Children learn ergative case marking in Hindi using statistical preemption and clause-level semantics (intentionality): evidence from acceptability judgment and elicited production studies with children and adults

儿童学习印地语作格标记时运用的是统计抢占和从句语义(意向性):来自可接受性判断和诱导产出研究(对象为儿童和成人)的证据

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Abstract

Background: A question that lies at the very heart of language acquisition research is how children learn semi-regular systems with exceptions (e.g., the English plural rule that yields cats, dogs, etc, with exceptions feet and men). We investigated this question for Hindi ergative ne marking; another semi-regular but exception-filled system. Generally, in the past tense, the subject of two-participant transitive verbs (e.g., Ram broke the cup) is marked with ne, but there are exceptions. How, then, do children learn when ne marking is required, when it is optional, and when it is ungrammatical? Methods: We conducted two studies using (a) acceptability judgment and (b) elicited production methods with children (aged 4-5, 5-6 and 9-10 years) and adults. Results: All age groups showed effects of statistical preemption: the greater the frequency with which a particular verb appears with versus without ne marking on the subject - relative to other verbs - the greater the extent to which participants (a) accepted and (b) produced ne over zero-marked subjects. Both children and adults also showed effects of clause-level semantics, showing greater acceptance of ne over zero-marked subjects for intentional than unintentional actions. Some evidence of semantic effects at the level of the verb was observed in the elicited production task for children and the judgment task for adults. Conclusions: participants mainly learn ergative marking on an input-based verb-by-verb basis (i.e., via statistical preemption; verb-level semantics), but are also sensitive to clause-level semantic considerations (i.e., the intentionality of the action). These findings add to a growing body of work which suggests that children learn semi-regular, exception-filled systems using both statistics and semantics.

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