Do Bilinguals Automatically Activate Their Native Language When They Are Not Using It?

Most models of lexical access assume that bilingual speakers activate their two languages even when they are in a context in which only one language is used. A critical piece of evidence used to support this notion is the observation that a given word automatically activates its translation equivalent in the other language. Here, we argue that these findings are compatible with a different account, in which bilinguals “carry over” the structure of their native language to the non‐native language during learning, and where there is no activation of translation equivalents. To demonstrate this, we describe a model in which language learning involves mapping native language phonological relationships to the non‐native language, and we show how it can explain the results attributed to automatic activation of translation equivalents.     

LES RAPPORTS ENTRE LE LANGAGE ET LA PENSÉE POINT DE VUE d'UN LINGUISTE

The author first wonders whether it was necessary to re-examine now the old problem of the relations between language and thought, as they are still too much related to traditional philosophical formulations, even among psychologists. He gives the reasons why, since Whitney, the scientific linguists, because of their epistemological prudence, voluntarily discarded this problem which they were not armed to study. Then he surveys some of the issues which, after a century of healthy anti-mentalistic views, an interdisciplinary scientific study could presently approach in a more valid and less ambitious experimental frame of work: thought without language, Whorf's hypothesis, relations between language and “splitting of the world”, lessons from experiences of translators, learning of language and pathology of language. Eventually, even though he seems to be avoiding it, he outlines the very complex problem of evaluating the still altogether unstructured draft of model with which Chomsky proposes to explain the relations between language and thought. The validity of this model is related to epistemological presuppositions and to a use of the notion of hypothetical-deductive models which it is difficult, but necessary to scrutinize at first.     

语言转换的认知及其神经机制

语言转换是双语研究领域的焦点问题之一,抑制控制模型、语言特异性选择模型、任务设置惯性模型和序列难度效应模型从不同的角度解释了语言转换的认知机制。认知神经科学的一些研究发现,语言转换与任务转换具有类似的神经机制,说明语言转换代价与任务转换代价的本质可能是相同的。抑制控制和语言熟练程度如何影响语言的转换,语言转换代价与任务转换代价的本质是否相同,一般领域的抑制机制是否可以迁移到语言转换领域等问题是未来需要进一步研究的问题。     

Modeling language acquisition in atypical phenotypes

An increasing number of connectionist models have been proposed to explain behavioral deficits in developmental disorders. These simulations motivate serious consideration of the theoretical implications of the claim that a developmental disorder fits within the parameter space of a particular computational model of normal development. The authors examine these issues in depth with respect to a series of new simulations investigating past-tense formation in Williams syndrome. This syndrome and the past-tense domain are highly relevant because both have been used to make strong theoretical claims about the processes underlying normal language acquisition. The authors conclude that computational models have great potential to advance psychologists' understanding of developmental deficits because they focus on the developmental process itself as a pivotal causal factor in producing atypical phenotypic outcomes.     

Using E-Z reader to model the effects of higher level language processing on eye movements during reading

Although computational models of eye-movement control during reading have been used to explain how saccadic programming, visual constraints, attention allocation, and lexical processing jointly affect eye movements during reading, these models have largely ignored the issue of how higher level, postlexical language processing affects eye movements. The present article shows how one of these models, E-Z Reader (Pollatsek, Reichle, & Rayner, 2006c), can be augmented to redress this limitation. Simulations show that with a few simple assumptions, the model can account for the fact that effects of higher level language processing are not observed on eye movements when such processing is occurring without difficulty, but can capture the patterns of eye movements that are observed when such processing is slowed or disrupted.     

No frills: Simple regularities in language can go a long way in the development of word knowledge

Recent years have seen a flourishing of Natural Language Processing models that can mimic many aspects of human language fluency. These models harness a simple, decades-old idea: It is possible to learn a lot about word meanings just from exposure to language, because words similar in meaning are used in language in similar ways. The successes of these models raise the intriguing possibility that exposure to word use in language also shapes the word knowledge that children amass during development. However, this possibility is strongly challenged by the fact that models use language input and learning mechanisms that may be unavailable to children. Across three studies, we found that unrealistically complex input and learning mechanisms are unnecessary. Instead, simple regularities of word use in children's language input that they have the capacity to learn can foster knowledge about word meanings. Thus, exposure to language may play a simple but powerful role in children's growing word knowledge. A video abstract of this article can be viewed at https://youtu.be/dT83dmMffnM .

Research Highlights

• Natural Language Processing (NLP) models can learn that words are similar in meaning from higher-order statistical regularities of word use.
• Unlike NLP models, infants and children may primarily learn only simple co-occurrences between words.
• We show that infants' and children's language input is rich in simple co-occurrence that can support learning similarities in meaning between words.
• We find that simple co-occurrences can explain infants' and children's knowledge that words are similar in meaning.

     

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.     

