Abstract
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.