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- W3037836314 abstract "Visual working memory is a capacity-limited cognitive system used to actively store and manipulate visual information. Visual working memory capacity is not fixed, but varies by stimulus type: Stimuli that are more meaningful are better remembered. In the current work, we investigate what conditions lead to the strongest benefits for meaningful stimuli. We propose that in some situations participants may try to encode the entire display holistically (i.e., in a quick snapshot). This may lead them to treat objects as simply meaningless, colored blobs, rather than individually and in a high-level way, which could reduce benefits of meaningful stimuli. In a series of experiments, we directly test whether real-world objects, colors, perceptually matched less-meaningful objects, and fully scrambled objects benefit from deeper processing. We systematically vary the presentation format of stimuli at encoding to be either simultaneous-encouraging a parallel, take-a-quick-snapshot strategy-or present the stimuli sequentially, promoting a serial, each-item-at-once strategy. We find large advantages for meaningful objects in all conditions, but find that real-world objects-and to a lesser degree lightly scrambled, still meaningful versions of the objects-benefit from the sequential encoding and thus deeper, focused-on-individual-items processing, while colors do not. Our results suggest single-feature objects may be an outlier in their affordance of parallel, quick processing, and that in more realistic memory situations, visual working memory likely relies upon representations resulting from in-depth processing of objects (e.g., in higher-level visual areas) rather than solely being represented in terms of their low-level features. (PsycInfo Database Record (c) 2022 APA, all rights reserved)." @default.
- W3037836314 created "2020-07-02" @default.
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- W3037836314 date "2022-07-01" @default.
- W3037836314 modified "2023-09-30" @default.
- W3037836314 title "The role of meaning in visual working memory: Real-world objects, but not simple features, benefit from deeper processing." @default.
- W3037836314 doi "https://doi.org/10.1037/xlm0001014" @default.
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