{"pk":32976,"title":"Strong Systematicity within Connectionism: The Tensor-Recurrent Network","subtitle":null,"abstract":"Systematicity, the ability to represent and process stnicturally related objects, is a significant and pervasive property of cognitive behaviour, and clearly evident in language. In the case of Connectionist models that leam from examples, systematicity is generalization over examples sharing a conmion structure. Although Connectionist models (e.g., the recurrent network and its variants) have demonstrated generalization over structured domains, there has not been a clear demonstration of strong systematicity (i.e., generalization across syntactic position). The tensor has been proposed as a way of representing structured objects, however, there has not been an effective learning mechanism (in the strongly systematic sense) to explain how these representations may be acquired. I address this issue through an analysis of tensor learning dynamics. These ideas are then implemented as the tensor-recurrent network which is shown to exhibit strong systematicity on a simple language task. Finally, it is suggested that the properties of the tensor-recurrent network that give rise to strong systematicity are analogous to the concepts of variables and types in the Classical paradigm.","language":"eng","license":{"name":"","short_name":"","text":null,"url":""},"keywords":[],"section":"Refereed Papers","is_remote":true,"remote_url":"https://escholarship.org/uc/item/0rp960zk","frozenauthors":[{"first_name":"Steven","middle_name":"","last_name":"Phillips","name_suffix":"","institution":"The University of Queensland","department":""}],"date_submitted":null,"date_accepted":null,"date_published":"1994-01-01T18:00:00Z","render_galley":null,"galleys":[{"label":"PDF","type":"pdf","path":"https://journalpub.escholarship.org/cognitivesciencesociety/article/32976/galley/24037/download/"}]}