially allocated to the cued location, with a gradual drop-off of attention over space to nearby uncued locations. In this case, memory for uncued locations should vary as a function of their distance from the cued location. As compared to a no-cue condition, memory was better for validly cued items but worse for uncued items. The spatial distance between the uncued and cued locations modulated the cuing costs: Items close in space to the cued location were insulated from cuing costs. The extension of this spatial proximity effect was larger for precues than for retrocues, mostly because the benefits of attention were larger for precues. These results point to similar selection principles between perceptual and internal attention and to a critical role of spatial distance in the selection of visual representations.

/pdf/the-precision-of-spatial-selection-into-the-focus-of-3dnvp85uvx.pdf

The precision of spatial selection into the focus of attention in working memory.

The role of domain-specific interference in the complex-span paradigm is still controversial. Here we distinguish two operations within the processing task of this paradigm-a cognitive operation to solve the task and a motor operation to give the answer. Their domain-specific interference with maintenance was investigated within the complex-span paradigm. Presentation of the memoranda-six words or four spatially distributed dots-was interleaved with the presentation of letter pairs to be processed. Cognitive operation domain was manipulated by asking participants to make either a rhyme judgment or a symmetry judgment for each letter pair. Motor operation domain was varied by requiring a response by either speaking or pointing. Cognitive load was held constant. Cognitive and motor operation showed separate domain-specific effects on memory: Visuospatial memory was impaired more by visuospatial than verbal cognitive operations and more by manual-spatial than oral motor operations. In contrast, verbal memory performance was worse when verbal cognitive operations and oral motor operations were executed. The results are discussed in relation to the multicomponent model, the time-based resource-sharing (TBRS) model, and the Serial Order in a Box-Complex-Span (SOB-CS) model. They imply that models of working memory must incorporate mechanisms that allow for domain-specific interference in the verbal and the visuospatial domain.

Domain-specific interference between storage and processing in complex span is driven by cognitive and motor operations:

Using mixed-effects models and Bayesian statistics has been advocated by statisticians in recent years. Mixed-effects models allow researchers to adequately account for the structure in the data. Bayesian statistics – in contrast to frequentist statistics – can state the evidence in favor of or against an effect of interest. For frequentist statistical methods, it is known that mixed models can lead to serious over-estimation of evidence in favor of an effect (i.e., inflated Type-I error rate) when models fail to include individual differences in the effect sizes of predictors ("random slopes") that are actually present in the data. Here, we show through simulation that the same problem exists for Bayesian mixed models. Yet, at present there is no easy-to-use application that allows for the estimation of Bayes Factors for mixed models with random slopes on continuous predictors. Here, we close this gap by introducing a new R package called BayesRS. We tested its functionality in four simulation studies. They show that BayesRS offers a reliable and valid tool to compute Bayes Factors. BayesRS also allows users to account for correlations between random effects. In a fifth simulation study we show, however, that doing so leads to slight underestimation of the evidence in favor of an actually present effect. We only recommend modeling correlations between random effects when they are of primary interest and when sample size is large enough. BayesRS is available under https://CRAN.R-project.org/package=BayesRS. BAYES FACTORS FOR LINEAR MIXED-EFFECTS MODELS 3

Estimating Bayes Factors for Linear Models with Random Slopes on Continuous Predictors

Bayes Factors for Hierarchical Linear Models with Continuous Predictors [R package BayesRS version 0.1.3]

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