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Enfield, N. J. – Cognitive Science, 2023
A central concern of the cognitive science of language since its origins has been the concept of the linguistic system. Recent approaches to the system concept in language point to the exceedingly complex relations that hold between many kinds of interdependent systems, but it can be difficult to know how to proceed when "everything is…
Descriptors: Psycholinguistics, Guidelines, Interdisciplinary Approach, Language Research
de Varda, Andrea Gregor; Strapparava, Carlo – Cognitive Science, 2022
The present paper addresses the study of non-arbitrariness in language within a deep learning framework. We present a set of experiments aimed at assessing the pervasiveness of different forms of non-arbitrary phonological patterns across a set of typologically distant languages. Different sequence-processing neural networks are trained in a set…
Descriptors: Learning Processes, Phonology, Language Patterns, Language Classification
Yadav, Himanshu; Vaidya, Ashwini; Shukla, Vishakha; Husain, Samar – Cognitive Science, 2020
Much previous work has suggested that word order preferences across languages can be explained by the dependency distance minimization constraint (Ferrer-i Cancho, 2008, 2015; Hawkins, 1994). Consistent with this claim, corpus studies have shown that the average distance between a head (e.g., verb) and its dependent (e.g., noun) tends to be short…
Descriptors: Word Order, Computational Linguistics, Contrastive Linguistics, Psycholinguistics
Dautriche, Isabelle; Mahowald, Kyle; Gibson, Edward; Piantadosi, Steven T. – Cognitive Science, 2017
Although the mapping between form and meaning is often regarded as arbitrary, there are in fact well-known constraints on words which are the result of functional pressures associated with language use and its acquisition. In particular, languages have been shown to encode meaning distinctions in their sound properties, which may be important for…
Descriptors: Semantics, Phonology, Cognitive Mapping, Correlation
Culbertson, Jennifer; Smolensky, Paul – Cognitive Science, 2012
In this article, we develop a hierarchical Bayesian model of learning in a general type of artificial language-learning experiment in which learners are exposed to a mixture of grammars representing the variation present in real learners' input, particularly at times of language change. The modeling goal is to formalize and quantify hypothesized…
Descriptors: Models, Bayesian Statistics, Artificial Languages, Language Acquisition
Lany, Jill; Gomez, Rebecca L.; Gerken, Lou Ann – Cognitive Science, 2007
Learners exposed to an artificial language recognize its abstract structural regularities when instantiated in a novel vocabulary (e.g., Gomez, Gerken, & Schvaneveldt, 2000; Tunney & Altmann, 2001). We asked whether such sensitivity accelerates subsequent learning, and enables acquisition of more complex structure. In Experiment 1, pre-exposure to…
Descriptors: Second Language Learning, Phonology, Artificial Languages, Prior Learning