后形式推理阶段的神经关联:发育阶段的生物学决定因素。

S. Leite, Cory David Barker, Marc G. Lucas
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引用次数: 2

摘要

根据递阶复杂度模型(Model of Hierarchical Complexity, MHC)对发育阶段的最新研究表明,发育阶段只有1个域,该阶段发展为log2(age),并且一个物种的神经元数量可以预测该物种达到的平均阶段。这可以解释为生物学控制了阶段。然而,人类有不同的发育阶段,限制发育阶段的生物学机制尚不清楚。基于这些发现,我们认为人类智力的认知神经科学研究应该从支配发育和大脑成熟的一般规律转向关注发展过程中的个体间差异,从而完成超越统计规范的人类认知图景。我们在这里提出了一项研究,寻找表现低于和高于正式阶段的受试者之间大脑激活模式的差异。这项研究与发展心理学和认知神经科学领域的其他研究的不同之处在于,它首次解释了为什么有些人天生就比其他人表现得更高,而不是如何做到的。我们打算分析跨不同层次复杂性任务的数据,并提取饱和指数(SI),该指数告知解决问题的处理负载。其次,我们比较了不同阶段受试者的SI。这些知识将有助于理解认知的生物学基础,有助于改进行为预测MHC,并有助于建立一个模拟整个生命发展的认知联结主义模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Neural correlates of postformal stages of reasoning: Biological determinants of developmental stage.
The latest research on developmental stage, according to the Model of Hierarchical Complexity (MHC), shows that there is only 1 domain, that stage develops as log2(age) and that the number of neurons of a species can predict the mean stage attained by that species. This can be interpreted as saying that biology controls stage. However, humans attain different stages and the biological mechanism that limits stage is still unknown. Based on these findings, we argue that cognitive neuroscience studies of human intelligence should shift from the general laws that govern development and brain maturation to focusing on interindividual differences across development, so as to complete the picture of human cognition beyond statistical norms. We here propose a study that looks for differences in patterns of the brain activation between subjects performing below and above formal stages. What differentiates this study from others that have been conducted in the field of developmental psychology and cognitive neuroscience is that this will explain for the first time not how, but why, some individuals are hardwired to perform at higher stages than others. We intend to analyze the data across different hierarchical complexity tasks and extract a saturation index (SI) that informs about the processing load of problem solving. Second, we compare the SI across subjects who attained different stages. This knowledge will provide for understanding the biological basis of cognition, for improving the behavioral predictive MHC, and for developing a connectionist model of cognition that emulates development throughout life.
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