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By testing human participants and comparing their performances in different learning conditions, we find that those embodied intentions facilitate both word discovery and word\u2010meaning association. In light of empirical findings, the main part of this article presents a computational model that can identify the sound patterns of individual words from continuous speech, using nonlinguistic contextual information, and employ body movements as deictic references to discover word\u2010meaning associations. To our knowledge, this work is the first model of word learning that not only learns lexical items from raw multisensory signals to closely resemble infant language development from natural environments, but also explores the computational role of social cognitive skills in lexical acquisition.<\/jats:p>","DOI":"10.1207\/s15516709cog0000_40","type":"journal-article","created":{"date-parts":[[2005,12,1]],"date-time":"2005-12-01T15:08:54Z","timestamp":1133449734000},"page":"961-1005","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":93,"title":["The Role of Embodied Intention in Early Lexical Acquisition"],"prefix":"10.1111","volume":"29","author":[{"given":"Chen","family":"Yu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dana H.","family":"Ballard","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Richard N.","family":"Aslin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2005,11]]},"reference":[{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/34.295913"},{"key":"e_1_2_1_3_1","volume-title":"Proceedings of the ACM Sigmod.","author":"Aggarwal C. 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