Syntactic structure and artificial grammar learning: The learnability of embedded hierarchical structures

Vries, M.H. de; Monaghan, Padraic; Knecht, Stefan; Zwitserlood, Pienie

doi:10.1016/j.cognition.2007.09.002

Cited by 93 publications

(151 citation statements)

References 27 publications

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“…The findings in (4) lacked the appropriate controls, but later replications claim that the AABB/ABAB task, which Fitch and Hauser (4) designed to obtain evidence for recursion, can be solved by humans using a simpler strategy instead (5,9) or by a conscious counting strategy that seems unrelated to language (11). At present, there is thus no convincing demonstration of the use of recursive rules in artificial language learning in any species.…”

Section: Testing For the Use Of Other Simpler Rules To Discriminate mentioning

confidence: 92%

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Simple rules can explain discrimination of putative recursive syntactic structures by a songbird species

Heijningen

Visser

Zuidema

et al. 2009

Proc. Natl. Acad. Sci. U.S.A.

157

167

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According to a controversial hypothesis, a characteristic unique to human language is recursion. Contradicting this hypothesis, it has been claimed that the starling, one of the two animal species tested for this ability to date, is able to distinguish acoustic stimuli based on the presence or absence of a center-embedded recursive structure. In our experiment we show that another songbird species, the zebra finch, can also discriminate between artificial song stimuli with these structures. Zebra finches are able to generalize this discrimination to new songs constructed using novel elements belonging to the same categories, similar to starlings. However, to demonstrate that this is based on the ability to detect the putative recursive structure, it is critical to test whether the birds can also distinguish songs with the same structure consisting of elements belonging to unfamiliar categories. We performed this test and show that seven out of eight zebra finches failed it. This suggests that the acquired discrimination was based on phonetic rather than syntactic generalization. The eighth bird, however, must have used more abstract, structural cues. Nevertheless, further probe testing showed that the results of this bird, as well as those of others, could be explained by simpler rules than recursive ones. Although our study casts doubts on whether the rules used by starlings and zebra finches really provide evidence for the ability to detect recursion as present in ''context-free'' syntax, it also provides evidence for abstract learning of vocal structure in a songbird.biolinguistics ͉ discrimination learning ͉ rule learning ͉ syntax

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Section: Testing For the Use Of Other Simpler Rules To Discriminate mentioning

confidence: 92%

“…The experiments gave rise to a lively debate, ranging from questions such as whether the stimuli really represented a suitable test of the ability to detect recursive structures, to whether animals as well as humans can ''solve'' the task by using less complex, non-recursive strategies (2,(5)(6)(7)(8)(9)(10)(11)(12).…”

mentioning

confidence: 99%

Simple rules can explain discrimination of putative recursive syntactic structures by a songbird species

Heijningen

Visser

Zuidema

et al. 2009

Proc. Natl. Acad. Sci. U.S.A.

157

167

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“…Much of the discussion of the recursion-only hypothesis, concerning the narrow faculty of language (FLN, Hauser et al, 2002), has been phrased in terms of concepts derived from the Chomsky hierarchy; for example, non-regular context-free vs. right-linear regular grammars (e.g., de Vries, Monaghan, Knecht, & Zwitserlood, 2008;Fitch & Hauser, 2004;Gentner et al, 2006;Perruchet & Rey, 2005;Uddén et al, 2009). Here, we will show why the Chomsky hierarchy is irrelevant in the context of finite processing systems.…”

Section: Finiteness Of Neural Systemsmentioning

confidence: 99%

What artificial grammar learning reveals about the neurobiology of syntax

2012

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a b s t r a c tIn this paper we examine the neurobiological correlates of syntax, the processing of structured sequences, by comparing FMRI results on artificial and natural language syntax. We discuss these and similar findings in the context of formal language and computability theory. We used a simple right-linear unification grammar in an implicit artificial grammar learning paradigm in 32 healthy Dutch university students (natural language FMRI data were already acquired for these participants). We predicted that artificial syntax processing would engage the left inferior frontal region (BA 44/45) and that this activation would overlap with syntax-related variability observed in the natural language experiment. The main findings of this study show that the left inferior frontal region centered on BA 44/45 is active during artificial syntax processing of well-formed (grammatical) sequence independent of local subsequence familiarity. The same region is engaged to a greater extent when a syntactic violation is present and structural unification becomes difficult or impossible. The effects related to artificial syntax in the left inferior frontal region (BA 44/45) were essentially identical when we masked these with activity related to natural syntax in the same subjects. Finally, the medial temporal lobe was deactivated during this operation, consistent with the view that implicit processing does not rely on declarative memory mechanisms that engage the medial temporal lobe. In the context of recent FMRI findings, we raise the question whether Broca's region (or subregions) is specifically related to syntactic movement operations or the processing of hierarchically nested non-adjacent dependencies in the discussion section. We conclude that this is not the case. Instead, we argue that the left inferior frontal region is a generic on-line sequence processor that unifies information from various sources in an incremental and recursive manner, independent of whether there are any processing requirements related to syntactic movement or hierarchically nested structures. In addition, we argue that the Chomsky hierarchy is not directly relevant for neurobiological systems.

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“…Likewise, Gentner, Fenn, Margoliash & Nusbaum (Gentner et al, 2006) claimed that their subjects (starlings) learnt CER using the same kind of structures as Fitch and Hauser (Fitch and Hauser, 2004). This in turn elicited some critique: Corballis (Corballis, 2007a;Corballis, 2007b) called attention to the fact that the sentences used could be parsed by simple counting, while others (De Vries et al, 2008;Perruchet and Rey, 2005) showed that in experimental situation similar to that of Fitch & Hauser (Fitch and Hauser, 2004) even human subjects used alternative strategies to solve the tasks.…”

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confidence: 99%

Parsing recursive sentences with a connectionist model including a neural stack and synaptic gating

Fedor¹,

Ittzés²,

Szathmáry³

2011

Journal of Theoretical Biology

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To cite this version:Anna Fedor, Péter Péter Ittzés, Eörs Eörs Szathmáry. Parsing recursive sentences with a connectionist model including a neural stack and synaptic gating. Journal of Theoretical Biology, Elsevier, 2011, 271 (1) This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. AbstractIt is supposed that humans are genetically predisposed to be able to recognize sequences of context free grammars with center-embedded recursion while other primates are restricted to the recognition of finite state grammars with tail-recursion. Our aim was to construct a minimalist neural network that is able to parse artificial sentences of both grammars in an efficient way without using the biologically unrealistic backpropagation algorithm. The core of this network is a neural stack-like memory where the push and pop operations are regulated by synaptic gating on the connections between the layers of the stack. The network correctly categorizes novel sentences of both grammars after training. We suggest that the introduction of the neural stack memory will turn out to be substantial for any biological 'hierarchical processor' and the minimalist design of the model suggests a quest for similar, realistic neural architectures.

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Syntactic structure and artificial grammar learning: The learnability of embedded hierarchical structures

Cited by 93 publications

References 27 publications

Simple rules can explain discrimination of putative recursive syntactic structures by a songbird species

Simple rules can explain discrimination of putative recursive syntactic structures by a songbird species

What artificial grammar learning reveals about the neurobiology of syntax

Parsing recursive sentences with a connectionist model including a neural stack and synaptic gating

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