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Contrary to the common assumption that the capacity of this system is fixed with respect to a single feature dimension, recent research has shown that working memory performance for a simple visual feature\u2014color\u2014is improved when this feature is encoded as part of a real-world object relative to an unrecognizable scrambled object. Using EEG (n = 24), we here demonstrate that this performance benefit is supported by increased neural engagement during the retention period, as indexed by enlarged contralateral delay activity during maintenance. Furthermore, the pattern of neural activity across parietal-occipital electrodes was more stable across time, suggesting that real-world objects may support more robust memory representations. Finally, we report a novel fronto-central ERP that distinguishes between real-world objects and scrambled objects during encoding and maintenance processes. Overall, our results demonstrate that active visual working memory capacity for simple features is not fixed but can expand depending on what context these features are encoded in.<\/jats:p>","DOI":"10.1162\/jocn.a.2427","type":"journal-article","created":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T18:42:27Z","timestamp":1767811347000},"page":"1171-1184","update-policy":"https:\/\/doi.org\/10.1162\/mitpressjournals.corrections.policy","source":"Crossref","is-referenced-by-count":1,"title":["Real-world Objects Scaffold Visual Working Memory for Features: Increased Neural Engagement When Colors Are Remembered as Part of Meaningful Objects"],"prefix":"10.1162","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-3943-7845","authenticated-orcid":true,"given":"Yong Hoon","family":"Chung","sequence":"first","affiliation":[{"name":"Dartmouth 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