{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T06:04:37Z","timestamp":1780553077271,"version":"3.54.1"},"reference-count":38,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T00:00:00Z","timestamp":1780531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100013139","name":"Humanities and Social Science Fund of Ministry of Education of China","doi-asserted-by":"publisher","award":["No. 24YJC630203"],"award-info":[{"award-number":["No. 24YJC630203"]}],"id":[{"id":"10.13039\/501100013139","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Psychol."],"abstract":"<jats:p>Recommendation algorithms increasingly shape the perceptual environments through which young users encounter information, evaluate options, and form preferences. Although prior studies have mainly examined algorithmic exposure as a problem of content filtering, less is known about how multimodal digital cues influence perceptual attention, intuitive judgment, and decision autonomy. This study proposes an Algorithmic Cognitive Colonization framework to explain how sustained exposure to personalized recommendation systems may progressively affect attentional selection, preference formation, and self-related decision processes. Two behavioral studies were conducted with educated Chinese undergraduate participants. Study 1 used a 14-day information diet field experiment in which participants switched from personalized recommendation feeds to chronological or social-graph-based feeds while maintaining normal platform use. The intervention was associated with increased reported content-exposure breadth, a U-shaped adjustment pattern in perceived decision autonomy, and a short-term return of content-exposure breadth toward baseline after personalized feeds were resumed. Study 2 used a dual-process decision paradigm to compare rapid consumer choices with deliberative opinion formation. Algorithmic recommendation labels produced stronger conformity in speeded consumer-choice conditions than in more deliberative opinion-formation conditions. Moreover, metacognitive awareness of algorithmic influence was not significantly associated with actual behavioral resistance, supporting a literacy paradox. These findings suggest that personalized recommendation systems may function as perceptual choice architectures that influence digital decision-making primarily by reducing decision friction and guiding intuitive responses. Rather than providing direct neural or physiological evidence, the present studies offer behavioral evidence for how algorithmically curated multimodal environments may shape attention-related choices, state-level preferences, and perceived autonomy. The results, therefore, provide a behavioral foundation for future multimodal neuroscience and perception research using eye-tracking, psychophysiological, neuroimaging, or cross-modal attention measures.<\/jats:p>","DOI":"10.3389\/fpsyg.2026.1868429","type":"journal-article","created":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T05:35:08Z","timestamp":1780551308000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Multimodal perceptual curation for cognitive autonomy in Generation Z decision making"],"prefix":"10.3389","volume":"17","author":[{"given":"Zhean","family":"Zhu","sequence":"first","affiliation":[{"name":"School of Data Science, Zhejiang University of Finance and Economics","place":["Hangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kai","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Management, Zhejiang University of Finance and Economics","place":["Hangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,6,4]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"500","DOI":"10.1177\/0743915619858057","article-title":"Algorithm overdependence: how the use of algorithmic recommendation systems can increase risks to consumer wellbeing","volume":"38","author":"Banker","year":"2019","journal-title":"J. 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