A tale of two lexica: Investigating computational pressures on word representation with neural networks.
A tale of two lexica: Investigating computational pressures on word representation with neural networks.
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DOI：
10.3389/frai.2023.1062230
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发表时间：
2023
影响因子：
4
通讯作者：
Gow, David W.
中科院分区：
文献类型：
--
作者：
Avcu, Enes;Hwang, Michael;Brown, Kevin Scott;Gow, David W.
关键词：
随着越来越多的证据表明，基于运动、知觉和概念过程的广泛分布的词形式神经表征已经变得越来越不可信。在这里，我们试图将机器学习方法和神经生物学框架结合起来，提出一个可能负责单词形式表示的大脑系统的计算模型。我们测试了一个假设，即大脑中单词表征的功能专门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发音vs.映射声音和意义的独特问题。我们发现，在声音和发音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，同时在两个任务上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，这些网络在没有明确训练的情况下开发了反映专门任务优化功能的内部表示。总之，这些发现表明，不同的任务导向表征导致更集中的反应和更好的表现，机器或算法，假设，大脑。因此，我们认为单词表示的功能专门化反映了人类大脑所面临的任务性质的计算优化策略。
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.
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