■单词表示的单一本地化存储的概念变得越来越不合理,因为已经积累了证据,证明了在运动中接地的单词形式的广泛分布的神经表示,感性的,和概念过程。这里,我们试图结合机器学习方法和神经生物学框架,提出一个可能负责词形表示的大脑系统的计算模型。我们测试了以下假设:大脑中单词表示的功能专业化部分是由计算优化驱动的。这个假设直接解决了映射声音和清晰度与映射声音和意义。
■我们发现,在声音和关节之间的映射上训练的人工神经网络在识别声音和意义之间的映射方面表现不佳,反之亦然。此外,与其他两个模型相比,在两个任务上同时训练的网络无法发现声音和高级认知状态之间有效映射所需的特征。此外,这些网络开发了内部表示,反映了专门的任务优化功能,而无需明确的培训。
■一起,这些发现表明,不同的任务导向表示导致更集中的响应和更好的性能的机器或算法,假设,大脑。因此,我们暗示,鉴于人类大脑面临的任务的性质,单词表示的功能专业化反映了一种计算优化策略。
UNASSIGNED: The notion of a single localized store of word representations has become increasingly less plausible as evidence has accumulated for the widely distributed neural representation of wordform grounded in motor, perceptual, and conceptual processes. Here, we attempt to combine machine learning methods and neurobiological frameworks to propose a computational model of brain systems potentially responsible for wordform representation. We tested the hypothesis that the functional specialization of word representation in the brain is driven partly by computational optimization. This hypothesis directly addresses the unique problem of mapping sound and articulation vs. mapping sound and meaning.
UNASSIGNED: We found that artificial neural networks trained on the mapping between sound and articulation performed poorly in recognizing the mapping between sound and meaning and vice versa. Moreover, a network trained on both tasks simultaneously could not discover the features required for efficient mapping between sound and higher-level cognitive states compared to the other two models. Furthermore, these networks developed internal representations reflecting specialized task-optimized functions without explicit training.
UNASSIGNED: Together, these findings demonstrate that different task-directed representations lead to more focused responses and better performance of a machine or algorithm and, hypothetically, the brain. Thus, we imply that the functional specialization of word representation mirrors a computational optimization strategy given the nature of the tasks that the human brain faces.