Dominant theoretical accounts of developmental language disorder (DLD) commonly invoke working memory capacity limitations. In the current report, we present an alternative view: That working memory in DLD is not under-resourced but overloaded due to operating on speech representations with low discriminability. This account is developed through computational simulations involving deep convolutional neural networks trained on spoken word spectrograms in which information is either retained to mimic typical development or degraded to mimic the auditory processing deficits identified among some children with DLD. We assess not only spoken word recognition accuracy and predictive probability and entropy (i.e., predictive distribution spread), but also use mean-field-theory based manifold analysis to assess; (a) internal speech representation dimensionality and (b) classification capacity, a measure of the networks’ ability to isolate any given internal speech representation that is used as a proxy for attentional control. We show that instantiating a low-level auditory processing deficit results in the formation of internal speech representations with atypically high dimensionality, and that classification capacity is exhausted due to low representation separability. These representation and control deficits underpin not only lower performance accuracy but also greater uncertainty even when making accurate predictions in a simulated spoken word recognition task (i.e., predictive distributions with low maximum probability and high entropy), which replicates the response delays and word finding difficulties often seen in DLD. Overall, these simulations demonstrate a theoretical account of speech representation and processing deficits in DLD in which working memory capacity limitations play no causal role.

中文翻译：

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资源不足还是超载？重新思考发育性语言障碍中的工作记忆缺陷。

发展性语言障碍 (DLD) 的主要理论解释通常会导致工作记忆容量限制。在本报告中，我们提出了另一种观点：DLD 中的工作记忆并非资源不足，而是由于对可辨别性低的语音表示进行操作而过载。这个帐户是通过计算模拟开发的，涉及在口语频谱图上训练的深度卷积神经网络，其中信息要么被保留以模仿典型的发展，要么被降级以模仿在某些 DLD 儿童中发现的听觉处理缺陷。我们不仅评估口语单词识别的准确性和预测概率和熵（即预测分布扩散），而且还使用基于平均场理论的流形分析来评估；(a) 内部语音表征维度和 (b) 分类能力，衡量网络隔离任何给定内部语音表征的能力，该内部语音表征用作注意力控制的代理。我们表明，实例化低级听觉处理缺陷会导致形成具有非典型高维的内部语音表示，并且由于表示可分离性低，分类能力会耗尽。这些表示和控制缺陷不仅导致性能准确性降低，而且不确定性更大，即使在模拟口语单词识别任务中做出准确预测（即，具有低最大概率和高熵的预测分布），也复制了响应延迟和单词查找困难经常出现在 DLD 中。总体，