D'Angiulli Amedeo, Devenyi Peter
Neuroscience of Imagination Cognition and Emotion Research Lab, Ottawa, ON, Canada.
Department of Neuroscience, Carleton University, Ottawa, ON, Canada.
Front Comput Neurosci. 2019 Feb 18;13:4. doi: 10.3389/fncom.2019.00004. eCollection 2019.
This paper describes continuing research on the building of neurocognitive models of the internal mental and brain processes of children using a novel adapted combination of existing computational approaches and tools, and using electro-encephalographic (EEG) data to validate the models. The guiding working model which was pragmatically selected for investigation was the established and widely used Adaptive Control of Thought-Rational (ACT-R) modeling architecture from cognitive science. The anatomo-functional circuitry covered by ACT-R is validated by MRI-based neuroscience research. The present experimental data was obtained from a cognitive neuropsychology study involving preschool children (aged 4-6), which measured their visual selective attention and word comprehension behaviors. The collection and analysis of Event-Related Potentials (ERPs) from the EEG data allowed for the identification of sources of electrical activity known as dipoles within the cortex, using a combination of computational tools (Independent Component Analysis, FASTICA; EEG-Lab DIPFIT). The results were then used to build neurocognitive models based on Python ACT-R such that the patterns and the timings of the measured EEG could be reproduced as simplified symbolic representations of spikes, built through simplified electric-field simulations. The models simulated ultimately accounted for more than three-quarters of variations spatially and temporally in all electrical potential measurements (fit of model to dipole data expressed as ranged between 0.75 and 0.98; < 0.0001). Implications for practical uses of the present work are discussed for learning and educational applications in non-clinical and special needs children's populations, and for the possible use of non-experts (teachers and parents).
本文描述了一项持续的研究，该研究利用现有计算方法和工具的新颖组合构建儿童内部心理和大脑过程的神经认知模型，并使用脑电图（EEG）数据来验证这些模型。为进行研究而实际选择的指导工作模型是认知科学中已确立且广泛使用的思维自适应控制-理性（ACT-R）建模架构。ACT-R所涵盖的解剖功能电路已通过基于MRI的神经科学研究得到验证。目前的实验数据来自一项涉及学龄前儿童（4至6岁）的认知神经心理学研究，该研究测量了他们的视觉选择性注意和单词理解行为。通过EEG数据收集和分析事件相关电位（ERP），结合计算工具（独立成分分析，FASTICA；EEG-Lab DIPFIT），可以识别皮质内被称为偶极子的电活动源。然后，利用这些结果基于Python ACT-R构建神经认知模型，以便通过简化的电场模拟构建的尖峰简化符号表示来再现测量EEG的模式和时间。最终模拟的模型在所有电位测量中在空间和时间上解释了超过四分之三的变化（模型与偶极子数据的拟合度表示为 ，范围在0.75至0.98之间； < 0.0001）。本文还讨论了这项工作在非临床和有特殊需求儿童群体的学习和教育应用中的实际用途，以及非专业人员（教师和家长）可能的使用情况。
