Unit of Computing Sciences, Faculty of Information Technology and Communication Sciences, Tampere University.
Département d'études cognitives, ENS, EHESS, CNRS, PSL University.
Cogn Sci. 2023 Jul;47(7):e13307. doi: 10.1111/cogs.13307.
Computational models of child language development can help us understand the cognitive underpinnings of the language learning process, which occurs along several linguistic levels at once (e.g., prosodic and phonological). However, in light of the replication crisis, modelers face the challenge of selecting representative and consolidated infant data. Thus, it is desirable to have evaluation methodologies that could account for robust empirical reference data, across multiple infant capabilities. Moreover, there is a need for practices that can compare developmental trajectories of infants to those of models as a function of language experience and development. The present study aims to take concrete steps to address these needs by introducing the concept of comparing models with large-scale cumulative empirical data from infants, as quantified by meta-analyses conducted across a large number of individual behavioral studies. We formalize the connection between measurable model and human behavior, and then present a conceptual framework for meta-analytic evaluation of computational models. We exemplify the meta-analytic model evaluation approach with two modeling experiments on infant-directed speech preference and native/non-native vowel discrimination.
儿童语言发展的计算模型可以帮助我们理解语言学习过程的认知基础，这个过程同时涉及多个语言层面（例如，韵律和语音）。然而，鉴于复制危机，建模者面临着选择具有代表性和一致性的婴儿数据的挑战。因此，最好有评估方法可以考虑到多个婴儿能力的强大实证参考数据。此外，还需要有实践方法可以将婴儿的发展轨迹与模型进行比较，作为语言经验和发展的函数。本研究旨在通过引入将模型与大规模累积婴儿实证数据进行比较的概念来具体解决这些需求，这些数据由对大量个体行为研究进行的荟萃分析来量化。我们将可衡量的模型和人类行为之间的联系形式化，然后提出一个计算模型的荟萃分析评估的概念框架。我们用两个关于婴儿指向性言语偏好和母语/非母语元音辨别力的建模实验来说明元分析模型评估方法。
