Biolinguistics (E-Journal)
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What in the World Makes Recursion so Easy to Learn? A Statistical Account of the Staged Input Effect on Learning a Center-Embedded Structure in Artificial Grammar Learning (AGL)
No full textIn an artificial grammar learning study, Lai & Poletiek (2011) found that human participants could learn a center-embedded recursive grammar only if the input during training was presented in a staged fashion. Previous studies on artificial grammar learning, with randomly ordered input, failed to demonstrate learning of such a center-embedded structure. In the account proposed here, the staged input effect is explained by a fine-tuned match between the statistical characteristics of the incrementally organized input and the development of human cognitive learning over time, from low level, linear associative, to hierarchical processing of long distance dependencies. Interestingly, staged input seems to be effective only for learning hierarchical structures, and unhelpful for learning linear grammars
Recursion in Language: A Layered-Derivation Approach
No full textThis paper argues that recursion in language is to be understood not in terms of embedding, but in terms of derivational layering. A construction is recursive if part of its input is the output of a separate derivational layer. Complex clauses may be derived recursively in this sense, but also iteratively, suggesting that standard arguments for or against recursion in language are misdirected. More generally, we cannot tell that a grammar is recursive by simply looking at its output; we have to know about the generative procedure. Using the new definition of recursion in terms of derivational layering, we once again inspect the recorded data of Pirahã, arguing that there is reason to believe that the grammar of Pirahã is recursive after all
Learning Recursion: Multiple Nested and Crossed Dependencies
No full textLanguage acquisition in both natural and artificial language learning settings crucially depends on extracting information from ordered sequences. A shared sequence learning mechanism is thus assumed to underlie both natural and artificial language learning. A growing body of empirical evidence is consistent with this hypothesis. By means of artificial language learning experiments, we may therefore gain more insight in this shared mechanism. In this paper, we review empirical evidence from artificial language learning and computational modeling studies, as well as natural language data, and suggest that there are two key factors that help deter-mine processing complexity in sequence learning, and thus in natural language processing. We propose that the specific ordering of non-adjacent dependencies (i.e. nested or crossed), as well as the number of non-adjacent dependencies to be resolved simultaneously (i.e. two or three) are important factors in gaining more insight into the boundaries of human sequence learning; and thus, also in natural language processing. The implications for theories of linguistic competence are discussed
The Neural Basis of Recursion and Complex Syntactic Hierarchy
No full textLanguage is a faculty specific to humans. It is characterized by hierarchical, recursive structures. The processing of hierarchically complex sentences is known to recruit Broca’s area. Comparisons across brain imaging studies investigating similar hierarchical structures in different domains revealed that complex hierarchical structures that mimic those of natural languages mainly activate Broca’s area, that is, left Brodmann area (BA) 44/45, whereas hierarchically structured mathematical formulae, moreover, strongly recruit more anteriorly located region BA 47. The present results call for a model of the prefrontal cortex assuming two systems of processing complex hierarchy: one system determined by cognitive control for which the posterior-to-anterior gradient applies active in the case of processing hierarchically structured mathematical formulae, and one system which is confined to the posterior parts of the prefrontal cortex processing complex syntactic hierarchies in language efficiently
Two Case Studies in Phonological Universals: A View from Artificial Grammars
No full textThis article summarizes the results of two experiments that use artificial grammar learning in order to test proposed phonological universals. The first universal involves limits on precedence-modification in phonological representations, drawn from a typology of ludlings (language games). It is found that certain unattested precedence-modifying operations in ludlings are also dispreferred in learning in experimental studies, suggesting that the typological gap reflects a principled and universal aspect of language structure. The second universal involves differences between vowels and consonants, and in particular, the fact that phonological typology finds vowel repetition and harmony to be widespread, while consonants are more likely to dissimilate. An artificial grammar task replicates this bias in the laboratory, suggesting that its presence in natural languages is not due to historical accident but to cognitive constraints on the form of linguistic grammars