Implicit Artificial Grammar Learning Research Articles

ERP Correlates of the Short-term Implicit Artificial Grammar Learning

We present a study investigating the neural correlates of artificial grammar learning – a process of implicit processing of regularities in the environment. Participants observed visual stimuli that were created using a set of complex rules and then classified items from a new stimulus set as either consistent with these rules or not. Unlike previous event-related potentials (ERP) studies in this area, we used a short-term learning procedure normally used in behavioral experiments. With this short-term learning paradigm, we were able to detect ERP-components related to two different types of implicit knowledge. We found component (P600) related to the violation of the learned abstract grammatical structure. We also found early ERP-components (N200) related to the violation of learned combinations of elements in stimuli (frequency structure). It was possible to observe these distinct results because of the specific design of the study in which frequency structure and abstract grammaticality were independently varied. The results show neural correlates of classical artificial grammar learning and speak in favor of two distinct mechanisms of implicit learning: one responsible for abstract rules learning and another – for the learning of frequency structure of the environment.

Modality effects in implicit artificial grammar learning: An EEG study

Recently, it has been proposed that sequence learning engages a combination of modality-specific operating networks and modality-independent computational principles. In the present study, we compared the behavioural and EEG outcomes of implicit artificial grammar learning in the visual vs. auditory modality. We controlled for the influence of surface characteristics of sequences (Associative Chunk Strength), thus focusing on the strictly structural aspects of sequence learning, and we adapted the paradigms to compensate for known frailties of the visual modality compared to audition (temporal presentation, fast presentation rate). The behavioural outcomes were similar across modalities. Favouring the idea of modality-specificity, ERPs in response to grammar violations differed in topography and latency (earlier and more anterior component in the visual modality), and ERPs in response to surface features emerged only in the auditory modality. In favour of modality-independence, we observed three common functional properties in the late ERPs of the two grammars: both were free of interactions between structural and surface influences, both were more extended in a grammaticality classification test than in a preference classification test, and both correlated positively and strongly with theta event-related-synchronization during baseline testing. Our findings support the idea of modality-specificity combined with modality-independence, and suggest that memory for visual vs. auditory sequences may largely contribute to cross-modal differences.

Eye movements in implicit artificial grammar learning.

Artificial grammar learning (AGL) has been probed with forced-choice behavioral tests (active tests). Recent attempts to probe the outcomes of learning (implicitly acquired knowledge) with eye-movement responses (passive tests) have shown null results. However, these latter studies have not tested for sensitivity effects, for example, increased eye movements on a printed violation. In this study, we tested for sensitivity effects in AGL tests with (Experiment 1) and without (Experiment 2) concurrent active tests (preference- and grammaticality classification) in an eye-tracking experiment. Eye movements discriminated between sequence types in passive tests and more so in active tests. The eye-movement profile did not differ between preference and grammaticality classification, and it resembled sensitivity effects commonly observed in natural syntax processing. Our findings show that the outcomes of implicit structured sequence learning can be characterized in eye tracking. More specifically, whole trial measures (dwell time, number of fixations) showed robust AGL effects, whereas first-pass measures (first-fixation duration) did not. Furthermore, our findings strengthen the link between artificial and natural syntax processing, and they shed light on the factors that determine performance differences in preference and grammaticality classification tests. (PsycINFO Database Record

The P600 in Implicit Artificial Grammar Learning.

The suitability of the artificial grammar learning (AGL) paradigm to capture relevant aspects of the acquisition of linguistic structures has been empirically tested in a number of EEG studies. Some have shown a syntax-related P600 component, but it has not been ruled out that the AGL P600 effect is a response to surface features (e.g., subsequence familiarity) rather than the underlying syntax structure. Therefore, in this study, we controlled for the surface characteristics of the test sequences (associative chunk strength) and recorded the EEG before (baseline preference classification) and after (preference and grammaticality classification) exposure to a grammar. After exposure, a typical, centroparietal P600 effect was elicited by grammatical violations and not by unfamiliar subsequences, suggesting that the AGL P600 effect signals a response to structural irregularities. Moreover, preference and grammaticality classification showed a qualitatively similar ERP profile, strengthening the idea that the implicit structural mere-exposure paradigm in combination with preference classification is a suitable alternative to the traditional grammaticality classification test.

Anti-opposition logic paradigm: An improvement on the opposition logic in implicit artificial grammar learning

Anti-opposition logic paradigm: An improvement on the opposition logic in implicit artificial grammar learning

Role of prior knowledge in implicit and explicit learning of artificial grammars.

