Recursive grammar is the sole cognitive mechanism unique to human languages. This study investigates three aspects of the acquisition of the recursive possessive marker de by Mandarin-speaking children: the feasibility of recursive possessive (RP) levels, performance in both cognition and competence, and the age at which these abilities emerge. Following a development theory of recursive possessives in language acquisition, we conducted two tasks—comprehension and production—each comprising 12 questions. The comprehension task involved a truth-value judgment, while the production task consisted of a question-and-answer session. Forty-eight preschoolers, aged three to five, were recruited from a kindergarten and divided into three age groups. Additionally, 16 adults were randomly recruited to serve as a baseline for comparison. The results revealed that significant effects of levels of recursive possessiveness were observed only in five-year-old Mandarin-speaking children, not in their three- and four-year-old counterparts. The comprehension task was significantly easier for all participants compared to the production task, suggesting that understanding the material was not challenging for any group, with no substantial differences noted among them. The results demonstrated that age effects were critical for younger children in adopting RPs. Specifically, three- and four-year-old Mandarin-speaking children demonstrated the ability to comprehend multiple levels of RP but struggled to produce them. In contrast, five-year-olds exhibited adult-like comprehension and the ability to produce Level 1 RP.

Abstract This article describes a simple PCFG induction model with a fixed category domain that predicts a large majority of attested constituent boundaries, and predicts labels consistent with nearly half of attested constituent labels on a standard evaluation data set of child-directed speech. The article then explores the idea that the difference between simple grammars exhibited by child learners and fully recursive grammars exhibited by adult learners may be an effect of increasing working memory capacity, where the shallow grammars are constrained images of the recursive grammars. An implementation of these memory bounds as limits on center embedding in a depth-specific transform of a recursive grammar yields a significant improvement over an equivalent but unbounded baseline, suggesting that this arrangement may indeed confer a learning advantage.

In this paper I explicate polyrhythm in the context of traditional West African music, framing it within a more general theory of polyrhythm and polymeter, then compare three approaches for the visual representation of both. In contrast to their analytical separation in Western theory and practice, traditional West African music features integral connections among all the expressive arts (music, poetry, dance, and drama), and the unity of rhythm and melody (what Nzewi calls “melo-rhythm”). Focusing on the Ewe people of south-eastern Ghana, I introduce the multi-art performance type called Agbekor, highlighting its poly-melo-rhythms, and representing them in three notational systems: the well-known but culturally biased Western notation; a more neutral tabular notation, widely used in ethnomusicology but more limited in its representation of structure; and a context-free recursive grammar of my own devising, which concisely summarizes structure, at the possible cost of readability. Examples are presented, and the strengths and drawbacks of each system are assessed. While undoubtedly useful, visual representations cannot replace audio-visual recordings, much less the experience of participation in a live performance.

Processing of recursion has been proposed as the foundation of human linguistic ability. Yet this ability may be shared with other domains, such as the musical or rhythmic domain. Lindenmayer grammars (L-systems) have been proposed as a recursive grammar for use in artificial grammar experiments to test recursive processing abilities, and previous work had shown that participants are able to learn such a grammar using linguistic stimuli (syllables). In the present work, we used two experimental paradigms (a yes/no task and a two-alternative forced choice) to test whether adult participants are able to learn a recursive Lindenmayer grammar composed of drum sounds. After a brief exposure phase, we found that participants at the group level were sensitive to the exposure grammar and capable of distinguishing the grammatical and ungrammatical test strings above chance level in both tasks. While we found evidence of participants’ sensitivity to a very complex L-system grammar in a non-linguistic, potentially musical domain, the results were not robust. We discuss the discrepancy within our results and with the previous literature using L-systems in the linguistic domain. Furthermore, we propose directions for future music cognition research using L-system grammars.

Multi-period data driven control strategy for real-time management of energy storages in virtual power plants integrated with power grid

Grammatical evolution (GE) is one of the most widespread techniques in evolutionary computation. Genotypes in GE are bit strings while phenotypes are strings, of a language defined by a user-provided context-free grammar. In this paper, we propose a novel procedure for mapping genotypes to phenotypes that we call weighted hierarchical GE (WHGE). WHGE imposes a form of hierarchy on the genotype and encodes grammar symbols with a varying number of bits based on the relative expressive power of those symbols. WHGE does not impose any constraint on the overall GE framework, in particular, WHGE may handle recursive grammars, uses the classical genetic operators, and does not need to define any bound in advance on the size of phenotypes. We assessed experimentally our proposal in depth on a set of challenging and carefully selected benchmarks, comparing the results of the standard GE framework as well as two of the most significant enhancements proposed in the literature: 1) position-independent GE and 2) structured GE. Our results show that WHGE delivers very good results in terms of fitness as well as in terms of the properties of the genotype-phenotype mapping procedure.

