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In traditional, generative phonology, sound patterns are represented in terms of abstract features, typically based on the articulatory properties of the sounds. The present study makes use of an artificial language learning experiment to explore when and how learners extend a novel phonological pattern to novel segments. Adult, English-speaking learners were exposed to a spirantization pattern in which a stop became a fricative between two vowels (e.g., /bib/ + /o/ [right arrow] [bivo]). Participants were trained on spirantization for two of four possible stop-fricative pairs, and were tested on their generalization to the held-out segments. Two groups of participants were trained on items based on voicing (e.g., the Voiced condition was trained on /b/ [right arrow] [v], and /d/ [right arrow] [z], and tested on /p/ [right arrow] [f], and /t/ [right arrow] [s]), and two groups of participants were trained on items based on place of articulation (e.g., the Labial condition was trained on /b/ [right arrow] [v], and /p/ [right arrow] [f] and tested on /t/ [right arrow] [s], and /d/ [right arrow] [z]). Participants in both Place and Voice conditions were successful at learning and generalizing the spirantization pattern to novel segments, but rates of generalization were higher in the Voice conditions. These results support a similarity-based approach to generalization, particularly one that takes into account articulatorily-based features and natural classes. Implications for phonological theory are discussed.

Morphological syncretism occurs in languages when one morphological category ‘merges’ with another. Cross-linguistic research on the prevalence and types of syncretic patterns has revealed that some types of syncretism are more common than others. For example, syncretism in nominal morphology is more likely to occur in non-singular categories (Baerman et al., 2005). In two artificial language learning experiments, participants were exposed to words from a miniature language with suffix markings for gender (feminine and masculine) and number (singular, dual, and plural). Participants in Experiment 1 showed no evidence of a bias for syncretism in non-singular forms. However, participants in Experiment 2 showed a general bias to infer that a suffix that marked a novel category should be identical to a known form. This bias was strongest for non-singular items, in line with the cross-linguistic typology of syncretism. Implications for learnability and typology are discussed.

In this commentary, I discuss why, despite the existence of gradience in phonetics and phonology, there is still a need for abstract representations. Most proponents of exemplar models assume multiple levels of abstraction, allowing for an integration of the gradient and the categorical. Ben Ambridge's dismissal of generative models such as Optimality Theory (OT) is problematic because OT not only allows for the abstract, but can also handle a variety of phenomena, including gradient representations, and similarity among output forms. [For Ben Ambridge's "Against Stored Abstractions: A Radical Exemplar Model of Language Acquisition," see EJ1269951.]

The present study explores the effects of frequency inlearning to parse novel morphological patterns. In twoexperiments, suffixes were divided into three classes: high,medium and low frequency, based on the proportion of stemsin the input that each suffix attached to (high frequency =12/12, medium frequency = 6/12, and low frequency = 2/12).In Experiment 1, learners were better at segmenting wordscontaining high frequency suffixes compared to lowfrequency suffixes, even when the stems were novel. InExperiment 2, token frequency was controlled for across allthree suffix frequency classes, but learners were still better atsegmenting high frequency suffixes, even when wordscontaining high frequency suffixes were less frequent. Theseresults suggest that learners are sensitive to the frequencydistributions of the morphemes in their language, supportingwork suggesting that a Zipfian distribution may be ideal forlanguage learning

The present study explores learning phonological alternationsthat contain exceptions. Participants were exposed to aback/round vowel harmony pattern in which a regular suffixfollowed harmony, varying between /e/ and /o/ depending onthe back/round phonetic features of the stem, and anexceptional suffix that was always /o/ regardless of thefeatures of the stem vowel. Participants in Experiment 1learned the behavior of both suffixes, but performance for thenon-alternating suffix was higher when the suffix happened toadhere to vowel harmony. In Experiment 2, participants wereexposed only to the same suffixes as Experiment 1, but thenon-alternating suffix only appeared in harmonic contexts,creating ambiguity between exceptionality and alternation.Participants only correctly selected the non-alternating suffixwhen it appeared in a harmonic context. This suggests thatlearners are biased towards alternating harmony patterns, butrequire concrete evidence of non-alternation to learn the nonalternatingsuffix.