Artificial grammar learning (AGL) performance reflects both implicit and explicit processes and has typically been modeled without incorporating any influence from general world knowledge. Our research provides a systematic investigation of the implicit vs. explicit nature of general knowledge and its interaction with knowledge types investigated by past AGL research (i.e., rule- and similarity-based knowledge). In an AGL experiment, a general knowledge manipulation involved expectations being either congruent or incongruent with training stimulus structure. Inconsistent observations paradoxically led to an advantage in structural knowledge and in the use of general world knowledge in both explicit (conscious) and implicit (unconscious) cases (as assessed by subjective measures). The above findings were obtained under conditions of reduced processing time and impaired executive resources. Key findings from our work are that implicit AGL can clearly be affected by general knowledge, and implicit learning can be enhanced by the violation of expectations.

Implicit Learning of Arithmetic Regularities Is Facilitated by Proximal Contrast

Natural number arithmetic is a simple, powerful and important symbolic system. Despite intense focus on learning in cognitive development and educational research many adults have weak knowledge of the system. In current study participants learn arithmetic principles via an implicit learning paradigm. Participants learn not by solving arithmetic equations, but through viewing and evaluating example equations, similar to the implicit learning of artificial grammars. We expand this to the symbolic arithmetic system. Specifically we find that exposure to principle-inconsistent examples facilitates the acquisition of arithmetic principle knowledge if the equations are presented to the learning in a temporally proximate fashion. The results expand on research of the implicit learning of regularities and suggest that contrasting cases, show to facilitate explicit arithmetic learning, is also relevant to implicit learning of arithmetic.

What artificial grammar learning reveals about the neurobiology of syntax

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.

The mechanics of implicit learning of contingencies: A commentary on Custers & Aarts’ paper

In their paper: "Learning of Predictive Relations Between Events Depends on Attention, Not on Awareness" Custers & Aarts demonstrate that when one is first exposed to a clear predictive relationship - a consequent predictive relationship will be represented as a unidirectional association ("predictor" to "predicted") in the percievers' minds regardless of their awareness of that relationship. Furthermore, a conscious intention to learn the relationship leads to the formation of a bidirectional (non-predictive) association. While these findings may prove to be a significant step in understanding other forms of implicit learning such as implicit artificial grammar learning and implicit sequence learning and why they are affected by intentional learning; Custers & Aarts' postulation that "top-down" regulation is at work here is debatable as their experimental manipulation can be understood as "bottom-up" activation of implicit learning processes.

Structural selection in implicit learning of artificial grammars.

In the contextual cueing paradigm, Endo and Takeda (in Percept Psychophys 66:293–302, 2004) provided evidence that implicit learning involves selection of the aspect of a structure that is most useful to one’s task. The present study attempted to replicate this finding in artificial grammar learning to investigate whether or not implicit learning commonly involves such a selection. Participants in Experiment 1 were presented with an induction task that could be facilitated by several characteristics of the exemplars. For some participants, those characteristics included a perfectly predictive feature. The results suggested that the aspect of the structure that was most useful to the induction task was selected and learned implicitly. Experiment 2 provided evidence that, although salience affected participants’ awareness of the perfectly predictive feature, selection for implicit learning was mainly based on usefulness.Electronic supplementary materialSupplementary material is available in the online version of this article at doi:10.1007/s00426-009-0227-1 and is accessible for authorized users.

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

Human implicit learning can be investigated with implicit artificial grammar learning, a paradigm that has been proposed as a simple model for aspects of natural language acquisition. In the present study we compared the typical yes–no grammaticality classification, with yes–no preference classification. In the case of preference instruction no reference to the underlying generative mechanism (i.e., grammar) is needed and the subjects are therefore completely uninformed about an underlying structure in the acquisition material. In experiment 1, subjects engaged in a short-term memory task using only grammatical strings without performance feedback for 5 days. As a result of the 5 acquisition days, classification performance was independent of instruction type and both the preference and the grammaticality group acquired relevant knowledge of the underlying generative mechanism to a similar degree. Changing the grammatical stings to random strings in the acquisition material (experiment 2) resulted in classification being driven by local substring familiarity. Contrasting repeated vs. non-repeated preference classification (experiment 3) showed that the effect of local substring familiarity decreases with repeated classification. This was not the case for repeated grammaticality classifications. We conclude that classification performance is largely independent of instruction type and that forced-choice preference classification is equivalent to the typical grammaticality classification.