In music, chords are organized into hierarchical structures based on recursive or embedded syntax. How the brain extracts recursive grammar is a central question in musical cognition and other cognitive neuroscience, but the precise mechanism remains unclear. By analyzing event related potentials (ERPs) and neural oscillatory activity, the present study investigated neurocognitive mechanisms underlying the processing of center-embedded structure in music by examining the differences in center-embedded and non-embedded structure processing and evaluating how these differences are affected by musical proficiency. Based on Western musical proficiency, the subjects were divided into two groups, non-experts and experts. The results revealed that for non-experts, the processing of center-embedded structure elicited greater early right-anterior negativity (ERAN) and N5 components as well as, reduced alpha and gamma activities than did the non-embedded structure. For experts, no significant difference in the ERP response was observed between the processing of non-embedded and center-embedded structures; however, the processing of center-embedded structure elicited increased beta activity compared to non-embedded structure. These findings indicate that listeners different in proficiency would rely on different cognitive neural mechanisms in music processing with the syntactic complexity increases.

It has been suggested that external and/or internal limitations paradoxically may lead to superior learning, that is, the concepts of starting small and less is more (Elman, 1993; Newport, 1990). In this paper, we explore the type of incremental ordering during training that might help learning, and what mechanism explains this facilitation. We report four artificial grammar learning experiments with human participants. In Experiments 1a and 1b we found a beneficial effect of starting small using two types of simple recursive grammars: right‐branching and center‐embedding, with recursive embedded clauses in fixed positions and fixed length. This effect was replicated in Experiment 2 (N = 100). In Experiment 3 and 4, we used a more complex center‐embedded grammar with recursive loops in variable positions, producing strings of variable length. When participants were presented an incremental ordering of training stimuli, as in natural language, they were better able to generalize their knowledge of simple units to more complex units when the training input “grew” according to structural complexity, compared to when it “grew” according to string length. Overall, the results suggest that starting small confers an advantage for learning complex center‐embedded structures when the input is organized according to structural complexity.

This paper presents several new techniques for volumetric cloud rendering using efficient algorithms and data structures based on ray-tracing methods for cumulus generation, achieving an optimum balance between realism and performance. These techniques target applications such as flight simulations, computer games, and educational software, even with conventional graphics hardware. The contours of clouds are defined by implicit mathematical expressions or triangulated structures inside which volumetric rendering is performed. Novel techniques are used to reproduce the asymmetrical nature of clouds and the effects of light-scattering, with low computing costs. The work includes a new method to create randomized fractal clouds using a recursive grammar. The graphical results are comparable to those produced by state-of-the-art, hyper-realistic algorithms. These methods provide real-time performance, and are superior to particle-based systems. These outcomes suggest that our methods offer a good balance between realism and performance, and are suitable for use in the standard graphics industry.

This study investigated the effect of semantic information on artificial grammar learning (AGL). Recursive grammars of different complexity levels (regular language, mirror language, copy language) were investigated in a series of AGL experiments. In the with-semantics condition, participants acquired semantic information prior to the AGL experiment; in the without-semantics control condition, participants did not receive semantic information. It was hypothesized that semantics would generally facilitate grammar acquisition and that the learning benefit in the with-semantics conditions would increase with increasing grammar complexity. Experiment 1 showed learning effects for all grammars but no performance difference between conditions. Experiment 2 replicated the absence of a semantic benefit for all grammars even though semantic information was more prominent during grammar acquisition as compared to Experiment 1. Thus, we did not find evidence for the idea that semantics facilitates grammar acquisition, which seems to support the view of an independent syntactic processing component.

