Children's acquisition of the English past tense has been widely studied as a testing ground for theories of language development, mostly because it comprises a set of quasi-regular mappings. English verbs are of two types: regular verbs, which form their past tense based on a productive rule, and irregular verbs, which form their past tenses through exceptions to that rule. Although many connectionist models exist for capturing language development, few consider individual differences. In this article, we explore the use of populations of artificial neural networks (ANNs) that evolve according to behavioral genetics principles in order to create computational models capable of capturing the population variability exhibited by children in acquiring English past tense verbs. Literature in the field of behavioral genetics views variability in children's learning in terms of genetic and environmental influences. In our model, the effects of genetic influences are simulated through variations in parameters controlling computational properties of ANNs, and the effects of environmental influences are simulated via a filter applied to the training set. This filter alters the quality of information available to the artificial learning system and creates a unique subsample of the training set for each simulated individual. Our approach uses a population of twins to disentangle genetic and environmental influences on past tense performance and to capture the wide range of variability exhibited by children as they learn English past tenses. We use a novel technique to create the population of ANN twins based on the biological processes of meiosis and fertilization. This approach allows modeling of both individual differences and development (within the lifespan of an individual) in a single framework. Finally, our approach permits the application of selection on developmental performance on the quasi-regular task across generations. Setting individual differences within an evolutionary framework is an important and novel contribution of our work. We present an experimental evaluation of this model, focusing on individual differences in performance. The experiments led to several novel findings, including: divergence of population attributes during selection to favor regular verbs, irregular verbs, or both; evidence of canalization, analogous to Waddington's developmental epigenetic landscape, once selection starts targeting a particular aspect of the task domain; and the limiting effect on the power of selection in the face of stochastic selection (roulette wheel), sexual reproduction, and a variable learning environment for each individual. Most notably, the heritability of traits showed an inverse relationship to optimization. Selected traits show lower heritability as the genetic variation of the population reduces. The simulations demonstrate the viability of linking concepts such as heritability of individual differences, cognitive development, and selection over generations within a single computational framework.

中文翻译：

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进化联结主义模型以捕捉语言发展中的人口变异：模拟儿童过去时的形成

儿童对英语过去时的习得作为语言发展理论的试验场被广泛研究，主要是因为它包含一组准正则映射。英语动词有两种类型：规则动词，根据生产规则形成过去时，不规则动词，通过该规则的例外形成过去时。尽管存在许多用于捕捉语言发展的联结主义模型，但很少有人考虑个体差异。在本文中，我们探索了根据行为遗传学原理进化的人工神经网络 (ANN) 群体的使用，以创建能够捕捉儿童在习得英语过去时动词时表现出的群体变异性的计算模型。行为遗传学领域的文献从遗传和环境影响的角度看待儿童学习的可变性。在我们的模型中，遗传影响的影响是通过控制人工神经网络计算特性的参数的变化来模拟的，环境影响的影响是通过应用于训练集的过滤器来模拟的。该过滤器改变了人工学习系统可用信息的质量，并为每个模拟个体创建了训练集的唯一子样本。我们的方法使用一对双胞胎来解决遗传和环境对过去时表现的影响，并捕捉儿童在学习英语过去时时表现出的广泛可变性。我们使用一种新技术根据减数分裂和受精的生物学过程来创建 ANN 双胞胎群体。这种方法允许在单个框架中对个体差异和发展（在个体的生命周期内）进行建模。最后，我们的方法允许对跨代准常规任务的发展表现进行选择。在进化框架内设置个体差异是我们工作的一项重要而新颖的贡献。我们对该模型进行了实验评估，重点关注性能的个体差异。这些实验导致了几个新的发现，包括：选择过程中群体属性的差异，以支持规则动词、不规则动词或两者兼而有之；运河化的证据，类似于 Waddington' s 发育表观遗传景观，一旦选择开始针对任务领域的特定方面；以及面对随机选择（轮盘赌）、有性生殖和每个人的可变学习环境时对选择权的限制作用。最值得注意的是，性状的遗传力与优化呈负相关。随着种群遗传变异的减少，选定的性状显示出较低的遗传力。模拟证明了在单个计算框架内连接概念的可行性，例如个体差异的遗传性、认知发展和世代选择。以及面对随机选择（轮盘赌）、有性生殖和每个人的可变学习环境时对选择权的限制作用。最值得注意的是，性状的遗传力与优化呈负相关。随着种群遗传变异的减少，选定的性状显示出较低的遗传力。模拟证明了在单个计算框架内连接概念的可行性，例如个体差异的遗传性、认知发展和世代选择。以及面对随机选择（轮盘赌）、有性生殖和每个人的可变学习环境时对选择权的限制作用。最值得注意的是，性状的遗传力与优化呈负相关。随着种群遗传变异的减少，选定的性状显示出较低的遗传力。模拟证明了在单个计算框架内连接概念的可行性，例如个体差异的遗传性、认知发展和世代选择。