Dataset Information

Implicit learning of recursive context-free grammars.

ABSTRACT: Context-free grammars are fundamental for the description of linguistic syntax. However, most artificial grammar learning experiments have explored learning of simpler finite-state grammars, while studies exploring context-free grammars have not assessed awareness and implicitness. This paper explores the implicit learning of context-free grammars employing features of hierarchical organization, recursive embedding and long-distance dependencies. The grammars also featured the distinction between left- and right-branching structures, as well as between centre- and tail-embedding, both distinctions found in natural languages. People acquired unconscious knowledge of relations between grammatical classes even for dependencies over long distances, in ways that went beyond learning simpler relations (e.g. n-grams) between individual words. The structural distinctions drawn from linguistics also proved important as performance was greater for tail-embedding than centre-embedding structures. The results suggest the plausibility of implicit learning of complex context-free structures, which model some features of natural languages. They support the relevance of artificial grammar learning for probing mechanisms of language learning and challenge existing theories and computational models of implicit learning.

SUBMITTER: Rohrmeier M

PROVIDER: S-EPMC3477156 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Implicit learning of recursive context-free grammars.

Rohrmeier Martin M Fu Qiufang Q Dienes Zoltan Z

PloS one 20121019 10

Context-free grammars are fundamental for the description of linguistic syntax. However, most artificial grammar learning experiments have explored learning of simpler finite-state grammars, while studies exploring context-free grammars have not assessed awareness and implicitness. This paper explores the implicit learning of context-free grammars employing features of hierarchical organization, recursive embedding and long-distance dependencies. The grammars also featured the distinction betwee ...[more]

PMID: 23094021

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Project description:BackgroundHuman populations are structured by social networks, in which individuals tend to form relationships based on shared attributes. Certain attributes that are ambiguous, stigmatized or illegal can create a OhiddenO population, so-called because its members are difficult to identify. Many hidden populations are also at an elevated risk of exposure to infectious diseases. Consequently, public health agencies are presently adopting modern survey techniques that traverse social networks in hidden populations by soliciting individuals to recruit their peers, e.g., respondent-driven sampling (RDS). The concomitant accumulation of network-based epidemiological data, however, is rapidly outpacing the development of computational methods for analysis. Moreover, current analytical models rely on unrealistic assumptions, e.g., that the traversal of social networks can be modeled by a Markov chain rather than a branching process.Methodology/principal findingsHere, we develop a new methodology based on stochastic context-free grammars (SCFGs), which are well-suited to modeling tree-like structure of the RDS recruitment process. We apply this methodology to an RDS case study of injection drug users (IDUs) in Tijuana, México, a hidden population at high risk of blood-borne and sexually-transmitted infections (i.e., HIV, hepatitis C virus, syphilis). Survey data were encoded as text strings that were parsed using our custom implementation of the inside-outside algorithm in a publicly-available software package (HyPhy), which uses either expectation maximization or direct optimization methods and permits constraints on model parameters for hypothesis testing. We identified significant latent variability in the recruitment process that violates assumptions of Markov chain-based methods for RDS analysis: firstly, IDUs tended to emulate the recruitment behavior of their own recruiter; and secondly, the recruitment of like peers (homophily) was dependent on the number of recruits.ConclusionsSCFGs provide a rich probabilistic language that can articulate complex latent structure in survey data derived from the traversal of social networks. Such structure that has no representation in Markov chain-based models can interfere with the estimation of the composition of hidden populations if left unaccounted for, raising critical implications for the prevention and control of infectious disease epidemics.

Project description:Humans can rapidly and seemingly implicitly learn to predict typical locations of relevant items when those items are encountered in familiar spatial contexts. Two important questions remain, however, concerning this type of learning: (1) which neural structures and mechanisms are involved in acquiring and exploiting such contextual knowledge? (2) Is this type of learning truly implicit and unconscious? We now answer both these questions after closely examining behavior and recording neural activity using MEG while observers (male and female) were acquiring and exploiting statistical regularities. Computational modeling of behavioral data suggested that, after repeated exposures to a spatial context, participants' behavior was marked by an abrupt switch to an exploitation strategy of the learnt regularities. MEG recordings showed that hippocampus and prefrontal cortex (PFC) were involved in the task and furthermore revealed a striking dissociation: only the initial learning phase was associated with hippocampal theta band activity, while the subsequent exploitation phase showed a shift in theta band activity to the PFC. Intriguingly, the behavioral benefit of repeated exposures to certain scenes was inversely related to explicit awareness of such repeats, demonstrating the implicit nature of the expectations acquired. Together, these findings demonstrate that (1a) hippocampus and PFC play complementary roles in the implicit, unconscious learning and exploitation of spatial statistical regularities; (1b) these mechanisms are implemented in the theta frequency band; and (2) contextual knowledge can indeed be acquired unconsciously, and awareness of such knowledge can even interfere with the exploitation thereof.SIGNIFICANCE STATEMENT Human visual perception is determined not just by the light that strikes our eyes, but also strongly by our prior knowledge and expectations. Such expectations, particularly about where to expect certain objects given scene context, might be learned implicitly and unconsciously, although this is hotly debated. Furthermore, it is unknown which brain mechanisms underpin this type of learning. We now show that, indeed, spatial prior expectations can be learned without awareness; in fact, strikingly, awareness seems to hinder the exploitation of the relevant knowledge. Furthermore, we demonstrate that one brain mechanism (hippocampal theta-band activity) is responsible for learning in these settings, whereas another mechanism (prefrontal theta-band activity) is involved in exploiting the learned associations.

Dataset Information

Implicit learning of recursive context-free grammars.

Publications

Implicit learning of recursive context-free grammars.

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