Laying down a path in talking

This paper aims to provide a starting point for a non-representational approach to language. It will do so by undoing some of the reifying tendencies that are at the heart of the ontology of scientific psychology. Although non-representational theories are beginning to emerge, they remain committed to giving explanations in terms of ontological structures that are independent of human activity. If they maintain this commitment it is unlikely that they will displace representationalism in domains such as language. By following some of Wittgenstein’s remarks on language, I explain the phenomenon of reification by carefully considering the formative, situational flow of language—thus without invoking representations. In this way, the paper sketches a direction of approach for a non-representational theory of language, undercutting the most important assumptions that justify an explanatory ontology devoid of human activity.     

A Unified Theory of the Atonement

Abstract:  Following Aulén, three broad, and competing, models of the atonement are outlined in this article, and an argument is offered to suggest that the mediatorial model should be considered normative. The argument proceeds through discussion with N.T. Wright's adoption of the victory model as normative in his expositions of Pauline theology. It is here suggested that the victory model is inherently unsuitable as a paradigm for the soteriology of Romans, and argued that the assertion that it can explain the process of reconciliation lacks foundation. Further, there is indication as to how the occasional language of victory in the New Testament can often be incorporated naturally within a mediation framework.     

A Probabilistic Constraints Approach to Language Acquisition and Processing

This article provides an overview of a probabilistic constraints framework for thinking about language acquisition and processing. The generative approach attempts to characterize knowledge of language (i.e., competence grammar) and then asks how this knowledge is acquired and used. Our approach is performance oriented: the goal is to explain how people comprehend and produce utterances and how children acquire this skill. Use of language involves exploiting multiple probabilistic constraints over various types of linguistic and nonlinguistic information. Acquisition is the process of accumulating this information, which begins in infancy. The constraint satisfaction processes that are central to language use are the same as the bootstrapping processes that provide entry to language for the child. Framing questions about acquisition in terms of models of adult performance unifies the two topics under a set of common principles and has important consequences for arguments concerning language learnability.     

Artificially induced valence of distractor words increases the effects of irrelevant speech on serial recall

In a game context, nonwords either were artificially associated with negative valence or were in some sense neutral or irrelevant. Subsequently, participants memorized target words in silence or while attempting to ignore the negatively valent, irrelevant, or neutral auditory distractor nonwords. The presence of distractor nonwords impaired recall performance, but negative distractor nonwords caused more disruption than neutral and irrelevant distractors, which did not differ in how much disruption they caused. These findings conceptually replicate earlier results showing disruption due to valence with natural language words and extend them by demonstrating that auditory features that may possibly be confounded with valence in natural language words cannot be the cause of the observed disruption. Explanations of the irrelevant speech effect within working memory models that specify an explicit role of attention in the maintenance of information for immediate serial recall can explain this pattern of results, whereas structural models of working memory cannot.     

Information processing in chronic pain disorder: A preliminary analysis

Researchers have alternately suggested selective attention, impaired stimulus filtering, and affective language deficiency models as accounting for multiple somatic complaints in persons with chronic pain disorder. The purpose of the present study was to establish the comparative usefulness of these three models for explaining somatic focus in a chronic pain population. Nine chronic pain patients evidencing high somatic focus (somatizing chronic pain patients), 10 chronic pain patients evidencing low somatic focus (nonsomatizing chronic pain patients), and 10 healthy control subjects were administered a computerized version of the emotional Stroop test as a test of these models. A total of 105 pain-, depression-, and neutral-content words were used as Stroop test stimuli. Stroop test color-naming response latencies were submitted to a two-way Group × Word Type mixed-model ANOVA, with Word Type as the repeated measure. Results reveal that neither the selective attention model nor the affective language deficiency model adequately explain Stroop test performance in somatizing chronic pain patients. Findings provide tentative support for the impaired stimulus filtering model, with somatizing chronic pain patients evidencing similar Stroop test response delays across all words. These results suggest that chronic pain sufferers misinterpret bodily sensations. Findings are discussed in terms of a bilevel approach for treatment of somatizing chronic pain patients that would include assisting the patient in accurate appraisal and interpretation of physical symptoms.     

利手与语言功能偏侧化的遗传模型

利手与语言功能偏侧化现象不仅具有跨文化一致性和发生、发展上的关联性, 还具有明显的生态学优势, 从遗传角度探讨它们的成因是必要的。早期遗传模型认为, 存在一对等位基因分别决定左、右利手。当代单基因模型则将语言功能偏侧化也纳入进来, 认为等位基因不仅可以决定具有方向性的偏侧化, 而且可以使偏侧化有一定几率向左或向右。在这个框架下形成了方向-随机等位基因模型、X-连锁隐性基因模型和右侧位移理论, 它们对等位基因的位置和功能有不同表述, 对利手的划分标准、性别差异以及遗传和环境的关系也有不同阐释。今后需加强对语言偏侧化的数据验证, 进一步阐明语言感知、理解与利手的关系, 并注重基因怎样决定表型、基因多效性和多基因合作等问题的研究。     

Cartographic systems and non-linguistic inference

It is often assumed that the capability to make inferences requires language. Against this assumption, I claim that inferential abilities do not necessarily require a language. On the contrary, certain cartographic systems could be used to explain some forms of inferences, and they are capable of warranting rational relations between contents they represent. By arguing that certain maps, as well as sentences, are adequate for inferential processes, I do not mean to neglect that there are important differences between maps and sentences. Instead, the purpose of this paper is to highlight interesting distinctions that might affect the way that cartographic thinking works.     

