Formal language theory: refining the Chomsky hierarchy

Jäger, Gerhard; Rogers, John H.

doi:10.1098/rstb.2012.0077

Cited by 122 publications

(138 citation statements)

References 26 publications

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“…Recent research using artificial grammar learning to probe the specific types of rules that organisms are capable of learning has offered a clearer picture (see ten Cate, 2016), and the use of formal language theory provides a useful metric and common description language for analyzing these rule types in terms of computational complexity (Jäger & Rogers, 2012). The most important distinction at present appears to be between the levels of regular or Bfinite state^grammars, some types of which are accessible to multiple animal species, and supra-regular grammars that go beyond this (including both context-sensitive and context-free grammars, sometimes termed Bphrase structure grammars^).…”

Section: Hierarchical Syntaxmentioning

confidence: 99%

Empirical approaches to the study of language evolution

2017

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The study of language evolution, and human cognitive evolution more generally, has often been ridiculed as unscientific, but in fact it differs little from many other disciplines that investigate past events, such as geology or cosmology. Well-crafted models of language evolution make numerous testable hypotheses, and if the principles of strong inference (simultaneous testing of multiple plausible hypotheses) are adopted, there is an increasing amount of relevant data allowing empirical evaluation of such models. The articles in this special issue provide a concise overview of current models of language evolution, emphasizing the testable predictions that they make, along with overviews of the many sources of data available to test them (emphasizing comparative, neural, and genetic data). The key challenge facing the study of language evolution is not a lack of data, but rather a weak commitment to hypothesistesting approaches and strong inference, exacerbated by the broad and highly interdisciplinary nature of the relevant data. This introduction offers an overview of the field, and a summary of what needed to evolve to provide our species with language-ready brains. It then briefly discusses different contemporary models of language evolution, followed by an overview of different sources of data to test these models. I conclude with my own multistage model of how different components of language could have evolved.

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Section: Hierarchical Syntaxmentioning

confidence: 99%

Empirical approaches to the study of language evolution

2017

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“…However, because temporal abstraction is defined in logical time, a word is always less temporally abstract than a sentence containing the word. The temporal abstraction view on sequence processing is a novel computational or a semiformal approach to artificial grammars, natural language, as well as other cognitive domains with sequential structure (see Jäger & Rogers [64] for more extended formal approaches to sequence processing and an explanation of the rationale). We will now focus on a possible temporal abstraction gradient within the LIFG.…”

Section: A Rostro-caudal Abstraction Gradient In Lateral Prefrontal Cmentioning

confidence: 99%

A rostro-caudal gradient of structured sequence processing in the left inferior frontal gyrus

Uddén

Bahlmann

2012

Phil. Trans. R. Soc. B

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In this paper, we present two novel perspectives on the function of the left inferior frontal gyrus (LIFG). First, a structured sequence processing perspective facilitates the search for functional segregation within the LIFG and provides a way to express common aspects across cognitive domains including language, music and action. Converging evidence from functional magnetic resonance imaging and transcranial magnetic stimulation studies suggests that the LIFG is engaged in sequential processing in artificial grammar learning, independently of particular stimulus features of the elements (whether letters, syllables or shapes are used to build up sequences). The LIFG has been repeatedly linked to processing of artificial grammars across all different grammars tested, whether they include non-adjacent dependencies or mere adjacent dependencies. Second, we apply the sequence processing perspective to understand how the functional segregation of semantics, syntax and phonology in the LIFG can be integrated in the general organization of the lateral prefrontal cortex (PFC). Recently, it was proposed that the functional organization of the lateral PFC follows a rostro-caudal gradient, such that more abstract processing in cognitive control is subserved by more rostral regions of the lateral PFC. We explore the literature from the viewpoint that functional segregation within the LIFG can be embedded in a general rostro-caudal abstraction gradient in the lateral PFC. If the lateral PFC follows a rostro-caudal abstraction gradient, then this predicts that the LIFG follows the same principles, but this prediction has not yet been tested or explored in the LIFG literature. Integration might provide further insights into the functional architecture of the LIFG and the lateral PFC.

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“…A grammar G is roughly a finite set of rules that specifies how items in a lexicon (alphabet) are combined into well-formed sequences, thus generating a formal language L(G) [39,[62][63][64]. The sequence set L(G) is called G's weak generative capacity and two grammars G 1 and…”

Section: Recursion Competence Grammars and Performance Modelsmentioning

confidence: 99%

“…Two grammars G 1 and G 2 are strongly equivalent if their sets of generated structural descriptions are equal, SD(G 1 ) ¼ SD(G 2 ). Many classes of grammars are described in the literature (see [63] for a review of some normal forms and the (extended) Chomsky hierarchy; grammar/language formalisms are however not restricted to these, [64,66,67]). Some important types of grammars generate classes of sequence (or string) sets that can be placed in a class hierarchy, the (extended) Chomsky hierarchy.…”

Section: Recursion Competence Grammars and Performance Modelsmentioning

confidence: 99%

The neurobiology of syntax: beyond string sets

Petersson

Hagoort

2012

Phil. Trans. R. Soc. B

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The human capacity to acquire language is an outstanding scientific challenge to understand. Somehow our language capacities arise from the way the human brain processes, develops and learns in interaction with its environment. To set the stage, we begin with a summary of what is known about the neural organization of language and what our artificial grammar learning (AGL) studies have revealed. We then review the Chomsky hierarchy in the context of the theory of computation and formal learning theory. Finally, we outline a neurobiological model of language acquisition and processing based on an adaptive, recurrent, spiking network architecture. This architecture implements an asynchronous, event-driven, parallel system for recursive processing. We conclude that the brain represents grammars (or more precisely, the parser/generator) in its connectivity, and its ability for syntax is based on neurobiological infrastructure for structured sequence processing. The acquisition of this ability is accounted for in an adaptive dynamical systems framework. Artificial language learning (ALL) paradigms might be used to study the acquisition process within such a framework, as well as the processing properties of the underlying neurobiological infrastructure. However, it is necessary to combine and constrain the interpretation of ALL results by theoretical models and empirical studies on natural language processing. Given that the faculty of language is captured by classical computational models to a significant extent, and that these can be embedded in dynamic network architectures, there is hope that significant progress can be made in understanding the neurobiology of the language faculty.Keywords: implicit artificial grammar learning; fMRI; repeated transcranial magnetic stimulation; language-related genes; CNTNAP2; spiking neural networks INTRODUCTIONRecent years have seen a renewed interest in using artificial grammar learning (AGL) as a window onto the organization of the language system. It has been exploited in cross-species comparisons, but also in studies on the neural architecture for language. Our focus is on the role AGL can play in unravelling the neural basis of human language. For this purpose, its role is relatively limited and mainly restricted to modelling aspects of structured sequence learning and structured sequence processing, uncontaminated by the semantic and phonological sources of information that co-determine the production and comprehension of natural language. Before going into more details related to the neurobiology of syntax and the role of AGL research, we outline what we think are the major conclusions from the research on the neurobiology of language:

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Formal language theory: refining the Chomsky hierarchy

Cited by 122 publications

References 26 publications

Empirical approaches to the study of language evolution

Empirical approaches to the study of language evolution

A rostro-caudal gradient of structured sequence processing in the left inferior frontal gyrus

The neurobiology of syntax: beyond string sets

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