Ntoumanis Ioannis, Shestakova Anna, Koriakina Maria, Kadieva Dzerassa, Kopytin Grigory, Jääskeläinen Iiro P
International Laboratory of Social Neurobiology, Institute for Cognitive Neuroscience, HSE University, Moscow, Russia.
Federal State Budgetary Institution the Turner Scientific Research Institute for Children's Orthopedics Under the Ministry of Health of the Russian Federation, Saint-Petersburg, Russia.
Front Hum Neurosci. 2023 Jan 10;16:1046277. doi: 10.3389/fnhum.2022.1046277. eCollection 2022.
It is widely believed that we are more attentive towards moving versus static stimuli. However, the neural correlates underlying the perception of human movements have not been extensively investigated in ecologically valid settings, nor has the developmental aspect of this phenomenon. Here, we set forth to investigate how human limb movements displayed in naturalistic videos influence the attentional engagement of children and young adults.
Thirty-nine healthy participants (4-26 years old) were presented with naturalistic videos featuring human goal-directed movements, while neural activity was recorded using electroencephalography (EEG). Video scenes were automatically annotated as containing arm, leg or no movement, using a machine learning algorithm. The viewers' attentional engagement was quantified by the intersubject correlation of EEG responses evoked by the videos.
Our results demonstrate that scenes featuring limb movements, especially simultaneous arm and leg movements, elicit higher attentional engagement than scenes with no limb movement. Interestingly, this effect was found to diminish with age.
Overall, our findings extend previous work on the perception of human motion by implementing naturalistic stimuli in the experimental design and extend the list of factors influencing the viewer's engagement exerted by naturalistic videos.
人们普遍认为,相较于静态刺激,我们对动态刺激更为关注。然而,在生态有效环境中,尚未对人类运动感知背后的神经关联进行广泛研究,这一现象的发展方面也未得到充分研究。在此,我们着手研究自然主义视频中展示的人类肢体运动如何影响儿童和年轻人的注意力参与度。
向39名健康参与者(年龄在4至26岁之间)展示包含人类目标导向运动的自然主义视频,同时使用脑电图(EEG)记录神经活动。使用机器学习算法将视频场景自动标注为包含手臂、腿部运动或无运动。通过视频诱发的脑电图反应的受试者间相关性来量化观看者的注意力参与度。
我们的结果表明,以肢体运动为特征的场景,尤其是手臂和腿部同时运动的场景,比无肢体运动的场景引发更高的注意力参与度。有趣的是,这种效应会随着年龄的增长而减弱。
总体而言,我们的研究结果通过在实验设计中采用自然主义刺激扩展了先前关于人类运动感知的研究,并扩展了影响自然主义视频观看者参与度的因素列表。