scholarly journals Rapid generalization in phonotactic learning

2017 ◽  
Vol 8 (1) ◽  
Author(s):  
Tal Linzen ◽  
Gillian Gallagher
Keyword(s):  
Phonotactic Learning

Contextual variability and exemplar strength in phonotactic learning.

2018 ◽  
Vol 44 (2) ◽  
pp. 280-294 ◽  
Author(s):  
Thomas Denby ◽  
Jeffrey Schecter ◽  
Sean Arn ◽  
Svetlin Dimov ◽  
Matthew Goldrick
Keyword(s):  
Contextual Variability ◽  
Phonotactic Learning

scholarly journals The Timecourse of Generalization in Phonotactic Learning

2014 ◽  
Vol 1 (1) ◽  
Author(s):  
Tal Linzen ◽  
Gillian Gallagher
Keyword(s):  
General Pattern ◽  
General Assumption ◽  
Artificial Language ◽  
Learning Models ◽  
Considerable Evidence ◽  
Phonotactic Learning

<p>There is considerable evidence that speakers show sensitivity to the phonotactic patterns of their language. These patterns can involve specific sound sequences (e.g. the consonant combination b-b) or more general classes of sequences (e.g. two identical consonants). In some models of phonotactic learning, generalizations can only be formed once some of their specific instantiations have been acquired (the specific-before-general assumption). To test this assumption, we designed an artificial language with both general and specific phonotactic patterns, and gave participants different amounts of exposure to the language. Contrary to the predictions of specific-before-general models, the general pattern required less exposure to be learned than did its specific instantiations. These results are most straightforwardly predicted by learning models that learn general and specific patterns simultaneously. We discuss the importance of modeling learners’ sensitivity to the amount of evidence supporting each phonotactic generalization, and show how specific-before-general models can be adapted to accommodate the results.</p>

Accidental Gaps and Surface-Based Phonotactic Learning: A Case Study of South Bolivian Quechua

2018 ◽  
Vol 49 (3) ◽  
pp. 610-623 ◽  
Author(s):  
Colin Wilson ◽  
Gillian Gallagher
Keyword(s):  
Whole Language ◽  
State Constraints ◽  
Learning Problem ◽  
Frequency Distributions ◽  
Distinctive Features ◽  
Feature Based ◽  
Complex Constraints ◽  
Multiple Levels ◽  
Phonotactic Learning

The lexicon of a natural language does not contain all of the phonological structures that are grammatical. This presents a fundamental challenge to the learner, who must distinguish linguistically significant restrictions from accidental gaps ( Fischer-Jørgensen 1952 , Halle 1962 , Chomsky and Halle 1965 , Pierrehumbert 1994 , Frisch and Zawaydeh 2001 , Iverson and Salmons 2005 , Gorman 2013 , Hayes and White 2013 ). The severity of the challenge depends on the size of the lexicon ( Pierrehumbert 2001 ), the number of sounds and their frequency distribution ( Sigurd 1968 , Tambovtsev and Martindale 2007 ), and the complexity of the generalizations that learners must entertain ( Pierrehumbert 1994 , Hayes and Wilson 2008 , Kager and Pater 2012 , Jardine and Heinz 2016 ). In this squib, we consider the problem that accidental gaps pose for learning phonotactic grammars stated on a single, surface level of representation. While the monostratal approach to phonology has considerable theoretical and computational appeal ( Ellison 1993 , Bird and Ellison 1994 , Scobbie, Coleman, and Bird 1996 , Burzio 2002 ), little previous research has investigated how purely surface-based phonotactic grammars can be learned from natural lexicons (but cf. Hayes and Wilson 2008 , Hayes and White 2013 ). The empirical basis of our study is the sound pattern of South Bolivian Quechua, with particular focus on the allophonic distribution of high and mid vowels. We show that, in characterizing the vowel distribution, a surface-based analysis must resort to generalizations of greater complexity than are needed in traditional accounts that derive outputs from underlying forms. This exacerbates the learning problem, because complex constraints are more likely to be surface-true by chance (i.e., the structures they prohibit are more likely to be accidentally absent from the lexicon). A comprehensive quantitative analysis of the Quechua lexicon and phonotactic system establishes that many accidental gaps of the relevant complexity level do indeed exist. We propose that, to overcome this problem, surface-based phonotactic models should have two related properties: they should use distinctive features to state constraints at multiple levels of granularity, and they should select constraints of appropriate granularity by statistical comparison of observed and expected frequency distributions. The central idea is that actual gaps typically belong to statistically robust feature-based classes, whereas accidental gaps are more likely to be featurally isolated and to contain independently rare sounds. A maximum-entropy learning model that incorporates these two properties is shown to be effective at distinguishing systematic and accidental gaps in a whole-language phonotactic analysis of Quechua, outperforming minimally different models that lack features or perform nonstatistical induction.

