Syntactic structure and artificial grammar learning: the learnability of embedded hierarchical structures

Embedded hierarchical structures, such as "the rat the cat ate was brown", constitute a core generative property of a natural language theory. Several recent studies have reported learning of hierarchical embeddings in artificial grammar learning (AGL) tasks, and described the functional specificity of Broca's area for processing such structures. In two experiments, we investigated whether alternative strategies can explain the learning success in these studies. We trained participants on hierarchical sequences, and found no evidence for the learning of hierarchical embeddings in test situations identical to those from other studies in the literature. Instead, participants appeared to solve the task by exploiting surface distinctions between legal and illegal sequences, and applying strategies such as counting or repetition detection. We suggest alternative interpretations for the observed activation of Broca's area, in terms of the application of calculation rules or of a differential role of working memory. We claim that the learnability of hierarchical embeddings in AGL tasks remains to be demonstrated.     

Learnability of embedded syntactic structures depends on prosodic cues

The ability to process center-embedded structures has been claimed to represent a core function of the language faculty. Recently, several studies have investigated the learning of center-embedded dependencies in artificial grammar settings. Yet some of the results seem to question the learnability of these structures in artificial grammar tasks. Here, we tested under which exposure conditions learning of center-embedded structures in an artificial grammar is possible. We used naturally spoken syllable sequences and varied the presence of prosodic cues. The results suggest that mere distributional information does not suffice for successful learning. Prosodic cues marking the boundaries of the major relevant units, however, can lead to learning success. Thus, our data are consistent with the hypothesis that center-embedded syntactic structures can be learned in artificial grammar tasks if language-like acoustic cues are provided.     

From mental strain to burnout

This study examines the relationships between burnout and short-term consequences of mental strain within and outside human services professions, at the same time contributing to the understanding of the development of burnout as a long-term effect of impairing work and job design. A total of 294 German employees working in human services (N = 149) and industrial production (N = 145) completed the Oldenburg Burnout Inventory (OLBI) and four scales measuring short-term consequences of mental strain. Results of factor analyses confirmed that the elements of the OLBI's two-factor structure—exhaustion and disengagement—are distinct from a range of impairing short-term consequences of mental strain. In addition, structural equation modelling showed that each burnout dimension is differentially related to specific short-term consequences of strain: Exhaustion is primarily related to mental fatigue, whereas disengagement is primarily related to satiation and the experience of monotony. The findings did not differ substantially for the two occupational groups. Burnout, as a long-term consequence of impairing mental strain, is distinct from and presumably temporally-causally related to specific impairing short-term consequences of mental strain, which, in turn, can be attributed to inadequate job design.     

The influence of temporal regularities on the implicit learning of pitch structures

Implicit learning is the acquisition of complex information without the intention to learn. The aim of this study was to investigate the influence of temporal regularities on the implicit learning of an artificial pitch grammar. According to the dynamic attending theory (DAT) external regularities can entrain internal oscillators that guide attention over time, inducing temporal expectations that influence perception of future events. In the present study, the presentation of the artificial pitch grammar in the exposure phase was temporally either regular or irregular for one of two participant groups. Based on the DAT, it was hypothesized that the regular temporal presentation would favour implicit learning of tone structures in comparison to the irregular temporal presentation. Results demonstrated learning of the artificial grammar for the group with the regular exposure phase and partial learning for the group with the irregular exposure phase. These findings suggest that the regular presentation helps listeners to develop perceptual expectations about the temporal occurrence of future tones and thus facilitates the learning of the artificial pitch grammar.     

On the learnability of frequent and infrequent word orders: An artificial language learning study

Most languages have a basic or “canonical” word order, which determines the relative positions of the subject (S), the verb (V), and the object (O) in a typical declarative sentence. The frequency of occurrence of the six possible word orders among world languages is not distributed uniformly. While SVO and SOV represent around 85% of world languages, orders like VSO (9%) or OSV (0.5%) are much less frequent or extremely rare. One possible explanation for this asymmetry is that biological and cognitive constraints for structured sequence processing make some word orders easier to be processed than others. Therefore, the high frequency of these word orders would be related to their higher learnability. The aim of the present study was to compare the learnability of different word orders between groups of adult subjects. Four artificial languages with different word orders were trained: two frequent (SVO, SOV) and two infrequent (VSO, OSV). In a test stage, subjects were asked to discriminate between new correct sentences and syntax or semantic violations. Higher performance rates and faster responses were observed for more frequent word orders. The results support the hypothesis that more frequent word orders are more easily learned.     

