Grammatical pattern learning by human infants and cotton-top tamarin monkeys

Saffran, Jenny R.; Hauser, Marc D.; Seibel, Rebecca L.; Kapfhamer, J.D.; Tsao, Fritz; Cushman, Fiery

doi:10.1016/j.cognition.2007.10.010

Cited by 139 publications

(172 citation statements)

References 35 publications

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“…Therefore the artificial grammar learning (AGL) paradigm has been used to create a relatively uncontaminated window onto the neurobiology of syntax (Gómez & Gerken, 2000;Petersson, Forkstam, & Ingvar, 2004;Reber, 1967). In addition, AGL has been used in cross-species comparisons in an attempt to establish the uniquely human component of language (Fitch & Hauser, 2004;Gentner, Fenn, Margoliash, & Nusbaum, 2006;Hauser, Chomsky, & Fitch, 2002;O'Donnell, Hauser, & Fitch, 2005;Saffran et al, 2008). Here, we will present data from an FMRI experiment that speaks to the neurobiology of syntax.…”

Section: Introductionmentioning

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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Section: Introductionmentioning

confidence: 99%

What artificial grammar learning reveals about the neurobiology of syntax

2012

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“…In such studies human participants or nonhuman animals have no a priori knowledge about the structure of the AG. Yet, by being habituated to or trained with exemplary sequences of sensory stimuli generated by the AG, the relationship between the elements in the sequence can be acquired [sometimes also referred to as "statistical learning" (Saffran et al, 1996(Saffran et al, , 1999]. Differential responses to novel well formed (correct) sequences compared with those that violate the AG structure suggest that some aspect of the AG structure was learned.…”

Section: Introductionmentioning

confidence: 99%

Auditory Artificial Grammar Learning in Macaque and Marmoset Monkeys

et al. 2013

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Artificial grammars (AG) are designed to emulate aspects of the structure of language, and AG learning (AGL) paradigms can be used to study the extent of nonhuman animals' structure-learning capabilities. However, different AG structures have been used with nonhuman animals and are difficult to compare across studies and species. We developed a simple quantitative parameter space, which we used to summarize previous nonhuman animal AGL results. This was used to highlight an under-studied AG with a forward-branching structure, designed to model certain aspects of the nondeterministic nature of word transitions in natural language and animal song. We tested whether two monkey species could learn aspects of this auditory AG. After habituating the monkeys to the AG, analysis of video recordings showed that common marmosets (New World monkeys) differentiated between well formed, correct testing sequences and those violating the AG structure based primarily on simple learning strategies. By comparison, Rhesus macaques (Old World monkeys) showed evidence for deeper levels of AGL. A novel eye-tracking approach confirmed this result in the macaques and demonstrated evidence for more complex AGL. This study provides evidence for a previously unknown level of AGL complexity in Old World monkeys that seems less evident in New World monkeys, which are more distant evolutionary relatives to humans. The findings allow for the development of both marmosets and macaques as neurobiological model systems to study different aspects of AGL at the neuronal level.

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“…In this context, we take the view that natural and artificial syntax share a common abstraction-structured sequence processing [19]. AGL was originally implemented to investigate implicit learning mechanisms shared with natural language acquisition [20] and has recently been used in cross-species comparisons to understand the evolutionary origins of language and communication [21][22][23][24][25].…”

Section: Introductionmentioning

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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Grammatical pattern learning by human infants and cotton-top tamarin monkeys

Cited by 139 publications

References 35 publications

What artificial grammar learning reveals about the neurobiology of syntax

What artificial grammar learning reveals about the neurobiology of syntax

Auditory Artificial Grammar Learning in Macaque and Marmoset Monkeys

The neurobiology of syntax: beyond string sets

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