Instruction effects in implicit artificial grammar learning: A preference for grammaticality

Forkstam, Christian; Elwér, Åsa; Ingvar, Martin; Petersson, Karl Magnus

doi:10.1016/j.brainres.2008.05.005

Cited by 25 publications

(60 citation statements)

References 34 publications

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“…In the reverse contrast (G > NG), we observed a significant effect in the inferior medial frontal cortex (cluster P FWE < 0.001), and we also replicated our previously reported caudate activation (Forkstam et al, 2006) with a small volume correction (centered at [3 18 À3]; radius: 5 mm; cluster P FWE = .039). There was no significant main effect of local subsequence familiarity (cluster P FWE > .83), neither were there any significant interactions (cluster P FWE > .64), consistent with our behavioral findings Forkstam et al, 2008). We also tested LG > HG and HG > LG, but found no significant effect (cluster P FWE > .95).…”

Section: Fmri Resultssupporting

confidence: 85%

“…It further included regions in the anterior cingulate (BA 24/32; cluster P FWE < .001), the right inferior parietal (BA 39; cluster P FWE < .001), and the fusiform and inferior temporal (BA 19/20; cluster P FWE < .001) regions, bilaterally. As in previous studies Forkstam, Elwér, Ingvar, & Petersson, 2008;Forkstam et al, 2006), we examined the artificial syntactic violation effect (NG > G) by maximally contrasting structural knowledge vs. local subsequence familiarity (i.e., HNG vs. LG) in the lateral prefrontal cortex.…”

Section: Fmri Resultsmentioning

confidence: 99%

“…In the standard version, subjects are informed after acquisition that the sequences were generated according to a complex set of rules, and are asked to classify novel sequences as grammatical or not, based on their immediate intuitive impression (i.e., guessing based on ''gut feeling"). A robust finding in this type of paradigm is that after several days of implicit acquisition subjects perform well above chance; they do so on regular (e.g., Folia et al, 2008;Forkstam, Elwér, Ingvar, & Petersson, 2008;Petersson et al, 2004;Stadler & Frensch, 1998) as well as non-regular grammars (Poletiek, 2002;Uddén et al, 2009), generating context-free and context-sensitive non-adjacent dependencies (Uddén, Ingvar, Hagoort, & Petersson, submitted for publication; Uddén et al, 2009). In passing, we note that a qualitative match between the performance of simple recurrent networks and human comprehension of nested (context-free) and crossed (context-sensitive) dependencies has been reported (Christiansen & Chater, 1999;Christiansen & MacDonald, 2009).…”

Section: Introductionmentioning

confidence: 99%

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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: Fmri Resultssupporting

confidence: 85%

Section: Fmri Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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What artificial grammar learning reveals about the neurobiology of syntax

2012

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“…studies of implicit AGL. The implicit AGL paradigm is based on the structural mere exposure effect and it provides a tool to investigate the aspects of structural acquisition from exposure to grammatical examples without any type of feedback, teaching instruction or engaging subjects in explicit problem-solving [41,42]. Generally, AGL paradigms consist of acquisition and classification phases.…”

Section: Multiple Regular and Non-regular Dependenciesmentioning

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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“…However, previous work assessing the impact of goals on learning in unintentional-learning paradigms has revealed null results, or even a counterintuitive decrease in the size of learning effects, from inducing a goal to learn. In artificial-grammar-learning work, Forkstam, Elwér, Ingvar, and Petersson (2008) compared learning between participants who were informed that there would be contingencies that made for an artificial grammar and participants who were not so informed. They found no effects of instruction on learning.…”

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confidence: 99%

Adding the goal to learn strengthens learning in an unintentional learning task

Schmidt

Houwer

2012

Psychon Bull Rev

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Previous research has demonstrated that contingency learning can take place in the absence of the intention to learn. For instance, in the color-word contingency learning task, each distracting word is presented most often in a given target color (e.g., "month" in red and "plate" in green), and less often in the other colors. Participants respond more quickly and accurately when the word is presented in the expected rather than an unexpected color, even though there is no reason why they would have the intention to learn the contingencies between the words and the colors. It remains to be determined, however, whether learning in such situations would benefit or suffer from adding the goal to learn contingencies. In the reported experiment, half of the participants were informed that each word was presented most often in a certain color, and they were instructed to try to learn these contingencies. The other half of the participants were not informed that contingencies would be present. The participants given the learning goal produced a larger response time contingency effect than did the control participants. In contrast to some results from other learning paradigms, these results suggest that intentional learning adds to, rather than interferes with, unintentional learning, and we propose an explanation for some of the conflicting results.Keywords Implicit learning . Human memory . Automaticity . GoalsWithin the literature on human contingency learning (Schmidt, 2012), several paradigms have emerged that have proved useful in the study of unintentional-learning processes. One example of this is the color-word contingency learning paradigm introduced by Schmidt and colleagues (Schmidt & Besner, 2008;Schmidt, Crump, Cheesman, & Besner, 2007; Schmidt & De Houwer, 2012, in press;Schmidt, De Houwer, & Besner, 2010). In this paradigm, each distracting word is presented most often in a certain target color (e.g., "month" most often in red, "plate" most often in green), and participants respond more quickly and accurately to trials in which the word is presented in its high-contingency color (e.g., "month" in red) rather than a low-contingency color (e.g., "month" in green). Contingency awareness is generally quite limited in this paradigm, and learning often seems to occur without awareness: Participants who say that they are not aware of the contingencies and who do not guess above chance which words went with which colors still show the effect (Schmidt et al., 2007). Because participants are typically unaware of the contingencies, it is unlikely that they have the intention to learn them. Moreover, the instructions remain silent about the presence of the contingencies, and participants are thus not asked to learn them. One could argue that participants might still form the intention to learn the contingencies in order to improve their performance in the response time task. However, the response time task is so simple (i.e., reacting to colors) and response times are so short that an intentional strategy to...

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Instruction effects in implicit artificial grammar learning: A preference for grammaticality

Cited by 25 publications

References 34 publications

What artificial grammar learning reveals about the neurobiology of syntax

What artificial grammar learning reveals about the neurobiology of syntax

The neurobiology of syntax: beyond string sets

Adding the goal to learn strengthens learning in an unintentional learning task

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