Providing evidence for the universal tendencies of patterns in the world's languages can be difficult, as it is impossible to sample all possible languages, and linguistic samples are subject to interpretation. However, experimental techniques, such as artificial grammar learning paradigms, make it possible to uncover the psychological reality of claimed universal tendencies. This article addresses learning of phonological patterns (systematic tendencies in the sounds in language). Specifically, I explore the role of phonetic grounding in learning round harmony, a phonological process in which words must contain either all round vowels ([o, u]) or all unround vowels ([i, e]). The phonetic precursors to round harmony are such that mid vowels ([o, e]), which receive the greatest perceptual benefit from harmony, are most likely to trigger harmony. High vowels ([i, u]), however, are cross-linguistically less likely to trigger round harmony. Adult participants were exposed to a miniature language that contained a round harmony pattern in which the harmony source triggers were either high vowels ([i, u]) (poor harmony source triggers) or mid vowels ([o, e]) (ideal harmony source triggers). Only participants who were exposed to the ideal mid vowel harmony source triggers were successfully able to generalise the harmony pattern to novel instances, suggesting that perception and phonetic naturalness play a role in learning. (Contains 7 footnotes, 2 tables and 1 figure.)

Traditional flat-structured bigram and trigram models of phonotactics are useful because they capture a large number of facts about phonological processes. Additionally, these models predict that local interactions should be easier to learn than long-distance ones because long-distance dependencies are difficult to capture with these models. Long-distance phonotactic patterns have been observed by linguists in many languages, who have proposed different kinds of models, including feature-based bigram and trigram models, as well as precedence models. Contrary to flat-structured bigram and trigram models, these alternatives capture unbounded dependencies because at an abstract level of representation, the relevant elements are locally dependent, even if they are not adjacent at the observable level. Using an artificial grammar learning paradigm, we provide additional support for these alternative models of phonotactics. Participants in two experiments were exposed to a long-distance consonant-harmony pattern in which the first consonant of a five-syllable word was [s] or ["esh"] ("sh") and triggered a suffix that was either [-su] or [-"esh"u] depending on the sibilant quality of this first consonant. Participants learned this pattern, despite the large distance between the trigger and the target, suggesting that when participants learn long-distance phonological patterns, that pattern is learned without specific reference to distance. (Contains 1 table, 3 figures, and 7 notes.)

While the vast majority of linguistic processes apply locally, consonant harmony appears to be an exception. In this phonological process, consonants share the same value of a phonological feature, such as secondary place of articulation. In sibilant harmony, [s] and [esh] ("sh") alternate such that if a word contains the sound [esh], all [s] sounds become [esh]. This can apply locally as a first-order or non-locally as a second-order pattern. In the first-order case, no consonants intervene between the two sibilants (e.g., [pisasu], [pi[esh]a[esh]u]). In second-order case, a consonant may intervene (e.g., [sipasu], [[esh]ipa[esh]u]). The fact that there are languages that allow second-order non-local agreement of consonant features has led some to question whether locality constraints apply to consonant harmony. This paper presents the results from two artificial grammar learning experiments that demonstrate the privileged role of locality constraints, even in patterns that allow second-order non-local interactions. In Experiment 1, we show that learners do not extend first-order non-local relationships in consonant harmony to second-order non-local relationships. In Experiment 2, we show that learners will extend a consonant harmony pattern with second-order long distance relationships to a consonant harmony with first-order long distance relationships. Because second-order non-local application implies first-order non-local application, but first-order non-local application does not imply second-order non-local application, we establish that local constraints are privileged even in consonant harmony.

Abstract representations such as subsegmental phonological features play such a vital role in explanations of phonological processes that many assume that these representations play an equally prominent role in the learning process. This assumption is tested in three artificial grammar experiments involving a mini language with morpho-phonological alternations based on back vowel harmony. In Experiments 1 and 2, adult participants were trained using positive data from four vowels in a six-vowel inventory: the two remaining vowels appeared at test only. If participants use subsegmental phonological features and natural classes for learning, they should generalize to the novel test segments. Results support a subsegmental feature-based learning strategy that makes use of phonetic information and knowledge of phonological principles. A third experiment (Experiment 3) tests for generalizations to novel suffixes, providing further evidence for the generality of learning. (Contains 3 tables and 4 figures.)