and in which limitations on the processing of recursive constructions stem from interactions between linguistic experience and intrinsic constraints on learning and processing.IntroductionEver since Humboldt (1836/1999, researchers have hypothesized that language makes infinite use of finite means. Yet the study of language had to wait nearly a century before the technical devices for adequately expressing the unboundedness of language became available through the development of recursion theory in the foundations of mathematics (cf. Chomsky, 1965). Recursion has subsequently become a fundamental property of grammar, permitting a finite set of rules and principles to process and produce an infinite number of expressions.(...)This article presents an alternative, usage-based view of recursive sentence structure, suggesting that recursion is not an innate property of grammar or an a priori computational property of the neural systems subserving language. Instead, we suggest that the ability to process recursive structure is acquired gradually, in an item-based fashion given experience with specific recursive constructions. In contrast to generative approaches, constraints on recursive regularities do not follow from extrinsic limitations on memory or processing; rather they arise from interactions between linguistic experience and architectural constraints on learning and processing (see also Engelmann & Vasishth, 2009; MacDonald & Christiansen, 2002), intrinsic to the system in which theknowledge of grammatical regularities is embedded. Constraints specific to particular recursive constructions are acquired as part of the knowledge of the recursive regularities themselves and therefore form an integrated part of the representation of those regularities.A Connectionist Model of Recursive Sentence ProcessingOur usage-based approach to recursion builds on a previously developed Simple Recurrent Network (SRN; Elman, 1990) model of recursive sentence processing (Christiansen, 1994; Christiansen & Chater, 1994). The SRN, as illustrated in Figure 1, is essentially a standard feed-forward network equipped with an extra layer of so-called context units. The hidden unit activations from the previous time step are copied back to these context units and paired with the current input. This means that the current state of the hidden units can influence the processing of subsequent inputs, providing the SRN with an ability to deal with integrated sequences of input presented successively.Usage-Based ConstituentsA key question for connectionist models of language is whether they are able to acquire knowledge of grammatical regularities going beyond simple co-occurrence statistics from the training corpus. Indeed, Hadley (1994) suggested that connectionist models could not afford the kind of generalization abilities necessary to account for human language processing (see Marcus, 1998, for a similar critique). Christiansen and Chater (1994) addressed this challenge using the SRN from Christiansen (1994).Deriving Novel PredictionsSimple Recurrent Networks have been employed successfully to model many aspects of psycholinguistic behavior, ranging from speech segmentation (e.g., Christiansen, Allen, & Seidenberg, 1998; Elman, 1990) and word learning (e.g., Sibley, Kello, Plaut, & Elman, 2008) to syntactic processing (e.g., Christiansen, Dale, & Reali, in press; Elman 1993; Rohde, 2002; see also Ellis & Larsen-Freeman, this issue) and reading (e.g., Plaut, 1999). Moreover, SRNs have also been shown to provide good models of nonlinguistic sequence learning (e.g., Botvinick & Plaut, 2004, 2006; Servan-Schreiber, Cleeremans, & McClelland, 1991). The human-like performance of