Artificial language learning and feature-based generalization

Finley, Sara; Badecker, William

doi:10.1016/j.jml.2009.05.002

Cited by 97 publications

(123 citation statements)

References 49 publications

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“…Our finding that participants generalized to new sounds is in line with the results of several other studies (Cristia et al, 2013;Finley & Badecker, 2009;Finley, 2011;Gallagher, 2013). However, those studies tested participants after extensive exposure to the language: 160 words (Cristia et al, 2013), 212 words (Gallagher, 2013) or 120 words (Finley & Badecker, 2009;Finley, 2011).…”

Section: Previous Studies Of Phonotactic Generalizationsupporting

confidence: 92%

“…Adult participants have shown evidence of learning the phonotactics of artificial languages in such diverse tasks as acceptability judgments (Richtsmeier, 2011), speech error patterns in production (Gaskell et al, 2014;Warker & Dell, 2006) and familiarity judgments (Cristia et al, 2013). Related findings have been reported in artificial language studies of morphological alternations (Finley & Badecker, 2009;Peperkamp et al, 2006;Wilson, 2003). Similar findings have been reported for infants, who are typically tested using the headturn preference paradigm (Chambers et al, 2003;Cristià & Seidl, 2008;Saffran & Thiessen, 2003;Seidl & Buckley, 2005;Cristia & Peperkamp, 2012).…”

Section: Introductionmentioning

confidence: 55%

“…Similar findings have been reported for infants, who are typically tested using the headturn preference paradigm (Chambers et al, 2003;Cristià & Seidl, 2008;Saffran & Thiessen, 2003;Seidl & Buckley, 2005;Cristia & Peperkamp, 2012). Most importantly for the present study, multiple artificial language learning experiments have reported generalization beyond attested sounds (Cristia & Peperkamp, 2012;Cristia et al, 2013;Gallagher, 2013;Finley & Badecker, 2009;Marcus et al, 1999), though some studies have failed to find this effect (Peperkamp et al, 2006;Peperkamp & Dupoux, 2007).…”

Section: Introductionmentioning

confidence: 62%

“…However, those studies tested participants after extensive exposure to the language: 160 words (Cristia et al, 2013), 212 words (Gallagher, 2013) or 120 words (Finley & Badecker, 2009;Finley, 2011). Our study enriches the empirical picture by charting how the generalizations that participants make depend on the amount of exposure to the artificial language, in particular when given a very small amount of exposure: Participants in the One Set condition in Experiment 1 received only five exposure words, a fraction of the number of exposure words used in previous studies.…”

Section: Previous Studies Of Phonotactic Generalizationmentioning

confidence: 99%

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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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Section: Previous Studies Of Phonotactic Generalizationsupporting

confidence: 92%

Section: Introductionmentioning

confidence: 55%

Section: Introductionmentioning

confidence: 62%

Section: Previous Studies Of Phonotactic Generalizationmentioning

confidence: 99%

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Rapid generalization in phonotactic learning

Linzen

Gallagher

2017

Laboratory Phonology: Journal of the Association for Laboratory Phonology

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“…), however, that speakers are prone to generalize properties of individual words to phonological categories, which is seen precisely in speakers' treatment of novel words (see e.g. Finley & Badecker 2009). That the category distinction between simple and complex word onsets is something speakers generalize over can be seen from processes in real languages.…”

Section: Discussionmentioning

confidence: 99%

Neighborhood Density in Phonological Alternations

Johnsen¹

2010

BLS

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in lieu of an abstract, here is a brief excerpt:Neighborhood density (ND) is a measure of how similar a word is to other words in the lexicon. In response to the growing evidence of the significance of ND for word identification and word production, linguists have started asking what role ND could play in phonological alternations, without reaching a consensus. This paper assesses the role of ND in a phonological alternation in Norwegian, by asking whether the alternation is best predicted by ND or by the phonological structure of the alternators. The results reveal how these factors are strongly correlated and equally good predictors of alternations.

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Structure and Substance in Artificial‐Phonology Learning, Part II: Substance

Moreton

Pater

2012

Language and Linguist. Compass

102

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Artificial analogues of natural-language phonological patterns can often be learned in the lab from small amounts of training or exposure. The difficulty of a featurallydefined pattern has been hypothesized to be affected by two main factors, its formal structure (the abstract logical relationships between the defining features) and its phonetic substance (the concrete phonetic interpretation of the pattern). This paper, the second of a two-part series, reviews the experimental literature on phonetic substance, which is hypothesized to facilitate the acquisition of phonological patterns that resemble naturally-occurring phonetic patterns. The effects of phonetic substance on pattern learning turn out to be elusive and unreliable in comparison with the robust effects of formal complexity (reviewed in Part I). If natural-language acquisition is guided by the same inductive biases as are found in the lab, these results support a theory in which inductive bias shapes only the form, and channel bias shapes the content, of the sound patterns of the worlds languages.

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Artificial language learning and feature-based generalization

Cited by 97 publications

References 49 publications

Rapid generalization in phonotactic learning

Rapid generalization in phonotactic learning

Neighborhood Density in Phonological Alternations

Structure and Substance in Artificial‐Phonology Learning, Part II: Substance

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