Tsank Yuliy, Eckstein Miguel P
Department of Psychological and Brain Sciences, University of California, Santa Barbara, California 93106-9660
Department of Psychological and Brain Sciences, University of California, Santa Barbara, California 93106-9660.
J Neurosci. 2017 Nov 22;37(47):11469-11484. doi: 10.1523/JNEUROSCI.1208-17.2017. Epub 2017 Oct 20.
Humans visually process the world with varying spatial resolution and can program their eye movements optimally to maximize information acquisition for a variety of everyday tasks. Diseases such as macular degeneration can change visual sensory processing, introducing central vision loss (a scotoma). However, humans can learn to direct a new preferred retinal location to regions of interest for simple visual tasks. Whether such learned compensatory saccades are optimal and generalize to more complex tasks, which require integrating information across a large area of the visual field, is not well understood. Here, we explore the possible effects of central vision loss on the optimal saccades during a face identification task, using a gaze-contingent simulated scotoma. We show that a new foveated ideal observer with a central scotoma correctly predicts that the human optimal point of fixation to identify faces shifts from just below the eyes to one that is at the tip of the nose and another at the top of the forehead. However, even after 5000 trials, humans of both sexes surprisingly do not change their initial fixations to adapt to the new optimal fixation points to faces. In contrast, saccades do change for tasks such as object following and to a lesser extent during search. Our findings argue against a central brain motor-compensatory mechanism that generalizes across tasks. They instead suggest task specificity in the learning of oculomotor plans in response to changes in front-end sensory processing and the possibility of separate domain-specific representations of learned oculomotor plans in the brain. The mechanism by which humans adapt eye movements in response to central vision loss is still not well understood and carries importance for gaining a fundamental understanding of brain plasticity. We show that although humans adapt their eye movements for simpler tasks such as object following and search, these adaptations do not generalize to more complex tasks such as face identification. We provide the first computational model to predict where humans with central vision loss should direct their eye movements in face identification tasks, which could become a critical tool in making patient-specific recommendations. Based on these results, we suggest a novel theory for oculomotor learning: a distributed representation of learned eye-movement plans represented in domain-specific areas of the brain.
人类以不同的空间分辨率视觉处理世界，并能最优地规划眼球运动，以在各种日常任务中最大化信息获取。诸如黄斑变性等疾病会改变视觉感觉处理，导致中心视力丧失（即盲点）。然而，人类可以学会将新的首选视网膜位置引导至感兴趣区域以完成简单视觉任务。对于更复杂的任务，即需要整合视野大面积区域信息的任务而言，这种习得的代偿性扫视是否最优并能推广应用，目前还不太清楚。在此，我们使用基于注视的模拟盲点，探究中心视力丧失在人脸识别任务中对最优扫视的可能影响。我们表明，带有中心盲点的新注视理想观察者正确预测出，用于识别面部的人类最优注视点会从眼睛下方转移至鼻尖处以及额头顶部。然而，即便经过5千次试验，令人惊讶的是，两性人类都不会改变其初始注视点以适应新的面部最优注视点。相比之下，在诸如物体跟踪任务以及在搜索过程中程度较轻的情况下，扫视的确会发生变化。我们的研究结果反对一种能在不同任务中推广应用的中枢脑运动代偿机制。相反，它们表明在响应前端感觉处理变化时，眼球运动计划学习中存在任务特异性，以及大脑中习得眼球运动计划可能存在单独的特定领域表征。人类如何响应中心视力丧失而调整眼球运动的机制仍未完全明了，对于深入理解大脑可塑性具有重要意义。我们表明，尽管人类会为诸如物体跟踪和搜索等较简单任务调整眼球运动，但这些调整不会推广至诸如人脸识别等更复杂任务。我们提供了首个计算模型，以预测中心视力丧失者在人脸识别任务中应将眼球运动引导至何处，这可能成为做出针对患者的建议的关键工具。基于这些结果，我们提出了一种关于眼球运动学习的新理论：习得眼球运动计划在大脑特定领域区域中的分布式表征。
