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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. 

Abstract Previous research has shown that when domain‐general transitional probability (TP) cues to word segmentation are in conflict with language‐specific stress cues, English‐learning 5‐ and 7‐month‐olds rely on TP, whereas 9‐month‐olds rely on stress. In two artificial languages, we evaluated English‐learning infants’ sensitivity to TP cues to word segmentation vis‐a‐vis language‐specific vowel phonotactic (VP) cues—English words do not end in lax vowels. These cues were either consistent or conflicting. When these cues were in conflict, 10‐month‐olds relied on the VP cues, whereas 5‐month‐olds relied on TP. These findings align with statistical bootstrapping accounts, where infants initially use domain‐general distributional information for word segmentation, and subsequently discover language‐specific patterns based on segmented words. Research HighlightsResearch indicates that when transitional probability (TP) conflicts with stress cues for word segmentation, English‐learning 9‐month‐olds rely on stress, whereas younger infants rely on TP.In two artificial languages, we evaluated English‐learning infants’ sensitivity to TP versus vowel phonotactic (VP) cues for word segmentation.When these cues conflicted, 10‐month‐olds relied on VPs, whereas 5‐month‐olds relied on TP.These findings align with statistical bootstrapping accounts, where infants first utilize domain‐general distributional information for word segmentation, and then identify language‐specific patterns from segmented words. 

Abstract Two types of discontinuous morphemes are thought to be the basic building blocks of words in Semitic languages: roots and templates. However, the role of these morphemes in lexical access and representation is debated. Priming experiments, where reaction times to target words are predicted to be faster when preceded by morphologically-related primes compared to unrelated control primes, provide conflicting evidence bearing on this debate. We used meta-analysis to synthesise the findings from 229 priming experiments on 4710 unique Semitic speakers. With Bayesian modelling of the aggregate effect sizes, we found credible root and template priming in both nouns and verbs in Arabic and Hebrew. Our results show that root priming effects can be distinguished from the effects of overlap in form and meaning. However, more experiments are needed to determine if template priming effects can be distinguished from overlap in form and morphosyntactic function. 

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. 

This study evaluates the malleability of adults' perception of probabilistic phonotactic (biphone) probabilities, building on a body of literature on statistical phonotactic learning. It was first replicated that listeners categorize phonetic continua as sounds that create higher-probability sequences in their native language. Listeners were also exposed to skewed distributions of biphone contexts, which resulted in the enhancement or reversal of these effects. Thus, listeners dynamically update biphone probabilities (BPs) and bring this to bear on perception of ambiguous acoustic information. These effects can override long-term BP effects rooted in native language experience. 

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. 

In six experiments we explored how biphone probability and lexical neighborhood density influence listeners’ categorization of vowels embedded in nonword sequences. We found independent effects of each. Listeners shifted categorization of a phonetic continuum to create a higher probability sequence, even when neighborhood density was controlled. Similarly, listeners shifted categorization to create a nonword from a denser neighborhood, even when biphone probability was controlled. Next, using a visual world eye-tracking task, we determined that biphone probability information is used rapidly by listeners in perception. In contrast, task complexity and irrelevant variability in the stimuli interfere with neighborhood density effects. These results support a model in which both biphone probability and neighborhood density independently affect word recognition, but only biphone probability effects are observed early in processing.