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Can children use a verb without exposure to its argument structure?
M D Braine1, R E Brody, S M Fisch
1Department of Psychology, New York University, NY 10003.
Canonical sentence schemas, not verb-specific rules, explain early verb argument structure acquisition. This challenges previous theories on how children learn verb transitivity and causative errors.
Area of Science:
- Psycholinguistics
- Language Acquisition
- Developmental Psychology
Background:
- Canonical sentence schemas, such as Agent-verb-Patient, may provide default argument structures for verbs during early language learning.
- This default assignment is particularly relevant when a verb's lexical entry initially lacks specific argument structure information.
Purpose of the Study:
- To test the hypothesis that canonical sentence schemas assign argument structure to verbs.
- To investigate the role of these schemas in causative verb errors and verb transitivity acquisition.
- To compare the usage of novel and familiar verbs by adults and children.
Main Methods:
- Novel action verbs were created, categorized as causative, intransitive, or unmarked for transitivity.
- Spontaneous verb usage was recorded, alongside responses to agent- and patient-focused questions.
- Data from novel verbs were compared with data from familiar English verbs of fixed and optional transitivity.
Main Results:
- Subjects readily used novel verbs both transitively and intransitively, with adults showing more adherence to assigned transitivity than children.
- Familiar verb transitivity was generally respected across age groups.
- Discourse pressure from specific question types significantly influenced observed verb transitivity.
Conclusions:
- The study found no evidence for an intransitive-to-causative derivational process as previously postulated.
- Causative verb errors are proposed to stem from argument structure assignment via canonical sentence schemas, especially when a specific argument (Agent or Patient) needs to be the sentence subject.
- This schema-based theory offers an alternative explanation for errors without requiring the unlearning of incorrect lexical entries and can be applied to other argument structure errors.
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