Chen Chi-Hsin, Zhang Yayun, Yu Chen
Department of Psychology, University of Liverpool.
Language Development Department, Max Planck Institute for Psycholinguistics.
Cogn Sci. 2025 Aug;49(8):e70099. doi: 10.1111/cogs.70099.
Learning the meaning of a verb is challenging because learners need to resolve two types of ambiguity: (1) word-referent mapping-finding the correct referent event of a verb, and (2) word-meaning mapping-inferring the correct meaning of the verb from the referent event (e.g., whether the meaning of an action word is TURNING or TWISTING). The present work examines how adult learners solve this challenge by utilizing both in-the-moment linguistic information within individual learning situations and cross-situational statistical information across multiple learning situations. We investigate how different cues provided in the moment affect information selection and how cross-situational learning as a general computational mechanism allows for information integration over time. Two experiments were designed based on a Human Simulation Paradigm, in which adult learners were presented with a sequence of short videos from parent-toddler toy play and asked to guess a mystery verb the parent produced in each video. In Experiment 1, we compared individual learning situations containing linguistic information to the exact same learning scenes without linguistic information and found that linguistic information helped learners narrow down the meaning of a verb embedded in individual situations, which was consistent with prior research. In Experiment 2, the videos sharing the same target verb were presented in a blocked design to incorporate cross-situational statistics for the same verb. We measured the variability, convergence, and accuracy of participants' guesses. Within-trial linguistic information allowed learners to quickly narrow down their search space and focus on a few relevant aspects in a scene, while cross-situational learning allowed them to fine-tune their learning further across trials. Our findings support a unified account wherein within-trial linguistic information and cross-situational statistical information are integrated for more efficient verb learning.
学习动词的含义具有挑战性，因为学习者需要解决两种类型的歧义：（1）词-指称映射——找到动词的正确指称事件，以及（2）词-意义映射——从指称事件中推断动词的正确含义（例如，一个动作词的含义是“转动”还是“扭转”）。本研究考察了成年学习者如何通过利用个体学习情境中的即时语言信息和多个学习情境中的跨情境统计信息来应对这一挑战。我们研究了即时提供的不同线索如何影响信息选择，以及跨情境学习作为一种通用的计算机制如何随着时间的推移实现信息整合。基于人类模拟范式设计了两个实验，在实验中向成年学习者展示一系列亲子玩具玩耍的短视频，并要求他们猜测家长在每个视频中说出的一个神秘动词。在实验1中，我们将包含语言信息的个体学习情境与没有语言信息的完全相同的学习场景进行了比较，发现语言信息有助于学习者缩小个体情境中嵌入动词的含义范围，这与先前的研究一致。在实验2中，共享相同目标动词的视频以分组设计呈现，以纳入同一动词的跨情境统计信息。我们测量了参与者猜测的变异性、收敛性和准确性。试验内的语言信息使学习者能够迅速缩小搜索空间，并专注于场景中的几个相关方面，而跨情境学习则使他们能够在不同试验中进一步微调学习。我们的研究结果支持一种统一的观点，即试验内语言信息和跨情境统计信息被整合起来以实现更有效的动词学习。
