Individual behavior in learning of an artificial grammar

2000

Memory & Cognition, 2008

2006

Sensitivity to distributional characteristics of sequential linguistic and nonlinguistic stimuli, have been shown to play a role in learning the underlying structure of these stimuli. A growing body of experimental and computational research with (artificial) grammars suggests that learners are sensitive to various distributional characteristics of their environment (Kuhl, 2004; Onnis, Monaghan, Richmond & Chater, 2005; Rohde & Plaut, 1999). We propose that, at a higher level, statistical characteristics of the full sample of stimuli on which learning is based, also affects learning. We provide a statistical model that accounts for such an effect, and experimental data with the Artificial Grammar Learning (AGL) methodology, showing that learners also are sensitive to distributional characteristics of a full sample of exemplars.

Psychonomic Bulletin & Review, 2008

To investigate the role of selective attention in artificial grammar (AG) learning, participants were presented with "GLOCAL" strings-that is, chains of compound global and local letters. The global and local levels instantiated different grammars. The results of this experiment revealed that participants learned only the grammar for the level to which they attended. The participants were not even able to choose presented but unattended strings themselves. These results show that selective attention plays a critical role in AG learning.

2013

A total of 78 adult participants were asked to read a sample of strings generated by a finite state grammar and, immediately after reading each string, to mark the natural segmentation positions with a slash bar. They repeated the same task after a phase of familiarization with the material, which consisted, depending on the group involved, of learning items by rote, performing a shortterm matching task, or searching for the rules of the grammar. Participants formed the same number of cognitive units before and after the training phase, thus indicating that they did not tend to form increasingly large units. However, the number of different units reliably decreased, whatever the task that participants had performed during familiarization. This result indicates that segmentation was increasingly consistent with the structure of the grammar. A theoretical account of this phenomenon, based on ubiquitous principles of associative memory and learning, is proposed. This account is support...

Brain and Language, 2012

In 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.

Consciousness and Cognition, 2011

Journal of Experimental Psychology: Learning, Memory, and Cognition, 1997

Three artificial grammar learning experiments investigate the memory processes underlying classification judgments. In Experiment 1, the effects of grammaticality, specific similarity and chunk frequency are found to be similar between classification and recognition. In Experiment 2a and 2b, instructions to exclude "old" and "similar" items under conditions that make conscious recollection difficult are found to dissociate grammaticality and similarity effects in classification. Finally, dividing attention at test in Experiment 3 also dissociates these effects, but the pattern of data underlying the dissociation is different from Experiments 2a and 2b. On the basis of these results, it is concluded that grammaticality effects are based on automatic memory processes, whereas intentional uses of memory largely support the specific similarity effect. It is argued that these results are consistent with a dual-process model of classification, but inconsistent with unitary mechanism accounts. The experiments also indicate that chunk frequency has both implicit and explicit influences on classification judgments.

Journal of Experimental Psychology: Learning, Memory, and Cognition, 2000

The authors examine the role of similarity in artificial grammar learning (AGL; A. S. . A standard finite-state language was used to create stimuli that were arrangements of embedded geometric shapes (Experiment 1), connected lines (Experiment 2), and sequences of shapes (Experiment 3). Main effects for well-known predictors from the literature (grammaticality, associative global and anchor chunk strength, novel global and anchor chunk strength, length of items, and edit distance) were observed, thus replicating previous work. However, the authors extend previous research by using a widely known similarity-based exemplar model of categorization (the generalized context model; R. M. to fit grammaticality judgments, by nested regression analyses. The results suggest that any explanation of AGL that is based on the existing theories is incomplete without a similarity process as well. Also, the results provide a foundation for further interpreting AGL in the wider context of categorization research.

Frontiers in Psychology

Whether pattern-parsing mechanisms are specific to language or apply across multiple cognitive domains remains unresolved. Formal language theory provides a mathematical framework for classifying pattern-generating rule sets (or "grammars") according to complexity. This framework applies to patterns at any level of complexity, stretching from simple sequences, to highly complex tree-like or net-like structures, to any Turingcomputable set of strings. Here, we explored human pattern-processing capabilities in the visual domain by generating abstract visual sequences made up of abstract tiles differing in form and color. We constructed different sets of sequences, using artificial "grammars" (rule sets) at three key complexity levels. Because human linguistic syntax is classed as "mildly context-sensitive," we specifically included a visual grammar at this complexity level. Acquisition of these three grammars was tested in an artificial grammar-learning paradigm: after exposure to a set of well-formed strings, participants were asked to discriminate novel grammatical patterns from non-grammatical patterns. Participants successfully acquired all three grammars after only minutes of exposure, correctly generalizing to novel stimuli and to novel stimulus lengths. A Bayesian analysis excluded multiple alternative hypotheses and shows that the success in rule acquisition applies both at the group level and for most participants analyzed individually. These experimental results demonstrate rapid pattern learning for abstract visual patterns, extending to the mildly context-sensitive level characterizing language. We suggest that a formal equivalence of processing at the mildly context sensitive level in the visual and linguistic domains implies that cognitive mechanisms with the computational power to process linguistic syntax are not specific to the domain of language, but extend to abstract visual patterns with no meaning.