Auditory expectations for newly acquired structures

Our study investigated whether newly acquired auditory structure knowledge allows listeners to develop perceptual expectations for future events. For that aim, we introduced a new experimental approach that combines implicit learning and priming paradigms. Participants were first exposed to structured tone sequences without being told about the underlying artificial grammar. They then made speeded judgements on a perceptual feature of target tones in new sequences (i.e., in-tune/out-of-tune judgements). The target tones respected or violated the structure of the artificial grammar and were thus supposed to be expected or unexpected. In this priming task, grammatical tones were processed faster and more accurately than ungrammatical ones. This processing advantage was observed for an experimental group performing a memory task during the exposure phase, but was not observed for a control group, which was lacking the exposure phase (Experiment 1). It persisted when participants realized an in-tune/out-of-tune detection task during exposure (Experiment 2). This finding suggests that the acquisition of new structure knowledge not only influences grammaticality judgements on entire sequences (as previously shown in implicit learning research), but allows developing perceptual expectations that influence single event processing. It further promotes the priming paradigm as an implicit access to acquired artificial structure knowledge.     

Methodological vs. strategic control in artificial grammar learning: A commentary on Norman, Price and Jones (2011)

Norman et al. (2011) reported that participants exposed in succession to two artificial grammars could be able to learn implicitly about them, and could apply their knowledge strategically to select which string corresponds to one of these two grammars. In this commentary, I identify an artifact that could account for the learning obtained not only in this study, but also in some previous studies using the same procedures. I claim that more methodological control is needed before jumping to conclusions on the kind of strategic control that could be achieved unconsciously.     

The Relationship Between Artificial and Second Language Learning

Artificial language learning (ALL) experiments have become an important tool in exploring principles of language and language learning. A persistent question in all of this work, however, is whether ALL engages the linguistic system and whether ALL studies are ecologically valid assessments of natural language ability. In the present study, we considered these questions by examining the relationship between performance in an ALL task and second language learning ability. Participants enrolled in a Spanish language class were evaluated using a number of different measures of Spanish ability and classroom performance, which was compared to IQ and a number of different measures of ALL performance. The results show that success in ALL experiments, particularly more complex artificial languages, correlates positively with indices of L2 learning even after controlling for IQ. These findings provide a key link between studies involving ALL and our understanding of second language learning in the classroom.     

Perceptual constraints and the learnability of simple grammars

Cognitive processes are often attributed to statistical or symbolic general-purpose mechanisms. Here we show that some spontaneous generalizations are driven by specialized, highly constrained symbolic operations. We explore how two types of artificial grammars are acquired, one based on repetitions and the other on characteristic relations between tones ("ordinal" grammars). Whereas participants readily acquire repetition-based grammars, displaying early electrophysiological responses to grammar violations, they perform poorly with ordinal grammars, displaying no such electrophysiological responses. This outcome is problematic for both general symbolic and statistical models, which predict that both types of grammars should be processed equally easily. This suggests that some simple grammars are acquired using perceptual primitives rather than general-purpose mechanisms; such primitives may be elements of a "toolbox" of specialized computational heuristics, which may ultimately allow constructing a psychological theory of symbol manipulation.     

Mindfulness reduces habitual responding based on implicit knowledge: Evidence from artificial grammar learning

Participants were unknowingly exposed to complex regularities in a working memory task. The existence of implicit knowledge was subsequently inferred from a preference for stimuli with similar grammatical regularities. Several affective traits have been shown to influence AGL performance positively, many of which are related to a tendency for automatic responding. We therefore tested whether the mindfulness trait predicted a reduction of grammatically congruent preferences, and used emotional primes to explore the influence of affect. Mindfulness was shown to correlate negatively with grammatically congruent responses. Negative primes were shown to result in faster and more negative evaluations. We conclude that grammatically congruent preference ratings rely on habitual responses, and that our findings provide empirical evidence for the non-reactive disposition of the mindfulness trait.     