Grammar came later: Triality of patterning and the gradual evolution of language

Recursive symmetries for geometrically complex and materially heterogeneous additive manufacturing

Language sciences have long maintained a close and supposedly necessary coupling between the infinite productivity of the human language faculty and recursive grammars. Because of the formal equivalence between recursion and non-recursive iteration; recursion, in the technical sense, is never a necessary component of a generative grammar. Contrary to some assertions this equivalence extends to both center-embedded relative clauses and hierarchical parse trees. Inspection of language usage suggests that recursive rule components in fact contribute very little, and likely nothing significant, to linguistic creativity. Further than this, if the productivity of human language is considered as not rigidly bound, but not infinite, then the need for any sort of iteration in generative grammars vanishes and can be replaced with a Non-iterative Explicit Alternatives Rule grammar. The knock-on effects of dispensing with recursive (or any iterative) grammar components are: that language diversity can simply be based on rule and lexicon combinatorics with no potentially infinite dimensions derived from recursive or iterative components of rules; the oddity of a vast, multiply-infinite competence set of `grammatical but unacceptable' productions is gone; and the development of a language faculty based on rules that eschew iterative rule components avoids any need for explaining `special' mechanisms. On the broader front of searching for the mechanisms of mind, our analysis can be similarly applied to the proposals for a recursive basis for mind as an explanation for humanity's great leap forward.

Modern pedagogical software is open-ended and flexible, allowing students to solve problems through exploration and trial-and-error. Such exploratory settings provide for a rich educational environment for students, but they challenge teachers to keep track of students’ progress and to assess their performance. This article presents techniques for recognizing students’ activities in such pedagogical software and visualizing these activities to teachers. It describes a new plan recognition algorithm that uses a recursive grammar that takes into account repetition and interleaving of activities. This algorithm was evaluated empirically using an exploratory environment for teaching chemistry used by thousands of students in several countries. It was always able to correctly infer students’ plans when the appropriate grammar was available. We designed two methods for visualizing students’ activities for teachers: one that visualizes students’ inferred plans, and one that visualizes students’ interactions over a timeline. Both of these visualization methods were preferred to and found more helpful than a baseline method which showed a movie of students’ interactions. These results demonstrate the benefit of combining novel AI techniques and visualization methods for the purpose of designing collaborative systems that support students in their problem solving and teachers in their understanding of students’ performance.

In this review we re-evaluate the recursion-only hypothesis, advocated by Fitch, Hauser and Chomsky (Hauser, Chomsky & Fitch, 2002; Fitch, Hauser & Chomsky, 2005). According to the recursion-only hypothesis, the property that distinguishes human language from animal communication systems is recursion, which refers to the potentially infinite embedding of one linguistic representation within another of the same type. This hypothesis predicts (1) that non-human primates and other animals lack the ability to learn recursive grammar, and (2) that recursive grammar is the sole cognitive mechanism that is unique to human language. We first review animal studies of recursive grammar, before turning to the claim that recursion is a property of all human languages. Finally, we discuss other views on what abilities may be unique to human language.

In an artificial grammar learning study, Lai & Poletiek (2011) found that human participants could learn a center-embedded recursive grammar only if the input during training was presented in a staged fashion. Previous studies on artificial grammar learning, with randomly ordered input, failed to demonstrate learning of such a center-embedded structure. In the account proposed here, the staged input effect is explained by a fine-tuned match between the statistical characteristics of the incrementally organized input and the development of human cognitive learning over time, from low level, linear associative, to hierarchical processing of long distance dependencies. Interestingly, staged input seems to be effective only for learning hierarchical structures, and unhelpful for learning linear grammars.

GLL Parsing

Many software development tools that assist with tasks such as testing and maintenance are specific to a particular development language and require a parser for that language. Because a grammar is required to develop a parser, construction of these software development tools is dependent upon the availability of a grammar for the development language. However, a grammar is not always available for a language and, in these cases, acquiring a grammar is the most difficult, costly, and time-consuming phase of tool construction. In this paper, we describe a methodology for grammar recovery from a hard-coded parser. Our methodology is comprised of manual instrumentation of the parser, a technique for automatic grammar recovery from parse trees, and a semi-automatic metrics-guided approach to refactoring an iterative grammar to obtain a recursive grammar. We present the results of a case study in which we recover and refactor a grammar from version 4.0.0 of the GNU C++ parser and then refactor the recovered grammar using our metrics-guided approach. Additionally, we present an evaluation of the recovered and refactored grammar by comparing it to the ISO C++98 grammar.

Behavioral contingency analysis

terms, how human beings develop Grammar. In the process of Grammar development (language acquisition process), children are exposed only to a small, finite number of utterances. Yet ali over the world, children 'instinctively' develop a recursive Grammar that allows them to produce and understand an infinite number of sentences of their language. Now, this 'instinct' that guides children to develop recursive Grammars, rather than some other kind of system, shows that the human mind must have an innate notion of the form of Grammar; that is, human beings are biologically endowed with specific mental structures for developing Grammar. Or, to put it in psychological terms, the human mind-brain must possess an innate language faculty, which determines the general form of Grammars that human languages must have.