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Abstract This paper presents the UCI Phonotactic Calculator (UCIPC), a new online tool for quantifying the occurrence of segments and segment sequences in a corpus. This tool has several advantages compared to existing tools: it allows users to supply their own training data, meaning it can be applied to any language for which a corpus is available; it computes a wider range of metrics than most existing tools; and it provides an accessible point-and-click interface that allows researchers with more modest technical backgrounds to take advantage of phonotactic models. After describing the metrics implemented by the calculator and how to use it, we present the results of a proof-of-concept study comparing how well different types of metrics implemented by the UCIPC predict human responses from eight published nonce word acceptability judgment studies across four different languages. These results suggest that metrics that take into account the relative position of sounds and include word boundaries are better at predicting human responses than those that are based on the absolute position of sounds and do not include word boundaries. We close by discussing the usefulness of tools like the UCIPC in experimental design and analysis and outline several areas of future research that this tool will help support. 

Should phonotactic knowledge be modeled as categorical or gradient? In this paper, I present new data from a Turkish acceptability judgment study that addresses some limitations of previous work on this question. This study shows that gradient models account for the variability in acceptability ratings better than categorical ones. However, I suggest that the distinction between gradient and categorical models is somewhat superficial when we think of models in a mathematically general way. I propose on this basis that both categorical and gradient models have a role to play in linguistic research. 

Models of phonotactics include subsegmental representations in order to generalize to unattested sequences. These representations can be encoded in at least two ways: as discrete, phonetically-based features, or as continuous, distribution-based representations induced from the statistical patterning of sounds. Because phonological theory typically assumes that representations are discrete, past work has reduced continuous representations to discrete ones, which eliminates potentially relevant information. In this paper we present a model of phonotactics that can use continuous representations directly, and show that this approach yields competitive performance on modeling experimental judgments of English sonority sequencing. The proposed model broadens the space of possible phonotactic models by removing requirements for discrete features, and is a step towards an integrated picture of phonotactic learning based on distributional statistics and continuous representations. 

We fuse two recent strands of work in subregular linguistics—probabilistic tier projections (Mayer, 2021) and tier-based perspectives on movement (Graf, 2022a)—into a probabilistic model of syntax that makes it easy to add gradience to traditional, categorical analyses from the syntactic literature. As a case study, we test this model on experimental data from Sprouse et al. (2016) for a number of island effects in English. We show that the model correctly replicates the superadditive effects and gradience that have been observed in the psycholinguistic literature.