Learning a Generative Probabilistic Grammar of Experience: A Process‐Level Model of Language Acquisition

We introduce a set of biologically and computationally motivated design choices for modeling the learning of language, or of other types of sequential, hierarchically structured experience and behavior, and describe an implemented system that conforms to these choices and is capable of unsupervised learning from raw natural‐language corpora. Given a stream of linguistic input, our model incrementally learns a grammar that captures its statistical patterns, which can then be used to parse or generate new data. The grammar constructed in this manner takes the form of a directed weighted graph, whose nodes are recursively (hierarchically) defined patterns over the elements of the input stream. We evaluated the model in seventeen experiments, grouped into five studies, which examined, respectively, (a) the generative ability of grammar learned from a corpus of natural language, (b) the characteristics of the learned representation, (c) sequence segmentation and chunking, (d) artificial grammar learning, and (e) certain types of structure dependence. The model's performance largely vindicates our design choices, suggesting that progress in modeling language acquisition can be made on a broad front—ranging from issues of generativity to the replication of human experimental findings—by bringing biological and computational considerations, as well as lessons from prior efforts, to bear on the modeling approach.     

Participation Patterns Among Mexican–American Parents Enrolled in a Universal Intervention and Their Association with Child Externalizing Outcomes

This study used growth mixture modeling to examine attendance trajectories among 292 Mexican–American primary female caregivers enrolled in a universal preventive intervention and the effects of health beliefs, participation intentions, cultural influences, and intervention group cohesion on trajectory group membership as well as trajectory group differences on a distal outcome, immediate posttest teacher report of child externalizing (T2). Results supported four trajectory groups—early terminators (ET), mid-program terminators (MPT), low-risk persistent attenders (LRPA), and high-risk persistent attenders (HRPA). Compared with LRPAs, caregivers classified as HRPAs had weaker familism values, less parenting efficacy, and higher externalizing children with lower GPAs. Caregivers in the two persistent attender groups reported strong group cohesion and providers rated these caregivers as having strong participation intentions. Children of caregivers in the LRPA group had the lowest T2 child externalizing. Children of caregivers in the MPT group had lower T2 externalizing than did those of the ET group, suggesting partial intervention dosage can benefit families. Despite high levels of attendance, children of caregivers in the HRPA had the highest T2 externalizing, suggesting this high-risk group needed either more intensive services or a longer period for parents to implement program skills to evidence change in child externalizing.     

A Computational Model for the Item‐Based Induction of Construction Networks

According to usage‐based approaches to language acquisition, linguistic knowledge is represented in the form of constructions—form‐meaning pairings—at multiple levels of abstraction and complexity. The emergence of syntactic knowledge is assumed to be a result of the gradual abstraction of lexically specific and item‐based linguistic knowledge. In this article, we explore how the gradual emergence of a network consisting of constructions at varying degrees of complexity can be modeled computationally. Linguistic knowledge is learned by observing natural language utterances in an ambiguous context. To determine meanings of constructions starting from ambiguous contexts, we rely on the principle of cross‐situational learning. While this mechanism has been implemented in several computational models, these models typically focus on learning mappings between words and referents. In contrast, in our model, we show how cross‐situational learning can be applied consistently to learn correspondences between form and meaning beyond such simple correspondences.     

Childhood intelligence predicts premature mortality: Results from a 40-year population-based longitudinal study

Childhood intelligence has been shown to predict mortality risk in adulthood. This relation has never been investigated in a Central European country with universal health care. The present study investigated whether childhood intelligence predicts mortality risk across 40 years in Luxembourg. 2543 participants completed an intelligence test at age 12 in 1968, and the mortality rate in this sample until 2008 was recorded. Our results showed that higher childhood intelligence predicted a lower risk for mortality, even when childhood socioeconomic status was controlled for. This effect was strongest in men belonging to the group of the lowest 20% in intelligence. These results indicate that even universal access to health care cannot fully offset the cumulative effects of intelligence on mortality.     

Intention, interpretation and the computational structure of language

I show how a conversational process that takes simple, intuitively meaningful steps may be understood as a sophisticated computation that derives the richly detailed, complex representations implicit in our knowledge of language. To develop the account, I argue that natural language is structured in a way that lets us formalize grammatical knowledge precisely in terms of rich primitives of interpretation. Primitives of interpretation can be correctly viewed intentionally, as explanations of our choices of linguistic actions; the model therefore fits our intuitions about meaning in conversation. Nevertheless, interpretations for complex utterances can be built from these primitives by simple operations of grammatical derivation. In bridging analyses of meaning at semantic and symbol-processing levels, this account underscores the fundamental place for computation in the cognitive science of language use.     

Artificial syntactic violations activate Broca’s region

In the present study, using event-related functional magnetic resonance imaging, we investigated a group of participants on a grammaticality classification task after they had been exposed to well-formed consonant strings generated from an artificial regular grammar. We used an implicit acquisition paradigm in which the participants were exposed to positive examples. The objective of this study was to investigate whether brain regions related to language processing overlap with the brain regions activated by the grammaticality classification task used in the present study. Recent meta-analyses of functional neuroimaging studies indicate that syntactic processing is related to the left inferior frontal gyrus (Brodmann’s areas 44 and 45) or Broca’s region. In the present study, we observed that artificial grammaticality violations activated Broca’s region in all participants. This observation lends some support to the suggestions that artificial grammar learning represents a model for investigating aspects of language learning in infants [TICS 4 (2000) 178] and adults [Proceedings of the National Academy of Sciences of United States of America 99 (2002) 529].     

《周易》古经词法探析

《周易》古经是研究上古汉语不可多得的宝贵资料，然而语言学界对它尚缺乏足够的重视，忽略了对它的研究。本文是继《〈周易〉古经句法探析》之后又一篇研究古经语法的文章。它系统地论述了古经文的构词法、各种词类及其语法作用；指出了作为上古汉语著作的古经在词法上的初始性和不完备性，以及词法与句法发展的不平衡性，揭示了上古语法发展的一个特点。