Acceptability judgments are reports of a speaker’s or signer’s subjective sense of the well-formedness, nativeness, or naturalness of (novel) linguistic forms. Their value comes in providing data about the nature of the human capacity to generalize beyond linguistic forms previously encountered in language comprehension. For this reason, acceptability judgments are often also called grammaticality judgments (particularly in syntax), although unlike the theory-dependent notion of grammaticality, acceptability is accessible to consciousness. While acceptability judgments have been used to test grammatical claims since ancient times, they became particularly prominent with the birth of generative syntax. Today they are also widely used in other linguistic schools (e.g., cognitive linguistics) and other linguistic domains (pragmatics, semantics, morphology, and phonology), and have been applied in a typologically diverse range of languages. As psychological responses to linguistic stimuli, acceptability judgments are experimental data. Their value thus depends on the validity of the experimental procedures, which, in their traditional version (where theoreticians elicit judgments from themselves or a few colleagues), have been criticized as overly informal and biased. Traditional responses to such criticisms have been supplemented in recent years by laboratory experiments that use formal psycholinguistic methods to collect and quantify judgments from nonlinguists under controlled conditions. Such formal experiments have played an increasingly influential role in theoretical linguistics, being used to justify subtle judgment claims or new grammatical models that incorporate gradience or lexical influences. They have also been used to probe the cognitive processes giving rise to the sense of acceptability itself, the central finding being that acceptability reflects processing ease. Exploring what this finding means will require not only further empirical work on the acceptability judgment process, but also theoretical work on the nature of grammar.
Article
Acceptability Judgments
James Myers
Article
Computational Phonology
Jane Chandlee and Jeffrey Heinz
Computational phonology studies the nature of the computations necessary and sufficient for characterizing phonological knowledge. As a field it is informed by the theories of computation and phonology.
The computational nature of phonological knowledge is important because at a fundamental level it is about the psychological nature of memory as it pertains to phonological knowledge. Different types of phonological knowledge can be characterized as computational problems, and the solutions to these problems reveal their computational nature. In contrast to syntactic knowledge, there is clear evidence that phonological knowledge is computationally bounded to the so-called regular classes of sets and relations. These classes have multiple mathematical characterizations in terms of logic, automata, and algebra with significant implications for the nature of memory. In fact, there is evidence that phonological knowledge is bounded by particular subregular classes, with more restrictive logical, automata-theoretic, and algebraic characterizations, and thus by weaker models of memory.
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Connectionism in Linguistic Theory
Xiaowei Zhao
Connectionism is an important theoretical framework for the study of human cognition and behavior. Also known as Parallel Distributed Processing (PDP) or Artificial Neural Networks (ANN), connectionism advocates that learning, representation, and processing of information in mind are parallel, distributed, and interactive in nature. It argues for the emergence of human cognition as the outcome of large networks of interactive processing units operating simultaneously. Inspired by findings from neural science and artificial intelligence, connectionism is a powerful computational tool, and it has had profound impact on many areas of research, including linguistics. Since the beginning of connectionism, many connectionist models have been developed to account for a wide range of important linguistic phenomena observed in monolingual research, such as speech perception, speech production, semantic representation, and early lexical development in children. Recently, the application of connectionism to bilingual research has also gathered momentum. Connectionist models are often precise in the specification of modeling parameters and flexible in the manipulation of relevant variables in the model to address relevant theoretical questions, therefore they can provide significant advantages in testing mechanisms underlying language processes.
