Avcu Enes, Hwang Michael, Brown Kevin Scott, Gow David W
Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States.
Harvard College, Boston, MA, United States.
Front Artif Intell. 2023 Mar 27;6:1062230. doi: 10.3389/frai.2023.1062230. eCollection 2023.
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.
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.
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.
随着越来越多的证据表明基于运动、感知和概念过程的词形在神经上广泛分布，单一局部词表征存储的概念变得越来越不可信。在此，我们尝试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征的脑系统计算模型。我们检验了这样一个假设，即大脑中词表征的功能特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与发音和映射声音与意义这一独特问题。
我们发现，在声音与发音之间映射上训练的人工神经网络在识别声音与意义之间的映射时表现不佳，反之亦然。此外，与其他两个模型相比，同时在这两项任务上训练的网络无法发现声音与更高层次认知状态之间高效映射所需的特征。此外，这些网络在没有明确训练的情况下发展出了反映专门任务优化功能的内部表征。
这些发现共同表明，不同的任务导向表征会导致机器或算法（以及假设的大脑）产生更集中的反应和更好的性能。因此，我们认为，鉴于人类大脑所面临任务的性质，词表征的功能特化反映了一种计算优化策略。
