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The model is trained on simple artificial languages. We find that the qualitative performance profile of the model matches human behavior, both on the relative difficulty of center\u2010embedding and cross\u2010dependency, and between the processing of these complex recursive structures and right\u2010branching recursive constructions. We analyze how these differences in performance are reflected in the internal representations of the model by performing discriminant analyses on these representations both before and after training. Furthermore, we show how a network trained to process recursive structures can also generate such structures in a probabilistic fashion. This work suggests a novel explanation of people's limited recursive performance, without assuming the existence of a mentally represented competence grammar allowing unbounded recursion.<\/jats:p>","DOI":"10.1207\/s15516709cog2302_2","type":"journal-article","created":{"date-parts":[[2007,11,28]],"date-time":"2007-11-28T04:09:39Z","timestamp":1196222979000},"page":"157-205","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":203,"title":["Toward a Connectionist Model of Recursion in Human Linguistic Performance"],"prefix":"10.1111","volume":"23","author":[{"given":"Morten H","family":"Christiansen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nick","family":"Chater","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[1999,4]]},"reference":[{"key":"e_1_2_1_2_1","first-page":"370","volume-title":"Proceedings of the Eighteenth Annual Cognitive Science Society Conference","author":"Allen J.","year":"1996"},{"key":"e_1_2_1_3_1","volume-title":"The architecture of cognition","author":"Anderson J. 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