Probabilistic Feature Attention as an Alternative to Variables in Phonotactic Learning

2021 ◽  
pp. 1-56
Author(s):  
Brandon Prickett
Keyword(s):  
Experimental Work ◽  
Experimental Results ◽  
Phonological Acquisition ◽  
Novel Approach ◽  
Free Model ◽  
Cast Doubt ◽  
Feature Attention ◽  
Phonotactic Learning ◽  
Past Experimental Work ◽  
Algebraic Representations

Abstract Since Halle (1962), explicit algebraic variables (often called alpha notation) have been commonplace in phonological theory. However, Hayes and Wilson (2008) proposed a variable-free model of phonotactic learning, sparking a debate about whether such algebraic representations are necessary to capture human phonological acquisition. While past experimental work has found evidence that suggested a need for variables in models of phonology (Berent et al. 2012, Moreton 2012, Gallagher 2013), this paper presents a novel mechanism, Probabilistic Feature Attention (PFA), that allows a variable-free model of phonotactics to predict a number of these phenomena. Additionally, experimental results involving phonological generalization that cannot be explained by variables are captured by this novel approach. These results cast doubt on whether variables are necessary to capture human-like phonotactic learning and provide a useful alternative to such representations.

Salient dimensions in implicit phonotactic learning

2015 ◽  
Author(s):  
Elise Michon ◽  
Emmanuel Dupoux ◽  
Alejandrina Cristia
Keyword(s):  
Phonotactic Learning

Reversal shift in phonotactic learning during language production: Evidence for incremental learning

2019 ◽  
Vol 106 ◽  
pp. 135-149 ◽  
Author(s):  
Nathaniel D. Anderson ◽  
Eric W. Holmes ◽  
Gary S. Dell ◽  
Erica L. Middleton
Keyword(s):  
Incremental Learning ◽  
Language Production ◽  
Reversal Shift ◽  
Phonotactic Learning

scholarly journals Exceptionality and derived environment effects: a comparison of Korean and Turkish

Phonology ◽  
2019 ◽  
Vol 36 (4) ◽  
pp. 543-572
Author(s):  
Adam J. Chong
Keyword(s):  
Environment Effects ◽  
Markedness Constraint ◽  
Morpheme Boundary ◽  
Phonotactic Learning

Morphologically derived environment effects (MDEEs) are well-known examples where phonotactic patterns in the lexicon mismatch with what is allowed at morphological boundaries – alternations. Analyses of MDEEs usually assume that the alternation is morphologically general, and that the sequences ‘repaired’ across morpheme boundaries are phonotactically well-formed in the lexicon. This paper examines the phonotactic patterns in the lexicon of two languages with MDEEs: Korean palatalisation and Turkish velar deletion. I show that Korean heteromorphemic sequences that undergo palatalisation are underattested in the lexicon. A computational learner learns a markedness constraint that drives palatalisation, suggesting a pattern of exceptional non-undergoing. This contrasts with Turkish, where the relevant constraint motivating velar deletion at the morpheme boundary is unavailable from phonotactic learning, and where the alternation is an example of exceptional triggering. These results indicate that MDEEs are not a unitary phenomenon, highlighting the need to examine these patterns in closer quantitative detail.

scholarly journals Phonological Naturalness and Phonotactic Learning

2013 ◽  
Vol 44 (1) ◽  
pp. 45-75 ◽  
Author(s):  
Bruce Hayes ◽  
James White
Keyword(s):  
Language Learners ◽  
Native Speaker ◽  
Learning System ◽  
Powerful Effect ◽  
Phonotactic Learning

We investigate whether the patterns of phonotactic well-formedness internalized by language learners are direct reflections of the phonological patterns they encounter, or reflect in addition principles of phonological naturalness. We employed the phonotactic learning system of Hayes and Wilson (2008) to search the English lexicon for phonotactic generalizations and found that it learned many constraints that are evidently unnatural, having no typological or phonetic basis. We tested 10 such constraints by obtaining native-speaker ratings of 40 nonce words: 10 violated our unnatural constraints, 10 violated natural constraints assigned comparable weights by the learner, and 20 were control forms. Violations of the natural constraints had a powerful effect on ratings, violations of the unnatural constraints at best a weak one. We assess various hypotheses intended to explain this disparity, and conclude in favor of a learning bias account.

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