By Guy C. Van Orden
First released in 2002. Routledge is an imprint of Taylor & Francis, an informa corporation.
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Additional resources for Nonlinear Dynamics and Psycholinguistics: A Special Double Issue of ecological Psychology
To address this question, I included a control netTABLE 5 Finite-State Symbol Generating Grammars Cl C2 C3 Finite, memoryless Finite with memory Infinite-state, memoryless C4 Finite-state with memory S→abc S→abac S → a (P1/P2) p P1 → a1 (P11/P12) P2 → a2 (P21/P22) P11 → a11 (P111/P112) P12 → a12 (P121/P122) P21 → a21 (P211/P212) P22 → a22 (P221/P222) … S → (NP) v (NP) p NP → n (NP) Note. A constituent in parentheses is present in half the instances and absent in the other half. Two or more constituents with slashes (/) between them split the instances equally among them.
I concur with Moore (1998) that the Chomsky hierarchy is an imperfectly designed tool, and it will be useful to replace it with a better apparatus. I will discuss part of the case for this claim below, but the hierarchy is not altogether useless, and it serves well for getting started. Grammars Counting. Chomsky (1957) argued for the need for (at least) stack-based computation (above finite state on the hierarchy) on the basis of constructions such as (6), where Si corresponds to some declarative sentence.
For example, one of the first insightful analyses of connectionist learning of complex languages, Rodriguez (2001), succeeds by training networks and then building similar dynamical automata. If, as Tabor (2001) suggests, the stack-based computers are attractors of the dynamical learning mechanism, then they may also play a central role in understanding what causes languages to be organized into constituent structures. The value, then, of symbolic, representationalist objects, is that they provide important bearing points for exploring complex dynamical learning systems, a psychologically appealing class of models.