Implicit acquisition of grammars with crossed and nested non-adjacent dependencies: investigating the push-down stack model

A recent hypothesis in empirical brain research on language is that the fundamental difference between animal and human communication systems is captured by the distinction between finite-state and more complex phrase-structure grammars, such as context-free and context-sensitive grammars. However, the relevance of this distinction for the study of language as a neurobiological system has been questioned and it has been suggested that a more relevant and partly analogous distinction is that between non-adjacent and adjacent dependencies. Online memory resources are central to the processing of non-adjacent dependencies as information has to be maintained across intervening material. One proposal is that an external memory device in the form of a limited push-down stack is used to process non-adjacent dependencies. We tested this hypothesis in an artificial grammar learning paradigm where subjects acquired non-adjacent dependencies implicitly. Generally, we found no qualitative differences between the acquisition of non-adjacent dependencies and adjacent dependencies. This suggests that although the acquisition of non-adjacent dependencies requires more exposure to the acquisition material, it utilizes the same mechanisms used for acquiring adjacent dependencies. We challenge the push-down stack model further by testing its processing predictions for nested and crossed multiple non-adjacent dependencies. The push-down stack model is partly supported by the results, and we suggest that stack-like properties are some among many natural properties characterizing the underlying neurophysiological mechanisms that implement the online memory resources used in language and structured sequence processing.     

Applying an exemplar model to the artificial-grammar task: String completion and performance on individual items

Jamieson and Mewhort (2009a) demonstrated that performance in the artificial-grammar task could be understood using an exemplar model of memory. We reinforce the position by testing the model against data for individual test items both in a standard artificial-grammar experiment and in a string-completion variant of the standard procedure. We argue that retrieval is sensitive to structure in memory. The work ties performance in the artificial-grammar task to principles of explicit memory.     

Modes of knowledge acquisition and retrieval in artificial grammar learning

The aim of this study was to conceptualize artificial grammar learning (AGL) in terms of two orthogonal dimensions—the mode of knowledge acquisition and the mode of knowledge retrieval—as was done by Perlman and Tzelgov (2006) for sequence learning. Experiment 1 was carried out to validate our experimental task; Experiments 2–4 tested, respectively, performance in the intentional, incidental, and automatic retrieval modes, for each of the three modes of acquisition. Furthermore, signal detection theory (SDT) was used as an analytic tool, consistent with our assumption that the processing of legality-relevant information involves decisions along a continuous dimension of fluency. The results presented support the analysis of AGL in terms of the proposed dimensions. They also indicate that knowledge acquired during training may include many aspects of the presented stimuli (whole strings, relations among elements, etc.). The contribution of the various components to performance depends on both the specific instruction in the acquisition phase and the requirements of the retrieval task.     

Development of Different Forms of Skill Learning Throughout the Lifespan

The acquisition of complex motor, cognitive, and social skills, like playing a musical instrument or mastering sports or a language, is generally associated with implicit skill learning (SL). Although it is a general view that SL is most effective in childhood, and such skills are best acquired if learning starts early, this idea has rarely been tested by systematic empirical studies on the developmental pathways of SL from childhood to old age. In this paper, we challenge the view that childhood and early school years are the prime time for skill learning by tracking age‐related changes in performance in three different paradigms of SL. We collected data from participants between 7 and 87 years for (1) a Serial Reaction Time Task (SRT) testing the learning of motor sequences, (2) an Artificial Grammar Learning (AGL) task testing the extraction of regularities from auditory sequences, and (3) Probabilistic Category Learning in the Weather Prediction task (WP), a non‐sequential categorization task. Results on all three tasks show that adolescence and adulthood are the most efficient periods for skill learning, since instead of becoming less and less effective with age, SL improves from childhood into adulthood and then later declines with aging.     

