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Speech production is an important aspect of linguistic competence. An attempt to understand linguistic morphology without speech production would be incomplete. A central research question develops from this perspective: what is the role of morphology in speech production. Speech production researchers collect many different types of data and much of that data has informed how linguists and psycholinguists characterize the role of linguistic morphology in speech production. Models of speech production play an important role in the investigation of linguistic morphology. These models provide a framework, which allows researchers to explore the role of morphology in speech production. However, models of speech production generally focus on different aspects of the production process. These models are split between phonetic models (which attempt to understand how the brain creates motor commands for uttering and articulating speech) and psycholinguistic models (which attempt to understand the cognitive processes and representation of the production process). Models that merge these two model types, phonetic and psycholinguistic models, have the potential to allow researchers the possibility to make specific predictions about the effects of morphology on speech production. Many studies have explored models of speech production, but the investigation of the role of morphology and how morphological properties may be represented in merged speech production models is limited.

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.

Naive discriminative learning (NDL) and linear discriminative learning (LDL) are simple computational algorithms for lexical learning and lexical processing. Both NDL and LDL assume that learning is discriminative, driven by prediction error, and that it is this error that calibrates the association strength between input and output representations. Both words’ forms and their meanings are represented by numeric vectors, and mappings between forms and meanings are set up. For comprehension, form vectors predict meaning vectors. For production, meaning vectors map onto form vectors. These mappings can be learned incrementally, approximating how children learn the words of their language. Alternatively, optimal mappings representing the end state of learning can be estimated. The NDL and LDL algorithms are incorporated in a computational theory of the mental lexicon, the ‘discriminative lexicon’. The model shows good performance both with respect to production and comprehension accuracy, and for predicting aspects of lexical processing, including morphological processing, across a wide range of experiments. Since, mathematically, NDL and LDL implement multivariate multiple regression, the ‘discriminative lexicon’ provides a cognitively motivated statistical modeling approach to lexical processing.