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.     

Essential unifiers

A substitution δ is less general than a substitution σ if there exists λ such that δ=σλ, which induces a notion of generality in the algebra of substitutions. We propose to look at this well known concept of generality again, and to impose a new quasi ordering on substitutions as a natural result of a stronger notion of the composition of substitutions. This new generality ordering has important consequences for the theory of E-unification (unification in equational theories) and changes the basic notion of the most general unifiers, now called essential unifiers, as well as the unification hierarchy. In particular we show that for idempotent Semigroups (associativity and idempotency), also known as Bands, the set of essential unifiers always exists and is finite.     

The Pentus Theorem for Lambek Calculus with Simple Nonlogical Axioms

The Lambek calculus introduced in Lambek [6] is a strengthening of the type reduction calculus of Ajdukiewicz [1]. We study Associative Lambek Calculus L in Gentzen style axiomatization enriched with a finite set Γ of nonlogical axioms, denoted by L(Γ).It is known that finite axiomatic extensions of Associative Lambek Calculus generate all recursively enumerable languages (see Buszkowski [2]). Then we confine nonlogical axioms to sequents of the form p → q, where p and q are atomic types. For calculus L(Γ) we prove interpolation lemma (modifying the Roorda proof for L [10]) and the binary reduction lemma (using the Pentus method [9] with modification from [3]). In consequence we obtain the weak equivalence of the Context-Free Grammars and grammars based on L(Γ).     

The International Significance of Human Rights

A comparative examination of four alternative ways of understandingwhat human rights are supports an institutional understanding assuggested by Article 28 of the Universal Declaration: Human rightsare weighty moral claims on any coercively imposed institutionalorder, national or international (as Article 28 confirms). Any suchorder must afford the persons on whom it is imposed secure accessto the objects of their human rights. This understanding of humanrights is broadly sharable across cultures and narrows the philosophical and practical differences between the friends ofcivil and political and the champions of social, economic, andcultural human rights. When applied to the global institutionalorder, it provides a new argument for conceiving human rights asuniversal – and a new basis for criticizing this order as tooencouraging of oppression, corruption, and poverty in the developing countries: We have a negative duty not to cooperatein the imposition of this global order if feasible reforms ofit would significantly improve the realization of human rights.     

Enhanced Verbal Statistical Learning in Glossolalia

Glossolalia (“speaking in tongues”) is a rhythmic utterance of word-like strings of sounds, regularly occurring in religious mass gatherings or various forms of private religious practices (e.g., prayer and meditation). Although specific verbal learning capacities may characterize glossolalists, empirical evidence is lacking. We administered three statistical learning tasks (artificial grammar, phoneme sequence, and visual-response sequence) to 30 glossolalists and 30 matched control volunteers. In artificial grammar, participants decide whether pseudowords and sentences follow previously acquired implicit rules or not. In sequence learning, they gradually draw out rules from repeating regularities in sequences of speech sounds or motor responses. Results revealed enhanced artificial grammar and phoneme sequence learning performances in glossolalists compared to control volunteers. There were significant positive correlations between daily glossolalia activity and artificial grammar learning. These results indicate that glossolalists exhibit enhanced abilities to extract the statistical regularities of verbal information, which may be related to their unusual language abilities.