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.     

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.     

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.     

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.     

Want to block earworms from conscious awareness?B(u)y gum!

In a recent article, Jamieson and Mewhort (2009) proposed a novel account of artificial grammar learning (AGL), which is based on a multitrace model of episodic memory, the Minerva 2 model. According to this account, test performance in AGL is based on an assessment of global similarity of the test strings to the memory traces of the training strings. In this article, simulation studies are presented, showing for three different AGL experiments that the predictions of the Minerva 2 model strikingly deviate from participants' performance. It is argued that participants' test performance is not generally based on general similarity.     

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.     

人工语法中的内隐学习实验研究

用Reber等人发明的人工语法和人工语法学习程序,研究在复杂规则学习中的内隐学习与外显学习过程.实验结果发现内隐学习中启动效应存在,内隐学习效应明显,支持Reber等人提出的内隐学习理论;对内隐记忆与外显记忆关系的研究,支持杨治良等(1998)提出的内隐和外显记忆的"钢筋水泥"结构性模型的假设.     

A Lambek Automaton

We define an automata-theoretic counterpart of (type-logical)grammars based on the (associative) Lambek-calculus L, a prominentformalism in computational linguistics. While the usual push-downautomaton (PDA) has the same weak generative power as the L-basedgrammars (Pentus, 1995), there is no direct relationship betweenthe computations of a PDA for some language L and the derivationsof an L-based grammar for L. In the Lambek-automaton, on theother hand, there is a tight relation (1-1) between automatoncomputations and grammar derivations. The automaton exhibitsa novel mode of operation, using hypothetical steps, directlyinspired by the hypothetical reasoning embodied by L.     

Artificial consciousness, artificial emotions, and autonomous robots

Nowadays for robots, the notion of behavior is reduced to a simple factual concept at the level of the movements. On another hand, consciousness is a very cultural concept, founding the main property of human beings, according to themselves. We propose to develop a computable transposition of the consciousness concepts into artificial brains, able to express emotions and consciousness facts. The production of such artificial brains allows the intentional and really adaptive behavior for the autonomous robots. Such a system managing the robot’s behavior will be made of two parts: the first one computes and generates, in a constructivist manner, a representation for the robot moving in its environment, and using symbols and concepts. The other part achieves the representation of the previous one using morphologies in a dynamic geometrical way. The robot’s body will be seen for itself as the morphologic apprehension of its material substrata. The model goes strictly by the notion of massive multi-agent’s organizations with a morphologic control.     

Operational Noology as a new methodology for the study of thought and language: theoretical aspects and possible practical applications

In this article, the author presents a new methodology for the study of two fundamental components of consciousness, that is thought and language. The fundamental presupposition that forms the basis of this methodology is that thought is not simply a passive "reflection" of an external "reality", but also (and especially) something active, i.e. that the fundamental components of thought are sequences of operations, amongst which the ones of attention play a key role. These sequences of elementary mental operations are called mental categories, and are the meanings of all the words that do not seem to indicate something physical (first of all, all the "grammatical" words, that is conjunctions, prepositions, articles, pronouns, fundamental verbs like "to be", "to have" etc., the main adverbs, and, in the large number of languages that have a more or less rich morphology, all morphemes (the ones which indicate cases, in languages that have cases, the number and the gender of nouns and adjectives, moods and tenses of the verb etc.). The author proposes a list of these elementary mental operations and he shows how it is possible, basing ourselves on them, to identify the meanings of these words, which are indispensable for any linguistic expression. The author also mentions a possible short-term practical application of these theories, i.e. a device in order to improve the quality of machine translation. He also formulates the hypothesis that these theories could allow us to look in a new way at the problem of the (partial) artificial reproduction of the human activity of thought and language.