Inter- and intra-dimensional dependencies in implicit phonotactic learning

Moreton, Elliott

doi:10.1016/j.jml.2011.12.003

Cited by 32 publications

(73 citation statements)

References 90 publications

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“…The size of the interaction was small, and detectable only among identical vowels. This offers the potential for a perceptual explanation for distance effects, but such an explanation is not the only plausible account; for example, it may very well be the case that biases in learning systematically disadvantage patterns that require associations across increasing distances (see Moreton, 2012;Moreton & Pater, 2012 for more on structural biases in learning).…”

Section: Discussionmentioning

confidence: 99%

Not crazy after all these years? Perceptual grounding for long-distance vowel harmony

Kimper

2017

Laboratory Phonology: Journal of the Association for Laboratory Phonology

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Long-distance (or 'transparent') vowel harmony systems have frequently been considered 'unnatural' and analyzed as 'crazy rules' (Bach & Harms, 1972) because they violate the principle of strict locality. Articulatory explanations for the phonetic grounding of vowel harmony are unable to extend to non-local processes, and attempts to re-analyze cases of transparent harmony as strictly local have been largely unsuccessful. In this paper, I present experimental evidence suggesting that vowel harmony may be perceptually (as well as articulatorily) grounded, and that this source of phonetic grounding does in fact extend to long-distance as well as local harmony. In a series of four experiments, subjects were presented with a nonsense word followed by an isolated vowel, and asked to report whether the isolated vowel had occurred in the preceding word. Subjects were consistently faster and more accurate in nonsense words which exhibited vowel harmony along the relevant feature dimension, regardless of locality. A fourth experiment included a task requiring subjects to identify whether the vowel occurred in a specific syllable, and here too they showed better performance on items with vowel harmony along the relevant feature dimension. I argue that strict locality is not a necessary component of a phonetically grounded theory of vowel harmony, suggesting that long-distance harmony can be analyzed as an explicitly non-local process without abandoning phonetic grounding.

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Section: Discussionmentioning

confidence: 99%

Not crazy after all these years? Perceptual grounding for long-distance vowel harmony

Kimper

2017

Laboratory Phonology: Journal of the Association for Laboratory Phonology

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“…In both phases, the words were presented in isolation-i.e., not in a continuous stream. Participants were told that the exposure phase would be followed by a test phase during which they will be required to decide if new words sounded like they could belong to the language they were listening to (for a similar task, see Moreton, 2008Moreton, , 2012Reeder et al, 2013). During the test phase, the instructions for the task were repeated after every test word.…”

Section: Methodsmentioning

confidence: 99%

“…The simplest type of such a generalization is sound identity (repetition). A large range of studies have shown that such generalizations can be acquired in artificial language studies (Gervain, 2014;Marcus et al, 1999;Moreton, 2012). In natural languages, generalizations that have to do with segment repetition or bans on repetition have been documented in Yucatec Mayan, Hebrew, Peruvian Aymara, and other languages (Berent et al, 2002;Gallagher, 2013).…”

Section: Experiments 2a: a Probabilistic Abstract Generalizationmentioning

confidence: 98%

Rapid generalization in phonotactic learning

Linzen

Gallagher

2017

Laboratory Phonology: Journal of the Association for Laboratory Phonology

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Speakers judge novel strings to be better potential words of their language if those strings consist of sound sequences that are attested in the language. These intuitions are often generalized to new sequences that share some properties with attested ones: Participants exposed to an artificial language where all words start with the voiced stops [b] and [d] will prefer words that start with other voiced stops (e.g., [g]) to words that start with vowels or nasals. The current study tracks the evolution of generalization across sounds during the early stages of artificial language learning. In Experiments 1 and 2, participants received varying amounts of exposure to an artificial language. Learners rapidly generalized to new sounds: In fact, following short exposure to the language, attested patterns were not distinguished from unattested patterns that were similar in their phonological properties to the attested ones. Following additional exposure, participants showed an increasing preference for attested sounds, alongside sustained generalization to unattested ones. Finally, Experiment 3 tested whether participants can rapidly generalize to new sounds based on a single type of sound. We discuss the implications of our results for computational models of phonotactic learning.

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“…In pattern 5, the two features relevant to the pattern are on the same consonant, and in pattern 6, they are on two separate consonants. Moreton (2012) claims that there is an advan- C1+voi and C1+cor C1-voi and C1-cor 6…”

Section: Patternsmentioning

confidence: 99%

Inherent Biases of Recurrent Neural Networks for Phonological Assimilation and Dissimilation

Doucette¹

2017

Proceedings of the 7th Workshop on Cognitive Modeling And Computational Linguistics (CMCL 2017)

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A recurrent neural network model of phonological pattern learning is proposed. The model is a relatively simple neural network with one recurrent layer, and displays biases in learning that mimic observed biases in human learning. Single-feature patterns are learned faster than two-feature patterns, and vowel or consonant-only patterns are learned faster than patterns involving vowels and consonants, mimicking the results of laboratory learning experiments. In non-recurrent models, capturing these biases requires the use of alpha features or some other representation of repeated features, but with a recurrent neural network, these elaborations are not necessary.

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Inter- and intra-dimensional dependencies in implicit phonotactic learning

Cited by 32 publications

References 90 publications

Not crazy after all these years? Perceptual grounding for long-distance vowel harmony

Not crazy after all these years? Perceptual grounding for long-distance vowel harmony

Rapid generalization in phonotactic learning

Inherent Biases of Recurrent Neural Networks for Phonological Assimilation and Dissimilation

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