Neuronal correlates of familiarity-driven decisions in artificial grammar learning.

It has been proposed on the basis of behavioural data that grammaticality judgments in implicit artificial grammar learning paradigms are largely driven by priming based on fragment familiarity. A prediction that follows from this account is that neural deactivation, a common correlate of repetition priming, should be observed for grammatical compared to ungrammatical stimuli. We conducted an event-related fMRI study to investigate neuronal correlates of such fragment-based priming. In a study phase, participants performed a short-term memory task on a series of strings of pseudofont characters. Scanning was performed in a subsequent test phase in which participants classified new strings as either grammatical or ungrammatical. Test strings differed systematically from training strings in terms of exemplar and fragment similarity. Behaviourally, participants classified strings as grammatical based on fragment familiarity. Differential activity was evident during string classification as reduced activity in left lateral occipital complex and bilateral lingual gyri for strings with high fragment familiarity compared to strings with low fragment familiarity. Thus, consistent with the hypothesis, neuronal facilitation in extrastriate occipital regions may constitute one basis of implicit grammaticality decisions based on fragment priming.

When it does hurt to try: adult age differences in the effects of instructions on implicit pattern learning.

Trying to learn sometimes impairs implicit learning of artificial grammars and of control systems. We asked whether such negative effects of trying also occur in implicit learning of subtle sequential regularities and whether such effects vary with adult age. Young (n = 12, age = 20-23) and older (n = 24, age = 60-80) adults completed an alternating serial response time task in which predictable pattern events alternated with random ones in a visual/spatial display. Half of the participants were informed about the pattern and were instructed to try to discover it (intentional instructions), and half were not (incidental instructions). Age-related deficits in implicit learning occurred for both conditions. In addition, for the older group, but not for the younger one, intentional instructions impaired implicit pattern learning. This negative effect of trying to learn demonstrates another similarity among implicit learning tasks, supporting the view that some common processes underlie different forms of implicit learning.

Learning artificial grammars: no evidence for the acquisition of rules.

Two experiments investigated whether there is evidence for acquisition of rules in implicit artificial grammar learning (AGL). Two different methods were used in meeting this goal, multiple regression analysis and analysis of receiver-operating characteristics (ROCs). By means of multiple regression analysis, several types of knowledge were identified that were used in judgments of grammaticality, for example, about single letters and about larger stimulus fragments. There was no evidence for the contribution of rule knowledge. The ROCs were in accord with a similarity-based account of AGL and thus did not support the notion that rule knowledge is acquired in AGL either. Simulations with a connectionist model corroborated the conclusion that the results were in accord with a similarity-based, associative account.

Learning Simple and Complex Second Language Rules Under Implicit, Incidental, Rule-Search, and Instructed Conditions

This study examines the generalizability of claims by Reber (1989, 1993) about the implicit learning of artificial grammars to the context of adult second language acquisition (SLA). In the field of SLA Krashen (1981, 1982, 1985, 1994) has made claims parallel to those of Reber regarding the differential effectiveness of conscious learning of rules and unconscious incidental acquisition of rules. Specifically addressed are Reber's and Krashen's claims that (a) implicit learning is more effective than explicit learning when the stimulus domain is complex, and (b) explicit learning of simple and complex stimulus domains is possible if the underlying rules are made salient. One hundred four adult learners of English as a second language were randomly assigned to implicit, incidental, rule-search, or instructed computerized training conditions. Speed and accuracy of judgments of novel tokens of easy and hard rule sentence types presented during training were used as dependent measures. Results do not support the first of Reber's and Krashen's claims but do support the second. Implicit learners do not outperform other learners on complex rules, but instructed learners outperform all others in learning simple rules. Analyses of the effect of sentence type and grammaticality on learning suggest a transfer-appropriate processing account of the relationship among consciousness, rule awareness, training, and transfer task performance.

