Social Construction of Intimate Partner Violence: A Brief Report on Quantitative Grammatical Analysis

This pilot study uses a quantitative linguistic analysis to assess evidence of agency and control, as well as treatment engagement, in letters written by men (N = 46, 37% Latino) participating in a batterer intervention program. At the program’s conclusion, men were asked to imagine their situation in 5 years and write a letter from their “future self” to their “present self.” Letters were scored for grammatical representations of agency and objectification, and temporal complexity. Results showed that the men tended to grant themselves the subject position (conferring agency) and to grant others the object position (deferring or denying agency) by almost equal margins, both rhetorical maneuvers suggesting an objectifying stance toward others. Moreover, they employed several strategies to address temporality, with more complex strategies suggesting greater treatment engagement. Findings suggest that quantitative linguistic analysis is a promising tool for assessing participants’ progress in and engagement with treatment.     

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.     

Learning Orthographic Structure With Sequential Generative Neural Networks

Learning the structure of event sequences is a ubiquitous problem in cognition and particularly in language. One possible solution is to learn a probabilistic generative model of sequences that allows making predictions about upcoming events. Though appealing from a neurobiological standpoint, this approach is typically not pursued in connectionist modeling. Here, we investigated a sequential version of the restricted Boltzmann machine (RBM), a stochastic recurrent neural network that extracts high‐order structure from sensory data through unsupervised generative learning and can encode contextual information in the form of internal, distributed representations. We assessed whether this type of network can extract the orthographic structure of English monosyllables by learning a generative model of the letter sequences forming a word training corpus. We show that the network learned an accurate probabilistic model of English graphotactics, which can be used to make predictions about the letter following a given context as well as to autonomously generate high‐quality pseudowords. The model was compared to an extended version of simple recurrent networks, augmented with a stochastic process that allows autonomous generation of sequences, and to non‐connectionist probabilistic models (n‐grams and hidden Markov models). We conclude that sequential RBMs and stochastic simple recurrent networks are promising candidates for modeling cognition in the temporal domain.     

The role of procedural memory in grammar and numeracy skills

The objective of this study was to examine the contribution of procedural memory to grammar and numeracy skills, which both involve the manipulation of abstract patterns. Seventy-six typically developing children between 5 and 7 years of age were assessed on grammar with a past tense production task and a sentence comprehension task, on numeracy with a counting task and a calculation task, and on procedural memory with a serial reaction time task. Moderate correlations were found between the measures of grammar and numeracy. Moreover, 4 hierarchical linear regressions indicated that procedural memory was associated with calculation but not with counting or grammar skills when age and working memory were taken into account. These novel findings suggest that procedural memory may have a role to play in the development of some numeracy skills. Several possible explanations for the absence of contribution to grammar are considered.     

Luther's Small and Large Catechisms: Defining and Confessing Christian Faith From the Centre in a Religiously Plural World

Abstract : In this article, the author argues that in his Small and Large Catechisms, which were both written in 1529, Martin Luther centres the Christian faith in a way that others can recognise as authentic and faithful to the Gospel vis‐à‐vis the relativism that is posited as the appropriate Christian articulation of the Gospel in a world of religious diversity. Luther's non‐negotiable centring on God for us in Jesus Christ, through whom God is uniquely and decisively revealed, speaks to the contemporary intra‐Christian and inter‐religious questions. The author finds evangelical and persuasive resonance in Lesslie Newbigin's call to indwell the Christian story and George Lindbeck's argument to attend to the grammar of the faith.     

From exemplar to grammar: a probabilistic analogy-based model of language learning

While rules and exemplars are usually viewed as opposites, this paper argues that they form end points of the same distribution. By representing both rules and exemplars as (partial) trees, we can take into account the fluid middle ground between the two extremes. This insight is the starting point for a new theory of language learning that is based on the following idea: If a language learner does not know which phrase-structure trees should be assigned to initial sentences, s/he allows (implicitly) for all possible trees and lets linguistic experience decide which is the "best" tree for each sentence. The best tree is obtained by maximizing "structural analogy" between a sentence and previous sentences, which is formalized by the most probable shortest combination of subtrees from all trees of previous sentences. Corpus-based experiments with this model on the Penn Treebank and the Childes database indicate that it can learn both exemplar-based and rule-based aspects of language, ranging from phrasal verbs to auxiliary fronting. By having learned the syntactic structures of sentences, we have also learned the grammar implicit in these structures, which can in turn be used to produce new sentences. We show that our model mimicks children's language development from item-based constructions to abstract constructions, and that the model can simulate some of the errors made by children in producing complex questions.     

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.     

The ideal scaffolding of language: Husser's fourth Logical Investigation in the light of cognitive linguistics

One of the central issues in linguistics is whether or not language should be considered a self-contained, autonomous formal system, essentially reducible to the syntactic algorithms of meaning construction (as Chomskyan grammar would have it), or a holistic-functional system serving the means of expressing pre-organized intentional contents and thus accessible with respect to features and structures pertaining to other cognitive subsystems or to human experience as such (as Cognitive Linguistics would have it). The latter claim depends critically on the existence of principles governing the composition of semantic contents. Husserl's fourth Logical Investigation is well known as a genuine precursor for Chomskyan grammar. However, I will establish the heterogeneous character of the Investigation and show that the whole first part of it is devoted to the exposition of a semantic combinatorial system cognate to the one elaborated within Cognitive Linguistics. I will thus show how theoretical results in linguistics may serve to corroborate and shed light on those parts of Husserl's Fourth Investigation that have traditionally been dismissed as vague or simply ignored.     

Sequential learning in individuals with agrammatic aphasia: evidence from artificial grammar learning

We examined sequential learning in individuals with agrammatic aphasia (n = 12) and healthy age-matched participants (n = 12) using an artificial grammar. Artificial grammar acquisition, 24-hour retention, and the potential benefits of additional training were examined by administering an artificial grammar judgment test (1) immediately following auditory exposure-based training, (2) one day after training, and (3) after a second training session on the second day. An untrained control group (n = 12 healthy age-matched participants) completed the tests on the same time schedule. The trained healthy and aphasic groups showed greater sensitivity to the detection of grammatical items than the control group. No significant correlations between sequential learning and language abilities were observed among the aphasic participants. The results suggest that individuals with agrammatic aphasia show sequential learning, but the underlying processes involved in this learning may be different than for healthy adults.     

Discovering syntactic deep structure via Bayesian statistics

In the Bayesian framework, a language learner should seek a grammar that explains observed data well and is also a priori probable. This paper proposes such a measure of prior probability. Indeed it develops a full statistical framework for lexicalized syntax. The learner's job is to discover the system of probabilistic transformations (often called lexical redundancy rules) that underlies the patterns of regular and irregular syntactic constructions listed in the lexicon. Specifically, the learner discovers what transformations apply in the language, how often they apply, and in what contexts. It considers simpler systems of transformations to be more probable a priori. Experiments show that the learned transformations are more effective than previous statistical models at predicting the probabilities of lexical entries, especially those for which the learner had no direct evidence.