Investigators have uncovered evidence for phonological learning biases: biases inherent in learners that favor certain language phonologies over others (e.g., Wilson 2006, Finley 2012, Moreton and Pater 2012, Hayes and White 2013, McMullin and Hansson 2014, White 2014). To what extent can a learning bias be defied in language? This question bears directly on the theory of phonological learning, as it addresses the limits of learner capability. A growing family of findings suggests that learners tend to favor phonological constraints that are morphosyntactically general—that is, obeyed by at least several morphemes, or in multiple or all grammatical contexts. That phonological alternations are typically corroborated by the phonotactics of a given language was observed as early as Chomsky and Halle 1968 and Kenstowicz and Kisseberth 1977, but the generalizing tendency just mentioned has also been observed in a number of recent corpus studies. Martin (2007, 2011), Shih and Zuraw (2018), and Breiss and Hayes (2019) observe cases of grammatical “leaking,” in which strong phonotactic restrictions tend to manifest across word boundaries or compound boundaries, or affect the choice between grammatical constructions. Chong (2017) found that certain alternations purported to be apparent derived-environment effects are just that—merely apparent. For example, though Korean t-palatalization triggered by high front vocoids was previously proposed to constitute a derived-environment effect because [ti]-sequences exist in some roots, Chong showed that such sequences are highly underattested in the Korean lexicon. Generalization effects have also been borne out in artificial-language-learning experiments: Myers and Padgett (2014) found that participants generalize a phrase-final devoicing pattern to the word-final domain without exposure to unambiguous evidence; Chong (2017) found that participants more readily learned a suffixal harmony alternation when they were exposed to higher rates of root harmony, corroborating proposals that phonotactic generalizations assist in acquiring alternations (e.g., Tesar and Prince 2003, Hayes 2004, Jarosz 2006). In light of these findings, Martin (2011) and Chong (2017) propose learning models whereby whenever the learner weights positively a structure-specific constraint (e.g., applying only across a suffix boundary), it gives positive weight to an analogous structure-insensitive constraint, leading to the generalizing tendency. If there were to exist an alternation that applies consistently in a constrained morphosyntactic context without even an analogous statistical tendency in phonotactics to accompany it, then that would complicate our understanding of learners’ preference for morphosyntactically general pat-

中文翻译：

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马达加斯加语OCP的目标是单个词缀：对语态句法概括在学习中的意义

研究者发现了语音学习偏向的证据：学习者固有的偏向于某些语言语音而不是其他语言（例如，Wilson 2006，Finley 2012，Moreton and Pater 2012，Hayes and White 2013，McMullin and Hansson 2014，White 2014）。在何种程度上可以克服语言学习障碍？这个问题直接关系到语音学习理论，因为它解决了学习者能力的局限性。越来越多的研究结果表明，学习者倾向于偏爱在语态句法上通用的语音限制，也就是说，至少受几个语素或在多个或所有语法环境中服从。早在Chomsky和Halle 1968年以及Kenstowicz和Kisseberth 1977年就已经观察到，给定语言的音位变化通常证实了语音变化。但是，在最近的语料库研究中也观察到了刚才提到的泛化趋势。Martin（2007，2011），Shih和Zuraw（2018）以及Breiss和Hayes（2019）观察到了语法“泄漏”的情况，在这种情况下，强烈的音位限制往往会跨越单词边界或复合边界，或影响语法选择建筑。Chong（2017）发现，据称是明显的派生环境效应的某些变化也只是显而易见的。例如，尽管先前提出了由高位前类化合物引发的韩国t-钯化以构成衍生环境效应，因为在某些根中存在ti-序列，但是Chong表明，在韩国词典中这种序列被充分低估了。人工语言学习实验也证实了普遍化的影响：Myers和Padgett（2014）发现，参与者在没有暴露于明确证据的情况下将词组-最终发音模式推广到词-最终域。Chong（2017）发现，当参与者暴露于更高的词根和声率时，他们更容易学到后缀和声的交替，从而佐证了光音法泛化有助于获得交替的提议（例如Tesar和Prince 2003，Hayes 2004，Jarosz 2006）。根据这些发现，Martin（2011）和Chong（2017）提出了学习模型，通过这种模型，只要学习者对结构特定的约束施加正的权重（例如，仅在后缀边界上应用），就对类似结构不敏感的类赋予正的权重约束，导致泛化趋势。如果存在在约束句法句法情境中始终如一地适用的替代方法，甚至没有类似的语音音标学统计趋势，那么这将使我们对学习者对形态句法学一般性偏好的理解更加复杂。