Glissades Are Altered by Lesions to the Oculomotor Vermis but Not by Saccadic Adaptation

Age-related decline of online visuomotor adaptation: a combined effect of deteriorations of motor anticipation and execution

The literature has established that the capability of visuomotor adaptation decreases with aging. However, the underlying mechanisms of this decline are yet to be fully understood. The current study addressed this issue by examining how aging affected visuomotor adaptation in a continuous manual tracking task with delayed visual feedback. To distinguish separate contributions of the declined capability of motor anticipation and deterioration of motor execution to this age-related decline, we recorded and analyzed participants' manual tracking performances and their eye movements during tracking. Twenty-nine older people and twenty-three young adults (control group) participated in this experiment. The results showed that the age-related decline of visuomotor adaptation was strongly linked to degraded performance in predictive pursuit eye movement, indicating that declined capability motor anticipation with aging had critical influences on the age-related decline of visuomotor adaptation. Additionally, deterioration of motor execution, measured by random error after controlling for the lag between target and cursor, was found to have an independent contribution to the decline of visuomotor adaptation. Taking these findings together, we see a picture that the age-related decline of visuomotor adaptation is a joint effect of the declined capability of motor anticipation and the deterioration of motor execution with aging.

Review and Evaluation of Eye Movement Event Detection Algorithms

Eye tracking is a technology aimed at understanding the direction of the human gaze. Event detection is a process of detecting and classifying eye movements that are divided into several types. Nowadays, event detection is almost exclusively done by applying a detection algorithm to the raw recorded eye-tracking data. However, due to the lack of a standard procedure for how to perform evaluations, evaluating and comparing various detection algorithms in eye-tracking signals is very challenging. In this paper, we used data from a high-speed eye-tracker SMI HiSpeed 1250 system and compared event detection performance. The evaluation focused on fixations, saccades and post-saccadic oscillation classification. It used sample-by-sample comparisons to compare the algorithms and inter-agreement between algorithms and human coders. The impact of varying threshold values on threshold-based algorithms was examined and the optimum threshold values were determined. This evaluation differed from previous evaluations by using the same dataset to evaluate the event detection algorithms and human coders. We evaluated and compared the different algorithms from threshold-based, machine learning-based and deep learning event detection algorithms. The evaluation results show that all methods perform well for fixation and saccade detection; however, there are substantial differences in classification results. Generally, CNN (Convolutional Neural Network) and RF (Random Forest) algorithms outperform threshold-based methods.