Ovsepian Rozana, Souto David, Schütz Alexander C
Sensorimotor Learning Unit, University of Marburg, 35032 Hessen, Germany.
School of Psychology and Vision Sciences, University of Leicester, Leicester LE1 7RH, UK.
iScience. 2025 May 2;28(6):112563. doi: 10.1016/j.isci.2025.112563. eCollection 2025 Jun 20.
Perceptual and sensorimotor learning is often specific to the trained stimuli and movement parameters. This specificity also applies to recalibrating sensory and motor maps, such as saccadic eye movements in response to systematic visual errors. Here, we show that the perceptual recalibration of stationarity during smooth pursuit eye movements generalizes to untrained eye movement speeds. During smooth pursuit, the retinal image motion of the stationary surround (reafference) must be compensated to maintain perceptual stability. Prior research revealed that the predicted reafference signal is continuously updated through interactions between the motor command and experienced retinal motion and is specific to movement direction and visual field location. Here, we show that stationarity recalibration transfers across pursuit speeds. The generalization pattern reveals two distinct mechanisms: a multiplicative gain for decreasing predicted reafference signals and a constant shift for increasing signals. The former is consistent with a gain control model of smooth pursuit.
知觉和感觉运动学习通常特定于所训练的刺激和运动参数。这种特异性也适用于重新校准感觉和运动图谱，例如对系统性视觉误差做出反应的眼球扫视运动。在这里，我们表明，在平稳跟踪眼球运动过程中，静止状态的知觉重新校准可推广到未训练的眼球运动速度。在平稳跟踪过程中，静止周围环境的视网膜图像运动（再传入）必须得到补偿，以维持知觉稳定性。先前的研究表明，预测的再传入信号通过运动指令与经历的视网膜运动之间的相互作用不断更新，并且特定于运动方向和视野位置。在这里，我们表明，静止状态重新校准可在不同的跟踪速度之间转移。这种推广模式揭示了两种不同的机制：一种用于减少预测再传入信号的乘法增益，以及一种用于增加信号的恒定偏移。前者与平稳跟踪的增益控制模型一致。