L'apprentissage implicite : le cas des grammaires artificielles

Summary: Implicit learning: The case of artificial grammars. This paper is an attempt to put the work of the past several decades on the problems of implicit learning and unconscious cognition in a diachronic perspective, through an illustration by the oldest and best known paradigm in the field, namely the «implicit learning of artificial grammars». In the first part, we expose Arthur Reber's pioneering work on artificial grammar learning since the I960's. According to this author, implicit learning is an inductive process whereby knowledge of a complex environment is acquired and used largely independently ofawareness ofeither the process of acquisition or the nature of that which has been learned. In the second part, we show that this interpretation has been the object of a controversy by numerous papers. We adopt the proposal that this phenomenon does not testify to the unconscious abstraction of the rules underlying the situation, as held by the prevalent, abstractionist interpretation. Indeed, performance improvement can be accounted for by a memory-based framework positing that subjects only learn specifie fragments of the material, which constitute the basic functional unit of knowledge in most learning conditions. In the third part, possible implications of this theory on the role of unconscious and conscious processes in adaptive behavior are discussed. Our proposai is that the processes engagea in implicit learning can be accounted for within the conceptual framework underlying implicit memory studies. The same processes may be underlying performance in the two types of implicit tasks. Key words : implicit learning, implicit memory, artificial grammars, cognitive unconscious, theory of evolution.

Affective Discrimination and the Implicit Learning Process

A modified version of the mere exposure effect paradigm was utilized in an implicit artificial grammar learning task in an attempt to develop a procedure that would be more sensitive in assesing nonconscious learning processes than the methods currently utilized within the field of implicit learning. Subjects were presented with stimuli generated from a finite-state artificial grammar and then had to either (a) decide if novel items conformed to the rule structure of the grammar or (b) rate the degree to which they liked novel items. Because the latter of the task was more indirect of the two procedures, subjects′ availability to discriminate between well-formed and ill-formed items on this liking task was taken as a more sensitive piece of evidence of implicit learning, compared to performance on the rule conformity task. A subsequent test of subjects′ explicit knowledge of the rules of the grammar showed that subjects do have some conscious knowledge of the artificial grammar, but subjects making liking decisions exhibited significantly less conscious rule knowledge than subjects initially making rule-conformity judgements. The findings are discussed in relation to synergistic relationships among implicit perception, implicit memory, and implicit learning proceses.

Role of implicit and explicit processes in learning from examples: A synergistic effect.

Four experiments are reported in which subjects gained extensive experience with artificial grammars in explicit and implicit processing tasks. Results indicated that (a) implicit processing was sufficient for learning a finite state grammar but was inadequate for learning another type of grammar based on logical rules, (b) Subjects were able to communicate some of their implicit knowledge of the grammars to another person, (c) Consistent with rule induction but not memory array models of learning, verbal protocols indicated there was no tendency to converge on the same set of cues used to identify valid strings, (d) A synergistic learning effect occurred when both implicit and explicit processing tasks were used in the grammar based on logical rules but not in the finite state grammar. A theoretical framework is proposed in which implicit learning is conceptualized as an automatic, memory-based mechanism for detecting patterns of family resemblance among exemplars. Explicit learning mechanisms for discovering and controlling task variables are similar to conscious problem solving. These processes include attempts to form a mental representation of the task, searching memory for knowledge of analogous systems, and attempts to build and test mental models of task performance (Gentner & Stevens, 1983; JohnsonLaird, 1983). Implicit learning is thought to be an alternate mode of learning that is automatic, nonconscious, and more powerful than explicit thinking for discovering nonsalient covariance between task variables (Lewicki, 1986; Reber, 1969, 1976; Reber & Allen, 1978). Demonstrations of implicit learning of artificial grammars typically involve comparisons between groups of subjects who experience exemplars of a grammar under (a) instructions to figure out the rules of the grammar (rule discovery instructions) or (b) instructions that require attention to the exemplars without attempting to determine the rules of the grammar (e.g., groups of subjects asked to memorize the exemplars for a subsequent memory test). Typically, groups of subjects who implicitly learned the grammar do as well or better on subsequent attempts to discriminate between new valid versus invalid strings as subjects who

Implicit learning of artificial grammars

Two experiments were carried out to investigate the process by which Ss respond to the statistical nature of the stimulus array, a process defined as “implicit learning”. An artificial grammar was used to generate the stimuli. Experiment I showed that Ss learned to become increasingly sensitive to the grammatical structure of the stimuli, but little was revealed about the nature of such learning. Experiment II showed that information gathered about the grammar in a memorization task could be extended to a recognition task with new stimuli. Various analyses of the data strongly implied that Ss were learning to respond to the general grammatical nature of the stimuli, rather than learning to respond according to specific coding systems imposed upon the stimuli. It was argued that this “implicit” learning is similar in nature to the “differentiation” process of perceptual learning espoused by Gibson and Gibson (1955) .