Applying an exemplar model to the artificial-grammar task: Inferring grammaticality from similarity

We present three artificial-grammar experiments. The first used position constraints, and the second used sequential constraints. The third varied both the amount of training and the degree of sequential constraint. Increasing both the amount of training and the redundancy of the grammar benefited participants' ability to infer grammatical status; nevertheless, they were unable to describe the grammar. We applied a multitrace model of memory to the task. The model used a global measure of similarity to assess the grammatical status of the probe and captured performance both in our experiments and in three classic studies from the literature. The model shows that retrieval is sensitive to structure in memory, even when individual exemplars are encoded sparsely. The work ties an understanding of performance in the artificial-grammar task to the principles used to understand performance in episodic-memory tasks.     

Acquisition of hierarchical reactive skills in a unified cognitive architecture

In this paper, we review Icarus, a cognitive architecture that utilizes hierarchical skills and concepts for reactive execution in physical environments. In addition, we present two extensions to the framework. The first involves the incorporation of means-ends analysis, which lets the system compose known skills to solve novel problems. The second involves the storage of new skills that are based on successful means-ends traces. We report experimental studies of these mechanisms on three distinct domains. Our results suggest that the two methods interact to acquire useful skill hierarchies that generalize well and that reduce the effort required to handle new tasks. We conclude with a discussion of related work on learning and prospects for additional research, including extending the framework to cover developmental phenomena.     

Exemplars, prototypes, similarities, and rules in category representation: an example of hierarchical bayesian analysis

This article demonstrates the potential of using hierarchical Bayesian methods to relate models and data in the cognitive sciences. This is done using a worked example that considers an existing model of category representation, the Varying Abstraction Model (VAM), which attempts to infer the representations people use from their behavior in category learning tasks. The VAM allows for a wide variety of category representations to be inferred, but this article shows how a hierarchical Bayesian analysis can provide a unifying explanation of the representational possibilities using 2 parameters. One parameter controls the emphasis on abstraction in category representations, and the other controls the emphasis on similarity. Using 30 previously published data sets, this work shows how inferences about these parameters, and about the category representations they generate, can be used to evaluate data in terms of the ongoing exemplar versus prototype and similarity versus rules debates in the literature. Using this concrete example, this article emphasizes the advantages of hierarchical Bayesian models in converting model selection problems to parameter estimation problems, and providing one way of specifying theoretically based priors for competing models.     

Learning the form of causal relationships using hierarchical bayesian models

People learn quickly when reasoning about causal relationships, making inferences from limited data and avoiding spurious inferences. Efficient learning depends on abstract knowledge, which is often domain or context specific, and much of it must be learned. While such knowledge effects are well documented, little is known about exactly how we acquire knowledge that constrains learning. This work focuses on knowledge of the functional form of causal relationships; there are many kinds of relationships that can apply between causes and their effects, and knowledge of the form such a relationship takes is important in order to quickly identify the real causes of an observed effect. We developed a hierarchical Bayesian model of the acquisition of knowledge of the functional form of causal relationships and tested it in five experimental studies, considering disjunctive and conjunctive relationships, failure rates, and cross-domain effects. The Bayesian model accurately predicted human judgments and outperformed several alternative models.     

Rapid learning of syllable classes from a perceptually continuous speech stream

To learn a language, speakers must learn its words and rules from fluent speech; in particular, they must learn dependencies among linguistic classes. We show that when familiarized with a short artificial, subliminally bracketed stream, participants can learn relations about the structure of its words, which specify the classes of syllables occurring in first and last word positions. By studying the effect of familiarization length, we compared the general predictions of associative theories of learning and those of models postulating separate mechanisms for quickly extracting the word structure and for tracking the syllable distribution in the stream. As predicted by the dual-mechanism model, the preference for structurally correct items was negatively correlated with the familiarization length. This result is difficult to explain by purely associative schemes; an extensive set of neural network simulations confirmed this difficulty. Still, we show that powerful statistical computations operating on the stream are available to our participants, as they are sensitive to co-occurrence statistics among non-adjacent syllables. We suggest that different learning mechanisms analyze speech on-line: A rapid mechanism extracting structural information about the stream, and a slower mechanism detecting statistical regularities among the items occurring in it.