Lévesque Mélanie, Arguin Martin
Département de Psychologie, Centre interdisciplinaire de recherche sur le cerveau et l'apprentissage, Université de Montréal, Montréal, QC, Canada.
Centre de Recherche, Institut Universitaire de Gériatrie de Montréal, Montréal, QC, Canada.
Front Psychol. 2024 Feb 21;15:1323493. doi: 10.3389/fpsyg.2024.1323493. eCollection 2024.
The temporal features of visual processing were compared between young and elderly healthy participants in visual object and word recognition tasks using the technique of random temporal sampling. The target stimuli were additively combined with a white noise field and were exposed very briefly (200 ms). Target visibility oscillated randomly throughout exposure duration by manipulating the signal-to-noise ratio (SNR). Classification images (CIs) based on response accuracy were calculated to reflect processing efficiency according to the time elapsed since target onset and the power of SNR oscillations in the 5-55 Hz range. CIs differed substantially across groups whereas individuals of the same group largely shared crucial features such that a machine learning algorithm reached 100% accuracy in classifying the data patterns of individual participants into their proper group. These findings demonstrate altered perceptual oscillations in healthy aging and are consistent with previous investigations showing brain oscillation anomalies in the elderly.
在视觉物体和单词识别任务中,使用随机时间采样技术,比较了年轻和老年健康参与者视觉处理的时间特征。目标刺激与白噪声场相加组合,并非常短暂地呈现(200毫秒)。通过操纵信噪比(SNR),目标可见度在整个呈现持续时间内随机振荡。根据自目标开始以来经过的时间以及5-55赫兹范围内SNR振荡的功率,计算基于反应准确性的分类图像(CIs),以反映处理效率。不同组之间的CIs有很大差异,而同一组的个体在很大程度上共享关键特征,以至于机器学习算法在将个体参与者的数据模式分类到其所属组时达到了100%的准确率。这些发现表明健康衰老过程中感知振荡发生了改变,并且与先前显示老年人脑振荡异常的研究结果一致。