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However, the tasks and visualizations used in prior work typically involve many additional perceptual and cognitive processes, making it difficult to isolate the contribution of VWM decline to age differences in visualization performance. Here, we used a glyph\u2010based discrimination paradigm in which set size (number of glyphs) was manipulated to vary VWM demand systematically. Young adults (YA), mid\u2010age adults (MA), and PLA completed a two\u2010alternative forced\u2010choice task in which they judged which of two glyph\u2010based maps presented a higher overall risk level encoded by the glyphs. For each participant, we fitted psychometric functions to obtain accuracy and precision, and analyzed the response time. The results show that set size impaired performance similarly across age groups, and the marked accuracy drop at the largest set size indicated a practical upper limit on meaningful set size for this task. Contrary to expectations based on cognitive aging, PLA did not perform worse overall or show disproportionate costs at higher VWM demands. Instead, the MA group showed higher precision and was more strongly influenced by the distribution of individual glyph values across the visualization, raising the possibility that strategic or effortful processing may partly compensate for capacity limits. These findings indicate that, for this glyph\u2010based estimation task, age\u2010related VWM decline alone is unlikely to fully explain age differences in performance. Therefore, our findings offer little evidence for treating reduced VWM demand as the primary design goal for visualizations targeting PLA, in the absence of evidence that age\u2010related VWM limitations substantially constrain task performance.<\/jats:p>","DOI":"10.1111\/cgf.70451","type":"journal-article","created":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T11:09:12Z","timestamp":1781176152000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Does Visual Working Memory Decline Explain Age Differences in Visualization Performance?"],"prefix":"10.1111","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0962-0922","authenticated-orcid":false,"given":"Y.","family":"Li","sequence":"first","affiliation":[{"name":"Department of Informatics King's College London  UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-6042-4927","authenticated-orcid":false,"given":"S.","family":"Shao","sequence":"additional","affiliation":[{"name":"Department of Informatics King's College London  UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6146-7147","authenticated-orcid":false,"given":"P.","family":"Baudains","sequence":"additional","affiliation":[{"name":"Department of Informatics King's College London  UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1919-7726","authenticated-orcid":false,"given":"A. 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