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The agent preference in ontogeny: predictability of agent and patient roles in child-directed utterances across languages

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Huber, Eva
Bickel, Balthasar
Stoll, Sabine

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Language comprehension unfolds incrementally, requiring listeners to continually predict and revise interpretations. Comprehenders across very diverse languages show a consistent preference for agents, anticipating the agent ("the doer" of an action) more strongly than the patient ("the undergoer"). An unresolved question is how the preference develops in children given incomplete utterances and argument omission in their input. Here, we approach this question by quantifying the incremental predictability of semantic roles (agents vs. patients), probing specifically what kind of contextual information impacts ease of learning. We use transcribed utterances from child-directed speech in three languages, differing in critical conditions of word order and argument omission: Tagalog (verb-initial), English (verb-medial), and Turkish (verb-final). To quantify incremental predictability at each position in the sentence, we use a computational model trained on naturalistic child-directed speech, which is first validated against experimental data in each language. Our results show that agents are highly predictable irrespective of sentence position or language, requiring barely any contextual information. In contrast, patient prediction requires additional information, varying by language. These findings suggest that the assignment of agent roles in child-directed speech is an easier task across typologically distinct languages, possibly reflecting the more general preference for agents outside language. Patients, by contrast, appear to be contextually induced roles that develop in ways that are largely shaped by the affordances of each language.

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Wiley

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Psychology

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Cognitive Science

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10.1111/cogs.70147

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CC BY-NC-ND (Attribution-NonCommercial-NoDerivs)

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Except where otherwised noted, this item's license is described as CC BY-NC-ND (Attribution-NonCommercial-NoDerivs)

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