自适应多维N-back认知负荷模型下的脑网络研究

Research on Brain Network under Self-adaptive Multi-dimensional N-back Cognition Tasks

目的弥补传统N-back实验只考察单一维度工作记忆内容的不足,设计多样化的脑力负荷任务,从多方位深入研究脑疲劳及认知负荷。方法从位置、颜色、形状3个维度划分3个等级,并根据反应正确率自动调节等级,构成自适应多维N-back认知负荷新范式,研究新范式下受试者的行为绩效、脑电频谱特征及脑网络变化。结果在1-back与2-back任务中,随着认知任务维度升级、脑力负荷增加,脑电alpha/beta功率比下降,PDC脑网络中各脑区之间的信息流增强且不同维度任务的信息流向与当前任务紧密相关。结论自适应多维N-back实验范式比传统单一维度N-back任务能多方位充分调动大脑认知功能、加速消耗脑力资源并影响其行为绩效,有望为更深层次研究脑认知与脑疲劳提供新思路。

Objective To cover the shortage of the traditional N-back task which only activates a single dimension of working memory,a diversified task is needed for in-depth study of brain fatigue and cognitive workload from multi-aspects. Methods A research on self-adaptive multi-dimensional N-back task based on traditional N-back task was conducted. Three difficulty levels were designed for three dimensions including the position,color,and shape respectively. The levels could be adjusted according to the reaction accuracy automatically.Then the behavioral performance,EEG frequency features as well as the brain network variations were studied. Results The results showed that the EEG power ratio of alpha to beta decreased and Partial Directed Connectivity（ PDC） among different brain areas increased with the addition of new dimensions of stimuli. It was also found that the information streams of different dimensions were closely related to the present task. Conclusion The results indicate that the self-adaptive multi-dimensional N-back task could activate more brain cognitive functions from multi-aspects and consume more brain resources than the traditional task. It provides a new thought for in-depth research on brain cognition and fatigue.