论文标题
婴儿口语识别期间词汇竞争的神经网络模型
A Neural Network Model of Lexical Competition during Infant Spoken Word Recognition
论文作者
论文摘要
视觉世界研究表明，在含有目标的视觉环境中听到一个单词，其中包含相关和无关的项目，幼儿和成人将目光简要地引导到语音相关的项目，然后转向语义和视觉上相关的项目。我们提出了一个神经网络模型，该模型可以处理动态展开语音表示并将其映射到静态的内部语义和视觉表示。该模型接受了来自真实语料库的表示形式的训练，它模拟了这一早期的语音/视觉偏好。我们的结果支持以下假设：口语的逐步展开本身足以说明语音竞争对手在无关和语义和视觉上相关的语音竞争者中的短暂偏好。语音表示以自下而上的方式映射到语义 - 视觉表示捕获了视觉世界任务中报道的早期语音偏好效应。在这样的试验中稍后观察到的语义视觉偏好不需要语义或视觉系统的自上而下的反馈。
Visual world studies show that upon hearing a word in a target-absent visual context containing related and unrelated items, toddlers and adults briefly direct their gaze towards phonologically related items, before shifting towards semantically and visually related ones. We present a neural network model that processes dynamic unfolding phonological representations and maps them to static internal semantic and visual representations. The model, trained on representations derived from real corpora, simulates this early phonological over semantic/visual preference. Our results support the hypothesis that incremental unfolding of a spoken word is in itself sufficient to account for the transient preference for phonological competitors over both unrelated and semantically and visually related ones. Phonological representations mapped dynamically in a bottom-up fashion to semantic-visual representations capture the early phonological preference effects reported in a visual world task. The semantic-visual preference observed later in such a trial does not require top-down feedback from a semantic or visual system.