From developmental metaphor to developmental model: the shrinking role of language in the talking cure

Psychoanalysts have invoked infant development diversely to understand nonverbal and unspoken aspects of lived experience. Two uses of developmental notions and their implications for understanding language and the therapeutic action of psychoanalysis are juxtaposed here: Hans Loewald's conception of developmental metaphors to illuminate ineffable aspects of the clinical situation and Daniel Stern's currently popular developmental model, which draws on findings from quantitative research to explain therapeutic action in the nonverbal realm. Loewald's metaphorical use of early development identifies and thus potentiates a central role for language in psychoanalytic treatment. By contrast, Stern and his colleagues exaggerate the abstract, orderly, and disembodied qualities of language, and consequently underestimate the degree to which lived interpersonal experience can be meaningfully verbalized, as demonstrated here with illustrations from published clinical material. As contemporary psychoanalysis moves toward embracing developmental models such as Stern's, it is concluded, psychoanalysts accept a shrinking role for language in the talking cure.     

Empirical approaches to the study of language evolution

The study of language evolution, and human cognitive evolution more generally, has often been ridiculed as unscientific, but in fact it differs little from many other disciplines that investigate past events, such as geology or cosmology. Well-crafted models of language evolution make numerous testable hypotheses, and if the principles of strong inference (simultaneous testing of multiple plausible hypotheses) are adopted, there is an increasing amount of relevant data allowing empirical evaluation of such models. The articles in this special issue provide a concise overview of current models of language evolution, emphasizing the testable predictions that they make, along with overviews of the many sources of data available to test them (emphasizing comparative, neural, and genetic data). The key challenge facing the study of language evolution is not a lack of data, but rather a weak commitment to hypothesis-testing approaches and strong inference, exacerbated by the broad and highly interdisciplinary nature of the relevant data. This introduction offers an overview of the field, and a summary of what needed to evolve to provide our species with language-ready brains. It then briefly discusses different contemporary models of language evolution, followed by an overview of different sources of data to test these models. I conclude with my own multistage model of how different components of language could have evolved.     

Language selection in bilingual speech: evidence for inhibitory processes

Although bilinguals rarely make random errors of language when they speak, research on spoken production provides compelling evidence to suggest that both languages are active when only one language is spoken (e.g., [Poulisse, N. (1999). Slips of the tongue: Speech errors in first and second language production. Amsterdam/Philadelphia: John Benjamins]). Moreover, the parallel activation of the two languages appears to characterize the planning of speech for highly proficient bilinguals as well as second language learners. In this paper, we first review the evidence for cross-language activity during single word production and then consider the two major alternative models of how the intended language is eventually selected. According to language-specific selection models, both languages may be active but bilinguals develop the ability to selectively attend to candidates in the intended language. The alternative model, that candidates from both languages compete for selection, requires that cross-language activity be modulated to allow selection to occur. On the latter view, the selection mechanism may require that candidates in the nontarget language be inhibited. We consider the evidence for such an inhibitory mechanism in a series of recent behavioral and neuroimaging studies.     

Explaining with Models: The Role of Idealizations

Because they contain idealizations, scientific models are often considered to be misrepresentations of their target systems. An important question is therefore how models can explain the behaviours of these systems. Most of the answers to this question are representationalist in nature. Proponents of this view are generally committed to the claim that models are explanatory if they represent their target systems to some degree of accuracy; in other words, they try to determine the conditions under which idealizations can be made without jeopardizing the representational function of models. In this article, we first outline several forms of this representationalist view. We then argue that this view, in each of these forms, omits an important role of idealizations: that of facilitating the identification of the explanatory components within a model. Via examination of a case study from contemporary astrophysics, we show that one way in which idealizations can do this is by creating a comparison case that serves to highlight the relevant features of the target system.     

A probabilistic computational model of cross-situational word learning

Words are the essence of communication: They are the building blocks of any language. Learning the meaning of words is thus one of the most important aspects of language acquisition: Children must first learn words before they can combine them into complex utterances. Many theories have been developed to explain the impressive efficiency of young children in acquiring the vocabulary of their language, as well as the developmental patterns observed in the course of lexical acquisition. A major source of disagreement among the different theories is whether children are equipped with special mechanisms and biases for word learning, or their general cognitive abilities are adequate for the task. We present a novel computational model of early word learning to shed light on the mechanisms that might be at work in this process. The model learns word meanings as probabilistic associations between words and semantic elements, using an incremental and probabilistic learning mechanism, and drawing only on general cognitive abilities. The results presented here demonstrate that much about word meanings can be learned from naturally occurring child-directed utterances (paired with meaning representations), without using any special biases or constraints, and without any explicit developmental changes in the underlying learning mechanism. Furthermore, our model provides explanations for the occasionally contradictory child experimental data, and offers predictions for the behavior of young word learners in novel situations.