Using Relational Syntactic Constraints in Content - Addressable Memory Architectures for Sentence Parsing

How linguistic representations in memory are searched and retrieved during sentence processing is an active question in psycholinguistics. Much work has independently suggested that retrieval operations require constant-time computations, are susceptible to interference, and operate under the constraint of a severely limited focus of attention. These features suggest a model for sentence processing that uses a content-addressable memory (CAM) architecture that accesses items in parallel while relying on only a limited amount of privileged, fast storage. A challenge for a CAM architecture comes from the potentially unbounded configurational relation c-command (equivalent to logical scope) that plays a pervasive role in linguistic phenomena. CAM is well-suited to retrieval of elements based on inherent properties of memory chunks, but relational notions such as c-command involve the properties of pairs of nodes in a structure, rather than inherent properties of individual chunks. To explore this problem, in this paper we adopt an explicit CAM-based model of sentence processing in the context of the ACT-R computational architecture (Lewis & Vasishth, 2005, Cognitive Science, 29, 375-419). We discuss why c-command is a challenge for CAM and explore algorithms for exploiting or approximating c-command online, and discuss their consequences for the model. We identify computational problems that any attention-limited, CAM-based model would have to address.