This interdisciplinary new work explores one of the central theoretical problems in linguistics: learnability. The authors, from different backgrounds---linguistics, philosophy, computer science, psychology and cognitive science-explore the idea that language acquisition proceeds through general purpose learning mechanisms, an approach that is broadly empiricist both methodologically and psychologically. Written by four researchers in the full range of relevant fields: linguistics, psychology, computer science, and cognitive science, the book sheds light on the central problems of learnability and language, and traces their implications (...) for key questions of theoretical linguistics and the study of language acquisition. (shrink)

The question of whether grammaticality is a binary categorical or a gradient property has been the subject of ongoing debate in linguistics and psychology for many years. Linguists have tended to use constructed examples to test speakers’ judgements on specific sorts of constraint violation. We applied machine translation to randomly selected subsets of the British National Corpus (BNC) to generate a large test set which contains well-formed English source sentences, and sentences that exhibit a wide variety of grammatical infelicities. We (...) tested a large number of speakers through (filtered) crowd sourcing, with three distinct modes of classification, one binary and two ordered scales. We found a high degree of correlation in mean judgements for sentences across the three classification tasks. We also did two visual image classification tasks to obtain benchmarks for binary and gradient judgement patterns, respectively. Finally, we did a second crowd source experiment on 100 randomly selected linguistic textbook example sentences. The sentence judgement distributions for individual speakers strongly resemble the gradience benchmark pattern. This evidence suggests that speakers represent grammatical well-formedness as a gradient property. (shrink)

Learning theory has frequently been applied to language acquisition, but discussion has largely focused on information theoretic problems—in particular on the absence of direct negative evidence. Such arguments typically neglect the probabilistic nature of cognition and learning in general. We argue first that these arguments, and analyses based on them, suffer from a major flaw: they systematically conflate the hypothesis class and the learnable concept class. As a result, they do not allow one to draw significant conclusions about the learner. (...) Second, we claim that the real problem for language learning is the computational complexity of constructing a hypothesis from input data. Studying this problem allows for a more direct approach to the object of study—the language acquisition device—rather than the learnable class of languages, which is epiphenomenal and possibly hard to characterize. The learnability results informed by complexity studies are much more insightful. They strongly suggest that target grammars need to be objective, in the sense that the primitive elements of these grammars are based on objectively definable properties of the language itself. These considerations support the view that language acquisition proceeds primarily through data-driven learning of some form. (shrink)

HARWOOD L and CLARK AM. Nursing Inquiry 2012; 19: 29–38Understanding health decisions using critical realism: home-dialysis decision-making during chronic kidney diseaseThis paper examines home-dialysis decision making in people with Chronic Kidney Disease (CKD) from the perspective of critical realism. CKD programmes focus on patient education for self-management to delay the progression of kidney disease and the preparation and support for renal replacement therapy e.g.) dialysis and transplantation. Home-dialysis has clear health, societal and economic benefits yet service usage is low despite (...) efforts to realign resources and educate individuals. Current research on the determinants of modality selection is superficial and insufficient to capture the complexities embedded in the process of dialysis modality selection. Predictors of home-dialysis selection and the effect of chronic kidney disease educational programmes provide a limited explanation of this experience. A re-conceptualization of the problem is required in order to fully understand this process. The epistemology and ontology of critical realism guides our knowledge and methodology particularly suited for examination of these complexities. This approach examines the deeper mechanisms and wider determinants associated with modality decision making, specifically who chooses home dialysis and under what circumstances. Until more is known regarding dialysis modality decision making service usage of home dialysis will remain low as interventions will be based on inadequate epistemology. (shrink)

Computational learning theory explores the limits of learnability. Studying language acquisition from this perspective involves identifying classes of languages that are learnable from the available data, within the limits of time and computational resources available to the learner. Different models of learning can yield radically different learnability results, where these depend on the assumptions of the model about the nature of the learning process, and the data, time, and resources that learners have access to. To the extent that such assumptions (...) accurately reflect human language learning, a model that invokes them can offer important insights into the formal properties of natural languages, and the way in which their representations might be efficiently acquired. In this chapter we consider several computational learning models that have been applied to the language learning task. Some of these have yielded results that suggest that the class of natural languages cannot be efficiently learned from the primary linguistic data (PLD) available to children, through.. (shrink)

Taking debates about the roles of structure and agency in health as a lens, this essay asks how Critical Realist and Feminist Intersectional approaches might inform health interventions research. Despite recognition of multiple determinants of health, health problems are often thought of as individual and interventions, in turn, target risky individual behaviours. Such approaches are rooted in a liberal model of personhood. This paper critiques enduring individualist assumptions linked to Western liberal underpinnings embedded in health interventions. It posits the need (...) to include a robust conception of the social world in which change depends on shifting power relations, and individual agency is shaped by power as well as individual will. We propose preliminary steps for undertaking critical realist intersectional interventions research. (shrink)

Indirect negative evidence is clearly an important way for learners to constrain overgeneralisation, and yet a good learning theoretic analysis has yet to be provided for this, whether in a PAC or a probabilistic identification in the limit framework. In this paper we suggest a theoretical analysis of indirect negative evidence that allows the presence of ungrammatical strings in the input and also accounts for the relationship between grammaticality/acceptability and probability. Given independently justified assumptions about lower bounds on the probabilities (...) of grammatical strings, we establish that a limited number of membership queries of some strings can be probabilistically simulated. (shrink)

The question of whether it is possible to characterise grammatical knowledge in probabilistic terms is central to determining the relationship of linguistic representation to other cognitive domains. We present a statistical model of grammaticality which maps the probabilities of a statistical model for sentences in parts of the British National Corpus (BNC) into grammaticality scores, using various functions of the parameters of the model. We test this approach with a classifier on test sets containing different levels of syntactic infelicity. With (...) appropriate tuning, the classifiers achieve encouraging levels of accuracy. These experiments suggest that it may be possible to characterise grammaticality judgements in probabilistic terms using an enriched language model. (shrink)